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examples/Molecule_mapping_beta_pictoris.ipynb	###Markdown Molecule mapping of the $\beta$ Pictoris system In this tutorial we will simulate an observation of the $\beta$ Pictoris system with a Medium-Resolution Integral Field Spectrograph similar to VLT/SINFONI or VLT/ERIS. After simulating the observation we will detect both planets using the molecular mapping technique from Hoeijmakers et al. 2018. ###Code #------ manually fix import import sys import os import pathlib module_path = os.path.abspath(os.path.join('..')) sys.path.append(module_path) #------ import hcipy as hp import numpy as np import matplotlib.pyplot as plt import astropy.units as u from ObservationSimulator import * from scipy.ndimage import gaussian_filter1d ###Output _____no_output_____ ###Markdown First, we define the wavelength grid that we observe. In our case, we simulate the wavelength sampling of K-band setting of VLT/SINFONI. ###Code spectral_resolution = 5000 wavelengths = np.arange(1.929, 2.472, 0.25e-3)u.micron ###Output _____no_output_____ ###Markdown SourcesNow we have to define the sources that we observe. Here, we simulate a star with the properties of beta pictoris and the two planets. We also include the sky background, as that may be important in the K-band. ###Code #Star star = Star(wavelengths, distance=19.44u.pc, radius=1.8u.R_sun, mass=1.75u.M_sun, radial_velocity=20u.km/u.s, temperature=8000u.K) #Planets beta_pic_b = Planet(wavelengths, host=star, orbital_phase=-np.pi/4u.rad, sma=9.9u.au, radius=1.46u.R_jup, inclination=90u.deg, temperature=1700u.K) beta_pic_c = Planet(wavelengths, host=star, orbital_phase=np.pi/2u.rad, sma=2.7u.au, radius=1.2u.R_jup, inclination=90u.deg, temperature=1200u.K) #Sky background sky_background = Background(wavelengths) sky_spec = get_sky_emission(fov=0.8u.arcsec) sky_background.spectral_model = InterpolatedSpectrum(sky_spec.wavelengths, sky_spec, spectral_resolution=spectral_resolution) ###Output _____no_output_____ ###Markdown Now we define the spectral models of the star and the plane. We use a PHOENIX spectrum for the star and BT-Settl models for the planets. ###Code star_spec = get_PHOENIX_spectrum(temperature=star.temperature, log_g=4) star.spectral_model = InterpolatedSpectrum(star_spec.wavelengths, star_spec, spectral_resolution=spectral_resolution) beta_pic_b_spec = get_BT_SETTL_spectrum(temperature = beta_pic_b.temperature, log_g=4) beta_pic_b.spectral_model = InterpolatedSpectrum(beta_pic_b_spec.wavelengths, beta_pic_b_spec, spectral_resolution=spectral_resolution) beta_pic_c_spec = get_BT_SETTL_spectrum(temperature = beta_pic_c.temperature, log_g=4) beta_pic_c.spectral_model = InterpolatedSpectrum(beta_pic_c_spec.wavelengths, beta_pic_c_spec, spectral_resolution=spectral_resolution) plot_spectrum(sky_background.spectrum, label='Sky background', alpha = 0.8) plot_spectrum(star.spectrum, label='Star') plot_spectrum(beta_pic_b.spectrum, label='Planet b') plot_spectrum(beta_pic_c.spectrum, label='Planet c') plt.yscale('log') plt.ylabel('Received Flux at the telescope') plt.xlabel('Wavelength [m]') plt.legend() plt.show() ###Output _____no_output_____ ###Markdown InstrumentNext, we define our instrument. ###Code #------------Define instrument--------- fov = 0.5u.arcsec read_noise = 7 dark_current_rate = 0.05 * u.Hz telescope = Telescope(diameter=8u.m, field_of_view=fov, pupil_grid_size=64, focal_grid_size=32) input_pup = hcipyPupilGenerator(telescope) x = input_pup x = AtmosphericTransmission(wavelengths)(input_pup) x = EffectiveThroughput(0.1)(x) x = telescope.aperture()(x) x = Atmosphere(seeing=0.9u.arcsec, tau_0=0.005u.s, L0=40u.m, N_layers=3)(x) x = IdealAO(num_modes=60, mode_basis='actuators', noise_level=400u.nm)(x) x = telescope.propagator()(x) #We make use of the Detector3D wrapper, which basically acts as a seperate detector at each wavelength. detector = PhysicalDetector(read_noise=read_noise, dark_current_rate=dark_current_rate ,well_depth=np.inf, gain=1, quantum_efficiency=0.8) ifs = Detector3D(detector, wavelengths)(x) ###Output _____no_output_____ ###Markdown And then we run the simulation. ###Code #---------Run simulation----------------- sim = Simulation(first_component=input_pup) DIT = 460u.s N_DIT = 10 time_resolution = 460u.s for x in range(N_DIT): print('Simulating exoposure {0}/{1}'.format(x, N_DIT)) sim.run([star, beta_pic_b, beta_pic_c], observing_time=DIT, time_resolution=time_resolution) if x==0: datacube = ifs.read_out(destructive=True) else: datacube += ifs.read_out(destructive=True) sim.statistics() np.save('/users/ricolandman/Documents/Research/MedRes/Simulations/sinfoni_like_beta_pic.npy', datacube) ###Output ========== Total runtime: 23.3 s. Time spent on overhead: -160 s. ---------- Time spent per component: <ObservationSimulator.simulation.components_hcipy.hcipyPupilGenerator object at 0x1357f45b0>: 16.6 s <ObservationSimulator.simulation.atmosphere.AtmosphericTransmission object at 0x1357f4130>: 17.7 s <ObservationSimulator.simulation.atmosphere.EffectiveThroughput object at 0x140642a30>: 2.81 s <ObservationSimulator.simulation.components_hcipy.Apodizer object at 0x1470d4d90>: 8.98 s <ObservationSimulator.simulation.atmosphere.Atmosphere object at 0x1355affa0>: 6.78 s <ObservationSimulator.simulation.ao.IdealAO object at 0x14654b5e0>: 17.5 s <ObservationSimulator.simulation.components_hcipy.FraunhoferPropagator object at 0x135459310>: 105 s <ObservationSimulator.simulation.detector.Detector3D object at 0x1354596a0>: 7.23 s ========== ###Markdown ResultsWe can then read out the detectors and plot for example the broadband image. ###Code if not isinstance(fov, float): fov = fov.value broadband_image = np.sum(datacube, axis=0) #strehl = broadband_image.max()/diff_lim_image.max() #print('Strehl ratio:', strehl) fig, axes = plt.subplots(1,2,sharey=True, sharex=True, figsize=(10,4)) plt.sca(axes[0]) plt.imshow(broadband_image, cmap='inferno', extent=[-fov, fov, -fov, fov]) plt.ylabel('Y [arcsec]') plt.xlabel('X [arcsec]') plt.colorbar(label='Counts [e-]') print(broadband_image) plt.sca(axes[1]) plt.imshow(np.log10(broadband_image), cmap='inferno', extent=[-fov, fov, -fov, fov]) plt.xlabel('X [arcsec]') plt.colorbar(label='Log10 counts [e-]') plt.tight_layout() plt.show() ###Output [[4051765.86945263 4245177.41097001 4436963.58726139 ... 4736518.2504104 4604384.58496592 4310411.10247798] [3710948.33391212 3946514.79092023 4273816.40501663 ... 4702018.61167024 4600049.86337742 4372461.89337557] [3304864.24150782 3498828.27780148 3853578.0829894 ... 4571151.89849611 4538799.61382801 4410062.71646856] ... [4499127.0559773 4511623.34894653 4590106.8204693 ... 3307737.77780764 3449785.53999612 3577776.91827851] [4345484.38041244 4424144.9395436 4511329.60245116 ... 3140671.11181435 3267643.91753326 3336856.02177418] [4238713.92046972 4343340.8975472 4363733.12628636 ... 3279144.63823972 3374286.30385627 3403542.50832658]] ###Markdown Data AnalysisHere, we apply the Molecule mapping technique from Hoeijmakers et al. 2018, where a template spectrum is cross-correlated with the datacube. ###Code def molecule_mapping(datacube, wavelengths, N_pca, template, rv_grid, smoothing_kernel_width=30, exclude_rv=1000): #Find brightest spaxels tot_image = np.sum(datacube,axis=0) pixel_brightness_sorted = np.argsort(tot_image.ravel())[::-1] reshaped_cube = datacube.reshape(datacube.shape[0],-1) brightest_pix = np.argsort(np.sum(reshaped_cube.ravel(),axis=0))[::-1][:20] #Normalize norm_cube = reshaped_cube/np.nansum(reshaped_cube,axis=0) #Construct master spectrum and divide by it brightest_spec = norm_cube[:,brightest_pix] master_spec = np.median(brightest_spec,axis=1) stellar_normalised = (norm_cube.T/master_spec).T #Remove low-pass filtered version of appropriate master spectrum data_kernel_width = smoothing_kernel_width low_pass = np.array([gaussian_filter1d(spec, data_kernel_width) for spec in stellar_normalised.T]).T stellar_model = (low_pass.Tmaster_spec).T star_removed = norm_cube-stellar_model #PCA um,sm,vm =np.linalg.svd(star_removed, full_matrices=False) s_new = sm.copy() s_new[:N_pca] = 0 pca_sub = (um.dot(np.diag(s_new))).dot(vm) #Molecule mapping beta = 1-rv_grid/3e5 shifted_wavelengths = wavelengths * beta[:, np.newaxis] T = template.at(shifted_wavelengths.ravel()).reshape(shifted_wavelengths.shape).value cross_corr = T.dot(pca_sub) cross_corr -= np.nanmedian(cross_corr[np.abs(rv_grid)>exclude_rv],axis=0) cross_corr /= np.nanstd(cross_corr[np.abs(rv_grid)>exclude_rv],axis=0) reshaped_ccf_map = cross_corr.reshape(rv_grid.size, datacube.shape[1], datacube.shape[2]) return reshaped_ccf_map flat_model = convolve_to_resolution(beta_pic_b_spec, spectral_resolution) continuum = gaussian_filter1d(flat_model, 30) flat_model = flat_model/continuum flat_model = InterpolatedSpectrum(flux_density=flat_model, wavelengths=flat_model.wavelengths) flat_model.initialise_for(beta_pic_b) plt.plot(flat_model.at(wavelengths)) plt.show() rv_grid = np.linspace(-3000,3000,500) result= molecule_mapping(datacube, wavelengths, N_pca=5, template=flat_model,rv_grid = rv_grid) rv_idx = np.argmin(np.abs(rv_grid-beta_pic_b.radial_velocity.to(u.km/u.s).value)) plt.imshow(result[rv_idx],extent=[-fov, fov, -fov, fov]) idx = np.argmax(result[result.shape[0]//2+2]) plt.colorbar(label='Signal-to-noise ratio') plt.scatter(beta_pic_b.location.to(u.arcsec), marker='x',color='red' ) plt.figure() plt.plot(rv_grid,result.reshape(result.shape[0],-1)[:,idx]) plt.axvline(beta_pic_b.radial_velocity.to(u.km/u.s).value, ls='--', color='black', alpha=0.8) plt.axhline(0, ls='--', color='black', alpha=0.8) plt.ylabel("Signal-to-noise ratio") plt.xlabel("Radial velocity [km/s]") flat_model = convolve_to_resolution(beta_pic_c_spec, spectral_resolution) continuum = gaussian_filter1d(flat_model, 30) flat_model = flat_model/continuum flat_model = InterpolatedSpectrum(flux_density=flat_model, wavelengths=flat_model.wavelengths) flat_model.initialise_for(beta_pic_c) rv_grid = np.linspace(-3000,3000,300) result= molecule_mapping(datacube, wavelengths, N_pca=30, template=flat_model,rv_grid = rv_grid) rv_idx = np.argmin(np.abs(rv_grid-beta_pic_c.radial_velocity.to(u.km/u.s).value)) plt.imshow(result[rv_idx],extent=[-fov, fov, -fov, fov]) plt.xlabel('X [arcsec]') plt.ylabel('Y [arcsec]') offset = 11 idx = [result.shape[1]//2, result.shape[2]//2+offset] plt.colorbar(label='Signal-to-noise ratio') plt.scatter(beta_pic_c.location.to(u.arcsec),marker='x', color='red') plt.figure() plt.plot(rv_grid, result[:,idx[0], idx[1]]) plt.axvline(beta_pic_c.radial_velocity.to(u.km/u.s).value, ls='--', color='black', alpha=0.8) plt.axhline(0, ls='--', color='black', alpha=0.8) plt.ylabel("Signal-to-noise ratio") plt.xlabel("Radial velocity [km/s]") ###Output _____no_output_____
Sentiment_Model_using_PyTorch.ipynb	###Markdown ###Code !pip install transformers==3 import pandas as pd import transformers from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup import torch import torch.nn as nn import numpy as np import seaborn as sns from sklearn.model_selection import train_test_split from torch.utils.data import Dataset, DataLoader from sklearn.utils import class_weight from collections import defaultdict import matplotlib.pyplot as plt from sklearn.metrics import classification_report,confusion_matrix df_train = pd.read_csv('train.csv', header = None) df_train.columns = ['rating', 'review'] df_test_set = pd.read_csv('test.csv', header = None) df_test_set.columns = ['rating', 'review'] df_train_set, df_valid_set = train_test_split(df_train, test_size = 0.2, random_state = 42) # sns.countplot(df.rating) class_name = ['most-negative', 'negative', 'neutral', 'positive', 'most-positive'] device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") PRE_TRAINED_MODEL_NAME = 'bert-base-cased' tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME) #from tqdm import tqdm #token_lens = [] #for txt in tqdm(df_train.review): # tokens = tokenizer.encode(txt, max_length = 512) # token_lens.append(len(tokens)) #print(max(token_lens)) # sns.distplot(token_lens) MAX_LEN = 120 df_train # a Dataset object loads training or test data into memory, and a DataLoader object # fetches data from a Dataset and serves the data up in batches. class YelpReviewDataset(Dataset): def __init__(self, reviews, targets, tokenizer, max_len): self.reviews = reviews self.targets = targets self.tokenizer = tokenizer self.max_len = max_len def __len__(self): return len(self.reviews) def __getitem__(self, item): review = str(self.reviews[item]) target = self.targets[item] encoding = self.tokenizer.encode_plus( # string review, # Whether or not to encode the sequences with the special tokens relative to their model. add_special_tokens = True, # Controls the maximum length to use by one of the truncation/padding parameters. max_length = self.max_len, # These require two different sequences to be joined in a # single “input_ids” entry, which usually is performed with the help of special tokens, # such as the classifier ([CLS]) and separator ([SEP]) tokens. return_token_type_ids = False, pad_to_max_length = True, return_attention_mask = True, # If set, will return tensors instead of list of python integers. # 'pt': Return PyTorch torch.Tensor objects. return_tensors = 'pt' ) # A BatchEncoding with the fields: return { 'review_text' : review, 'input_ids' : encoding['input_ids'].flatten(), 'attention_mask' : encoding['attention_mask'].flatten(), 'targets' : torch.tensor(target, dtype=torch.long) } # print(df_train_set.shape) # print(df_valid_set.shape) # print(df_test_set.shape) # Checking the uniform distribution of a dataset # class_weights = class_weight.compute_class_weight('balanced', # np.unique(df_train.rating.values), # df_train.rating.values) def create_data_loader(df, tokenizer, max_len, batch_size): reviewDataset = YelpReviewDataset( # Convert the DataFrame (df-column in this case) to a NumPy array. reviews = df.review.to_numpy(), targets = df.rating.to_numpy(), tokenizer = tokenizer, max_len = max_len ) # DataLoader represents a Python iterable over a dataset return DataLoader(reviewDataset, batch_size = batch_size, num_workers = 4) BATCH_SIZE = 32 train_data_loader = create_data_loader(df_train_set, tokenizer, MAX_LEN, BATCH_SIZE) valid_data_loader = create_data_loader(df_valid_set, tokenizer, MAX_LEN, BATCH_SIZE) test_valid_loader = create_data_loader(df_test_set, tokenizer, MAX_LEN, BATCH_SIZE) # bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) class SentimentClassifier(nn.Module): def __init__(self, n_classes): super(SentimentClassifier, self).__init__() self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) self.drop = nn.Dropout(p = 0.4) self.out1 = nn.Linear(self.bert.config.hidden_size, 128) self.drop1 = nn.Dropout(p = 0.4) self.relu = nn.ReLU() self.out = nn.Linear(128, n_classes) def forward(self, input_ids, attention_mask): _, pooled_output = self.bert( input_ids = input_ids, attention_mask = attention_mask ) output = self.drop(pooled_output) output = self.out1(output) output = self.relu(output) output = self.drop1(output) return self.out(output) model = SentimentClassifier(len(class_name)+1) model = model.to(device) EPOCHS = 5 optimizer = AdamW(model.parameters(), lr = 2e-5, correct_bias = False) total_steps = len(train_data_loader) * EPOCHS scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps = 0, num_training_steps = total_steps ) loss_fn = nn.CrossEntropyLoss().to(device) def train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples): # Set the module in training mode model = model.train() losses = [] correct_predictions = 0 for data in data_loader: input_ids = data['input_ids'].to(device) attention_mask = data['attention_mask'].to(device) targets = data['targets'].to(device) # Although the recipe for forward pass needs to be defined within this function, one should call the #Module instance afterwards instead of this since the former takes care of running the registered hooks #while the latter silently ignores them. outputs = model( input_ids = input_ids, attention_mask = attention_mask ) _, preds = torch.max(outputs, dim=1) loss = loss_fn(outputs, targets) correct_predictions += torch.sum(preds == targets) losses.append(loss.item()) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), max_norm = 1.0) optimizer.step() scheduler.step() optimizer.zero_grad() return correct_predictions.double() / n_examples, np.mean(losses) def eval_model(model, data_loader, loss_fn, device, n_examples): model = model.eval() losses = [] correct_predictions = 0 with torch.no_grad(): for d in data_loader: input_ids = d["input_ids"].to(device) attention_mask = d["attention_mask"].to(device) targets = d["targets"].to(device) outputs = model( input_ids=input_ids, attention_mask=attention_mask ) _, preds = torch.max(outputs) loss = loss_fn(outputs, targets) correct_predictions += torch.sum(preds == targets) losses.append(loss.item()) return correct_predictions.double() / n_examples, np.mean(losses) history = defaultdict(list) best_accuracy = 0 for epoch in range(EPOCHS): print(f'Epoch {epoch + 1}/{EPOCHS}') print('-' * 10) train_acc, train_loss = train_epoch( model, train_data_loader, loss_fn, optimizer, device, scheduler, len(df_train_set) ) print(f'Train loss {train_loss} accuracy {train_acc}') val_acc, val_loss = eval_model( model, valid_data_loader, loss_fn, device, len(df_valid_set) ) print(f'Val loss {val_loss} accuracy {val_acc}') print() history['train_acc'].append(train_acc) history['train_loss'].append(train_loss) history['val_acc'].append(val_acc) history['val_loss'].append(val_loss) if val_acc > best_accuracy: torch.save(model.state_dict(), 'best_model_state.bin') best_accuracy = val_acc #plt.plot(history['train_acc'], label='train accuracy') #plt.plot(history['val_acc'], label='validation accuracy') #plt.title('Training history') #plt.ylabel('Accuracy') #plt.xlabel('Epoch') #plt.legend() #plt.ylim([0, 1]); def get_predictions(model, data_loader): model = model.eval() review_texts = [] predictions = [] prediction_probs = [] real_values = [] with torch.no_grad(): for d in data_loader: texts = d["review_text"] input_ids = d["input_ids"].to(device) attention_mask = d["attention_mask"].to(device) targets = d["targets"].to(device) outputs = model( input_ids=input_ids, attention_mask=attention_mask ) _, preds = torch.max(outputs, dim=1) review_texts.extend(texts) predictions.extend(preds) prediction_probs.extend(outputs) real_values.extend(targets) predictions = torch.stack(predictions).cpu() prediction_probs = torch.stack(prediction_probs).cpu() real_values = torch.stack(real_values).cpu() return review_texts, predictions, prediction_probs, real_values y_review_texts, y_pred, y_pred_probs, y_test = get_predictions( model, test_data_loader ) print(classification_report(y_test, y_pred, target_names=class_name)) review_text = "the food was delicious but it was spicy" encoded_review = tokenizer.encode_plus( review_text, max_length=MAX_LEN, add_special_tokens=True, return_token_type_ids=False, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt', ) input_ids = encoded_review['input_ids'].to(device) attention_mask = encoded_review['attention_mask'].to(device) output = model(input_ids, attention_mask) _, prediction = torch.max(output, dim=1) print(f'Review text: {review_text}') print(f'Sentiment : {class_name[prediction]}') ###Output _____no_output_____
Mersenne Numbers and Primes.ipynb	###Markdown Mersenne Prime (메르센 소수) 메르센?- 마린 메르센(1588-1648)은 프랑스 수도승으로 페르마, 파스칼, 갈릴레오 등과 동시대를 산 인물- 당시에는 수학 저널 같은 것이 없어서 메르센은 수학에서의 발견을 받아 정리하고 배포하는 역할(clearing house)을 했다고 함- 물론 본인도 철학자이면서 수학자 소수를 공식으로 표현하고자 하는 노력- 무한대의 소수가 있음을 증명할 수 있는 간단한 방법- $x^{2} + x + 41$이 대표적 ###Code [i for i in range(1, 50) if not sympy.ntheory.isprime(i * i + i + 41)] ###Output _____no_output_____ ###Markdown 1~39까지는 참이지만 40부터 성립하지 않는 결과가 나오기 시작 Mersenne numbers and primes- 메르센도 공식을 하나 만들었다. $M_{n} = 2^{n} - 1$- 이 수를 Mersenne numbers라고 불렀고 이 중 소수인 숫자가 Mersenne prime이라고 불린다- $n$이 소수가 아니면 $M_{n}$도 소수가 아니다- $n$이 소수면? ###Code primes = [sympy.prime(n) for n in range(1, 9)] [(n, 2 n - 1, sympy.ntheory.isprime(2 n - 1)) for n in primes] ###Output _____no_output_____ ###Markdown - 11을 제외하고 19까지 공식이 성립한 걸 본 메르센은 19 이상의 소수에 대해 조사를 하기 시작 ~~수도승은 시간이 많았겠...~~- $M_{31}$, $M_{67}$, $M_{127}$, $M_{257}$이 소수라고 결론 ###Code [(n, 2 n - 1, sympy.ntheory.isprime(2 n - 1)) for n in [31, 67, 127, 257]] ###Output _____no_output_____ ###Markdown - 메르센이 어떻게 이 숫자 찾은 과정은 알려지지 않음- $M_{31}$만 해도 10자리 숫자로 아주 큰 숫자 ~~당시 기술로는 ...~~ 그 후- 1750년 오일러 형님이 $M_{31}$은 소수가 맞다고 증명- 1876년 Edouard Lucas가 $M_{67}$이 소수가 아니라고 발표했지만 어떤 수로 소인수분해되는지는 밝히지 못함- 이후 Derrick Lehmer라는 사람이 Lucas의 방법을 간결하게 개선 ###Code def lucas(p): U = 4 Mp = 2 ** p - 1 for _ in range(p - 2): U = (U * U - 2) % Mp return U == 0 [lucas(p) for p in (31, 67, 127, 257)] ###Output _____no_output_____ ###Markdown - 된다! 근데 왜 되는거지? ~~저는 몰라요~~- 그 뒤로도 $M_{61}$, $M_{89}$, $M_{107}$이 메르센 소수임이 밝혀짐- 결론적으로 메르센 형님의 찍기는 반타작: $M_{31}$(O), $M_{67}$(X), $M_{127}$(O), $M_{257}$(X) ~~많은 수학자들을 낚아 이걸 증명하게 만든 것은 큰 그림~~ $M_{67}$의 소인수 분해- Lucas의 방법으로 $M_{67}$이 소수가 아닌 건 알았지만 이 수는 어떤 수들의 곱인지는 밝혀지지 않은 상황- 1903년, 수학자 Frank Nelson Cole(1861-1926)은 American Mathematical Society에서 'On the Factorisation of Large Numbers'라는 강의를 하게 되는데...- 아무말 하지 않고 $2^{67}$을 계산하기 시작, 그리고 1을 신중하게(carefully) 뺀다- 이 숫자는 무려 ###Code m67 = 2*67 - 1 f'{m67:_}' ###Output _____no_output_____ ###Markdown - 그리고 다른 칠판에 193,707,721와 761,838,257,287를 쓰고 곱하기 시작 ###Code 193_707_721 761_838_257_287 ###Output _____no_output_____ ###Markdown - 아무 말 없이 계산을 끝냈고, Cole은 기립 박수를 받음- Cole이 후에 밝히길 이걸 찾아내는데 매주 일요일을 투자했고 3년이 걸렸다고 함 ###Code sympy.ntheory.isprime(267 - 1) %time print(sympy.factorint(267 - 1)) ###Output Wall time: 0 ns {193707721: 1, 761838257287: 1}
online-retail-k-means-Tableau.ipynb	###Markdown CAPSTONE PROJECT:3 - RETAIL Importing Neccessary Python Packages and Libraries. ###Code import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import datetime as dt # importing required libraries for clustering import sklearn from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score ###Output _____no_output_____ ###Markdown Reading and Understanding Data ###Code x={1,2,3,4,4} x.update([4,5,6,7]) print(x) print(bool('False')) print(bool) i=0 while i<3: print(i) i+=1 else: print(0) # Reading the data from excel to pandas dataframe on which analysis needs to be done. retail = pd.read_excel('Online_Retail.xlsx') retail.head() ###Output _____no_output_____ ###Markdown Performing a preliminary data inspection and data cleaning. ###Code # shape of df retail.shape #showing no. of rows and columns in the dataset. # df info retail.info() #showing no. of rows with non null entries and datatype of each variable with name. # df description retail.describe() #This gives statistical summary of numerical variable by default. ###Output _____no_output_____ ###Markdown Describe function has revealed strange insight with negative values in Quantity variable and UnitPrice variable, which needs further investigation. ###Code retail[retail.Quantity<0],retail[retail.Quantity>0] ###Output _____no_output_____ ###Markdown Filtering and comparing data for both negative and non negative values of Quantity, and correlating with information provided we can infer that negative value in Quantity is associated with cancelled order. ###Code retail[retail.UnitPrice<0] ###Output _____no_output_____ ###Markdown From above filtering and description it's not clear as to what is 'adjustment of bad debt' and needs consultation from client or domain expertise for it's treatment. Checking for missing data and formulating an apt strategy to treat them. ###Code # Calculating the Missing Values % contribution in DF df_null = round(100(retail.isnull().sum())/len(retail), 5) df_null # Droping rows having missing values retail = retail.dropna() retail.shape ###Output _____no_output_____ ###Markdown Dealing with duplicate records. ###Code retail[retail.duplicated()].shape retail.loc[retail.duplicated(keep=False)] retail[['StockCode','Description']][retail[['StockCode','Description']].duplicated(keep=False)] # Dropping duplicate records. retail.drop_duplicates(inplace= True) retail.shape ###Output _____no_output_____ ###Markdown Descriptive analysis on dataset. ###Code retail.describe() #This is giving the statistical summary such as count, mean, min, max and quartiles values. retail[retail.Quantity<0].shape # This shows that 8872 no. of products has been returned or cancelled. retail.CustomerID.nunique() #This shows the total no. of unique customers who are contributing to overall sales. retail.Country.unique(),retail.Country.nunique() #This shows that there are 37 countries with their names contributing to sales. retail.Country.value_counts() # This shows the total no. of product bought for each country for that period, with UK being the highest contributor in sales. retail.Country.value_counts().plot(kind= 'bar') retail.InvoiceNo.nunique() # this shows the total no. of unique transactions for the given period. len(retail.InvoiceNo.value_counts())# this shows the total no. of unique transactions for the given period. len(retail.Description.unique()),retail.Description.nunique() # This shows the no. of distinct products bought by the customer during the given period. sns.boxplot(retail.UnitPrice) retail.UnitPrice.quantile([0,.25,.50,.75,.8,.9,.99,1])#This shows the relative prices of products where price of maximum products lie below 15 sterling #per unit, with max price being 38970. retail.UnitPrice[retail.UnitPrice<25].plot(kind='hist') retail.Description.mode() #Highest selling product. retail.Description.value_counts().head(25) # This is a list of top 25 selling products. ###Output _____no_output_____ ###Markdown Cohort analysis : As per requirement we will build a cohort on the basis of the 1st invoice date of available data in a month and then check for retention metric. Cohort size will be 1 month and we will consider the whole data as range under analysis. ###Code retail.info() #Converting Customer ID to object datatype. retail1=retail.copy() retail1['CustomerID']=retail1['CustomerID'].astype(str) retail1.info() # for this analysis we are going to drop negative Quantity orders if not done so it would be calculated in ordered more than once. retail1.drop(retail1[retail1.Quantity<1].index, inplace = True) retail1[retail1.Quantity<1] n_orders = retail1.groupby(['CustomerID'])['InvoiceNo'].nunique() mult_orders_perc = np.sum(n_orders > 1) / retail1['CustomerID'].nunique() print(f'{100 mult_orders_perc:.2f}% of customers ordered more than once.') # This show that 65% out of all data provided to us in this given period ordered more than once. # We can have a look at the distribution of the number of orders per customer. ax = sns.distplot(n_orders, kde=False, hist=True) ax.set(title='Distribution of number of orders per customer', xlabel='no. of orders', ylabel='no. of customers'); ###Output _____no_output_____ ###Markdown We keep only the relevant columns and drop duplicated values — one order (indicated by InvoiceNo) can contain multiple items (indicated by StockCode) for analysis ###Code retail1 = retail1[['CustomerID', 'InvoiceNo', 'InvoiceDate']].drop_duplicates() retail1 ###Output _____no_output_____ ###Markdown Creating the cohort and order_month variables. The first one indicates the monthly cohort based on the first purchase date (calculated per customer).The latter one is the truncated month of the purchase date. ###Code retail1['order_month'] = retail1['InvoiceDate'].dt.to_period('M') retail1['cohort'] = retail1.groupby('CustomerID')['InvoiceDate'] \ .transform('min') \ .dt.to_period('M') retail1.head(25) ###Output _____no_output_____ ###Markdown Aggregating the data per cohort and order_month and count the number of unique customers in each group. Additionally, we add the period_number, which indicates the number of periods between the cohort month and the month of the purchase. ###Code df_cohort = retail1.groupby(['cohort', 'order_month']) \ .agg(n_customers=('CustomerID', 'nunique')) \ .reset_index(drop=False) from operator import attrgetter df_cohort['period_number'] = (df_cohort.order_month - df_cohort.cohort).apply(attrgetter('n')) df_cohort.head(14) ###Output _____no_output_____ ###Markdown Now we will pivot the df_cohort table in a way that each row contains information about a given cohort and each column contains values for certain period. ###Code cohort_pivot = df_cohort.pivot_table(index = 'cohort', columns = 'period_number', values = 'n_customers') ###Output _____no_output_____ ###Markdown To obtain the retention matrix, we need to divide the values each row by the row's first value, which is actually the cohort size — all customers who made their first purchase in the given month. ###Code cohort_size = cohort_pivot.iloc[:,0] retention_matrix = cohort_pivot.divide(cohort_size, axis = 0) ###Output _____no_output_____ ###Markdown We plot the retention matrix as a heatmap. Additionally, we wanted to include extra information regarding the cohort size. That is why we in fact created two heatmaps, where the one indicating the cohort size is using a white only colormap — no coloring at all. ###Code import matplotlib.colors as mcolors with sns.axes_style("white"): fig, ax = plt.subplots(1, 2, figsize=(12, 8), sharey=True, gridspec_kw={'width_ratios': [1, 11]}) # retention matrix sns.heatmap(retention_matrix, mask=retention_matrix.isnull(), annot=True, fmt='.0%', cmap='RdYlGn', ax=ax[1]) ax[1].set_title('Monthly Cohorts: User Retention', fontsize=16) ax[1].set(xlabel='no. of periods', ylabel='') # cohort size cohort_size_df = pd.DataFrame(cohort_size).rename(columns={0: 'cohort_size'}) white_cmap = mcolors.ListedColormap(['white']) sns.heatmap(cohort_size_df, annot=True, cbar=False, fmt='g', cmap=white_cmap, ax=ax[0]) fig.tight_layout() ###Output _____no_output_____ ###Markdown After going through the heatmap it can be inferred that the Cohort in 2010-12 had maximum retention with 27% for the 12th period and an average of around 34% throughout the year. Whereas there is sharp decrease in retention rate for the 2nd group at the end period, average retention rate is around 26% throughout the period. Horizontally it shows gradual increase except for final sharp decline. All in all we can say- the cohort in proceeding months are observed to show a gradual decline in retention rate. But the average retention rate for the end period for all cohort is seen to be around 10%, except for the 1st cohort. Data Preparation for RFM analysis. We are going to analyse the Customers based on below 3 factors:- R (Recency): Number of days since last purchase- F (Frequency): Number of transactions- M (Monetary): Total amount of transactions (revenue contributed) ###Code # New Attribute : Monetary retail['Amount'] = retail['Quantity']retail['UnitPrice'] rfm_m = retail.groupby('CustomerID')['Amount'].sum() rfm_m = rfm_m.reset_index() rfm_m.shape rfm_m.head() # New Attribute : Frequency rfm_f = retail.groupby('CustomerID')['InvoiceNo'].count() rfm_f = rfm_f.reset_index() rfm_f.columns = ['CustomerID', 'Frequency'] rfm_f.head() # Merging the two dfs rfm = pd.merge(rfm_m, rfm_f, on='CustomerID', how='inner') rfm.head() # New Attribute : Recency # Convert 'InvoiceDate' to datetime datatype. retail['InvoiceDate'] = pd.to_datetime(retail['InvoiceDate'],format='%d-%m-%Y %H:%M') retail.info() # Compute the maximum date to know the last transaction date max_date = max(retail['InvoiceDate']) max_date # Compute the difference between max date and transaction date retail['Diff'] = max_date - retail['InvoiceDate'] retail # Compute last transaction date to get the recency of customers rfm_p = retail.groupby('CustomerID')['Diff'].min() rfm_p = rfm_p.reset_index() rfm_p.head() # Extract number of days only rfm_p['Diff'] = rfm_p['Diff'].dt.days rfm_p.head() # Merging tha dataframes to get the final RFM dataframe rfm = pd.merge(rfm, rfm_p, on='CustomerID', how='inner') rfm.columns = ['CustomerID', 'Amount', 'Frequency', 'Recency'] rfm.head() x=[] for i in rfm.Amount: if i<=rfm.Amount.quantile(.25): x.append(1) elif i<=rfm.Amount.quantile(0.50): x.append(2) elif i<=rfm.Amount.quantile(0.75): x.append(3) elif i<=rfm.Amount.quantile(1.0): x.append(4) rfm["Amount_star"]=x rfm y=[] for i in rfm.Frequency: if i<=rfm.Frequency.quantile(.25): y.append(1) elif i<=rfm.Frequency.quantile(0.50): y.append(2) elif i<=rfm.Frequency.quantile(0.75): y.append(3) elif i<=rfm.Frequency.quantile(1.0): y.append(4) rfm["Frequency_star"]=y rfm z=[] for i in rfm.Recency: if i<=rfm.Recency.quantile(.25): z.append(4) elif i<=rfm.Recency.quantile(0.50): z.append(3) elif i<=rfm.Recency.quantile(0.75): z.append(2) elif i<=rfm.Recency.quantile(1.0): z.append(1) rfm['Recency_star']=z rfm rfm['rfm_score']=rfm.Amount_star+rfm.Frequency_star+rfm.Recency_star rfm rfm['rfm_score%']=round(((rfm.rfm_score/12)100),2) #this I have created to have better clarity while analysing. rfm rfm['rfm_segment']=rfm.Amount_star.astype(str) + "-" + rfm.Frequency_star.astype(str) + "-" + rfm.Recency_star.astype(str) rfm rfm['rfm_segment1']=rfm.Amount_star.astype(str) + rfm.Frequency_star.astype(str) + rfm.Recency_star.astype(str) rfm rfm.rfm_segment.value_counts().head(60) # this shows all the unique combination of star ratings. #((rfm.Amount_star== 4).sum()/retail.CustomerID.nunique())100 ###Output _____no_output_____ ###Markdown Inferences from RFM segmentation:->From above rfm_segment we can easily filter and make inferences that any value starting with 4 or 3 (or say at hundreds place) will be a high revenue generating customer, thus they should be made to feel valued customer.->Any customer with rfm_score% of 75 or higher can be considered a high value customer even if they have not made more purchases because they can be approached to gain insight as to what offer lures them to buy more, and thus creating proper strategy to target those group to give offers or discounts.-> Best Customers – This group consists of those customers who are found in M-4, F-4, and R-4 meaning that they transacted recently, do so often and spend more than other customers. A shortened notation for this segment is 4-4-4;->High-spending New Customers – This group consists of those customers in 4-1-1 and 4-2-1. These are customers who transacted only once, but very recently and they spent a lot.->Lowest-Spending Active Loyal Customers – This group consists of those customers in segments 1-1-3 and 1-1-4 (they transacted recently and do so often, but spend the least).->Churned Best Customers – This segment consists of those customers in groups 4-1-1, 4-1-2, 4-2-1 and 4-2-2 (they transacted frequently and spent a lot, but it’s been a long time since they’ve transacted)->Best Customers – Communications with this group should make them feel valued and appreciated. These customers likely generate a disproportionately high percentage of overall revenues and thus focusing on keeping them happy should be a top priority. Further analyzing their individual preferences and affinities will provide additional information.->High-spending New Customers – It is always a good idea to carefully incubate all new customers, but because these new customers spent a lot on their first purchase, it’s even more important. Like with the Best Customers group, it’s important to make them feel valued and appreciated – and to give them terrific incentives to continue interacting with the client.->Lowest-Spending Active Loyal Customers – These repeat customers are active and loyal, but they are low spenders. Marketers should create campaigns for this group that make them feel valued, and incentivize them to increase their spend levels. As loyal customers, it often also pays to reward them with special offers if they spread the word about the brand to their friends, e.g., via social networks.->Churned Best Customers – These are valuable customers who stopped transacting a long time ago. While it’s often challenging to re-engage churned customers, the high value of these customers makes it worthwhile trying. Like with the Best Customers group, it’s important to communicate with them on the basis of their specific preferences, as known from earlier transaction data. Data Preparation for Algorithm. There are 2 types of outliers and we will treat outliers as it can skew our dataset.- Statistical- Domain specific ###Code # Outlier Analysis of Amount Frequency and Recency attributes = ['Amount','Frequency','Recency'] plt.rcParams['figure.figsize'] = [10,8] sns.boxplot(data = rfm[attributes], orient="v", palette="Set2" ,whis=1.5,saturation=1, width=0.7) plt.title("Outliers Variable Distribution", fontsize = 14, fontweight = 'bold') plt.ylabel("Range", fontweight = 'bold') plt.xlabel("Attributes", fontweight = 'bold') # Removing (statistical) outliers for Amount Q1 = rfm.Amount.quantile(0.05) Q3 = rfm.Amount.quantile(0.95) IQR = Q3 - Q1 rfm = rfm[(rfm.Amount >= Q1 - 1.5IQR) & (rfm.Amount <= Q3 + 1.5IQR)] # Removing (statistical) outliers for Recency Q1 = rfm.Recency.quantile(0.05) Q3 = rfm.Recency.quantile(0.95) IQR = Q3 - Q1 rfm = rfm[(rfm.Recency >= Q1 - 1.5IQR) & (rfm.Recency <= Q3 + 1.5IQR)] # Removing (statistical) outliers for Frequency Q1 = rfm.Frequency.quantile(0.05) Q3 = rfm.Frequency.quantile(0.95) IQR = Q3 - Q1 rfm = rfm[(rfm.Frequency >= Q1 - 1.5IQR) & (rfm.Frequency <= Q3 + 1.5IQR)] ###Output _____no_output_____ ###Markdown Standardizing the data.It is extremely important to rescale the variables so that they have a comparable scale.\|There are two common ways of rescaling:1. Min-Max scaling 2. Standardisation (mean-0, sigma-1) Here, we will use Standardisation Scaling. ###Code # Rescaling the attributes rfm_df = rfm[['Amount', 'Frequency', 'Recency']] # Instantiate scaler = StandardScaler() # fit_transform rfm_df_scaled = scaler.fit_transform(rfm_df) rfm_df_scaled.shape rfm_df_scaled = pd.DataFrame(rfm_df_scaled) rfm_df_scaled.columns = ['Amount', 'Frequency', 'Recency'] rfm_df_scaled.head() ###Output _____no_output_____ ###Markdown Building the Model K-Means Clustering K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.The algorithm works as follows:- First we initialize k points, called means, randomly.- We categorize each item to its closest mean and we update the mean’s coordinates, which are the averages of the items categorized in that mean so far.- We repeat the process for a given number of iterations and at the end, we have our clusters. ###Code # k-means with some arbitrary k kmeans = KMeans(n_clusters=3, max_iter=50) kmeans.fit(rfm_df_scaled) rfm_df_scaled kmeans.labels_ ###Output _____no_output_____ ###Markdown Deciding the optimum number of clusters to be formed. Using Elbow Curve to get the right number of Clusters.A fundamental step for any unsupervised algorithm is to determine the optimal number of clusters into which the data may be clustered. The Elbow Method is one of the most popular methods to determine this optimal value of k. ###Code # Elbow-curve/SSD ssd = [] range_n_clusters = [2, 3, 4, 5, 6, 7, 8] for num_clusters in range_n_clusters: kmeans = KMeans(n_clusters=num_clusters, max_iter=50) kmeans.fit(rfm_df_scaled) ssd.append(kmeans.inertia_) # plot the SSDs for each n_clusters plt.plot(range_n_clusters,ssd) ###Output _____no_output_____ ###Markdown Exporting dataframe to excel for data visualization in Tableau ###Code import openpyxl import xlsxwriter error_cluster= pd.DataFrame(list(zip(range_n_clusters,ssd)), columns=["no.of clusters","Error"]) error_cluster.to_excel("error_cluster.xlsx") ###Output _____no_output_____ ###Markdown Silhouette Analysis$$\text{silhouette score}=\frac{p-q}{max(p,q)}$$$p$ is the mean distance to the points in the nearest cluster that the data point is not a part of$q$ is the mean intra-cluster distance to all the points in its own cluster. The value of the silhouette score range lies between -1 to 1. * A score closer to 1 indicates that the data point is very similar to other data points in the cluster, * A score closer to -1 indicates that the data point is not similar to the data points in its cluster. ###Code # Silhouette analysis range_n_clusters = [2, 3, 4, 5, 6, 7, 8] for num_clusters in range_n_clusters: # intialise kmeans kmeans = KMeans(n_clusters=num_clusters, max_iter=50) kmeans.fit(rfm_df_scaled) cluster_labels = kmeans.labels_ # silhouette score silhouette_avg = silhouette_score(rfm_df_scaled, cluster_labels) print("For n_clusters={0}, the silhouette score is {1}".format(num_clusters, silhouette_avg)) # Final model with k=3 kmeans = KMeans(n_clusters=3, max_iter=50) kmeans.fit(rfm_df_scaled) kmeans.labels_ # assign the label rfm['Cluster_Id'] = kmeans.labels_ rfm.head(60) # Box plot to visualize Cluster Id vs Amount sns.boxplot(x='Cluster_Id', y='Amount', data=rfm) # Box plot to visualize Cluster Id vs Frequency sns.boxplot(x='Cluster_Id', y='Frequency', data=rfm) # Box plot to visualize Cluster Id vs Recency sns.boxplot(x='Cluster_Id', y='Recency', data=rfm) ###Output _____no_output_____ ###Markdown Final Analysis Inferences:K-Means Clustering with 3 Cluster Ids- Customers with Cluster Id 1 are frequent buyers, recent buyers and high spender as well, should be considered as the best customer group.- Customers with Cluster Id 0 are the customers with comparatively good monetary value, frequency and receny for all transactions under observation. Thus these customer group can be a target for conversion into high spending cohort.- Customers with Cluster Id 2 are not recent buyers, frequency is also low and hence least of importance from business point of view. ###Code rfm ###Output _____no_output_____ ###Markdown Exporting files from dataframe to excel for visualization and dashboard building in Tableau. ###Code import openpyxl import xlsxwriter rfm.to_excel("rfm.xlsx") ###Output _____no_output_____
examples/Practical_tutorial/ELS_practical.ipynb	###Markdown The ELS matching procedureThis practical is based on the concepts introduced for optimising electrical contacts in photovoltaic cells. The procedure was published in [J. Mater. Chem. C (2016)]((http://pubs.rsc.org/en/content/articlehtml/2016/tc/c5tc04091d).In this practical we screen electrical contact materials for CH$_3$NH$_3$PbI$_3$. There are three main steps:* Electronic matching of band energies* Lattice matching of surface vectors * Site matching of under-coordinated surface atoms 1. Electronic matching BackgroundEffective charge extraction requires a low barrier to electron or hole transport accross an the interface. This barrier is exponential in the discontinuity of the band energies across the interface. To a first approximation the offset or discontinuity can be estimated by comparing the ionisation potentials (IPs) or electron affinities (EAs) of the two materials, this is known as [Anderson's rule](https://en.wikipedia.org/wiki/Anderson%27s_rule).Here we have collected a database of 173 measured or estimated semiconductor IPs and EAs (`CollatedData.txt`). We use it as the first step in our screening. The screening is performed by the script `scan_energies.py`. We enforce several criteria:* The IP and EA of the target material are supplied using the flags `-i` and `-e`* The IP/EA must be within a certain range from the target material; by default this is set to 0.5 eV, but it can be contolled by the flag `-w`. The window is the full width so the max offset is 0.5window A selective contact should be a semiconductor, so we apply a criterion based on its band gap. If the gap is too large we consider that it would be an insulator. By default this is set to 4.0 eV and is controlled by the flag `-g` ###Code %%bash cd Electronic/ python scan_energies.py -h ###Output _____no_output_____ ###Markdown Now let's do a proper scan* IP = 5.7 eV* EA = 4.0 eV* Window = 0.25 eV* Insulating threshold = 4.0 eV ###Code %%bash cd Electronic/ python scan_energies.py -i 5.7 -e 4.0 -w 0.5 -g 4.0 ###Output _____no_output_____ ###Markdown 2. Lattice matching BackgroundFor stable interfaces there should be an integer relation between the lattice constants of the two surfaces in contact, which allows for perfect matching, with minimal strain. Generally a strain value of ~ 3% is considered acceptable, above this the interface will be incoherent.This section uses the [ASE package](https://wiki.fysik.dtu.dk/ase/) to construct the low index surfaces of the materials identified in the electronic step, as well as those of the target material. The code `LatticeMatch.py` to identify optimal matches.First we need `.cif` files of the materials obtained from the electronic matching. These are obtained from the [Materials Project website](https://www.materialsproject.org). Most of the `.cif` files are there already, but we should add Cu$_2$O and GaN, just for practice. Lattice matching routineThe lattice matching routine involves obtaining reduced cells for each surface and looking for multiples of each side which match. The procedure is described in more detail in [our paper](http://pubs.rsc.org/en/content/articlehtml/2016/tc/c5tc04091d).The actual clever stuff of the algorithm comes from a paper from Zur and McGill in [J. Appl. Physics (1984)](http://scitation.aip.org/content/aip/journal/jap/55/2/10.1063/1.333084). The scriptThe work is done by a python script called `LatticeMatch.py`. As input it reads `.cif` files. It takes a number of flags: * `-a` the file containing the crystallographic information of the first material* `-b` the file containing the crystallographic information of the second material * `-s` the strain threshold above which to cutoff, defaults to 0.05* `-l` the maximum number of times to expand either surface to find matching conditions, defaults to 5We will run the script in a bash loop to iterate over all interfaces of our contact materials with the (100) and (110) surfaces of pseudo-cubic CH$_3$NH$_3$PbI$_3$. Note that I have made all lattice parameters of CH$_3$NH$_3$PbI$_3$ exactly equal, this is to facilitate the removal of duplicate surfaces by the script. ###Code %%bash cd Lattice/ for file in .cif; do python LatticeMatch.py -a MAPI/CH3NH3PbI3.cif -b $file -s 0.03; done ###Output _____no_output_____ ###Markdown 3. Site matchingSo far the interface matching considered only the magnitude of the lattice vectors. It would be nice to be able to include some measure of how well the dangling bonds can passivate one another. We do this by calculating the site overlap. Basically, we determine the undercoordinated surface atoms on each side and project their positions into a 2D plane. We then lay the planes over each other and slide them around until there is the maximum coincidence. We calculate the overlap factor from $$ ASO = \frac{2S_C}{S_A + S_B}$$where $S_C$ is the number of overlapping sites in the interface, and $S_A$ and $S_B$ are the number of sites in each surface. The scriptThis section can be run in a stand-alone script called `csl.py`. It relies on a library of the 2D projections of lattice sites from different surfaces, which is called `surface_points.py`. Currently this contains a number of common materials types, but sometimes must be expanded as new materials are identified from the electronic and lattice steps.`csl.py` takes the following input parameters: `-a` The first material to consider* `-b` The second material to consider* `-x` The first materials miller index to consider, format : 001* `-y` The second materials miller index to consider, format : 001* `-u` The first materials multiplicity, format : 2,2* `-v` The second materials multiplicity, format : 2,2We can run it for one example from the previous step, let's say GaN (010)x(2,5) with CH$_3$NH$_3$PbI$_3$ (110)x(1,3) ###Code %%bash cd Site/ python csl.py -a CH3NH3PbI3 -b GaN -x 110 -y 010 -u 1,3 -v 2,5 ###Output _____no_output_____ ###Markdown All togetherThe lattice and site examples above give a a feel for what is going on. For a proper screening procedure it would be nice to be able to run them together. That's exactly what happens with the `LatticeSite.py` script. It uses a new class `Pair` to store and pass information about the interface pairings. This includes the materials names, miller indices of matching surfaces, strians, multiplicities etc.The `LatticeSite.py` script takes the same variables as `LatticeMatch.py`. It just takes a little longer to run, so a bit of patience is required.This script outputs the standard pair information as well as the site matching factor, which is calculated as$$ \frac{100\times ASO}{1 + \|\epsilon\|}$$where the $ASO$ was defined above, and $\epsilon$ in the average of the $u$ and $v$ strains. The number is a measure of the mechanical stability of an interface. A perfect interface of a material with itself would have a fator of 100.Where lattices match but no information on the structure of the surface exists it is flagged up. You can always add new surfaces as required. ###Code %%bash cd Site/ for file in *cif; do python LatticeSite.py -a MAPI/CH3NH3PbI3.cif -b $file -s 0.03; done ###Output _____no_output_____
notebooks/figure_1/figure_1.ipynb	###Markdown Read and process image ###Code image = plt.imread('./c0911b81ee5266fa.jpg') image = np.sum(image, axis=2) image = image[:np.min(image.shape),:np.min(image.shape)] image = image.astype('float') image = image[180:-181,180:-181 ] image = image - image.mean() image = image - image.min() image = image/image.max() image.shape ###Output _____no_output_____ ###Markdown Show image and its FT power spectrum ###Code r = np.arange(180).reshape(180,1) radial_masks = np.apply_along_axis(radial_mask, 1, r, int(image.shape[0]/2), int(image.shape[0]/2), \ np.arange(0, image.shape[0]), np.arange(0, image.shape[0]), 1 ) radial_masks.shape ft_2d = np.fft.fft2(image) ft_2d = np.fft.fftshift(ft_2d) ft_2d = np.abs(ft_2d) ft_2d[radial_masks[170]] = 0 ft_2d[radial_masks[85]] = 0 ft_2d[radial_masks[42]] = 0 fig = plt.figure(figsize=(2*1.75, 1.7)) #subplot 1 ax1 = plt.Axes(fig, [0.03, 0.02, 0.38, 0.95]) fig.add_axes(ax1) ax1.imshow(image, cmap='gray') plt.xticks([]) plt.yticks([]) ax1.set_title('A', size=8) #subplot 2 ax2 = plt.Axes(fig, [0.44, 0.0, 0.415, 0.99]) fig.add_axes(ax2) im = ax2.imshow(np.log2(ft_2d), cmap='twilight_shifted') plt.xticks([]) plt.yticks([]) ax2.set_title('B') divider = make_axes_locatable(ax2) cax = divider.new_horizontal(size="5%", pad=0.05) fig = ax2.get_figure() fig.add_axes(cax) cbar = plt.colorbar(im ,cax=cax) cbar.set_label(r'$Log_2$ magnitude', rotation=270,labelpad=10) fig.savefig('figure_1.png', dpi=300) ###Output /Users/rzepiela/anaconda/envs/py37_tf2/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log2 from ipykernel import kernelapp as app
examples/reference/elements/bokeh/Bars.ipynb	###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib Plotly ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown A ``Bars`` element can be sliced and selecting on like any other element: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown It is possible to define an explicit ordering for a set of Bars by explicit declaring `Dimension.values` either in the Dimension constructor or using the `.redim.values()` approach: ###Code occupants = hv.Dimension('Car occupants', values=['three', 'two', 'four', 'one', 'five', 'six']) hv.Bars(data, occupants, 'Count') # or using .redim.values(*{'Car Occupants': ['three', 'two', 'four', 'one', 'five', 'six']}) ###Output _____no_output_____ ###Markdown ``Bars`` support nested categorical groupings, e.g. here we will create a random sample of pets sub-divided by male and female: ###Code N = 100 pets = ['Cat', 'Dog', 'Hamster', 'Rabbit'] genders = ['Female', 'Male'] pets_sample = np.random.choice(pets, N) gender_sample = np.random.choice(genders, N) bars = hv.Bars((pets_sample, gender_sample, np.ones(N)), ['Pets', 'Gender']).aggregate(function=np.sum) bars.opts(width=500) ###Output _____no_output_____ ###Markdown Just as before we can provide an explicit ordering by declaring the `Dimension.values`. Alternatively we can also make use of the `.sort` method, internally `Bars` will use topological sorting to ensure consistent ordering. ###Code bars.redim.values(Pets=pets, Gender=genders) + bars.sort() ###Output _____no_output_____ ###Markdown To drop the second level of tick labels we can set `multi_level=False`, which will indicate the groupings using a legend instead: ###Code bars.sort() + bars.clone().opts(multi_level=False) ###Output _____no_output_____ ###Markdown Lastly, Bars can be also be stacked by setting `stacked=True`: ###Code bars.opts(stacked=True) ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support nested categorical grouping as well as stacking if more than one key dimension is defined, to switch between the two set ``stacked=True/False``: ###Code %%opts Bars.Stacked [stacked=True] from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], ['Index', 'Group'], 'Count') bars.relabel(group='Grouped') + bars.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be stacked by supplying a second key dimensions representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions and one value dimension. See the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, kdims=[hv.Dimension('Car occupants')], vdims=['Count']) bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support stacking just like the ``Area`` element as well as grouping by a second key dimension. To activate grouping and stacking set the ``group_index`` or ``stack_index`` to the dimension name or dimension index: ###Code %%opts Bars.Grouped [group_index='Group'] Bars.Stacked [stack_index='Group'] from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], kdims=['Index', 'Group'], vdims=['Count']) bars.relabel(group='Grouped') + bars.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support nested categorical grouping as well as stacking if more than one key dimension is defined, to switch between the two set ``stacked=True/False``: ###Code from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], ['Index', 'Group'], 'Count') stacked = bars.opts(stacked=True, clone=True) bars.relabel(group='Grouped') + stacked.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv from holoviews import opts hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support nested categorical grouping as well as stacking if more than one key dimension is defined, to switch between the two set ``stacked=True/False``: ###Code from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], ['Index', 'Group'], 'Count') stacked = bars.opts(stacked=True, clone=True) bars.relabel(group='Grouped') + stacked.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title: Bars ElementDependencies: BokehBackends: [Bokeh](./Bars.ipynb), [Matplotlib](../matplotlib/Bars.ipynb), [Plotly](../plotly/Bars.ipynb) ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown A ``Bars`` element can be sliced and selecting on like any other element: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown It is possible to define an explicit ordering for a set of Bars by explicit declaring `Dimension.values` either in the Dimension constructor or using the `.redim.values()` approach: ###Code occupants = hv.Dimension('Car occupants', values=['three', 'two', 'four', 'one', 'five', 'six']) # or using .redim.values({'Car Occupants': ['three', 'two', 'four', 'one', 'five', 'six']}) hv.Bars(data, occupants, 'Count') ###Output _____no_output_____ ###Markdown ``Bars`` support nested categorical groupings, e.g. here we will create a random sample of pets sub-divided by male and female: ###Code samples = 100 pets = ['Cat', 'Dog', 'Hamster', 'Rabbit'] genders = ['Female', 'Male'] pets_sample = np.random.choice(pets, samples) gender_sample = np.random.choice(genders, samples) bars = hv.Bars((pets_sample, gender_sample, np.ones(samples)), ['Pets', 'Gender']).aggregate(function=np.sum) bars.opts(width=500) ###Output _____no_output_____ ###Markdown Just as before we can provide an explicit ordering by declaring the `Dimension.values`. Alternatively we can also make use of the `.sort` method, internally `Bars` will use topological sorting to ensure consistent ordering. ###Code bars.redim.values(Pets=pets, Gender=genders) + bars.sort() ###Output _____no_output_____ ###Markdown To drop the second level of tick labels we can set `multi_level=False`, which will indicate the groupings using a legend instead: ###Code bars.sort() + bars.clone().opts(multi_level=False) ###Output _____no_output_____ ###Markdown Lastly, Bars can be also be stacked by setting `stacked=True`: ###Code bars.opts(stacked=True) ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be stacked by supplying a second key dimensions representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions and one value dimension. See the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support stacking just like the ``Area`` element as well as grouping by a second key dimension. To activate grouping and stacking set the ``group_index`` or ``stack_index`` to the dimension name or dimension index: ###Code %%opts Bars.Grouped [group_index='Group'] Bars.Stacked [stack_index='Group'] from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], ['Index', 'Group'], 'Count') bars.relabel(group='Grouped') + bars.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title Bars Element Dependencies Bokeh Backends Bokeh Matplotlib ###Code import numpy as np import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be stacked by supplying a second key dimensions representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions and one value dimension. See the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown You can 'slice' a ``Bars`` element by selecting categories as follows: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown ``Bars`` support stacking just like the ``Area`` element as well as grouping by a second key dimension. To activate grouping and stacking set the ``group_index`` or ``stack_index`` to the dimension name or dimension index: ###Code %%opts Bars.Grouped [group_index='Group'] Bars.Stacked [stack_index='Group'] from itertools import product np.random.seed(3) index, groups = ['A', 'B'], ['a', 'b'] keys = product(index, groups) bars = hv.Bars([k+(np.random.rand()100.,) for k in keys], ['Index', 'Group'], 'Count') bars.relabel(group='Grouped') + bars.relabel(group='Stacked') ###Output _____no_output_____ ###Markdown Title: Bars ElementDependencies: BokehBackends: [Bokeh](./Bars.ipynb), [Matplotlib](../matplotlib/Bars.ipynb), [Plotly](../plotly/Bars.ipynb) ###Code import numpy as np import pandas as pd import holoviews as hv hv.extension('bokeh') ###Output _____no_output_____ ###Markdown The ``Bars`` Element uses bars to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a continuous value.Bars may also be grouped or stacked by supplying a second key dimension representing sub-categories. Therefore the ``Bars`` Element expects a tabular data format with one or two key dimensions (``kdims``) and one or more value dimensions (``vdims``). See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays. ###Code data = [('one',8),('two', 10), ('three', 16), ('four', 8), ('five', 4), ('six', 1)] bars = hv.Bars(data, hv.Dimension('Car occupants'), 'Count') bars ###Output _____no_output_____ ###Markdown We can achieve the same plot using a Pandas DataFrame:```hv.Bars(pd.DataFrame(data, columns=['Car occupants','Count']))``` A ``Bars`` element can be sliced and selecting on like any other element: ###Code bars[['one', 'two', 'three']] + bars[['four', 'five', 'six']] ###Output _____no_output_____ ###Markdown It is possible to define an explicit ordering for a set of Bars by explicit declaring `Dimension.values` either in the Dimension constructor or using the `.redim.values()` approach: ###Code occupants = hv.Dimension('Car occupants', values=['three', 'two', 'four', 'one', 'five', 'six']) # or using .redim.values(*{'Car Occupants': ['three', 'two', 'four', 'one', 'five', 'six']}) hv.Bars(data, occupants, 'Count') ###Output _____no_output_____ ###Markdown ``Bars`` also supports nested categorical groupings. Next we'll use a Pandas DataFrame to construct a random sample of pets sub-divided by male and female: ###Code samples = 100 pets = ['Cat', 'Dog', 'Hamster', 'Rabbit'] genders = ['Female', 'Male'] pets_sample = np.random.choice(pets, samples) gender_sample = np.random.choice(genders, samples) count = np.random.randint(1, 5, size=samples) df = pd.DataFrame({'Pets': pets_sample, 'Gender': gender_sample, 'Count': count}) df.head(2) bars = hv.Bars(df, kdims=['Pets', 'Gender']).aggregate(function=np.sum) bars.opts(width=500) ###Output _____no_output_____ ###Markdown Just as before we can provide an explicit ordering by declaring the `Dimension.values`. Alternatively we can also make use of the `.sort` method, internally `Bars` will use topological sorting to ensure consistent ordering. ###Code bars.redim.values(Pets=pets, Gender=genders) + bars.sort() ###Output _____no_output_____ ###Markdown To drop the second level of tick labels we can set `multi_level=False`, which will indicate the groupings using a legend instead: ###Code bars.sort() + bars.clone().opts(multi_level=False) ###Output _____no_output_____ ###Markdown Lastly, Bars can be also be stacked by setting `stacked=True`: ###Code bars.opts(stacked=True) ###Output _____no_output_____
Anomaly Detection/Multi-Objective Generative Adversarial Active Learning/MO_GAAL_MinMaxScaler.ipynb	###Markdown Multi-Objective Generative Adversarial Active Learning with MinMaxScaler This code template is for Anomaly detection/outlier analysis using the MO_GAAL Algorithm implemented using pyod library and feature scaling using MinMaxScaler. Required Packages ###Code !pip install plotly !pip install pyod import time import warnings import pandas as pd import numpy as np from scipy import stats import seaborn as sns import plotly.express as px import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn.manifold import Isomap from pyod.models.mo_gaal import MO_GAAL from sklearn.preprocessing import LabelEncoder,MinMaxScaler from sklearn.model_selection import train_test_split warnings.filterwarnings("ignore") ###Output _____no_output_____ ###Markdown InitializationFilepath of CSV file ###Code file_path= '' ###Output _____no_output_____ ###Markdown List of features which are required for model training ###Code features = [] ###Output _____no_output_____ ###Markdown Data FetchingPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry. ###Code df=pd.read_csv(file_path) df.head() ###Output _____no_output_____ ###Markdown Feature SelectionsIt is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.We will assign all the required input features to X. ###Code X=df[features] ###Output _____no_output_____ ###Markdown Data PreprocessingSince the majority of the machine learning models in the Sklearn library doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippet have functions, which removes the null value if any exists. And convert the string classes data in the datasets by encoding them to integer classes. ###Code def NullClearner(df): if(isinstance(df, pd.Series) and (df.dtype in ["float64","int64"])): df.fillna(df.mean(),inplace=True) return df elif(isinstance(df, pd.Series)): df.fillna(df.mode()[0],inplace=True) return df else:return df def EncodeX(df): return pd.get_dummies(df) ###Output _____no_output_____ ###Markdown Calling preprocessing functions on the feature set. ###Code x=X.columns.to_list() for i in x: X[i]=NullClearner(X[i]) X=EncodeX(X) X.head() ###Output _____no_output_____ ###Markdown Data RescalingMinMaxScalerMinMaxScaler subtracts the minimum value in the feature and then divides by the range, where range is the difference between the original maximum and original minimum. We will fit an object of MinMaxScaler to train data then transform the same data via fit_transform(X_train) method, following which we will transform test data via transform(X_test) method. ###Code X_Scaled=MinMaxScaler().fit_transform(X) X_Scaled=pd.DataFrame(data = X_Scaled,columns = X.columns) X_Scaled.head() ###Output _____no_output_____ ###Markdown Data SplittingThe train-test split is a procedure for evaluating the performance of an algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of the model on new data. ###Code x_train,x_test=train_test_split(X_Scaled,test_size=0.2,random_state=123) ###Output _____no_output_____ ###Markdown ModelMO_GAALMO_GAAL directly generates informative potential outliers to assist the classifier in describing a boundary that can separate outliers from normal data effectively. Moreover, to prevent the generator from falling into the mode collapsing problem, the network structure of SO-GAAL is expanded from a single generator (SO-GAAL) to multiple generators with different objectives (MO-GAAL) to generate a reasonable reference distribution for the whole dataset.[For more information](https://pyod.readthedocs.io/en/latest/pyod.models.htmlpyod.models.mo_gaal.MO_GAAL) ###Code model = MO_GAAL(contamination=0.001,stop_epochs=11,k=3) model.fit(x_train) ###Output Epoch 1 of 33 Testing for epoch 1 index 1: Testing for epoch 1 index 2: Testing for epoch 1 index 3: Testing for epoch 1 index 4: Testing for epoch 1 index 5: Testing for epoch 1 index 6: Testing for epoch 1 index 7: Testing for epoch 1 index 8: Epoch 2 of 33 Testing for epoch 2 index 1: Testing for epoch 2 index 2: Testing for epoch 2 index 3: Testing for epoch 2 index 4: Testing for epoch 2 index 5: Testing for epoch 2 index 6: Testing for epoch 2 index 7: Testing for epoch 2 index 8: Epoch 3 of 33 Testing for epoch 3 index 1: Testing for epoch 3 index 2: Testing for epoch 3 index 3: Testing for epoch 3 index 4: Testing for epoch 3 index 5: Testing for epoch 3 index 6: Testing for epoch 3 index 7: Testing for epoch 3 index 8: Epoch 4 of 33 Testing for epoch 4 index 1: Testing for epoch 4 index 2: Testing for epoch 4 index 3: Testing for epoch 4 index 4: Testing for epoch 4 index 5: Testing for epoch 4 index 6: Testing for epoch 4 index 7: Testing for epoch 4 index 8: Epoch 5 of 33 Testing for epoch 5 index 1: Testing for epoch 5 index 2: Testing for epoch 5 index 3: Testing for epoch 5 index 4: Testing for epoch 5 index 5: Testing for epoch 5 index 6: Testing for epoch 5 index 7: Testing for epoch 5 index 8: Epoch 6 of 33 Testing for epoch 6 index 1: Testing for epoch 6 index 2: Testing for epoch 6 index 3: Testing for epoch 6 index 4: Testing for epoch 6 index 5: Testing for epoch 6 index 6: Testing for epoch 6 index 7: Testing for epoch 6 index 8: Epoch 7 of 33 Testing for epoch 7 index 1: Testing for epoch 7 index 2: Testing for epoch 7 index 3: Testing for epoch 7 index 4: Testing for epoch 7 index 5: Testing for epoch 7 index 6: Testing for epoch 7 index 7: Testing for epoch 7 index 8: Epoch 8 of 33 Testing for epoch 8 index 1: Testing for epoch 8 index 2: Testing for epoch 8 index 3: Testing for epoch 8 index 4: Testing for epoch 8 index 5: Testing for epoch 8 index 6: Testing for epoch 8 index 7: Testing for epoch 8 index 8: Epoch 9 of 33 Testing for epoch 9 index 1: Testing for epoch 9 index 2: Testing for epoch 9 index 3: Testing for epoch 9 index 4: Testing for epoch 9 index 5: Testing for epoch 9 index 6: Testing for epoch 9 index 7: Testing for epoch 9 index 8: Epoch 10 of 33 Testing for epoch 10 index 1: Testing for epoch 10 index 2: Testing for epoch 10 index 3: Testing for epoch 10 index 4: Testing for epoch 10 index 5: Testing for epoch 10 index 6: Testing for epoch 10 index 7: Testing for epoch 10 index 8: Epoch 11 of 33 Testing for epoch 11 index 1: Testing for epoch 11 index 2: Testing for epoch 11 index 3: Testing for epoch 11 index 4: Testing for epoch 11 index 5: Testing for epoch 11 index 6: Testing for epoch 11 index 7: Testing for epoch 11 index 8: Epoch 12 of 33 Testing for epoch 12 index 1: Testing for epoch 12 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6868 16/16 [==============================] - 0s 1ms/step - loss: 0.7024 16/16 [==============================] - 0s 1ms/step - loss: 0.7061 Testing for epoch 12 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6864 16/16 [==============================] - 0s 1ms/step - loss: 0.7011 16/16 [==============================] - 0s 1ms/step - loss: 0.7046 Testing for epoch 12 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6880 16/16 [==============================] - 0s 1ms/step - loss: 0.7028 16/16 [==============================] - 0s 1ms/step - loss: 0.7063 Testing for epoch 12 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6888 16/16 [==============================] - 0s 1ms/step - loss: 0.7020 16/16 [==============================] - 0s 1ms/step - loss: 0.7051 Testing for epoch 12 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6878 16/16 [==============================] - 0s 1ms/step - loss: 0.7024 16/16 [==============================] - 0s 1ms/step - loss: 0.7059 Testing for epoch 12 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6870 16/16 [==============================] - 0s 2ms/step - loss: 0.7032 16/16 [==============================] - 0s 1ms/step - loss: 0.7070 Testing for epoch 12 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6851 16/16 [==============================] - 0s 2ms/step - loss: 0.7029 16/16 [==============================] - 0s 2ms/step - loss: 0.7072 Epoch 13 of 33 Testing for epoch 13 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6872 16/16 [==============================] - 0s 1ms/step - loss: 0.7027 16/16 [==============================] - 0s 1ms/step - loss: 0.7064 Testing for epoch 13 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6867 16/16 [==============================] - 0s 1ms/step - loss: 0.7030 16/16 [==============================] - 0s 1ms/step - loss: 0.7069 Testing for epoch 13 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6878 16/16 [==============================] - 0s 1ms/step - loss: 0.7033 16/16 [==============================] - 0s 1ms/step - loss: 0.7069 Testing for epoch 13 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6874 16/16 [==============================] - 0s 1ms/step - loss: 0.7037 16/16 [==============================] - 0s 1ms/step - loss: 0.7076 Testing for epoch 13 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6875 16/16 [==============================] - 0s 1ms/step - loss: 0.7036 16/16 [==============================] - 0s 1ms/step - loss: 0.7074 Testing for epoch 13 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6865 16/16 [==============================] - 0s 1ms/step - loss: 0.7044 16/16 [==============================] - 0s 2ms/step - loss: 0.7086 Testing for epoch 13 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6871 16/16 [==============================] - 0s 1ms/step - loss: 0.7048 16/16 [==============================] - 0s 1ms/step - loss: 0.7089 Testing for epoch 13 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6884 16/16 [==============================] - 0s 1ms/step - loss: 0.7047 16/16 [==============================] - 0s 1ms/step - loss: 0.7085 Epoch 14 of 33 Testing for epoch 14 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6881 16/16 [==============================] - 0s 1ms/step - loss: 0.7051 16/16 [==============================] - 0s 1ms/step - loss: 0.7091 Testing for epoch 14 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6869 16/16 [==============================] - 0s 1ms/step - loss: 0.7043 16/16 [==============================] - 0s 1ms/step - loss: 0.7084 Testing for epoch 14 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6888 16/16 [==============================] - 0s 1ms/step - loss: 0.7038 16/16 [==============================] - 0s 1ms/step - loss: 0.7072 Testing for epoch 14 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6861 16/16 [==============================] - 0s 1ms/step - loss: 0.7054 16/16 [==============================] - 0s 1ms/step - loss: 0.7100 Testing for epoch 14 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6841 16/16 [==============================] - 0s 1ms/step - loss: 0.7055 16/16 [==============================] - 0s 1ms/step - loss: 0.7106 Testing for epoch 14 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6864 16/16 [==============================] - 0s 1ms/step - loss: 0.7057 16/16 [==============================] - 0s 1ms/step - loss: 0.7103 Testing for epoch 14 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6851 16/16 [==============================] - 0s 1ms/step - loss: 0.7057 16/16 [==============================] - 0s 1ms/step - loss: 0.7106 Testing for epoch 14 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6877 16/16 [==============================] - 0s 1ms/step - loss: 0.7061 16/16 [==============================] - 0s 1ms/step - loss: 0.7104 Epoch 15 of 33 Testing for epoch 15 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6854 16/16 [==============================] - 0s 1ms/step - loss: 0.7066 16/16 [==============================] - 0s 2ms/step - loss: 0.7116 Testing for epoch 15 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6849 16/16 [==============================] - 0s 1ms/step - loss: 0.7062 16/16 [==============================] - 0s 1ms/step - loss: 0.7112 Testing for epoch 15 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6855 16/16 [==============================] - 0s 1ms/step - loss: 0.7072 16/16 [==============================] - 0s 1ms/step - loss: 0.7123 Testing for epoch 15 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6855 16/16 [==============================] - 0s 1ms/step - loss: 0.7056 16/16 [==============================] - 0s 2ms/step - loss: 0.7102 Testing for epoch 15 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6845 16/16 [==============================] - 0s 1ms/step - loss: 0.7088 16/16 [==============================] - 0s 1ms/step - loss: 0.7146 Testing for epoch 15 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6840 16/16 [==============================] - 0s 2ms/step - loss: 0.7078 16/16 [==============================] - 0s 2ms/step - loss: 0.7134 Testing for epoch 15 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6846 16/16 [==============================] - 0s 2ms/step - loss: 0.7088 16/16 [==============================] - 0s 2ms/step - loss: 0.7144 Testing for epoch 15 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6829 16/16 [==============================] - 0s 1ms/step - loss: 0.7101 16/16 [==============================] - 0s 1ms/step - loss: 0.7165 Epoch 16 of 33 Testing for epoch 16 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6848 16/16 [==============================] - 0s 1ms/step - loss: 0.7087 16/16 [==============================] - 0s 2ms/step - loss: 0.7142 Testing for epoch 16 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6846 16/16 [==============================] - 0s 1ms/step - loss: 0.7089 16/16 [==============================] - 0s 1ms/step - loss: 0.7145 Testing for epoch 16 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6867 16/16 [==============================] - 0s 1ms/step - loss: 0.7081 16/16 [==============================] - 0s 2ms/step - loss: 0.7129 Testing for epoch 16 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6840 16/16 [==============================] - 0s 1ms/step - loss: 0.7090 16/16 [==============================] - 0s 1ms/step - loss: 0.7147 Testing for epoch 16 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6844 16/16 [==============================] - 0s 2ms/step - loss: 0.7097 16/16 [==============================] - 0s 1ms/step - loss: 0.7154 Testing for epoch 16 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6832 16/16 [==============================] - 0s 1ms/step - loss: 0.7095 16/16 [==============================] - 0s 1ms/step - loss: 0.7154 Testing for epoch 16 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6851 16/16 [==============================] - 0s 1ms/step - loss: 0.7075 16/16 [==============================] - 0s 2ms/step - loss: 0.7125 Testing for epoch 16 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6847 16/16 [==============================] - 0s 1ms/step - loss: 0.7106 16/16 [==============================] - 0s 2ms/step - loss: 0.7164 Epoch 17 of 33 Testing for epoch 17 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6836 16/16 [==============================] - 0s 2ms/step - loss: 0.7104 16/16 [==============================] - 0s 1ms/step - loss: 0.7164 Testing for epoch 17 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6833 16/16 [==============================] - 0s 1ms/step - loss: 0.7092 16/16 [==============================] - 0s 2ms/step - loss: 0.7150 Testing for epoch 17 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6866 16/16 [==============================] - 0s 1ms/step - loss: 0.7088 16/16 [==============================] - 0s 1ms/step - loss: 0.7137 Testing for epoch 17 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6859 16/16 [==============================] - 0s 1ms/step - loss: 0.7104 16/16 [==============================] - 0s 1ms/step - loss: 0.7158 Testing for epoch 17 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6818 16/16 [==============================] - 0s 1ms/step - loss: 0.7116 16/16 [==============================] - 0s 1ms/step - loss: 0.7183 Testing for epoch 17 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6827 16/16 [==============================] - 0s 1ms/step - loss: 0.7110 16/16 [==============================] - 0s 1ms/step - loss: 0.7174 Testing for epoch 17 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6826 16/16 [==============================] - 0s 1ms/step - loss: 0.7122 16/16 [==============================] - 0s 1ms/step - loss: 0.7189 Testing for epoch 17 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6847 16/16 [==============================] - 0s 1ms/step - loss: 0.7112 16/16 [==============================] - 0s 1ms/step - loss: 0.7171 Epoch 18 of 33 Testing for epoch 18 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6829 16/16 [==============================] - 0s 1ms/step - loss: 0.7129 16/16 [==============================] - 0s 1ms/step - loss: 0.7196 Testing for epoch 18 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6825 16/16 [==============================] - 0s 2ms/step - loss: 0.7133 16/16 [==============================] - 0s 2ms/step - loss: 0.7202 Testing for epoch 18 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6785 16/16 [==============================] - 0s 1ms/step - loss: 0.7128 16/16 [==============================] - 0s 1ms/step - loss: 0.7205 Testing for epoch 18 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6850 16/16 [==============================] - 0s 1ms/step - loss: 0.7125 16/16 [==============================] - 0s 1ms/step - loss: 0.7186 Testing for epoch 18 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6815 16/16 [==============================] - 0s 1ms/step - loss: 0.7130 16/16 [==============================] - 0s 2ms/step - loss: 0.7201 Testing for epoch 18 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6836 16/16 [==============================] - 0s 1ms/step - loss: 0.7154 16/16 [==============================] - 0s 1ms/step - loss: 0.7225 Testing for epoch 18 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6839 16/16 [==============================] - 0s 2ms/step - loss: 0.7124 16/16 [==============================] - 0s 1ms/step - loss: 0.7187 Testing for epoch 18 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6803 16/16 [==============================] - 0s 1ms/step - loss: 0.7138 16/16 [==============================] - 0s 1ms/step - loss: 0.7213 Epoch 19 of 33 Testing for epoch 19 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6834 16/16 [==============================] - 0s 1ms/step - loss: 0.7111 16/16 [==============================] - 0s 1ms/step - loss: 0.7172 Testing for epoch 19 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6808 16/16 [==============================] - 0s 1ms/step - loss: 0.7126 16/16 [==============================] - 0s 2ms/step - loss: 0.7196 Testing for epoch 19 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6819 16/16 [==============================] - 0s 1ms/step - loss: 0.7143 16/16 [==============================] - 0s 1ms/step - loss: 0.7215 Testing for epoch 19 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6801 16/16 [==============================] - 0s 2ms/step - loss: 0.7152 16/16 [==============================] - 0s 1ms/step - loss: 0.7230 Testing for epoch 19 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6814 16/16 [==============================] - 0s 2ms/step - loss: 0.7149 16/16 [==============================] - 0s 1ms/step - loss: 0.7224 Testing for epoch 19 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6833 16/16 [==============================] - 0s 1ms/step - loss: 0.7137 16/16 [==============================] - 0s 1ms/step - loss: 0.7204 Testing for epoch 19 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6809 16/16 [==============================] - 0s 1ms/step - loss: 0.7157 16/16 [==============================] - 0s 2ms/step - loss: 0.7235 Testing for epoch 19 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6802 16/16 [==============================] - 0s 1ms/step - loss: 0.7170 16/16 [==============================] - 0s 1ms/step - loss: 0.7251 Epoch 20 of 33 Testing for epoch 20 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6817 16/16 [==============================] - 0s 1ms/step - loss: 0.7170 16/16 [==============================] - 0s 1ms/step - loss: 0.7247 Testing for epoch 20 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6814 16/16 [==============================] - 0s 1ms/step - loss: 0.7162 16/16 [==============================] - 0s 1ms/step - loss: 0.7238 Testing for epoch 20 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6780 16/16 [==============================] - 0s 2ms/step - loss: 0.7188 16/16 [==============================] - 0s 1ms/step - loss: 0.7278 Testing for epoch 20 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6773 16/16 [==============================] - 0s 2ms/step - loss: 0.7195 16/16 [==============================] - 0s 1ms/step - loss: 0.7288 Testing for epoch 20 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6784 16/16 [==============================] - 0s 2ms/step - loss: 0.7160 16/16 [==============================] - 0s 1ms/step - loss: 0.7242 Testing for epoch 20 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6802 16/16 [==============================] - 0s 1ms/step - loss: 0.7193 16/16 [==============================] - 0s 1ms/step - loss: 0.7279 Testing for epoch 20 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6839 16/16 [==============================] - 0s 2ms/step - loss: 0.7176 16/16 [==============================] - 0s 1ms/step - loss: 0.7249 Testing for epoch 20 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6810 16/16 [==============================] - 0s 2ms/step - loss: 0.7166 16/16 [==============================] - 0s 1ms/step - loss: 0.7243 Epoch 21 of 33 Testing for epoch 21 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6799 16/16 [==============================] - 0s 2ms/step - loss: 0.7169 16/16 [==============================] - 0s 2ms/step - loss: 0.7250 Testing for epoch 21 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6782 16/16 [==============================] - 0s 1ms/step - loss: 0.7199 16/16 [==============================] - 0s 2ms/step - loss: 0.7291 Testing for epoch 21 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6783 16/16 [==============================] - 0s 1ms/step - loss: 0.7216 16/16 [==============================] - 0s 2ms/step - loss: 0.7311 Testing for epoch 21 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6798 16/16 [==============================] - 0s 2ms/step - loss: 0.7184 16/16 [==============================] - 0s 2ms/step - loss: 0.7268 Testing for epoch 21 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6783 16/16 [==============================] - 0s 1ms/step - loss: 0.7175 16/16 [==============================] - 0s 1ms/step - loss: 0.7260 Testing for epoch 21 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6771 16/16 [==============================] - 0s 1ms/step - loss: 0.7170 16/16 [==============================] - 0s 1ms/step - loss: 0.7256 Testing for epoch 21 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6765 16/16 [==============================] - 0s 1ms/step - loss: 0.7182 16/16 [==============================] - 0s 2ms/step - loss: 0.7273 Testing for epoch 21 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6784 16/16 [==============================] - 0s 1ms/step - loss: 0.7204 16/16 [==============================] - 0s 1ms/step - loss: 0.7296 Epoch 22 of 33 Testing for epoch 22 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6796 16/16 [==============================] - 0s 1ms/step - loss: 0.7219 16/16 [==============================] - 0s 1ms/step - loss: 0.7311 Testing for epoch 22 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6817 16/16 [==============================] - 0s 2ms/step - loss: 0.7181 16/16 [==============================] - 0s 1ms/step - loss: 0.7258 Testing for epoch 22 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6788 16/16 [==============================] - 0s 1ms/step - loss: 0.7232 16/16 [==============================] - 0s 1ms/step - loss: 0.7329 Testing for epoch 22 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6792 16/16 [==============================] - 0s 2ms/step - loss: 0.7219 16/16 [==============================] - 0s 1ms/step - loss: 0.7312 Testing for epoch 22 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6797 16/16 [==============================] - 0s 1ms/step - loss: 0.7214 16/16 [==============================] - 0s 2ms/step - loss: 0.7304 Testing for epoch 22 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6815 16/16 [==============================] - 0s 1ms/step - loss: 0.7210 16/16 [==============================] - 0s 1ms/step - loss: 0.7295 Testing for epoch 22 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6782 16/16 [==============================] - 0s 1ms/step - loss: 0.7215 16/16 [==============================] - 0s 2ms/step - loss: 0.7309 Testing for epoch 22 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6801 16/16 [==============================] - 0s 1ms/step - loss: 0.7227 16/16 [==============================] - 0s 1ms/step - loss: 0.7320 Epoch 23 of 33 Testing for epoch 23 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6756 16/16 [==============================] - 0s 1ms/step - loss: 0.7229 16/16 [==============================] - 0s 1ms/step - loss: 0.7333 Testing for epoch 23 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6766 16/16 [==============================] - 0s 1ms/step - loss: 0.7234 16/16 [==============================] - 0s 1ms/step - loss: 0.7336 Testing for epoch 23 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6762 16/16 [==============================] - 0s 1ms/step - loss: 0.7237 16/16 [==============================] - 0s 2ms/step - loss: 0.7339 Testing for epoch 23 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6749 16/16 [==============================] - 0s 1ms/step - loss: 0.7252 16/16 [==============================] - 0s 1ms/step - loss: 0.7362 Testing for epoch 23 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6755 16/16 [==============================] - 0s 1ms/step - loss: 0.7267 16/16 [==============================] - 0s 1ms/step - loss: 0.7378 Testing for epoch 23 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6782 16/16 [==============================] - 0s 2ms/step - loss: 0.7235 16/16 [==============================] - 0s 1ms/step - loss: 0.7333 Testing for epoch 23 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6802 16/16 [==============================] - 0s 2ms/step - loss: 0.7227 16/16 [==============================] - 0s 2ms/step - loss: 0.7318 Testing for epoch 23 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6804 16/16 [==============================] - 0s 2ms/step - loss: 0.7231 16/16 [==============================] - 0s 1ms/step - loss: 0.7323 Epoch 24 of 33 Testing for epoch 24 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6793 16/16 [==============================] - 0s 1ms/step - loss: 0.7268 16/16 [==============================] - 0s 1ms/step - loss: 0.7370 Testing for epoch 24 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6772 16/16 [==============================] - 0s 2ms/step - loss: 0.7259 16/16 [==============================] - 0s 1ms/step - loss: 0.7365 Testing for epoch 24 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6807 16/16 [==============================] - 0s 1ms/step - loss: 0.7251 16/16 [==============================] - 0s 1ms/step - loss: 0.7346 Testing for epoch 24 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6785 16/16 [==============================] - 0s 2ms/step - loss: 0.7240 16/16 [==============================] - 0s 2ms/step - loss: 0.7338 Testing for epoch 24 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6754 16/16 [==============================] - 0s 2ms/step - loss: 0.7257 16/16 [==============================] - 0s 2ms/step - loss: 0.7365 Testing for epoch 24 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6755 16/16 [==============================] - 0s 1ms/step - loss: 0.7255 16/16 [==============================] - 0s 2ms/step - loss: 0.7363 Testing for epoch 24 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6796 16/16 [==============================] - 0s 2ms/step - loss: 0.7291 16/16 [==============================] - 0s 2ms/step - loss: 0.7398 Testing for epoch 24 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6801 16/16 [==============================] - 0s 2ms/step - loss: 0.7283 16/16 [==============================] - 0s 2ms/step - loss: 0.7387 Epoch 25 of 33 Testing for epoch 25 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6776 16/16 [==============================] - 0s 1ms/step - loss: 0.7289 16/16 [==============================] - 0s 1ms/step - loss: 0.7399 Testing for epoch 25 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6800 16/16 [==============================] - 0s 1ms/step - loss: 0.7260 16/16 [==============================] - 0s 2ms/step - loss: 0.7358 Testing for epoch 25 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6732 16/16 [==============================] - 0s 2ms/step - loss: 0.7336 16/16 [==============================] - 0s 2ms/step - loss: 0.7466 Testing for epoch 25 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6759 16/16 [==============================] - 0s 2ms/step - loss: 0.7289 16/16 [==============================] - 0s 2ms/step - loss: 0.7403 Testing for epoch 25 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6741 16/16 [==============================] - 0s 1ms/step - loss: 0.7305 16/16 [==============================] - 0s 2ms/step - loss: 0.7425 Testing for epoch 25 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6746 16/16 [==============================] - 0s 2ms/step - loss: 0.7304 16/16 [==============================] - 0s 1ms/step - loss: 0.7422 Testing for epoch 25 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6695 16/16 [==============================] - 0s 2ms/step - loss: 0.7328 16/16 [==============================] - 0s 1ms/step - loss: 0.7464 Testing for epoch 25 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6743 16/16 [==============================] - 0s 2ms/step - loss: 0.7314 16/16 [==============================] - 0s 1ms/step - loss: 0.7436 Epoch 26 of 33 Testing for epoch 26 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6774 16/16 [==============================] - 0s 1ms/step - loss: 0.7273 16/16 [==============================] - 0s 2ms/step - loss: 0.7378 Testing for epoch 26 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6738 16/16 [==============================] - 0s 2ms/step - loss: 0.7297 16/16 [==============================] - 0s 1ms/step - loss: 0.7415 Testing for epoch 26 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6726 16/16 [==============================] - 0s 2ms/step - loss: 0.7335 16/16 [==============================] - 0s 1ms/step - loss: 0.7464 Testing for epoch 26 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6766 16/16 [==============================] - 0s 1ms/step - loss: 0.7290 16/16 [==============================] - 0s 1ms/step - loss: 0.7399 Testing for epoch 26 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6745 16/16 [==============================] - 0s 2ms/step - loss: 0.7328 16/16 [==============================] - 0s 2ms/step - loss: 0.7452 Testing for epoch 26 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6699 16/16 [==============================] - 0s 1ms/step - loss: 0.7323 16/16 [==============================] - 0s 1ms/step - loss: 0.7456 Testing for epoch 26 index 7: 16/16 [==============================] - 0s 1ms/step - loss: 0.6741 16/16 [==============================] - 0s 2ms/step - loss: 0.7293 16/16 [==============================] - 0s 2ms/step - loss: 0.7409 Testing for epoch 26 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6782 16/16 [==============================] - 0s 1ms/step - loss: 0.7319 16/16 [==============================] - 0s 1ms/step - loss: 0.7432 Epoch 27 of 33 Testing for epoch 27 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6732 16/16 [==============================] - 0s 1ms/step - loss: 0.7324 16/16 [==============================] - 0s 1ms/step - loss: 0.7449 Testing for epoch 27 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6735 16/16 [==============================] - 0s 2ms/step - loss: 0.7338 16/16 [==============================] - 0s 1ms/step - loss: 0.7466 Testing for epoch 27 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6696 16/16 [==============================] - 0s 2ms/step - loss: 0.7340 16/16 [==============================] - 0s 1ms/step - loss: 0.7477 Testing for epoch 27 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6686 16/16 [==============================] - 0s 1ms/step - loss: 0.7360 16/16 [==============================] - 0s 1ms/step - loss: 0.7502 Testing for epoch 27 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6719 16/16 [==============================] - 0s 1ms/step - loss: 0.7390 16/16 [==============================] - 0s 2ms/step - loss: 0.7532 Testing for epoch 27 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6703 16/16 [==============================] - 0s 3ms/step - loss: 0.7373 16/16 [==============================] - 0s 2ms/step - loss: 0.7514 Testing for epoch 27 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6632 16/16 [==============================] - 0s 2ms/step - loss: 0.7418 16/16 [==============================] - 0s 2ms/step - loss: 0.7586 Testing for epoch 27 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6701 16/16 [==============================] - 0s 1ms/step - loss: 0.7355 16/16 [==============================] - 0s 1ms/step - loss: 0.7493 Epoch 28 of 33 Testing for epoch 28 index 1: 16/16 [==============================] - 0s 1ms/step - loss: 0.6728 16/16 [==============================] - 0s 2ms/step - loss: 0.7317 16/16 [==============================] - 0s 2ms/step - loss: 0.7441 Testing for epoch 28 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6662 16/16 [==============================] - 0s 1ms/step - loss: 0.7400 16/16 [==============================] - 0s 2ms/step - loss: 0.7557 Testing for epoch 28 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6725 16/16 [==============================] - 0s 1ms/step - loss: 0.7374 16/16 [==============================] - 0s 1ms/step - loss: 0.7510 Testing for epoch 28 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6688 16/16 [==============================] - 0s 1ms/step - loss: 0.7388 16/16 [==============================] - 0s 2ms/step - loss: 0.7535 Testing for epoch 28 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6668 16/16 [==============================] - 0s 2ms/step - loss: 0.7388 16/16 [==============================] - 0s 2ms/step - loss: 0.7540 Testing for epoch 28 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6711 16/16 [==============================] - 0s 2ms/step - loss: 0.7348 16/16 [==============================] - 0s 2ms/step - loss: 0.7481 Testing for epoch 28 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6648 16/16 [==============================] - 0s 2ms/step - loss: 0.7408 16/16 [==============================] - 0s 2ms/step - loss: 0.7568 Testing for epoch 28 index 8: 16/16 [==============================] - 0s 1ms/step - loss: 0.6680 16/16 [==============================] - 0s 1ms/step - loss: 0.7397 16/16 [==============================] - 0s 2ms/step - loss: 0.7547 Epoch 29 of 33 Testing for epoch 29 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6685 16/16 [==============================] - 0s 2ms/step - loss: 0.7427 16/16 [==============================] - 0s 2ms/step - loss: 0.7582 Testing for epoch 29 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6666 16/16 [==============================] - 0s 2ms/step - loss: 0.7444 16/16 [==============================] - 0s 2ms/step - loss: 0.7606 Testing for epoch 29 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6685 16/16 [==============================] - 0s 2ms/step - loss: 0.7439 16/16 [==============================] - 0s 2ms/step - loss: 0.7596 Testing for epoch 29 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6728 16/16 [==============================] - 0s 1ms/step - loss: 0.7408 16/16 [==============================] - 0s 1ms/step - loss: 0.7547 Testing for epoch 29 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6722 16/16 [==============================] - 0s 2ms/step - loss: 0.7401 16/16 [==============================] - 0s 1ms/step - loss: 0.7540 Testing for epoch 29 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6683 16/16 [==============================] - 0s 2ms/step - loss: 0.7409 16/16 [==============================] - 0s 2ms/step - loss: 0.7558 Testing for epoch 29 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6687 16/16 [==============================] - 0s 2ms/step - loss: 0.7447 16/16 [==============================] - 0s 1ms/step - loss: 0.7603 Testing for epoch 29 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6710 16/16 [==============================] - 0s 2ms/step - loss: 0.7430 16/16 [==============================] - 0s 2ms/step - loss: 0.7577 Epoch 30 of 33 Testing for epoch 30 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6710 16/16 [==============================] - 0s 1ms/step - loss: 0.7382 16/16 [==============================] - 0s 2ms/step - loss: 0.7519 Testing for epoch 30 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6762 16/16 [==============================] - 0s 1ms/step - loss: 0.7392 16/16 [==============================] - 0s 1ms/step - loss: 0.7518 Testing for epoch 30 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6759 16/16 [==============================] - 0s 2ms/step - loss: 0.7424 16/16 [==============================] - 0s 2ms/step - loss: 0.7558 Testing for epoch 30 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6683 16/16 [==============================] - 0s 1ms/step - loss: 0.7396 16/16 [==============================] - 0s 2ms/step - loss: 0.7540 Testing for epoch 30 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6706 16/16 [==============================] - 0s 2ms/step - loss: 0.7416 16/16 [==============================] - 0s 2ms/step - loss: 0.7559 Testing for epoch 30 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6692 16/16 [==============================] - 0s 2ms/step - loss: 0.7460 16/16 [==============================] - 0s 2ms/step - loss: 0.7614 Testing for epoch 30 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6738 16/16 [==============================] - 0s 2ms/step - loss: 0.7442 16/16 [==============================] - 0s 2ms/step - loss: 0.7582 Testing for epoch 30 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6706 16/16 [==============================] - 0s 2ms/step - loss: 0.7394 16/16 [==============================] - 0s 2ms/step - loss: 0.7531 Epoch 31 of 33 Testing for epoch 31 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6634 16/16 [==============================] - 0s 2ms/step - loss: 0.7495 16/16 [==============================] - 0s 1ms/step - loss: 0.7669 Testing for epoch 31 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6644 16/16 [==============================] - 0s 2ms/step - loss: 0.7506 16/16 [==============================] - 0s 2ms/step - loss: 0.7680 Testing for epoch 31 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6649 16/16 [==============================] - 0s 2ms/step - loss: 0.7535 16/16 [==============================] - 0s 2ms/step - loss: 0.7713 Testing for epoch 31 index 4: 16/16 [==============================] - 0s 1ms/step - loss: 0.6685 16/16 [==============================] - 0s 2ms/step - loss: 0.7478 16/16 [==============================] - 0s 2ms/step - loss: 0.7636 Testing for epoch 31 index 5: 16/16 [==============================] - 0s 1ms/step - loss: 0.6669 16/16 [==============================] - 0s 2ms/step - loss: 0.7477 16/16 [==============================] - 0s 2ms/step - loss: 0.7639 Testing for epoch 31 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6657 16/16 [==============================] - 0s 2ms/step - loss: 0.7470 16/16 [==============================] - 0s 2ms/step - loss: 0.7632 Testing for epoch 31 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6640 16/16 [==============================] - 0s 2ms/step - loss: 0.7529 16/16 [==============================] - 0s 2ms/step - loss: 0.7707 Testing for epoch 31 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6626 16/16 [==============================] - 0s 2ms/step - loss: 0.7533 16/16 [==============================] - 0s 1ms/step - loss: 0.7715 Epoch 32 of 33 Testing for epoch 32 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6692 16/16 [==============================] - 0s 2ms/step - loss: 0.7507 16/16 [==============================] - 0s 2ms/step - loss: 0.7669 Testing for epoch 32 index 2: 16/16 [==============================] - 0s 2ms/step - loss: 0.6641 16/16 [==============================] - 0s 2ms/step - loss: 0.7470 16/16 [==============================] - 0s 2ms/step - loss: 0.7635 Testing for epoch 32 index 3: 16/16 [==============================] - 0s 1ms/step - loss: 0.6641 16/16 [==============================] - 0s 2ms/step - loss: 0.7496 16/16 [==============================] - 0s 2ms/step - loss: 0.7667 Testing for epoch 32 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6635 16/16 [==============================] - 0s 2ms/step - loss: 0.7544 16/16 [==============================] - 0s 2ms/step - loss: 0.7727 Testing for epoch 32 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6643 16/16 [==============================] - 0s 1ms/step - loss: 0.7505 16/16 [==============================] - 0s 2ms/step - loss: 0.7676 Testing for epoch 32 index 6: 16/16 [==============================] - 0s 1ms/step - loss: 0.6655 16/16 [==============================] - 0s 2ms/step - loss: 0.7450 16/16 [==============================] - 0s 1ms/step - loss: 0.7607 Testing for epoch 32 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6615 16/16 [==============================] - 0s 1ms/step - loss: 0.7555 16/16 [==============================] - 0s 2ms/step - loss: 0.7742 Testing for epoch 32 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6670 16/16 [==============================] - 0s 2ms/step - loss: 0.7488 16/16 [==============================] - 0s 2ms/step - loss: 0.7650 Epoch 33 of 33 Testing for epoch 33 index 1: 16/16 [==============================] - 0s 2ms/step - loss: 0.6591 16/16 [==============================] - 0s 1ms/step - loss: 0.7608 16/16 [==============================] - 0s 1ms/step - loss: 0.7811 Testing for epoch 33 index 2: 16/16 [==============================] - 0s 1ms/step - loss: 0.6614 16/16 [==============================] - 0s 2ms/step - loss: 0.7509 16/16 [==============================] - 0s 1ms/step - loss: 0.7687 Testing for epoch 33 index 3: 16/16 [==============================] - 0s 2ms/step - loss: 0.6687 16/16 [==============================] - 0s 1ms/step - loss: 0.7495 16/16 [==============================] - 0s 2ms/step - loss: 0.7654 Testing for epoch 33 index 4: 16/16 [==============================] - 0s 2ms/step - loss: 0.6572 16/16 [==============================] - 0s 2ms/step - loss: 0.7565 16/16 [==============================] - 0s 2ms/step - loss: 0.7762 Testing for epoch 33 index 5: 16/16 [==============================] - 0s 2ms/step - loss: 0.6616 16/16 [==============================] - 0s 1ms/step - loss: 0.7621 16/16 [==============================] - 0s 2ms/step - loss: 0.7821 Testing for epoch 33 index 6: 16/16 [==============================] - 0s 2ms/step - loss: 0.6626 16/16 [==============================] - 0s 1ms/step - loss: 0.7550 16/16 [==============================] - 0s 2ms/step - loss: 0.7733 Testing for epoch 33 index 7: 16/16 [==============================] - 0s 2ms/step - loss: 0.6590 16/16 [==============================] - 0s 2ms/step - loss: 0.7566 16/16 [==============================] - 0s 1ms/step - loss: 0.7759 Testing for epoch 33 index 8: 16/16 [==============================] - 0s 2ms/step - loss: 0.6620 16/16 [==============================] - 0s 2ms/step - loss: 0.7623 16/16 [==============================] - 0s 1ms/step - loss: 0.7821 ###Markdown Anomaly Prediction ###Code result=x_test.copy(deep=True) result['Anomaly']=model.predict(x_test) result.head() ###Output _____no_output_____ ###Markdown Anomaly Visualization Bar Plot ###Code result['Anomaly'].value_counts().plot(kind='bar',color=['green','red']) ###Output _____no_output_____ ###Markdown Pie Chart ###Code fig = px.pie(result['Anomaly'],names=result['Anomaly'], title='Anomaly rate',) fig.show() ###Output _____no_output_____ ###Markdown AnomaliesIn this part we will perform Dimensionality Reduction technique to visualize data. This can be performed using technique such as PCA or TSNE algorithms. ###Code pca = PCA(n_components=2) pca_results = pca.fit_transform(result.drop('Anomaly',axis=1)) plt.rcParams["figure.figsize"] = (20,10) plt.scatter(x=pca_results[:,0],y=pca_results[:,1],c=result.iloc[:,result.columns.get_loc('Anomaly')]) plt.show() ###Output _____no_output_____
AI_Class/000/전처리-참고서.ipynb	###Markdown 전처리 참고서사용한 데이터는 타이타닉 데이터데이터는 df라는 변수에 담겨져있다데이터를 불러오는 코드는 가장 아래줄에 배치 ###Code import numpy as np # 수학적인 함수가 많이 들어있는 라이브러리 import pandas as pd # 데이터프레임을 다루기 위한 라이브러리 ###Output _____no_output_____ ###Markdown 데이터 확인.head(x)x개의 줄을 보여줌.tail(y)데이터 마지막의 y개의 줄을 보여줌 ###Code df.head() df.tail() ###Output _____no_output_____ ###Markdown 데이터의 칼럼명을 수정해야 할 경우 ###Code df.columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o'] df.head() df.columns.values[0] = 'survived' df.head() ###Output _____no_output_____ ###Markdown 데이터 자료형 확인 ###Code df.info() ###Output <class 'pandas.core.frame.DataFrame'> Int64Index: 889 entries, 0 to 890 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 survived 889 non-null int64 1 pclass 889 non-null int64 2 sex 889 non-null object 3 age 712 non-null float64 4 sibsp 889 non-null int64 5 parch 889 non-null int64 6 fare 889 non-null float64 7 embarked 889 non-null object 8 class 889 non-null category 9 who 889 non-null object 10 adult_male 889 non-null bool 11 deck 201 non-null category 12 embark_town 889 non-null object 13 alive 889 non-null object 14 alone 889 non-null float64 dtypes: bool(1), category(2), float64(3), int64(4), object(5) memory usage: 93.4+ KB ###Markdown 데이터 통계 요약정보 확인데이터의 자료형이 int, float, double 등의 숫자형인 칼럼만 적용 ###Code df.describe() ###Output _____no_output_____ ###Markdown 결측치 확인 ###Code df.isna().sum() ###Output _____no_output_____ ###Markdown 칼럼의 고유 값 확인데이터명.['칼럼명'].unique() ###Code df['alone'].unique() df['embark_town'].unique() ###Output _____no_output_____ ###Markdown 누락된 행 삭제nan으로 표현된 데이터 삭제axis : 0은 행단위, 1은 열단위로 처리 ###Code df.dropna(subset=['embark_town'], axis=0, inplace=True) df.head() ###Output _____no_output_____ ###Markdown 자료형 변환.astype('바꿀 자료형') ###Code df = df[['alone','pclass']] df.info() df['alone'] = df['alone'].astype('int') df.info() ###Output <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 alone 891 non-null int64 1 pclass 891 non-null int64 dtypes: int64(2) memory usage: 14.0 KB ###Markdown 자료내용중 특정 값들을 변환.replace('a', 'b', inplace=True)자료안의 a 값들을 b로 변환 ###Code df['embark_town'].unique() df['embark_town'].replace('Queenstown', 'Q', inplace=True) df['embark_town'].unique() ###Output _____no_output_____ ###Markdown 특정 열(row) 선택 ###Code new_row = df[['pclass', 'age', 'embarked']] new_row.head() ###Output _____no_output_____ ###Markdown 특정 열(row) 삭제 ###Code df.head() df.drop(['deck', 'embark_town'], axis=1, inplace=True) df.head() ###Output _____no_output_____ ###Markdown 원핫인코딩 기본적으로 머신러닝 알고리즘은 문자열 값을 입력 값으로 인식하지 않음. 그렇기 때문에 모든 문자열 값들을 숫자 형으로 인코딩하는 전처리 작업 후에 머신러닝 모델에 학습을 시켜야 함.간단하게 피처 값의 유형에 따라 새로운 피처를 추가해 고유 값에 해당하는 칼럼에만 1을 표시하고 나머지 칼럼에는 0을 표시하는 방법. ###Code # 원핫인코딩 onehot_embarked = pd.get_dummies(df['embarked'], prefix='town') # 데이터 합치기 df = pd.concat([df, onehot_embarked], axis=1) onehot_embarked.head() df.head() import seaborn as sns df = sns.load_dataset('titanic') ###Output _____no_output_____
examples/car_simulation_to_analysis.ipynb	###Markdown From CMB simulations to power spectra analysisIn this tutorial, we will show how to generate CMB simulations from theoritical $C_\ell$, produce the corresponding CMB map tiles and finally analyze them using `psplay`. CMB simulationUsing the attached $C_\ell$ [file](bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat), we will first generate simulations with `pspy` in `CAR` pixellisation. ###Code from pspy import so_map ncomp = 3 ra0, ra1, dec0, dec1 = -30, 30, -30, 30 res = 0.5 template = so_map.car_template(ncomp, ra0, ra1, dec0, dec1, res) cl_file = "bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat" cmb = template.synfast(cl_file) ###Output _____no_output_____ ###Markdown Then, we make 2 splits out of it, each with $5$ µK.arcmin rms in temperature and $5\times\sqrt{2}$ µK.arcmin in polarisation ###Code import numpy as np nsplits = 2 splits = [cmb.copy() for i in range(nsplits)] for i in range(nsplits): noise = so_map.white_noise(cmb, rms_uKarcmin_T=5, rms_uKarcmin_pol=np.sqrt(2)5) splits[i].data += noise.data ###Output _____no_output_____ ###Markdown We finally write on disk the two corresponding `fits` files ###Code for i in range(nsplits): splits[i].write_map("split{}_IQU_CAR.fits".format(i)) ###Output _____no_output_____ ###Markdown Generation of tile filesWe now have a total of 6 maps in `CAR` pixellisation we want to plot them into tile files. A tile file corresponds to a static `PNG` file representing a part of the sky at a given zoom level. To convert the maps, we will use a set of tools from `psplay`. Conversion to tile filesYou should get two files of ~ 1 Gb size. We should keep these files since we will use them later for the power spectra computation. Last step in the conversion process, we have to generate the different tiles ###Code from psplay import tools for i in range(nsplits): tools.car2tiles(input_file="split{}_IQU_car.fits".format(i), output_dir="tiles/split{}_IQU_car.fits".format(i)) ###Output _____no_output_____ ###Markdown At the end of the process, we get a new directory `tiles` with two sub-directories `split0_IQU_car.fits` and `split1_IQU_car_fits`. Within these two directories we have 6 directories which names refer to the zoom level : the directory `-5` correspond to the smallest zoom whereas the directory `0` corresponds to the most precise tiles. Using `psplay` to visualize the mapsNow that we have generated the different tiles, we can interactively see the maps with `psplay`. The configuration of `psplay` can be either done using a dictionary or more easily with a `yaml`file. Basically, the configuration file needs to know where the original `FITS` files and the tile files are located. Other options can be set but we won't enter into too much details for this tutorial. For the purpose of this tutorial we have already created the `yaml` [file](simulation_to_analysis.yml) associated to the files we have created so far. Here is a copy-paste of it```yamlmap: layers: - cmb: tags: splits: values: [0, 1] keybindings: [j, k] components: values: [0, 1, 2] keybindings: [c, v] substitutes: ["T", "Q", "U"] tile: files/tiles/split{splits}_IQU_car.fits/{z}/tile_{y}_{x}_{components}.png name: CMB simulation - split {splits} - {components}data: maps: - id: split0 file: split0_IQU_car.fits - id: split1 file: split1_IQU_car.fits theory_file: bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat plot: lmax: 2000```There are 3 sections related to the 3 main steps namely the map visualization, the spectra computation and the graphical representation of the spectra. The two first sections are mandatory. The `map` section corresponds to the tile files generated so far and can be dynamically expanded given different `tags`. Here for instance, we will built all the combination of split and component values. The tile and the name fields will be generated for each combination given the tag values. Dedicated keybindings can also be defined in order to switch between the different split and/or components.The tricker part of this configuration is to set the path to tiles relatively to where your notebook/JupyterLab instance has been started. We can't set an absolute path and so you have to make sure that your notebook has been initiated from the `examples` directory. Otherwise, you should change the path to tile files given that you have access to them from your JupyterLab instance. So make sure to initiate your JupyterLab session from a "top" directory.We can now create an instance of `psplay` application and show the different maps ###Code from psplay import App my_app = App("simulation_to_analysis.yml") my_app.show_map() ###Output _____no_output_____ ###Markdown If we unzoom enough, we will see the $\pm$ 30° patch size. We can also switch between the different I, Q, U and split layers. There are other options like the colormap and color scale which one can play and see how things change. Selecting sub-patches and computing the corresponding power spectraGiven the different map, we can now select patches by clicking on the square or the disk icons located just below the +/- zoom button. For instance, if we select a rectangle and another disk whose size are more or less the total size of our patch, we will get two surfaces of almost 3000 square degrees. Now we can ask `psplay` to compute the power spectra of both regions. Let's initiate the plot application ###Code my_app.show_plot() ###Output _____no_output_____ ###Markdown From CMB simulations to power spectra analysisIn this tutorial, we will show how to generate CMB simulations from theoritical $C_\ell$, produce the corresponding CMB map tiles and finally analyze them using `psplay`. CMB simulationUsing the attached $C_\ell$ [file](bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat), we will first generate simulations with `pspy` in `CAR` pixellisation. ###Code from pspy import so_map ncomp= 3 ra0, ra1, dec0, dec1 = -30, 30, -30, 30 res = 0.5 template = so_map.car_template(ncomp, ra0, ra1, dec0, dec1, res) cl_file = "bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat" cmb = template.synfast(cl_file) ###Output _____no_output_____ ###Markdown Then, we make 2 splits out of it, each with $5$ µK.arcmin rms in temperature and $5\times\sqrt{2}$ µK.arcmin in polarisation ###Code import numpy as np nsplits = 2 splits = [cmb.copy() for i in range(nsplits)] for i in range(nsplits): noise = so_map.white_noise(cmb, rms_uKarcmin_T=5, rms_uKarcmin_pol=np.sqrt(2)5) splits[i].data += noise.data ###Output _____no_output_____ ###Markdown We finally write on disk the two corresponding `fits` files ###Code for i in range(nsplits): splits[i].write_map("split{}_IQU_CAR.fits".format(i)) ###Output _____no_output_____ ###Markdown Generation of tile filesWe now have a total of 6 maps in `CAR` pixellisation we want to plot them into tile files. A tile file corresponds to a static `PNG` file representing a part of the sky at a given zoom level. To convert the maps, we will use a set of tools from `psplay`. Conversion to tile filesYou should get two files of ~ 1 Gb size. We should keep these files since we will use them later for the power spectra computation. Last step in the conversion process, we have to generate the different tiles ###Code from psplay import tools for i in range(nsplits): tools.car2tiles(input_file="split{}_IQU_car.fits".format(i), output_dir="tiles/split{}_IQU_car.fits".format(i)) ###Output _____no_output_____ ###Markdown At the end of the process, we get a new directory `tiles` with two sub-directories `split0_IQU_car.fits` and `split1_IQU_car_fits`. Within these two directories we have 6 directories which names refer to the zoom level : the directory `-5` correspond to the smallest zoom whereas the directory `0` corresponds to the most precise tiles. Using `psplay` to visualize the mapsNow that we have generated the different tiles, we can interactively see the maps with `psplay`. The configuration of `psplay` can be either done using a dictionary or more easily with a `yaml`file. Basically, the configuration file needs to know where the original `FITS` files and the tile files are located. Other options can be set but we won't enter into too much details for this tutorial. For the purpose of this tutorial we have already created the `yaml` [file](simulation_to_analysis.yml) associated to the files we have created so far. Here is a copy-paste of it```yamlmap: layers: - cmb: tags: splits: values: [0, 1] keybindings: [j, k] components: values: [0, 1, 2] keybindings: [c, v] substitutes: ["T", "Q", "U"] tile: files/tiles/split{splits}_IQU_car.fits/{z}/tile_{y}_{x}_{components}.png name: CMB simulation - split {splits} - {components}data: maps: - id: split0 file: split0_IQU_car.fits - id: split1 file: split1_IQU_car.fits theory_file: bode_almost_wmap5_lmax_1e4_lensedCls_startAt2.dat plot: lmax: 2000```There are 3 sections related to the 3 main steps namely the map visualization, the spectra computation and the graphical representation of the spectra. The two first sections are mandatory. The `map` section corresponds to the tile files generated so far and can be dynamically expanded given different `tags`. Here for instance, we will built all the combination of split and component values. The tile and the name fields will be generated for each combination given the tag values. Dedicated keybindings can also be defined in order to switch between the different split and/or components.The tricker part of this configuration is to set the path to tiles relatively to where your notebook/JupyterLab instance has been started. We can't set an absolute path and so you have to make sure that your notebook has been initiated from the `examples` directory. Otherwise, you should change the path to tile files given that you have access to them from your JupyterLab instance. So make sure to initiate your JupyterLab session from a "top" directory.We can now create an instance of `psplay` application and show the different maps ###Code from psplay import App my_app = App("simulation_to_analysis.yml") my_app.show_map() ###Output _____no_output_____ ###Markdown If we unzoom enough, we will see the $\pm$ 30° patch size. We can also switch between the different I, Q, U and split layers. There are other options like the colormap and color scale which one can play and see how things change. Selecting sub-patches and computing the corresponding power spectraGiven the different map, we can now select patches by clicking on the square or the disk icons located just below the +/- zoom button. For instance, if we select a rectangle and another disk whose size are more or less the total size of our patch, we will get two surfaces of almost 3000 square degrees. Now we can ask `psplay` to compute the power spectra of both regions. Let's initiate the plot application ###Code my_app.show_plot() ###Output _____no_output_____
Chapter7/ufo-algorithms-lab.ipynb	###Markdown UFO Sightings Algorithms LabThe goal of this notebook is to build out models to use for predicting the legitimacy of a UFO sighting using the XGBoost and Linear Learner algorithm.What we plan on accompishling is the following:1. [Load dataset onto Notebook instance memory from S3](Step-1:-Load-the-data-from-Amazon-S3)1. [Cleaning, transforming, analyize, and preparing the dataset](Step-2:-Cleaning,-transforming,-analyize,-and-preparing-the-dataset)1. [Create and train our model (XGBoost)](Step-3:-Creating-and-training-our-model-(XGBoost))1. [Create and train our model (Linear Learner)](Step-4:-Creating-and-training-our-model-(Linear-Learner)) First let's go ahead and import all the needed libraries. ###Code import io from datetime import datetime import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import boto3 import sagemaker import sagemaker.amazon.common as smac from sagemaker import get_execution_role ###Output _____no_output_____ ###Markdown Step 1: Loading the data from Amazon S3Let's get the UFO sightings data that is stored in S3 and load it into memory. ###Code role = get_execution_role() boto3_sess = boto3.Session() bucket = "tc-ml-cert-training" data_key = "ufo_dataset/ufo_fullset.csv" data_location = f"s3://{bucket}/{data_key}" df = pd.read_csv(data_location, low_memory=False) df.head() ###Output _____no_output_____ ###Markdown Step 2: Cleaning, transforming, analysing, and preparing the datasetThis step is so important. It's crucial that we clean and prepare our data before we do anything else.Let's check to see if there are any missing values: ###Code missing_values = df.isnull().values.any() if missing_values: display(df[df.isnull().any(axis=1)]) df["shape"].value_counts() # Replace the missing values with the most common shape df["shape"].fillna(df["shape"].value_counts().index[0], inplace=True) ###Output _____no_output_____ ###Markdown Let's go ahead and start preparing our dataset by transforming some of the values into the correct data types. Here is what we are going to take care of.1. Convert the `reportedTimestamp` and `eventDate` to a datetime data types.1. Convert the `shape` and `weather` to a category data type.1. Map the `physicalEvidence` and `contact` from 'Y', 'N' to `0`, `1`.1. Convert the `researchOutcome` to a category data type (target attribute). ###Code df["reportedTimestamp"] = pd.to_datetime(df["reportedTimestamp"]) df["eventDate"] = pd.to_datetime(df["eventDate"]) df["shape"] = df["shape"].astype("category") df["weather"] = df["weather"].astype("category") df["physicalEvidence"] = df["physicalEvidence"].replace({"Y": 1, "N": 0}) df["contact"] = df["contact"].replace({"Y": 1, "N": 0}) df["researchOutcome"] = df["researchOutcome"].astype("category") df.dtypes ###Output _____no_output_____ ###Markdown Let's visualize some of the data to see if we can find out any important information. ###Code %matplotlib inline sns.set_context("paper", font_scale=1.4) m_cts = df["contact"].value_counts() m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5, 5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title("UFO Sightings and Contact") ax.set_xlabel("Was contact made?") ax.set_ylabel("Number of Sightings") ax.set_xticklabels(["No", "Yes"]) plt.xticks(rotation=45) plt.show() m_cts = df["physicalEvidence"].value_counts() m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5, 5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title("UFO Sightings and Physical Evidence") ax.set_xlabel("Was there physical evidence?") ax.set_ylabel("Number of Sightings") ax.set_xticklabels(["No", "Yes"]) plt.xticks(rotation=45) plt.show() m_cts = df["shape"].value_counts() m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(9, 5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title("UFO Sightings by Shape") ax.set_xlabel("UFO Shape") ax.set_ylabel("Number of Sightings") plt.xticks(rotation=45) plt.show() m_cts = df["weather"].value_counts() m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5, 5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title("UFO Sightings by Weather") ax.set_xlabel("Weather") ax.set_ylabel("Number of Sightings") plt.xticks(rotation=45) plt.show() m_cts = df["researchOutcome"].value_counts() m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5, 5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title("UFO Sightings and Research Outcome") ax.set_xlabel("Research Outcome") ax.set_ylabel("Number of Sightings") plt.xticks(rotation=45) plt.show() ufo_yr = df["eventDate"].dt.year # series with the year exclusively ## Set axes ## years_data = ufo_yr.value_counts() years_index = years_data.index # x ticks years_values = years_data.to_numpy() ## Create Bar Plot ## plt.figure(figsize=(15, 8)) plt.xticks(rotation=60) plt.title("UFO Sightings by Year") plt.ylabel("Number of Sightings") plt.xlabel("Year") years_plot = sns.barplot(x=years_index[:60], y=years_values[:60]) df.corr() ###Output _____no_output_____ ###Markdown Let's drop the columns that are not important. 1. We can drop `sighting` becuase it is always 'Y' or Yes. 1. Let's drop the `firstName` and `lastName` becuase they are not important in determining the `researchOutcome`.1. Let's drop the `reportedTimestamp` becuase when the sighting was reporting isn't going to help us determine the legitimacy of the sighting.1. We would need to create some sort of buckets for the `eventDate` and `eventTime`, like seasons for example, but since the distribution of dates is pretty even, let's go ahead and drop them. ###Code df.drop( columns=[ "firstName", "lastName", "sighting", "reportedTimestamp", "eventDate", "eventTime", ], inplace=True, ) df.head() ###Output _____no_output_____ ###Markdown Let's apply one-hot encoding1. We need to one-hot both the `weather` attribute and the `shape` attribute. 1. We also need to transform or map the researchOutcome (target) attribute into numeric values. This is what the alogrithm is expecting. We can do this by mapping unexplained, explained, and probable to 0, 1, 2. ###Code # Let's one-hot the weather and shape attribute df = pd.get_dummies(df, columns=["weather", "shape"]) # Let's replace the researchOutcome values with 0, 1, 2 for Unexplained, Explained, and Probable df["researchOutcome"] = df["researchOutcome"].replace( {"unexplained": 0, "explained": 1, "probable": 2} ) display(df.head()) display(df.shape) ###Output _____no_output_____ ###Markdown Let's randomize and split the data into training, validation, and testing.1. First we need to randomize the data.1. Next Let's use 80% of the dataset for our training set.1. Then use 10% for validation during training.1. Finally we will use 10% for testing our model after it is deployed. ###Code # Let's go ahead and randomize our data. df = df.sample(frac=1).reset_index(drop=True) # Next, Let's split the data into a training, validation, and testing. rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 # 80% for training val_list = (rand_split >= 0.8) & (rand_split < 0.9) # 10% for validation test_list = rand_split >= 0.9 # 10% for testing # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] ###Output _____no_output_____ ###Markdown Next, let's go ahead and rearrange our attributes so the first attribute is our target attribute `researchOutcome`. This is what AWS requires and the XGBoost algorithms expects. You can read all about it here in the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.htmlInputOutput-XGBoost).After that we will go ahead and create those files on our Notebook instance (stored as CSV) and then upload them to S3. ###Code # Simply moves the researchOutcome attribute to the first position before creating CSV files pd.concat( [data_train["researchOutcome"], data_train.drop(["researchOutcome"], axis=1)], axis=1, ).to_csv("train.csv", index=False, header=False) pd.concat( [data_val["researchOutcome"], data_val.drop(["researchOutcome"], axis=1)], axis=1 ).to_csv("validation.csv", index=False, header=False) # Next we can take the files we just stored onto our Notebook instance and upload them to S3. ml_bucket = boto3_sess.resource("s3").Bucket(bucket) ml_bucket.Object("algorithms_lab/xgboost_train/train.csv").upload_file("train.csv") ml_bucket.Object("algorithms_lab/xgboost_validation/validation.csv").upload_file( "validation.csv" ) ###Output _____no_output_____ ###Markdown Step 3: Creating and training our model (XGBoost)This is where the magic happens. We will get the ECR container hosted in ECR for the XGBoost algorithm. ###Code container = sagemaker.image_uris.retrieve("xgboost", boto3_sess.region_name, "1") ###Output _____no_output_____ ###Markdown Next, because we're training with the CSV file format, we'll create inputs that our training function can use as a pointer to the files in S3, which also specify that the content type is CSV. ###Code s3_input_train = sagemaker.inputs.TrainingInput( s3_data=f"s3://{bucket}/algorithms_lab/xgboost_train", content_type="csv" ) s3_input_validation = sagemaker.inputs.TrainingInput( s3_data=f"s3://{bucket}/algorithms_lab/xgboost_validation", content_type="csv", ) ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a XGBoost model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `xgboost` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [XGBoost Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code # Create a training job name dt_now = datetime.now().strftime("%Y%m%d%H%M%S") job_name = f"ufo-xgboost-job-{dt_now}" print(f"Job name: {job_name}") # Here is where the model artifact will be stored output_location = f"s3://{bucket}/algorithms_lab/xgboost_output" sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator( container, role, instance_count=1, instance_type="ml.m4.xlarge", output_path=output_location, sagemaker_session=sess, ) xgb.set_hyperparameters(objective="multi:softmax", num_class=3, num_round=100) data_channels = {"train": s3_input_train, "validation": s3_input_validation} xgb.fit(data_channels, job_name=job_name) print( f"Location of the trained XGBoost model: {output_location}/{job_name}/output/model.tar.gz" ) ###Output _____no_output_____ ###Markdown After we train our model we can see the default evaluation metric in the logs. The `merror` is used in multiclass classification error rate. It is calculated as (wrong cases)/(all cases). We want this to be minimized (so we want this to be super small). --- Step 4: Creating and training our model (Linear Learner)Let's evaluate the Linear Learner algorithm as well. Let's go ahead and randomize the data again and get it ready for the Linear Leaner algorithm. We will also rearrange the columns so it is ready for the algorithm (it expects the first column to be the target attribute) ###Code np.random.seed(0) rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 val_list = (rand_split >= 0.8) & (rand_split < 0.9) test_list = rand_split >= 0.9 # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] # This rearranges the columns cols = list(data_train) cols.insert(0, cols.pop(cols.index("researchOutcome"))) data_train = data_train[cols] cols = list(data_val) cols.insert(0, cols.pop(cols.index("researchOutcome"))) data_val = data_val[cols] cols = list(data_test) cols.insert(0, cols.pop(cols.index("researchOutcome"))) data_test = data_test[cols] # Breaks the datasets into attribute numpy.ndarray and the same for target attribute. train_X = data_train.drop(columns="researchOutcome").values train_y = data_train["researchOutcome"].values val_X = data_val.drop(columns="researchOutcome").values val_y = data_val["researchOutcome"].values test_X = data_test.drop(columns="researchOutcome").values test_y = data_test["researchOutcome"].values ###Output _____no_output_____ ###Markdown Next, Let's create recordIO file for the training data and upload it to S3. ###Code train_file = "ufo_sightings_train_recordIO_protobuf.data" f = io.BytesIO() smac.write_numpy_to_dense_tensor( f, train_X.astype("float32"), train_y.astype("float32") ) f.seek(0) ml_bucket.Object(f"algorithms_lab/linearlearner_train/{train_file}").upload_fileobj(f) training_recordIO_protobuf_location = ( f"s3://{bucket}/algorithms_lab/linearlearner_train/{train_file}" ) print( f"The Pipe mode recordIO protobuf training data: {training_recordIO_protobuf_location}" ) ###Output _____no_output_____ ###Markdown Let's create recordIO file for the validation data and upload it to S3 ###Code validation_file = "ufo_sightings_validatioin_recordIO_protobuf.data" f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, val_X.astype("float32"), val_y.astype("float32")) f.seek(0) ml_bucket.Object( f"algorithms_lab/linearlearner_validation/{validation_file}" ).upload_fileobj(f) validate_recordIO_protobuf_location = ( f"s3://{bucket}/algorithms_lab/linearlearner_validation/{validation_file}" ) print( f"The Pipe mode recordIO protobuf validation data: {validate_recordIO_protobuf_location}" ) ###Output _____no_output_____ ###Markdown ---Alright we are good to go for the Linear Learner algorithm. Let's get everything we need from the ECR repository to call the Linear Learner algorithm. ###Code container = sagemaker.image_uris.retrieve("linear-learner", boto3_sess.region_name, "1") # Create a training job name dt_now = datetime.now().strftime("%Y%m%d%H%M%S") job_name = f"ufo-linear-learner-job-{dt_now}" print(f"Job name {job_name}") # Here is where the model-artifact will be stored output_location = f"s3://{bucket}/algorithms_lab/linearlearner_output" ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a Linear Learner model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `linear-learner` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [The input type (Pipe)](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)1. [Linear Learner Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code print(f"The feature_dim hyperparameter needs to be set to {data_train.shape[1] - 1}.") sess = sagemaker.Session() # Setup the LinearLeaner algorithm from the ECR container linear = sagemaker.estimator.Estimator( container, role, instance_count=1, instance_type="ml.c4.xlarge", output_path=output_location, sagemaker_session=sess, input_mode="Pipe", ) # Setup the hyperparameters linear.set_hyperparameters( feature_dim=22, # number of attributes (minus the researchOutcome attribute) predictor_type="multiclass_classifier", # type of classification problem num_classes=3, ) # number of classes in out researchOutcome (explained, unexplained, probable) # Launch a training job. This method calls the CreateTrainingJob API call data_channels = { "train": training_recordIO_protobuf_location, "validation": validate_recordIO_protobuf_location, } linear.fit(data_channels, job_name=job_name) print( f"Location of the trained Linear Learner model: {output_location}/{job_name}/output/model.tar.gz" ) ###Output _____no_output_____ ###Markdown UFO Sightings Algorithms LabThe goal of this notebook is to build out models to use for predicting the legitimacy of a UFO sighting using the XGBoost and Linear Learner algorithm.What we plan on accompishling is the following:1. [Load dataset onto Notebook instance memory from S3](Step-1:-Load-the-data-from-Amazon-S3)1. [Cleaning, transforming, analyize, and preparing the dataset](Step-2:-Cleaning,-transforming,-analyize,-and-preparing-the-dataset)1. [Create and train our model (XGBoost)](Step-3:-Creating-and-training-our-model-(XGBoost))1. [Create and train our model (Linear Learner)](Step-4:-Creating-and-training-our-model-(Linear-Learner)) First let's go ahead and import all the needed libraries. ###Code import pandas as pd import numpy as np from datetime import datetime import io import sagemaker.amazon.common as smac import boto3 from sagemaker import get_execution_role import sagemaker import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Step 1: Loading the data from Amazon S3Let's get the UFO sightings data that is stored in S3 and load it into memory. ###Code role = get_execution_role() bucket='<INSERT_YOUR_BUCKET_NAME_HERE>' sub_folder = 'ufo_dataset' data_key = 'ufo_fullset.csv' data_location = 's3://{}/{}/{}'.format(bucket, sub_folder, data_key) df = pd.read_csv(data_location, low_memory=False) df.head() ###Output _____no_output_____ ###Markdown Step 2: Cleaning, transforming, analyize, and preparing the datasetThis step is so important. It's crucial that we clean and prepare our data before we do anything else. ###Code # Let's check to see if there are any missing values missing_values = df.isnull().values.any() if(missing_values): display(df[df.isnull().any(axis=1)]) df['shape'].value_counts() # Replace the missing values with the most common shape df['shape'] = df['shape'].fillna(df['shape'].value_counts().index[0]) ###Output _____no_output_____ ###Markdown Let's go ahead and start preparing our dataset by transforming some of the values into the correct data types. Here is what we are going to take care of.1. Convert the `reportedTimestamp` and `eventDate` to a datetime data types.1. Convert the `shape` and `weather` to a category data type.1. Map the `physicalEvidence` and `contact` from 'Y', 'N' to `0`, `1`.1. Convert the `researchOutcome` to a category data type (target attribute). ###Code df['reportedTimestamp'] = pd.to_datetime(df['reportedTimestamp']) df['eventDate'] = pd.to_datetime(df['eventDate']) df['shape'] = df['shape'].astype('category') df['weather'] = df['weather'].astype('category') df['physicalEvidence'] = df['physicalEvidence'].replace({'Y': 1, 'N': 0}) df['contact'] = df['contact'].replace({'Y': 1, 'N': 0}) df['researchOutcome'] = df['researchOutcome'].astype('category') df.dtypes ###Output _____no_output_____ ###Markdown Let's visualize some of the data to see if we can find out any important information. ###Code %matplotlib inline sns.set_context("paper", font_scale=1.4) m_cts = (df['contact'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Contact') ax.set_xlabel('Was contact made?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['physicalEvidence'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Physical Evidence') ax.set_xlabel('Was there physical evidence?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['shape'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(9,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Shape') ax.set_xlabel('UFO Shape') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['weather'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Weather') ax.set_xlabel('Weather') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['researchOutcome'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Research Outcome') ax.set_xlabel('Research Outcome') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() ufo_yr = df['eventDate'].dt.year # series with the year exclusively ## Set axes ## years_data = ufo_yr.value_counts() years_index = years_data.index # x ticks years_values = years_data.get_values() ## Create Bar Plot ## plt.figure(figsize=(15,8)) plt.xticks(rotation = 60) plt.title('UFO Sightings by Year') plt.ylabel('Number of Sightings') plt.xlabel('Year') years_plot = sns.barplot(x=years_index[:60],y=years_values[:60]) df.corr() ###Output _____no_output_____ ###Markdown Let's drop the columns that are not important. 1. We can drop `sighting` becuase it is always 'Y' or Yes. 1. Let's drop the `firstName` and `lastName` becuase they are not important in determining the `researchOutcome`.1. Let's drop the `reportedTimestamp` becuase when the sighting was reporting isn't going to help us determine the legitimacy of the sighting.1. We would need to create some sort of buckets for the `eventDate` and `eventTime`, like seasons for example, but since the distribution of dates is pretty even, let's go ahead and drop them. ###Code df.drop(columns=['firstName', 'lastName', 'sighting', 'reportedTimestamp', 'eventDate', 'eventTime'], inplace=True) df.head() ###Output _____no_output_____ ###Markdown Let's apply one-hot encoding1. We need to one-hot both the `weather` attribute and the `shape` attribute. 1. We also need to transform or map the researchOutcome (target) attribute into numeric values. This is what the alogrithm is expecting. We can do this by mapping unexplained, explained, and probable to 0, 1, 2. ###Code # Let's one-hot the weather and shape attribute df = pd.get_dummies(df, columns=['weather', 'shape']) # Let's replace the researchOutcome values with 0, 1, 2 for Unexplained, Explained, and Probable df['researchOutcome'] = df['researchOutcome'].replace({'unexplained': 0, 'explained': 1, 'probable': 2}) display(df.head()) display(df.shape) ###Output _____no_output_____ ###Markdown Let's randomize and split the data into training, validation, and testing.1. First we need to randomize the data.1. Next Let's use 80% of the dataset for our training set.1. Then use 10% for validation during training.1. Finally we will use 10% for testing our model after it is deployed. ###Code # Let's go ahead and randomize our data. df = df.sample(frac=1).reset_index(drop=True) # Next, Let's split the data into a training, validation, and testing. rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 # 80% for training val_list = (rand_split >= 0.8) & (rand_split < 0.9) # 10% for validation test_list = rand_split >= 0.9 # 10% for testing # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] ###Output _____no_output_____ ###Markdown Next, let's go ahead and rearrange our attributes so the first attribute is our target attribute `researchOutcome`. This is what AWS requires and the XGBoost algorithms expects. You can read all about it here in the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.htmlInputOutput-XGBoost).After that we will go ahead and create those files on our Notebook instance (stored as CSV) and then upload them to S3. ###Code # Simply moves the researchOutcome attribute to the first position before creating CSV files pd.concat([data_train['researchOutcome'], data_train.drop(['researchOutcome'], axis=1)], axis=1).to_csv('train.csv', index=False, header=False) pd.concat([data_val['researchOutcome'], data_val.drop(['researchOutcome'], axis=1)], axis=1).to_csv('validation.csv', index=False, header=False) # Next we can take the files we just stored onto our Notebook instance and upload them to S3. boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_train/train.csv').upload_file('train.csv') boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_validation/validation.csv').upload_file('validation.csv') ###Output _____no_output_____ ###Markdown Step 3: Creating and training our model (XGBoost)This is where the magic happens. We will get the ECR container hosted in ECR for the XGBoost algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri container = get_image_uri(boto3.Session().region_name, 'xgboost') ###Output _____no_output_____ ###Markdown Next, because we're training with the CSV file format, we'll create inputs that our training function can use as a pointer to the files in S3, which also specify that the content type is CSV. ###Code s3_input_train = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_train'.format(bucket), content_type='csv') s3_input_validation = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_validation'.format(bucket), content_type='csv') ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a XGBoost model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `xgboost` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [XGBoost Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code # Create a training job name job_name = 'ufo-xgboost-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model artifact will be stored output_location = 's3://{}/algorithms_lab/xgboost_output'.format(bucket) sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.m4.xlarge', output_path=output_location, sagemaker_session=sess) xgb.set_hyperparameters(objective='multi:softmax', num_class=3, num_round=100) data_channels = { 'train': s3_input_train, 'validation': s3_input_validation } xgb.fit(data_channels, job_name=job_name) print('Here is the location of the trained XGBoost model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown After we train our model we can see the default evaluation metric in the logs. The `merror` is used in multiclass classification error rate. It is calculated as (wrong cases)/(all cases). We want this to be minimized (so we want this to be super small). --- Step 4: Creating and training our model (Linear Learner)Let's evaluate the Linear Learner algorithm as well. Let's go ahead and randomize the data again and get it ready for the Linear Leaner algorithm. We will also rearrange the columns so it is ready for the algorithm (it expects the first column to be the target attribute) ###Code np.random.seed(0) rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 val_list = (rand_split >= 0.8) & (rand_split < 0.9) test_list = rand_split >= 0.9 # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] # This rearranges the columns cols = list(data_train) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_train = data_train[cols] cols = list(data_val) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_val = data_val[cols] cols = list(data_test) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_test = data_test[cols] # Breaks the datasets into attribute numpy.ndarray and the same for target attribute. train_X = data_train.drop(columns='researchOutcome').values train_y = data_train['researchOutcome'].values val_X = data_val.drop(columns='researchOutcome').values val_y = data_val['researchOutcome'].values test_X = data_test.drop(columns='researchOutcome').values test_y = data_test['researchOutcome'].values ###Output _____no_output_____ ###Markdown Next, Let's create recordIO file for the training data and upload it to S3. ###Code train_file = 'ufo_sightings_train_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, train_X.astype('float32'), train_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_train/{}'.format(train_file)).upload_fileobj(f) training_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_train/{}'.format(bucket, train_file) print('The Pipe mode recordIO protobuf training data: {}'.format(training_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown Let's create recordIO file for the validation data and upload it to S3 ###Code validation_file = 'ufo_sightings_validatioin_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, val_X.astype('float32'), val_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_validation/{}'.format(validation_file)).upload_fileobj(f) validate_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_validation/{}'.format(bucket, validation_file) print('The Pipe mode recordIO protobuf validation data: {}'.format(validate_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown ---Alright we are good to go for the Linear Learner algorithm. Let's get everything we need from the ECR repository to call the Linear Learner algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri import sagemaker container = get_image_uri(boto3.Session().region_name, 'linear-learner', "1") # Create a training job name job_name = 'ufo-linear-learner-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model-artifact will be stored output_location = 's3://{}/algorithms_lab/linearlearner_output'.format(bucket) ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a Linear Learner model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `linear-learner` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [The input type (Pipe)](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)1. [Linear Learner Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code print('The feature_dim hyperparameter needs to be set to {}.'.format(data_train.shape[1] - 1)) sess = sagemaker.Session() # Setup the LinearLeaner algorithm from the ECR container linear = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.c4.xlarge', output_path=output_location, sagemaker_session=sess, input_mode='Pipe') # Setup the hyperparameters linear.set_hyperparameters(feature_dim=22, # number of attributes (minus the researchOutcome attribute) predictor_type='multiclass_classifier', # type of classification problem num_classes=3) # number of classes in out researchOutcome (explained, unexplained, probable) # Launch a training job. This method calls the CreateTrainingJob API call data_channels = { 'train': training_recordIO_protobuf_location, 'validation': validate_recordIO_protobuf_location } linear.fit(data_channels, job_name=job_name) print('Here is the location of the trained Linear Learner model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown UFO Sightings Algorithms LabThe goal of this notebook is to build out models to use for predicting the legitimacy of a UFO sighting using the XGBoost and Linear Learner algorithm.What we plan on accompishling is the following:1. [Load dataset onto Notebook instance memory from S3](Step-1:-Load-the-data-from-Amazon-S3)1. [Cleaning, transforming, analyize, and preparing the dataset](Step-2:-Cleaning,-transforming,-analyize,-and-preparing-the-dataset)1. [Create and train our model (XGBoost)](Step-3:-Creating-and-training-our-model-(XGBoost))1. [Create and train our model (Linear Learner)](Step-4:-Creating-and-training-our-model-(Linear-Learner)) First let's go ahead and import all the needed libraries. ###Code import pandas as pd import numpy as np from datetime import datetime import io import sagemaker.amazon.common as smac import boto3 from sagemaker import get_execution_role import sagemaker import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Step 1: Loading the data from Amazon S3Let's get the UFO sightings data that is stored in S3 and load it into memory. ###Code role = get_execution_role() bucket='<INSERT_YOUR_BUCKET_NAME_HERE>' sub_folder = 'ufo_dataset' data_key = 'ufo_fullset.csv' data_location = 's3://{}/{}/{}'.format(bucket, sub_folder, data_key) df = pd.read_csv(data_location, low_memory=False) df.head() ###Output _____no_output_____ ###Markdown Step 2: Cleaning, transforming, analyize, and preparing the datasetThis step is so important. It's crucial that we clean and prepare our data before we do anything else. ###Code # Let's check to see if there are any missing values missing_values = df.isnull().values.any() if(missing_values): display(df[df.isnull().any(axis=1)]) df['shape'].value_counts() # Replace the missing values with the most common shape df['shape'] = df['shape'].fillna(df['shape'].value_counts().index[0]) ###Output _____no_output_____ ###Markdown Let's go ahead and start preparing our dataset by transforming some of the values into the correct data types. Here is what we are going to take care of.1. Convert the `reportedTimestamp` and `eventDate` to a datetime data types.1. Convert the `shape` and `weather` to a category data type.1. Map the `physicalEvidence` and `contact` from 'Y', 'N' to `0`, `1`.1. Convert the `researchOutcome` to a category data type (target attribute). ###Code df['reportedTimestamp'] = pd.to_datetime(df['reportedTimestamp']) df['eventDate'] = pd.to_datetime(df['eventDate']) df['shape'] = df['shape'].astype('category') df['weather'] = df['weather'].astype('category') df['physicalEvidence'] = df['physicalEvidence'].replace({'Y': 1, 'N': 0}) df['contact'] = df['contact'].replace({'Y': 1, 'N': 0}) df['researchOutcome'] = df['researchOutcome'].astype('category') df.dtypes ###Output _____no_output_____ ###Markdown Let's visualize some of the data to see if we can find out any important information. ###Code %matplotlib inline sns.set_context("paper", font_scale=1.4) m_cts = (df['contact'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Contact') ax.set_xlabel('Was contact made?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['physicalEvidence'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Physical Evidence') ax.set_xlabel('Was there physical evidence?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['shape'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(9,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Shape') ax.set_xlabel('UFO Shape') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['weather'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Weather') ax.set_xlabel('Weather') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['researchOutcome'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.to_numpy() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Research Outcome') ax.set_xlabel('Research Outcome') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() ufo_yr = df['eventDate'].dt.year # series with the year exclusively ## Set axes ## years_data = ufo_yr.value_counts() years_index = years_data.index # x ticks years_values = years_data.to_numpy() ## Create Bar Plot ## plt.figure(figsize=(15,8)) plt.xticks(rotation = 60) plt.title('UFO Sightings by Year') plt.ylabel('Number of Sightings') plt.xlabel('Year') years_plot = sns.barplot(x=years_index[:60],y=years_values[:60]) df.corr() ###Output _____no_output_____ ###Markdown Let's drop the columns that are not important. 1. We can drop `sighting` becuase it is always 'Y' or Yes. 1. Let's drop the `firstName` and `lastName` becuase they are not important in determining the `researchOutcome`.1. Let's drop the `reportedTimestamp` becuase when the sighting was reporting isn't going to help us determine the legitimacy of the sighting.1. We would need to create some sort of buckets for the `eventDate` and `eventTime`, like seasons for example, but since the distribution of dates is pretty even, let's go ahead and drop them. ###Code df.drop(columns=['firstName', 'lastName', 'sighting', 'reportedTimestamp', 'eventDate', 'eventTime'], inplace=True) df.head() ###Output _____no_output_____ ###Markdown Let's apply one-hot encoding1. We need to one-hot both the `weather` attribute and the `shape` attribute. 1. We also need to transform or map the researchOutcome (target) attribute into numeric values. This is what the alogrithm is expecting. We can do this by mapping unexplained, explained, and probable to 0, 1, 2. ###Code # Let's one-hot the weather and shape attribute df = pd.get_dummies(df, columns=['weather', 'shape']) # Let's replace the researchOutcome values with 0, 1, 2 for Unexplained, Explained, and Probable df['researchOutcome'] = df['researchOutcome'].replace({'unexplained': 0, 'explained': 1, 'probable': 2}) display(df.head()) display(df.shape) ###Output _____no_output_____ ###Markdown Let's randomize and split the data into training, validation, and testing.1. First we need to randomize the data.1. Next Let's use 80% of the dataset for our training set.1. Then use 10% for validation during training.1. Finally we will use 10% for testing our model after it is deployed. ###Code # Let's go ahead and randomize our data. df = df.sample(frac=1).reset_index(drop=True) # Next, Let's split the data into a training, validation, and testing. rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 # 80% for training val_list = (rand_split >= 0.8) & (rand_split < 0.9) # 10% for validation test_list = rand_split >= 0.9 # 10% for testing # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] ###Output _____no_output_____ ###Markdown Next, let's go ahead and rearrange our attributes so the first attribute is our target attribute `researchOutcome`. This is what AWS requires and the XGBoost algorithms expects. You can read all about it here in the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.htmlInputOutput-XGBoost).After that we will go ahead and create those files on our Notebook instance (stored as CSV) and then upload them to S3. ###Code # Simply moves the researchOutcome attribute to the first position before creating CSV files pd.concat([data_train['researchOutcome'], data_train.drop(['researchOutcome'], axis=1)], axis=1).to_csv('train.csv', index=False, header=False) pd.concat([data_val['researchOutcome'], data_val.drop(['researchOutcome'], axis=1)], axis=1).to_csv('validation.csv', index=False, header=False) # Next we can take the files we just stored onto our Notebook instance and upload them to S3. boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_train/train.csv').upload_file('train.csv') boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_validation/validation.csv').upload_file('validation.csv') ###Output _____no_output_____ ###Markdown Step 3: Creating and training our model (XGBoost)This is where the magic happens. We will get the ECR container hosted in ECR for the XGBoost algorithm. ###Code from sagemaker import image_uris container = image_uris.retrieve('xgboost', boto3.Session().region_name, '1') ###Output _____no_output_____ ###Markdown Next, because we're training with the CSV file format, we'll create inputs that our training function can use as a pointer to the files in S3, which also specify that the content type is CSV. ###Code s3_input_train = sagemaker.inputs.TrainingInput(s3_data='s3://{}/algorithms_lab/xgboost_train'.format(bucket), content_type='csv') s3_input_validation = sagemaker.inputs.TrainingInput(s3_data='s3://{}/algorithms_lab/xgboost_validation'.format(bucket), content_type='csv') ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a XGBoost model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `xgboost` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [XGBoost Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code # Create a training job name job_name = 'ufo-xgboost-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model artifact will be stored output_location = 's3://{}/algorithms_lab/xgboost_output'.format(bucket) sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator(container, role, instance_count=1, instance_type='ml.m4.xlarge', output_path=output_location, sagemaker_session=sess) xgb.set_hyperparameters(objective='multi:softmax', num_class=3, num_round=100) data_channels = { 'train': s3_input_train, 'validation': s3_input_validation } xgb.fit(data_channels, job_name=job_name) print('Here is the location of the trained XGBoost model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown After we train our model we can see the default evaluation metric in the logs. The `merror` is used in multiclass classification error rate. It is calculated as (wrong cases)/(all cases). We want this to be minimized (so we want this to be super small). --- Step 4: Creating and training our model (Linear Learner)Let's evaluate the Linear Learner algorithm as well. Let's go ahead and randomize the data again and get it ready for the Linear Leaner algorithm. We will also rearrange the columns so it is ready for the algorithm (it expects the first column to be the target attribute) ###Code np.random.seed(0) rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 val_list = (rand_split >= 0.8) & (rand_split < 0.9) test_list = rand_split >= 0.9 # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] # This rearranges the columns cols = list(data_train) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_train = data_train[cols] cols = list(data_val) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_val = data_val[cols] cols = list(data_test) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_test = data_test[cols] # Breaks the datasets into attribute numpy.ndarray and the same for target attribute. train_X = data_train.drop(columns='researchOutcome').values train_y = data_train['researchOutcome'].values val_X = data_val.drop(columns='researchOutcome').values val_y = data_val['researchOutcome'].values test_X = data_test.drop(columns='researchOutcome').values test_y = data_test['researchOutcome'].values ###Output _____no_output_____ ###Markdown Next, Let's create recordIO file for the training data and upload it to S3. ###Code train_file = 'ufo_sightings_train_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, train_X.astype('float32'), train_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_train/{}'.format(train_file)).upload_fileobj(f) training_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_train/{}'.format(bucket, train_file) print('The Pipe mode recordIO protobuf training data: {}'.format(training_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown Let's create recordIO file for the validation data and upload it to S3 ###Code validation_file = 'ufo_sightings_validatioin_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, val_X.astype('float32'), val_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_validation/{}'.format(validation_file)).upload_fileobj(f) validate_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_validation/{}'.format(bucket, validation_file) print('The Pipe mode recordIO protobuf validation data: {}'.format(validate_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown ---Alright we are good to go for the Linear Learner algorithm. Let's get everything we need from the ECR repository to call the Linear Learner algorithm. ###Code from sagemaker import image_uris container = image_uris.retrieve('linear-learner', boto3.Session().region_name, '1') # Create a training job name job_name = 'ufo-linear-learner-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model-artifact will be stored output_location = 's3://{}/algorithms_lab/linearlearner_output'.format(bucket) ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a Linear Learner model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `linear-learner` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [The input type (Pipe)](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)1. [Linear Learner Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code print('The feature_dim hyperparameter needs to be set to {}.'.format(data_train.shape[1] - 1)) sess = sagemaker.Session() # Setup the LinearLeaner algorithm from the ECR container linear = sagemaker.estimator.Estimator(container, role, instance_count=1, instance_type='ml.c4.xlarge', output_path=output_location, sagemaker_session=sess, input_mode='Pipe') # Setup the hyperparameters linear.set_hyperparameters(feature_dim=22, # number of attributes (minus the researchOutcome attribute) predictor_type='multiclass_classifier', # type of classification problem num_classes=3) # number of classes in out researchOutcome (explained, unexplained, probable) # Launch a training job. This method calls the CreateTrainingJob API call data_channels = { 'train': training_recordIO_protobuf_location, 'validation': validate_recordIO_protobuf_location } linear.fit(data_channels, job_name=job_name) print('Here is the location of the trained Linear Learner model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown UFO Sightings Algorithms LabThe goal of this notebook is to build out models to use for predicting the legitimacy of a UFO sighting using the XGBoost and Linear Learner algorithm.What we plan on accompishling is the following:1. [Load dataset onto Notebook instance memory from S3](Step-1:-Load-the-data-from-Amazon-S3)1. [Cleaning, transforming, analyize, and preparing the dataset](Step-2:-Cleaning,-transforming,-analyize,-and-preparing-the-dataset)1. [Create and train our model (XGBoost)](Step-3:-Creating-and-training-our-model-(XGBoost))1. [Create and train our model (Linear Learner)](Step-4:-Creating-and-training-our-model-(Linear-Learner)) First let's go ahead and import all the needed libraries. ###Code import pandas as pd import numpy as np from datetime import datetime import io import sagemaker.amazon.common as smac import boto3 from sagemaker import get_execution_role import sagemaker import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Step 1: Loading the data from Amazon S3Let's get the UFO sightings data that is stored in S3 and load it into memory. ###Code role = get_execution_role() bucket='<INSERT_YOUR_BUCKET_NAME_HERE>' sub_folder = 'ufo_dataset' data_key = 'ufo_fullset.csv' data_location = 's3://{}/{}/{}'.format(bucket, sub_folder, data_key) df = pd.read_csv(data_location, low_memory=False) df.head() ###Output _____no_output_____ ###Markdown Step 2: Cleaning, transforming, analyize, and preparing the datasetThis step is so important. It's crucial that we clean and prepare our data before we do anything else. ###Code # Let's check to see if there are any missing values missing_values = df.isnull().values.any() if(missing_values): display(df[df.isnull().any(axis=1)]) df['shape'].value_counts() # Replace the missing values with the most common shape df['shape'] = df['shape'].fillna(df['shape'].value_counts().index[0]) ###Output _____no_output_____ ###Markdown Let's go ahead and start preparing our dataset by transforming some of the values into the correct data types. Here is what we are going to take care of.1. Convert the `reportedTimestamp` and `eventDate` to a datetime data types.1. Convert the `shape` and `weather` to a category data type.1. Map the `physicalEvidence` and `contact` from 'Y', 'N' to `0`, `1`.1. Convert the `researchOutcome` to a category data type (target attribute). ###Code df['reportedTimestamp'] = pd.to_datetime(df['reportedTimestamp']) df['eventDate'] = pd.to_datetime(df['eventDate']) df['shape'] = df['shape'].astype('category') df['weather'] = df['weather'].astype('category') df['physicalEvidence'] = df['physicalEvidence'].replace({'Y': 1, 'N': 0}) df['contact'] = df['contact'].replace({'Y': 1, 'N': 0}) df['researchOutcome'] = df['researchOutcome'].astype('category') df.dtypes ###Output _____no_output_____ ###Markdown Let's visualize some of the data to see if we can find out any important information. ###Code %matplotlib inline sns.set_context("paper", font_scale=1.4) m_cts = (df['contact'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Contact') ax.set_xlabel('Was contact made?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['physicalEvidence'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Physical Evidence') ax.set_xlabel('Was there physical evidence?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['shape'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(9,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Shape') ax.set_xlabel('UFO Shape') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['weather'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Weather') ax.set_xlabel('Weather') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['researchOutcome'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Research Outcome') ax.set_xlabel('Research Outcome') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() ufo_yr = df['eventDate'].dt.year # series with the year exclusively ## Set axes ## years_data = ufo_yr.value_counts() years_index = years_data.index # x ticks years_values = years_data.get_values() ## Create Bar Plot ## plt.figure(figsize=(15,8)) plt.xticks(rotation = 60) plt.title('UFO Sightings by Year') plt.ylabel('Number of Sightings') plt.xlabel('Year') years_plot = sns.barplot(x=years_index[:60],y=years_values[:60]) df.corr() ###Output _____no_output_____ ###Markdown Let's drop the columns that are not important. 1. We can drop `sighting` becuase it is always 'Y' or Yes. 1. Let's drop the `firstName` and `lastName` becuase they are not important in determining the `researchOutcome`.1. Let's drop the `reportedTimestamp` becuase when the sighting was reporting isn't going to help us determine the legitimacy of the sighting.1. We would need to create some sort of buckets for the `eventDate` and `eventTime`, like seasons for example, but since the distribution of dates is pretty even, let's go ahead and drop them. ###Code df.drop(columns=['firstName', 'lastName', 'sighting', 'reportedTimestamp', 'eventDate', 'eventTime'], inplace=True) df.head() ###Output _____no_output_____ ###Markdown Let's apply one-hot encoding1. We need to one-hot both the `weather` attribute and the `shape` attribute. 1. We also need to transform or map the researchOutcome (target) attribute into numeric values. This is what the alogrithm is expecting. We can do this by mapping unexplained, explained, and probable to 0, 1, 2. ###Code # Let's one-hot the weather and shape attribute df = pd.get_dummies(df, columns=['weather', 'shape']) # Let's replace the researchOutcome values with 0, 1, 2 for Unexplained, Explained, and Probable df['researchOutcome'] = df['researchOutcome'].replace({'unexplained': 0, 'explained': 1, 'probable': 2}) display(df.head()) display(df.shape) ###Output _____no_output_____ ###Markdown Let's randomize and split the data into training, validation, and testing.1. First we need to randomize the data.1. Next Let's use 80% of the dataset for our training set.1. Then use 10% for validation during training.1. Finally we will use 10% for testing our model after it is deployed. ###Code # Let's go ahead and randomize our data. df = df.sample(frac=1).reset_index(drop=True) # Next, Let's split the data into a training, validation, and testing. rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 # 80% for training val_list = (rand_split >= 0.8) & (rand_split < 0.9) # 10% for validation test_list = rand_split >= 0.9 # 10% for testing # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] ###Output _____no_output_____ ###Markdown Next, let's go ahead and rearrange our attributes so the first attribute is our target attribute `researchOutcome`. This is what AWS requires and the XGBoost algorithms expects. You can read all about it here in the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.htmlInputOutput-XGBoost).After that we will go ahead and create those files on our Notebook instance (stored as CSV) and then upload them to S3. ###Code # Simply moves the researchOutcome attribute to the first position before creating CSV files pd.concat([data_train['researchOutcome'], data_train.drop(['researchOutcome'], axis=1)], axis=1).to_csv('train.csv', index=False, header=False) pd.concat([data_val['researchOutcome'], data_val.drop(['researchOutcome'], axis=1)], axis=1).to_csv('validation.csv', index=False, header=False) # Next we can take the files we just stored onto our Notebook instance and upload them to S3. boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_train/train.csv').upload_file('train.csv') boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_validation/validation.csv').upload_file('validation.csv') ###Output _____no_output_____ ###Markdown Step 3: Creating and training our model (XGBoost)This is where the magic happens. We will get the ECR container hosted in ECR for the XGBoost algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri container = get_image_uri(boto3.Session().region_name, 'xgboost') ###Output _____no_output_____ ###Markdown Next, because we're training with the CSV file format, we'll create inputs that our training function can use as a pointer to the files in S3, which also specify that the content type is CSV. ###Code s3_input_train = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_train'.format(bucket), content_type='csv') s3_input_validation = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_validation'.format(bucket), content_type='csv') ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a XGBoost model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `xgboost` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [XGBoost Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code # Create a training job name job_name = 'ufo-xgboost-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model artifact will be stored output_location = 's3://{}/algorithms_lab/xgboost_output'.format(bucket) sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.m4.xlarge', output_path=output_location, sagemaker_session=sess) xgb.set_hyperparameters(objective='multi:softmax', num_class=3, num_round=100) data_channels = { 'train': s3_input_train, 'validation': s3_input_validation } xgb.fit(data_channels, job_name=job_name) print('Here is the location of the trained XGBoost model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown After we train our model we can see the default evaluation metric in the logs. The `merror` is used in multiclass classification error rate. It is calculated as (wrong cases)/(all cases). We want this to be minimized (so we want this to be super small). --- Step 4: Creating and training our model (Linear Learner)Let's evaluate the Linear Learner algorithm as well. Let's go ahead and randomize the data again and get it ready for the Linear Leaner algorithm. We will also rearrange the columns so it is ready for the algorithm (it expects the first column to be the target attribute) ###Code np.random.seed(0) rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 val_list = (rand_split >= 0.8) & (rand_split < 0.9) test_list = rand_split >= 0.9 # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] # This rearranges the columns cols = list(data_train) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_train = data_train[cols] cols = list(data_val) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_val = data_val[cols] cols = list(data_test) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_test = data_test[cols] # Breaks the datasets into attribute numpy.ndarray and the same for target attribute. train_X = data_train.drop(columns='researchOutcome').as_matrix() train_y = data_train['researchOutcome'].as_matrix() val_X = data_val.drop(columns='researchOutcome').as_matrix() val_y = data_val['researchOutcome'].as_matrix() test_X = data_test.drop(columns='researchOutcome').as_matrix() test_y = data_test['researchOutcome'].as_matrix() ###Output _____no_output_____ ###Markdown Next, Let's create recordIO file for the training data and upload it to S3. ###Code train_file = 'ufo_sightings_train_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, train_X.astype('float32'), train_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_train/{}'.format(train_file)).upload_fileobj(f) training_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_train/{}'.format(bucket, train_file) print('The Pipe mode recordIO protobuf training data: {}'.format(training_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown Let's create recordIO file for the validation data and upload it to S3 ###Code validation_file = 'ufo_sightings_validatioin_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, val_X.astype('float32'), val_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_validation/{}'.format(validation_file)).upload_fileobj(f) validate_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_validation/{}'.format(bucket, validation_file) print('The Pipe mode recordIO protobuf validation data: {}'.format(validate_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown ---Alright we are good to go for the Linear Learner algorithm. Let's get everything we need from the ECR repository to call the Linear Learner algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri import sagemaker container = get_image_uri(boto3.Session().region_name, 'linear-learner', "1") # Create a training job name job_name = 'ufo-linear-learner-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model-artifact will be stored output_location = 's3://{}/algorithms_lab/linearlearner_output'.format(bucket) ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a Linear Learner model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `linear-learner` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [The input type (Pipe)](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)1. [Linear Learner Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code print('The feature_dim hyperparameter needs to be set to {}.'.format(data_train.shape[1] - 1)) sess = sagemaker.Session() # Setup the LinearLeaner algorithm from the ECR container linear = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.c4.xlarge', output_path=output_location, sagemaker_session=sess, input_mode='Pipe') # Setup the hyperparameters linear.set_hyperparameters(feature_dim=22, # number of attributes (minus the researchOutcome attribute) predictor_type='multiclass_classifier', # type of classification problem num_classes=3) # number of classes in out researchOutcome (explained, unexplained, probable) # Launch a training job. This method calls the CreateTrainingJob API call data_channels = { 'train': training_recordIO_protobuf_location, 'validation': validate_recordIO_protobuf_location } linear.fit(data_channels, job_name=job_name) print('Here is the location of the trained Linear Learner model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown UFO Sightings Algorithms LabThe goal of this notebook is to build out models to use for predicting the legitimacy of a UFO sighting using the XGBoost and Linear Learner algorithm.What we plan on accompishling is the following:1. [Load dataset onto Notebook instance memory from S3](Step-1:-Load-the-data-from-Amazon-S3)1. [Cleaning, transforming, analyize, and preparing the dataset](Step-2:-Cleaning,-transforming,-analyize,-and-preparing-the-dataset)1. [Create and train our model (XGBoost)](Step-3:-Creating-and-training-our-model-(XGBoost))1. [Create and train our model (Linear Learner)](Step-4:-Creating-and-training-our-model-(Linear-Learner)) First let's go ahead and import all the needed libraries. ###Code import pandas as pd import numpy as np from datetime import datetime import io import sagemaker.amazon.common as smac import boto3 from sagemaker import get_execution_role import sagemaker import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Step 1: Loading the data from Amazon S3Let's get the UFO sightings data that is stored in S3 and load it into memory. ###Code role = get_execution_role() bucket='<INSERT_YOUR_BUCKET_NAME_HERE>' sub_folder = 'ufo_dataset' data_key = 'ufo_fullset.csv' data_location = 's3://{}/{}/{}'.format(bucket, sub_folder, data_key) df = pd.read_csv(data_location, low_memory=False) df.head() ###Output _____no_output_____ ###Markdown Step 2: Cleaning, transforming, analyize, and preparing the datasetThis step is so important. It's crucial that we clean and prepare our data before we do anything else. ###Code # Let's check to see if there are any missing values missing_values = df.isnull().values.any() if(missing_values): display(df[df.isnull().any(axis=1)]) df['shape'].value_counts() # Replace the missing values with the most common shape df['shape'] = df['shape'].fillna(df['shape'].value_counts().index[0]) ###Output _____no_output_____ ###Markdown Let's go ahead and start preparing our dataset by transforming some of the values into the correct data types. Here is what we are going to take care of.1. Convert the `reportedTimestamp` and `eventDate` to a datetime data types.1. Convert the `shape` and `weather` to a category data type.1. Map the `physicalEvidence` and `contact` from 'Y', 'N' to `0`, `1`.1. Convert the `researchOutcome` to a category data type (target attribute). ###Code df['reportedTimestamp'] = pd.to_datetime(df['reportedTimestamp']) df['eventDate'] = pd.to_datetime(df['eventDate']) df['shape'] = df['shape'].astype('category') df['weather'] = df['weather'].astype('category') df['physicalEvidence'] = df['physicalEvidence'].replace({'Y': 1, 'N': 0}) df['contact'] = df['contact'].replace({'Y': 1, 'N': 0}) df['researchOutcome'] = df['researchOutcome'].astype('category') df.dtypes ###Output _____no_output_____ ###Markdown Let's visualize some of the data to see if we can find out any important information. ###Code %matplotlib inline sns.set_context("paper", font_scale=1.4) m_cts = (df['contact'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Contact') ax.set_xlabel('Was contact made?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['physicalEvidence'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Physical Evidence') ax.set_xlabel('Was there physical evidence?') ax.set_ylabel('Number of Sightings') ax.set_xticklabels(['No', 'Yes']) plt.xticks(rotation=45) plt.show() m_cts = (df['shape'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(9,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Shape') ax.set_xlabel('UFO Shape') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['weather'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings by Weather') ax.set_xlabel('Weather') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() m_cts = (df['researchOutcome'].value_counts()) m_ctsx = m_cts.index m_ctsy = m_cts.get_values() f, ax = plt.subplots(figsize=(5,5)) sns.barplot(x=m_ctsx, y=m_ctsy) ax.set_title('UFO Sightings and Research Outcome') ax.set_xlabel('Research Outcome') ax.set_ylabel('Number of Sightings') plt.xticks(rotation=45) plt.show() ufo_yr = df['eventDate'].dt.year # series with the year exclusively ## Set axes ## years_data = ufo_yr.value_counts() years_index = years_data.index # x ticks years_values = years_data.get_values() ## Create Bar Plot ## plt.figure(figsize=(15,8)) plt.xticks(rotation = 60) plt.title('UFO Sightings by Year') plt.ylabel('Number of Sightings') plt.ylabel('Year') years_plot = sns.barplot(x=years_index[:60],y=years_values[:60]) df.corr() ###Output _____no_output_____ ###Markdown Let's drop the columns that are not important. 1. We can drop `sighting` becuase it is always 'Y' or Yes. 1. Let's drop the `firstName` and `lastName` becuase they are not important in determining the `researchOutcome`.1. Let's drop the `reportedTimestamp` becuase when the sighting was reporting isn't going to help us determine the legitimacy of the sighting.1. We would need to create some sort of buckets for the `eventDate` and `eventTime`, like seasons for example, but since the distribution of dates is pretty even, let's go ahead and drop them. ###Code df.drop(columns=['firstName', 'lastName', 'sighting', 'reportedTimestamp', 'eventDate', 'eventTime'], inplace=True) df.head() ###Output _____no_output_____ ###Markdown Let's apply one-hot encoding1. We need to one-hot both the `weather` attribute and the `shape` attribute. 1. We also need to transform or map the researchOutcome (target) attribute into numeric values. This is what the alogrithm is expecting. We can do this by mapping unexplained, explained, and probable to 0, 1, 2. ###Code # Let's one-hot the weather and shape attribute df = pd.get_dummies(df, columns=['weather', 'shape']) # Let's replace the researchOutcome values with 0, 1, 2 for Unexplained, Explained, and Probable df['researchOutcome'] = df['researchOutcome'].replace({'unexplained': 0, 'explained': 1, 'probable': 2}) display(df.head()) display(df.shape) ###Output _____no_output_____ ###Markdown Let's randomize and split the data into training, validation, and testing.1. First we need to randomize the data.1. Next Let's use 80% of the dataset for our training set.1. Then use 10% for validation during training.1. Finally we will use 10% for testing our model after it is deployed. ###Code # Let's go ahead and randomize our data. df = df.sample(frac=1).reset_index(drop=True) # Next, Let's split the data into a training, validation, and testing. rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 # 80% for training val_list = (rand_split >= 0.8) & (rand_split < 0.9) # 10% for validation test_list = rand_split >= 0.9 # 10% for testing # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] ###Output _____no_output_____ ###Markdown Next, let's go ahead and rearrange our attributes so the first attribute is our target attribute `researchOutcome`. This is what AWS requires and the XGBoost algorithms expects. You can read all about it here in the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.htmlInputOutput-XGBoost).After that we will go ahead and create those files on our Notebook instance (stored as CSV) and then upload them to S3. ###Code # Simply moves the researchOutcome attribute to the first position before creating CSV files pd.concat([data_train['researchOutcome'], data_train.drop(['researchOutcome'], axis=1)], axis=1).to_csv('train.csv', index=False, header=False) pd.concat([data_val['researchOutcome'], data_val.drop(['researchOutcome'], axis=1)], axis=1).to_csv('validation.csv', index=False, header=False) # Next we can take the files we just stored onto our Notebook instance and upload them to S3. boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_train/train.csv').upload_file('train.csv') boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/xgboost_validation/validation.csv').upload_file('validation.csv') ###Output _____no_output_____ ###Markdown Step 3: Creating and training our model (XGBoost)This is where the magic happens. We will get the ECR container hosted in ECR for the XGBoost algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri container = get_image_uri(boto3.Session().region_name, 'xgboost') ###Output _____no_output_____ ###Markdown Next, because we're training with the CSV file format, we'll create inputs that our training function can use as a pointer to the files in S3, which also specify that the content type is CSV. ###Code s3_input_train = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_train'.format(bucket), content_type='csv') s3_input_validation = sagemaker.s3_input(s3_data='s3://{}/algorithms_lab/xgboost_validation'.format(bucket), content_type='csv') ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a XGBoost model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `xgboost` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [XGBoost Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code # Create a training job name job_name = 'ufo-xgboost-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model artifact will be stored output_location = 's3://{}/algorithms_lab/xgboost_output'.format(bucket) sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.m4.xlarge', output_path=output_location, sagemaker_session=sess) xgb.set_hyperparameters(objective='multi:softmax', num_class=3, num_round=100) data_channels = { 'train': s3_input_train, 'validation': s3_input_validation } xgb.fit(data_channels, job_name=job_name) print('Here is the location of the trained XGBoost model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____ ###Markdown After we train our model we can see the default evaluation metric in the logs. The `merror` is used in multiclass classification error rate. It is calculated as (wrong cases)/(all cases). We want this to be minimized (so we want this to be super small). --- Step 4: Creating and training our model (Linear Learner)Let's evaluate the Linear Learner algorithm as well. Let's go ahead and randomize the data again and get it ready for the Linear Leaner algorithm. We will also rearrange the columns so it is ready for the algorithm (it expects the first column to be the target attribute) ###Code np.random.seed(0) rand_split = np.random.rand(len(df)) train_list = rand_split < 0.8 val_list = (rand_split >= 0.8) & (rand_split < 0.9) test_list = rand_split >= 0.9 # This dataset will be used to train the model. data_train = df[train_list] # This dataset will be used to validate the model. data_val = df[val_list] # This dataset will be used to test the model. data_test = df[test_list] # This rearranges the columns cols = list(data_train) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_train = data_train[cols] cols = list(data_val) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_val = data_val[cols] cols = list(data_test) cols.insert(0, cols.pop(cols.index('researchOutcome'))) data_test = data_test[cols] # Breaks the datasets into attribute numpy.ndarray and the same for target attribute. train_X = data_train.drop(columns='researchOutcome').as_matrix() train_y = data_train['researchOutcome'].as_matrix() val_X = data_val.drop(columns='researchOutcome').as_matrix() val_y = data_val['researchOutcome'].as_matrix() test_X = data_test.drop(columns='researchOutcome').as_matrix() test_y = data_test['researchOutcome'].as_matrix() ###Output _____no_output_____ ###Markdown Next, Let's create recordIO file for the training data and upload it to S3. ###Code train_file = 'ufo_sightings_train_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, train_X.astype('float32'), train_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_train/{}'.format(train_file)).upload_fileobj(f) training_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_train/{}'.format(bucket, train_file) print('The Pipe mode recordIO protobuf training data: {}'.format(training_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown Let's create recordIO file for the validation data and upload it to S3 ###Code validation_file = 'ufo_sightings_validatioin_recordIO_protobuf.data' f = io.BytesIO() smac.write_numpy_to_dense_tensor(f, val_X.astype('float32'), val_y.astype('float32')) f.seek(0) boto3.Session().resource('s3').Bucket(bucket).Object('algorithms_lab/linearlearner_validation/{}'.format(validation_file)).upload_fileobj(f) validate_recordIO_protobuf_location = 's3://{}/algorithms_lab/linearlearner_validation/{}'.format(bucket, validation_file) print('The Pipe mode recordIO protobuf validation data: {}'.format(validate_recordIO_protobuf_location)) ###Output _____no_output_____ ###Markdown ---Alright we are good to go for the Linear Learner algorithm. Let's get everything we need from the ECR repository to call the Linear Learner algorithm. ###Code from sagemaker.amazon.amazon_estimator import get_image_uri import sagemaker container = get_image_uri(boto3.Session().region_name, 'linear-learner', "1") # Create a training job name job_name = 'ufo-linear-learner-job-{}'.format(datetime.now().strftime("%Y%m%d%H%M%S")) print('Here is the job name {}'.format(job_name)) # Here is where the model-artifact will be stored output_location = 's3://{}/algorithms_lab/linearlearner_output'.format(bucket) ###Output _____no_output_____ ###Markdown Next we start building out our model by using the SageMaker Python SDK and passing in everything that is required to create a Linear Learner model.First I like to always create a specific job name.Next, we'll need to specify training parameters.1. The `linear-learner` algorithm container1. The IAM role to use1. Training instance type and count1. S3 location for output data/model artifact1. [The input type (Pipe)](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html)1. [Linear Learner Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)Finally, after everything is included and ready, then we can call the `.fit()` function which specifies the S3 location for training and validation data. ###Code print('The feature_dim hyperparameter needs to be set to {}.'.format(data_train.shape[1] - 1)) sess = sagemaker.Session() # Setup the LinearLeaner algorithm from the ECR container linear = sagemaker.estimator.Estimator(container, role, train_instance_count=1, train_instance_type='ml.c4.xlarge', output_path=output_location, sagemaker_session=sess, input_mode='Pipe') # Setup the hyperparameters linear.set_hyperparameters(feature_dim=22, # number of attributes (minus the researchOutcome attribute) predictor_type='multiclass_classifier', # type of classification problem num_classes=3) # number of classes in out researchOutcome (explained, unexplained, probable) # Launch a training job. This method calls the CreateTrainingJob API call data_channels = { 'train': training_recordIO_protobuf_location, 'validation': validate_recordIO_protobuf_location } linear.fit(data_channels, job_name=job_name) print('Here is the location of the trained Linear Learner model: {}/{}/output/model.tar.gz'.format(output_location, job_name)) ###Output _____no_output_____
sierra_leone.ipynb	###Markdown SEIR model for the 2014 outbreak in Sierra Leone.Numbers are taken from: [this article](10.1371/currents.outbreaks.91afb5e0f279e7f29e7056095255b288) ###Code %matplotlib notebook import dateutil.parser from matplotlib import pyplot as plt import pandas as pd from model import run_model from plotting import ( plot_model_and_raw_data, plot_model_evolution, plot_model_evolution_item, ) plt.style.use('seaborn-notebook') raw = pd.read_csv('data/ebola_sierra_leone.csv') raw['Date'] = [dateutil.parser.parse(i) for i in raw['Date'].values] time_zero = dateutil.parser.parse('23 Apr 2014') parameters = { 'infectiousness': 5.61, 'incubation': 5.3, 'r0': 2.53, 'fatality': 0.48, 'kappa': 0.0097, 'tau': 0, 'time_zero': time_zero, } parameters['beta_0'] = parameters['r0'] / parameters['infectiousness'] parameters population = 7.6 * 10**6 initial_values = [population, 0, 1, 0, 0, 1] time_span = [0, 350] result, _, time_date, model = run_model(time_span, parameters, initial_values) plot_model_and_raw_data( time_date, model, raw, max_model_date=dateutil.parser.parse('01 Sep 2014') ) plot_model_evolution(time_date, model) plot_model_evolution_item(time_date, model, 'infected') ###Output _____no_output_____
notebooks/.ipynb_checkpoints/similar-duplicate-images-in-aptos-data-checkpoint.ipynb	###Markdown Calculating the Hash, Shape, Mode, Length and Ratio of each image ###Code def getImageMetaData(file_path): with Image.open(file_path) as img: img_hash = imagehash.phash(img) return img.size, img.mode, img_hash def get_train_input(): train_input = df.copy() m = train_input.path.apply(lambda x: getImageMetaData(x)) train_input["Hash"] = [str(i[2]) for i in m] train_input["Shape"] = [i[0] for i in m] train_input["Mode"] = [str(i[1]) for i in m] train_input["Length"] = train_input["Shape"].apply(lambda x: x[0]*x[1]) train_input["Ratio"] = train_input["Shape"].apply(lambda x: x[0]/x[1]) img_counts = train_input.path.value_counts().to_dict() train_input["Id_Count"] = train_input.path.apply(lambda x: img_counts[x]) return train_input train_input = get_train_input() train_input1 = train_input[['Hash']] # Getting the Hash from the new data train_input1['New']=1 # Creating a dummy column 1 train_input2 = train_input1.groupby('Hash').count().reset_index() # Grouping the column by Hash to aggregate at Hash level train_input2 = train_input2[train_input2['New']>1] # Filtering those instances where the hash is occuring multiple times train_input2.shape # Checking the shape train_input2 = train_input2.sort_values('Hash') # Sorting the data by Hash train_input2.head(5) # Checking the top 5 records train_input[train_input['Hash']=='808d0f3513f33f2d'] # FIltering only one Hash from master data import matplotlib.pyplot as plt import matplotlib.image as mpimg from IPython import display import time %matplotlib inline PATH = "../input/train_images/1632c4311fc9.png" image = mpimg.imread(PATH) # images are color images plt.imshow(image); import matplotlib.pyplot as plt import matplotlib.image as mpimg from IPython import display import time %matplotlib inline PATH = "../input/train_images/a75bab2463d4.png" image = mpimg.imread(PATH) # images are color images plt.imshow(image); ###Output _____no_output_____
labs/4-12/4-12_Morality_Sentiment_Analysis.ipynb	###Markdown [LEGALST-190] Lab 4/12: Morality and Sentiment Analysis This lab will cover morality and sentiment analysis using the Moral Foundations Theory with dictionary-based analysis, connecting to topic modeling and classifications ideas from previous labs. Table of Contents[The Data](section data)[Goal and Question](section goal)1 - [Text Pre-processing](section 1)2 - [Polarity](section 2)3 - [Moral Foundations Theory](section 3)4 - [Non-negative matrix factorization](section 4)Dependencies: ###Code import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import json from sklearn.decomposition import NMF, LatentDirichletAllocation from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer import nltk from nltk.stem.snowball import SnowballStemmer import seaborn as sns !pip install textblob from textblob import TextBlob ###Output _____no_output_____ ###Markdown ---- The DataFor this lab, we'll use the Old Bailey dataset, something you all should be familiar with now. The size of the dataset is also rather large so we will compare two year-long periods, one from before 1827 and one after. Read the question to better understand why we look at 1827. Goal and QuestionThe goal of today's lab is to explore sentiment analysis with three different approaches – [polarity scoring](section 2), [topic-specific dictionary methods](section 3), and [topic modeling](section 4).We'll look at sentiment in the context of the following question: Did the way judges, prosecutors, and witnesses talk about moral culpability change after the Bloody Code was mostly repealed in 1827 (at the leading edge of a wave of legal reform in England)?Note: this is a question that could encompass an entire research project. Today's lab uses a very small subset of data due to datahub memory limitations, and skips over many of the steps needed for truly robust conclusions. Something to think about: What are some things you would need to consider before answering this question?---- Section 1: Text Pre-processing Before we startThis dataset we are about to look at is incredibly large, so to avoid crashing our datahub kernel, we only consider two years: 1822 and 1832. These two years were chosen as periods that were equally far from 1827 (when the Bloody Code was mostly repealed), while not being so far from each other that we'd expect to see major language usage change due only to time. ---- Getting startedLet's get working with the data. ###Code # contains Old Bailey trial data from 1822 and 1832 old_bailey = pd.read_csv('data/obc_1822_1832.csv', index_col='trial_id') # select only the columns we need for this lab old_bailey = old_bailey.loc[:, ['year', 'transcript']] old_bailey.head() ###Output _____no_output_____ ###Markdown Awesome! We now have data we can work with. Before we start anything, we must clean the text!Just to review, we want to process our text by:1) Lowercasing the words2) Cleaning up punctuation3) Splitting into individual words4) Stemming the word tokensFor the sake of time (and to get to the good stuff), we've provided the pre-processing code below. This a big data set, so the code will take up to a minute to run. ###Code # pre-process the data lower_cased = old_bailey['transcript'].str.lower() punct_re = r'[^\w\s]' lower_no_punc = lower_cased.str.replace(punct_re, ' ') tokens = lower_no_punc.str.split() old_bailey['tokens'] = tokens stemmer = SnowballStemmer('english') stem_lists = [] for token_list in old_bailey['tokens']: stem_lists.append([stemmer.stem(wd) for wd in token_list]) old_bailey['stemmed_tokens'] = stem_lists old_bailey.head() ###Output _____no_output_____ ###Markdown ---- Section 2: Polarity One way to measure the tone of a text is to look at the text polarity: a measure of how positive or negative it is perceived to be. For example, a sentence like "I love Berkeley!" would be considered positive, while a sentence like "Stanford is terrible!" would be negative. And, because polarity is represented as a scale, some words have stronger positive or negative sentiment than others- "I like data science" is positive, but not as positive as "I love data science."We will use the [TextBlob](https://textblob.readthedocs.io/en/dev/quickstart.htmlsentiment-analysis) tools to analyze the sentiment of Old Bailey. TextBlob provides access to many common text-processing operations, and includes a lexicon and rule-based sentiment analysis tool.A TextBlob is created around string of text: ###Code # creates a sentiment analyzer blob = TextBlob("This is a super exciting, totally awesome test sentence.") blob ###Output _____no_output_____ ###Markdown We can access the sentiment by using `.sentiment`. ###Code blob.sentiment ###Output _____no_output_____ ###Markdown `sentiment` returns two values: the polarity and the subjectivity. The polarity ranges between -1 and 1 where -1 is a very negative text and 1 is a very positive text. Subjectivity ranges between 0 and 1 where 0 is a very objective text and 1 is a very subjective text (i.e. one that can be interpreted many different ways). You can get the polarity by using `.polarity`. ###Code blob.sentiment.polarity ###Output _____no_output_____ ###Markdown Polarity is calculated fairly simply: TextBlob accesses a dictionary of words that have been assigned polarity and subjectivity scores, looks up each word in the given text, and averages over the sentence. It also employs a few rules, such as changing the polarity of a word that comes after a negation. ###Code happy = TextBlob('Happy') print(happy.sentiment.polarity) negation = TextBlob('Not') print(negation.sentiment.polarity) negated_happy = TextBlob('Not happy') print(negated_happy.sentiment.polarity) ###Output _____no_output_____ ###Markdown QUESTION: Try calculating the polarity scores of a few of your own sentences in the cell below. ###Code # test the polarity scoring for different sentences my_blob = ... ... ###Output _____no_output_____ ###Markdown Next, we want to get the average polarity for each transcript. EXERCISE: define a function that will take in a string of text and return the polarity of that text. ###Code def get_polarity(text): """Return the polarity of TEXT""" ... return ... ###Output _____no_output_____ ###Markdown EXERCISE: Using `.apply` and your `get_polarity` function, get the polarity of every transcript in the Old Bailey data. ###Code polarities = ... # add the polarities as a column old_bailey['polarity'] = polarities old_bailey.head() ###Output _____no_output_____ ###Markdown QUESTION: - What was the most negative transcript/transcripts?- What was the most positive transcript/transcripts? ###Code # find the transcript with the highest polarity most_pos = ... most_pos # find the transcript with the lowest polarity most_neg = ... most_neg ###Output _____no_output_____ ###Markdown EXERCISE: Let's take a look at violin plots of these two datasets to better compare how the average compound polarity is distributed for each of the two years, before and after 1827.To show both years at once, it's easiest to use the Seaborn (abbreviated as `sns`) visualization library function. `y` is set to the name of the variable (a string) whose distributions we want to see. `x` is set to the name of the variable (also a string)that we want to compare distributions for . `data` is set to the dataframe (not a string) with all the values. ###Code # uncomment the next line and fill in the code to create the violin plots #sns.violinplot(x=..., y=..., data=...) ###Output _____no_output_____ ###Markdown QUESTION: What does this plot show us? What are some advantages to using polarity as a way to measure moral tone? What are some issues with this approach? Consider also how these answers might change for a different data set. Write your answer here. ---- Section 3: Moral Foundations TheoryAnother approach is to create specialized dictionaries containing specific words of interest to try to analyze sentiment from a particular angle (i.e. use a dictionary method). One set of researchers did just that from the perspective of [Moral Foundations Theory](http://moralfoundations.org/). We will now use it to see if we can understand more about the moral tone of Old Bailey transcripts than by using general polarity. You should be doing something like this for your homework. We will be using a provided moral foundations dictionary. ###Code with open('data/haidt_dict.json') as json_data: mft_dict = json.load(json_data) ###Output _____no_output_____ ###Markdown Moral Foundations Theory posits that there are five (with an occasional sixth) innane, universal psychological foundations of morality, and that those foundations shape human cultures and institutions (including legal). The keys of the dictionary correspond to the five foundations. ###Code #look at the keys of the dictionary provided keys = mft_dict.keys() list(keys) ###Output _____no_output_____ ###Markdown And the values of the dictionary are lists of words associated with each foundation. ###Code mft_dict[list(keys)[0]] #one example of the values provided for the first key ###Output _____no_output_____ ###Markdown Calculating Percentages In this approach, we'll use the frequency of Moral Foundations-related words as a measure of how the transcripts talk about morality and see if there's a difference between pre- and post-1827 trends. As a first step, we need to know the total number of words in each transcript. EXERCISE: Add a column to `old_bailey` with the number of words corresponding to each transcript. ###Code # create a new column called 'total_words' old_bailey['total_words'] = ... old_bailey.head() ###Output _____no_output_____ ###Markdown Next, we need to calculate the number of matches to entries in our dictionary for each foundation for each speech.Run the next cell to add six new columns to `old_bailey`, one per foundation, that show the number of word matches. This cell will also likely take some time to run (no more than a minute). Note that by now, you have the skills to write all the code in the next cell- we're just giving it to you because it's long, fiddly, and writing nested for-loops is not the focus of this lab. Make sure you know what it does before you move on, though. ###Code # Will take a bit of time to run due to the large size. # do the following code for each foundation for foundation in mft_dict.keys(): # create a new, empty column num_match_words = np.zeros(len(old_bailey)) stems = mft_dict[foundation] # do the following code for each foundation word for stem in stems: # find related word matches wd_count = np.array([sum([wd == stem for wd in transcript])for transcript in old_bailey['stemmed_tokens']]) # add the number of matches to the total num_match_words += wd_count # create a new column for each foundation with the number of related words per transcript old_bailey[foundation] = num_match_words old_bailey.head() ###Output _____no_output_____ ###Markdown EXERCISE: The columns for each foundation currently contain the number of words related to that foundation for each of the trials. Calculate the percentage of foundation words per trial by dividing the number of matched words by the number of total words and multiplying by 100. ###Code # do this for each foundation column for foundation in mft_dict.keys(): old_bailey[foundation] = ... old_bailey.head() ###Output _____no_output_____ ###Markdown Let's compare the average percentage of foundation words per transcript for the two dates, 1822, and 1832.EXERCISE: Create a dataframe that only has columns for the five foundations plus the year. Then, use the pandas dataframe function `groupby` to group rows by the year, and call the `mean` function on the `groupby` output to get the averages for each foundation. ###Code # the names of the columns we want to keep mft_columns = ['authority/subversion', 'care/harm', 'fairness/cheating', 'loyalty/betrayal', 'sanctity/degradation', 'year'] # create a data frame with only the above columns included mft_df = ... # groups the rows of mft_df by year, then take the mean foundation_avgs = ... foundation_avgs ###Output _____no_output_____ ###Markdown Next, create a bar graph. The simplest way is to call `.plot.barh()` on your dataframe of the averages. Also try calling `.transpose()` on your averages dataframe, then making a bar graph of that. The transpose function flips the rows and columns and can make it easier to compare the percentages. ###Code # create a bar graph ... ###Output _____no_output_____ ###Markdown QUESTION: What do you see from the bar graphs you created? Why would this be a good approach to answering the question of how talk about morality changed between these two periods? What are some limitations of this approach (Hint: look at the values on the graphs you calculated, and remember: these are percentages, not proportions)? Write your answer here. ---- Section 4: Non-negative matrix factorizationIn this section, you can get an idea of sentiment using topic modeling algorithms, something you touched on in the 4/10 lab earlier this week, to help look for patterns.On Tuesday, you explored Latent Dirichlet Allocation (LDA) in gensim to look for topics in a corpus. Non-negative matrix factorization (NMF), not included in gensim, is another such way to look for topics in unstructured text data. The two methods differ in what kinds of math they use 'under the hood': LDA relies on probabilistic graphical modeling, while NMF uses linear algebra. We want to generate the topics found for 1822 and 1832 trials, look for topics related to tone or morality, and see if there's a difference between the two.Run the cell below to make two lists: one list of the trial transcripts for each year. ###Code # trial transcripts for 1822 transcripts_1822 = old_bailey[old_bailey['year'] == 1822]['transcript'] # trial transcripts for 1832 transcripts_1832 = old_bailey[old_bailey['year'] == 1832]['transcript'] ###Output _____no_output_____ ###Markdown We'll start by looking at 1822. The following cell creates the tfidf vectorizer, fits the text data, and assigns the list of feature name (i.e. the words in the document) to `tfidf_feature_names_1822`.Check out the [documentation for TfidfVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html) if you need a refresher on what it does. ###Code # create the vectorizer tfidf_vectorizer_1822 = TfidfVectorizer(max_df=0.95, min_df=2, max_features=1000, stop_words='english') # fit the data tfidf_1822 = tfidf_vectorizer_1822.fit_transform(transcripts_1822) # get the feature names tfidf_feature_names_1822 = tfidf_vectorizer_1822.get_feature_names() ###Output _____no_output_____ ###Markdown EXERCISE: Create the TfidfVectorizer, fit_transform the data, and get the feature names for 1832. ###Code # create the vectorizer tfidf_vectorizer_1832 = ... # fit the data tfidf_1832 = ... # get the feature names tfidf_feature_names_1832 = ... ###Output _____no_output_____ ###Markdown As mentioned previously the algorithms are not able to automatically determine the number of topics and this value must be set when running the algorithm. Initialising NMF with ‘nndsvd’ rather than random initialisation improves the time it takes for NMF to converge.`random_state` gives the seed for the random number generator to use: this lets us reproduce our results in the future. ###Code num_topics = 20 # Run NMF for 1822 nmf_1822 = NMF(n_components=num_topics, random_state=1, init='nndsvd').fit(tfidf_1822) ###Output _____no_output_____ ###Markdown EXERCISE: Run NMF using `num_topics` for the number of components on the data from 1832. ###Code # Run NMF for 1832 nmf_1832 = ... ###Output _____no_output_____ ###Markdown We've provided you the function to display the topics shown by the NMF. ###Code def display_topics(model, feature_names, num_top_words): """Displays NUM_TOP_WORDS topics for MODEL """ for topic_idx, topic in enumerate(model.components_): print("Topic %d:" % (topic_idx)) print(" ".join([feature_names[i] for i in topic.argsort()[:-num_top_words - 1:-1]])) # the number of words to display per topic num_top_words = 10 # display the topics for 1822 display_topics(nmf_1822, tfidf_feature_names_1822, num_top_words) # display the topics for 1832 display_topics(nmf_1832, tfidf_feature_names_1832, num_top_words) ###Output _____no_output_____
docs/notebooks/Starters.ipynb	###Markdown For StartersThe basic idea of a starter is:> - pick a destination in the _JupyterLab File Browser_> - click a button in the _JupyterLab Launcher_> - see useful filesA slightly more accurate version is:> - configure via> [traitlets](https://jupyter-notebook.readthedocs.io/en/stable/config.html)> - advertise to JupyterLab via the [REST API](./REST%20API.ipynb)> - display in the _JupyterLab Launcher_> - click a button in the _JupyterLab Launcher_> - or immediately start with a [Starter Tree URL](Starter-Tree-URL)> - zero or more (but usually one) times:> - gather more information from the user via> [react-jsonschema-form](https://react-jsonschema-form.readthedocs.io)> - perform further processing> - copy files via the> [Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html)> - see useful files in the _JupyterLab File Browser_> - run _JupyterLab Commands_ to do other things to JupyterLabWhich of these steps a particular starter performs depends primarily on its type. Types of Starters Copy> `"type": "copy"`>> `"src": ""`The simplest starter, `copy`, just... copies. It can copy a single file, or a directoryof files (and subdirectories). The `src` attribute tells the starter where to get thefiles. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a copy starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> contents[label=③] contents -> files[label=④] files -> contents[label=⑤] contents -> post[label=⑥] post -> launcher[label=⑦] launcher -> launcher[label=⑧] } ###Output _____no_output_____ ###Markdown `copy`, like all the starters, makes use of the[Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html)directly. Existing files will _not_ be overwritten. Python> `"type": "python"`>> `"callable": ""`A Python Starter is a function. This type has the fewest limitations, as it has fullaccess to the `StarterManager` (and by extension, it's `parent`, the `NotebookApp`).This powers both the [Cookiecutter](Cookiecutter) the [Notebook](Notebook) starters,with the latter directly using the notebook server's _Kernel Manager_ to startshort-lifespan kernels. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a python starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents callable } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> callable[label=③] callable -> contents[label=④] contents -> files[label=⑤] files -> contents[label=⑥] contents -> callable[label=⑦] callable -> post[label=⑧] post -> launcher[label=⑨] launcher -> launcher[label=⑩] } ###Output _____no_output_____ ###Markdown Notebook> `"type": "notebook"`A notebook can be a starter. Each starter run gets its own, private kernel which canpersist between interactions with the user. Communication with the server manager ishandled through manipulating a copy of the notebook, specfically the notebook metadata.The advantages of this approach over the Python starter is:- works with any installed kernel- state is maintained between successive re-executions- `jupyterlab-starters` provides authoring support for editing and validating the starter ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a notebook starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents kernel tmpdir } subgraph cluster_lab { label="JupyterLab" launcher form1[label="initial form"] form2[label="dynamic form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> tmpdir[label=④] tmpdir -> post[label=⑤] tmpdir -> kernel[label=⑥] kernel -> tmpdir[label=⑦] post -> form2[label=⑧] form2 -> post[label=⑨] post -> tmpdir[label=⑩] tmpdir -> kernel[label=⑪] kernel -> tmpdir[label=⑫] tmpdir -> contents[label=⑬] contents -> files[label=⑭] files -> contents[label=⑮] contents -> post[label=⑯] post -> launcher[label=⑰] launcher -> launcher[label=⑲] } ###Output _____no_output_____ ###Markdown Built-ins CookiecutterThe cookiecutter starter will be available if `cookiecutter` is[installed](./Users.ipynbCookiecutter) in the same Python environment as the `notebook`server.> Additionally, if available, `importlib_metadata` will be used to list the (previously)> curated list of community-contributed cookiecutters. It is now recommended to search> for them directly on GitHub by> [topic](https://github.com/topics/cookiecutter-template) or> [advanced search](https://github.com/search?utf8=%E2%9C%93&q=path%3A%2F+filename%3Acookiecutter.json).One of the original motivations for _Jupyter Starters_ was a way to provide aconvenient, consistent, web-based experience for the[cookiecutter](https://cookiecutter.rtfd.io) ecosystem. Briefly, a cookiecutter is:> - a repository, zip archive, or directory that contains> - `cookiecutter.json`> - a (potentially nested) directory that uses> [Jinja2](https://jinja.palletsprojects.com) to describe file names and contentsWhat they may lack in dynamism, the make up for in consistency and robustness. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of the cookiecutter starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents cookiecutter } subgraph cluster_lab { label="JupyterLab" launcher form1[label="template form"] form2[label="cookiecutter form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> cookiecutter[label=④] cookiecutter -> git[label=⑤] git -> cookiecutter[label=⑥] cookiecutter -> post[label=⑧] post -> form2[label=⑨] form2 -> post[label=⑩] post -> cookiecutter[label=⑪] cookiecutter -> contents[label=⑫] contents -> files[label=⑬] files -> contents[label=⑭] contents -> post[label=⑮] post -> launcher[label=⑯] launcher -> launcher[label=⑰] } ###Output _____no_output_____ ###Markdown For StartersThe basic idea of a starter is:> - pick a destination in the _JupyterLab File Browser_> - click a button in the _JupyterLab Launcher_> - see useful filesA slightly more accurate version is:> - configure via> [traitlets](https://jupyter-notebook.readthedocs.io/en/stable/config.html)> - advertise to JupyterLab via the [REST API](./REST%20API.ipynb)> - display in the _JupyterLab Launcher_> - click a button in the _JupyterLab Launcher_> - or immediately start with a [Starter Tree URL](Starter-Tree-URL)> - zero or more (but usually one) times:> - gather more information from the user via> [react-jsonschema-form](https://react-jsonschema-form.readthedocs.io)> - perform further processing> - copy files via the> [Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html)> - see useful files in the _JupyterLab File Browser_> - run _JupyterLab Commands_ to do other things to JupyterLabWhich of these steps a particular starter performs depends primarily on its type. Types of Starters Copy> `"type": "copy"`>> `"src": ""`The simplest starter, `copy`, just... copies. It can copy a single file, or a directoryof files (and subdirectories). The `src` attribute tells the starter where to get thefiles. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a copy starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> contents[label=③] contents -> files[label=④] files -> contents[label=⑤] contents -> post[label=⑥] post -> launcher[label=⑦] launcher -> launcher[label=⑧] } ###Output _____no_output_____ ###Markdown `copy`, like all the starters, makes use of the[Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html)directly. Existing files will _not_ be overwritten. Python> `"type": "python"`>> `"callable": ""`A Python Starter is a function. This type has the fewest limitations, as it has fullaccess to the `StarterManager` (and by extension, it's `parent`, the `NotebookApp`).This powers both the [Cookiecutter](Cookiecutter) the [Notebook](Notebook) starters,with the latter directly using the notebook server's _Kernel Manager_ to startshort-lifespan kernels. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a python starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents callable } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> callable[label=③] callable -> contents[label=④] contents -> files[label=⑤] files -> contents[label=⑥] contents -> callable[label=⑦] callable -> post[label=⑧] post -> launcher[label=⑨] launcher -> launcher[label=⑩] } ###Output _____no_output_____ ###Markdown Notebook> `"type": "notebook"`A notebook can be a starter. Each starter run gets its own, private kernel which canpersist between interactions with the user. Communication with the server manager ishandled through manipulating a copy of the notebook, specfically the notebook metadata.The advantages of this approach over the Python starter is:- works with any installed kernel- state is maintained between successive re-executions- `jupyterlab-starters` provides authoring support for editing and validating the starter ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a notebook starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents kernel tmpdir } subgraph cluster_lab { label="JupyterLab" launcher form1[label="initial form"] form2[label="dynamic form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> tmpdir[label=④] tmpdir -> post[label=⑤] tmpdir -> kernel[label=⑥] kernel -> tmpdir[label=⑦] post -> form2[label=⑧] form2 -> post[label=⑨] post -> tmpdir[label=⑩] tmpdir -> kernel[label=⑪] kernel -> tmpdir[label=⑫] tmpdir -> contents[label=⑬] contents -> files[label=⑭] files -> contents[label=⑮] contents -> post[label=⑯] post -> launcher[label=⑰] launcher -> launcher[label=⑲] } ###Output _____no_output_____ ###Markdown Built-ins CookiecutterThe cookiecutter starter will be available if `cookiecutter` is[installed](./Users.ipynbCookiecutter) in the same Python environment as the `notebook`server.> Find more cookiecutter URLs on GitHub by> [topic](https://github.com/topics/cookiecutter-template) or> [advanced search](https://github.com/search?utf8=%E2%9C%93&q=path%3A%2F+filename%3Acookiecutter.json).One of the original motivations for _Jupyter Starters_ was a way to provide aconvenient, consistent, web-based experience for the[cookiecutter](https://cookiecutter.rtfd.io) ecosystem. Briefly, a cookiecutter is:> - a repository, zip archive, or directory that contains> - `cookiecutter.json`> - a (potentially nested) directory that uses> [Jinja2](https://jinja.palletsprojects.com) to describe file names and contentsWhat they may lack in dynamism, the make up for in consistency and robustness. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of the cookiecutter starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents cookiecutter } subgraph cluster_lab { label="JupyterLab" launcher form1[label="template form"] form2[label="cookiecutter form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> cookiecutter[label=④] cookiecutter -> git[label=⑤] git -> cookiecutter[label=⑥] cookiecutter -> post[label=⑧] post -> form2[label=⑨] form2 -> post[label=⑩] post -> cookiecutter[label=⑪] cookiecutter -> contents[label=⑫] contents -> files[label=⑬] files -> contents[label=⑭] contents -> post[label=⑮] post -> launcher[label=⑯] launcher -> launcher[label=⑰] } ###Output _____no_output_____ ###Markdown For StartersThe basic idea of a starter is:> - pick a destination in the _JupyterLab File Browser_> - click a button in the _JupyterLab Launcher_> - see useful filesA slightly more accurate version is:> - configure via [traitlets](https://jupyter-notebook.readthedocs.io/en/stable/config.html) > - advertise to JupyterLab via the [REST API](./REST%20API.ipynb)> - display in the _JupyterLab Launcher_> - click a button in the _JupyterLab Launcher_> - or immediately start with a [Starter Tree URL](Starter-Tree-URL)> - zero or more (but usually one) times: > - gather more information from the user via [react-jsonschema-form](https://react-jsonschema-form.readthedocs.io)> - perform further processing> - copy files via the [Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html)> - see useful files in the _JupyterLab File Browser_> - run _JupyterLab Commands_ to do other things to JupyterLabWhich of these steps a particular starter performs depends primarily on its type. Types of Starters Copy> `"type": "copy"`>> `"src": ""`The simplest starter, `copy`, just... copies. It can copy a single file, or a directory of files (and subdirectories). The `src` attribute tells the starter where to get the files. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a copy starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> contents[label=③] contents -> files[label=④] files -> contents[label=⑤] contents -> post[label=⑥] post -> launcher[label=⑦] launcher -> launcher[label=⑧] } ###Output _____no_output_____ ###Markdown `copy`, like all the starters, makes use of the [Contents API](https://jupyter-notebook.readthedocs.io/en/stable/extending/contents.html) directly. Existing files will _not_ be overwritten. Python> `"type": "python"`>> `"callable": ""`A Python Starter is a function. This type has the fewest limitations, as it has full access to the `StarterManager` (and by extension, it's `parent`, the `NotebookApp`). This powers both the [Cookiecutter](Cookiecutter) the [Notebook](Notebook) starters, with the latter directly using the notebook server's _Kernel Manager_ to start short-lifespan kernels. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a python starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/{:name}/{:path}" fontname=monospace] contents callable } subgraph cluster_lab { label="JupyterLab" launcher } get -> launcher[label=①] launcher -> post[label=②] post -> callable[label=③] callable -> contents[label=④] contents -> files[label=⑤] files -> contents[label=⑥] contents -> callable[label=⑦] callable -> post[label=⑧] post -> launcher[label=⑨] launcher -> launcher[label=⑩] } ###Output _____no_output_____ ###Markdown Notebook> `"type": "notebook"`A notebook can be a starter. Each starter run gets its own, private kernel which can persist between interactions with the user. Communication with the server manager is handled through manipulating a copy of the notebook, specfically the notebook metadata. The advantages of this approach over the Python starter is:- works with any installed kernel- state is maintained between successive re-executions- `jupyterlab-starters` provides authoring support for editing and validating the starter ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of a notebook starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents kernel tmpdir } subgraph cluster_lab { label="JupyterLab" launcher form1[label="initial form"] form2[label="dynamic form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> tmpdir[label=④] tmpdir -> post[label=⑤] tmpdir -> kernel[label=⑥] kernel -> tmpdir[label=⑦] post -> form2[label=⑧] form2 -> post[label=⑨] post -> tmpdir[label=⑩] tmpdir -> kernel[label=⑪] kernel -> tmpdir[label=⑫] tmpdir -> contents[label=⑬] contents -> files[label=⑭] files -> contents[label=⑮] contents -> post[label=⑯] post -> launcher[label=⑰] launcher -> launcher[label=⑲] } ###Output _____no_output_____ ###Markdown Built-ins CookiecutterThe cookiecutter starter will be available if `cookiecutter` is [installed](./Users.ipynbCookiecutter) in the same Python environment as the `notebook` server.> Additionally, if available, `importlib_metadata` will be used to list the (previously) curated list of community-contributed cookiecutters. It is now recommended to search for them directly on GitHub by [topic](https://github.com/topics/cookiecutter-template) or [advanced search](https://github.com/search?utf8=%E2%9C%93&q=path%3A%2F+filename%3Acookiecutter.json).One of the original motivations for _Jupyter Starters_ was a way to provide a convenient, consistent, web-based experience for the [cookiecutter](https://cookiecutter.rtfd.io) ecosystem. Briefly, a cookiecutter is:> - a repository, zip archive, or directory that contains> - `cookiecutter.json`> - a (potentially nested) directory that uses [Jinja2](https://jinja.palletsprojects.com) to describe file names and contentsWhat they may lack in dynamism, the make up for in consistency and robustness. ###Code %%dot digraph g { compound=true layout=dot rankdir=TB title="woooo" node[shape=none fontname="sans-serif"] graph[fontname="sans-serif" fontcolor="grey" color="none" fillcolor="#eeeeee" style=filled] label="a notional execution of the cookiecutter starter" subgraph cluster_files { label="Your Files" files } subgraph cluster_server { label="Notebook Server" get[label="/starters" fontname=monospace] post[label="/starters/cookiecutter/{:path}" fontname=monospace] contents cookiecutter } subgraph cluster_lab { label="JupyterLab" launcher form1[label="template form"] form2[label="cookiecutter form"] } get -> launcher[label=①] launcher -> form1[label=②] form1 -> post[label=③] post -> cookiecutter[label=④] cookiecutter -> git[label=⑤] git -> cookiecutter[label=⑥] cookiecutter -> post[label=⑧] post -> form2[label=⑨] form2 -> post[label=⑩] post -> cookiecutter[label=⑪] cookiecutter -> contents[label=⑫] contents -> files[label=⑬] files -> contents[label=⑭] contents -> post[label=⑮] post -> launcher[label=⑯] launcher -> launcher[label=⑰] } ###Output _____no_output_____
notebooks/Tutorial_09_Xarray_Compare_two_gridded_datasets.ipynb	###Markdown SST in Hurricane Irene Authors* [Dr Chelle Gentemann](mailto:[email protected]) - Earth and Space Research, USA* [Dr Marisol Garcia-Reyes](mailto:[email protected]) - Farallon Institute, USA * PODAACPY file search added by [Lewis John McGibbney](mailto:[email protected]) -JPL, NASA, USA ------------------- Import python packages* You are going to want numpy, pandas, matplotlib.pyplot, podaaacpy, and xarray* This cell also imports a parser so that a login file can be read to use podaacpy ###Code import warnings warnings.simplefilter('ignore') # filter some warning messages import numpy as np import pandas as pd import matplotlib.pyplot as plt import xarray as xr import cartopy.crs as ccrs #This is for reading in and parsing the login file credentials from pathlib import Path import configparser from lxml import objectify #The podaacpy api import podaac.podaac as podaac from podaac import drive as podaacdrive import podaac.podaac_utils as putil # then create an instance of the Podaac class p = podaac.Podaac() #read in the login credentials and pass them to podaac drive with open('./podaac.ini', 'r') as f: config = configparser.ConfigParser() config.read_file(f) d = podaacdrive.Drive(None, config['drive']['urs_username'], config['drive']['urs_password']) ###Output _____no_output_____ ###Markdown Analysis of SSTs during Hurricane IreneIrene was a massive storm, with tropical storm force winds extending outward 300 miles (485 km). The storm was also slow moving as it traversed the Mid-Atlantic. Irene claimed at least 48 lives and caused over 7 billion U.S. dollars in damages in the U.S. and 3.1 billion U.S. dollars of damage in the Caribbean. (source: https://www.ncdc.noaa.gov/sotc/tropical-cyclones/201113).For this tutorial we will use the podaacpy and a virtually aggregated dataset to search for SST2 Chl-a during Hurricane Irene and look at the change in upper ocean heat content and chlorophyll-a. https://www.livescience.com/30759-how-a-hurricane-impacts-the-ocean.html Read in Storm data from a thredds server- Note update - the thredds server has disappeared, so I have left the url in the code, but commented out and replaced it with a local copy of the data. ###Code #url = 'http://mrtee.europa.renci.org:8080/thredds/dodsC/DataLayers/IBTrACS.NA.v04r00.nc?name[0:1:2211],time[0:1:2211][0:1:359],lat[0:1:2211][0:1:359],lon[0:1:2211][0:1:359]' url = './../data/IBTrACS.NA.v04r00.nc' ds_storm=xr.open_dataset(url) irene = ds_storm.isel(storm=2092).isel(date_time=slice(0,78)) plt.scatter(irene.lon,irene.lat,c=irene.time.dt.dayofyear) print('storm start and end:', irene.time[0].data,irene.time[-1].data) ###Output _____no_output_____ ###Markdown For plotting the data, try using cartopy`ax = plt.axes(projection=ccrs.Orthographic(-70, 30))`you will need to add `transform=ccrs.PlateCarree()` to your plotting routine`ax.scatter(irene.lon,irene.lat,c=irene.time.dt.dayofyear,transform=ccrs.PlateCarree())``ax.set_extent([-82, -50, 10, 60], crs=ccrs.PlateCarree())``ax.coastlines('50m')``ax.stock_img()` ###Code # try plotting here with land mask start_time = '2011-08-20T00:00:00Z' end_time = '2011-09-05T23:59:59Z' start_time2 = '2011-08-15' end_time2 = '2011-09-25' minlat,maxlat = 15,45 minlon,maxlon = -100,-40 #dataset_id = 'PODAAC-GHCMC-4FM03' #CMC SST looked up on podaac website, on dataset page this is the persistant id #dataset_id = 'PODAAC-GHCMC-4FM02' #CMC SST looked up on podaac website, on dataset page this is the persistant id #dataset_id = 'PODAAC-GHGMR-4FJ04' #MUR SST #dataset_id = 'PODAAC-GHGDM-4FD02' #DMI SST #dataset_id = 'PODAAC-GHGPB-4FO02' #ospo sst dataset_id = 'PODAAC-GHCMC-4FM02' #CMC SST gresult = p.granule_search(dataset_id=dataset_id, start_time=start_time, end_time=end_time, items_per_page='100') urls = putil.PodaacUtils.mine_opendap_urls_from_granule_search(gresult) urls_sst = [w[:-5] for w in urls] #remove html from urlsurls_sst = [w.replace('-tools.jpl.nasa.gov/drive/files/', '-opendap.jpl.nasa.gov/opendap/') for w in urls_sst] print('num files:',len(urls_sst)) ds_sst = xr.open_dataset(urls_sst[0]) subset_sst = ds_sst.sel(lat=slice(minlat,maxlat) ,lon=slice(minlon,maxlon)) print('opening:', urls_sst[0],subset_sst) #subset_sst.analysed_sst.plot() fig, axes = plt.subplots(ncols=2,figsize=[12,4]) subset_sst.analysed_sst[0,:,:].plot(ax=axes[0]) axes[0].scatter(irene.lon[0:78],irene.lat[0:78],c=irene.time.dt.dayofyear[0:78],cmap='seismic') subset_sst.mask[0,:,:].plot(ax=axes[1]) axes[0].scatter(irene.lon[0:78],irene.lat[0:78],c=irene.time.dt.dayofyear[0:78],cmap='seismic') ###Output _____no_output_____ ###Markdown Mask out land values using .where`subset_sst_masked = subset_sst.where(subset_sst.mask==1)``subset_sst_masked.analysed_sst[0,:,:].plot()` ###Code # plot masked data here ###Output _____no_output_____ ###Markdown Compare time series of the cold wake after Hurricane as measured by MUR and OSTIA SSTs When you open a multi-file dataset, xarray uses dask for lasy loading. * Lazy loading: It mostly just loads the metadata. You can do data searching, selecting, subsetting without acutally loading the data. * Here we have loaded in 14 days of data for a very high resolution SST global datasets. Before we actually load the data, we are going to want to do some subsetting so that it will fit into our memory.* Notice below when you print out the dataset details that they are all stored as dask.array types. ###Code ds_sst = xr.open_mfdataset(urls_sst,coords='minimal') ds_sst = ds_sst.where(ds_sst.mask==1) #subset data subset_sst = ds_sst.sel(lat=slice(minlat,maxlat), lon=slice(minlon,maxlon)) ###Output _____no_output_____ ###Markdown Check the size of the data ###Code print('GB of data:', subset_sst.nbytes/1e9) #load the data subset_sst.load() ###Output _____no_output_____ ###Markdown Load chlorophyll-a data from a virtually aggregated dataset at COASTWATCH ###Code url = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/pmlEsaCCI31OceanColorDaily' ds_chl = xr.open_dataset(url).rename({'latitude':'lat','longitude':'lon'}) ds_chl_subset = ds_chl.sel(time=slice(start_time2,end_time2), lat=slice(45,15), lon=slice(-100,-40)) chl = ds_chl_subset.chlor_a.sortby('lat') ###Output _____no_output_____ ###Markdown * Create a 5-day resampled dataset since the chl-a data is missing when clouds are present ###Code chl_5dy = chl.resample(time='5D').mean('time') ###Output _____no_output_____ ###Markdown * Interpolate onto daily maps ###Code chl_1dy = chl_5dy.resample(time='1D').interpolate('linear') ###Output _____no_output_____ ###Markdown Look at the Chlorophyll-a data* Create a subplot* plot a 5-day average of the data* plot IRENE's track on the image* plot the difference poststorm - prestorm Chl-a* add a grid to the data for georeference ###Code fig, axes = plt.subplots(ncols=2,figsize=[12,4]) chl_5dy[0,:,:].plot(vmin=0,vmax=.5,ax=axes[0]) axes[0].scatter(irene.lon,irene.lat, c=irene.time.dt.dayofyear, cmap='seismic') (chl_5dy[4,:,:]-chl_5dy[0,:,:]).plot(vmin=-0.2,vmax=.2,ax=axes[1],cmap='seismic') axes[1].scatter(irene.lon,irene.lat, c=irene.time.dt.dayofyear, cmap='jet') axes[1].grid() ###Output _____no_output_____ ###Markdown Plot a timeseries of Chl-a at -80,32 near the coast* The Chl-a changes from 0.4 to 1.0 * It takes almost a month before the Chl-a returns to normal ###Code chl_ts = chl_1dy.sel(lat=32.0,method='nearest').sel(lon=-80.0,method='nearest') chl_ts.plot() ###Output _____no_output_____ ###Markdown Regridding to look at both SST and Chl-a* First interpolate in time* Next interpolate in space* use the SST mask to mask the Chl-a data ###Code #first interpolate onto same time sampling subset_chl_interp_time = chl_1dy.interp(time=subset_sst.time, method='linear') #now interpolate onto same spatial grid subset_chl_interp = subset_chl_interp_time.interp(lat=subset_sst.lat, lon=subset_sst.lon, method='nearest') #now mask the data subset_chl_masked = subset_chl_interp.where(subset_sst.mask==1) ###Output _____no_output_____ ###Markdown Plot both the change in SST and the change in Chl-a* There is a large region with substantial cooling, a 'cold-wake'* The Chl-a increase is only near the coast ###Code fig, axes = plt.subplots(ncols=2,figsize=[12,4]) dif = (subset_sst.analysed_sst[10,:,:]-subset_sst.analysed_sst[0,:,:]) dif.plot(vmin=-1,vmax=1,ax=axes[0],cmap='seismic') dif2 = (subset_chl_masked[10,:,:]-subset_chl_masked[0,:,:]) dif2.plot(vmin=-1,vmax=1,ax=axes[1],cmap='seismic') ###Output _____no_output_____ ###Markdown Make the figure easier to interpret by adding land ###Code f = plt.figure(figsize=(12,4)) dif = (subset_sst.analysed_sst[10,:,:]-subset_sst.analysed_sst[0,:,:]) ax1 = plt.subplot(121, projection=ccrs.Orthographic(-70, 30)) dif.plot(vmin=-1,vmax=1,ax=ax1,cmap='seismic',transform=ccrs.PlateCarree()) ax1.set_extent([-82, -50, 15, 45], crs=ccrs.PlateCarree()) ax1.coastlines('50m') ax1.stock_img() dif2 = (subset_chl_masked[11,:,:]-subset_chl_masked[0,:,:]) ax2 = plt.subplot(122, projection=ccrs.Orthographic(-70, 30)) (dif*0).plot(vmin=-1,vmax=1,ax=ax2,cmap='seismic',transform=ccrs.PlateCarree(),add_colorbar=False) dif2.plot(vmin=-1,vmax=1,ax=ax2,cmap='seismic',transform=ccrs.PlateCarree()) ax2.set_extent([-82, -50, 15, 45], crs=ccrs.PlateCarree()) ax2.coastlines('50m') ax2.stock_img() ###Output _____no_output_____
chap7/chapter_7_examples.ipynb	###Markdown `def func(pos1, pos2, ..., keywd1, keywd2, ..., args, kwargs):` • Changes to string, variable, and tuple arguments of a function within the function do not affect their values in the calling program.• Changes to values of elements in list and array arguments of a function within the function are reflected in the values of the same list and array elements in the calling function.The point is that simple numerics, strings and tuples are immutable while lists and arrays are mutable. Because immutable objects can’t be changed, changing them within a function creates new objects with the same name inside of the function, but the old immutable objects that were used as arguments in the function call remain unchanged in the calling program. On the other hand, if elements of mutable objects like those in lists or arrays are changed, then those elements that are changed inside the function are also changed in the calling program. ###Code import numpy as np a = np.random.random(10) a.size a.dtype a a.sum() a.mean() a.var() a.sort() a a.clip(0.3, 0.8) a def f(a, b): return 3 a + b*2 f(2, 3) g = lambda a, b: 3 a + b**2 g(2, 3) ###Output _____no_output_____
analyze_results10.ipynb	###Markdown ###Code import os import pandas as pd url = 'https://raw.githubusercontent.com/m-rafiul-islam/driver-behavior-model/data/parameter_estimation_combined_data_version10.csv' data = pd.read_csv(url) data.dropna(inplace=True) data.describe() data_ODE = data[data['alpha']==1].reset_index() data_FDE = data[data['alpha']!=1].reset_index() data_FDE.describe() data_ODE.describe() data_FDE data_FDE['alpha'].hist(bins=100) import seaborn as sns import matplotlib.pyplot as plt sns.histplot(data_FDE['alpha'], bins = 100,kde = True) sns.histplot(data_FDE['delta'], bins = 50,kde = True) sns.histplot(data_ODE['delta'], bins = 50,kde = True) fig, ax = plt.subplots() ax = (data_ODE['objective']/data_FDE['objective']).plot() import numpy as np # fig, ax = plt.subplots() # ax.plot(range(len(data_ODE['objective'])),np.array(data_ODE['objective']),'blue') # ax2= plt.twinx() # ax2.plot(range(len(data_FDE['objective'])),np.array(data_FDE['objective']),'green') # plt.figure() # plt.plot(range(len(data_ODE['objective'])),np.array(data_ODE['objective']),'blue') # plt.plot(range(len(data_FDE['objective'])),np.array(data_FDE['objective']),'green') ###Output _____no_output_____ ###Markdown ![image.png](data:image/png;base64,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) 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) ###Code ###Output _____no_output_____
quantum gravity/convwave/src/jupyter/get_snr_distributions_from_training.ipynb	###Markdown Read in the training data ###Code # Path to the directory where all data is stored data_path = '../data' snrs = dict() for event in ['GW150914', 'GW151226', 'GW170104']: snrs[event] = dict() print('Starting on event: {}'.format(event)) for dist in ['0100_0300', '0250_0500', '0400_0800', '0700_1200']: print('--Reading in distances: {}'.format(dist), end=' ') snrs[event][dist] = dict() # Read in the HDF file with h5py.File(os.path.join(data_path, 'training', 'timeseries', 'training_{}_{}_8k.h5'.format(event, dist)), 'r') as file: snrs[event][dist]['H1'] = np.array(file['snrs_H1']) print('({}, '.format(len(snrs[event][dist]['H1'])), end='') snrs[event][dist]['L1'] = np.array(file['snrs_L1']) print('{})'.format(len(snrs[event][dist]['L1']))) for event in ['GW150914', 'GW151226', 'GW170104']: display(HTML('<h3>{}</h3>'.format(event))) rows = [] for dist in ['0100_0300', '0250_0500', '0400_0800', '0700_1200']: median_H1 = '{:.1E}'.format(np.nanmedian(snrs[event][dist]['H1'].flatten())) median_L1 = '{:.1E}'.format(np.nanmedian(snrs[event][dist]['L1'].flatten())) min_H1 = '{:.1E}'.format(np.nanmin(snrs[event][dist]['H1'].flatten())) min_L1 = '{:.1E}'.format(np.nanmin(snrs[event][dist]['L1'].flatten())) max_H1 = '{:.1E}'.format(np.nanmax(snrs[event][dist]['H1'].flatten())) max_L1 = '{:.1E}'.format(np.nanmax(snrs[event][dist]['L1'].flatten())) rows.append([dist, median_H1, min_H1, max_H1, median_L1, min_L1, max_L1]) display(HTML(tabulate(rows, tablefmt='html', headers=['Distances', 'Median H1', 'Minimum H1', 'Maximum H1', 'Median L1', 'Minimum L1', 'Maximum L1']))) print(tabulate(rows, tablefmt='latex', headers=['Distances', 'Median H1', 'Minimum H1', 'Maximum H1', 'Median L1', 'Minimum L1', 'Maximum L1'])) 1.7e-4 ###Output _____no_output_____
Data_Visualization/Multivariate_Exploration_of_data/Adapted_Plot_Practice.ipynb	###Markdown In this workspace, you will work with the fuel economy dataset from the previous lesson on bivariate plots. ###Code fuel_econ = pd.read_csv('./data/fuel_econ.csv') fuel_econ.head() ###Output _____no_output_____ ###Markdown Task 1: Plot the city ('city') vs. highway ('highway') fuel efficiencies (both in mpg) for each vehicle class ('VClass'). Don't forget that vehicle class is an ordinal variable with levels {Minicompact Cars, Subcompact Cars, Compact Cars, Midsize Cars, Large Cars}. ###Code g = sb.FacetGrid(data = fuel_econ, col = 'VClass', size = 3, col_wrap = 3) g.map(plt.scatter, 'city', 'highway', alpha = 1/4); # run this cell to check your work against ours adaptedplot_solution_1() ###Output Due to overplotting, I've taken a faceting approach to this task. There don't seem to be any obvious differences in the main cluster across vehicle classes, except that the minicompact and large sedans' arcs are thinner than the other classes due to lower counts. The faceted plots clearly show that most of the high-efficiency cars are in the mid-size and compact car classes. ###Markdown Task 2: Plot the relationship between engine size ('displ', in meters), vehicle class, and fuel type ('fuelType'). For the lattermost feature, focus only on Premium Gasoline and Regular Gasoline cars. What kind of relationships can you spot in this plot? ###Code sb.boxplot(data = fuel_econ, x = 'VClass', y = 'displ', hue = 'fuelType') plt.legend(loc = 6, bbox_to_anchor = (1, 0)) plt.xticks(rotation = 90); # run this cell to check your work against ours adaptedplot_solution_2() ###Output I went with a clustered box plot on this task since there were too many levels to make a clustered violin plot accessible. The plot shows that in each vehicle class, engine sizes were larger for premium-fuel cars than regular-fuel cars. Engine size generally increased with vehicle class within each fuel type, but the trend was noisy for the smallest vehicle classes.
jupyter_script/hard_triplet_lstm_classification.ipynb	###Markdown Baseline of 3-convolutional layer and lstm classification- network - embedding: ```conv(5x5)->maxpool(2x2)->conv(5x5)->maxpool(2x2)->conv(3x3)->bi-lstm(32 hidden units)``` - classifier: ```linear(64, 10)``` - loss function: ```CrossEntropyLoss``` ###Code import sys sys.path.append('..') %load_ext autoreload %autoreload 2 from experiment import hard_triplet_baseline kwargs = { 'device': '2', 'lr': 1e-3, 'n_epochs': 100, 'n_classes': 10, 'n_samples': 12, 'margin': 0.3, 'log_interval': 50 } kwargs hard_triplet_baseline.hard_triplet_baseline_exp(**kwargs) ###Output 10080it [00:00, 307924.02it/s] 10080it [00:00, 205046.73it/s] 10080it [00:00, 488594.66it/s]
7. Python - Functions.ipynb	###Markdown Setup First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures: ###Code # To support both python 2 and python 3 from __future__ import division, print_function, unicode_literals # Common imports import numpy as np import os # to make this notebook's output stable across runs def reset_graph(seed=42): tf.reset_default_graph() tf.set_random_seed(seed) np.random.seed(seed) ###Output _____no_output_____ ###Markdown Basics of Python ###Code #Functions are a convenient way to divide your code into useful blocks, allowing us to order our code, #make it more readable, reuse it and save some time. #Also functions are a key way to define interfaces so programmers can share their code. def my_function(): print("x") def myfunction(): print("y") #default argument usage for "efgh" variable def my_function(abcd, efgh=101): print("abcd = %d, efgh = %d" %(abcd, efgh)) print("Hello there!") my_function(5) myfunction() my_function(3.9, 7) #function arguments def my_function_with_args(username, greeting, greeting2): print("Hello, %s , From My Function!, I wish you %s. Today %s"%(username, greeting, greeting2)) my_function_with_args("Vivek", "How are you", "is a good day") #def sum_two_numbers(a, b): # return a + b # Define our 3 functions def my_function(): print("Hello From My Function!") def my_function_with_args(username, greeting): print("Hello, %s , From My Function!, I wish you %s"%(username, greeting)) # print(a simple greeting) my_function() #prints - "Hello, John Doe, From My Function!, I wish you a great year!" my_function_with_args("John Doe", "a great year!") def sum_two_numbers(a, b): return a + b # after this line x will hold the value 3! x = sum_two_numbers(1,2) print(x) #Exercise """ In this exercise you'll use an existing function, and while adding your own to create a fully functional program. Add a function named list_benefits() that returns the following list of strings: "More organized code", "More readable code", "Easier code reuse", "Allowing programmers to share and connect code together" Add a function named build_sentence(info) which receives a single argument containing a string and returns a sentence starting with the given string and ending with the string " is a benefit of functions!" Run and see all the functions work together! """ # Modify this function to return a list of strings as defined above def list_benefits(): pass # Modify this function to concatenate to each benefit - " is a benefit of functions!" def build_sentence(benefit): pass def name_the_benefits_of_functions(): list_of_benefits = list_benefits() for benefit in list_of_benefits: print(build_sentence(benefit)) name_the_benefits_of_functions() ###Output _____no_output_____
_acquire_data/BeautifulSoup_xedotcom.ipynb	###Markdown BeautifulSoup Exchange rates from XE.com ###Code url = 'https://www.xe.com/currencyconverter/convert/?Amount=1&From=USD&To=EUR' import pandas as pd import requests from bs4 import BeautifulSoup def result_page(url, keywords=''): response = requests.get(url + keywords) if not response.status_code == 200: return None return BeautifulSoup(response.content, 'lxml') def get_data(url, keywords='', selector=''): rate_list = [] try: soup = result_page(url, keywords) rates = soup.find_all('td', class_='rateCell') for rate in rates: rate_ = rate.get_text() try: currency = rate.find('a').get('rel')[0][:7] rate_list.append((currency, rate_)) except: currency = '' return rate_list except: return None xe_data = get_data(url) pd.DataFrame(xe_data, columns=['currency', 'rate']) ###Output _____no_output_____
site/en-snapshot/swift/tutorials/python_interoperability.ipynb	###Markdown Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). ###Code // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" } // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // https://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. ###Output _____no_output_____ ###Markdown View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. ###Code import PythonKit print(Python.version) ###Output _____no_output_____ ###Markdown Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTHON_LIBRARY="~/anaconda3/lib/libpython3.7m.so"`The exact filename will differ across Python environments and platforms. Alternatively, you can set the `PYTHON_VERSION` environment variable, which instructs Swift to search system library paths for a matching Python version. Note that `PYTHON_LIBRARY` takes precedence over `PYTHON_VERSION`.In code, you can also call the `PythonLibrary.useVersion` function, which is equivalent to setting `PYTHON_VERSION`. ###Code // PythonLibrary.useVersion(2) // PythonLibrary.useVersion(3, 7) ###Output _____no_output_____ ###Markdown __Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). BasicsIn Swift, `PythonObject` represents an object from Python.All Python APIs use and return `PythonObject` instances.Basic types in Swift (like numbers and arrays) are convertible to `PythonObject`. In some cases (for literals and functions taking `PythonConvertible` arguments), conversion happens implicitly. To explicitly cast a Swift value to `PythonObject`, use the `PythonObject` initializer.`PythonObject` defines many standard operations, including numeric operations, indexing, and iteration. ###Code // Convert standard Swift types to Python. let pythonInt: PythonObject = 1 let pythonFloat: PythonObject = 3.0 let pythonString: PythonObject = "Hello Python!" let pythonRange: PythonObject = PythonObject(5..<10) let pythonArray: PythonObject = [1, 2, 3, 4] let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]] // Perform standard operations on Python objects. print(pythonInt + pythonFloat) print(pythonString[0..<6]) print(pythonRange) print(pythonArray[2]) print(pythonDict["bar"]) // Convert Python objects back to Swift. let int = Int(pythonInt)! let float = Float(pythonFloat)! let string = String(pythonString)! let range = Range<Int>(pythonRange)! let array: [Int] = Array(pythonArray)! let dict: [String: [Int]] = Dictionary(pythonDict)! // Perform standard operations. // Outputs are the same as Python! print(Float(int) + float) print(string.prefix(6)) print(range) print(array[2]) print(dict["bar"]!) ###Output _____no_output_____ ###Markdown `PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality from an incompatible `PythonObject` instance. ###Code let one: PythonObject = 1 print(one == one) print(one < one) print(one + one) let array: PythonObject = [1, 2, 3] for (i, x) in array.enumerated() { print(i, x) } ###Output _____no_output_____ ###Markdown To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` ###Code let pythonTuple = Python.tuple([1, 2, 3]) print(pythonTuple, Python.len(pythonTuple)) // Convert to Swift. let tuple = pythonTuple.tuple3 print(tuple) ###Output _____no_output_____ ###Markdown Python builtinsAccess Python builtins via the global `Python` interface. ###Code // `Python.builtins` is a dictionary of all Python builtins. _ = Python.builtins // Try some Python builtins. print(Python.type(1)) print(Python.len([1, 2, 3])) print(Python.sum([1, 2, 3])) ###Output _____no_output_____ ###Markdown Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. ###Code let np = Python.import("numpy") print(np) let zeros = np.ones([2, 3]) print(zeros) ###Output _____no_output_____ ###Markdown Use the throwing function `Python.attemptImport` to perform safe importing. ###Code let maybeModule = try? Python.attemptImport("nonexistent_module") print(maybeModule) ###Output _____no_output_____ ###Markdown Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only if the `numpy.ndarray` is 1-D. ###Code import TensorFlow let numpyArray = np.ones([4], dtype: np.float32) print("Swift type:", type(of: numpyArray)) print("Python type:", Python.type(numpyArray)) print(numpyArray.shape) // Examples of converting `numpy.ndarray` to Swift types. let array: [Float] = Array(numpy: numpyArray)! let shapedArray = ShapedArray<Float>(numpy: numpyArray)! let tensor = Tensor<Float>(numpy: numpyArray)! // Examples of converting Swift types to `numpy.ndarray`. print(array.makeNumpyArray()) print(shapedArray.makeNumpyArray()) print(tensor.makeNumpyArray()) // Examples with different dtypes. let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))! let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))! ###Output _____no_output_____ ###Markdown Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. ###Code // This cell is here to display plots inside a Jupyter Notebook. // Do not copy it into another environment. %include "EnableIPythonDisplay.swift" IPythonDisplay.shell.enable_matplotlib("inline") let np = Python.import("numpy") let plt = Python.import("matplotlib.pyplot") let time = np.arange(0, 10, 0.01) let amplitude = np.exp(-0.1 * time) let position = amplitude * np.sin(3 * time) plt.figure(figsize: [15, 10]) plt.plot(time, position) plt.plot(time, amplitude) plt.plot(time, -amplitude) plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.title("Oscillations") plt.show() ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). ###Code // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" } // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // https://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. ###Output _____no_output_____ ###Markdown View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. ###Code import PythonKit print(Python.version) ###Output _____no_output_____ ###Markdown Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTHON_LIBRARY="~/anaconda3/lib/libpython3.7m.so"`The exact filename will differ across Python environments and platforms. Alternatively, you can set the `PYTHON_VERSION` environment variable, which instructs Swift to search system library paths for a matching Python version. Note that `PYTHON_LIBRARY` takes precedence over `PYTHON_VERSION`.In code, you can also call the `PythonLibrary.useVersion` function, which is equivalent to setting `PYTHON_VERSION`. ###Code // PythonLibrary.useVersion(2) // PythonLibrary.useVersion(3, 7) ###Output _____no_output_____ ###Markdown __Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). BasicsIn Swift, `PythonObject` represents an object from Python.All Python APIs use and return `PythonObject` instances.Basic types in Swift (like numbers and arrays) are convertible to `PythonObject`. In some cases (for literals and functions taking `PythonConvertible` arguments), conversion happens implicitly. To explicitly cast a Swift value to `PythonObject`, use the `PythonObject` initializer.`PythonObject` defines many standard operations, including numeric operations, indexing, and iteration. ###Code // Convert standard Swift types to Python. let pythonInt: PythonObject = 1 let pythonFloat: PythonObject = 3.0 let pythonString: PythonObject = "Hello Python!" let pythonRange: PythonObject = PythonObject(5..<10) let pythonArray: PythonObject = [1, 2, 3, 4] let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]] // Perform standard operations on Python objects. print(pythonInt + pythonFloat) print(pythonString[0..<6]) print(pythonRange) print(pythonArray[2]) print(pythonDict["bar"]) // Convert Python objects back to Swift. let int = Int(pythonInt)! let float = Float(pythonFloat)! let string = String(pythonString)! let range = Range<Int>(pythonRange)! let array: [Int] = Array(pythonArray)! let dict: [String: [Int]] = Dictionary(pythonDict)! // Perform standard operations. // Outputs are the same as Python! print(Float(int) + float) print(string.prefix(6)) print(range) print(array[2]) print(dict["bar"]!) ###Output _____no_output_____ ###Markdown `PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality from an incompatible `PythonObject` instance. ###Code let one: PythonObject = 1 print(one == one) print(one < one) print(one + one) let array: PythonObject = [1, 2, 3] for (i, x) in array.enumerated() { print(i, x) } ###Output _____no_output_____ ###Markdown To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` ###Code let pythonTuple = Python.tuple([1, 2, 3]) print(pythonTuple, Python.len(pythonTuple)) // Convert to Swift. let tuple = pythonTuple.tuple3 print(tuple) ###Output _____no_output_____ ###Markdown Python builtinsAccess Python builtins via the global `Python` interface. ###Code // `Python.builtins` is a dictionary of all Python builtins. _ = Python.builtins // Try some Python builtins. print(Python.type(1)) print(Python.len([1, 2, 3])) print(Python.sum([1, 2, 3])) ###Output _____no_output_____ ###Markdown Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. ###Code let np = Python.import("numpy") print(np) let zeros = np.ones([2, 3]) print(zeros) ###Output _____no_output_____ ###Markdown Use the throwing function `Python.attemptImport` to perform safe importing. ###Code let maybeModule = try? Python.attemptImport("nonexistent_module") print(maybeModule) ###Output _____no_output_____ ###Markdown Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only if the `numpy.ndarray` is 1-D. ###Code import TensorFlow let numpyArray = np.ones([4], dtype: np.float32) print("Swift type:", type(of: numpyArray)) print("Python type:", Python.type(numpyArray)) print(numpyArray.shape) // Examples of converting `numpy.ndarray` to Swift types. let array: [Float] = Array(numpy: numpyArray)! let shapedArray = ShapedArray<Float>(numpy: numpyArray)! let tensor = Tensor<Float>(numpy: numpyArray)! // Examples of converting Swift types to `numpy.ndarray`. print(array.makeNumpyArray()) print(shapedArray.makeNumpyArray()) print(tensor.makeNumpyArray()) // Examples with different dtypes. let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))! let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))! ###Output _____no_output_____ ###Markdown Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. ###Code // This cell is here to display plots inside a Jupyter Notebook. // Do not copy it into another environment. %include "EnableIPythonDisplay.swift" IPythonDisplay.shell.enable_matplotlib("inline") let np = Python.import("numpy") let plt = Python.import("matplotlib.pyplot") let time = np.arange(0, 10, 0.01) let amplitude = np.exp(-0.1 * time) let position = amplitude * np.sin(3 * time) plt.figure(figsize: [15, 10]) plt.plot(time, position) plt.plot(time, amplitude) plt.plot(time, -amplitude) plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.title("Oscillations") plt.show() ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). ###Code // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" } // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // https://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. ###Output _____no_output_____ ###Markdown View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. ###Code // comment so that Colab does not interpret `#if ...` as a comment #if canImport(PythonKit) import PythonKit #else import Python #endif print(Python.version) ###Output _____no_output_____ ###Markdown Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTHON_LIBRARY="~/anaconda3/lib/libpython3.7m.so"`The exact filename will differ across Python environments and platforms. Alternatively, you can set the `PYTHON_VERSION` environment variable, which instructs Swift to search system library paths for a matching Python version. Note that `PYTHON_LIBRARY` takes precedence over `PYTHON_VERSION`.In code, you can also call the `PythonLibrary.useVersion` function, which is equivalent to setting `PYTHON_VERSION`. ###Code // PythonLibrary.useVersion(2) // PythonLibrary.useVersion(3, 7) ###Output _____no_output_____ ###Markdown __Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). BasicsIn Swift, `PythonObject` represents an object from Python.All Python APIs use and return `PythonObject` instances.Basic types in Swift (like numbers and arrays) are convertible to `PythonObject`. In some cases (for literals and functions taking `PythonConvertible` arguments), conversion happens implicitly. To explicitly cast a Swift value to `PythonObject`, use the `PythonObject` initializer.`PythonObject` defines many standard operations, including numeric operations, indexing, and iteration. ###Code // Convert standard Swift types to Python. let pythonInt: PythonObject = 1 let pythonFloat: PythonObject = 3.0 let pythonString: PythonObject = "Hello Python!" let pythonRange: PythonObject = PythonObject(5..<10) let pythonArray: PythonObject = [1, 2, 3, 4] let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]] // Perform standard operations on Python objects. print(pythonInt + pythonFloat) print(pythonString[0..<6]) print(pythonRange) print(pythonArray[2]) print(pythonDict["bar"]) // Convert Python objects back to Swift. let int = Int(pythonInt)! let float = Float(pythonFloat)! let string = String(pythonString)! let range = Range<Int>(pythonRange)! let array: [Int] = Array(pythonArray)! let dict: [String: [Int]] = Dictionary(pythonDict)! // Perform standard operations. // Outputs are the same as Python! print(Float(int) + float) print(string.prefix(6)) print(range) print(array[2]) print(dict["bar"]!) ###Output _____no_output_____ ###Markdown `PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality from an incompatible `PythonObject` instance. ###Code let one: PythonObject = 1 print(one == one) print(one < one) print(one + one) let array: PythonObject = [1, 2, 3] for (i, x) in array.enumerated() { print(i, x) } ###Output _____no_output_____ ###Markdown To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` ###Code let pythonTuple = Python.tuple([1, 2, 3]) print(pythonTuple, Python.len(pythonTuple)) // Convert to Swift. let tuple = pythonTuple.tuple3 print(tuple) ###Output _____no_output_____ ###Markdown Python builtinsAccess Python builtins via the global `Python` interface. ###Code // `Python.builtins` is a dictionary of all Python builtins. _ = Python.builtins // Try some Python builtins. print(Python.type(1)) print(Python.len([1, 2, 3])) print(Python.sum([1, 2, 3])) ###Output _____no_output_____ ###Markdown Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. ###Code let np = Python.import("numpy") print(np) let zeros = np.ones([2, 3]) print(zeros) ###Output _____no_output_____ ###Markdown Use the throwing function `Python.attemptImport` to perform safe importing. ###Code let maybeModule = try? Python.attemptImport("nonexistent_module") print(maybeModule) ###Output _____no_output_____ ###Markdown Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only if the `numpy.ndarray` is 1-D. ###Code import TensorFlow let numpyArray = np.ones([4], dtype: np.float32) print("Swift type:", type(of: numpyArray)) print("Python type:", Python.type(numpyArray)) print(numpyArray.shape) // Examples of converting `numpy.ndarray` to Swift types. let array: [Float] = Array(numpy: numpyArray)! let shapedArray = ShapedArray<Float>(numpy: numpyArray)! let tensor = Tensor<Float>(numpy: numpyArray)! // Examples of converting Swift types to `numpy.ndarray`. print(array.makeNumpyArray()) print(shapedArray.makeNumpyArray()) print(tensor.makeNumpyArray()) // Examples with different dtypes. let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))! let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))! ###Output _____no_output_____ ###Markdown Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. ###Code // This cell is here to display plots inside a Jupyter Notebook. // Do not copy it into another environment. %include "EnableIPythonDisplay.swift" IPythonDisplay.shell.enable_matplotlib("inline") let np = Python.import("numpy") let plt = Python.import("matplotlib.pyplot") let time = np.arange(0, 10, 0.01) let amplitude = np.exp(-0.1 * time) let position = amplitude * np.sin(3 * time) plt.figure(figsize: [15, 10]) plt.plot(time, position) plt.plot(time, amplitude) plt.plot(time, -amplitude) plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.title("Oscillations") plt.show() ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). ###Code // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" } // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // https://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. ###Output _____no_output_____ ###Markdown View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. ###Code import PythonKit print(Python.version) ###Output _____no_output_____ ###Markdown Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTHON_LIBRARY="~/anaconda3/lib/libpython3.7m.so"`The exact filename will differ across Python environments and platforms. Alternatively, you can set the `PYTHON_VERSION` environment variable, which instructs Swift to search system library paths for a matching Python version. Note that `PYTHON_LIBRARY` takes precedence over `PYTHON_VERSION`.In code, you can also call the `PythonLibrary.useVersion` function, which is equivalent to setting `PYTHON_VERSION`. ###Code // PythonLibrary.useVersion(2) // PythonLibrary.useVersion(3, 7) ###Output _____no_output_____ ###Markdown __Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). BasicsIn Swift, `PythonObject` represents an object from Python.All Python APIs use and return `PythonObject` instances.Basic types in Swift (like numbers and arrays) are convertible to `PythonObject`. In some cases (for literals and functions taking `PythonConvertible` arguments), conversion happens implicitly. To explicitly cast a Swift value to `PythonObject`, use the `PythonObject` initializer.`PythonObject` defines many standard operations, including numeric operations, indexing, and iteration. ###Code // Convert standard Swift types to Python. let pythonInt: PythonObject = 1 let pythonFloat: PythonObject = 3.0 let pythonString: PythonObject = "Hello Python!" let pythonRange: PythonObject = PythonObject(5..<10) let pythonArray: PythonObject = [1, 2, 3, 4] let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]] // Perform standard operations on Python objects. print(pythonInt + pythonFloat) print(pythonString[0..<6]) print(pythonRange) print(pythonArray[2]) print(pythonDict["bar"]) // Convert Python objects back to Swift. let int = Int(pythonInt)! let float = Float(pythonFloat)! let string = String(pythonString)! let range = Range<Int>(pythonRange)! let array: [Int] = Array(pythonArray)! let dict: [String: [Int]] = Dictionary(pythonDict)! // Perform standard operations. // Outputs are the same as Python! print(Float(int) + float) print(string.prefix(6)) print(range) print(array[2]) print(dict["bar"]!) ###Output _____no_output_____ ###Markdown `PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality from an incompatible `PythonObject` instance. ###Code let one: PythonObject = 1 print(one == one) print(one < one) print(one + one) let array: PythonObject = [1, 2, 3] for (i, x) in array.enumerated() { print(i, x) } ###Output _____no_output_____ ###Markdown To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` ###Code let pythonTuple = Python.tuple([1, 2, 3]) print(pythonTuple, Python.len(pythonTuple)) // Convert to Swift. let tuple = pythonTuple.tuple3 print(tuple) ###Output _____no_output_____ ###Markdown Python builtinsAccess Python builtins via the global `Python` interface. ###Code // `Python.builtins` is a dictionary of all Python builtins. _ = Python.builtins // Try some Python builtins. print(Python.type(1)) print(Python.len([1, 2, 3])) print(Python.sum([1, 2, 3])) ###Output _____no_output_____ ###Markdown Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. ###Code let np = Python.import("numpy") print(np) let zeros = np.ones([2, 3]) print(zeros) ###Output _____no_output_____ ###Markdown Use the throwing function `Python.attemptImport` to perform safe importing. ###Code let maybeModule = try? Python.attemptImport("nonexistent_module") print(maybeModule) ###Output _____no_output_____ ###Markdown Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only if the `numpy.ndarray` is 1-D. ###Code import TensorFlow let numpyArray = np.ones([4], dtype: np.float32) print("Swift type:", type(of: numpyArray)) print("Python type:", Python.type(numpyArray)) print(numpyArray.shape) // Examples of converting `numpy.ndarray` to Swift types. let array: [Float] = Array(numpy: numpyArray)! let shapedArray = ShapedArray<Float>(numpy: numpyArray)! let tensor = Tensor<Float>(numpy: numpyArray)! // Examples of converting Swift types to `numpy.ndarray`. print(array.makeNumpyArray()) print(shapedArray.makeNumpyArray()) print(tensor.makeNumpyArray()) // Examples with different dtypes. let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))! let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))! ###Output _____no_output_____ ###Markdown Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. ###Code // This cell is here to display plots inside a Jupyter Notebook. // Do not copy it into another environment. %include "EnableIPythonDisplay.swift" print(IPythonDisplay.shell.enable_matplotlib("inline")) let np = Python.import("numpy") let plt = Python.import("matplotlib.pyplot") let time = np.arange(0, 10, 0.01) let amplitude = np.exp(-0.1 * time) let position = amplitude * np.sin(3 * time) plt.figure(figsize: [15, 10]) plt.plot(time, position) plt.plot(time, amplitude) plt.plot(time, -amplitude) plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.title("Oscillations") plt.show() ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). ###Code // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" } // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // https://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. ###Output _____no_output_____ ###Markdown View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. ###Code import PythonKit print(Python.version) ###Output _____no_output_____ ###Markdown Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTHON_LIBRARY="~/anaconda3/lib/libpython3.7m.so"`The exact filename will differ across Python environments and platforms. Alternatively, you can set the `PYTHON_VERSION` environment variable, which instructs Swift to search system library paths for a matching Python version. Note that `PYTHON_LIBRARY` takes precedence over `PYTHON_VERSION`.In code, you can also call the `PythonLibrary.useVersion` function, which is equivalent to setting `PYTHON_VERSION`. ###Code // PythonLibrary.useVersion(2) // PythonLibrary.useVersion(3, 7) ###Output _____no_output_____ ###Markdown __Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). BasicsIn Swift, `PythonObject` represents an object from Python.All Python APIs use and return `PythonObject` instances.Basic types in Swift (like numbers and arrays) are convertible to `PythonObject`. In some cases (for literals and functions taking `PythonConvertible` arguments), conversion happens implicitly. To explicitly cast a Swift value to `PythonObject`, use the `PythonObject` initializer.`PythonObject` defines many standard operations, including numeric operations, indexing, and iteration. ###Code // Convert standard Swift types to Python. let pythonInt: PythonObject = 1 let pythonFloat: PythonObject = 3.0 let pythonString: PythonObject = "Hello Python!" let pythonRange: PythonObject = PythonObject(5..<10) let pythonArray: PythonObject = [1, 2, 3, 4] let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]] // Perform standard operations on Python objects. print(pythonInt + pythonFloat) print(pythonString[0..<6]) print(pythonRange) print(pythonArray[2]) print(pythonDict["bar"]) // Convert Python objects back to Swift. let int = Int(pythonInt)! let float = Float(pythonFloat)! let string = String(pythonString)! let range = Range<Int>(pythonRange)! let array: [Int] = Array(pythonArray)! let dict: [String: [Int]] = Dictionary(pythonDict)! // Perform standard operations. // Outputs are the same as Python! print(Float(int) + float) print(string.prefix(6)) print(range) print(array[2]) print(dict["bar"]!) ###Output _____no_output_____ ###Markdown `PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality from an incompatible `PythonObject` instance. ###Code let one: PythonObject = 1 print(one == one) print(one < one) print(one + one) let array: PythonObject = [1, 2, 3] for (i, x) in array.enumerated() { print(i, x) } ###Output _____no_output_____ ###Markdown To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` ###Code let pythonTuple = Python.tuple([1, 2, 3]) print(pythonTuple, Python.len(pythonTuple)) // Convert to Swift. let tuple = pythonTuple.tuple3 print(tuple) ###Output _____no_output_____ ###Markdown Python builtinsAccess Python builtins via the global `Python` interface. ###Code // `Python.builtins` is a dictionary of all Python builtins. _ = Python.builtins // Try some Python builtins. print(Python.type(1)) print(Python.len([1, 2, 3])) print(Python.sum([1, 2, 3])) ###Output _____no_output_____ ###Markdown Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. ###Code let np = Python.import("numpy") print(np) let zeros = np.ones([2, 3]) print(zeros) ###Output _____no_output_____ ###Markdown Use the throwing function `Python.attemptImport` to perform safe importing. ###Code let maybeModule = try? Python.attemptImport("nonexistent_module") print(maybeModule) ###Output _____no_output_____ ###Markdown Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only if the `numpy.ndarray` is 1-D. ###Code import TensorFlow let numpyArray = np.ones([4], dtype: np.float32) print("Swift type:", type(of: numpyArray)) print("Python type:", Python.type(numpyArray)) print(numpyArray.shape) // Examples of converting `numpy.ndarray` to Swift types. let array: [Float] = Array(numpy: numpyArray)! let shapedArray = ShapedArray<Float>(numpy: numpyArray)! let tensor = Tensor<Float>(numpy: numpyArray)! // Examples of converting Swift types to `numpy.ndarray`. print(array.makeNumpyArray()) print(shapedArray.makeNumpyArray()) print(tensor.makeNumpyArray()) // Examples with different dtypes. let doubleArray: [Double] = Array(numpy: np.ones([3], dtype: np.float))! let intTensor = Tensor<Int32>(numpy: np.ones([2, 3], dtype: np.int32))! ###Output _____no_output_____ ###Markdown Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. ###Code // This cell is here to display plots inside a Jupyter Notebook. // Do not copy it into another environment. %include "EnableIPythonDisplay.swift" print(IPythonDisplay.shell.enable_matplotlib("inline")) let np = Python.import("numpy") let plt = Python.import("matplotlib.pyplot") let time = np.arange(0, 10, 0.01) let amplitude = np.exp(-0.1 * time) let position = amplitude * np.sin(3 * time) plt.figure(figsize: [15, 10]) plt.plot(time, position) plt.plot(time, amplitude) plt.plot(time, -amplitude) plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.title("Oscillations") plt.show() ###Output _____no_output_____
site/ko/lattice/tutorials/shape_constraints_for_ethics.ipynb	###Markdown *Copyright 2020 The TensorFlow Authors.* ###Code #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ###Output _____no_output_____ ###Markdown Tensorflow Lattice를 사용한 윤리에 대한 형상 제약 조건 TensorFlow.org에서 보기 Google Colab에서 실행하기 GitHub에서소스 보기 노트북 다운로드하기 개요이 튜토리얼에서는 TensorFlow Lattice(TFL) 라이브러리를 사용하여 책임감 있게 작동하고 윤리적이거나 공정한 특정 가정을 위반하지 않는 모델을 훈련하는 방법을 보여줍니다. 특히 특정 속성에 대한 불공정한 불이익을 피하기 위해 단조성 제약 조건을 사용하는 데 초점을 맞출 것입니다. 이 튜토리얼에는 Serena Wang 및 Maya Gupta이 [AISTATS 2020](https://www.aistats.org/)에 게재한 [Deontological Ethics By Monotonicity Shape Constraints(단조성 형상 제약 조건에 의한 의무론적 윤리)](https://arxiv.org/abs/2001.11990) 논문의 실험 데모가 포함되어 있습니다.공개된 데이터세트에 TFL 사전 구성 estimator를 사용할 것이지만 이 튜토리얼의 모든 내용은 TFL Keras 레이어로 구성된 모델로도 수행할 수 있습니다.계속하기 전에 필요한 모든 패키지가 런타임에 설치되어 있는지 확인하세요(아래 코드 셀에서 가져옴). 설정 TF Lattice 패키지 설치하기: ###Code #@test {"skip": true} !pip install tensorflow-lattice seaborn ###Output _____no_output_____ ###Markdown 필수 패키지 가져오기: ###Code import tensorflow as tf import logging import matplotlib.pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns from sklearn.model_selection import train_test_split import sys import tensorflow_lattice as tfl logging.disable(sys.maxsize) ###Output _____no_output_____ ###Markdown 이 튜토리얼에서 사용되는 기본값: ###Code # List of learning rate hyperparameters to try. # For a longer list of reasonable hyperparameters, try [0.001, 0.01, 0.1]. LEARNING_RATES = [0.01] # Default number of training epochs and batch sizes. NUM_EPOCHS = 1000 BATCH_SIZE = 1000 # Directory containing dataset files. DATA_DIR = 'https://raw.githubusercontent.com/serenalwang/shape_constraints_for_ethics/master' ###Output _____no_output_____ ###Markdown 사례 연구 1: 로스쿨 입학이 튜토리얼의 첫 번째 부분에서는 로스쿨 입학 위원회(LSAC)의 로스쿨 입학 데이터세트를 사용한 사례 연구를 살펴봅니다. 학생의 LSAT 점수와 학부 GPA의 두 가지 특성을 사용하여 학생이 기준점을 통과할지 여부를 예측하도록 분류자를 훈련할 것입니다.분류자의 점수가 로스쿨 입학 또는 장학금 판단 요소로 사용되었다고 가정합니다. 성과 기반 사회 규범에 따르면 GPA와 LSAT 점수가 높은 학생이 분류자로부터 더 높은 점수를 받아야 합니다. 그러나 모델이 이러한 직관적인 규범을 위반하기 쉽고 때로는 더 높은 GPA 또는 LSAT 점수를 받은 학생들에게 불이익을 주는 것을 관찰하게 됩니다.이 불공정한 불이익 문제를 해결하기 위해 모델이 더 높은 GPA 또는 더 높은 LSAT 점수에 불이익을 주지 않도록 단조성 제약 조건을 적용할 수 있습니다. 이 튜토리얼에서는 TFL을 사용하여 이러한 단조성 제약 조건을 적용하는 방법을 보여줍니다. 로스쿨 데이터 로드하기 ###Code # Load data file. law_file_name = 'lsac.csv' law_file_path = os.path.join(DATA_DIR, law_file_name) raw_law_df = pd.read_csv(law_file_path, delimiter=',') ###Output _____no_output_____ ###Markdown 데이터세트 전처리하기: ###Code # Define label column name. LAW_LABEL = 'pass_bar' def preprocess_law_data(input_df): # Drop rows with where the label or features of interest are missing. output_df = input_df[~input_df[LAW_LABEL].isna() & ~input_df['ugpa'].isna() & (input_df['ugpa'] > 0) & ~input_df['lsat'].isna()] return output_df law_df = preprocess_law_data(raw_law_df) ###Output _____no_output_____ ###Markdown 데이터를 훈련/검증/테스트 세트로 분할하기 ###Code def split_dataset(input_df, random_state=888): """Splits an input dataset into train, val, and test sets.""" train_df, test_val_df = train_test_split( input_df, test_size=0.3, random_state=random_state) val_df, test_df = train_test_split( test_val_df, test_size=0.66, random_state=random_state) return train_df, val_df, test_df law_train_df, law_val_df, law_test_df = split_dataset(law_df) ###Output _____no_output_____ ###Markdown 데이터 분포 시각화하기먼저 데이터 분포를 시각화합니다. 기준점을 통과한 모든 학생들과 통과하지 못한 모든 학생들에 대한 GPA 및 LSAT 점수를 플롯할 것입니다. ###Code def plot_dataset_contour(input_df, title): plt.rcParams['font.family'] = ['serif'] g = sns.jointplot( x='ugpa', y='lsat', data=input_df, kind='kde', xlim=[1.4, 4], ylim=[0, 50]) g.plot_joint(plt.scatter, c='b', s=10, linewidth=1, marker='+') g.ax_joint.collections[0].set_alpha(0) g.set_axis_labels('Undergraduate GPA', 'LSAT score', fontsize=14) g.fig.suptitle(title, fontsize=14) # Adust plot so that the title fits. plt.subplots_adjust(top=0.9) plt.show() law_df_pos = law_df[law_df[LAW_LABEL] == 1] plot_dataset_contour( law_df_pos, title='Distribution of students that passed the bar') law_df_neg = law_df[law_df[LAW_LABEL] == 0] plot_dataset_contour( law_df_neg, title='Distribution of students that failed the bar') ###Output _____no_output_____ ###Markdown 기준점 시험 통과를 예측하도록 보정된 선형 모델 훈련하기다음으로, TFL에서 보정된 선형 모델을 훈련하여 학생이 기준점을 통과할지 여부를 예측합니다. 두 가지 입력 특성은 LSAT 점수와 학부 GPA이며, 훈련 레이블은 학생이 기준점을 통과했는지 여부입니다.먼저 제약 조건 없이 보정된 선형 모델을 훈련합니다. 그런 다음, 단조성 제약 조건을 사용하여 보정된 선형 모델을 훈련하고 모델 출력 및 정확성의 차이를 관찰합니다. TFL 보정 선형 estimator를 훈련하기 위한 도우미 함수이들 함수는 이 로스쿨 사례 연구와 아래의 대출 연체 사례 연구에 사용됩니다. ###Code def train_tfl_estimator(train_df, monotonicity, learning_rate, num_epochs, batch_size, get_input_fn, get_feature_columns_and_configs): """Trains a TFL calibrated linear estimator. Args: train_df: pandas dataframe containing training data. monotonicity: if 0, then no monotonicity constraints. If 1, then all features are constrained to be monotonically increasing. learning_rate: learning rate of Adam optimizer for gradient descent. num_epochs: number of training epochs. batch_size: batch size for each epoch. None means the batch size is the full dataset size. get_input_fn: function that returns the input_fn for a TF estimator. get_feature_columns_and_configs: function that returns TFL feature columns and configs. Returns: estimator: a trained TFL calibrated linear estimator. """ feature_columns, feature_configs = get_feature_columns_and_configs( monotonicity) model_config = tfl.configs.CalibratedLinearConfig( feature_configs=feature_configs, use_bias=False) estimator = tfl.estimators.CannedClassifier( feature_columns=feature_columns, model_config=model_config, feature_analysis_input_fn=get_input_fn(input_df=train_df, num_epochs=1), optimizer=tf.keras.optimizers.Adam(learning_rate)) estimator.train( input_fn=get_input_fn( input_df=train_df, num_epochs=num_epochs, batch_size=batch_size)) return estimator def optimize_learning_rates( train_df, val_df, test_df, monotonicity, learning_rates, num_epochs, batch_size, get_input_fn, get_feature_columns_and_configs, ): """Optimizes learning rates for TFL estimators. Args: train_df: pandas dataframe containing training data. val_df: pandas dataframe containing validation data. test_df: pandas dataframe containing test data. monotonicity: if 0, then no monotonicity constraints. If 1, then all features are constrained to be monotonically increasing. learning_rates: list of learning rates to try. num_epochs: number of training epochs. batch_size: batch size for each epoch. None means the batch size is the full dataset size. get_input_fn: function that returns the input_fn for a TF estimator. get_feature_columns_and_configs: function that returns TFL feature columns and configs. Returns: A single TFL estimator that achieved the best validation accuracy. """ estimators = [] train_accuracies = [] val_accuracies = [] test_accuracies = [] for lr in learning_rates: estimator = train_tfl_estimator( train_df=train_df, monotonicity=monotonicity, learning_rate=lr, num_epochs=num_epochs, batch_size=batch_size, get_input_fn=get_input_fn, get_feature_columns_and_configs=get_feature_columns_and_configs) estimators.append(estimator) train_acc = estimator.evaluate( input_fn=get_input_fn(train_df, num_epochs=1))['accuracy'] val_acc = estimator.evaluate( input_fn=get_input_fn(val_df, num_epochs=1))['accuracy'] test_acc = estimator.evaluate( input_fn=get_input_fn(test_df, num_epochs=1))['accuracy'] print('accuracies for learning rate %f: train: %f, val: %f, test: %f' % (lr, train_acc, val_acc, test_acc)) train_accuracies.append(train_acc) val_accuracies.append(val_acc) test_accuracies.append(test_acc) max_index = val_accuracies.index(max(val_accuracies)) return estimators[max_index] ###Output _____no_output_____ ###Markdown 로스쿨 데이터세트 특성을 구성하기 위한 도우미 함수이들 도우미 함수는 로스쿨 사례 연구에만 해당됩니다. ###Code def get_input_fn_law(input_df, num_epochs, batch_size=None): """Gets TF input_fn for law school models.""" return tf.compat.v1.estimator.inputs.pandas_input_fn( x=input_df[['ugpa', 'lsat']], y=input_df['pass_bar'], num_epochs=num_epochs, batch_size=batch_size or len(input_df), shuffle=False) def get_feature_columns_and_configs_law(monotonicity): """Gets TFL feature configs for law school models.""" feature_columns = [ tf.feature_column.numeric_column('ugpa'), tf.feature_column.numeric_column('lsat'), ] feature_configs = [ tfl.configs.FeatureConfig( name='ugpa', lattice_size=2, pwl_calibration_num_keypoints=20, monotonicity=monotonicity, pwl_calibration_always_monotonic=False), tfl.configs.FeatureConfig( name='lsat', lattice_size=2, pwl_calibration_num_keypoints=20, monotonicity=monotonicity, pwl_calibration_always_monotonic=False), ] return feature_columns, feature_configs ###Output _____no_output_____ ###Markdown 훈련된 모델의 출력 시각화를 위한 도우미 함수 ###Code def get_predicted_probabilities(estimator, input_df, get_input_fn): predictions = estimator.predict( input_fn=get_input_fn(input_df=input_df, num_epochs=1)) return [prediction['probabilities'][1] for prediction in predictions] def plot_model_contour(estimator, input_df, num_keypoints=20): x = np.linspace(min(input_df['ugpa']), max(input_df['ugpa']), num_keypoints) y = np.linspace(min(input_df['lsat']), max(input_df['lsat']), num_keypoints) x_grid, y_grid = np.meshgrid(x, y) positions = np.vstack([x_grid.ravel(), y_grid.ravel()]) plot_df = pd.DataFrame(positions.T, columns=['ugpa', 'lsat']) plot_df[LAW_LABEL] = np.ones(len(plot_df)) predictions = get_predicted_probabilities( estimator=estimator, input_df=plot_df, get_input_fn=get_input_fn_law) grid_predictions = np.reshape(predictions, x_grid.shape) plt.rcParams['font.family'] = ['serif'] plt.contour( x_grid, y_grid, grid_predictions, colors=('k',), levels=np.linspace(0, 1, 11)) plt.contourf( x_grid, y_grid, grid_predictions, cmap=plt.cm.bone, levels=np.linspace(0, 1, 11)) # levels=np.linspace(0,1,8)); plt.xticks(fontsize=20) plt.yticks(fontsize=20) cbar = plt.colorbar() cbar.ax.set_ylabel('Model score', fontsize=20) cbar.ax.tick_params(labelsize=20) plt.xlabel('Undergraduate GPA', fontsize=20) plt.ylabel('LSAT score', fontsize=20) ###Output _____no_output_____ ###Markdown 제약이 없는(단조가 아닌) 보정 선형 모델 훈련하기 ###Code nomon_linear_estimator = optimize_learning_rates( train_df=law_train_df, val_df=law_val_df, test_df=law_test_df, monotonicity=0, learning_rates=LEARNING_RATES, batch_size=BATCH_SIZE, num_epochs=NUM_EPOCHS, get_input_fn=get_input_fn_law, get_feature_columns_and_configs=get_feature_columns_and_configs_law) plot_model_contour(nomon_linear_estimator, input_df=law_df) ###Output _____no_output_____ ###Markdown 단조성 보정 선형 모델 훈련하기 ###Code mon_linear_estimator = optimize_learning_rates( train_df=law_train_df, val_df=law_val_df, test_df=law_test_df, monotonicity=1, learning_rates=LEARNING_RATES, batch_size=BATCH_SIZE, num_epochs=NUM_EPOCHS, get_input_fn=get_input_fn_law, get_feature_columns_and_configs=get_feature_columns_and_configs_law) plot_model_contour(mon_linear_estimator, input_df=law_df) ###Output _____no_output_____ ###Markdown 다른 제약이 없는 모델 훈련하기TFL 보정 선형 모델이 정확성을 크게 희생하지 않고도 LSAT 점수와 GPA 모두에서 단조롭도록 훈련될 수 있음을 입증했습니다.그렇다면 보정 선형 모델이 심층 신경망(DNN) 또는 그래디언트 부스트 트리(GBT)와 같은 다른 형태의 모델과 어떻게 비교될까요? DNN과 GBT가 합리적으로 공정한 출력을 제공하는 것으로 보입니까? 이 질문의 해답을 얻기 위해 이제 제약이 없는 DNN 및 GBT를 훈련할 것입니다. 실제로, DNN과 GBT 모두 LSAT 점수와 학부 GPA에서 단조성을 쉽게 위반한다는 사실을 관찰하게 될 것입니다. 제약이 없는 심층 신경망(DNN) 모델 훈련하기앞서 높은 검증 정확성을 얻기 위해 아키텍처를 최적화했습니다. ###Code feature_names = ['ugpa', 'lsat'] dnn_estimator = tf.estimator.DNNClassifier( feature_columns=[ tf.feature_column.numeric_column(feature) for feature in feature_names ], hidden_units=[100, 100], optimizer=tf.keras.optimizers.Adam(learning_rate=0.008), activation_fn=tf.nn.relu) dnn_estimator.train( input_fn=get_input_fn_law( law_train_df, batch_size=BATCH_SIZE, num_epochs=NUM_EPOCHS)) dnn_train_acc = dnn_estimator.evaluate( input_fn=get_input_fn_law(law_train_df, num_epochs=1))['accuracy'] dnn_val_acc = dnn_estimator.evaluate( input_fn=get_input_fn_law(law_val_df, num_epochs=1))['accuracy'] dnn_test_acc = dnn_estimator.evaluate( input_fn=get_input_fn_law(law_test_df, num_epochs=1))['accuracy'] print('accuracies for DNN: train: %f, val: %f, test: %f' % (dnn_train_acc, dnn_val_acc, dnn_test_acc)) plot_model_contour(dnn_estimator, input_df=law_df) ###Output _____no_output_____ ###Markdown 제약이 없는 그래디언트 부스트 트리(GBT) 모델 훈련하기앞서 높은 검증 정확성을 얻기 위해 트리 구조를 최적화했습니다. ###Code tree_estimator = tf.estimator.BoostedTreesClassifier( feature_columns=[ tf.feature_column.numeric_column(feature) for feature in feature_names ], n_batches_per_layer=2, n_trees=20, max_depth=4) tree_estimator.train( input_fn=get_input_fn_law( law_train_df, num_epochs=NUM_EPOCHS, batch_size=BATCH_SIZE)) tree_train_acc = tree_estimator.evaluate( input_fn=get_input_fn_law(law_train_df, num_epochs=1))['accuracy'] tree_val_acc = tree_estimator.evaluate( input_fn=get_input_fn_law(law_val_df, num_epochs=1))['accuracy'] tree_test_acc = tree_estimator.evaluate( input_fn=get_input_fn_law(law_test_df, num_epochs=1))['accuracy'] print('accuracies for GBT: train: %f, val: %f, test: %f' % (tree_train_acc, tree_val_acc, tree_test_acc)) plot_model_contour(tree_estimator, input_df=law_df) ###Output _____no_output_____ ###Markdown 사례 연구 2: 대출 연체이 튜토리얼에서 고려할 두 번째 사례 연구는 개인의 대출 연체 확률을 예측하는 것입니다. UCI 리포지토리의 Default of Credit Card Clients 데이터세트를 사용합니다. 이 데이터는 30,000명의 대만 신용카드 사용자로부터 수집되었으며 사용자가 일정 기간 내에 결제를 불이행했는지 여부를 나타내는 바이너리 레이블을 포함하고 있습니다. 특성에는 결혼 여부, 성별, 학력, 사용자가 2005년 4월부터 9월까지 월별로 기존 청구액을 연체한 기간이 포함됩니다.첫 번째 사례 연구에서와 마찬가지로, 불공정한 불이익을 피하기 위해 단조성 제약 조건을 사용하는 방법을 다시 설명합니다. 모델을 사용하여 사용자의 신용 점수를 결정하는 경우, 다른 모든 조건이 동일할 때 청구액을 조기에 지불하는 것에 대해 불이익을 받는다면 많은 사람들이 불공정하다고 느낄 수 있습니다. 따라서 모델이 조기 결제에 불이익을 주지 않도록 하는 단조성 제약 조건을 적용합니다. 대출 연체 데이터 로드하기 ###Code # Load data file. credit_file_name = 'credit_default.csv' credit_file_path = os.path.join(DATA_DIR, credit_file_name) credit_df = pd.read_csv(credit_file_path, delimiter=',') # Define label column name. CREDIT_LABEL = 'default' ###Output _____no_output_____ ###Markdown 데이터를 훈련/검증/테스트 세트로 분할하기 ###Code credit_train_df, credit_val_df, credit_test_df = split_dataset(credit_df) ###Output _____no_output_____ ###Markdown 데이터 분포 시각화하기먼저 데이터 분포를 시각화합니다. 결혼 여부와 상환 상태가 서로 다른 사람들에 대해 관찰된 연체율의 평균 및 표준 오차를 플롯할 것입니다. 상환 상태는 대출 상환 기간(2005년 4월 현재)에 연체된 개월 수를 나타냅니다. ###Code def get_agg_data(df, x_col, y_col, bins=11): xbins = pd.cut(df[x_col], bins=bins) data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem']) return data def plot_2d_means_credit(input_df, x_col, y_col, x_label, y_label): plt.rcParams['font.family'] = ['serif'] _, ax = plt.subplots(nrows=1, ncols=1) plt.setp(ax.spines.values(), color='black', linewidth=1) ax.tick_params( direction='in', length=6, width=1, top=False, right=False, labelsize=18) df_single = get_agg_data(input_df[input_df['MARRIAGE'] == 1], x_col, y_col) df_married = get_agg_data(input_df[input_df['MARRIAGE'] == 2], x_col, y_col) ax.errorbar( df_single[(x_col, 'mean')], df_single[(y_col, 'mean')], xerr=df_single[(x_col, 'sem')], yerr=df_single[(y_col, 'sem')], color='orange', marker='s', capsize=3, capthick=1, label='Single', markersize=10, linestyle='') ax.errorbar( df_married[(x_col, 'mean')], df_married[(y_col, 'mean')], xerr=df_married[(x_col, 'sem')], yerr=df_married[(y_col, 'sem')], color='b', marker='^', capsize=3, capthick=1, label='Married', markersize=10, linestyle='') leg = ax.legend(loc='upper left', fontsize=18, frameon=True, numpoints=1) ax.set_xlabel(x_label, fontsize=18) ax.set_ylabel(y_label, fontsize=18) ax.set_ylim(0, 1.1) ax.set_xlim(-2, 8.5) ax.patch.set_facecolor('white') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(1) plt.show() plot_2d_means_credit(credit_train_df, 'PAY_0', 'default', 'Repayment Status (April)', 'Observed default rate') ###Output _____no_output_____ ###Markdown 대출 연체율을 예측하도록 보정 선형 모델 훈련하기다음으로, TFL에서 보정 선형 모델을 훈련하여 개인이 대출을 불이행할지 여부를 예측합니다. 두 가지 입력 특성은 결혼 여부와 4월에 대출금을 연체한 개월 수(상환 상태)입니다. 훈련 레이블은 대출을 연체했는지 여부입니다.먼저 제약 조건 없이 보정된 선형 모델을 훈련합니다. 그런 다음, 단조성 제약 조건을 사용하여 보정된 선형 모델을 훈련하고 모델 출력 및 정확성의 차이를 관찰합니다. 대출 연체 데이터세트 특성을 구성하기 위한 도우미 함수이들 도우미 함수는 대출 연체 사례 연구에만 해당합니다. ###Code def get_input_fn_credit(input_df, num_epochs, batch_size=None): """Gets TF input_fn for credit default models.""" return tf.compat.v1.estimator.inputs.pandas_input_fn( x=input_df[['MARRIAGE', 'PAY_0']], y=input_df['default'], num_epochs=num_epochs, batch_size=batch_size or len(input_df), shuffle=False) def get_feature_columns_and_configs_credit(monotonicity): """Gets TFL feature configs for credit default models.""" feature_columns = [ tf.feature_column.numeric_column('MARRIAGE'), tf.feature_column.numeric_column('PAY_0'), ] feature_configs = [ tfl.configs.FeatureConfig( name='MARRIAGE', lattice_size=2, pwl_calibration_num_keypoints=3, monotonicity=monotonicity, pwl_calibration_always_monotonic=False), tfl.configs.FeatureConfig( name='PAY_0', lattice_size=2, pwl_calibration_num_keypoints=10, monotonicity=monotonicity, pwl_calibration_always_monotonic=False), ] return feature_columns, feature_configs ###Output _____no_output_____ ###Markdown 훈련된 모델의 출력 시각화를 위한 도우미 함수 ###Code def plot_predictions_credit(input_df, estimator, x_col, x_label='Repayment Status (April)', y_label='Predicted default probability'): predictions = get_predicted_probabilities( estimator=estimator, input_df=input_df, get_input_fn=get_input_fn_credit) new_df = input_df.copy() new_df.loc[:, 'predictions'] = predictions plot_2d_means_credit(new_df, x_col, 'predictions', x_label, y_label) ###Output _____no_output_____ ###Markdown 제약이 없는(단조가 아닌) 보정 선형 모델 훈련하기 ###Code nomon_linear_estimator = optimize_learning_rates( train_df=credit_train_df, val_df=credit_val_df, test_df=credit_test_df, monotonicity=0, learning_rates=LEARNING_RATES, batch_size=BATCH_SIZE, num_epochs=NUM_EPOCHS, get_input_fn=get_input_fn_credit, get_feature_columns_and_configs=get_feature_columns_and_configs_credit) plot_predictions_credit(credit_train_df, nomon_linear_estimator, 'PAY_0') ###Output _____no_output_____ ###Markdown 단조 보정 선형 모델 훈련하기 ###Code mon_linear_estimator = optimize_learning_rates( train_df=credit_train_df, val_df=credit_val_df, test_df=credit_test_df, monotonicity=1, learning_rates=LEARNING_RATES, batch_size=BATCH_SIZE, num_epochs=NUM_EPOCHS, get_input_fn=get_input_fn_credit, get_feature_columns_and_configs=get_feature_columns_and_configs_credit) plot_predictions_credit(credit_train_df, mon_linear_estimator, 'PAY_0') ###Output _____no_output_____
notebooks/Top 15 violations by Total Tickets Analysis.ipynb	###Markdown Get the top 15 violations by total tickets ###Code vc_df = dmv_df.groupby(['violation_code']).counter.sum().reset_index('violation_code') counter_codes_15 = vc_df.sort_values(by='counter', ascending=False)[:15].violation_code top_codes = dmv_df[dmv_df.violation_code.isin(counter_codes_15)] top_violation_by_state = top_codes.groupby(['violation_description', 'rp_plate_state']).counter.sum() #.unstack().unstack().unstack() top_violation_by_state.unstack().plot.barh() top_violation_by_state_revenue = top_codes.groupby(['violation_description', 'rp_plate_state']).fine.sum() ax = top_violation_by_state_revenue.unstack().plot.barh(legend=True) ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0f')) plt.draw() ###Output _____no_output_____
notebooks/Glob-RRL.ipynb	###Markdown From GC-Orbits-Monte-Carlo.ipynb: ###Code df = gd.FardalStreamDF() names = ['NGC 6809', 'NGC 288', 'NGC 5897', 'NGC 6362', 'IC 4499', 'NGC 7099', 'Pal 13', 'NGC 6144', 'NGC 6101', 'NGC 1904'] for name in names: row = tbl[tbl['Name_1'] == name] this_w0 = w0[tbl['Name_1'] == name][0] # Run mock stream models from samples from the orbit error distribution: mass = row['mass'][0]u.Msun prog_pot = gp.PlummerPotential(m=mass, b=row['r_hm'][0] / 1.3 u.pc, units=galactic) gen = gd.MockStreamGenerator(df, mw_pot, progenitor_potential=prog_pot) streams = [] for i in tqdm(range(32)): stream, _ = gen.run(this_w0[i], mass, release_every=8, dt=-1u.Myr, n_steps=2000) streams.append(stream) # Now transform to sky coordinates, centered on the cluster: fr = coord.SkyOffsetFrame(origin=coord.ICRS(ra=row['RA'][0]u.deg, dec=row['DEC'][0]u.deg)) all_sky = [] for stream in streams: stream_c = stream.to_coord_frame(fr, galactocentric_frame=gc_frame) all_sky.append(np.stack((stream_c.lon.wrap_at(180u.deg).degree, stream_c.lat.degree)).T) all_sky = np.vstack(all_sky) all_sky = all_sky[np.sqrt(all_sky[:, 0]2 + all_sky[:, 1]2) > 0.25] # Predict where we expect to see tidal debris: kde = KernelDensity(bandwidth=0.2) _ = kde.fit(all_sky) grid = np.arange(-10, 10+1e-3, 0.25) xgrid, ygrid = np.meshgrid(grid, grid) X_grid = np.stack((xgrid.ravel(), ygrid.ravel())).T H = np.exp(kde.score_samples(X_grid)) H = H.reshape(grid.size, grid.size) # --- plot fig, ax = plt.subplots(1, 1, figsize=(6.2, 6)) ax.pcolormesh(xgrid, ygrid, H) ax.set_xlabel(r'$\phi_1$') ax.set_ylabel(r'$\phi_2$') ax.set_title('Predicted stream ({})'.format(row['Name_1'][0]), fontsize=16) for axis in [ax.xaxis, ax.yaxis]: axis.set_ticks(np.arange(-10, 10+1e-3, 5)) fig.set_facecolor('w') fig.tight_layout() fig.savefig('../plots/predicted-debris-{}.png'.format(row['Name_1'][0].replace(' ', '_')), dpi=250) ###Output _____no_output_____ ###Markdown --- ###Code # row = tbl[tbl['Name_1'] == 'NGC 5897'] row = tbl[tbl['Name_1'] == 'Pal 13'] # Now transform to sky coordinates, centered on the cluster: fr = coord.SkyOffsetFrame(origin=coord.ICRS(ra=row['RA'][0]u.deg, dec=row['DEC'][0]u.deg)) rrl_c = rrl.get_skycoord(distance=rrl.D_kpcu.kpc) rrl_c_fr = rrl_c.transform_to(fr) sky_mask = (np.abs(rrl_c_fr.lon) < 10u.deg) & (np.abs(rrl_c_fr.lat) < 10u.deg) pm_mask = np.sqrt((rrl_c.pm_ra_cosdec.value - (row['PMRA'][0]))2 + (rrl_c.pm_dec.value - row['PMDEC'][0])2) < 1.5 dist_mask = (np.abs(rrl_c.distance - row['dist'][0]u.kpc) < 5u.kpc) # dist_mask = (np.abs(rrl_c.distance - 10u.kpc) < 2*u.kpc) mask = sky_mask & dist_mask & pm_mask fig, axes = plt.subplots(1, 2, figsize=(10, 5)) ax = axes[0] ax.plot(rrl_c_fr.lon.degree[mask], rrl_c_fr.lat.degree[mask], ls='', marker='o', mew=0, ms=2., alpha=0.5) ax.set_xlim(-10, 10) ax.set_ylim(-10, 10) ax = axes[1] ax.plot(rrl_c.pm_ra_cosdec.value[mask], rrl_c.pm_dec.value[mask], ls='', marker='o', mew=0, ms=2., alpha=0.5) # ax.scatter(row['PMRA'], row['PMDEC']) ###Output _____no_output_____
modeling/AppMdl/profile_functions.ipynb	###Markdown AppMdl * A complex App with 16 functions.* There are 2 branches, 2 parallels, 2 cycles, and 2 self-loops in App16 ###Code import os import logging from io import BytesIO import time import zipfile import numpy as np import boto3 from datetime import datetime, timezone from time import gmtime, strftime import json import pandas as pd import matplotlib.pyplot as plt import pickle import math client = boto3.client('lambda') function_prefix='AppMdl' function_count = 16 # The difference between UTC and local timezone timezone_offset = 0 ###Output _____no_output_____ ###Markdown Create Functions of AppMdl Function Name List ###Code function_name_list = [function_prefix+'_f'+str(i) for i in range(1, function_count+1)] print(function_name_list) ###Output ['AppMdl_f1', 'AppMdl_f2', 'AppMdl_f3', 'AppMdl_f4', 'AppMdl_f5', 'AppMdl_f6', 'AppMdl_f7', 'AppMdl_f8', 'AppMdl_f9', 'AppMdl_f10', 'AppMdl_f11', 'AppMdl_f12', 'AppMdl_f13', 'AppMdl_f14', 'AppMdl_f15', 'AppMdl_f16'] ###Markdown Send Requests to Create Lambda Functions ###Code function_creation_response = [] for function in function_name_list: response = client.create_function( FunctionName=function, Runtime='python3.7', Role='arn:aws:iam::499537426559:role/ServerlessAppPerfOpt', Handler='lambda_function.lambda_handler', Code={ 'ZipFile': b"PK\x03\x04\x14\x00\x00\x00\x00\x00\xf3s;P\x84\xf0r\x96Z\x00\x00\x00Z\x00\x00\x00\x12\x00\x00\x00lambda_function.pydef lambda_handler(event, context):\n pass\n return {\n 'statusCode': 200\n }\nPK\x03\x04\x14\x00\x00\x00\x00\x00\x05q;P\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x13\x00\x00\x00.ipynb_checkpoints/PK\x01\x02\x14\x03\x14\x00\x00\x00\x00\x00\xf3s;P\x84\xf0r\x96Z\x00\x00\x00Z\x00\x00\x00\x12\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xb4\x81\x00\x00\x00\x00lambda_function.pyPK\x01\x02\x14\x03\x14\x00\x00\x00\x00\x00\x05q;P\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x13\x00\x00\x00\x00\x00\x00\x00\x00\x00\x10\x00\xfdA\x8a\x00\x00\x00.ipynb_checkpoints/PK\x05\x06\x00\x00\x00\x00\x02\x00\x02\x00\x81\x00\x00\x00\xbb\x00\x00\x00\x00\x00" }, Description='Analytical Model Evaluation {}'.format(function), Timeout=60, MemorySize=128 ) function_creation_response.append(response) time.sleep(0.1) print([item['StateReasonCode'] for item in function_creation_response]) ###Output _____no_output_____ ###Markdown Update all Functions in AppMdl Update Function Code ###Code functions=[] for file in os.listdir('functions'): path=os.path.abspath(os.path.join(os.path.dirname('__file__'), 'functions/'+file)) if not file.startswith('.') and os.path.isdir(path): functions.append(file) for function_folder in functions: buf = BytesIO() with zipfile.ZipFile(buf, 'w') as z: for file in os.listdir('functions/'+function_folder): z.write(os.path.abspath(os.path.join(os.path.dirname('__file__'), 'functions/{}/{}'.format(function_folder,file))), os.path.basename(os.path.join(os.path.dirname('__file__'), 'functions/{}/{}'.format(function_folder,file)))) buf.seek(0) pkg = buf.read() client.update_function_code(FunctionName='{}_{}'.format(function_prefix, function_folder),ZipFile=pkg) ###Output _____no_output_____ ###Markdown Update Function Memory Configuration* Available Memory Configurations: 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152, 1216, 1280, 1344, 1408, 1472, 1536, 1600, 1664, 1728, 1792, 1856, 1920, 1984, 2048, 2112, 2176, 2240, 2304, 2368, 2432, 2496, 2560, 2624, 2688, 2752, 2816, 2880, 2944, 3008 ###Code mem_config_list={ 'f1':1536, 'f2':1792, 'f3':576, 'f4':2240, 'f5':896, 'f6':1728, 'f7':128, 'f8':128, 'f9':256, 'f10':320, 'f11':1920, 'f12':1984, 'f13':1088, 'f14':640, 'f15':896, 'f16':1088 } for function in mem_config_list.keys(): client.update_function_configuration(FunctionName='{}_{}'.format(function_prefix, function), MemorySize=mem_config_list[function]) ###Output _____no_output_____ ###Markdown Profile Functions Test Run ###Code np.random.seed(256) payload_str="{"+ "\"para1\":{}, \"para2\":{}, \"para4\":{}, \"para6\":{}".format( np.random.randint(1, 101), list(np.random.randint(1, 101, 20)), np.random.randint(1, 101), list(np.random.randint(1, 101, 20)) ) +"}" client.invoke(FunctionName='{}_{}'.format(function_prefix, 'f1'), InvocationType='Event', Payload=payload_str) ###Output _____no_output_____ ###Markdown Configure Logging ###Code logging.basicConfig(filename='invoke.log', encoding='utf-8', format='%(asctime)s.%(msecs)03d %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO) ###Output _____no_output_____ ###Markdown Run ###Code for i in range(10000): time.sleep(3) for name in function_name_list: payload_str="{"+ "\"para1\":{}, \"para2\":{}, \"para4\":{}, \"para6\":{}".format( np.random.randint(1, 101), list(np.random.randint(1, 101, 20)), np.random.randint(1, 101), list(np.random.randint(1, 101, 20)) ) +"}" response = client.invoke(FunctionName=name, InvocationType='Event', Payload=payload_str) RequestId = response.get('ResponseMetadata', {}).get('RequestId') StatusCode = response.get('StatusCode', 'ERR') logging.info(f'{i+1} {StatusCode} {name} {RequestId}') time.sleep(0.1) ###Output _____no_output_____ ###Markdown Get the start time and the end time ###Code profile_function_start_time = ' '.join(os.popen('head -1 invoke.log').read().split(' ')[:2]) profile_function_end_time = ' '.join(os.popen('tail -1 invoke.log').read().split(' ')[:2]) profile_function_start_time = datetime.strptime(profile_function_start_time, '%Y-%m-%d %H:%M:%S.%f') profile_function_end_time = datetime.strptime(profile_function_end_time, '%Y-%m-%d %H:%M:%S.%f') profile_function_start_time profile_function_end_time profile_function_start_time = int(datetime.timestamp(profile_function_start_time)) - 10 profile_function_end_time = int(datetime.timestamp(profile_function_end_time)) + 10 ###Output _____no_output_____ ###Markdown CloudWatch Logs ###Code logclient = boto3.client('logs') ###Output _____no_output_____ ###Markdown Query AppMdl Lambda Function Logs Functions for parsing Logs ###Code def lambda_report_log_to_dict(log): res={} lis=[item.split(': ') for item in log[1]['value'].split('\t')] res['RequestId']=lis[0][1] res['Duration']=float(lis[1][1].split(' ')[0]) res['Billed_Duration']=int(lis[2][1].split(' ')[0]) res['Memory_Size']=int(lis[3][1].split(' ')[0]) res['Max_Memory_Used']=int(lis[4][1].split(' ')[0]) res['UTC_Timestamp'] = time.mktime(datetime.strptime(log[0]['value'], "%Y-%m-%d %H:%M:%S.%f").timetuple()) +timezone_offset3600 return res ###Output _____no_output_____ ###Markdown Prepare Logs ###Code query_lambda = [] for function in function_name_list: query_lambda.append(logclient.start_query( logGroupName='/aws/lambda/{}'.format(function), queryString="fields @timestamp, @message\| filter @message like 'REPORT'\| sort @timestamp asc", startTime=profile_function_start_time, endTime=profile_function_end_time, limit=10000 )) time.sleep(4) time.sleep(10) ###Output _____no_output_____ ###Markdown Retrieve Logs ###Code query_lambda_results = [] for q in query_lambda: query_lambda_results.append(logclient.get_query_results( queryId=q['queryId'] )) time.sleep(4) with open('query_lambda_results_performance_profile.pickle', 'wb') as f: f.write(pickle.dumps(query_lambda_results)) AppMdl_lambda_logs_dict = {'f'+str(i):None for i in range(1, function_count+1)} for i in range(1, function_count+1): AppMdl_lambda_logs_dict['f'+str(i)] = [lambda_report_log_to_dict(item) for item in query_lambda_results[i-1]['results']] for item in AppMdl_lambda_logs_dict['f'+str(i)]: item['Function']='f'+str(i) len(AppMdl_lambda_logs_dict['f1']) ###Output _____no_output_____ ###Markdown Convert Logs into DataFrame and Save as CSV ###Code AppMdl_lambda_logs=pd.DataFrame() for i in range(1, function_count+1): AppMdl_lambda_logs = AppMdl_lambda_logs.append(pd.DataFrame(AppMdl_lambda_logs_dict['f'+str(i)])) AppMdl_lambda_logs.index=range(AppMdl_lambda_logs.shape[0]) AppMdl_lambda_logs=AppMdl_lambda_logs[['Function', 'Memory_Size', 'Max_Memory_Used', 'Duration', 'Billed_Duration', 'UTC_Timestamp', 'RequestId']] AppMdl_lambda_logs.to_csv('AppMdl_lambda_logs_performance_profile.csv',index=False) AppMdl_lambda_logs = pd.read_csv('AppMdl_lambda_logs_performance_profile.csv', low_memory=False) AppMdl_lambda_logs.columns = ['Function', 'Memory_Size', 'Max_Memory_Used', 'Duration', 'Billed_Duration', 'UTCTimestamp', 'RequestId'] AppMdl_lambda_logs.head() for i in range(1, function_count+1): print(f"f{i}", AppMdl_lambda_logs.query(f"Function == 'f{i}'").shape[0], AppMdl_lambda_logs.query(f"Function == 'f{i}'")['Duration'].mean()) def calculate_cost(rt: float, mem: float, pmms: float = 1.627607421875e-11, ppi: float = 0.0000002) -> float: return math.ceil(rt) mem * pmms + ppi def adjacent_values(vals, q1, q3): upper_adjacent_value = q3 + (q3 - q1) * 1.5 upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1]) lower_adjacent_value = q1 - (q3 - q1) * 1.5 lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1) return lower_adjacent_value, upper_adjacent_value f1_duration = AppMdl_lambda_logs.query("Function == 'f1'")['Duration'].to_list()[500:9501] f1_cost = [calculate_cost(duration, mem_config_list['f1'] * 1000000) for duration in f1_duration] f2_duration = AppMdl_lambda_logs.query("Function == 'f2'")['Duration'].to_list()[500:9501] f2_cost = [calculate_cost(duration, mem_config_list['f2'] * 1000000) for duration in f2_duration] f3_duration = AppMdl_lambda_logs.query("Function == 'f3'")['Duration'].to_list()[500:9501] f3_cost = [calculate_cost(duration, mem_config_list['f3'] * 1000000) for duration in f3_duration] f4_duration = AppMdl_lambda_logs.query("Function == 'f4'")['Duration'].to_list()[500:9501] f4_cost = [calculate_cost(duration, mem_config_list['f4'] * 1000000) for duration in f4_duration] f5_duration = AppMdl_lambda_logs.query("Function == 'f5'")['Duration'].to_list()[500:9501] f5_cost = [calculate_cost(duration, mem_config_list['f5'] * 1000000) for duration in f5_duration] f6_duration = AppMdl_lambda_logs.query("Function == 'f6'")['Duration'].to_list()[500:9501] f6_cost = [calculate_cost(duration, mem_config_list['f6'] * 1000000) for duration in f6_duration] f7_duration = AppMdl_lambda_logs.query("Function == 'f7'")['Duration'].to_list()[500:9501] f7_cost = [calculate_cost(duration, mem_config_list['f7'] * 1000000) for duration in f7_duration] f8_duration = AppMdl_lambda_logs.query("Function == 'f8'")['Duration'].to_list()[500:9501] f8_cost = [calculate_cost(duration, mem_config_list['f8'] * 1000000) for duration in f8_duration] f9_duration = AppMdl_lambda_logs.query("Function == 'f9'")['Duration'].to_list()[500:9501] f9_cost = [calculate_cost(duration, mem_config_list['f9'] * 1000000) for duration in f9_duration] f10_duration = AppMdl_lambda_logs.query("Function == 'f10'")['Duration'].to_list()[500:9501] f10_cost = [calculate_cost(duration, mem_config_list['f10'] * 1000000) for duration in f10_duration] f11_duration = AppMdl_lambda_logs.query("Function == 'f11'")['Duration'].to_list()[500:9501] f11_cost = [calculate_cost(duration, mem_config_list['f11'] * 1000000) for duration in f11_duration] f12_duration = AppMdl_lambda_logs.query("Function == 'f12'")['Duration'].to_list()[500:9501] f12_cost = [calculate_cost(duration, mem_config_list['f12'] * 1000000) for duration in f12_duration] f13_duration = AppMdl_lambda_logs.query("Function == 'f13'")['Duration'].to_list()[500:9501] f13_cost = [calculate_cost(duration, mem_config_list['f13'] * 1000000) for duration in f13_duration] f14_duration = AppMdl_lambda_logs.query("Function == 'f14'")['Duration'].to_list()[500:9501] f14_cost = [calculate_cost(duration, mem_config_list['f14'] * 1000000) for duration in f14_duration] f15_duration = AppMdl_lambda_logs.query("Function == 'f15'")['Duration'].to_list()[500:9501] f15_cost = [calculate_cost(duration, mem_config_list['f15'] * 1000000) for duration in f15_duration] f16_duration = AppMdl_lambda_logs.query("Function == 'f16'")['Duration'].to_list()[500:9501] f16_cost = [calculate_cost(duration, mem_config_list['f16'] * 1000000) for duration in f16_duration] fig, ((ax_f1, ax_f2, ax_f3, ax_f4, ax_f5, ax_f6, ax_f7, ax_f8), (ax_f9, ax_f10, ax_f11, ax_f12, ax_f13, ax_f14, ax_f15, ax_f16)) = plt.subplots(nrows=2, ncols=8, figsize=(12, 3.2)) duration_y_lim = { "f1": (483, 880, 28), "f2": (194, 224, 2), "f3": (498, 584, 6), "f4": (700, 1200, 35), "f5": (190, 230, 2.5), "f6": (490, 550, 4), "f7": (260, 360, 6), "f8": (160, 260, 6), "f9": (112, 192, 5), "f10": (320, 420, 6), "f11": (640, 1140, 30), "f12": (600, 1200, 40), "f13": (350, 450, 6), "f14": (200, 300, 6), "f15": (240, 320, 5), "f16": (130, 330, 12) } cost_y_lim = { "f1": (12.2, 21.8, 0.6), "f2": (5.6, 6.6, 0.2), "f3": (4.6, 5.6, 0.2), "f4": (24, 44, 1.5), "f5": (2.6, 3.6, 0.2), "f6": (13.4, 15.6, 0.2), "f7": (0.5, 0.9, 0.05), "f8": (0.25, 0.7, 0.05), "f9": (0.3, 0.8, 0.05), "f10": (1.6, 2.4, 0.1), "f11": (24, 40, 1.5), "f12": (20, 42, 1.5), "f13": (6.2, 7.4, 0.1), "f14": (2.1, 3.1, 0.1), "f15": (3.5, 4.6, 0.1), "f16": (2.4, 5.8, 0.2) } for i in range(1, 17): string = f""" # plot f{i} vp{i} = ax_f{i}.violinplot(f{i}_duration, [1.5], widths=1.5, showmeans=False, showmedians=False, showextrema=False) percentile10, quartile25, medians, quartile75, percentile90 = np.percentile(f{i}_duration, [10, 25, 50, 75, 90]) mean = np.mean(f{i}_duration) duration_y1, duration_y2, duration_step = duration_y_lim["f{i}"] whiskers_min, whiskers_max = adjacent_values(np.sort(f{i}_duration), quartile25, quartile75) ax_f{i}.scatter([1.5], medians, marker='o', color='white', s=30, zorder=3) ax_f{i}.scatter([1.5], mean, marker='x', color='white', s=30, zorder=3) ax_f{i}.scatter([1.5], percentile10, marker='+', color='white', s=30, zorder=3) ax_f{i}.scatter([1.5], percentile90, marker='+', color='white', s=30, zorder=3) ax_f{i}.vlines([1.5], quartile25, quartile75, color='#666666', linestyle='-', lw=6, alpha=0.5) ax_f{i}.vlines([1.5], whiskers_min, whiskers_max, color='#666666', linestyle='-', lw=1, alpha=0.5) ax_f{i}.set(xlim=(0, 5), xticks=np.arange(0, 5), ylim=(duration_y1, duration_y2), yticks=np.arange(duration_y1, duration_y2, duration_step)) ax_f{i}.set_title('f{i}', fontsize=7) ax_f{i}_cost = ax_f{i}.twinx() vp{i}_twin = ax_f{i}_cost.violinplot( f{i}_cost, [3.5], showmeans=False, showmedians=False, showextrema=False, widths=1.5) percentile10, quartile25, medians, quartile75, percentile90 = np.percentile(f{i}_cost, [10, 25, 50, 75, 90]) mean = np.mean(f{i}_cost) cost_y1, cost_y2, cost_step = cost_y_lim["f{i}"] whiskers_min, whiskers_max = adjacent_values(np.sort(f{i}_cost), quartile25, quartile75) ax_f{i}_cost.scatter([3.5], medians, marker='o', color='white', s=30, zorder=3) ax_f{i}_cost.scatter([3.5], mean, marker='x', color='white', s=30, zorder=3) ax_f{i}_cost.scatter([3.5], percentile10, marker='+', color='white', s=30, zorder=3) ax_f{i}_cost.scatter([3.5], percentile90, marker='+', color='white', s=30, zorder=3) ax_f{i}_cost.vlines([3.5], quartile25, quartile75, color='#666666', linestyle='-', lw=6, alpha=0.5) ax_f{i}_cost.vlines([3.5], whiskers_min, whiskers_max, color='#666666', linestyle='-', lw=1, alpha=0.5) ax_f{i}_cost.set(xlim=(0, 5), xticks=np.arange(0, 5), ylim=(cost_y1, cost_y2), yticks=np.arange(cost_y1, cost_y2, cost_step)) for pc in vp{i}["bodies"]: pc.set_facecolor('#BBD5E8') pc.set_edgecolor('grey') pc.set_alpha(1) for pc in vp{i}_twin['bodies']: pc.set_facecolor('#FFB570') pc.set_edgecolor('grey') pc.set_alpha(1) ax_f{i}.tick_params(axis='both', which='major', labelsize=6) ax_f{i}_cost.tick_params(axis='both', which='major', labelsize=6) ax_f{i}.set_xticklabels([]) ax_f{i}_cost.set_xticklabels([]) ax_f{i}.tick_params(direction='in', bottom=False) ax_f{i}_cost.tick_params(direction='in', bottom=False) """ exec(string) # ax_f1.set_ylabel('Duration in milliseconds') # ax_f16_cost.set_ylabel('Cost per 1M invocations in USD') plt.tight_layout() plt.show() fig.savefig("16_function_pp.pdf") ###Output _____no_output_____
site/public/courses/DS-2.3/Lessons/map_reduce.ipynb	###Markdown Map-Reduce- Lets watch this video: https://www.youtube.com/watch?v=cKlnR-CB3tk&t=137s ###Code f = lambda x: x * x # def f(x): # return x * x print(f(5)) ###Output 25 ###Markdown Two ways to apply a function to the elements of a list ###Code nums = [1, 2, 3, 4, 5, 6] nums_squared = list(map(lambda x: x * x, nums)) print(nums_squared) nums_squared = [x * x for x in nums] print(nums_squared) words = ['Deer', 'Bear', 'River', 'Car', 'Car', 'River', 'Deer', 'Car', 'Bear'] mapping = map(lambda x : (x, 1), words) for i in mapping: print(i) for i in mapping: print(i) mapping = map(lambda x : {x: 1}, words) for i in mapping: print(i) ###Output {'Deer': 1} {'Bear': 1} {'River': 1} {'Car': 1} {'Car': 1} {'River': 1} {'Deer': 1} {'Car': 1} {'Bear': 1} ###Markdown It is somehow similar to generator unless we do list(map()) ###Code n = list(map(lambda char: dict([[char, 1]]), 'testing yeah it works')) n ###Output _____no_output_____ ###Markdown Reduce ###Code from functools import reduce print(reduce(lambda x, y: x & y, [{1, 2, 3}, {2, 3, 4}, {3, 4, 5}])) print(reduce(lambda x, y: x + y, [1, 2, 4])) ###Output 7 ###Markdown Count how many words do we have in a list ###Code from collections import Counter words = ['Deer', 'Bear', 'River', 'Car', 'Car', 'River', 'Deer', 'Car', 'Bear'] mapping = map(lambda x : {x: 1}, words) def fn(x, y): return dict(Counter(x) + Counter(y)) reduce(fn, mapping) reduce(fn, [{'a': 1}, {'a': 1}, {'b': 1}]) Counter({'a': 1}) + Counter({'a': 1}) print(fn({'a': 1}, {'a': 1})) print(fn({'a': 1}, {'b': 1})) ###Output {'a': 1, 'b': 1} ###Markdown arg ###Code def intersection(arg): result = set(arg[0]) for i in range(1,len(arg)): result = result & set(arg[i]) return list(result) print(intersection(['a', 'b'], ['a', 'c'], ['a', 'b'])) ###Output ['a']
examples/trending_prediction.ipynb	###Markdown Trending Prediction Notebook Import the packages ###Code import pandas as pd import matplotlib.pyplot as plt import numpy as np from scipy import interpolate ###Output _____no_output_____ ###Markdown Read the original data file ###Code df = pd.read_csv("../data/US_youtube_trending_data.csv") ###Output _____no_output_____ ###Markdown Preview the data after cleaning ###Code df_cleaned = df.drop(columns=['channelId', 'channelTitle', 'tags', 'thumbnail_link', 'comments_disabled', 'ratings_disabled', 'description'], axis=1) df_cleaned = df_cleaned[df_cleaned['view_count'] > 0] df_cleaned["trending_date"]= pd.to_datetime(df_cleaned.trending_date) df_cleaned["publishedAt"]= pd.to_datetime(df_cleaned.publishedAt) df_cleaned = df_cleaned.groupby(['video_id']).filter(lambda x: len(x) > 4) df_grouped = df_cleaned.drop(columns=['publishedAt', 'likes', 'dislikes', 'comment_count']) df_grouped ###Output _____no_output_____ ###Markdown Get a dictionary containing dataset for training ###Code video_ids = df_cleaned.video_id.unique() df_grouped_by = df_cleaned.groupby(['video_id']) video_dict = {} for i in video_ids: video_dict[i] = df_grouped_by.get_group(i) ###Output _____no_output_____ ###Markdown Get a dictionary containing dataset for inference ###Code start_date = df_grouped['trending_date'].max() + pd.DateOffset(-4) end_date = df_grouped['trending_date'].max() current_trending = df_grouped[df_grouped['trending_date'].between(start_date, end_date)] current_trending = current_trending.groupby(['video_id']).filter(lambda x: len(x) > 3) current_ids = current_trending.video_id.unique() current_trending_group = current_trending.groupby(['video_id']) current_dict = {} for i in current_ids: current_dict[i] = current_trending_group.get_group(i).reset_index(drop=True) list(current_dict.items())[:5] ###Output _____no_output_____ ###Markdown Inference the data ###Code for i in current_ids: category = int(current_dict[i].categoryId.unique()) title = str(current_dict[i].title.unique()[0]) trending_date = current_dict[i].trending_date.max() + pd.DateOffset(1) last_view_count = current_dict[i].view_count.max() x_points = list(current_dict[i].index) y_points = list(current_dict[i].view_count) tck = interpolate.splrep(x_points, y_points) view_count_pred = int(interpolate.splev(max(x_points) + 1, tck)) if view_count_pred < last_view_count: view_count_pred = last_view_count + last_view_count - view_count_pred new_row = {'video_id': i, 'categoryId': category, 'trending_date': trending_date, 'view_count': view_count_pred} current_dict[i].loc[len(current_dict[i].index)] = [i, title, category, trending_date, view_count_pred] list(current_dict.items())[:5] ###Output _____no_output_____ ###Markdown Plot one of the predicted trending ###Code df_plot = current_dict['pXDx6DjNLDU'] title_plot = str(df_plot.title[0]) trending_date_plot = df_plot['trending_date'] view_count_plot = df_plot['view_count'] plt.plot(trending_date_plot, view_count_plot) plt.title(title_plot) plt.show() ###Output _____no_output_____ ###Markdown Show the data with predicted values ###Code df_output = pd.DataFrame(columns=['video_id', 'title', 'categoryId', 'trending_date', 'view_count']) for i in current_ids: df_output = df_output.append(current_dict[i], ignore_index=True) df_output ###Output _____no_output_____
examples/gallery/demos/matplotlib/scatter_economic.ipynb	###Markdown Most examples work across multiple plotting backends, this example is also available for:* [Bokeh - scatter_economic](../bokeh/scatter_economic.ipynb) ###Code import pandas as pd import holoviews as hv from holoviews import dim, opts hv.extension('matplotlib') hv.output(dpi=100) ###Output _____no_output_____ ###Markdown Declaring data ###Code macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t') key_dimensions = [('year', 'Year'), ('country', 'Country')] value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'), ('gdp', 'GDP Growth'), ('trade', 'Trade')] macro = hv.Table(macro_df, key_dimensions, value_dimensions) ###Output _____no_output_____ ###Markdown Plot ###Code gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment']) gdp_unem_scatter.overlay('Country').opts( opts.Scatter(color=hv.Cycle('tab20'), edgecolors='k', show_grid=True, aspect=2, fig_size=250, s=dim('Unemployment')20, show_frame=False), opts.NdOverlay(legend_position='right')) ###Output _____no_output_____ ###Markdown Most examples work across multiple plotting backends, this example is also available for: [Bokeh - scatter_economic](../bokeh/scatter_economic.ipynb) ###Code import pandas as pd import holoviews as hv from holoviews import dim, opts hv.extension('matplotlib') hv.output(dpi=100) ###Output _____no_output_____ ###Markdown Declaring data ###Code macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t') key_dimensions = [('year', 'Year'), ('country', 'Country')] value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'), ('gdp', 'GDP Growth'), ('trade', 'Trade')] macro = hv.Table(macro_df, key_dimensions, value_dimensions) ###Output _____no_output_____ ###Markdown Plot ###Code gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment']) gdp_unem_scatter.overlay('Country').opts( opts.Scatter(color=hv.Cycle('tab20'), edgecolors='k', show_grid=True, aspect=2, fig_size=250, s=dim('Unemployment')20, padding=0.1, show_frame=False), opts.NdOverlay(legend_position='right')) ###Output _____no_output_____ ###Markdown Most examples work across multiple plotting backends, this example is also available for: [Bokeh - scatter_economic](../bokeh/scatter_economic.ipynb) ###Code import numpy as np import pandas as pd import holoviews as hv hv.extension('matplotlib') ###Output _____no_output_____ ###Markdown Declaring data ###Code macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t') key_dimensions = [('year', 'Year'), ('country', 'Country')] value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'), ('gdp', 'GDP Growth'), ('trade', 'Trade')] macro = hv.Table(macro_df, key_dimensions, value_dimensions) ###Output _____no_output_____ ###Markdown Plot ###Code %%output dpi=100 %%opts Scatter [scaling_method='width' scaling_factor=2 size_index=2 show_grid=True] %%opts Scatter (color=Cycle('tab20') edgecolors='k') %%opts NdOverlay [legend_position='right' aspect=2, fig_size=250, show_frame=False] gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment']) gdp_unem_scatter.overlay('Country') ###Output _____no_output_____ ###Markdown Most examples work across multiple plotting backends, this example is also available for:* [Bokeh - scatter_economic](../bokeh/scatter_economic.ipynb) ###Code import numpy as np import pandas as pd import holoviews as hv hv.extension('matplotlib') ###Output _____no_output_____ ###Markdown Declaring data ###Code macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t') key_dimensions = [('year', 'Year'), ('country', 'Country')] value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'), ('gdp', 'GDP Growth'), ('trade', 'Trade')] macro = hv.Table(macro_df, kdims=key_dimensions, vdims=value_dimensions) ###Output _____no_output_____ ###Markdown Plot ###Code %%output dpi=100 %%opts Scatter [scaling_method='width' scaling_factor=2 size_index=2 show_grid=True] %%opts Scatter (color=Cycle('tab20') edgecolors='k') %%opts NdOverlay [legend_position='right' aspect=2, fig_size=250, show_frame=False] gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment']) gdp_unem_scatter.overlay('Country') ###Output _____no_output_____ ###Markdown Most examples work across multiple plotting backends, this example is also available for:* [Bokeh - scatter_economic](../bokeh/scatter_economic.ipynb) ###Code import pandas as pd import holoviews as hv hv.extension('matplotlib') ###Output _____no_output_____ ###Markdown Declaring data ###Code macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t') key_dimensions = [('year', 'Year'), ('country', 'Country')] value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'), ('gdp', 'GDP Growth'), ('trade', 'Trade')] macro = hv.Table(macro_df, key_dimensions, value_dimensions) ###Output _____no_output_____ ###Markdown Plot ###Code %%output dpi=100 %%opts Scatter [scaling_method='width' scaling_factor=2 size_index=2 show_grid=True] %%opts Scatter (color=Cycle('tab20') edgecolors='k') %%opts NdOverlay [legend_position='right' aspect=2, fig_size=250, show_frame=False] gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment']) gdp_unem_scatter.overlay('Country') ###Output _____no_output_____
Emotion-detection-main/Emotion_Detection.ipynb	###Markdown Building our Model To train the data ###Code # Working with pre trained model base_model = MobileNet( input_shape=(224,224,3), include_top= False ) for layer in base_model.layers: layer.trainable = False x = Flatten()(base_model.output) x = Dense(units=7 , activation='softmax' )(x) # creating our model. model = Model(base_model.input, x) model.compile(optimizer='adam', loss= categorical_crossentropy , metrics=['accuracy'] ) ###Output _____no_output_____ ###Markdown Preparing our data using data generator ###Code train_datagen = ImageDataGenerator( zoom_range = 0.2, shear_range = 0.2, horizontal_flip=True, rescale = 1./255 ) train_data = train_datagen.flow_from_directory(directory= "/content/train", target_size=(224,224), batch_size=32, ) train_data.class_indices val_datagen = ImageDataGenerator(rescale = 1./255 ) val_data = val_datagen.flow_from_directory(directory= "/content/test", target_size=(224,224), batch_size=32, ) ###Output Found 7178 images belonging to 7 classes. ###Markdown visualizing the data that is fed to train data gen ###Code # to visualize the images in the traing data denerator t_img , label = train_data.next() #----------------------------------------------------------------------------- # function when called will prot the images def plotImages(img_arr, label): """ input :- images array output :- plots the images """ count = 0 for im, l in zip(img_arr,label) : plt.imshow(im) plt.title(im.shape) plt.axis = False plt.show() count += 1 if count == 10: break #----------------------------------------------------------------------------- # function call to plot the images plotImages(t_img, label) ###Output _____no_output_____ ###Markdown having early stopping and model check point ###Code ## having early stopping and model check point from keras.callbacks import ModelCheckpoint, EarlyStopping # early stopping es = EarlyStopping(monitor='val_accuracy', min_delta= 0.01 , patience= 5, verbose= 1, mode='auto') # model check point mc = ModelCheckpoint(filepath="best_model.h5", monitor= 'val_accuracy', verbose= 1, save_best_only= True, mode = 'auto') # puting call back in a list call_back = [es, mc] hist = model.fit_generator(train_data, steps_per_epoch= 10, epochs= 30, validation_data= val_data, validation_steps= 8, callbacks=[es,mc]) # Loading the best fit model from keras.models import load_model model = load_model("/content/best_model.h5") h = hist.history h.keys() plt.plot(h['accuracy']) plt.plot(h['val_accuracy'] , c = "red") plt.title("acc vs v-acc") plt.show() plt.plot(h['loss']) plt.plot(h['val_loss'] , c = "red") plt.title("loss vs v-loss") plt.show() # just to map o/p values op = dict(zip( train_data.class_indices.values(), train_data.class_indices.keys())) # path for the image to see if it predics correct class path = "/content/test/angry/PrivateTest_1054527.jpg" img = load_img(path, target_size=(224,224) ) i = img_to_array(img)/255 input_arr = np.array([i]) input_arr.shape pred = np.argmax(model.predict(input_arr)) print(f" the image is of {op[pred]}") # to display the image plt.imshow(input_arr[0]) plt.title("input image") plt.show() ###Output the image is of neutral
jiuqu/AutoArrangementDashboard.ipynb	###Markdown 为什么会删除房间 ###Code room_id = 7122459 sql = "select begin_at, schedule_id, room_type_id, lesson_id from schedules s join rooms r on s.id = r.schedule_id \ where r.id = 7122459" with conn.cursor() as cur: cur.execute(sql) room = cur.fetchone() print("ROOM INFO:",room) slot = room[0].strftime('%Y-%m-%d %H:%M:00') print(slot) df_chapter, df_room_type = get_free_teacher_capacity_dataframe(slot) # course_conn = get_course_connection() sql = "select id, chapter_id from lessons where published = 1 and deleted_at is null" lessons = pd.read_sql(sql, course_conn, index_col='id') chapter_id = lessons.loc[room[3], 'chapter_id'] ## 可用老师为 print('可用老师为') t1 = df_chapter.loc[df_chapter.loc[:, chapter_id] > 0].index t2 = df_room_type.loc[df_room_type.loc[:, room[2]] > 0].index print(t1.intersection(t2)) ###Output ROOM INFO: (datetime.datetime(2019, 6, 3, 19, 20), 2964373, 2, 1674) 2019-06-03 19:20:00 可用老师为 Int64Index([ 4293, 4630, 6316, 100190, 100644, 100840, 100947, 100953, 101859, 101902, 102405, 102488, 102674, 103213, 103430, 103504, 105686, 106956, 110949, 111052, 111873, 112139, 112148, 112523, 112818, 112873, 117047, 119725, 128249], dtype='int64', name='teacher_id') ###Markdown 为什么这个L 没有课了空闲的老师 ###Code sql = "select r.id, schedule_id, room_type_id, course_id, lesson_id, student_count, teacher_id \ from schedules s join rooms r on s.id = r.schedule_id and r.status = 0 and klass_id is null \ and r.is_internal = 0 and ((teacher_id > 0 and student_count =0) or (teacher_id = 0 and student_count > 0)) \ where begin_at = %r " % slot data = pd.read_sql(sql, conn) rooms = data.loc[data['teacher_id'] == 0] ###Output _____no_output_____ ###Markdown 获取老师的chapter 和 room type ###Code teacher_conn = get_teacher_connection() tlist = str(tuple([i for i in data['teacher_id'].values if i > 0])) sql = "select teacher_id, chapter_id, status from teacher_chapter_tables where teacher_id in {} and status > 0".format(tlist) with teacher_conn.cursor() as cur: cur.execute(sql) chapter_records = cur.fetchall() chapters = pd.DataFrame(columns = ('a',), dtype='int8') for row in chapter_records: chapters.loc[row[0], row[1]] = int(row[2]) chapters.drop('a', axis=1,inplace=True) sql = "select teacher_id, room_type, status from teacher_room_types where teacher_id in {} and status > 0".format(tlist) with teacher_conn.cursor() as cur: cur.execute(sql) room_type_records = cur.fetchall() roomtypes = pd.DataFrame(columns = ('a',), dtype='int8') for row in room_type_records: roomtypes.loc[row[0],row[1]] = int(row[2]) roomtypes.drop('a', axis = 1, inplace=True) ###Output _____no_output_____ ###Markdown Lesson 数据转chapter ###Code course_conn = get_course_connection() sql = "select id, chapter_id from lessons where published = 1 and deleted_at is null" lessons = pd.read_sql(sql, course_conn, index_col='id') ###Output _____no_output_____ ###Markdown 房间匹配 ###Code def match(room_chapter, room_type): rt = set(roomtypes.loc[roomtypes[room_type] == 2].index.tolist()) c = set(chapters.loc[chapters[room_chapter] == 2].index.tolist()) print(rt&c) rt = set(roomtypes.loc[roomtypes[room_type] == 2].index.tolist()) c = set(chapters.loc[chapters[room_chapter] == 1].index.tolist()) print(rt&c) rt = set(roomtypes.loc[roomtypes[room_type] == 1].index.tolist()) c = set(chapters.loc[chapters[room_chapter] == 2].index.tolist()) print(rt&c) rt = set(roomtypes.loc[roomtypes[room_type] == 1].index.tolist()) c = set(chapters.loc[chapters[room_chapter] == 1].index.tolist()) print(rt&c) for index, room in rooms.iterrows(): chapter = lessons['chapter_id'][room['lesson_id']] print('-' 32) print("room[{}]:".format(room['id'])) match(chapter, room['room_type_id']) ###Output -------------------------------- room[7031682]: set() {4610, 104963, 102405, 526, 101395, 3604, 102419, 4630, 5654, 101912, 106521, 111643, 101405, 200224, 105508, 113188, 128036, 120871, 150052, 1066, 107563, 112683, 101933, 122926, 105524, 100919, 110135, 115767, 174136, 166972, 110653, 103998, 109631, 1089, 6211, 100420, 105541, 112195, 134211, 162377, 101965, 3155, 105046, 165979, 136285, 138846, 171618, 105575, 103016, 107111, 112235, 110189, 4718, 106606, 112750, 128621, 3191, 155256, 163450, 4219, 123003, 5755, 6782, 175233, 131722, 4747, 3213, 112269, 110740, 109205, 113301, 4759, 129177, 1690, 6810, 169113, 104603, 106153, 170666, 105645, 112814, 124077, 130734, 113329, 3764, 103093, 105141, 126646, 127669, 136372, 108730, 115898, 238262, 105663, 711, 116935, 108235, 135375, 125137, 107221, 1238, 1239, 111829, 100061, 110815, 132833, 141537, 106214, 109286, 170729, 127722, 2284, 103660, 102126, 121075, 104182, 109304, 168185, 109313, 128770, 112899, 205573, 119049, 107276, 111884, 102162, 111385, 171289, 237338, 179488, 111917, 120621, 105775, 108335, 5937, 118577, 4915, 112948, 116532, 114506, 107862, 2392, 101210, 3932, 105821, 100190, 108380, 110942, 7009, 6498, 130914, 108901, 113510, 108903, 360, 2408, 107372, 110961, 103285, 113533, 102272, 102785, 110978, 131971, 106888, 113033, 104331, 167307, 110990, 128400, 113041, 112530, 109460, 112024, 106906, 103838, 105374, 106913, 109987, 105893, 1447, 2472, 129959, 107946, 170919, 119725, 132526, 5555, 126389, 103862, 139190, 107962, 3003, 109499, 4541, 107967, 104384, 3525, 141766, 108487, 5578, 104911, 170959, 144338, 979, 110552, 101340, 109533, 101859, 106981, 108518, 999, 111079, 111081, 4586, 104943, 1521, 4083, 107507, 106486, 107511, 132602, 103931, 109053, 102399} set() {124943, 2064, 102417, 110607, 147493, 102444, 50, 100413, 6206, 6210, 104515, 108612, 110660, 4168, 106586, 100442, 104543, 106598, 112759, 123006, 2181, 106637, 106638, 112787, 4244, 143507, 112802, 165, 110760, 116904, 6314, 6316, 108722, 112818, 106677, 108728, 2242, 4293, 104648, 108752, 102616, 116958, 100575, 2278, 112873, 2282, 110827, 176370, 102643, 6391, 102655, 100618, 112906, 271, 102674, 4376, 106780, 102691, 112933, 114987, 117047, 4409, 104761, 121153, 106821, 110923, 6478, 112976, 121170, 151892, 102742, 115034, 106844, 121180, 104804, 100709, 106856, 113005, 108915, 104821, 104830, 6528, 102784, 110986, 104848, 121234, 106899, 119190, 111007, 102816, 113059, 106917, 100780, 2477, 111020, 113083, 108989, 100800, 102848, 106946, 119233, 108999, 106956, 2509, 111052, 109010, 4563, 111058, 109013, 109015, 129496, 4572, 104925, 123357, 100836, 111077, 100840, 113130, 503, 2558, 113162, 102934, 115225, 119325, 542, 2593, 6694, 109103, 115248, 105017, 105022, 102977, 585, 109130, 2639, 593, 100947, 109144, 100953, 115291, 115292, 100961, 4709, 113253, 109159, 113258, 113268, 631, 105087, 111237, 103047, 2696, 117384, 103053, 109199, 105105, 156313, 111259, 109214, 105129, 148139, 4782, 101042, 105143, 109244, 103118, 105168, 113364, 4821, 103128, 101081, 105176, 107232, 2785, 105189, 107240, 101113, 109305, 4862, 4863, 6913, 105218, 103173, 105228, 103189, 2839, 103197, 4898, 101156, 103211, 103213, 156461, 105269, 101178, 115530, 107339, 103244, 2893, 109387, 101199, 109390, 103252, 107348, 127833, 4956, 111455, 107362, 125805, 101231, 4976, 107377, 101237, 2942, 4990, 111494, 105354, 107403, 101259, 103316, 103320, 134044, 121759, 115619, 123819, 953, 107454, 109510, 113607, 113617, 103378, 170961, 3036, 117725, 3040, 1000, 101359, 103417, 107523, 103430, 105478, 103441, 105490, 111636, 103450, 101406, 107554, 5164, 5172, 105530, 1086, 111678, 101440, 103496, 109643, 103504, 5205, 109664, 109666, 3177, 111724, 109678, 109682, 103539, 111734, 119926, 3196, 111740, 111744, 109705, 111753, 119948, 105626, 122012, 109729, 3238, 122023, 105640, 124076, 103597, 1204, 119996, 105668, 103621, 105673, 142541, 105686, 120022, 120025, 122076, 109789, 120032, 120034, 1258, 107762, 128249, 1274, 111873, 3332, 1289, 124169, 3344, 124180, 5401, 107804, 1310, 103712, 122147, 3366, 101673, 109870, 130363, 122176, 103752, 109902, 111966, 109922, 111993, 5512, 112009, 120202, 3467, 5522, 109971, 109986, 1446, 109994, 112042, 5552, 103864, 112059, 122325, 103903, 101856, 112095, 105954, 108002, 103913, 112110, 112116, 112123, 105981, 103946, 112139, 101902, 167442, 108051, 112148, 5655, 153115, 112158, 112164, 110117, 106022, 5672, 110120, 126505, 106030, 108080, 110130, 106045, 108093, 104000, 110144, 104006, 3655, 126542, 110159, 110182, 106097, 110207, 138885, 128654, 108187, 102052, 112294, 104113, 102072, 130746, 104126, 110287, 159442, 1747, 104161, 1771, 1772, 110321, 112370, 112371, 3829, 100091, 104201, 112393, 104203, 124681, 106253, 100117, 100119, 106273, 114467, 102190, 3887, 1840, 104240, 110384, 112435, 112438, 5943, 112441, 100160, 100169, 112459, 110413, 1871, 114526, 122728, 112499, 110458, 112506, 6020, 100235, 6028, 102283, 3982, 112523, 147340, 6036, 100247, 108440, 112542, 102303, 108447, 1956, 106415, 100276, 4022, 106422, 116664, 6074, 4030, 108488, 4041, 104394, 1995, 110536, 110538, 102360, 106458, 104413, 112606, 112608, 106465, 112609, 116706, 2021, 118762, 112620, 106481, 6130, 102389, 112637, 110591} -------------------------------- room[7031776]: set() {4610, 104963, 102405, 526, 101395, 3604, 102419, 4630, 5654, 101912, 106521, 111643, 101405, 200224, 105508, 113188, 128036, 120871, 150052, 1066, 107563, 112683, 101933, 122926, 105524, 100919, 110135, 115767, 174136, 166972, 110653, 103998, 109631, 1089, 6211, 100420, 105541, 112195, 134211, 162377, 101965, 3155, 105046, 165979, 136285, 138846, 171618, 105575, 103016, 107111, 112235, 110189, 4718, 106606, 112750, 128621, 3191, 155256, 163450, 4219, 123003, 5755, 6782, 175233, 131722, 4747, 3213, 112269, 110740, 109205, 113301, 4759, 129177, 1690, 6810, 169113, 104603, 106153, 170666, 105645, 112814, 124077, 130734, 113329, 3764, 103093, 105141, 126646, 127669, 136372, 108730, 115898, 238262, 105663, 711, 116935, 108235, 135375, 125137, 107221, 1238, 1239, 111829, 100061, 110815, 132833, 141537, 106214, 109286, 170729, 127722, 2284, 103660, 102126, 121075, 104182, 109304, 168185, 109313, 128770, 112899, 205573, 119049, 107276, 111884, 102162, 111385, 171289, 237338, 179488, 111917, 120621, 105775, 108335, 5937, 118577, 4915, 112948, 116532, 114506, 107862, 2392, 101210, 3932, 105821, 100190, 108380, 110942, 7009, 6498, 130914, 108901, 113510, 108903, 360, 2408, 107372, 110961, 103285, 113533, 102272, 102785, 110978, 131971, 106888, 113033, 104331, 167307, 110990, 128400, 113041, 112530, 109460, 112024, 106906, 103838, 105374, 106913, 109987, 105893, 1447, 2472, 129959, 107946, 170919, 119725, 132526, 5555, 126389, 103862, 139190, 107962, 3003, 109499, 4541, 107967, 104384, 3525, 141766, 108487, 5578, 104911, 170959, 144338, 979, 110552, 101340, 109533, 101859, 106981, 108518, 999, 111079, 111081, 4586, 104943, 1521, 4083, 107507, 106486, 107511, 132602, 103931, 109053, 102399} set() {124943, 2064, 102417, 110607, 147493, 102444, 50, 100413, 6206, 6210, 104515, 108612, 110660, 4168, 106586, 100442, 104543, 106598, 112759, 123006, 2181, 106637, 106638, 112787, 4244, 143507, 112802, 165, 110760, 116904, 6314, 6316, 108722, 112818, 106677, 108728, 2242, 4293, 104648, 108752, 102616, 116958, 100575, 2278, 112873, 2282, 110827, 176370, 102643, 6391, 102655, 100618, 112906, 271, 102674, 4376, 106780, 102691, 112933, 114987, 117047, 4409, 104761, 121153, 106821, 110923, 6478, 112976, 121170, 151892, 102742, 115034, 106844, 121180, 104804, 100709, 106856, 113005, 108915, 104821, 104830, 6528, 102784, 110986, 104848, 121234, 106899, 119190, 111007, 102816, 113059, 106917, 100780, 2477, 111020, 113083, 108989, 100800, 102848, 106946, 119233, 108999, 106956, 2509, 111052, 109010, 4563, 111058, 109013, 109015, 129496, 4572, 104925, 123357, 100836, 111077, 100840, 113130, 503, 2558, 113162, 102934, 115225, 119325, 542, 2593, 6694, 109103, 115248, 105017, 105022, 102977, 585, 109130, 2639, 593, 100947, 109144, 100953, 115291, 115292, 100961, 4709, 113253, 109159, 113258, 113268, 631, 105087, 111237, 103047, 2696, 117384, 103053, 109199, 105105, 156313, 111259, 109214, 105129, 148139, 4782, 101042, 105143, 109244, 103118, 105168, 113364, 4821, 103128, 101081, 105176, 107232, 2785, 105189, 107240, 101113, 109305, 4862, 4863, 6913, 105218, 103173, 105228, 103189, 2839, 103197, 4898, 101156, 103211, 103213, 156461, 105269, 101178, 115530, 107339, 103244, 2893, 109387, 101199, 109390, 103252, 107348, 127833, 4956, 111455, 107362, 125805, 101231, 4976, 107377, 101237, 2942, 4990, 111494, 105354, 107403, 101259, 103316, 103320, 134044, 121759, 115619, 123819, 953, 107454, 109510, 113607, 113617, 103378, 170961, 3036, 117725, 3040, 1000, 101359, 103417, 107523, 103430, 105478, 103441, 105490, 111636, 103450, 101406, 107554, 5164, 5172, 105530, 1086, 111678, 101440, 103496, 109643, 103504, 5205, 109664, 109666, 3177, 111724, 109678, 109682, 103539, 111734, 119926, 3196, 111740, 111744, 109705, 111753, 119948, 105626, 122012, 109729, 3238, 122023, 105640, 124076, 103597, 1204, 119996, 105668, 103621, 105673, 142541, 105686, 120022, 120025, 122076, 109789, 120032, 120034, 1258, 107762, 128249, 1274, 111873, 3332, 1289, 124169, 3344, 124180, 5401, 107804, 1310, 103712, 122147, 3366, 101673, 109870, 130363, 122176, 103752, 109902, 111966, 109922, 111993, 5512, 112009, 120202, 3467, 5522, 109971, 109986, 1446, 109994, 112042, 5552, 103864, 112059, 122325, 103903, 101856, 112095, 105954, 108002, 103913, 112110, 112116, 112123, 105981, 103946, 112139, 101902, 167442, 108051, 112148, 5655, 153115, 112158, 112164, 110117, 106022, 5672, 110120, 126505, 106030, 108080, 110130, 106045, 108093, 104000, 110144, 104006, 3655, 126542, 110159, 110182, 106097, 110207, 138885, 128654, 108187, 102052, 112294, 104113, 102072, 130746, 104126, 110287, 159442, 1747, 104161, 1771, 1772, 110321, 112370, 112371, 3829, 100091, 104201, 112393, 104203, 124681, 106253, 100117, 100119, 106273, 114467, 102190, 3887, 1840, 104240, 110384, 112435, 112438, 5943, 112441, 100160, 100169, 112459, 110413, 1871, 114526, 122728, 112499, 110458, 112506, 6020, 100235, 6028, 102283, 3982, 112523, 147340, 6036, 100247, 108440, 112542, 102303, 108447, 1956, 106415, 100276, 4022, 106422, 116664, 6074, 4030, 108488, 4041, 104394, 1995, 110536, 110538, 102360, 106458, 104413, 112606, 112608, 106465, 112609, 116706, 2021, 118762, 112620, 106481, 6130, 102389, 112637, 110591} ###Markdown 需要扩充的schedule ###Code sql = "select schedule_id, room_type_id, course_id, lesson_id \ from schedules s join rooms r on s.id = r.schedule_id and r.status = 0 and klass_id is null \ and r.is_internal = 0 where begin_at = %r group by schedule_id having sum(max_student_count) = \ sum(student_count)" % slot df = pd.read_sql(sql, conn) for index, row in df.iterrows(): chapter = lessons['chapter_id'][row['lesson_id']] print('-' 32) print("Schedule[{}]:".format(row['schedule_id'])) match(chapter, row['room_type_id']) ###Output -------------------------------- Schedule[2907662]: {111873, 110949, 112873, 112139, 111052, 112523, 112818, 112148, 117047, 128249} set() set() set()
doc/gallery/horizontal_stacked_bar_chart.ipynb	###Markdown Horizontal Stacked Bar ChartThis is an example of a horizontal stacked bar chart using data which contains crop yields over different regions and different years in the 1930s. ###Code import altair as alt from vega_datasets import data source = data.barley() alt.Chart(source).mark_bar().encode( x='sum(yield)', y='variety', color='site' ) ###Output _____no_output_____
module1-join-and-reshape-data/Eyve_Geo_LS_DS7_121_Join_and_Reshape_Data_Assignment.ipynb	###Markdown _Lambda School Data Science_ Join and Reshape datasetsObjectives- concatenate data with pandas- merge data with pandas- understand tidy data formatting- melt and pivot data with pandasLinks- [Pandas Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)- [Tidy Data](https://en.wikipedia.org/wiki/Tidy_data) - Combine Data Sets: Standard Joins - Tidy Data - Reshaping Data- Python Data Science Handbook - [Chapter 3.6](https://jakevdp.github.io/PythonDataScienceHandbook/03.06-concat-and-append.html), Combining Datasets: Concat and Append - [Chapter 3.7](https://jakevdp.github.io/PythonDataScienceHandbook/03.07-merge-and-join.html), Combining Datasets: Merge and Join - [Chapter 3.8](https://jakevdp.github.io/PythonDataScienceHandbook/03.08-aggregation-and-grouping.html), Aggregation and Grouping - [Chapter 3.9](https://jakevdp.github.io/PythonDataScienceHandbook/03.09-pivot-tables.html), Pivot Tables Reference- Pandas Documentation: [Reshaping and Pivot Tables](https://pandas.pydata.org/pandas-docs/stable/reshaping.html)- Modern Pandas, Part 5: [Tidy Data](https://tomaugspurger.github.io/modern-5-tidy.html) ###Code !wget https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz !tar --gunzip --extract --verbose --file=instacart_online_grocery_shopping_2017_05_01.tar.gz %cd instacart_2017_05_01 !ls -lh .csv ###Output -rw-r--r-- 1 502 staff 2.6K May 2 2017 aisles.csv -rw-r--r-- 1 502 staff 270 May 2 2017 departments.csv -rw-r--r-- 1 502 staff 551M May 2 2017 order_products__prior.csv -rw-r--r-- 1 502 staff 24M May 2 2017 order_products__train.csv -rw-r--r-- 1 502 staff 104M May 2 2017 orders.csv -rw-r--r-- 1 502 staff 2.1M May 2 2017 products.csv ###Markdown Assignment Join Data PracticeThese are the top 10 most frequently ordered products. How many times was each ordered? 1. Banana2. Bag of Organic Bananas3. Organic Strawberries4. Organic Baby Spinach 5. Organic Hass Avocado6. Organic Avocado7. Large Lemon 8. Strawberries9. Limes 10. Organic Whole MilkFirst, write down which columns you need and which dataframes have them.Next, merge these into a single dataframe.Then, use pandas functions from the previous lesson to get the counts of the top 10 most frequently ordered products. Columns Needed/Location: - product ID/products.csv- product name/products.csv- product id with order/order products. ###Code import pandas as pd pd.options.display.max_rows=150 #read in and join up the orders information df1=pd.read_csv('order_products__prior.csv') df2=pd.read_csv('order_products__train.csv') ordersDF=pd.concat([df1,df2]) ordersDF.head() #read in product name information products=pd.read_csv('products.csv') products.head() #create a dataframe of only the top ten items topTen=ordersDF['product_id'].value_counts().head(10).index.tolist() topTenDF=products[products['product_id'].isin(topTen)] #remove any order lines that don't have a product in the top ten (make it go faster) ordersSubset=ordersDF[ordersDF['product_id'].isin(topTen)] topTenDF['product_id'].value_counts() ordersSubset.head() topTenDF['product_id'] columns=['product_id','product_name'] merged=pd.merge(ordersSubset['product_id'], topTenDF[columns], how='inner', on='product_id') merged.head() topTenRate=merged['product_name'].value_counts() #this is a list of the top ordered items and how often they were ordered topTenRate ###Output _____no_output_____ ###Markdown Reshape Data Section- Replicate the lesson code- Complete the code cells we skipped near the beginning of the notebook- Table 2 --> Tidy- Tidy --> Table 2- Load seaborn's `flights` dataset by running the cell below. Then create a pivot table showing the number of passengers by month and year. Use year for the index and month for the columns. You've done it right if you get 112 passengers for January 1949 and 432 passengers for December 1960. ###Code import numpy as np #set up table 2 table1 = pd.DataFrame( [[np.nan, 2], [16, 11], [3, 1]], index=['John Smith', 'Jane Doe', 'Mary Johnson'], columns=['treatmenta', 'treatmentb']) table2 = table1.T table2 treat=table2.index.to_list() cols=table2.columns.to_list() cols table2=table2.reset_index() table2 tidy=table2.melt(id_vars='index', value_vars=cols) tidy import seaborn as sns flights = sns.load_dataset('flights') ##### YOUR CODE HERE ##### flights.pivot_table(index='year', columns='month', values='passengers') ###Output _____no_output_____ ###Markdown Join Data Stretch ChallengeThe [Instacart blog post](https://tech.instacart.com/3-million-instacart-orders-open-sourced-d40d29ead6f2) has a visualization of "Popular products* purchased earliest in the day (green) and latest in the day (red)." The post says,> "We can also see the time of day that users purchase specific products.> Healthier snacks and staples tend to be purchased earlier in the day, whereas ice cream (especially Half Baked and The Tonight Dough) are far more popular when customers are ordering in the evening.> In fact, of the top 25 latest ordered products, the first 24 are ice cream! The last one, of course, is a frozen pizza."Your challenge is to reproduce the list of the top 25 latest ordered popular products.We'll define "popular products" as products with more than 2,900 orders. ###Code ##### YOUR CODE HERE ##### #create a list of the most common items purchased values=ordersDF['product_id'].value_counts() orderRate=values[values>29000] orderRate=orderRate.reset_index() orderRate=orderRate.rename(columns={'index':'product_id','product_id':'purchase_rate'}) orderRate.head() #add the names of the products back in orderRate=pd.merge(orderRate, products[columns]) orderRate #this data has the times of orders orders=pd.read_csv('orders.csv') orders.head() #combine into one dataframe that has all the info we need in it orderCols=['order_id','order_hour_of_day'] idCols=['order_id','product_id'] timeOrderDF=pd.merge(orders[orderCols], ordersDF[idCols]) timeOrderDF final=pd.merge(timeOrderDF, orderRate[columns]) final.columns.to_list() ct=pd.crosstab(final['product_name'],final['order_hour_of_day'], normalize='index') ct ct.T.plot(legend=False); ###Output _____no_output_____ ###Markdown Reshape Data Stretch Challenge_Try whatever sounds most interesting to you!_- Replicate more of Instacart's visualization showing "Hour of Day Ordered" vs "Percent of Orders by Product"- Replicate parts of the other visualization from [Instacart's blog post](https://tech.instacart.com/3-million-instacart-orders-open-sourced-d40d29ead6f2), showing "Number of Purchases" vs "Percent Reorder Purchases"- Get the most recent order for each user in Instacart's dataset. This is a useful baseline when [predicting a user's next order](https://www.kaggle.com/c/instacart-market-basket-analysis)- Replicate parts of the blog post linked at the top of this notebook: [Modern Pandas, Part 5: Tidy Data](https://tomaugspurger.github.io/modern-5-tidy.html) ###Code ##### YOUR CODE HERE ##### ###Output _____no_output_____
vanderplas/PythonDataScienceHandbook-master/notebooks/04.11-Settings-and-Stylesheets.ipynb	###Markdown This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)! Customizing Matplotlib: Configurations and Stylesheets Matplotlib's default plot settings are often the subject of complaint among its users.While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer stylesheets feature, which contains some nice sets of default configurations. Plot Customization by HandThrough this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.It's possible to do these customizations for each individual plot.For example, here is a fairly drab default histogram: ###Code import matplotlib.pyplot as plt plt.style.use('classic') import numpy as np %matplotlib inline x = np.random.randn(1000) plt.hist(x); ###Output _____no_output_____ ###Markdown We can adjust this by hand to make it a much more visually pleasing plot: ###Code # use a gray background ax = plt.axes(axisbg='#E6E6E6') ax.set_axisbelow(True) # draw solid white grid lines plt.grid(color='w', linestyle='solid') # hide axis spines for spine in ax.spines.values(): spine.set_visible(False) # hide top and right ticks ax.xaxis.tick_bottom() ax.yaxis.tick_left() # lighten ticks and labels ax.tick_params(colors='gray', direction='out') for tick in ax.get_xticklabels(): tick.set_color('gray') for tick in ax.get_yticklabels(): tick.set_color('gray') # control face and edge color of histogram ax.hist(x, edgecolor='#E6E6E6', color='#EE6666'); ###Output _____no_output_____ ###Markdown This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.But this took a whole lot of effort!We definitely do not want to have to do all that tweaking each time we create a plot.Fortunately, there is a way to adjust these defaults once in a way that will work for all plots. Changing the Defaults: ``rcParams``Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session: ###Code IPython_default = plt.rcParams.copy() ###Output _____no_output_____ ###Markdown Now we can use the ``plt.rc`` function to change some of these settings: ###Code from matplotlib import cycler colors = cycler('color', ['#EE6666', '#3388BB', '#9988DD', '#EECC55', '#88BB44', '#FFBBBB']) plt.rc('axes', facecolor='#E6E6E6', edgecolor='none', axisbelow=True, grid=True, prop_cycle=colors) plt.rc('grid', color='w', linestyle='solid') plt.rc('xtick', direction='out', color='gray') plt.rc('ytick', direction='out', color='gray') plt.rc('patch', edgecolor='#E6E6E6') plt.rc('lines', linewidth=2) ###Output _____no_output_____ ###Markdown With these settings defined, we can now create a plot and see our settings in action: ###Code plt.hist(x); ###Output _____no_output_____ ###Markdown Let's see what simple line plots look like with these rc parameters: ###Code for i in range(4): plt.plot(np.random.rand(10)) ###Output _____no_output_____ ###Markdown I find this much more aesthetically pleasing than the default styling.If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!These settings can be saved in a .matplotlibrc file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).That said, I prefer to customize Matplotlib using its stylesheets instead. StylesheetsThe version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the .matplotlibrc files mentioned earlier, but must be named with a .mplstyle extension.Even if you don't create your own style, the stylesheets included by default are extremely useful.The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity: ###Code plt.style.available[:5] ###Output _____no_output_____ ###Markdown The basic way to switch to a stylesheet is to call``` pythonplt.style.use('stylename')```But keep in mind that this will change the style for the rest of the session!Alternatively, you can use the style context manager, which sets a style temporarily:``` pythonwith plt.style.context('stylename'): make_a_plot()``` Let's create a function that will make two basic types of plot: ###Code def hist_and_lines(): np.random.seed(0) fig, ax = plt.subplots(1, 2, figsize=(11, 4)) ax[0].hist(np.random.randn(1000)) for i in range(3): ax[1].plot(np.random.rand(10)) ax[1].legend(['a', 'b', 'c'], loc='lower left') ###Output _____no_output_____ ###Markdown We'll use this to explore how these plots look using the various built-in styles. Default styleThe default style is what we've been seeing so far throughout the book; we'll start with that.First, let's reset our runtime configuration to the notebook default: ###Code # reset rcParams plt.rcParams.update(IPython_default); ###Output _____no_output_____ ###Markdown Now let's see how it looks: ###Code hist_and_lines() ###Output _____no_output_____ ###Markdown FiveThiryEight styleThe ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).As you can see here, it is typified by bold colors, thick lines, and transparent axes: ###Code with plt.style.context('fivethirtyeight'): hist_and_lines() ###Output _____no_output_____ ###Markdown ggplotThe ``ggplot`` package in the R language is a very popular visualization tool.Matplotlib's ``ggplot`` style mimics the default styles from that package: ###Code with plt.style.context('ggplot'): hist_and_lines() ###Output _____no_output_____ ###Markdown Bayesian Methods for Hackers( styleThere is a very nice short online book called [Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.This style is reproduced in the ``bmh`` stylesheet: ###Code with plt.style.context('bmh'): hist_and_lines() ###Output _____no_output_____ ###Markdown Dark backgroundFor figures used within presentations, it is often useful to have a dark rather than light background.The ``dark_background`` style provides this: ###Code with plt.style.context('dark_background'): hist_and_lines() ###Output _____no_output_____ ###Markdown GrayscaleSometimes you might find yourself preparing figures for a print publication that does not accept color figures.For this, the ``grayscale`` style, shown here, can be very useful: ###Code with plt.style.context('grayscale'): hist_and_lines() ###Output _____no_output_____ ###Markdown Seaborn styleMatplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.I've found these settings to be very nice, and tend to use them as defaults in my own data exploration. ###Code import seaborn hist_and_lines() ###Output _____no_output_____
ipynb/US-Montana.ipynb	###Markdown United States: Montana* Homepage of project: https://oscovida.github.io* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb) ###Code import datetime import time start = datetime.datetime.now() print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}") %config InlineBackend.figure_formats = ['svg'] from oscovida import * overview(country="US", region="Montana"); # load the data cases, deaths, region_label = get_country_data("US", "Montana") # compose into one table table = compose_dataframe_summary(cases, deaths) # show tables with up to 500 rows pd.set_option("max_rows", 500) # display the table table ###Output _____no_output_____ ###Markdown Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyter.org for more details on how to use Jupyter Notebook Acknowledgements:- Johns Hopkins University provides data for countries- Robert Koch Institute provides data for within Germany- Open source and scientific computing community for the data tools- Github for hosting repository and html files- Project Jupyter for the Notebook and binder service- The H2020 project Photon and Neutron Open Science Cloud ([PaNOSC](https://www.panosc.eu/))-------------------- ###Code print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and " f"deaths at {fetch_deaths_last_execution()}.") # to force a fresh download of data, run "clear_cache()" print(f"Notebook execution took: {datetime.datetime.now()-start}") ###Output _____no_output_____ ###Markdown United States: Montana* Homepage of project: https://oscovida.github.io* Plots are explained at http://oscovida.github.io/plots.html* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb) ###Code import datetime import time start = datetime.datetime.now() print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}") %config InlineBackend.figure_formats = ['svg'] from oscovida import * overview(country="US", region="Montana", weeks=5); overview(country="US", region="Montana"); compare_plot(country="US", region="Montana"); # load the data cases, deaths = get_country_data("US", "Montana") # compose into one table table = compose_dataframe_summary(cases, deaths) # show tables with up to 500 rows pd.set_option("max_rows", 500) # display the table table ###Output _____no_output_____ ###Markdown Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyter.org for more details on how to use Jupyter Notebook Acknowledgements:- Johns Hopkins University provides data for countries- Robert Koch Institute provides data for within Germany- Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)- Open source and scientific computing community for the data tools- Github for hosting repository and html files- Project Jupyter for the Notebook and binder service- The H2020 project Photon and Neutron Open Science Cloud ([PaNOSC](https://www.panosc.eu/))-------------------- ###Code print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and " f"deaths at {fetch_deaths_last_execution()}.") # to force a fresh download of data, run "clear_cache()" print(f"Notebook execution took: {datetime.datetime.now()-start}") ###Output _____no_output_____ ###Markdown United States: Montana* Homepage of project: https://oscovida.github.io* Plots are explained at http://oscovida.github.io/plots.html* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb) ###Code import datetime import time start = datetime.datetime.now() print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}") %config InlineBackend.figure_formats = ['svg'] from oscovida import * overview(country="US", region="Montana", weeks=5); overview(country="US", region="Montana"); compare_plot(country="US", region="Montana"); # load the data cases, deaths = get_country_data("US", "Montana") # get population of the region for future normalisation: inhabitants = population(country="US", region="Montana") print(f'Population of country="US", region="Montana": {inhabitants} people') # compose into one table table = compose_dataframe_summary(cases, deaths) # show tables with up to 1000 rows pd.set_option("max_rows", 1000) # display the table table ###Output _____no_output_____ ###Markdown Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/US-Montana.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyter.org for more details on how to use Jupyter Notebook Acknowledgements:- Johns Hopkins University provides data for countries- Robert Koch Institute provides data for within Germany- Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)- Open source and scientific computing community for the data tools- Github for hosting repository and html files- Project Jupyter for the Notebook and binder service- The H2020 project Photon and Neutron Open Science Cloud ([PaNOSC](https://www.panosc.eu/))-------------------- ###Code print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and " f"deaths at {fetch_deaths_last_execution()}.") # to force a fresh download of data, run "clear_cache()" print(f"Notebook execution took: {datetime.datetime.now()-start}") ###Output _____no_output_____
end_to_end/nlp_mlops_company_sentiment/02_nlp_company_earnings_analysis_pipeline.ipynb	###Markdown Understanding Trends in Company Valuation with NLP - Part 2: NLP Company Earnings Analysis Pipeline Introduction Orchestrating company earnings trend analysis, using SEC filings, news sentiment with the Hugging Face transformers, and Amazon SageMaker PipelinesIn this notebook, we demonstrate how to summarize and derive sentiments out of Security and Exchange Commission reports filed by a publicly traded organization. We will derive the overall market sentiments about the said organization through financial news articles within the same financial period to present a fair view of the organization vs. market sentiments and outlook about the company's overall valuation and performance. In addition to this we will also identify the most popular keywords and entities within the news articles about that organization.In order to achieve the above we will be using multiple SageMaker Hugging Face based NLP transformers for the downstream NLP tasks of Summarization (e.g., of the news and SEC MDNA sections) and Sentiment Analysis (of the resulting summaries). --- Using SageMaker Pipelines Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. Orchestrating workflows across each step of the machine learning process (e.g. exploring and preparing data, experimenting with different algorithms and parameters, training and tuning models, and deploying models to production) can take months of coding.Since it is purpose-built for machine learning, SageMaker Pipelines helps you automate different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment. With SageMaker Pipelines, you can build dozens of ML models a week, manage massive volumes of data, thousands of training experiments, and hundreds of different model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization. --- Understanding trends in company valuation (or similar) with NLPNatural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. (Source: [Wikipedia](https://en.wikipedia.org/wiki/Natural_language_processing))We are going to demonstrate how to summarize and derive sentiments out of Security and Exchange Commission reports filed by a publicly traded organization. We are also going to derive the overall market sentiments about the said organization through financial news articles within the same financial period to present a fair view of the organization vs. market sentiments and outlook about the company's overall valuation and performance. In addition to this we will also identify the most popular keywords and entities within the news articles about that organization.In order to achieve the above we will be using multiple SageMaker Hugging Face based NLP transformers with summarization and sentiment analysis downstream tasks.* Summarization of financial text from SEC reports and news articles will be done via [Pegasus for Financial Summarization model](https://huggingface.co/human-centered-summarization/financial-summarization-pegasus) based on the paper [Towards Human-Centered Summarization: A Case Study on Financial News](https://aclanthology.org/2021.hcinlp-1.4/). * Sentiment analysis on summarized SEC financial report and news articles will be done via pre-trained NLP model to analyze sentiment of financial text called [FinBERT](https://huggingface.co/ProsusAI/finbert). Paper: [ FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063)--- SEC DatasetThe starting point for a vast amount of financial NLP is text in SEC filings. The SEC requires companies to report different types of information related to various events involving companies. The full list of SEC forms is here: https://www.sec.gov/forms.SEC filings are widely used by financial services companies as a source of information about companies in order to make trading, lending, investment, and risk management decisions. Because these filings are required by regulation, they are of high quality and veracity. They contain forward-looking information that helps with forecasts and are written with a view to the future, required by regulation. In addition, in recent times, the value of historical time-series data has degraded, since economies have been structurally transformed by trade wars, pandemics, and political upheavals. Therefore, text as a source of forward-looking information has been increasing in relevance. Obtain the dataset using the SageMaker JumpStart Industry Python SDKDownloading SEC filings is done from the SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR) website, which provides open data access. EDGAR is the primary system under the U.S. Securities And Exchange Commission (SEC) for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. EDGAR contains millions of company and individual filings. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average.There are several ways to download the data, and some open source packages available to extract the text from these filings. However, these require extensive programming and are not always easy-to-use. We provide a simple one-API call that will create a dataset in a few lines of code, for any period of time and for numerous tickers.We have wrapped the extraction functionality into a SageMaker processing container and provide this notebook to enable users to download a dataset of filings with metadata such as dates and parsed plain text that can then be used for machine learning using other SageMaker tools. This is included in the [SageMaker Industry Jumpstart Industry](https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/) library for financial language models. Users only need to specify a date range and a list of ticker symbols, and the library will take care of the rest.As of now, the solution supports extracting a popular subset of SEC forms in plain text (excluding tables): 10-K, 10-Q, 8-K, 497, 497K, S-3ASR, and N-1A. For each of these, we provide examples throughout this notebook and a brief description of each form. For the 10-K and 10-Q forms, filed every year or quarter, we also extract the Management Discussion and Analysis (MDNA) section, which is the primary forward-looking section in the filing. This is the section that has been most widely used in financial text analysis. Therefore, we provide this section automatically in a separate column of the dataframe alongside the full text of the filing.The extracted dataframe is written to S3 storage and to the local notebook instance. --- News articles related to the stock symbol -- datasetWe will use the MIT Licensed [NewsCatcher API](https://docs.newscatcherapi.com/) to grab top 4-5 articles about the specific organization using filters, however other sources such as Social media feeds, RSS Feeds can also be used. The first step in the pipeline is to fetch the SEC report from the EDGAR database using the [SageMaker Industry Jumpstart Industry](https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/) library for Financial language models. This library provides us an easy to use functionality to obtain either one or multiple SEC reports for one or more Ticker symbols or CIKs. The ticker or CIK number will be passed to the SageMaker Pipeline using Pipeline parameter `inference_ticker_cik`. For demo purposes of this Pipeline we will focus on a single Ticker/CIK number at a time and the MDNA section of the 10-K form. The first processing will extract the MDNA from the 10-K form for a company and will also gather few news articles related to the company from the NewsCatcher API. This data will ultimately be used for summarization and then finally sentiment analysis. --- MLOps for NLP using SageMaker PipelinesWe will set up the following SageMaker Pipeline. The Pipleline has two flows depending on what the value for `model_register_deploy` Pipeline parameter is set to. If the value is set to `Y` we want the pipeline to register the model and deploy the latest version of the model from the model registry to the SageMaker endpoint. If the value is set to `N` then we simply want to run inferences using the FinBert and the Pegasus models using the Ticker symbol (or CIK number) that is passed to the pipeline using the `inference_ticker_cik` Pipeline parameter. Note: You must execute the script-processor-custom-container.ipynb notebook before you can set up the SageMaker Pipeline. This notebook creates a custom Docker image and registers it in Amazon Elastic Container Registry (Amazon ECR) for the pipeline to use. The image contains of all the dependencies required. --- Set Up SageMaker Project Install and import packages ###Code # Install updated version of SageMaker # !pip install -q sagemaker==2.49 !pip install sagemaker --upgrade !pip install transformers !pip install typing !pip install sentencepiece !pip install fiscalyear #Install SageMaker Jumpstart Industry !pip install smjsindustry ###Output _____no_output_____ ###Markdown NOTE: After installing an updated version of SageMaker and PyTorch, save the notebook and then restart your kernel. ###Code import boto3 import botocore import pandas as pd import sagemaker print(f'SageMaker version: {sagemaker.__version__}') from sagemaker.huggingface import HuggingFace from sagemaker.huggingface import HuggingFaceModel from sagemaker.workflow.pipeline import Pipeline from sagemaker.workflow.steps import CreateModelStep from sagemaker.workflow.step_collections import RegisterModel from sagemaker.workflow.steps import ProcessingStep from sagemaker.workflow.steps import TransformStep from sagemaker.workflow.properties import PropertyFile from sagemaker.workflow.parameters import (ParameterInteger, ParameterString) from sagemaker.sklearn.processing import ScriptProcessor from sagemaker.lambda_helper import Lambda from sagemaker.workflow.lambda_step import ( LambdaStep, LambdaOutput, LambdaOutputTypeEnum, ) ###Output _____no_output_____ ###Markdown Define parameters that you'll use throughout the notebook ###Code s3 = boto3.resource("s3") region = boto3.Session().region_name sagemaker_session = sagemaker.Session() role = sagemaker.get_execution_role() sagemaker_role = role default_bucket = sagemaker_session.default_bucket() prefix = 'nlp-e2e-mlops' s3_client = boto3.client('s3', region_name=region) sagemaker_boto_client = boto3.client("sagemaker", region_name=region) #deploy_model_instance_type = "ml.m4.8xlarge" deploy_model_instance_type = "ml.m4.xlarge" inference_instances=["ml.t2.medium", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge"] transform_instances=["ml.m5.xlarge"] PROCESSING_INSTANCE="ml.m4.4xlarge" ticker='AMZN' %store -r print(f's3://{default_bucket}/{prefix}/code/model_deploy.py') print(f'SageMaker Role: {role}') ###Output _____no_output_____ ###Markdown Define parameters to parametrize Pipeline ExecutionUsing SageMaker Pipelines, we can define the steps to be included in a pipeline but then use parameters to modify that pipeline when we go to execute the pipeline, without having to modify the pipeline definition. We'll provide some default parameter values that can be overridden on pipeline execution. ###Code #Define some default parameters: #specify default number of instances for processing step processing_instance_count = ParameterInteger( name="ProcessingInstanceCount", default_value=1 ) #specify default instance type for processing step processing_instance_type = ParameterString( name="ProcessingInstanceType", default_value=PROCESSING_INSTANCE ) #specify location of inference data for data processing step inference_input_data = ParameterString( name="InferenceData", default_value=f's3://{default_bucket}/{prefix}/nlp-pipeline/inf-data', ) #Specify the Ticker CIK for the pipeline inference_ticker_cik = ParameterString( name="InferenceTickerCik", default_value=ticker, ) #specify default method for model approval model_approval_status = ParameterString( name="ModelApprovalStatus", default_value="PendingManualApproval" ) #specify if new model needs to be registered and deployed model_register_deploy = ParameterString( name="ModelRegisterDeploy", default_value="Y" ) %store # These are the stored variables, the container is created in the # previous notebook 01_script-processor-custom-container.ipynb %pylab inline %store -r ###Output _____no_output_____ ###Markdown --- Preparing SEC dataset Before we dive right into setting up the pipeline, let's take a look at how the SageMaker Jumpstart Industry SDK for Financial language model helps obtain the dataset from SEC forms and what are the features available for us to use. Note: The code cells in this section are completely optional and for information purposes only; we will use the SageMaker JumpStart Industry SDK directly in the pipeline. Let's install the required dependencies first. Install the SageMaker JumpStart Industry SDKThe functionality is delivered through a client-side SDK. The first step requires pip installing a Python package that interacts with a SageMaker processing container. The retrieval, parsing, transforming, and scoring of text is a complex process and uses different algorithms and packages. In order to make this seamless and stable for the user, the functionality is packaged into a SageMaker container. This lifts the load of installation and maintenance of the workflow, reducing the user effort down to a pip install followed by a single API call. ###Code !pip install --no-index smjsindustry==1.0.0 ###Output _____no_output_____ ###Markdown As an example, we will try to pull AMZN ticker 10k/10q filings from EDGAR and write the data as CSV to S3. Below is the single block of code that contains the API call. The options are all self-explanatory. ###Code # from smfinance import SECDataSetConfig, DataLoader from smjsindustry.finance import DataLoader from smjsindustry.finance.processor_config import EDGARDataSetConfig ###Output _____no_output_____ ###Markdown The extracted reports will be saved to an S3 bucket for us to review. This code will also be used in the Pipeline to fetch the report for the Ticker or CIK number passed to the SageMaker Pipeline. Executing the following code cell will run a processing job which will fetch the SEC reports from the EDGAR database. Obtain SEC data using the SageMaker JumpStart Industry SDK ###Code %%time dataset_config = EDGARDataSetConfig( tickers_or_ciks=['amzn','goog', '27904', 'FB'], # list of stock tickers or CIKs form_types=['10-K', '10-Q'], # list of SEC form types filing_date_start='2019-01-01', # starting filing date filing_date_end='2020-12-31', # ending filing date email_as_user_agent='[email protected]') # user agent email data_loader = DataLoader( role=sagemaker.get_execution_role(), # loading job execution role instance_count=1, # instances number, limit varies with instance type instance_type='ml.c5.2xlarge', # instance type volume_size_in_gb=30, # size in GB of the EBS volume to use volume_kms_key=None, # KMS key for the processing volume output_kms_key=None, # KMS key ID for processing job outputs max_runtime_in_seconds=None, # timeout in seconds. Default is 24 hours. sagemaker_session=sagemaker.Session(), # session object tags=None) # a list of key-value pairs data_loader.load( dataset_config, 's3://{}/{}'.format(default_bucket, 'sample-sec-data'), # output s3 prefix (both bucket and folder names are required) 'dataset_10k_10q.csv', # output file name wait=True, logs=True) ###Output _____no_output_____ ###Markdown OutputThe output of the `data_loader` processing job is a `CSV` file. We see the filings for different quarters. The filing date comes within a month of the end date of the reporting period. Both these dates are collected and displayed in the dataframe. The column `text` contains the full text of the report, but the tables are not extracted. The values in the tables in the filings are balance-sheet and income-statement data (numeric/tabular) and are easily available elsewhere as they are reported in numeric databases. The last column of the dataframe comprises the Management Discussion & Analysis section, the column is named `mdna`, which is the primary forward-looking section in the filing. This is the section that has been most widely used in financial text analysis. Therefore, we will use the `mdna` text to derive the sentiment of the overall filing in this example. ###Code print (f"{default_bucket}/{prefix}/") s3_client.download_file(default_bucket, '{}/{}'.format(f'sample-sec-data', f'dataset_10k_10q.csv'), f'./data/dataset_10k_10q.csv') data_frame_10k_10q = pd.read_csv(f'./data/dataset_10k_10q.csv') data_frame_10k_10q ###Output _____no_output_____ ###Markdown --- Set Up Your MLOps NLP Pipeline with SageMaker Pipelines Step 1: Data pre-processing - extract SEC data and news about the company Define a processing step to prepare SEC data for inference We will define a processing step to extract 10K and 10Q forms for a specific Organization either using the company [Stock Ticker](https://www.investopedia.com/ask/answers/12/what-is-a-stock-ticker.asp) Symbol or [CIK (Central Index Key)](https://www.sec.gov/edgar/searchedgar/cik.htm) used to lookup reports in SEC's EDGAR System. You can find the company Stock Ticker Symbol to CIK Number mapping [here](https://www.sec.gov/include/ticker.txt). This step will also collect news article snippets related to the company using the NewsCatcher API. Important:It is recommended to use CIKs as the input. The tickers will be internally converted to CIKs according to the [mapping file](https://www.sec.gov/include/ticker.txt). One ticker may map to multiple CIKs, but we only support the latest ticker to CIK mapping. Please provide the old CIKs in the input when you want historical filings. Also note that even though the Client side SDK allows you to download multiple SEC reports for multiple CIKs at a time, we will set up our data preprocessing step to grab exactly 1 SEC Report for 1 CIK (Company/Organization). ###Code ''' we used store magic in the previous note book script-processor-custom-container.ipynb to instantiate the container in the region of choice ''' CONTAINER_IMAGE_URI loader_instance_type = "ml.c5.2xlarge" create_dataset_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) ###Output _____no_output_____ ###Markdown Create a processing step to process the SEC data for inference: ###Code create_dataset_script_uri = f's3://{default_bucket}/{prefix}/code/data-processing.py' s3_client.upload_file(Filename='./scripts/data-processing.py', Bucket=default_bucket, Key=f'{prefix}/code/data-processing.py') create_dataset_step = ProcessingStep( name='HFSECFinBertCreateDataset', processor=create_dataset_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='report_data', source='/opt/ml/processing/output/10k10q', destination=f'{inference_input_data}/10k10q'), sagemaker.processing.ProcessingOutput(output_name='article_data', source='/opt/ml/processing/output/articles', destination=f'{inference_input_data}/articles')], job_arguments=["--ticker-cik", inference_ticker_cik, "--instance-type", loader_instance_type, "--region", region, "--bucket", default_bucket, "--prefix", prefix, "--role", role], code=create_dataset_script_uri) ###Output _____no_output_____ ###Markdown Step 2: Create models for summarization and sentiment analysis ###Code sentiment_model_name="HFSECFinbertModel" summarization_model_name="HFSECPegasusModel" ###Output _____no_output_____ ###Markdown Create the `finBert` model for Sentiment Analysis ###Code # Download pre-trained model using HuggingFaceModel class from sagemaker.huggingface import HuggingFaceModel hub = { 'HF_MODEL_ID':'ProsusAI/finbert', 'HF_TASK':'text-classification' } # create Hugging Face Model Class (documentation here: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model) sentiment_huggingface_model = HuggingFaceModel( name=sentiment_model_name, transformers_version='4.6.1', pytorch_version='1.7.1', py_version='py36', env=hub, role=role, sagemaker_session=sagemaker_session, ) inputs = sagemaker.inputs.CreateModelInput( instance_type="ml.m4.xlarge" ) create_sentiment_model_step = CreateModelStep( name="HFSECFinBertCreateModel", model=sentiment_huggingface_model, inputs=inputs, # depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Create the Pegasus summarization model ###Code hub = { 'HF_MODEL_ID':'human-centered-summarization/financial-summarization-pegasus', 'HF_TASK':'summarization' } # create Hugging Face Model Class (documentation here: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model) summary_huggingface_model = HuggingFaceModel( name=summarization_model_name, transformers_version='4.6.1', pytorch_version='1.7.1', py_version='py36', env=hub, role=role, sagemaker_session=sagemaker_session, ) create_summary_model_step = CreateModelStep( name="HFSECPegasusCreateModel", model=summary_huggingface_model, inputs=inputs, # depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Step 3: Register modelUse HuggingFace register method to register Hugging Face Model for deployment. Set up step as a custom processing step ###Code sentiment_model_package_group_name = "HuggingFaceSECSentimentModelPackageGroup" summary_model_package_group_name = "HuggingFaceSECSummaryModelPackageGroup" model_approval_status = "Approved" register_sentiment_model_step = RegisterModel( name="HFSECFinBertRegisterModel", model = sentiment_huggingface_model, content_types=["application/json"], response_types=["application/json"], inference_instances=["ml.t2.medium", "ml.m4.4xlarge"], transform_instances=["ml.m4.4xlarge"], model_package_group_name = sentiment_model_package_group_name, approval_status = model_approval_status, depends_on=['HFSECFinBertCreateModel'] ) register_summary_model_step = RegisterModel( name="HFSECPegasusRegisterModel", model = summary_huggingface_model, content_types=["application/json"], response_types=["application/json"], inference_instances=["ml.t2.medium", "ml.m4.4xlarge"], transform_instances=["ml.m4.4xlarge"], model_package_group_name = summary_model_package_group_name, approval_status = model_approval_status, depends_on=['HFSECPegasusCreateModel'] ) ###Output _____no_output_____ ###Markdown Step 4: Deploy model We deploy the FinBert and Pegasus models from the model registry. NOTE: The models in the model registry are the pre-trained version from HuggingFace Model Hub. Each of the deployment step will attempt to deploy a SageMaker Endpoint with the model and will write a property file upon successful completion. The Pipeline will make use of these property files to decide whether to execute the subsequent summarization and sentiment analysis inference steps. ###Code deploy_model_instance_type = "ml.m4.4xlarge" deploy_model_instance_count = "1" sentiment_endpoint_name = "HFSECFinBertModel-endpoint" summarization_endpoint_name = "HFSECPegasusModel-endpoint" %store -r print (f"using ecr container in {CONTAINER_IMAGE_URI}") s3_client.upload_file(Filename='./scripts/model_deploy_v2.py', Bucket=default_bucket, Key=f'{prefix}/code/model_deploy_v2.py') deploy_model_script_uri = f's3://{default_bucket}/{prefix}/code/model_deploy_v2.py' deploy_model_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) sentiment_deploy_response = PropertyFile( name="SentimentPropertyFile", output_name="sentiment_deploy_response", path="success.json" # the property file generated by the script ) sentiment_deploy_step = ProcessingStep( name='HFSECFinBertDeployModel', processor=deploy_model_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_deploy_response', source='/opt/ml/processing/output', destination=f's3://{default_bucket}/{prefix}/nlp-pipeline/sentimentResponse')], job_arguments=[ "--initial-instance-count", deploy_model_instance_count, "--endpoint-instance-type", deploy_model_instance_type, "--endpoint-name", sentiment_endpoint_name, "--model-package-group-name", sentiment_model_package_group_name, "--role", role, "--region", region, ], property_files=[sentiment_deploy_response], code=deploy_model_script_uri, depends_on=['HFSECFinBertRegisterModel']) summary_deploy_response = PropertyFile( name="SummaryPropertyFile", output_name="summary_deploy_response", path="success.json" # the property file generated by the script ) summary_deploy_step = ProcessingStep( name='HFSECPegasusDeployModel', processor=deploy_model_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='summary_deploy_response', source='/opt/ml/processing/output', destination=f's3://{default_bucket}/{prefix}/nlp-pipeline/summaryResponse')], job_arguments=[ "--initial-instance-count", deploy_model_instance_count, "--endpoint-instance-type", deploy_model_instance_type, "--endpoint-name", summarization_endpoint_name, "--model-package-group-name", summary_model_package_group_name, "--role", role, "--region", region, ], property_files=[summary_deploy_response], code=deploy_model_script_uri, depends_on=['HFSECPegasusRegisterModel']) ###Output _____no_output_____ ###Markdown Create pipeline conditions to check if the Endpoint deployments were successfulWe will define a condition that checks to see if our model deployment was successful based on the property files generated by the deployment steps of both the FinBert and Pegasus Models. If both the conditions evaluates to `True` then we will run or subsequent inferences for Summarization and Sentiment analysis. ###Code from sagemaker.workflow.conditions import ConditionEquals from sagemaker.workflow.condition_step import ( ConditionStep ) from sagemaker.workflow.functions import JsonGet sentiment_condition_eq = ConditionEquals( left=JsonGet( #the left value of the evaluation expression step_name="HFSECFinBertDeployModel", #the step from which the property file will be grabbed property_file=sentiment_deploy_response, #the property file instance that was created earlier in Step 4 json_path="model_created" #the JSON path of the property within the property file success.json ), right="Y" #the right value of the evaluation expression, i.e. the AUC threshold ) summary_condition_eq = ConditionEquals( left=JsonGet( #the left value of the evaluation expression step_name="HFSECPegasusDeployModel", #the step from which the property file will be grabbed property_file=summary_deploy_response, #the property file instance that was created earlier in Step 4 json_path="model_created" #the JSON path of the property within the property file success.json ), right="Y" #the right value of the evaluation expression, i.e. the AUC threshold ) summarize_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) summarize_step_2 = ProcessingStep( name='HFSECPegasusSummarizer_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) deploy_condition_step = ConditionStep( name="HFSECFinBertDeployConditionCheck", conditions=[sentiment_condition_eq,summary_condition_eq], #the equal to conditions defined above if_steps=[summarize_step_2], #if the condition evaluates to true then run the summarization step else_steps=[], #there are no else steps so we will keep it empty depends_on=['HFSECFinBertDeployModel','HFSECPegasusDeployModel'] #dependencies on both Finbert and Pegasus Deployment steps ) ###Output _____no_output_____ ###Markdown Step 5: Summarize SEC report step This step is to make use of the Pegasus Summarizer model endpoint to summarize the MDNA text from the SEC report. Because the MDNA text is usually large, we want to derive a short summary of the overall text to be able to determine the overall sentiment. ###Code summarize_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) summarize_script_uri = f's3://{default_bucket}/{prefix}/code/summarize.py' s3_client.upload_file(Filename='./scripts/summarize.py', Bucket=default_bucket, Key=f'{prefix}/code/summarize.py') summarize_step_1 = ProcessingStep( name='HFSECPegasusSummarizer_1', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) summarize_step_2 = ProcessingStep( name='HFSECPegasusSummarizer_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) ###Output _____no_output_____ ###Markdown Step 6: Sentiment inference step - SEC summary and news articles This step uses the MDNA summary (determined by the previous step) and the news articles to find out the sentiment of the company's financial and what the Market trends are indicating. This would help us understand the overall position of the company's financial outlook and current position without leaning solely on the company's forward-looking statements and bring objective market opinions into the picture. ###Code sentiment_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) sentiment_script_uri = f's3://{default_bucket}/{prefix}/code/sentiment.py' s3_client.upload_file(Filename='./scripts/sentiment.py', Bucket=default_bucket, Key=f'{prefix}/code/sentiment.py') sentiment_step_1 = ProcessingStep( name='HFSECFinBertSentiment_1', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='sec_summary', source=f'{inference_input_data}/10k10q/summary', destination='/opt/ml/processing/input/10k10q'), sagemaker.processing.ProcessingInput(input_name='articles', source=f'{inference_input_data}/articles', destination='/opt/ml/processing/input/articles')], outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/sentiment')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", sentiment_endpoint_name], code=sentiment_script_uri, depends_on=["HFSECPegasusSummarizer_1"]) sentiment_step_2 = ProcessingStep( name='HFSECFinBertSentiment_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='sec_summary', source=f'{inference_input_data}/10k10q/summary', destination='/opt/ml/processing/input/10k10q'), sagemaker.processing.ProcessingInput(input_name='articles', source=f'{inference_input_data}/articles', destination='/opt/ml/processing/input/articles')], outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/sentiment')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", sentiment_endpoint_name], code=sentiment_script_uri, depends_on=["HFSECPegasusSummarizer_2"]) ###Output _____no_output_____ ###Markdown Condition Step As explained earlier, this is a top level condition step. This step will determine based on the value of the pipeline parameter `model_register_deploy` on whether we want to register and deploy a new version of the models and then run inference, or to simply run inference using the existing endpoints. ###Code from sagemaker.workflow.conditions import ConditionEquals from sagemaker.workflow.condition_step import ( ConditionStep ) condition_eq = ConditionEquals( left=model_register_deploy, right="Y" ) #Define the condition step condition_step = ConditionStep( name="HFSECFinBertConditionCheck", conditions=[condition_eq], #the parameter is Y if_steps=[ create_sentiment_model_step, register_sentiment_model_step, sentiment_deploy_step, create_summary_model_step, register_summary_model_step, summary_deploy_step ], # if the condition evaluates to true then create model, register, and deploy else_steps=[summarize_step_1], depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Combine Pipeline steps and run ###Code pipeline_name = 'FinbertSECDeploymentPipeline' pipeline = Pipeline( name=pipeline_name, parameters=[ processing_instance_type, processing_instance_count, model_register_deploy, inference_ticker_cik, inference_input_data], steps=[ create_dataset_step, condition_step, deploy_condition_step, sentiment_step_1, sentiment_step_2 ], ) pipeline.upsert(role_arn=role) %%time start_response = pipeline.start() start_response.wait(delay=60, max_attempts=200) start_response.describe() ###Output _____no_output_____ ###Markdown The following image shows a successful execution of the NLP end-to-end Pipeline. --- View Evaluation ResultsOnce the pipeline execution completes, we can download the evaluation data from S3 and view it. ###Code s3_client.download_file(default_bucket, f'{prefix}/nlp-pipeline/inf-data/sentiment/{ticker}_sentiment_result.csv', f'./data/{ticker}_sentiment_result.csv') sentiment_df = pd.read_csv(f'./data/{ticker}_sentiment_result.csv') sentiment_df ###Output _____no_output_____ ###Markdown --- Clean up Delete the SageMaker Pipeline and the SageMaker Endpoints created by the pipeline. ###Code def clean_up_resources(): pipeline.delete() sagemaker_boto_client.delete_endpoint(EndpointName=sentiment_endpoint_name) sagemaker_boto_client.delete_endpoint(EndpointName=summarization_endpoint_name) ###Output _____no_output_____ ###Markdown Understanding Trends in Company Valuation with NLP - Part 2: NLP Company Earnings Analysis Pipeline Introduction Orchestrating company earnings trend analysis, using SEC filings, news sentiment with the Hugging Face transformers, and Amazon SageMaker PipelinesIn this notebook, we demonstrate how to summarize and derive sentiments out of Security and Exchange Commission reports filed by a publicly traded organization. We will derive the overall market sentiments about the said organization through financial news articles within the same financial period to present a fair view of the organization vs. market sentiments and outlook about the company's overall valuation and performance. In addition to this we will also identify the most popular keywords and entities within the news articles about that organization.In order to achieve the above we will be using multiple SageMaker Hugging Face based NLP transformers for the downstream NLP tasks of Summarization (e.g., of the news and SEC MDNA sections) and Sentiment Analysis (of the resulting summaries). --- Using SageMaker Pipelines Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. Orchestrating workflows across each step of the machine learning process (e.g. exploring and preparing data, experimenting with different algorithms and parameters, training and tuning models, and deploying models to production) can take months of coding.Since it is purpose-built for machine learning, SageMaker Pipelines helps you automate different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment. With SageMaker Pipelines, you can build dozens of ML models a week, manage massive volumes of data, thousands of training experiments, and hundreds of different model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization. --- Understanding trends in company valuation (or similar) with NLPNatural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. (Source: [Wikipedia](https://en.wikipedia.org/wiki/Natural_language_processing))We are going to demonstrate how to summarize and derive sentiments out of Security and Exchange Commission reports filed by a publicly traded organization. We are also going to derive the overall market sentiments about the said organization through financial news articles within the same financial period to present a fair view of the organization vs. market sentiments and outlook about the company's overall valuation and performance. In addition to this we will also identify the most popular keywords and entities within the news articles about that organization.In order to achieve the above we will be using multiple SageMaker Hugging Face based NLP transformers with summarization and sentiment analysis downstream tasks.* Summarization of financial text from SEC reports and news articles will be done via [Pegasus for Financial Summarization model](https://huggingface.co/human-centered-summarization/financial-summarization-pegasus) based on the paper [Towards Human-Centered Summarization: A Case Study on Financial News](https://aclanthology.org/2021.hcinlp-1.4/). * Sentiment analysis on summarized SEC financial report and news articles will be done via pre-trained NLP model to analyze sentiment of financial text called [FinBERT](https://huggingface.co/ProsusAI/finbert). Paper: [ FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063)--- SEC DatasetThe starting point for a vast amount of financial NLP is text in SEC filings. The SEC requires companies to report different types of information related to various events involving companies. The full list of SEC forms is here: https://www.sec.gov/forms.SEC filings are widely used by financial services companies as a source of information about companies in order to make trading, lending, investment, and risk management decisions. Because these filings are required by regulation, they are of high quality and veracity. They contain forward-looking information that helps with forecasts and are written with a view to the future, required by regulation. In addition, in recent times, the value of historical time-series data has degraded, since economies have been structurally transformed by trade wars, pandemics, and political upheavals. Therefore, text as a source of forward-looking information has been increasing in relevance. Obtain the dataset using the SageMaker JumpStart Industry Python SDKDownloading SEC filings is done from the SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR) website, which provides open data access. EDGAR is the primary system under the U.S. Securities And Exchange Commission (SEC) for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. EDGAR contains millions of company and individual filings. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average.There are several ways to download the data, and some open source packages available to extract the text from these filings. However, these require extensive programming and are not always easy-to-use. We provide a simple one-API call that will create a dataset in a few lines of code, for any period of time and for numerous tickers.We have wrapped the extraction functionality into a SageMaker processing container and provide this notebook to enable users to download a dataset of filings with metadata such as dates and parsed plain text that can then be used for machine learning using other SageMaker tools. This is included in the [SageMaker Industry Jumpstart Industry](https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/) library for financial language models. Users only need to specify a date range and a list of ticker symbols, and the library will take care of the rest.As of now, the solution supports extracting a popular subset of SEC forms in plain text (excluding tables): 10-K, 10-Q, 8-K, 497, 497K, S-3ASR, and N-1A. For each of these, we provide examples throughout this notebook and a brief description of each form. For the 10-K and 10-Q forms, filed every year or quarter, we also extract the Management Discussion and Analysis (MDNA) section, which is the primary forward-looking section in the filing. This is the section that has been most widely used in financial text analysis. Therefore, we provide this section automatically in a separate column of the dataframe alongside the full text of the filing.The extracted dataframe is written to S3 storage and to the local notebook instance. --- News articles related to the stock symbol -- datasetWe will use the MIT Licensed [NewsCatcher API](https://docs.newscatcherapi.com/) to grab top 4-5 articles about the specific organization using filters, however other sources such as Social media feeds, RSS Feeds can also be used. The first step in the pipeline is to fetch the SEC report from the EDGAR database using the [SageMaker Industry Jumpstart Industry](https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/) library for Financial language models. This library provides us an easy to use functionality to obtain either one or multiple SEC reports for one or more Ticker symbols or CIKs. The ticker or CIK number will be passed to the SageMaker Pipeline using Pipeline parameter `inference_ticker_cik`. For demo purposes of this Pipeline we will focus on a single Ticker/CIK number at a time and the MDNA section of the 10-K form. The first processing will extract the MDNA from the 10-K form for a company and will also gather few news articles related to the company from the NewsCatcher API. This data will ultimately be used for summarization and then finally sentiment analysis. --- MLOps for NLP using SageMaker PipelinesWe will set up the following SageMaker Pipeline. The Pipleline has two flows depending on what the value for `model_register_deploy` Pipeline parameter is set to. If the value is set to `Y` we want the pipeline to register the model and deploy the latest version of the model from the model registry to the SageMaker endpoint. If the value is set to `N` then we simply want to run inferences using the FinBert and the Pegasus models using the Ticker symbol (or CIK number) that is passed to the pipeline using the `inference_ticker_cik` Pipeline parameter. Note: You must execute the script-processor-custom-container.ipynb notebook before you can set up the SageMaker Pipeline. This notebook creates a custom Docker image and registers it in Amazon Elastic Container Registry (Amazon ECR) for the pipeline to use. The image contains of all the dependencies required. --- Set Up SageMaker Project Install and import packages ###Code # Install updated version of SageMaker # !pip install -q sagemaker==2.49 !pip install sagemaker --upgrade !pip install transformers !pip install typing !pip install sentencepiece !pip install fiscalyear #Install SageMaker Jumpstart Industry !pip install smjsindustry ###Output _____no_output_____ ###Markdown NOTE: After installing an updated version of SageMaker and PyTorch, save the notebook and then restart your kernel. ###Code import boto3 import botocore import pandas as pd import sagemaker print(f'SageMaker version: {sagemaker.__version__}') from sagemaker.huggingface import HuggingFace from sagemaker.huggingface import HuggingFaceModel from sagemaker.workflow.pipeline import Pipeline from sagemaker.workflow.steps import CreateModelStep from sagemaker.workflow.step_collections import RegisterModel from sagemaker.workflow.steps import ProcessingStep from sagemaker.workflow.steps import TransformStep from sagemaker.workflow.properties import PropertyFile from sagemaker.workflow.parameters import (ParameterInteger, ParameterString) from sagemaker.sklearn.processing import ScriptProcessor from sagemaker.lambda_helper import Lambda from sagemaker.workflow.lambda_step import ( LambdaStep, LambdaOutput, LambdaOutputTypeEnum, ) ###Output _____no_output_____ ###Markdown Define parameters that you'll use throughout the notebook ###Code s3 = boto3.resource("s3") region = boto3.Session().region_name sagemaker_session = sagemaker.Session() role = sagemaker.get_execution_role() sagemaker_role = role default_bucket = sagemaker_session.default_bucket() prefix = 'nlp-e2e-mlops' s3_client = boto3.client('s3', region_name=region) sagemaker_boto_client = boto3.client("sagemaker", region_name=region) #deploy_model_instance_type = "ml.m4.8xlarge" deploy_model_instance_type = "ml.m4.xlarge" inference_instances=["ml.t2.medium", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge"] transform_instances=["ml.m5.xlarge"] PROCESSING_INSTANCE="ml.m4.4xlarge" ticker='AMZN' %store -r print(f's3://{default_bucket}/{prefix}/code/model_deploy.py') print(f'SageMaker Role: {role}') ###Output _____no_output_____ ###Markdown Define parameters to parametrize Pipeline ExecutionUsing SageMaker Pipelines, we can define the steps to be included in a pipeline but then use parameters to modify that pipeline when we go to execute the pipeline, without having to modify the pipeline definition. We'll provide some default parameter values that can be overridden on pipeline execution. ###Code #Define some default parameters: #specify default number of instances for processing step processing_instance_count = ParameterInteger( name="ProcessingInstanceCount", default_value=1 ) #specify default instance type for processing step processing_instance_type = ParameterString( name="ProcessingInstanceType", default_value=PROCESSING_INSTANCE ) #specify location of inference data for data processing step inference_input_data = ParameterString( name="InferenceData", default_value=f's3://{default_bucket}/{prefix}/nlp-pipeline/inf-data', ) #Specify the Ticker CIK for the pipeline inference_ticker_cik = ParameterString( name="InferenceTickerCik", default_value=ticker, ) #specify default method for model approval model_approval_status = ParameterString( name="ModelApprovalStatus", default_value="PendingManualApproval" ) #specify if new model needs to be registered and deployed model_register_deploy = ParameterString( name="ModelRegisterDeploy", default_value="Y" ) %store # These are the stored variables, the container is created in the # previous notebook 01_script-processor-custom-container.ipynb %pylab inline %store -r ###Output _____no_output_____ ###Markdown --- Preparing SEC dataset Before we dive right into setting up the pipeline, let's take a look at how the SageMaker Jumpstart Industry SDK for Financial language model helps obtain the dataset from SEC forms and what are the features available for us to use. Note: The code cells in this section are completely optional and for information purposes only; we will use the SageMaker JumpStart Industry SDK directly in the pipeline. Let's install the required dependencies first. Install the SageMaker JumpStart Industry SDKThe functionality is delivered through a client-side SDK. The first step requires pip installing a Python package that interacts with a SageMaker processing container. The retrieval, parsing, transforming, and scoring of text is a complex process and uses different algorithms and packages. In order to make this seamless and stable for the user, the functionality is packaged into a SageMaker container. This lifts the load of installation and maintenance of the workflow, reducing the user effort down to a pip install followed by a single API call. ###Code !pip install --no-index smjsindustry==1.0.0 ###Output _____no_output_____ ###Markdown As an example, we will try to pull AMZN ticker 10k/10q filings from EDGAR and write the data as CSV to S3. Below is the single block of code that contains the API call. The options are all self-explanatory. ###Code # from smfinance import SECDataSetConfig, DataLoader from smjsindustry.finance import DataLoader from smjsindustry.finance.processor_config import EDGARDataSetConfig ###Output _____no_output_____ ###Markdown The extracted reports will be saved to an S3 bucket for us to review. This code will also be used in the Pipeline to fetch the report for the Ticker or CIK number passed to the SageMaker Pipeline. Executing the following code cell will run a processing job which will fetch the SEC reports from the EDGAR database. Obtain SEC data using the SageMaker JumpStart Industry SDK ###Code %%time dataset_config = EDGARDataSetConfig( tickers_or_ciks=['amzn','goog', '27904', 'FB'], # list of stock tickers or CIKs form_types=['10-K', '10-Q'], # list of SEC form types filing_date_start='2019-01-01', # starting filing date filing_date_end='2020-12-31', # ending filing date email_as_user_agent='[email protected]') # user agent email data_loader = DataLoader( role=sagemaker.get_execution_role(), # loading job execution role instance_count=1, # instances number, limit varies with instance type instance_type='ml.c5.2xlarge', # instance type volume_size_in_gb=30, # size in GB of the EBS volume to use volume_kms_key=None, # KMS key for the processing volume output_kms_key=None, # KMS key ID for processing job outputs max_runtime_in_seconds=None, # timeout in seconds. Default is 24 hours. sagemaker_session=sagemaker.Session(), # session object tags=None) # a list of key-value pairs data_loader.load( dataset_config, 's3://{}/{}'.format(default_bucket, 'sample-sec-data'), # output s3 prefix (both bucket and folder names are required) 'dataset_10k_10q.csv', # output file name wait=True, logs=True) ###Output _____no_output_____ ###Markdown OutputThe output of the `data_loader` processing job is a `CSV` file. We see the filings for different quarters. The filing date comes within a month of the end date of the reporting period. Both these dates are collected and displayed in the dataframe. The column `text` contains the full text of the report, but the tables are not extracted. The values in the tables in the filings are balance-sheet and income-statement data (numeric/tabular) and are easily available elsewhere as they are reported in numeric databases. The last column of the dataframe comprises the Management Discussion & Analysis section, the column is named `mdna`, which is the primary forward-looking section in the filing. This is the section that has been most widely used in financial text analysis. Therefore, we will use the `mdna` text to derive the sentiment of the overall filing in this example. ###Code !mkdir data print (f"{default_bucket}/{prefix}/") s3_client.download_file(default_bucket, '{}/{}'.format(f'sample-sec-data', f'dataset_10k_10q.csv'), f'./data/dataset_10k_10q.csv') data_frame_10k_10q = pd.read_csv(f'./data/dataset_10k_10q.csv') data_frame_10k_10q ###Output _____no_output_____ ###Markdown --- Set Up Your MLOps NLP Pipeline with SageMaker Pipelines Step 1: Data pre-processing - extract SEC data and news about the company Define a processing step to prepare SEC data for inference We will define a processing step to extract 10K and 10Q forms for a specific Organization either using the company [Stock Ticker](https://www.investopedia.com/ask/answers/12/what-is-a-stock-ticker.asp) Symbol or [CIK (Central Index Key)](https://www.sec.gov/edgar/searchedgar/cik.htm) used to lookup reports in SEC's EDGAR System. You can find the company Stock Ticker Symbol to CIK Number mapping [here](https://www.sec.gov/include/ticker.txt). This step will also collect news article snippets related to the company using the NewsCatcher API. Important:It is recommended to use CIKs as the input. The tickers will be internally converted to CIKs according to the [mapping file](https://www.sec.gov/include/ticker.txt). One ticker may map to multiple CIKs, but we only support the latest ticker to CIK mapping. Please provide the old CIKs in the input when you want historical filings. Also note that even though the Client side SDK allows you to download multiple SEC reports for multiple CIKs at a time, we will set up our data preprocessing step to grab exactly 1 SEC Report for 1 CIK (Company/Organization). ###Code ''' we used store magic in the previous note book script-processor-custom-container.ipynb to instantiate the container in the region of choice ''' CONTAINER_IMAGE_URI loader_instance_type = "ml.c5.2xlarge" create_dataset_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) ###Output _____no_output_____ ###Markdown Create a processing step to process the SEC data for inference: ###Code create_dataset_script_uri = f's3://{default_bucket}/{prefix}/code/data-processing.py' s3_client.upload_file(Filename='./scripts/data-processing.py', Bucket=default_bucket, Key=f'{prefix}/code/data-processing.py') create_dataset_step = ProcessingStep( name='HFSECFinBertCreateDataset', processor=create_dataset_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='report_data', source='/opt/ml/processing/output/10k10q', destination=f'{inference_input_data}/10k10q'), sagemaker.processing.ProcessingOutput(output_name='article_data', source='/opt/ml/processing/output/articles', destination=f'{inference_input_data}/articles')], job_arguments=["--ticker-cik", inference_ticker_cik, "--instance-type", loader_instance_type, "--region", region, "--bucket", default_bucket, "--prefix", prefix, "--role", role], code=create_dataset_script_uri) ###Output _____no_output_____ ###Markdown Step 2: Create models for summarization and sentiment analysis ###Code sentiment_model_name="HFSECFinbertModel" summarization_model_name="HFSECPegasusModel" ###Output _____no_output_____ ###Markdown Create the `finBert` model for Sentiment Analysis ###Code # Download pre-trained model using HuggingFaceModel class from sagemaker.huggingface import HuggingFaceModel hub = { 'HF_MODEL_ID':'ProsusAI/finbert', 'HF_TASK':'text-classification' } # create Hugging Face Model Class (documentation here: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model) sentiment_huggingface_model = HuggingFaceModel( name=sentiment_model_name, transformers_version='4.6.1', pytorch_version='1.7.1', py_version='py36', env=hub, role=role, sagemaker_session=sagemaker_session, ) inputs = sagemaker.inputs.CreateModelInput( instance_type="ml.m4.xlarge" ) create_sentiment_model_step = CreateModelStep( name="HFSECFinBertCreateModel", model=sentiment_huggingface_model, inputs=inputs, # depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Create the Pegasus summarization model ###Code hub = { 'HF_MODEL_ID':'human-centered-summarization/financial-summarization-pegasus', 'HF_TASK':'summarization' } # create Hugging Face Model Class (documentation here: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model) summary_huggingface_model = HuggingFaceModel( name=summarization_model_name, transformers_version='4.6.1', pytorch_version='1.7.1', py_version='py36', env=hub, role=role, sagemaker_session=sagemaker_session, ) create_summary_model_step = CreateModelStep( name="HFSECPegasusCreateModel", model=summary_huggingface_model, inputs=inputs, # depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Step 3: Register modelUse HuggingFace register method to register Hugging Face Model for deployment. Set up step as a custom processing step ###Code sentiment_model_package_group_name = "HuggingFaceSECSentimentModelPackageGroup" summary_model_package_group_name = "HuggingFaceSECSummaryModelPackageGroup" model_approval_status = "Approved" register_sentiment_model_step = RegisterModel( name="HFSECFinBertRegisterModel", model = sentiment_huggingface_model, content_types=["application/json"], response_types=["application/json"], inference_instances=["ml.t2.medium", "ml.m4.4xlarge"], transform_instances=["ml.m4.4xlarge"], model_package_group_name = sentiment_model_package_group_name, approval_status = model_approval_status, depends_on=['HFSECFinBertCreateModel'] ) register_summary_model_step = RegisterModel( name="HFSECPegasusRegisterModel", model = summary_huggingface_model, content_types=["application/json"], response_types=["application/json"], inference_instances=["ml.t2.medium", "ml.m4.4xlarge"], transform_instances=["ml.m4.4xlarge"], model_package_group_name = summary_model_package_group_name, approval_status = model_approval_status, depends_on=['HFSECPegasusCreateModel'] ) ###Output _____no_output_____ ###Markdown Step 4: Deploy model We deploy the FinBert and Pegasus models from the model registry. NOTE: The models in the model registry are the pre-trained version from HuggingFace Model Hub. Each of the deployment step will attempt to deploy a SageMaker Endpoint with the model and will write a property file upon successful completion. The Pipeline will make use of these property files to decide whether to execute the subsequent summarization and sentiment analysis inference steps. ###Code deploy_model_instance_type = "ml.m4.4xlarge" deploy_model_instance_count = "1" sentiment_endpoint_name = "HFSECFinBertModel-endpoint" summarization_endpoint_name = "HFSECPegasusModel-endpoint" %store -r print (f"using ecr container in {CONTAINER_IMAGE_URI}") s3_client.upload_file(Filename='./scripts/model_deploy_v2.py', Bucket=default_bucket, Key=f'{prefix}/code/model_deploy_v2.py') deploy_model_script_uri = f's3://{default_bucket}/{prefix}/code/model_deploy_v2.py' deploy_model_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) sentiment_deploy_response = PropertyFile( name="SentimentPropertyFile", output_name="sentiment_deploy_response", path="success.json" # the property file generated by the script ) sentiment_deploy_step = ProcessingStep( name='HFSECFinBertDeployModel', processor=deploy_model_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_deploy_response', source='/opt/ml/processing/output', destination=f's3://{default_bucket}/{prefix}/nlp-pipeline/sentimentResponse')], job_arguments=[ "--initial-instance-count", deploy_model_instance_count, "--endpoint-instance-type", deploy_model_instance_type, "--endpoint-name", sentiment_endpoint_name, "--model-package-group-name", sentiment_model_package_group_name, "--role", role, "--region", region, ], property_files=[sentiment_deploy_response], code=deploy_model_script_uri, depends_on=['HFSECFinBertRegisterModel']) summary_deploy_response = PropertyFile( name="SummaryPropertyFile", output_name="summary_deploy_response", path="success.json" # the property file generated by the script ) summary_deploy_step = ProcessingStep( name='HFSECPegasusDeployModel', processor=deploy_model_processor, outputs=[sagemaker.processing.ProcessingOutput(output_name='summary_deploy_response', source='/opt/ml/processing/output', destination=f's3://{default_bucket}/{prefix}/nlp-pipeline/summaryResponse')], job_arguments=[ "--initial-instance-count", deploy_model_instance_count, "--endpoint-instance-type", deploy_model_instance_type, "--endpoint-name", summarization_endpoint_name, "--model-package-group-name", summary_model_package_group_name, "--role", role, "--region", region, ], property_files=[summary_deploy_response], code=deploy_model_script_uri, depends_on=['HFSECPegasusRegisterModel']) ###Output _____no_output_____ ###Markdown Create pipeline conditions to check if the Endpoint deployments were successfulWe will define a condition that checks to see if our model deployment was successful based on the property files generated by the deployment steps of both the FinBert and Pegasus Models. If both the conditions evaluates to `True` then we will run or subsequent inferences for Summarization and Sentiment analysis. ###Code from sagemaker.workflow.conditions import ConditionEquals from sagemaker.workflow.condition_step import ( ConditionStep ) from sagemaker.workflow.functions import JsonGet summarize_script_uri = f's3://{default_bucket}/{prefix}/code/summarize.py' sentiment_condition_eq = ConditionEquals( left=JsonGet( #the left value of the evaluation expression step_name="HFSECFinBertDeployModel", #the step from which the property file will be grabbed property_file=sentiment_deploy_response, #the property file instance that was created earlier in Step 4 json_path="model_created" #the JSON path of the property within the property file success.json ), right="Y" #the right value of the evaluation expression, i.e. the AUC threshold ) summary_condition_eq = ConditionEquals( left=JsonGet( #the left value of the evaluation expression step_name="HFSECPegasusDeployModel", #the step from which the property file will be grabbed property_file=summary_deploy_response, #the property file instance that was created earlier in Step 4 json_path="model_created" #the JSON path of the property within the property file success.json ), right="Y" #the right value of the evaluation expression, i.e. the AUC threshold ) summarize_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) summarize_step_2 = ProcessingStep( name='HFSECPegasusSummarizer_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) deploy_condition_step = ConditionStep( name="HFSECFinBertDeployConditionCheck", conditions=[sentiment_condition_eq,summary_condition_eq], #the equal to conditions defined above if_steps=[summarize_step_2], #if the condition evaluates to true then run the summarization step else_steps=[], #there are no else steps so we will keep it empty depends_on=['HFSECFinBertDeployModel','HFSECPegasusDeployModel'] #dependencies on both Finbert and Pegasus Deployment steps ) ###Output _____no_output_____ ###Markdown Step 5: Summarize SEC report step This step is to make use of the Pegasus Summarizer model endpoint to summarize the MDNA text from the SEC report. Because the MDNA text is usually large, we want to derive a short summary of the overall text to be able to determine the overall sentiment. ###Code summarize_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) s3_client.upload_file(Filename='./scripts/summarize.py', Bucket=default_bucket, Key=f'{prefix}/code/summarize.py') summarize_step_1 = ProcessingStep( name='HFSECPegasusSummarizer_1', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) summarize_step_2 = ProcessingStep( name='HFSECPegasusSummarizer_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='summary_data', source=f'{inference_input_data}/10k10q', destination='/opt/ml/processing/input')], outputs=[sagemaker.processing.ProcessingOutput(output_name='summarized_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/10k10q/summary')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", summarization_endpoint_name], code=summarize_script_uri) ###Output _____no_output_____ ###Markdown Step 6: Sentiment inference step - SEC summary and news articles This step uses the MDNA summary (determined by the previous step) and the news articles to find out the sentiment of the company's financial and what the Market trends are indicating. This would help us understand the overall position of the company's financial outlook and current position without leaning solely on the company's forward-looking statements and bring objective market opinions into the picture. ###Code sentiment_processor = ScriptProcessor(command=['python3'], image_uri=CONTAINER_IMAGE_URI, role=role, instance_count=processing_instance_count, instance_type=processing_instance_type) sentiment_script_uri = f's3://{default_bucket}/{prefix}/code/sentiment.py' s3_client.upload_file(Filename='./scripts/sentiment.py', Bucket=default_bucket, Key=f'{prefix}/code/sentiment.py') sentiment_step_1 = ProcessingStep( name='HFSECFinBertSentiment_1', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='sec_summary', source=f'{inference_input_data}/10k10q/summary', destination='/opt/ml/processing/input/10k10q'), sagemaker.processing.ProcessingInput(input_name='articles', source=f'{inference_input_data}/articles', destination='/opt/ml/processing/input/articles')], outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/sentiment')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", sentiment_endpoint_name], code=sentiment_script_uri, depends_on=["HFSECPegasusSummarizer_1"]) sentiment_step_2 = ProcessingStep( name='HFSECFinBertSentiment_2', processor=summarize_processor, inputs=[sagemaker.processing.ProcessingInput(input_name='sec_summary', source=f'{inference_input_data}/10k10q/summary', destination='/opt/ml/processing/input/10k10q'), sagemaker.processing.ProcessingInput(input_name='articles', source=f'{inference_input_data}/articles', destination='/opt/ml/processing/input/articles')], outputs=[sagemaker.processing.ProcessingOutput(output_name='sentiment_data', source='/opt/ml/processing/output', destination=f'{inference_input_data}/sentiment')], job_arguments=["--ticker-cik", inference_ticker_cik, "--region", region, "--endpoint-name", sentiment_endpoint_name], code=sentiment_script_uri, depends_on=["HFSECPegasusSummarizer_2"]) ###Output _____no_output_____ ###Markdown Condition Step As explained earlier, this is a top level condition step. This step will determine based on the value of the pipeline parameter `model_register_deploy` on whether we want to register and deploy a new version of the models and then run inference, or to simply run inference using the existing endpoints. ###Code from sagemaker.workflow.conditions import ConditionEquals from sagemaker.workflow.condition_step import ( ConditionStep ) condition_eq = ConditionEquals( left=model_register_deploy, right="Y" ) #Define the condition step condition_step = ConditionStep( name="HFSECFinBertConditionCheck", conditions=[condition_eq], #the parameter is Y if_steps=[ create_sentiment_model_step, register_sentiment_model_step, sentiment_deploy_step, create_summary_model_step, register_summary_model_step, summary_deploy_step ], # if the condition evaluates to true then create model, register, and deploy else_steps=[summarize_step_1], depends_on=['HFSECFinBertCreateDataset'] ) ###Output _____no_output_____ ###Markdown Combine Pipeline steps and run ###Code pipeline_name = 'FinbertSECDeploymentPipeline' pipeline = Pipeline( name=pipeline_name, parameters=[ processing_instance_type, processing_instance_count, model_register_deploy, inference_ticker_cik, inference_input_data], steps=[ create_dataset_step, condition_step, deploy_condition_step, sentiment_step_1, sentiment_step_2 ], ) pipeline.upsert(role_arn=role) %%time start_response = pipeline.start() start_response.wait(delay=60, max_attempts=200) start_response.describe() ###Output _____no_output_____ ###Markdown The following image shows a successful execution of the NLP end-to-end Pipeline. --- View Evaluation ResultsOnce the pipeline execution completes, we can download the evaluation data from S3 and view it. ###Code s3_client.download_file(default_bucket, f'{prefix}/nlp-pipeline/inf-data/sentiment/{ticker}_sentiment_result.csv', f'./data/{ticker}_sentiment_result.csv') sentiment_df = pd.read_csv(f'./data/{ticker}_sentiment_result.csv') sentiment_df ###Output _____no_output_____ ###Markdown --- Clean up Delete the SageMaker Pipeline and the SageMaker Endpoints created by the pipeline. ###Code def clean_up_resources(): pipeline.delete() sagemaker_boto_client.delete_endpoint(EndpointName=sentiment_endpoint_name) sagemaker_boto_client.delete_endpoint(EndpointName=summarization_endpoint_name) ###Output _____no_output_____
VizuGraphe/Algos_Graphes_en_NSI/algos_graphes_en_nsi.ipynb	###Markdown Ce notebook nécessite de disposer du logiciel [graphviz](https://graphviz.org/download/) ainsi que du module graphviz pour python (`pip install graphviz`). Installation de graphviz sous Windows (méthode 1)- On se rendra sur la page dédiée : [Downloads](https://graphviz.org/download/) de Graphviz. - Normalement le lien pour l'installeur de la version stable (parfois rompu lors des MAJ des dépôts) vous conduit sur cette page : - (https://www2.graphviz.org/Packages/stable/windows/). Il faudra ensuite suivre les liens pour parvenir à - (https://www2.graphviz.org/Packages/stable/windows/10/cmake/Release/)- Lors de l'installation du logiciel, demandez bien à actualiser le PATH Windows.- Une fois le logiciel installé, il vous faut installer le module permettant à Python de communiquer avec le logiciel Graphviz : - `pip install graphviz` Installation de graphviz sous Windows (méthode 2)- Si ce qui est indiqué sur la page [Downloads](https://graphviz.org/download/) de Graphviz conduit au dépôt Github (cela arrive parfois lorsque Graphviz effectue une MAJ sur ses serveus de dépôt) on préférera l'installeur .msi de la version 2.38 qui est toujours disponible ici : [installeur MSI pour Windows](https://graphviz.gitlab.io/_pages/Download/Download_windows.html) - Une fois l'installation du logiciel effectuée, on ajoutera alors graphviz au Path Windows (pour que python puisse le trouver) manuellement car la 2.38 ne le propose pas lors de l'installation : - Paramètres > Propriétés Système > Variables d'environnement > Variables système > Path > Modifier > Nouveau - copier le chemin vers l'installation de graphviz (sans doute "C:\Program Files (x86)\Graphviz2.38\bin") - Il suffira ensuite d'installer le module permettant à Python de communiquer avec le logiciel Graphviz : - `pip install graphviz` ###Code from random import random from xile import Pile, File from generateurs import maillage, soleil, etoiles_reliees from vizu_graphe import VizuGraphe #nécessite graphviz (pip install graphviz) ###Output _____no_output_____ ###Markdown Parcours de graphes & Plus courts chemins et Détections de cycles Les détails techniques sont donnés ci-dessous dans le code. On retiendra que si l'algorithme de parcours récursif et l'algorithme de parcours itératif 1 s'adaptent facilement pour détecter les cycles, il n'en est pas de même pour le parcours itératif 2. En résumé, et avec les précautions d'implémentation disséminées dans ce notebook :``` largeur profondeur cycles plus court chemin recursif N O O N iteratif 1 O O O (L/P) N iteratif 2 O O N O (L)```Remarque 1:Ici il s'agit de recherche de cycles sur des graphes non-orientés.Remarque 2:La recherche de cycles sur un graphe orienté ne peut pas se baser sur le parcours en profondeur ou en largeur. Il faut utiliser d'autres algorithmes (par exemple ici : http://perso.ens-lyon.fr/eric.thierry/Graphes2007/adrien-panhaleux.pdf) ###Code ############################## # explorer = "explorer les successeurs" # visiter = "faire ce qu'il y a à faire avec le sommet" (ici relever la date de visite) # marquer = "empecher le sommet d'être ultérieurement rajouté à la xile" # ici on marque et visite en même temps ######################### algorithme récursif ############################ # Pour le récursif, la variante où l'on teste si le sommet # est marqué en début de fonction (avant #) et pas avant l'appel de fonction #* # est contraignante pour la découverte de cycle # # En effet pour les cycles, en cas de successeur déjà marqué en # , on a besoin de savoir s'il # s'agit d'un faux-cycle "A -> B -> A". On utilisera donc (pour les détections de cycles) # une fonction dfs modifiée comme suit : dfs(sommet, predecesseur) # # En mettant le test au niveau de #, on aura accès pour le test à # la fois à prédécesseur et successeur --> facile de tester "A -> B -> A" # # Dans le cas de la variante avec le test en #, on n'aurait pas accès à prédécesseur et successeur # et cela imposerait d'utiliser la visite pour stocker le prédécesseur du prédécesseur ce qui serait # possible mais moins élégant def parcours_recursif(graphe, depart): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter la date de visite du sommet. On renvoie un dictionnaire {sommet : date_de_visite for sommet in graphe} ''' dates = { 'en_cours':0, 'historique':dict() } def visiter(sommet): dates['historique'][sommet] = dates['en_cours'] dates['en_cours'] = dates['en_cours'] + 1 def dfs(sommet): dejavu[sommet] = True # visiter(sommet) for successeur in graphe[sommet]: if successeur not in dejavu.keys(): # dfs(successeur) dejavu = dict() dfs(depart) return dates['historique'] ####################### algorithmes itératifs ############################ # Il y a deux versions. # 1 : # - un sommet vient d'être découvert depuis un(des) prédecesseur(s) # - si pas marqué, on enxile (éventuellement plusieurs fois, depuis des prédecesseurs différents) # - plus tard on déxile, # - on marque/visite (si non déjà marqué auparavant) # - on explore ses successeurs # # 2 : # - un sommet vient d'être découvert depuis un prédecesseur # - si pas marqué on marque/visite # - si pas marqué, on enxile (une seule fois donc) # - plus tard on déxile # - on explore les successeurs # # Dans le 2 un sommet ne peut être mis q'une fois dans la xile # car il est marqué avant d'y être introduit. # # Dans 1 ce n'est plus vrai : un sommet peut être mis plusieurs fois dans la xile # Cela se produit si : # - un premier prédecesseur a mis S dans la pile # - le parcours a continué sans dépiler S # - un second prédécesseur a remis S dans la pile # - (puisque S n'a pas été dépilé, on ne peut pas être dans le cas du demi-tour) # # 1 et 2 donnent les mêmes dates de visite en largeur # mais pour le parcours en profondeur 2 ratisse plus large, c'est # à dire moins profond. def parcours_1(graphe, depart, larg_ou_prof): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart - larg_ou_prof : 'prof' : en profondeur, 'larg' en largeur. La visite consiste à noter la date de visite du sommet On renvoie un dictionnaire {sommet : date_de_visite for sommet in graphe} ''' dates = { 'en_cours':0, 'historique':dict() } def visiter(sommet): dates['historique'][sommet] = dates['en_cours'] dates['en_cours'] = dates['en_cours'] + 1 a_explorer = Pile() if larg_ou_prof == 'prof' else File() a_explorer.ajouter(depart) #<sss deja_vu = dict() while not a_explorer.est_vide(): sommet = a_explorer.extraire() if sommet not in deja_vu.keys(): #<---------- # n'est pas en trop même si on a le if plus deja_vu[sommet] = True # bas en effet un sommet peut être enxilé visiter(sommet) # plusieurs fois avant déxilage for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): #<-------- # nécessaire si on veut étendre a_explorer.ajouter(successeur) # à la recherche de cycle return dates['historique'] def parcours_2(graphe, depart, larg_ou_prof): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter la date de visite du sommet. On renvoie un dictionnaire {sommet : date_de_visite for sommet in graphe} ''' dates = { 'en_cours':0, 'historique':dict() } def visiter(sommet): dates['historique'][sommet] = dates['en_cours'] dates['en_cours'] = dates['en_cours'] + 1 a_explorer = Pile() if larg_ou_prof == 'prof' else File() deja_vu = dict() deja_vu[depart] = True #<sss visiter(depart) #<sss a_explorer.ajouter(depart) while not a_explorer.est_vide(): sommet = a_explorer.extraire() for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): deja_vu[successeur] = True visiter(successeur) a_explorer.ajouter(successeur) return dates['historique'] ###Output _____no_output_____ ###Markdown Une fois qu'on a les dates de visite du parcours en profondeur, on peut facilement rajouter ces informations sur le graphe représenté grâce à l'attribut `etiquettes_secondaires`. On peut ensuite "convertir" ce dictionnaire des dates de parcours en dictionnaire de couleurs `(H, S, V)` avec `H, S` et `V` de type `float` entre 0 et 1. Et demander à représenter le graphe avec ces couleurs. ###Code from vizu_graphe import VizuGraphe ma_liste_adjacence = maillage(11, 11, 0.65) dates = parcours_1(ma_liste_adjacence, 'CC', 'larg') date_max = max(dates.values()) dates_en_couleur = dict() for key, val in dates.items(): dates_en_couleur[key] = (val/date_max, 0.6, 1) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = dates, couleurs = dates_en_couleur) ###Output _____no_output_____ ###Markdown Pour un graphe en étoile : ###Code ma_liste_adjacence = etoiles_reliees('M') dates = parcours_1(ma_liste_adjacence, 0, 'larg') date_max = max(dates.values()) dates_en_couleur = dict() for key, val in dates.items(): dates_en_couleur[key] = (val/date_max, 0.6, 1) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = dates, couleurs = dates_en_couleur, moteur = 'circo') ###Output _____no_output_____ ###Markdown Pour un graphe en soleil : ###Code ma_liste_adjacence = soleil(10, 5) dates = parcours_1(ma_liste_adjacence, 0, 'prof') date_max = max(dates.values()) dates_en_couleur = dict() for key, val in dates.items(): dates_en_couleur[key] = (val/date_max, 0.6, 1) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = dates, couleurs = dates_en_couleur) ###Output _____no_output_____ ###Markdown Applications Plus courts chemins On se base sur le parcours en largeur. Il va s'agir de modifier la visite pour passer d'une date calculée comme suit :- date du dernier sommet visité + 1 à un calcul comme suit :- date du prédécesseur lors du parcours + 1Il faut avoir compris que l'avant-dernier sommet visité n'est en général pas le prédécesseur (pendant le parcours) du dernier sommet en cours de visite (à cause de la xile qui fait tampon). Pour cela, un bref regard sur les algorithmes nous montre que le parcours 2 est plus adapté que le 1 puisque lors de l'appel au marquage/visite on a accès à la fois au sommet et à son prédécesseur : en passant les deux à la fonction visite, on doit pouvoir s'en sortir en modifiant à la marge le dictionnaire `dates` que l'on renommera en `distances` pour l'occasion.A contrario, le parcours 1 ne permet pas cet accès lors de la visite puisque dans l'algorithme, encore une fois, ce qui est écrit comme `successeur` en fin de boucle n'est PAS le `sommet` en début de tour de boucle suivant (toujours à cause du rôle tampon de la xile). ###Code def parcours_2_bis(graphe, depart, larg_ou_prof): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter la distance depuis le départ. On renvoie un dictionnaire {sommet : distance depuis le départ for sommet in graphe} ''' distances = dict() def visiter(successeur, sommet): #<sss distances[successeur] = distances[sommet] + 1 a_explorer = Pile() if larg_ou_prof == 'prof' else File() deja_vu = dict() deja_vu[depart] = True distances[depart] = 0 #<sss a_explorer.ajouter(depart) while not a_explorer.est_vide(): sommet = a_explorer.extraire() for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): deja_vu[successeur] = True visiter(successeur, sommet) #<sss a_explorer.ajouter(successeur) return distances ma_liste_adjacence = maillage(11, 11, 0.55) #mettre 'CC' par exemple en départ #ma_liste_adjacence = soleil(10, 5) #mettre 0 par exemple en départ #ma_liste_adjacence = etoiles_reliees('M') #mettre 0 par exemple en départ distances = parcours_2_bis(ma_liste_adjacence, 'CC', 'larg') distance_max = max(distances.values()) distances_en_couleur = dict() for key, val in distances.items(): distances_en_couleur[key] = (val/distance_max, 0.6, 1) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = distances, couleurs = distances_en_couleur) ###Output _____no_output_____ ###Markdown Plus court chemin d'un sommet au départ En modifiant très légèrement le code ci-dessus on va pouvoir s'en sortir. Lors de la visite, on peut directement stocker le prédécesseur à la place de la distance. Ainsi, en "remontant" les prédécesseurs, on pourra reconstituer le chemin qui a abouti à un sommet donné depuis le départ.On a donc deux modifications à opérer :- modifier légèrement le code de la visite pour qu'elle donne le dictionnaire des prédécesseurs- ajouter une fonction de post-traitement capable de reconstituer un chemin `depart --x--x--x--x--> sommet` à partir du dictionnaire des paires `sommet:predecesseur` (ou `successeur:sommet` selon le point de vue) obtenus lors du parcours.Attention : la clef est bien le successeur qui a un unique prédécesseur lors du parcours (alors que l'unicté est perdue dans l'autre sens).Question :** si sommet est mutable (plateau de jeu), a-t-on des effets de bord ? ###Code def parcours_2_ter(graphe, depart, larg_ou_prof): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter le prédécesseur du sommet dans le parcours en largeur. On renvoie un dictionnaire {sommet : predecesseur for sommet in graphe} ''' parents = dict() def visiter(successeur, sommet): #<sss parents[successeur] = sommet a_explorer = Pile() if larg_ou_prof == 'prof' else File() deja_vu = dict() deja_vu[depart] = True parents[depart] = None #<sss a_explorer.ajouter(depart) while not a_explorer.est_vide(): sommet = a_explorer.extraire() for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): deja_vu[successeur] = True visiter(successeur, sommet) #<sss a_explorer.ajouter(successeur) return parents # on donne le chemin sous forme d'un dictionnaire avec les distances # depuis le départ pour pouvoir l'afficher ensuite # on pourrait faire plus léger mais c'est l'occasion d'utiliser une pile def donner_chemin(depart, sommet, parents): if sommet not in parents.keys(): return dict() chemin_inverse = Pile() chemin_inverse.ajouter(sommet) longueur = 0 while parents[sommet] != None: sommet = parents[sommet] chemin_inverse.ajouter(sommet) longueur = longueur + 1 longueur_chemin = longueur chemin = dict() while longueur>=0: sommet = chemin_inverse.extraire() chemin[sommet] = longueur_chemin - longueur longueur = longueur - 1 return chemin ma_liste_adjacence = maillage(11, 11, 0.55) #mettre 'CC' par exemple en départ #ma_liste_adjacence = soleil(10, 5) #mettre 0 par exemple en départ #ma_liste_adjacence = etoiles_reliees('M') #mettre 0 par exemple en départ parents = parcours_2_ter(ma_liste_adjacence, 'CC', 'larg') chemin = donner_chemin('CC', 'JJ', parents) chemin_en_couleur = dict() for key in chemin.keys(): chemin_en_couleur[key] = (0.7, 0.3, 1) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = chemin, couleurs = chemin_en_couleur) ###Output _____no_output_____ ###Markdown Découverte de cycles (uniquement pour les graphes non orientés - limité à une composante connexe)L'idée fondamentale est que, si l'on si prend bien (c'est à dire en réussissant à écarter simplement le cas du cycle "A --> B --> A"), le fait d'explorer un sommet déjà marqué signifie que l'on a réussi à accéder à un sommet depuis deux chemins différents qui partent du départ. On a donc nécessairement un cycle.Si on regarde en détail ce qui suit, on s'aperçoit que contrairement au cas des plus courts chemins, cette fois-ci ce sont les algorithmes récursifs et itératifs n°1 qui sont les plus adaptés (alors que l'itératif n°2 demanderait des adaptations peu élégantes). ###Code ############################################################################# ## Recherche de cycles en récursif ############################################################################# def parcours_recursif_bis(graphe, depart): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter la date de visite du sommet. On renvoie : - un booléen indiquant si le graphe possède un cycle - un dictionnaire {sommet : date_de_visite for sommet in {sommets parcourus}} ''' dates = { 'en_cours':0, 'historique':dict() } def visiter(sommet): dates['historique'][sommet] = dates['en_cours'] dates['en_cours'] = dates['en_cours'] + 1 def dfs(sommet, predecesseur): dejavu[sommet] = True visiter(sommet) for successeur in graphe[sommet]: if successeur in dejavu.keys() : if predecesseur != successeur: #<sss # pas un demi-tour --> on a un cycle return True else: if dfs(successeur, sommet): #<sss # cycle plus haut dans pile d'appels return True # --> on sort return False dejavu = dict() return dfs(depart, None), dates['historique'] ############################################################################## ## recherche de cycle sur parcours 1 ############################################################################## # # Le cas du cycle "sommetA --> sommetB --> sommetA" - possible en récursif - # est ici exclu puisque si un successeur (B) est mis dans la xile, son parent (A) # dans le parcours est déjà marqué donc ne pourra pas être empilé en tant # que successeur de successeur de lui même. # #------------------------------------ # renvoie True => il existe un cycle #------------------------------------ # On renvoie True si un sommet sort de la pile et a déjà été marqué # Cela se produit si on arrive à la situation où un sommet est # passé deux fois dans la xile. # # Cela n'est possible que si la xile contient à un instant donné deux fois # le même sommet. En effet, une fois déxilé/marqué une première fois un sommet ne peut # plus être réintroduit dans la xile (d'où l'importance du if en bas d'algo). # C'est donc que le second exemplaire a été réintroduit dans la xile AVANT # la sortie du premier exemplaire # # Cette situation se produit uniquement si : # - un premier prédecesseur a mis S dans la xile # - le parcours a continué sans déxiler S # - un second prédécesseur a remis S dans la xile # - (puisque S n'a pas été déxilé, on ne peut pas être dans le cas du demi-tour) # Donc uniquement si S a au moins deux prédécesseurs différents # ce qui signifie qu'on a un cycle (car on est dans le cas d'un graphe non orienté) # # Remarque : sans le if additionnel, on pourrait visiter un sommet S, visiter un de ses successeurs # et revisiter S (cas du cycle "A -> B -> A") => entrainerait une détection de cycle erronée #-------------------------------------- # Il existe un cycle => renvoie True #-------------------------------------- # Puisque tous les sommets du cycle finiront par être visités # (sauf si la fonction renvoie True avant) # notons Sb le dernier sommet du cycle à être marqué. # Juste avant de sortir de la xile pour être marqué, les deux sommets adjacents à Sb # (notons les Sa et Sc) dans le cycle ont déjà été marqués # (puisque Sb est par définition le dernier à l'être). # Ces deux sommets Sa et Sc ont donc chacun envoyé un exemplaire de Sb dans la xile # Juste avant de sortir de la xile pour être marqué, Sb se trouve donc en double # exemplaire dans la xile # ---> renvoie True def parcours_1_bis(graphe, depart, larg_ou_prof): ''' - graphe liste ou matrice d'adjacence - depart : sommet de depart La visite consiste à noter la date de visite du sommet. On renvoie : - un booléen indiquant si le graphe possède un cycle - un dictionnaire {sommet : date_de_visite for sommet in {sommets parcourus}} ''' dates = { 'en_cours':0, 'historique':dict() } def visiter(sommet): dates['historique'][sommet] = dates['en_cours'] dates['en_cours'] = dates['en_cours'] + 1 a_explorer = Pile() if larg_ou_prof == 'prof' else File() a_explorer.ajouter(depart) deja_vu = dict() while not a_explorer.est_vide(): sommet = a_explorer.extraire() if sommet not in deja_vu.keys(): deja_vu[sommet] = True visiter(sommet) for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): a_explorer.ajouter(successeur) else: return True, dates['historique'] #<sss return False, dates['historique'] #<sss ################################ # On regardera avec profit ce qui se passe sur le parcours 1 dans cette configuration # pour pouvoir comparer avec la méthode 2 (qui elle sera mise en défaut) # # ----* # / # >>----x # \ # O----* # # si on parcourt de la gauche vers la droite # d'abord vers le haut, quand on arrivera # sur O il n'y aura pas de détection de cycle "x -> O -> x" ###################################################################### ## recherche de cycle sur parcours 2 : Ne fonctionne pas ## sauf à implémenter une visite avec mémorisation du prédécesseur ###################################################################### # # La xile casse le lien prédecesseur --> suivant # Lien de filiation dont on peut avoir besoin pour détecter les cycle "A -> B -> A" # Alors qu'en récursif il est facile d'avoir ce lien puisque c'est le lien # appelant ---> appelé # Donc en récursif, détection de cycle "sommetA --> sommetB --> sommetA" # facilement écartée. # # La détection du cycle "A -> B -> A" est également exclue en méthode 1 puisque # si un successeur (B) est mis dans la xile, son parent (A) dans le parcours # est déjà marqué donc ne pourra pas être enxilé par (B). # (Si (A) est une seconde fois dans la xile, c'est en tant que successeur d'un # sommet (C). C'est donc qu'il y a un cycle). def parcours_2_quattro(graphe, depart, larg_ou_prof): def visiter(sommet): pass a_explorer = Pile() if larg_ou_prof == 'prof' else File() deja_vu = dict() deja_vu[depart] = True #<sss visiter(depart) #<sss a_explorer.ajouter(depart) while not a_explorer.est_vide(): sommet = a_explorer.extraire() for successeur in graphe[sommet]: if successeur not in deja_vu.keys(): deja_vu[successeur] = True visiter(successeur) a_explorer.ajouter(successeur) else: #<-----------------<#### return True # return False # # # ########################################## # Comment savoir qu'on est pas dans le cas du cycle "A -> B -> A" ? # garder en mémoire le dernier sommet visité n'est pas # suffisant puisqu'on fait parfois des sauts en arrière # où le dernier sommet visité n'est pas le prédécesseur # (dans le parcours) du sommet sur lequel on est # ----D # / # >>----x # \ # O----* # sur cet exemple, si on parcourt de la gauche vers la droite # d'abord vers le haut, que se passera-t-il quand on arrivera # sur O ? Comment éviter de détecter le cycle "x -> O -> x" ? # Alors que quand on arrive sur O, c'est D qui a été visité en dernier (pas x) ? # Cette difficulté doit nous pousser à privilégier le parcours 1 # pour la recherche de cycle. from vizu_graphe import VizuGraphe ma_liste_adjacence = maillage(3, 3, 0.45) existe_cycle, cycle = parcours_1_bis(ma_liste_adjacence, 'BC', 'larg') print(existe_cycle) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = cycle) from vizu_graphe import VizuGraphe ma_liste_adjacence = maillage(5, 5, 0.45) existe_cycle, cycle = parcours_recursif_bis(ma_liste_adjacence, 'BC') cycle_en_couleur = dict() for key in cycle.keys(): cycle_en_couleur[key] = (0.8, 0.6, 1) print(existe_cycle) mon_vizualisateur = VizuGraphe('liste', ma_liste_adjacence, etiquettes_secondaires = cycle, couleurs = cycle_en_couleur) ###Output _____no_output_____
0_Aleutian_Enumeration_Example.ipynb	###Markdown This is to demonstrate how to use the `s1-enumerator` to get a full time series of GUNWs.We are going basically take each month in acceptable date range and increment by a month and make sure the temporal window is large enough to ensure connectivity across data gaps. ###Code %load_ext autoreload %autoreload 2 from s1_enumerator import get_aoi_dataframe, distill_all_pairs, enumerate_ifgs, get_s1_coverage_tiles, enumerate_ifgs_from_stack, get_s1_stack_by_dataframe import concurrent from s1_enumerator import duplicate_gunw_found from tqdm import tqdm from shapely.geometry import Point import datetime import pandas as pd import geopandas as gpd import matplotlib.pyplot as plt from dateutil.relativedelta import relativedelta import networkx as nx import boto3 df_aoi = gpd.read_file('aois/Aleutians_pathNumber124.geojson') aoi = df_aoi.geometry.unary_union aoi df_aoi ###Output _____no_output_____ ###Markdown Currently, there is a lot of data in each of the rows above. We really only need the AOI `geometry` and the `path_number`. ParametersThis is what the operator is going to have to change. Will provide some comments. ###Code today = datetime.datetime.now() # Earliest year for reference frames START_YEAR = 2016 # Latest year for reference frames END_YEAR = today.year # Adjust depending on seasonality # For annual IFGs, select a single months of interest and you will get what you want. MONTHS_OF_INTEREST = [6, 7, 8, 9, 10] ###Output _____no_output_____ ###Markdown Below, we select the boundary for minimum reference dates. ###Code min_reference_dates = [datetime.datetime(year, month, 1) for year in range(START_YEAR, END_YEAR + 1) for month in MONTHS_OF_INTEREST if datetime.datetime(year, month, 1) < today] min_reference_dates = list(reversed(min_reference_dates)) min_reference_dates[:3] ###Output _____no_output_____ ###Markdown The maximum secondary dates for enumeration are going to be a month after the previous `min_reference_date`. What we are going to do is: select 3 SLCs before and after the 1st of the month. When we reach the earliest month of interest in a given year, we select secondaries occuring before the latest occuring month of interest in the previous calendar year (at the end of that month). Here the number of neighnbors (i.e. 3) is arbitrary and can be changed below. We found this parameter useful to making sure the graph was connected. So, don't change it unless you are careful.In the setup above and below, we are thus picking three dates occuring before one item of the `max_secondary_dates` and three dates after one item of `min_reference_dates`. ###Code max_secondary_dates = [ref_date + relativedelta(months=1) for ref_date in min_reference_dates] max_secondary_dates = max_secondary_dates[1:] + min_reference_dates[-1:] max_secondary_dates[:3] for ref, sec in zip(min_reference_dates[:6], max_secondary_dates): print('############################') print(ref.date(), ' to ', sec.date()) print('or: days backward', (ref - sec).days) path_numbers = df_aoi.path_number.unique().tolist() path_numbers ###Output _____no_output_____ ###Markdown Generate tiles used for coverageThis function just does a geometric query based on each frame and using one pass from the start date. ###Code df_coverage_tiles = get_s1_coverage_tiles(aoi, # the date is used to get coverage tiles for extracting stack. # Recent data has reliable coverage. start_date=datetime.datetime(2021, 1, 1)) ## Uncomment if you want to inspect the dataframe # df_coverage_tiles.to_file('coverage_tiles.geojson', driver='GeoJSON', index=False) fig, ax = plt.subplots() df_coverage_tiles.plot(ax=ax, alpha=.5, color='green', label='Frames interesecting tile') df_aoi.exterior.plot(color='black', ax=ax, label='AOI') plt.legend() ###Output _____no_output_____ ###Markdown For the Aleutians above, note there is:1. Above the coverage was 12 days + 1 after January 1 2021. It will used to generate an entire collection of frames below.2. the track we are interested doesn't "jiggle" above the AOI because of the restriction to 12 days + 1 (see below when we make the stack, for more frames "jiggling").3. the other tracks have some more "jiggle" or "seams" partially because of how the frames fell during this limited time period.For larger frames, piecemealing the queries ensures that 1000 product limit that ASF imposes will no prevent us from collecting all the data we need over the AOI. Generate a stackUsing all the tiles that are needed to cover the AOI we make a geometric query based on the frame. We now include only the path we are interested in. ###Code df_stack = get_s1_stack_by_dataframe(df_coverage_tiles, path_numbers=path_numbers) f'We have {df_stack.shape[0]} frames in our stack' fig, ax = plt.subplots() df_stack.plot(ax=ax, alpha=.5, color='green', label='Frames interesecting tile') df_aoi.exterior.plot(color='black', ax=ax, label='AOI') plt.legend() ###Output _____no_output_____ ###Markdown Note, we now see the frames cover the entire AOI as we expect. Next, we filter the stack by month to ensure we only have SLCs we need. ###Code df_stack_month = df_stack[df_stack.start_date.dt.month.isin(MONTHS_OF_INTEREST)] df_stack_month.shape ifg_pairs = [] for min_ref_date, max_sec_date in zip(tqdm(min_reference_dates), (max_secondary_dates)): # Mostly this is 0 unless we get to our earliest month of interest in the calendar year min_days_backward = (min_ref_date - max_sec_date).days # because of s1 availability/gaps, we just make this huge so that we can look up slcs in the stack # occuring in previous years - the gaps can cause temporal jumps that you have to be mindful of # That's why we have neighbors being 3. temporal_window_days=364 * 3 temp = enumerate_ifgs_from_stack(df_stack_month, aoi, min_ref_date, # options are 'tile' and 'path' # 'path' processes multiple references simultaneously enumeration_type='tile', min_days_backward=min_days_backward, num_neighbors_ref=3, num_neighbors_sec=3, temporal_window_days=temporal_window_days, ) ifg_pairs += temp f'The number of GUNWs (likely lots of duplicates) is {len(ifg_pairs)}' ###Output _____no_output_____ ###Markdown Get Dataframe ###Code df_pairs = distill_all_pairs(ifg_pairs) df_pairs.head() f"# of GUNWs: ' {df_pairs.shape[0]}" ###Output _____no_output_____ ###Markdown Deduplication Pt. 1A `GUNW` is uniquely determined by the reference and secondary IDs. We contanenate these sorted lists and generate a lossy hash to deduplicate products we may have introduced from the enumeration above. ###Code import hashlib import json def get_gunw_hash_id(reference_ids: list, secondary_ids: list) -> str: all_ids = json.dumps([' '.join(sorted(reference_ids)), ' '.join(sorted(secondary_ids)) ]).encode('utf8') hash_id = hashlib.md5(all_ids).hexdigest() return hash_id def hasher(row): return get_gunw_hash_id(row['reference'], row['secondary']) df_pairs['hash_id'] = df_pairs.apply(hasher, axis=1) df_pairs.head() f"# of duplicated entries: {df_pairs.duplicated(subset=['hash_id']).sum()}" df_pairs = df_pairs.drop_duplicates(subset=['hash_id']).reset_index(drop=True) f"# of UNIQUE GUNWs: {df_pairs.shape[0]}" ###Output _____no_output_____ ###Markdown Viewing GUNW pairs ###Code # start index M = 0 # number of pairs to view N = 5 for J in range(M, M + N): pair = ifg_pairs[J] fig, axs = plt.subplots(1, 2, sharey=True, sharex=True) df_ref_plot = pair['reference'] df_sec_plot = pair['secondary'] df_ref_plot.plot(column='start_date_str', legend=True, ax=axs[0], alpha=.15) df_aoi.exterior.plot(ax=axs[0], alpha=.5, color='black') axs[0].set_title('Reference') df_sec_plot.plot(column='start_date_str', legend=True, ax=axs[1], alpha=.15) df_aoi.exterior.plot(ax=axs[1], alpha=.5, color='black') axs[0].set_title(f'Reference {J}') axs[1].set_title('Secondary') ###Output _____no_output_____ ###Markdown Update types for Graphical AnalysisWe want to do some basic visualization to support the understanding if we traverse time correctly. We do some simple standard pandas manipulation. ###Code df_pairs['reference_date'] = pd.to_datetime(df_pairs['reference_date']) df_pairs['secondary_date'] = pd.to_datetime(df_pairs['secondary_date']) df_pairs.head() ###Output _____no_output_____ ###Markdown Visualize a Date Graph from Time SeriesWe can put this into a network Directed Graph and use some simple network functions to check connectivity.We are going to use just dates for nodes, though you could use `(ref_date, hash_id)` for nodes and then inspect connected components. That is for another notebook. ###Code list(zip(df_pairs.reference_date, df_pairs.secondary_date))[:15] unique_dates = df_pairs.reference_date.tolist() + df_pairs.secondary_date.tolist() unique_dates = sorted(list(set(unique_dates))) unique_dates[:4] date2node = {date: k for (k, date) in enumerate(unique_dates)} node2date = {k: date for (date, k) in date2node.items()} G = nx.DiGraph() edges = [(date2node[ref_date], date2node[sec_date]) for (ref_date, sec_date) in zip(df_pairs.reference_date, df_pairs.secondary_date)] G.add_edges_from(edges) nx.draw(G) ###Output _____no_output_____ ###Markdown This function checks there is a path from the first date to the last one. ###Code nx.has_path(G, target=date2node[unique_dates[0]], source=date2node[unique_dates[-1]]) ###Output _____no_output_____ ###Markdown The y-axis is created purely for display so doesn't really indicated anything but flow by month. ###Code fig, ax = plt.subplots(figsize=(15, 5)) increment = [date.month + date.day for date in unique_dates] # source: https://stackoverflow.com/a/27852570 scat = ax.scatter(unique_dates, increment) position = scat.get_offsets().data pos = {date2node[date]: position[k] for (k, date) in enumerate(unique_dates)} nx.draw_networkx_edges(G, pos=pos, ax=ax) ax.grid('on') ax.tick_params(axis='x', which='major', labelbottom=True, labelleft=True) ymin, ymax = ax.get_ylim() for y in range(2015, 2022): label = 'Oct to Nov' if y == 2016 else None ax.fill_between([datetime.datetime(y, 6, 1), datetime.datetime(y, 11, 1)], ymin, ymax, alpha=.5, color='green', zorder=0, label=label) plt.legend() ###Output _____no_output_____ ###Markdown Observe there is a gap in 2018 over are area of interest. This is where our 3 year "temporal_window_days" parameter in our enumeration was essential. Deduplication Pt. 2This is to ensure that previous processing hasn't generate any of the products we have just enumerated. Check CMRThis function checks the ASF DAAC if there are GUNWs with the same spatial extent and same date pairs as the ones created. At some point, we will be able to check the input SLC ids from CMR, but currently that is not possible.If you are processing a new AOI whose products have not been delivered, you can ignore this step. It is a bit time consuming as the queries are done product by product. ###Code from s1_enumerator import duplicate_gunw_found import concurrent from tqdm import tqdm n = df_pairs.shape[0] with concurrent.futures.ThreadPoolExecutor(max_workers=15) as executor: results = list(tqdm(executor.map(duplicate_gunw_found, df_pairs.to_dict('records')), total=n)) df_pairs['existing_gunw'] = [r != '' for r in results] df_pairs['existing_gunw_id'] = results total_existing_gunws = df_pairs['existing_gunw'].sum() print('existing_gunws: ', total_existing_gunws) print('Total pairs', df_pairs.shape[0]) df_pairs_filtered = df_pairs[~df_pairs['existing_gunw']].reset_index(drop=True) # df_pairs_filtered.drop_duplicates(subset=['hash_id'], inplace=True) print('after filtering, total pairs: ', df_pairs_filtered.shape[0]) ###Output after filtering, total pairs: 140 ###Markdown Check Hyp3 AccountWe are now going to check1. check products in the open s3 bucket2. check running/pending jobsNotes:1. Above, to accomplish step 1., there is some verbose code (see below). Once we automate delivery, this step will be obsolete. However, until we have delivery, we have to make sure that there are no existing products. Additionally, if we are using a separate (non-operational account), then would be good to use this.2. If we are debugging products and some of our previously generated products were made incorrectly, we will want to ignore this step. ###Code import hyp3_sdk # uses .netrc; add `prompt=True` to prompt for credentials; hyp3_isce = hyp3_sdk.HyP3('https://hyp3-isce.asf.alaska.edu/') pending_jobs = hyp3_isce.find_jobs(status_code='PENDING') + hyp3_isce.find_jobs(status_code='RUNNING') all_jobs = hyp3_isce.find_jobs() print(all_jobs) ###Output 416 HyP3 Jobs: 416 succeeded, 0 failed, 0 running, 0 pending. ###Markdown 1. Get existing products in s3 bucket ###Code job_data = [j.to_dict() for j in all_jobs] job_data[0] ###Output _____no_output_____ ###Markdown Get bucket (there is only one) ###Code job_data_s3 = list(filter(lambda job: 'files' in job.keys(), job_data)) len(job_data_s3) bucket = job_data_s3[0]['files'][0]['s3']['bucket'] bucket ###Output _____no_output_____ ###Markdown Get all keys ###Code job_keys = [job['files'][0]['s3']['key'] for job in job_data_s3] job_keys[0] s3 = boto3.resource('s3') prod_bucket = s3.Bucket(bucket) objects = list(prod_bucket.objects.all()) ncs = list(filter(lambda x: x.key.endswith('.nc'), objects)) ncs[:10] ###Output _____no_output_____ ###Markdown Need to physically check if the products are not there (could have been deleted!) ###Code nc_keys = [nc_ob.key for nc_ob in ncs] jobs_with_prods_in_s3 = [job for (k, job) in enumerate(job_data_s3) if job_keys[k] in nc_keys] len(jobs_with_prods_in_s3) slcs = [(job['job_parameters']['granules'], job['job_parameters']['secondary_granules']) for job in jobs_with_prods_in_s3] slcs[:2] hash_ids_of_prods_in_s3 = [get_gunw_hash_id(slc) for slc in slcs] hash_ids_of_prods_in_s3[0] f"We are removing {df_pairs_filtered['hash_id'].isin(hash_ids_of_prods_in_s3).sum()} GUNWs for submission" items = hash_ids_of_prods_in_s3 df_pairs_filtered = df_pairs_filtered[~df_pairs_filtered['hash_id'].isin(items)].reset_index(drop=True) f"Current # of GUNWs: {df_pairs_filtered.shape[0]}" ###Output _____no_output_____ ###Markdown 2. Running or Pending Jobs ###Code pending_job_data = [j.to_dict() for j in pending_jobs] pending_slcs = [(job['job_parameters']['granules'], job['job_parameters']['secondary_granules']) for job in pending_job_data] hash_ids_of_pending_jobs = [get_gunw_hash_id(slc) for slc in pending_slcs] hash_ids_of_pending_jobs[:4] items = hash_ids_of_pending_jobs f"We are removing {df_pairs_filtered['hash_id'].isin(items).sum()} GUNWs for submission" items = hash_ids_of_pending_jobs df_pairs_filtered = df_pairs_filtered[~df_pairs_filtered['hash_id'].isin(items)].reset_index(drop=True) f"Current # of GUNWs: {df_pairs_filtered.shape[0]}" ###Output _____no_output_____ ###Markdown Submit jobs to Hyp3 ###Code records_to_submit = df_pairs_filtered.to_dict('records') records_to_submit[0] import hyp3_sdk # uses .netrc; add `prompt=True` to prompt for credentials; hyp3_isce = hyp3_sdk.HyP3('https://hyp3-isce.asf.alaska.edu/') ###Output _____no_output_____ ###Markdown The below puts the records in a format that we can submit to the Hyp3 API.Note 1: there is an index in the records to submit to ensure we don't over submit jobs for generating GUNWs. \Note 2: uncomment the code to actually submit the jobs. ###Code import hyp3_sdk # uses .netrc; add `prompt=True` to prompt for credentials; hyp3_isce = hyp3_sdk.HyP3('https://hyp3-isce.asf.alaska.edu/') job_dicts = [{'name': 'test-aleutian-95-new', # NOTE: we are still using the `dev` branch. Change this to "INSAR_ISCE" to use the `main` branch. 'job_type': 'INSAR_ISCE_TEST', 'job_parameters': {'granules': r['reference'], 'secondary_granules': r['secondary']}} # NOTE THERE IS AN INDEX - this is to submit only a subset of Jobs for r in records_to_submit[:5]] # UNCOMMENT TO SUBMIT # submitted_jobs = hyp3_isce.submit_prepared_jobs(job_dicts) jobs = hyp3_isce.find_jobs() print(jobs) ###Output 416 HyP3 Jobs: 416 succeeded, 0 failed, 0 running, 0 pending. ###Markdown Below, we show how to download files. The multi-threading example will download products in parallel much faster than `jobs.download_files()`. ###Code # import concurrent.futures # from tqdm import tqdm # with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: # results = list(tqdm(executor.map(lambda job: job.download_files(), jobs), total=len(jobs))) ###Output _____no_output_____
Jud Taylor - reference_regression_classification_1.ipynb	###Markdown Lambda School Data ScienceUnit 2, Sprint 1, Module 1--- Regression 1- Begin with baselines for regression- Use scikit-learn to fit a linear regression- Explain the coefficients from a linear regression Brandon Rohrer wrote a good blog post, [“What questions can machine learning answer?”](https://brohrer.github.io/five_questions_data_science_answers.html)We’ll focus on two of these questions in Unit 2. These are both types of “supervised learning.”- “How Much / How Many?” (Regression)- “Is this A or B?” (Classification)This unit, you’ll build supervised learning models with “tabular data” (data in tables, like spreadsheets). Including, but not limited to:- Predict New York City real estate prices <-- Today, we'll start this!- Predict which water pumps in Tanzania need repairs- Choose your own labeled, tabular dataset, train a predictive model, and publish a blog post or web app with visualizations to explain your model! SetupRun the code cell below. You can work locally (follow the [local setup instructions](https://lambdaschool.github.io/ds/unit2/local/)) or on Colab.Libraries:- ipywidgets- pandas- plotly- scikit-learn ###Code import sys # If you're on Colab: if 'google.colab' in sys.modules: DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/' # If you're working locally: else: DATA_PATH = '../data/' # Ignore this Numpy warning when using Plotly Express: # FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead. import warnings warnings.filterwarnings(action='ignore', category=FutureWarning, module='numpy') ###Output _____no_output_____ ###Markdown Begin with baselines for regression Overview Predict how much a NYC condo costs 🏠💸Regression models output continuous numbers, so we can use regression to answer questions like "How much?" or "How many?" Often, the question is "How much will this cost? How many dollars?" For example, here's a fun YouTube video, which we'll use as our scenario for this lesson:[Amateurs & Experts Guess How Much a NYC Condo With a Private Terrace Costs](https://www.youtube.com/watch?v=JQCctBOgH9I)> Real Estate Agent Leonard Steinberg just sold a pre-war condo in New York City's Tribeca neighborhood. We challenged three people - an apartment renter, an apartment owner and a real estate expert - to try to guess how much the apartment sold for. Leonard reveals more and more details to them as they refine their guesses. The condo from the video is 1,497 square feet, built in 1852, and is in a desirable neighborhood. According to the real estate agent, _"Tribeca is known to be one of the most expensive ZIP codes in all of the United States of America."_How can we guess what this condo sold for? Let's look at 3 methods:1. Heuristics2. Descriptive Statistics3. Predictive Model Follow Along 1. HeuristicsHeuristics are "rules of thumb" that people use to make decisions and judgments. The video participants discussed their heuristics: Participant 1, Chinwe, is a real estate amateur. She rents her apartment in New York City. Her first guess was `8 million, and her final guess was 15 million.[She said](https://youtu.be/JQCctBOgH9I?t=465), _"People just go crazy for numbers like 1852. You say 'pre-war' to anyone in New York City, they will literally sell a kidney. They will just give you their children."_ Participant 3, Pam, is an expert. She runs a real estate blog. Her first guess was 1.55 million, and her final guess was 2.2 million.[She explained](https://youtu.be/JQCctBOgH9I?t=280) her first guess: _"I went with a number that I think is kind of the going rate in the location, and that's a thousand bucks a square foot."_ Participant 2, Mubeen, is between the others in his expertise level. He owns his apartment in New York City. His first guess was 1.7 million, and his final guess was also 2.2 million. 2. Descriptive Statistics We can use data to try to do better than these heuristics. How much have other Tribeca condos sold for?Let's answer this question with a relevant dataset, containing most of the single residential unit, elevator apartment condos sold in Tribeca, from January through April 2019.We can get descriptive statistics for the dataset's `SALE_PRICE` column.How many condo sales are in this dataset? What was the average sale price? The median? Minimum? Maximum? ###Code import pandas as pd df = pd.read_csv(DATA_PATH+'condos/tribeca.csv') pd.options.display.float_format = '{:,.0f}'.format df['SALE_PRICE'].describe() ###Output _____no_output_____ ###Markdown On average, condos in Tribeca have sold for \$3.9 million. So that could be a reasonable first guess.In fact, here's the interesting thing: we could use this one number as a "prediction", if we didn't have any data except for sales price... Imagine we didn't have any any other information about condos, then what would you tell somebody? If you had some sales prices like this but you didn't have any of these other columns. If somebody asked you, "How much do you think a condo in Tribeca costs?"You could say, "Well, I've got 90 sales prices here, and I see that on average they cost \$3.9 million."So we do this all the time in the real world. We use descriptive statistics for prediction. And that's not wrong or bad, in fact that's where you should start. This is called the _mean baseline_. Baseline is an overloaded term, with multiple meanings:1. [The score you'd get by guessing](https://twitter.com/koehrsen_will/status/1088863527778111488)2. [Fast, first models that beat guessing](https://blog.insightdatascience.com/always-start-with-a-stupid-model-no-exceptions-3a22314b9aaa) 3. Complete, tuned "simpler" model (Simpler mathematically, computationally. Or less work for you, the data scientist.)4. Minimum performance that "matters" to go to production and benefit your employer and the people you serve.5. Human-level performance Baseline type 1 is what we're doing now.(Linear models can be great for 2, 3, 4, and [sometimes even 5 too!](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.5825)) ---Let's go back to our mean baseline for Tribeca condos. If we just guessed that every Tribeca condo sold for \$3.9 million, how far off would we be, on average? ###Code guess = df['SALE_PRICE'].mean() errors = guess - df['SALE_PRICE'] mean_absolute_error = errors.abs().mean() print(f'If we just guessed every Tribeca condo sold for ${guess:,.0f},') print(f'we would be off by ${mean_absolute_error:,.0f} on average.') ###Output _____no_output_____ ###Markdown That sounds like a lot of error! But fortunately, we can do better than this first baseline — we can use more data. For example, the condo's size.Could sale price be dependent on square feet? To explore this relationship, let's make a scatterplot, using [Plotly Express](https://plot.ly/python/plotly-express/): ###Code import plotly.express as px px.scatter(df, x='GROSS_SQUARE_FEET', y='SALE_PRICE') ###Output _____no_output_____ ###Markdown 3. Predictive ModelTo go from a _descriptive_ [scatterplot](https://www.plotly.express/plotly_express/plotly_express.scatter) to a _predictive_ regression, just add a _line of best fit:_ ###Code ###Output _____no_output_____ ###Markdown Roll over the Plotly regression line to see its equation and predictions for sale price, dependent on gross square feet.Linear Regression helps us interpolate. For example, in this dataset, there's a gap between 4016 sq ft and 4663 sq ft. There were no 4300 sq ft condos sold, but what price would you predict, using this line of best fit?Linear Regression also helps us extrapolate. For example, in this dataset, there were no 6000 sq ft condos sold, but what price would you predict? The line of best fit tries to summarize the relationship between our x variable and y variable in a way that enables us to use the equation for that line to make predictions. Synonyms for "y variable"- Dependent Variable- Response Variable- Outcome Variable - Predicted Variable- Measured Variable- Explained Variable- Label- Target Synonyms for "x variable"- Independent Variable- Explanatory Variable- Regressor- Covariate- Correlate- Feature The bolded terminology will be used most often by your instructors this unit. ChallengeIn your assignment, you will practice how to begin with baselines for regression, using a new dataset! Use scikit-learn to fit a linear regression Overview We can use visualization libraries to do simple linear regression ("simple" means there's only one independent variable). But during this unit, we'll usually use the scikit-learn library for predictive models, and we'll usually have multiple independent variables. In [_Python Data Science Handbook,_ Chapter 5.2: Introducing Scikit-Learn](https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.htmlBasics-of-the-API), Jake VanderPlas explains how to structure your data for scikit-learn:> The best way to think about data within Scikit-Learn is in terms of tables of data. >> ![](https://jakevdp.github.io/PythonDataScienceHandbook/figures/05.02-samples-features.png)>>The features matrix is often stored in a variable named `X`. The features matrix is assumed to be two-dimensional, with shape `[n_samples, n_features]`, and is most often contained in a NumPy array or a Pandas `DataFrame`.>>We also generally work with a label or target array, which by convention we will usually call `y`. The target array is usually one dimensional, with length `n_samples`, and is generally contained in a NumPy array or Pandas `Series`. The target array may have continuous numerical values, or discrete classes/labels. >>The target array is the quantity we want to _predict from the data:_ in statistical terms, it is the dependent variable. VanderPlas also lists a 5 step process for scikit-learn's "Estimator API":> Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications.>> Most commonly, the steps in using the Scikit-Learn estimator API are as follows:>> 1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.> 2. Choose model hyperparameters by instantiating this class with desired values.> 3. Arrange data into a features matrix and target vector following the discussion above.> 4. Fit the model to your data by calling the `fit()` method of the model instance.> 5. Apply the Model to new data: For supervised learning, often we predict labels for unknown data using the `predict()` method.Let's try it! Follow AlongFollow the 5 step process, and refer to [Scikit-Learn LinearRegression documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). ###Code # 1. Import the appropriate estimator class from Scikit-Learn # 2. Instantiate this class # 3. Arrange X features matrix & y target vector # 4. Fit the model # 5. Apply the model to new data ###Output _____no_output_____ ###Markdown So, we used scikit-learn to fit a linear regression, and predicted the sales price for a 1,497 square foot Tribeca condo, like the one from the video.Now, what did that condo actually sell for? ___The final answer is revealed in [the video at 12:28](https://youtu.be/JQCctBOgH9I?t=748)!___ ###Code ###Output _____no_output_____ ###Markdown What was the error for our prediction, versus the video participants?Let's use [scikit-learn's mean absolute error function](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html). ###Code chinwe_final_guess = [15000000] mubeen_final_guess = [2200000] pam_final_guess = [2200000] ###Output _____no_output_____ ###Markdown This [diagram](https://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/tutorial/text_analytics/general_concepts.htmlsupervised-learning-model-fit-x-y) shows what we just did! Don't worry about understanding it all now. But can you start to match some of these boxes/arrows to the corresponding lines of code from above? Here's [another diagram](https://livebook.manning.com/book/deep-learning-with-python/chapter-1/), which shows how machine learning is a "new programming paradigm":> A machine learning system is "trained" rather than explicitly programmed. It is presented with many "examples" relevant to a task, and it finds statistical structure in these examples which eventually allows the system to come up with rules for automating the task. —[Francois Chollet](https://livebook.manning.com/book/deep-learning-with-python/chapter-1/) Wait, are we saying that linear regression could be considered a machine learning algorithm? Maybe it depends? What do you think? We'll discuss throughout this unit. ChallengeIn your assignment, you will use scikit-learn for linear regression with one feature. For a stretch goal, you can do linear regression with two or more features. Explain the coefficients from a linear regression OverviewWhat pattern did the model "learn", about the relationship between square feet & price? Follow Along To help answer this question, we'll look at the `coef_` and `intercept_` attributes of the `LinearRegression` object. (Again, [here's the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).) ###Code ###Output _____no_output_____ ###Markdown We can repeatedly apply the model to new/unknown data, and explain the coefficient: ###Code def predict(square_feet): y_pred = model.predict([[square_feet]]) estimate = y_pred[0] coefficient = model.coef_[0] result = f'${estimate:,.0f} estimated price for {square_feet:,.0f} square foot condo in Tribeca.' explanation = f'In this linear regression, each additional square foot adds ${coefficient:,.0f}.' return result + '\n' + explanation predict(1497) # What does the model predict for low square footage? predict(500) # For high square footage? predict(10000) ###Output _____no_output_____
tutorials/opf_dcline.ipynb	###Markdown DC Line dispatch with pandapower OPFThis is an introduction into the usage of the pandapower optimal power flow with dc lines. Example NetworkWe use the following four bus example network for this tutorial:We first create this network in pandapower: ###Code import pandapower as pp from numpy import array net = pp.create_empty_network() b1 = pp.create_bus(net, 380) b2 = pp.create_bus(net, 380) b3 = pp.create_bus(net, 380) b4 = pp.create_bus(net, 380) b5 = pp.create_bus(net, 380) l1 = pp.create_line(net, b1, b2, 30, "490-AL1/64-ST1A 380.0") l2 = pp.create_line(net, b3, b4, 20, "490-AL1/64-ST1A 380.0") l3 = pp.create_line(net, b4, b5, 20, "490-AL1/64-ST1A 380.0") dcl1 = pp.create_dcline(net, name="dc line", from_bus=b2, to_bus=b3, p_kw=0.2e6, loss_percent=1.0, loss_kw=500, vm_from_pu=1.01, vm_to_pu=1.012, max_p_kw=1e6, in_service=True) eg1 = pp.create_ext_grid(net, b1, 1.02, max_p_kw=0.) eg2 = pp.create_ext_grid(net, b5, 1.02, max_p_kw=0.) l1 = pp.create_load(net, bus=b4, p_kw=800e3, controllable = False) ###Output _____no_output_____ ###Markdown We now run a regular load flow to check out the DC line model: ###Code pp.runpp(net) ###Output _____no_output_____ ###Markdown The transmission power of the DC line is defined in the loadflow as given by the p_kw parameter, which was set to 0.2 GW: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown The losses amount to 2500 kW, which are made up of 500 kW conversion loss and 200 MW * 0.01 = 2 MW transmission losses. The voltage setpoints defined at from and to bus are complied with. Now lets define costs for the external grids to run an OPF: ###Code costeg0 = pp.create_polynomial_cost(net, 0, 'ext_grid', array([.1, 0])) costeg1 = pp.create_polynomial_cost(net, 1, 'ext_grid', array([.08, 0])) net.bus['max_vm_pu'] = 1.5 net.line['max_loading_percent'] = 1000 pp.runopp(net) ###Output _____no_output_____ ###Markdown Since we defined lower costs for Ext Grid 2, it fully services the load: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown While the DC line does not transmit any power: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown If we set the costs of the left grid to a lower value than the right grid and run the loadflow again: ###Code net.polynomial_cost.c.at[costeg0]= array([[0.08, 0]]) net.polynomial_cost.c.at[costeg1]= array([[0.1, 0]]) pp.runopp(net) ###Output _____no_output_____ ###Markdown We can see that the power now comes from the left ext_grid: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And is transmitted over the DC line: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown We can however see that the lines on the left hand side are now overloaded: ###Code net.res_line ###Output _____no_output_____ ###Markdown If we set the maximum line loading to 100% and run the OPF again: ###Code net.line["max_loading_percent"] = 100 pp.runopp(net) ###Output _____no_output_____ ###Markdown We can see that the lines are no longer overloaded: ###Code net.res_line ###Output _____no_output_____ ###Markdown Because the load is serviced from both grids: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line transmits only part of the power needed to service the load: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Finally, we can also define transmission costs for the DC line: ###Code costeg1 = pp.create_polynomial_cost(net, 0, 'dcline', array([.03, 0])) pp.runopp(net) ###Output _____no_output_____ ###Markdown Because the sum of the costs for generating power on the left hand side (0.08) and transmitting it to the right side (0.03) is now larger than for generating on the right side (0.1), the OPF draws as much power from the right side as is possible without violating line loading constraints: ###Code net.res_line net.res_dcline ###Output _____no_output_____ ###Markdown If we broaden the line loading constraint and run the OPF again: ###Code net.line["max_loading_percent"] = 1000 pp.runopp(net) ###Output _____no_output_____ ###Markdown The load is once again fully serviced by the grid on the right hand side: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line is in open loop operation: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown DC Line dispatch with pandapower OPFThis is an introduction into the usage of the pandapower optimal power flow with dc lines. Example NetworkWe use the following four bus example network for this tutorial:We first create this network in pandapower: ###Code import pandapower as pp from numpy import array net = pp.create_empty_network() b1 = pp.create_bus(net, 380) b2 = pp.create_bus(net, 380) b3 = pp.create_bus(net, 380) b4 = pp.create_bus(net, 380) b5 = pp.create_bus(net, 380) l1 = pp.create_line(net, b1, b2, 30, "490-AL1/64-ST1A 380.0") l2 = pp.create_line(net, b3, b4, 20, "490-AL1/64-ST1A 380.0") l3 = pp.create_line(net, b4, b5, 20, "490-AL1/64-ST1A 380.0") dcl1 = pp.create_dcline(net, name="dc line", from_bus=b2, to_bus=b3, p_kw=0.2e6, loss_percent=1.0, loss_kw=500, vm_from_pu=1.01, vm_to_pu=1.012, max_p_kw=1e6, in_service=True) eg1 = pp.create_ext_grid(net, b1, 1.02, max_p_kw=0.) eg2 = pp.create_ext_grid(net, b5, 1.02, max_p_kw=0.) l1 = pp.create_load(net, bus=b4, p_kw=800e3, controllable = False) ###Output _____no_output_____ ###Markdown We now run a regular load flow to check out the DC line model: ###Code pp.runpp(net) ###Output _____no_output_____ ###Markdown The transmission power of the DC line is defined in the loadflow as given by the p_kw parameter, which was set to 0.2 GW: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown The losses amount to 2500 kW, which are made up of 500 kW conversion loss and 200 MW * 0.01 = 2 MW transmission losses. The voltage setpoints defined at from and to bus are complied with. Now lets define costs for the external grids to run an OPF: ###Code costeg0 = pp.create_polynomial_cost(net, 0, 'ext_grid', array([-.1, 0])) costeg1 = pp.create_polynomial_cost(net, 1, 'ext_grid', array([-.08, 0])) net.bus['max_vm_pu'] = 1.5 net.line['max_loading_percent'] = 1000 pp.runopp(net) ###Output hp.pandapower.run - WARNING: The OPF cost definition has changed! Please check out the tutorial 'opf_changes-may18.ipynb' or the documentation! hp.pandapower.run - INFO: These missing columns in ext_grid are considered in OPF as +- 1000 TW.: ['min_p_kw' 'min_q_kvar' 'max_q_kvar'] hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Since we defined lower costs for Ext Grid 2, it fully services the load: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown While the DC line does not transmit any power: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown If we set the costs of the left grid to a lower value than the right grid and run the loadflow again: ###Code net.polynomial_cost.c.at[costeg0]= array([[-0.08, 0]]) net.polynomial_cost.c.at[costeg1]= array([[-0.1, 0]]) pp.runopp(net) ###Output hp.pandapower.run - WARNING: The OPF cost definition has changed! Please check out the tutorial 'opf_changes-may18.ipynb' or the documentation! hp.pandapower.run - INFO: These missing columns in ext_grid are considered in OPF as +- 1000 TW.: ['min_p_kw' 'min_q_kvar' 'max_q_kvar'] hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the power now comes from the left ext_grid: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And is transmitted over the DC line: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown We can however see that the lines on the left hand side are now overloaded: ###Code net.res_line ###Output _____no_output_____ ###Markdown If we set the maximum line loading to 100% and run the OPF again: ###Code net.line["max_loading_percent"] = 100 pp.runopp(net) ###Output hp.pandapower.run - WARNING: The OPF cost definition has changed! Please check out the tutorial 'opf_changes-may18.ipynb' or the documentation! hp.pandapower.run - INFO: These missing columns in ext_grid are considered in OPF as +- 1000 TW.: ['min_p_kw' 'min_q_kvar' 'max_q_kvar'] hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the lines are no longer overloaded: ###Code net.res_line ###Output _____no_output_____ ###Markdown Because the load is serviced from both grids: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line transmits only part of the power needed to service the load: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Finally, we can also define transmission costs for the DC line: ###Code costeg1 = pp.create_polynomial_cost(net, 0, 'dcline', array([.03, 0])) pp.runopp(net) ###Output hp.pandapower.run - WARNING: The OPF cost definition has changed! Please check out the tutorial 'opf_changes-may18.ipynb' or the documentation! hp.pandapower.run - INFO: These missing columns in ext_grid are considered in OPF as +- 1000 TW.: ['min_p_kw' 'min_q_kvar' 'max_q_kvar'] hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Because the sum of the costs for generating power on the left hand side (0.08) and transmitting it to the right side (0.03) is now larger than for generating on the right side (0.1), the OPF draws as much power from the right side as is possible without violating line loading constraints: ###Code net.res_line net.res_dcline ###Output _____no_output_____ ###Markdown If we broaden the line loading constraint and run the OPF again: ###Code net.line["max_loading_percent"] = 1000 pp.runopp(net) ###Output hp.pandapower.run - WARNING: The OPF cost definition has changed! Please check out the tutorial 'opf_changes-may18.ipynb' or the documentation! hp.pandapower.run - INFO: These missing columns in ext_grid are considered in OPF as +- 1000 TW.: ['min_p_kw' 'min_q_kvar' 'max_q_kvar'] hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown The load is once again fully serviced by the grid on the right hand side: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line is in open loop operation: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Little consistency check: ###Code net.res_ext_grid.p_kw.at[0]-0.08 + net.res_ext_grid.p_kw.at[1]-0.1 + net.res_dcline.p_from_kw.at[0]0.03 net.res_cost ###Output _____no_output_____ ###Markdown DC Line dispatch with pandapower OPFThis is an introduction into the usage of the pandapower optimal power flow with dc lines. Example NetworkWe use the following four bus example network for this tutorial:We first create this network in pandapower: ###Code import pandapower as pp from numpy import array net = pp.create_empty_network() b1 = pp.create_bus(net, 380) b2 = pp.create_bus(net, 380) b3 = pp.create_bus(net, 380) b4 = pp.create_bus(net, 380) b5 = pp.create_bus(net, 380) l1 = pp.create_line(net, b1, b2, 30, "490-AL1/64-ST1A 380.0") l2 = pp.create_line(net, b3, b4, 20, "490-AL1/64-ST1A 380.0") l3 = pp.create_line(net, b4, b5, 20, "490-AL1/64-ST1A 380.0") dcl1 = pp.create_dcline(net, name="dc line", from_bus=b2, to_bus=b3, p_mw=200, loss_percent=1.0, loss_mw=0.5, vm_from_pu=1.01, vm_to_pu=1.012, max_p_mw=1000, in_service=True) eg1 = pp.create_ext_grid(net, b1, 1.02, min_p_mw=0.) eg2 = pp.create_ext_grid(net, b5, 1.02, min_p_mw=0.) l1 = pp.create_load(net, bus=b4, p_mw=800, controllable = False) ###Output _____no_output_____ ###Markdown We now run a regular load flow to check out the DC line model: ###Code pp.runpp(net) ###Output _____no_output_____ ###Markdown The transmission power of the DC line is defined in the loadflow as given by the p_kw parameter, which was set to 200 MW: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown The losses amount to 2.5 MW, which are made up of 0.5 MW conversion loss and 200 MW 0.01 = 2 MW transmission losses. The voltage setpoints defined at from and to bus are complied with. Now lets define costs for the external grids to run an OPF: ###Code costeg0 = pp.create_poly_cost(net, 0, 'ext_grid', cp1_eur_per_mw=10) costeg1 = pp.create_poly_cost(net, 1, 'ext_grid', cp1_eur_per_mw=8) net.bus['max_vm_pu'] = 1.5 net.line['max_loading_percent'] = 1000 pp.runopp(net) ###Output tazan.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] tazan.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Since we defined lower costs for Ext Grid 2, it fully services the load: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown While the DC line does not transmit any power: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown If we set the costs of the left grid to a lower value than the right grid and run the loadflow again: ###Code net.poly_cost.cp1_eur_per_mw.at[costeg0] = 8 net.poly_cost.cp1_eur_per_mw.at[costeg1] = 10 pp.runopp(net) ###Output tazan.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] tazan.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the power now comes from the left ext_grid: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And is transmitted over the DC line: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown We can however see that the lines on the left hand side are now overloaded: ###Code net.res_line.loading_percent ###Output _____no_output_____ ###Markdown If we set the maximum line loading to 100% and run the OPF again: ###Code net.line["max_loading_percent"] = 100 pp.runopp(net) ###Output tazan.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] tazan.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the lines are no longer overloaded: ###Code net.res_line.loading_percent ###Output _____no_output_____ ###Markdown Because the load is serviced from both grids: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line transmits only part of the power needed to service the load: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Finally, we can also define transmission costs for the DC line: ###Code costeg1 = pp.create_poly_cost(net, 0, 'dcline', cp1_eur_per_mw=3) pp.runopp(net) ###Output tazan.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] tazan.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Because the sum of the costs for generating power on the left hand side (0.08) and transmitting it to the right side (0.03) is now larger than for generating on the right side (0.1), the OPF draws as much power from the right side as is possible without violating line loading constraints: ###Code net.res_line.loading_percent net.res_dcline ###Output _____no_output_____ ###Markdown If we broaden the line loading constraint and run the OPF again: ###Code net.line["max_loading_percent"] = 1000 pp.runopp(net) ###Output tazan.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] tazan.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown The load is once again fully serviced by the grid on the right hand side: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line is in open loop operation: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Little consistency check: ###Code net.res_ext_grid.p_mw.at[0]8 + net.res_ext_grid.p_mw.at[1]10 + net.res_dcline.p_from_mw.at[0]3 net.res_cost ###Output _____no_output_____ ###Markdown DC Line dispatch with pandapower OPFThis is an introduction into the usage of the pandapower optimal power flow with dc lines. Example NetworkWe use the following four bus example network for this tutorial:We first create this network in pandapower: ###Code import pandapower as pp from numpy import array net = pp.create_empty_network() b1 = pp.create_bus(net, 380) b2 = pp.create_bus(net, 380) b3 = pp.create_bus(net, 380) b4 = pp.create_bus(net, 380) b5 = pp.create_bus(net, 380) l1 = pp.create_line(net, b1, b2, 30, "490-AL1/64-ST1A 380.0") l2 = pp.create_line(net, b3, b4, 20, "490-AL1/64-ST1A 380.0") l3 = pp.create_line(net, b4, b5, 20, "490-AL1/64-ST1A 380.0") dcl1 = pp.create_dcline(net, name="dc line", from_bus=b2, to_bus=b3, p_mw=200, loss_percent=1.0, loss_mw=0.5, vm_from_pu=1.01, vm_to_pu=1.012, max_p_mw=1000, in_service=True) eg1 = pp.create_ext_grid(net, b1, 1.02, min_p_mw=0.) eg2 = pp.create_ext_grid(net, b5, 1.02, min_p_mw=0.) l1 = pp.create_load(net, bus=b4, p_mw=800, controllable = False) ###Output _____no_output_____ ###Markdown We now run a regular load flow to check out the DC line model: ###Code pp.runpp(net) ###Output _____no_output_____ ###Markdown The transmission power of the DC line is defined in the loadflow as given by the p_kw parameter, which was set to 200 MW: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown The losses amount to 2.5 MW, which are made up of 0.5 MW conversion loss and 200 MW 0.01 = 2 MW transmission losses. The voltage setpoints defined at from and to bus are complied with. Now lets define costs for the external grids to run an OPF: ###Code costeg0 = pp.create_poly_cost(net, 0, 'ext_grid', cp1_eur_per_mw=10) costeg1 = pp.create_poly_cost(net, 1, 'ext_grid', cp1_eur_per_mw=8) net.bus['max_vm_pu'] = 1.5 net.line['max_loading_percent'] = 1000 pp.runopp(net) ###Output hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Since we defined lower costs for Ext Grid 2, it fully services the load: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown While the DC line does not transmit any power: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown If we set the costs of the left grid to a lower value than the right grid and run the loadflow again: ###Code net.poly_cost.cp1_eur_per_mw.at[costeg0] = 8 net.poly_cost.cp1_eur_per_mw.at[costeg1] = 10 pp.runopp(net) ###Output hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the power now comes from the left ext_grid: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And is transmitted over the DC line: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown We can however see that the lines on the left hand side are now overloaded: ###Code net.res_line.loading_percent ###Output _____no_output_____ ###Markdown If we set the maximum line loading to 100% and run the OPF again: ###Code net.line["max_loading_percent"] = 100 pp.runopp(net) ###Output hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown We can see that the lines are no longer overloaded: ###Code net.res_line.loading_percent ###Output _____no_output_____ ###Markdown Because the load is serviced from both grids: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line transmits only part of the power needed to service the load: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Finally, we can also define transmission costs for the DC line: ###Code costeg1 = pp.create_poly_cost(net, 0, 'dcline', cp1_eur_per_mw=3) pp.runopp(net) ###Output hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown Because the sum of the costs for generating power on the left hand side (8) and transmitting it to the right side (3) is now larger than for generating on the right side (10), the OPF draws as much power from the right side as is possible without violating line loading constraints: ###Code net.res_line.loading_percent net.res_dcline ###Output _____no_output_____ ###Markdown If we relax the line loading constraint and run the OPF again: ###Code net.line["max_loading_percent"] = 1000 pp.runopp(net) ###Output hp.pandapower.run - INFO: These elements have missing power constraint values, which are considered in OPF as +- 1000 TW: ['dcline'] hp.pandapower.run - INFO: min_vm_pu is missing in bus table. In OPF these limits are considered as 0.0 pu. ###Markdown The load is once again fully serviced by the grid on the right hand side: ###Code net.res_ext_grid ###Output _____no_output_____ ###Markdown And the DC line is in open loop operation: ###Code net.res_dcline ###Output _____no_output_____ ###Markdown Little consistency check: ###Code net.res_ext_grid.p_mw.at[0]8 + net.res_ext_grid.p_mw.at[1]10 + net.res_dcline.p_from_mw.at[0]*3 net.res_cost ###Output _____no_output_____
Mini_Project_Linear_Regression.ipynb	###Markdown Regression in Python**This is a very quick run-through of some basic statistical concepts, adapted from [Lab 4 in Harvard's CS109](https://github.com/cs109/2015lab4) course. Please feel free to try the original lab if you're feeling ambitious :-) The CS109 git repository also has the solutions if you're stuck. Linear Regression Models* Prediction using linear regression* Some re-sampling methods * Train-Test splits * Cross ValidationLinear regression is used to model and predict continuous outcomes while logistic regression is used to model binary outcomes. We'll see some examples of linear regression as well as Train-test splits.The packages we'll cover are: `statsmodels`, `seaborn`, and `scikit-learn`. While we don't explicitly teach `statsmodels` and `seaborn` in the Springboard workshop, those are great libraries to know.* * ###Code # special IPython command to prepare the notebook for matplotlib and other libraries %pylab inline import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt import sklearn import seaborn as sns # special matplotlib argument for improved plots from matplotlib import rcParams sns.set_style("whitegrid") sns.set_context("poster") ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown * Part 1: Linear Regression Purpose of linear regression* Given a dataset $X$ and $Y$, linear regression can be used to: Build a predictive model to predict future values of $X_i$ without a $Y$ value. Model the strength of the relationship between each dependent variable $X_i$ and $Y$ Sometimes not all $X_i$ will have a relationship with $Y$ Need to figure out which $X_i$ contributes most information to determine $Y$ Linear regression is used in so many applications that I won't warrant this with examples. It is in many cases, the first pass prediction algorithm for continuous outcomes. A brief recap (feel free to skip if you don't care about the math)**[Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) is a method to model the relationship between a set of independent variables $X$ (also knowns as explanatory variables, features, predictors) and a dependent variable $Y$. This method assumes the relationship between each predictor $X$ is linearly related to the dependent variable $Y$. $$ Y = \beta_0 + \beta_1 X + \epsilon$$where $\epsilon$ is considered as an unobservable random variable that adds noise to the linear relationship. This is the simplest form of linear regression (one variable), we'll call this the simple model. $\beta_0$ is the intercept of the linear model* Multiple linear regression is when you have more than one independent variable * $X_1$, $X_2$, $X_3$, $\ldots$$$ Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon$$ * Back to the simple model. The model in linear regression is the conditional mean of $Y$ given the values in $X$ is expressed a linear function. $$ y = f(x) = E(Y \| X = x)$$ ![conditional mean](images/conditionalmean.png)http://www.learner.org/courses/againstallodds/about/glossary.html* The goal is to estimate the coefficients (e.g. $\beta_0$ and $\beta_1$). We represent the estimates of the coefficients with a "hat" on top of the letter. $$ \hat{\beta}_0, \hat{\beta}_1 $$* Once you estimate the coefficients $\hat{\beta}_0$ and $\hat{\beta}_1$, you can use these to predict new values of $Y$$$\hat{y} = \hat{\beta}_0 + \hat{\beta}_1 x_1$$* How do you estimate the coefficients? * There are many ways to fit a linear regression model * The method called least squares is one of the most common methods * We will discuss least squares today Estimating $\hat\beta$: Least squares**[Least squares](http://en.wikipedia.org/wiki/Least_squares) is a method that can estimate the coefficients of a linear model by minimizing the difference between the following: $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$where $N$ is the number of observations. We will not go into the mathematical details, but the least squares estimates $\hat{\beta}_0$ and $\hat{\beta}_1$ minimize the sum of the squared residuals $r_i = y_i - (\beta_0 + \beta_1 x_i)$ in the model (i.e. makes the difference between the observed $y_i$ and linear model $\beta_0 + \beta_1 x_i$ as small as possible). The solution can be written in compact matrix notation as$$\hat\beta = (X^T X)^{-1}X^T Y$$ We wanted to show you this in case you remember linear algebra, in order for this solution to exist we need $X^T X$ to be invertible. Of course this requires a few extra assumptions, $X$ must be full rank so that $X^T X$ is invertible, etc. This is important for us because this means that having redundant features in our regression models will lead to poorly fitting (and unstable) models. We'll see an implementation of this in the extra linear regression example.Note: The "hat" means it is an estimate of the coefficient. * Part 2: Boston Housing Data SetThe [Boston Housing data set](https://archive.ics.uci.edu/ml/datasets/Housing) contains information about the housing values in suburbs of Boston. This dataset was originally taken from the StatLib library which is maintained at Carnegie Mellon University and is now available on the UCI Machine Learning Repository. Load the Boston Housing data set from `sklearn`This data set is available in the [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.htmlsklearn.datasets.load_boston) python module which is how we will access it today. ###Code from sklearn.datasets import load_boston boston = load_boston() boston.keys() boston.data.shape # Print column names print (boston.feature_names) # Print description of Boston housing data set print (boston.DESCR) ###Output Boston House Prices dataset Notes ------ Data Set Characteristics: :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive :Median Value (attribute 14) is usually the target :Attribute Information (in order): - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built prior to 1940 - DIS weighted distances to five Boston employment centres - RAD index of accessibility to radial highways - TAX full-value property-tax rate per $10,000 - PTRATIO pupil-teacher ratio by town - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town - LSTAT % lower status of the population - MEDV Median value of owner-occupied homes in $1000's :Missing Attribute Values: None :Creator: Harrison, D. and Rubinfeld, D.L. This is a copy of UCI ML housing dataset. http://archive.ics.uci.edu/ml/datasets/Housing This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter. The Boston house-price data has been used in many machine learning papers that address regression problems. References* - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing) ###Markdown Now let's explore the data set itself. ###Code bos = pd.DataFrame(boston.data) bos.head() ###Output _____no_output_____ ###Markdown There are no column names in the DataFrame. Let's add those. ###Code bos.columns = boston.feature_names bos.head() ###Output _____no_output_____ ###Markdown Now we have a pandas DataFrame called `bos` containing all the data we want to use to predict Boston Housing prices. Let's create a variable called `PRICE` which will contain the prices. This information is contained in the `target` data. ###Code print (boston.target.shape) bos['PRICE'] = boston.target bos.head() ###Output _____no_output_____ ###Markdown EDA and Summary Statistics*Let's explore this data set. First we use `describe()` to get basic summary statistics for each of the columns. ###Code bos.describe() ###Output _____no_output_____ ###Markdown Scatter plotsLet's look at some scatter plots for three variables: 'CRIM', 'RM' and 'PTRATIO'. What kind of relationship do you see? e.g. positive, negative? linear? non-linear? ###Code plt.scatter(bos.CRIM, bos.PRICE) plt.xlabel("Per capita crime rate by town (CRIM)") plt.ylabel("Housing Price") plt.title("Relationship between CRIM and Price") ###Output _____no_output_____ ###Markdown Your turn: Create scatter plots between RM* and PRICE, and PTRATIO and PRICE. What do you notice? ###Code #your turn: scatter plot between RM and PRICE plt.scatter(bos.RM, bos.PRICE) plt.xlabel("average number of rooms per dwelling (RM)") plt.ylabel("Housing Price") plt.title("Relationship between RM and Price") #your turn: scatter plot between PTRATIO and PRICE plt.scatter(bos.PTRATIO, bos.PRICE) plt.xlabel("pupil-teacher ratio by town (PTRATIO)") plt.ylabel("Housing Price") plt.title("Relationship between PTRATIO and Price") ###Output _____no_output_____ ###Markdown Your turn: What are some other numeric variables of interest? Plot scatter plots with these variables and PRICE. ###Code #your turn: create some other scatter plots plt.scatter(bos.AGE, bos.PRICE) plt.xlabel("proportion of owner-occupied units built prior to 1940 (AGE)") plt.ylabel("Housing Price") plt.title("Relationship between House Ages and Price") ###Output _____no_output_____ ###Markdown Scatter Plots using Seaborn*[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) is a cool Python plotting library built on top of matplotlib. It provides convenient syntax and shortcuts for many common types of plots, along with better-looking defaults.We can also use [seaborn regplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/regression.htmlfunctions-to-draw-linear-regression-models) for the scatterplot above. This provides automatic linear regression fits (useful for data exploration later on). Here's one example below. ###Code sns.regplot(y="PRICE", x="RM", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Histograms* Histograms are a useful way to visually summarize the statistical properties of numeric variables. They can give you an idea of the mean and the spread of the variables as well as outliers. ###Code plt.hist(bos.CRIM) plt.title("CRIM") plt.xlabel("Crime rate per capita") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Your turn: Plot separate histograms and one for RM, one for PTRATIO. Any interesting observations? ###Code #your turn plt.hist(bos.RM) plt.title("RM") plt.xlabel("average number of rooms per dwelling") plt.ylabel("Frequency") plt.show() # Histogram for pupil-teacher ratio by town plt.hist(bos.PTRATIO) plt.title("PTRATIO") plt.xlabel("pupil-teacher ratio by town") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Linear regression with Boston housing data example*Here, $Y$ = boston housing prices (also called "target" data in python)and$X$ = all the other features (or independent variables)which we will use to fit a linear regression model and predict Boston housing prices. We will use the least squares method as the way to estimate the coefficients. We'll use two ways of fitting a linear regression. We recommend the first but the second is also powerful in its features. Fitting Linear Regression using `statsmodels`[Statsmodels](http://statsmodels.sourceforge.net/) is a great Python library for a lot of basic and inferential statistics. It also provides basic regression functions using an R-like syntax, so it's commonly used by statisticians. While we don't cover statsmodels officially in the Data Science Intensive, it's a good library to have in your toolbox. Here's a quick example of what you could do with it. ###Code # Import regression modules # ols - stands for Ordinary least squares, we'll use this import statsmodels.api as sm from statsmodels.formula.api import ols # statsmodels works nicely with pandas dataframes # The thing inside the "quotes" is called a formula, a bit on that below m = ols('PRICE ~ RM',bos).fit() print (m.summary()) ###Output OLS Regression Results ============================================================================== Dep. Variable: PRICE R-squared: 0.484 Model: OLS Adj. R-squared: 0.483 Method: Least Squares F-statistic: 471.8 Date: Sat, 17 Jun 2017 Prob (F-statistic): 2.49e-74 Time: 14:39:37 Log-Likelihood: -1673.1 No. Observations: 506 AIC: 3350. Df Residuals: 504 BIC: 3359. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>\|t\| [0.025 0.975] ------------------------------------------------------------------------------ Intercept -34.6706 2.650 -13.084 0.000 -39.877 -29.465 RM 9.1021 0.419 21.722 0.000 8.279 9.925 ============================================================================== Omnibus: 102.585 Durbin-Watson: 0.684 Prob(Omnibus): 0.000 Jarque-Bera (JB): 612.449 Skew: 0.726 Prob(JB): 1.02e-133 Kurtosis: 8.190 Cond. No. 58.4 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown Interpreting coefficientsThere is a ton of information in this output. But we'll concentrate on the coefficient table (middle table). We can interpret the `RM` coefficient (9.1021) by first noticing that the p-value (under `P>\|t\|`) is so small, basically zero. We can interpret the coefficient as, if we compare two groups of towns, one where the average number of rooms is say $5$ and the other group is the same except that they all have $6$ rooms. For these two groups the average difference in house prices is about $9.1$ (in thousands) so about $\$9,100$ difference. The confidence interval fives us a range of plausible values for this difference, about ($\$8,279, \$9,925$), deffinitely not chump change. `statsmodels` formulasThis formula notation will seem familiar to `R` users, but will take some getting used to for people coming from other languages or are new to statistics.The formula gives instruction for a general structure for a regression call. For `statsmodels` (`ols` or `logit`) calls you need to have a Pandas dataframe with column names that you will add to your formula. In the below example you need a pandas data frame that includes the columns named (`Outcome`, `X1`,`X2`, ...), bbut you don't need to build a new dataframe for every regression. Use the same dataframe with all these things in it. The structure is very simple:`Outcome ~ X1`But of course we want to to be able to handle more complex models, for example multiple regression is doone like this:`Outcome ~ X1 + X2 + X3`This is the very basic structure but it should be enough to get you through the homework. Things can get much more complex, for a quick run-down of further uses see the `statsmodels` [help page](http://statsmodels.sourceforge.net/devel/example_formulas.html). Let's see how our model actually fit our data. We can see below that there is a ceiling effect, we should probably look into that. Also, for large values of $Y$ we get underpredictions, most predictions are below the 45-degree gridlines. Your turn: Create a scatterpot between the predicted prices, available in `m.fittedvalues` and the original prices. How does the plot look? ###Code # your turn plt.scatter(bos.PRICE, m.fittedvalues) plt.xlabel("Housing Price") plt.ylabel("Predicted Housing Price") plt.title("Relationship between Predicted and Actual Price") ###Output _____no_output_____ ###Markdown Fitting Linear Regression using `sklearn` ###Code from sklearn.linear_model import LinearRegression X = bos.drop('PRICE', axis = 1) # This creates a LinearRegression object lm = LinearRegression() lm ###Output _____no_output_____ ###Markdown What can you do with a LinearRegression object? Check out the scikit-learn [docs here](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). We have listed the main functions here. Main functions \| Description--- \| --- `lm.fit()` \| Fit a linear model`lm.predit()` \| Predict Y using the linear model with estimated coefficients`lm.score()` \| Returns the coefficient of determination (R^2). A measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model* What output can you get? ###Code # Look inside lm object #lm.<tab> ###Output _____no_output_____ ###Markdown Output \| Description--- \| --- `lm.coef_` \| Estimated coefficients`lm.intercept_` \| Estimated intercept Fit a linear model*The `lm.fit()` function estimates the coefficients the linear regression using least squares. ###Code # Use all 13 predictors to fit linear regression model lm.fit(X, bos.PRICE) ###Output _____no_output_____ ###Markdown Your turn: How would you change the model to not fit an intercept term? Would you recommend not having an intercept? Estimated intercept and coefficientsLet's look at the estimated coefficients from the linear model using `1m.intercept_` and `lm.coef_`. After we have fit our linear regression model using the least squares method, we want to see what are the estimates of our coefficients $\beta_0$, $\beta_1$, ..., $\beta_{13}$: $$ \hat{\beta}_0, \hat{\beta}_1, \ldots, \hat{\beta}_{13} $$ ###Code print ('Estimated intercept coefficient:', lm.intercept_) print ('Number of coefficients:', len(lm.coef_)) # The coefficients pd.DataFrame(list(zip(X.columns, lm.coef_)), columns = ['features', 'estimatedCoefficients']) ###Output _____no_output_____ ###Markdown Predict Prices We can calculate the predicted prices ($\hat{Y}_i$) using `lm.predict`. $$ \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_1 + \ldots \hat{\beta}_{13} X_{13} $$ ###Code # first five predicted prices lm.predict(X)[0:5] ###Output _____no_output_____ ###Markdown Your turn:** * Histogram: Plot a histogram of all the predicted prices* Scatter Plot: Let's plot the true prices compared to the predicted prices to see they disagree (we did this with `statsmodels` before). ###Code # your turn # Plot a histogram of all the predicted prices plt.hist(lm.predict(X)) plt.title("Predicted Prices") plt.xlabel("Predicted Prices") plt.ylabel("Frequency") plt.show() # Let's plot the true prices compared to the predicted prices to see they disagree plt.scatter(bos.PRICE, lm.predict(X)) plt.xlabel("Housing Price") plt.ylabel("Predicted Housing Price") plt.title("Relationship between Predicted and Actual Price") ###Output _____no_output_____ ###Markdown Residual sum of squaresLet's calculate the residual sum of squares $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$ ###Code print (np.sum((bos.PRICE - lm.predict(X)) 2)) ###Output 11080.2762841 ###Markdown Mean squared errorThis is simple the mean of the residual sum of squares.Your turn:* Calculate the mean squared error and print it. ###Code #your turn print ('Mean squared error: ', np.mean((bos.PRICE - lm.predict(X)) 2)) ###Output Mean squared error: 21.8977792177 ###Markdown Relationship between `PTRATIO` and housing priceTry fitting a linear regression model using only the 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code lm = LinearRegression() lm.fit(X[['PTRATIO']], bos.PRICE) msePTRATIO = np.mean((bos.PRICE - lm.predict(X[['PTRATIO']])) * 2) print (msePTRATIO) ###Output 62.6522000138 ###Markdown We can also plot the fitted linear regression line. ###Code plt.scatter(bos.PTRATIO, bos.PRICE) plt.xlabel("Pupil-to-Teacher Ratio (PTRATIO)") plt.ylabel("Housing Price") plt.title("Relationship between PTRATIO and Price") plt.plot(bos.PTRATIO, lm.predict(X[['PTRATIO']]), color='blue', linewidth=3) plt.show() ###Output _____no_output_____ ###Markdown Your turn*Try fitting a linear regression model using three independent variables1. 'CRIM' (per capita crime rate by town)2. 'RM' (average number of rooms per dwelling)3. 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code # your turn lm.fit(X[['CRIM']], bos.PRICE) print ('(MSE) Per capita crime rate by town: ', np.mean((bos.PRICE - lm.predict(X[['CRIM']])) 2)) lm.fit(X[['RM']], bos.PRICE) print ('(MSE) Average number of rooms per dwelling: ', np.mean((bos.PRICE - lm.predict(X[['RM']])) 2)) lm.fit(X[['PTRATIO']], bos.PRICE) print ('(MSE) Pupil-teacher ratio by town: ', np.mean((bos.PRICE - lm.predict(X[['PTRATIO']])) 2)) ###Output (MSE) Per capita crime rate by town: 71.8523466653 (MSE) Average number of rooms per dwelling: 43.6005517712 (MSE) Pupil-teacher ratio by town: 62.6522000138 ###Markdown Other important things to think about when fitting a linear regression model* Linearity. The dependent variable $Y$ is a linear combination of the regression coefficients and the independent variables $X$. Constant standard deviation. The SD of the dependent variable $Y$ should be constant for different values of X. e.g. PTRATIO Normal distribution for errors. The $\epsilon$ term we discussed at the beginning are assumed to be normally distributed. $$ \epsilon_i \sim N(0, \sigma^2)$$Sometimes the distributions of responses $Y$ may not be normally distributed at any given value of $X$. e.g. skewed positively or negatively. Independent errors. The observations are assumed to be obtained independently. e.g. Observations across time may be correlated ###Code sns.set(font_scale=.8) sns.heatmap(X.corr(), vmax=.8, square=True, annot=True) ###Output _____no_output_____ ###Markdown Part 3: Training and Test Data sets Purpose of splitting data into Training/testing sets* Let's stick to the linear regression example: We built our model with the requirement that the model fit the data well. As a side-effect, the model will fit THIS dataset well. What about new data? We wanted the model for predictions, right? One simple solution, leave out some data (for testing) and train the model on the rest This also leads directly to the idea of cross-validation, next section. *One way of doing this is you can create training and testing data sets manually. ###Code X_train = X[:-50] X_test = X[-50:] Y_train = bos.PRICE[:-50] Y_test = bos.PRICE[-50:] print (X_train.shape) print (X_test.shape) print (Y_train.shape) print (Y_test.shape) ###Output (456, 13) (50, 13) (456,) (50,) ###Markdown Another way, is to split the data into random train and test subsets using the function `train_test_split` in `sklearn.cross_validation`. Here's the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html). ###Code X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split( X, bos.PRICE, test_size=0.33, random_state = 5) print (X_train.shape) print (X_test.shape) print (Y_train.shape) print (Y_test.shape) ###Output (339, 13) (167, 13) (339,) (167,) ###Markdown Your turn:** Let's build a linear regression model using our new training data sets. * Fit a linear regression model to the training set* Predict the output on the test set ###Code # your turn # Fit a linear regression model to the training set lm.fit(X_train, Y_train) lm.predict(X_test) ###Output _____no_output_____ ###Markdown Your turn:Calculate the mean squared error * using just the test data* using just the training dataAre they pretty similar or very different? What does that mean? ###Code # your turn # Calculate MSE using just the test data print ('(MSE) using just the test data: ', np.mean((Y_test - lm.predict(X_test)) 2)) # Calculate MSE using just the training data print ('(MSE) using just the training data: ', np.mean((Y_train - lm.predict(X_train)) 2)) ###Output (MSE) using just the test data: 28.5413672756 (MSE) using just the training data: 19.5467584735 ###Markdown Are they pretty similar or very different? What does that mean?-> They are very different because the model us based on training data so it will be accurate compared to the test data. The model is not exposed to test data so it will give a greater mean square error. It means there are data in test data which are different with the training data. Residual plots ###Code plt.scatter(lm.predict(X_train), lm.predict(X_train) - Y_train, c='b', s=40, alpha=0.5) plt.scatter(lm.predict(X_test), lm.predict(X_test) - Y_test, c='g', s=40) plt.hlines(y = 0, xmin=0, xmax = 50) plt.title('Residual Plot using training (blue) and test (green) data') plt.ylabel('Residuals') ###Output _____no_output_____ ###Markdown Your turn: Do you think this linear regression model generalizes well on the test data?-> No, the scatter points are not close to zero so the model needs improvements. Check the features to see highly correlated predictors and remove one of them or check the parameters of the model and do fine-tuning. K-fold Cross-validation as an extension of this idea* A simple extension of the Test/train split is called K-fold cross-validation. Here's the procedure: randomly assign your $n$ samples to one of $K$ groups. They'll each have about $n/k$ samples For each group $k$: Fit the model (e.g. run regression) on all data excluding the $k^{th}$ group Use the model to predict the outcomes in group $k$ Calculate your prediction error for each observation in $k^{th}$ group (e.g. $(Y_i - \hat{Y}_i)^2$ for regression, $\mathbb{1}(Y_i = \hat{Y}_i)$ for logistic regression). Calculate the average prediction error across all samples $Err_{CV} = \frac{1}{n}\sum_{i=1}^n (Y_i - \hat{Y}_i)^2$ Luckily you don't have to do this entire process all by hand (``for`` loops, etc.) every single time, ``sci-kit learn`` has a very nice implementation of this, have a look at the [documentation](http://scikit-learn.org/stable/modules/cross_validation.html). Your turn (extra credit):* Implement K-Fold cross-validation using the procedure above and Boston Housing data set using $K=4$. How does the average prediction error compare to the train-test split above? ###Code from sklearn import cross_validation, linear_model # If the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. # In all other cases, KFold is used scores = cross_validation.cross_val_score(lm, X, bos.PRICE, scoring='mean_squared_error', cv=4) # This will print metric for evaluation print ('(MSE) Using k-fold: ', np.mean(scores)) print ('The K-fold cross-validation is not performaing well compared to the previous train-test split above') ###Output (MSE) Using k-fold: -42.4894695275 The K-fold cross-validation is not performaing well compared to the previous train-test split above ###Markdown Regression in Python**This is a very quick run-through of some basic statistical concepts, adapted from [Lab 4 in Harvard's CS109](https://github.com/cs109/2015lab4) course. Please feel free to try the original lab if you're feeling ambitious :-) The CS109 git repository also has the solutions if you're stuck. Linear Regression Models* Prediction using linear regressionLinear regression is used to model and predict continuous outcomes with normal random errors. There are nearly an infinite number of different types of regression models and each regression model is typically defined by the distribution of the prediction errors (called "residuals") of the type of data. Logistic regression is used to model binary outcomes whereas Poisson regression is used to predict counts. In this exercise, we'll see some examples of linear regression as well as Train-test splits.The packages we'll cover are: `statsmodels`, `seaborn`, and `scikit-learn`. While we don't explicitly teach `statsmodels` and `seaborn` in the Springboard workshop, those are great libraries to know.* * ###Code # special IPython command to prepare the notebook for matplotlib and other libraries %matplotlib inline import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt import sklearn import seaborn as sns # special matplotlib argument for improved plots from matplotlib import rcParams sns.set_style("whitegrid") sns.set_context("poster") ###Output _____no_output_____ ###Markdown * Part 1: Introduction to Linear Regression Purpose of linear regression* Given a dataset containing predictor variables $X$ and outcome/response variable $Y$, linear regression can be used to: Build a predictive model to predict future values of $\hat{Y}$, using new data $X^$ where $Y$ is unknown. Model the strength of the relationship between each independent variable $X_i$ and $Y$ Many times, only a subset of independent variables $X_i$ will have a linear relationship with $Y$ Need to figure out which $X_i$ contributes most information to predict $Y$ It is in many cases, the first pass prediction algorithm for continuous outcomes. A Brief Mathematical Recap[Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) is a method to model the relationship between a set of independent variables $X$ (also knowns as explanatory variables, features, predictors) and a dependent variable $Y$. This method assumes the relationship between each predictor $X$ is linearly** related to the dependent variable $Y$. The most basic linear regression model contains one independent variable $X$, we'll call this the simple model. $$ Y = \beta_0 + \beta_1 X + \epsilon$$where $\epsilon$ is considered as an unobservable random variable that adds noise to the linear relationship. In linear regression, $\epsilon$ is assumed to be normally distributed with a mean of 0. In other words, what this means is that on average, if we know $Y$, a roughly equal number of predictions $\hat{Y}$ will be above $Y$ and others will be below $Y$. That is, on average, the error is zero. The residuals, $\epsilon$ are also assumed to be "i.i.d.": independently and identically distributed. Independence means that the residuals are not correlated -- the residual from one prediction has no effect on the residual from another prediction. Correlated errors are common in time series analysis and spatial analyses.* $\beta_0$ is the intercept of the linear model and represents the average of $Y$ when all independent variables $X$ are set to 0.* $\beta_1$ is the slope of the line associated with the regression model and represents the average effect of a one-unit increase in $X$ on $Y$.* Back to the simple model. The model in linear regression is the conditional mean of $Y$ given the values in $X$ is expressed a linear function. $$ y = f(x) = E(Y \| X = x)$$ ![conditional mean](images/conditionalmean.png)Image from http://www.learner.org/courses/againstallodds/about/glossary.html. Note this image uses $\alpha$ and $\beta$ instead of $\beta_0$ and $\beta_1$.* The goal is to estimate the coefficients (e.g. $\beta_0$ and $\beta_1$). We represent the estimates of the coefficients with a "hat" on top of the letter. $$ \hat{\beta}_0, \hat{\beta}_1 $$* Once we estimate the coefficients $\hat{\beta}_0$ and $\hat{\beta}_1$, we can use these to predict new values of $Y$ given new data $X$.$$\hat{y} = \hat{\beta}_0 + \hat{\beta}_1 x_1$$* Multiple linear regression is when you have more than one independent variable and the estimation involves matrices * $X_1$, $X_2$, $X_3$, $\ldots$* How do you estimate the coefficients? * There are many ways to fit a linear regression model * The method called least squares is the most common methods * We will discuss least squares$$ Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon$$ Estimating $\hat\beta$: Least squares*[Least squares](http://en.wikipedia.org/wiki/Least_squares) is a method that can estimate the coefficients of a linear model by minimizing the squared residuals: $$ \mathscr{L} = \sum_{i=1}^N \epsilon_i^2 = \sum_{i=1}^N \left( y_i - \hat{y}_i \right)^2 = \sum_{i=1}^N \left(y_i - \left(\beta_0 + \beta_1 x_i\right)\right)^2 $$where $N$ is the number of observations and $\epsilon$ represents a residual or error, ACTUAL - PREDICTED. Estimating the intercept $\hat{\beta_0}$ for the simple linear modelWe want to minimize the squared residuals and solve for $\hat{\beta_0}$ so we take the partial derivative of $\mathscr{L}$ with respect to $\hat{\beta_0}$ $\begin{align}\frac{\partial \mathscr{L}}{\partial \hat{\beta_0}} &= \frac{\partial}{\partial \hat{\beta_0}} \sum_{i=1}^N \epsilon^2 \\&= \frac{\partial}{\partial \hat{\beta_0}} \sum_{i=1}^N \left( y_i - \hat{y}_i \right)^2 \\&= \frac{\partial}{\partial \hat{\beta_0}} \sum_{i=1}^N \left( y_i - \left( \hat{\beta}_0 + \hat{\beta}_1 x_i \right) \right)^2 \\&= -2 \sum_{i=1}^N \left( y_i - \left( \hat{\beta}_0 + \hat{\beta}_1 x_i \right) \right) \hspace{25mm} \mbox{(by chain rule)} \\&= -2 \sum_{i=1}^N (y_i - \hat{\beta}_0 - \hat{\beta}_1 x_i) \\&= -2 \left[ \left( \sum_{i=1}^N y_i \right) - N \hat{\beta_0} - \hat{\beta}_1 \left( \sum_{i=1}^N x_i\right) \right] \\& 2 \left[ N \hat{\beta}_0 + \hat{\beta}_1 \sum_{i=1}^N x_i - \sum_{i=1}^N y_i \right] = 0 \hspace{20mm} \mbox{(Set equal to 0 and solve for $\hat{\beta}_0$)} \\& N \hat{\beta}_0 + \hat{\beta}_1 \sum_{i=1}^N x_i - \sum_{i=1}^N y_i = 0 \\& N \hat{\beta}_0 = \sum_{i=1}^N y_i - \hat{\beta}_1 \sum_{i=1}^N x_i \\& \hat{\beta}_0 = \frac{\sum_{i=1}^N y_i - \hat{\beta}_1 \sum_{i=1}^N x_i}{N} \\& \hat{\beta}_0 = \frac{\sum_{i=1}^N y_i}{N} - \hat{\beta}_1 \frac{\sum_{i=1}^N x_i}{N} \\& \boxed{\hat{\beta}_0 = \bar{y} - \hat{\beta}_1 \bar{x}}\end{align}$ Using this new information, we can compute the estimate for $\hat{\beta}_1$ by taking the partial derivative of $\mathscr{L}$ with respect to $\hat{\beta}_1$. $\begin{align}\frac{\partial \mathscr{L}}{\partial \hat{\beta_1}} &= \frac{\partial}{\partial \hat{\beta_1}} \sum_{i=1}^N \epsilon^2 \\&= \frac{\partial}{\partial \hat{\beta_1}} \sum_{i=1}^N \left( y_i - \hat{y}_i \right)^2 \\&= \frac{\partial}{\partial \hat{\beta_1}} \sum_{i=1}^N \left( y_i - \left( \hat{\beta}_0 + \hat{\beta}_1 x_i \right) \right)^2 \\&= 2 \sum_{i=1}^N \left( y_i - \left( \hat{\beta}_0 + \hat{\beta}_1 x_i \right) \right) \left( -x_i \right) \hspace{25mm}\mbox{(by chain rule)} \\&= -2 \sum_{i=1}^N x_i \left( y_i - \hat{\beta}_0 - \hat{\beta}_1 x_i \right) \\&= -2 \sum_{i=1}^N x_i (y_i - \hat{\beta}_0 x_i - \hat{\beta}_1 x_i^2) \\&= -2 \sum_{i=1}^N x_i (y_i - \left( \bar{y} - \hat{\beta}_1 \bar{x} \right) x_i - \hat{\beta}_1 x_i^2) \\&= -2 \sum_{i=1}^N (x_i y_i - \bar{y}x_i + \hat{\beta}_1\bar{x}x_i - \hat{\beta}_1 x_i^2) \\&= -2 \left[ \sum_{i=1}^N x_i y_i - \bar{y} \sum_{i=1}^N x_i + \hat{\beta}_1\bar{x}\sum_{i=1}^N x_i - \hat{\beta}_1 \sum_{i=1}^N x_i^2 \right] \\&= -2 \left[ \hat{\beta}_1 \left\{ \bar{x} \sum_{i=1}^N x_i - \sum_{i=1}^N x_i^2 \right\} + \left\{ \sum_{i=1}^N x_i y_i - \bar{y} \sum_{i=1}^N x_i \right\}\right] \\& 2 \left[ \hat{\beta}_1 \left\{ \sum_{i=1}^N x_i^2 - \bar{x} \sum_{i=1}^N x_i \right\} + \left\{ \bar{y} \sum_{i=1}^N x_i - \sum_{i=1}^N x_i y_i \right\} \right] = 0 \\& \hat{\beta}_1 = \frac{-\left( \bar{y} \sum_{i=1}^N x_i - \sum_{i=1}^N x_i y_i \right)}{\sum_{i=1}^N x_i^2 - \bar{x}\sum_{i=1}^N x_i} \\&= \frac{\sum_{i=1}^N x_i y_i - \bar{y} \sum_{i=1}^N x_i}{\sum_{i=1}^N x_i^2 - \bar{x} \sum_{i=1}^N x_i} \\& \boxed{\hat{\beta}_1 = \frac{\sum_{i=1}^N x_i y_i - \bar{x}\bar{y}n}{\sum_{i=1}^N x_i^2 - n \bar{x}^2}}\end{align}$ The solution can be written in compact matrix notation as$$\hat\beta = (X^T X)^{-1}X^T Y$$ We wanted to show you this in case you remember linear algebra, in order for this solution to exist we need $X^T X$ to be invertible. Of course this requires a few extra assumptions, $X$ must be full rank so that $X^T X$ is invertible, etc. Basically, $X^T X$ is full rank if all rows and columns are linearly independent. This has a loose relationship to variables and observations being independent respective. This is important for us because this means that having redundant features in our regression models will lead to poorly fitting (and unstable) models. We'll see an implementation of this in the extra linear regression example. * Part 2: Exploratory Data Analysis for Linear RelationshipsThe [Boston Housing data set](https://archive.ics.uci.edu/ml/datasets/Housing) contains information about the housing values in suburbs of Boston. This dataset was originally taken from the StatLib library which is maintained at Carnegie Mellon University and is now available on the UCI Machine Learning Repository. Load the Boston Housing data set from `sklearn`*This data set is available in the [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.htmlsklearn.datasets.load_boston) python module which is how we will access it today. ###Code from sklearn.datasets import load_boston import pandas as pd boston = load_boston() boston.keys() boston.data.shape # Print column names print(boston.feature_names) # Print description of Boston housing data set print(boston.DESCR) ###Output .. _boston_dataset: Boston house prices dataset --------------------------- Data Set Characteristics: :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target. :Attribute Information (in order): - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built prior to 1940 - DIS weighted distances to five Boston employment centres - RAD index of accessibility to radial highways - TAX full-value property-tax rate per $10,000 - PTRATIO pupil-teacher ratio by town - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town - LSTAT % lower status of the population - MEDV Median value of owner-occupied homes in $1000's :Missing Attribute Values: None :Creator: Harrison, D. and Rubinfeld, D.L. This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter. The Boston house-price data has been used in many machine learning papers that address regression problems. .. topic:: References - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. ###Markdown Now let's explore the data set itself. ###Code bos = pd.DataFrame(boston.data) bos.head() ###Output _____no_output_____ ###Markdown There are no column names in the DataFrame. Let's add those. ###Code bos.columns = boston.feature_names bos.head() ###Output _____no_output_____ ###Markdown Now we have a pandas DataFrame called `bos` containing all the data we want to use to predict Boston Housing prices. Let's create a variable called `PRICE` which will contain the prices. This information is contained in the `target` data. ###Code print(boston.target.shape) bos['PRICE'] = boston.target bos.head() ###Output _____no_output_____ ###Markdown EDA and Summary StatisticsLet's explore this data set. First we use `describe()` to get basic summary statistics for each of the columns. ###Code bos.describe() ###Output _____no_output_____ ###Markdown Scatterplots*Let's look at some scatter plots for three variables: 'CRIM' (per capita crime rate), 'RM' (number of rooms) and 'PTRATIO' (pupil-to-teacher ratio in schools). ###Code plt.scatter(bos.CRIM, bos.PRICE, s=10) plt.xlabel("Per capita crime rate by town (CRIM)") plt.ylabel("Housing Price") plt.title("Relationship between CRIM and Price") ###Output _____no_output_____ ###Markdown Part 2 Checkup Exercise Set IExercise: What kind of relationship do you see? e.g. positive, negative? linear? non-linear? Is there anything else strange or interesting about the data? What about outliers?Exercise: Create scatter plots between RM* and PRICE, and PTRATIO and PRICE. Label your axes appropriately using human readable labels. Tell a story about what you see.Exercise: What are some other numeric variables of interest? Why do you think they are interesting? Plot scatterplots with these variables and PRICE (house price) and tell a story about what you see. -- your turn: describe relationshipIt seems clear that there is a negative relationship between crime rate and price. It might be better fitted with a curve but, in general, it's fairly linear. It is somewhat interesting that houses at the bottom of the pricing scale have a large range of crime ratios. ###Code # your turn: scatter plot between RM and PRICE plt.scatter(bos.RM, bos.PRICE, s=12) plt.xlabel('Number of Rooms') plt.ylabel('Housing Price') plt.title('Relationship between number of rooms and price') plt.show() # your turn: scatter plot between PTRATIO and PRICE plt.scatter(bos.PTRATIO, bos.PRICE, s=12) plt.xlabel('Pupil-Teacher ratio') plt.ylabel('Housing Price') plt.title('Relationship between PTRATIO and PRICE') # your turn: create some other scatter plots plt.scatter(bos.B, bos.PRICE, s=12) plt.xlabel('Proportion of blacks') plt.ylabel('Housing Price') plt.scatter(bos.NOX, bos.PRICE, s=12) plt.xlabel('Nitric Oxide Level') plt.ylabel('Housing Price') ###Output _____no_output_____ ###Markdown Nitric oxide levels do seem to have an effect on pricing. Or it may be that areas without facilities that produce NOX are naturally more appealing, which correlates to price. Scatterplots using Seaborn*[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) is a cool Python plotting library built on top of matplotlib. It provides convenient syntax and shortcuts for many common types of plots, along with better-looking defaults.We can also use [seaborn regplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/regression.htmlfunctions-to-draw-linear-regression-models) for the scatterplot above. This provides automatic linear regression fits (useful for data exploration later on). Here's one example below. ###Code _ = sns.regplot(y="PRICE", x="RM", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Histograms* ###Code plt.hist(np.log(bos.CRIM)) plt.title("CRIM") plt.xlabel("Crime rate per capita") plt.ylabel("Frequencey") plt.show() ###Output _____no_output_____ ###Markdown Part 2 Checkup Exercise Set IIExercise: In the above histogram, we took the logarithm of the crime rate per capita. Repeat this histogram without taking the log. What was the purpose of taking the log? What do we gain by making this transformation? What do you now notice about this variable that is not obvious without making the transformation?Exercise: Plot the histogram for RM and PTRATIO against each other, along with the two variables you picked in the previous section. We are looking for correlations in predictors here. ###Code #your turn #histogram without the log plt.hist(bos.CRIM) plt.title("CRIM") plt.xlabel("Crime rate per capita") plt.ylabel("Frequencey") plt.show() plt.subplot(2,2,1) plt.hist(np.log(bos.RM)) plt.xlabel('RM', fontsize=14) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.title('Room Count', fontsize=14) plt.subplot(2,2,2) plt.hist(np.log(bos.PTRATIO)) plt.xlabel('PTRATIO', fontsize=14) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.title('Pupil-Teacher Ratio', fontsize=14) plt.subplot(2,2,3) plt.hist(bos.B) plt.xlabel('B', fontsize=14) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.title('Proportion of Blacks', fontsize=14) plt.subplot(2,2,4) plt.hist(np.log(bos.NOX)) plt.xlabel('NOX', fontsize=14) plt.xticks(fontsize=14, rotation=60) plt.yticks(fontsize=14) plt.title('Nitric Oxide Level', fontsize=14) plt.tight_layout() ###Output _____no_output_____ ###Markdown Part 3: Linear Regression with Boston Housing Data Example*Here, $Y$ = boston housing prices (called "target" data in python, and referred to as the dependent variable or response variable)and$X$ = all the other features (or independent variables, predictors or explanatory variables)which we will use to fit a linear regression model and predict Boston housing prices. We will use the least-squares method to estimate the coefficients. We'll use two ways of fitting a linear regression. We recommend the first but the second is also powerful in its features. Fitting Linear Regression using `statsmodels`[Statsmodels](http://statsmodels.sourceforge.net/) is a great Python library for a lot of basic and inferential statistics. It also provides basic regression functions using an R-like syntax, so it's commonly used by statisticians. While we don't cover statsmodels officially in the Data Science Intensive workshop, it's a good library to have in your toolbox. Here's a quick example of what you could do with it. The version of least-squares we will use in statsmodels is called ordinary least-squares (OLS). There are many other versions of least-squares such as [partial least squares (PLS)](https://en.wikipedia.org/wiki/Partial_least_squares_regression) and [weighted least squares (WLS)](https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares). ###Code # Import regression modules import statsmodels.api as sm from statsmodels.formula.api import ols # statsmodels works nicely with pandas dataframes # The thing inside the "quotes" is called a formula, a bit on that below m = ols('PRICE ~ RM',bos).fit() print(m.summary()) ###Output OLS Regression Results ============================================================================== Dep. Variable: PRICE R-squared: 0.484 Model: OLS Adj. R-squared: 0.483 Method: Least Squares F-statistic: 471.8 Date: Sat, 08 Jun 2019 Prob (F-statistic): 2.49e-74 Time: 11:10:27 Log-Likelihood: -1673.1 No. Observations: 506 AIC: 3350. Df Residuals: 504 BIC: 3359. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>\|t\| [0.025 0.975] ------------------------------------------------------------------------------ Intercept -34.6706 2.650 -13.084 0.000 -39.877 -29.465 RM 9.1021 0.419 21.722 0.000 8.279 9.925 ============================================================================== Omnibus: 102.585 Durbin-Watson: 0.684 Prob(Omnibus): 0.000 Jarque-Bera (JB): 612.449 Skew: 0.726 Prob(JB): 1.02e-133 Kurtosis: 8.190 Cond. No. 58.4 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown Interpreting coefficientsThere is a ton of information in this output. But we'll concentrate on the coefficient table (middle table). We can interpret the `RM` coefficient (9.1021) by first noticing that the p-value (under `P>\|t\|`) is so small, basically zero. This means that the number of rooms, `RM`, is a statistically significant predictor of `PRICE`. The regression coefficient for `RM` of 9.1021 means that on average, each additional room is associated with an increase of $\$9,100$ in house price net of the other variables. The confidence interval gives us a range of plausible values for this average change, about ($\$8,279, \$9,925$), definitely not chump change. In general, the $\hat{\beta_i}, i > 0$ can be interpreted as the following: "A one unit increase in $x_i$ is associated with, on average, a $\hat{\beta_i}$ increase/decrease in $y$ net of all other variables."On the other hand, the interpretation for the intercept, $\hat{\beta}_0$ is the average of $y$ given that all of the independent variables $x_i$ are 0. `statsmodels` formulasThis formula notation will seem familiar to `R` users, but will take some getting used to for people coming from other languages or are new to statistics.The formula gives instruction for a general structure for a regression call. For `statsmodels` (`ols` or `logit`) calls you need to have a Pandas dataframe with column names that you will add to your formula. In the below example you need a pandas data frame that includes the columns named (`Outcome`, `X1`,`X2`, ...), but you don't need to build a new dataframe for every regression. Use the same dataframe with all these things in it. The structure is very simple:`Outcome ~ X1`But of course we want to to be able to handle more complex models, for example multiple regression is done like this:`Outcome ~ X1 + X2 + X3`In general, a formula for an OLS multiple linear regression is`Y ~ X1 + X2 + ... + Xp`This is the very basic structure but it should be enough to get you through the homework. Things can get much more complex. You can force statsmodels to treat variables as categorical with the `C()` function, call numpy functions to transform data such as `np.log` for extremely-skewed data, or fit a model without an intercept by including `- 1` in the formula. For a quick run-down of further uses see the `statsmodels` [help page](http://statsmodels.sourceforge.net/devel/example_formulas.html). Let's see how our model actually fit our data. We can see below that there is a ceiling effect, we should probably look into that. Also, for large values of $Y$ we get underpredictions, most predictions are below the 45-degree gridlines. Part 3 Checkup Exercise Set IExercise: Create a scatterplot between the predicted prices, available in `m.fittedvalues` (where `m` is the fitted model) and the original prices. How does the plot look? Do you notice anything interesting or weird in the plot? Comment on what you see. ###Code plt.scatter(m.fittedvalues, bos.PRICE, s=12) plt.xlabel('Predicted Prices') plt.ylabel('Actual Prices') plt.title('Predicted vs. Actual') ###Output _____no_output_____ ###Markdown For the most part, the original prices and the predicted prices correlate to each other. Most of the observations that are predicted in the $20k range actually concentrate in a similar area on the Y-axis. However, there are a few observations where the actual price is much higher that the predicted price, and there is one very noticeable example of the opposite. One final curiosity, there looks to be one house with a predicted negative value, which is odd, especially given that the actual price is above the median. Fitting Linear Regression using `sklearn` ###Code from sklearn.linear_model import LinearRegression X = bos.drop('PRICE', axis = 1) # This creates a LinearRegression object lm = LinearRegression() lm ###Output _____no_output_____ ###Markdown What can you do with a LinearRegression object? Check out the scikit-learn [docs here](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). We have listed the main functions here. Most machine learning models in scikit-learn follow this same API of fitting a model with `fit`, making predictions with `predict` and the appropriate scoring function `score` for each model. Main functions \| Description--- \| --- `lm.fit()` \| Fit a linear model`lm.predit()` \| Predict Y using the linear model with estimated coefficients`lm.score()` \| Returns the coefficient of determination (R^2). A measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model* What output can you get? ###Code # Look inside lm object # lm.<tab> ###Output _____no_output_____ ###Markdown Output \| Description--- \| --- `lm.coef_` \| Estimated coefficients`lm.intercept_` \| Estimated intercept Fit a linear model*The `lm.fit()` function estimates the coefficients the linear regression using least squares. ###Code # Use all 13 predictors to fit linear regression model lm.fit(X, bos.PRICE) ###Output _____no_output_____ ###Markdown Part 3 Checkup Exercise Set IIExercise: How would you change the model to not fit an intercept term? Would you recommend not having an intercept? Why or why not? For more information on why to include or exclude an intercept, look [here](https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-is-regression-through-the-origin/).Exercise: One of the assumptions of the linear model is that the residuals must be i.i.d. (independently and identically distributed). To satisfy this, is it enough that the residuals are normally distributed? Explain your answer.Exercise: True or false. To use linear regression, $Y$ must be normally distributed. Explain your answer. \ your turnIf we wanted to not fit an intercept, we could set the fit_intercept argument when calling LinearRegression() to =False. However, if the model predicts that there is a constant or base value of Y given all X variables to be zero, it seems we would want to capture that information. Looking at the housing example, even an empty plot of land has value regardless of the fact that there are zero rooms. It would be useful to be able to predict the value of that plot.While normally distributed residuals is ideal, it's not the only consideration. For one thing, the sample size might be too low to indicate normality, but doesn't guarantee that the residuals are not i.i.d. As for Y, the dependent variable does not need to be normally distributed. The distribution can be bi-modal, or otherwise, and still 'fit the line.' Estimated intercept and coefficientsLet's look at the estimated coefficients from the linear model using `1m.intercept_` and `lm.coef_`. After we have fit our linear regression model using the least squares method, we want to see what are the estimates of our coefficients $\beta_0$, $\beta_1$, ..., $\beta_{13}$: $$ \hat{\beta}_0, \hat{\beta}_1, \ldots, \hat{\beta}_{13} $$ ###Code print('Estimated intercept coefficient: {}'.format(lm.intercept_)) print('Number of coefficients: {}'.format(len(lm.coef_))) # The coefficients coef_df = pd.DataFrame({'features': X.columns, 'estimatedCoefficients': lm.coef_})[['features', 'estimatedCoefficients']] coef_df ###Output _____no_output_____ ###Markdown Predict Prices We can calculate the predicted prices ($\hat{Y}_i$) using `lm.predict`. $$ \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_1 + \ldots \hat{\beta}_{13} X_{13} $$ ###Code # first five predicted prices lm.predict(X)[0:5] ###Output _____no_output_____ ###Markdown Part 3 Checkup Exercise Set IIIExercise: Histogram: Plot a histogram of all the predicted prices. Write a story about what you see. Describe the shape, center and spread of the distribution. Are there any outliers? What might be the reason for them? Should we do anything special with them?Exercise: Scatterplot: Let's plot the true prices compared to the predicted prices to see if they disagree (we did this with `statsmodels` before).Exercise: We have looked at fitting a linear model in both `statsmodels` and `scikit-learn`. What are the advantages and disadvantages of each based on your exploration? Based on the information provided by both packages, what advantage does `statsmodels` provide? ###Code # your turn #histogram of predicted prices _ = plt.hist(lm.predict(X)) _ = plt.xlabel('Predicted prices') _ = plt.title('Predicted home values') _ = plt.scatter(lm.predict(X), bos.PRICE, s=12) _ = plt.xlabel('Predicted Prices') _ = plt.ylabel('Actual Prices') ###Output _____no_output_____ ###Markdown Evaluating the Model: Sum-of-SquaresThe partitioning of the sum-of-squares shows the variance in the predictions explained by the model and the variance that is attributed to error.$$TSS = ESS + RSS$$ Residual Sum-of-Squares (aka $RSS$)The residual sum-of-squares is one of the basic ways of quantifying how much error exists in the fitted model. We will revisit this in a bit.$$ RSS = \sum_{i=1}^N r_i^2 = \sum_{i=1}^N \left(y_i - \left(\beta_0 + \beta_1 x_i\right)\right)^2 $$ ###Code print(np.sum((bos.PRICE - lm.predict(X)) 2)) ###Output 11078.784577954977 ###Markdown Explained Sum-of-Squares (aka $ESS$)The explained sum-of-squares measures the variance explained by the regression model.$$ESS = \sum_{i=1}^N \left( \hat{y}_i - \bar{y} \right)^2 = \sum_{i=1}^N \left( \left( \hat{\beta}_0 + \hat{\beta}_1 x_i \right) - \bar{y} \right)^2$$ ###Code print(np.sum((lm.predict(X) - np.mean(bos.PRICE)) ** 2)) ###Output 31637.510837065056 ###Markdown Evaluating the Model: The Coefficient of Determination ($R^2$)The coefficient of determination, $R^2$, tells us the percentage of the variance in the response variable $Y$ that can be explained by the linear regression model.$$ R^2 = \frac{ESS}{TSS} $$The $R^2$ value is one of the most common metrics that people use in describing the quality of a model, but it is important to note that $R^2$ increases artificially as a side-effect of increasing the number of independent variables. While $R^2$ is reported in almost all statistical packages, another metric called the adjusted $R^2$ is also provided as it takes into account the number of variables in the model, and can sometimes even be used for non-linear regression models!$$R_{adj}^2 = 1 - \left( 1 - R^2 \right) \frac{N - 1}{N - K - 1} = R^2 - \left( 1 - R^2 \right) \frac{K}{N - K - 1} = 1 - \frac{\frac{RSS}{DF_R}}{\frac{TSS}{DF_T}}$$where $N$ is the number of observations, $K$ is the number of variables, $DF_R = N - K - 1$ is the degrees of freedom associated with the residual error and $DF_T = N - 1$ is the degrees of the freedom of the total error. Evaluating the Model: Mean Squared Error and the $F$-Statistic**The mean squared errors are just the averages* of the sum-of-squares errors over their respective degrees of freedom.$$MSE = \frac{RSS}{N-K-1}$$$$MSR = \frac{ESS}{K}$$Remember: Notation may vary across resources particularly the use of $R$ and $E$ in $RSS/ESS$ and $MSR/MSE$. In some resources, E = explained and R = residual. In other resources, E = error and R = regression (explained). This is a very important distinction that requires looking at the formula to determine which naming scheme is being used.Given the MSR and MSE, we can now determine whether or not the entire model we just fit is even statistically significant. We use an $F$-test for this. The null hypothesis is that all of the $\beta$ coefficients are zero, that is, none of them have any effect on $Y$. The alternative is that at least one $\beta$ coefficient is nonzero, but it doesn't tell us which one in a multiple regression:$$H_0: \beta_i = 0, \mbox{for all $i$} \\H_A: \beta_i > 0, \mbox{for some $i$}$$ $$F = \frac{MSR}{MSE} = \left( \frac{R^2}{1 - R^2} \right) \left( \frac{N - K - 1}{K} \right)$$ Once we compute the $F$-statistic, we can use the $F$-distribution with $N-K$ and $K-1$ degrees of degrees of freedom to get a p-value.Warning! The $F$-statistic mentioned in this section is NOT the same as the F1-measure or F1-value discused in Unit 7. Part 3 Checkup Exercise Set IVLet's look at the relationship between `PTRATIO` and housing price.Exercise: Try fitting a linear regression model using only the 'PTRATIO' (pupil-teacher ratio by town) and interpret the intercept and the coefficients.Exercise: Calculate (or extract) the $R^2$ value. What does it tell you?Exercise: Compute the $F$-statistic. What does it tell you?Exercise: Take a close look at the $F$-statistic and the $t$-statistic for the regression coefficient. What relationship do you notice? Note that this relationship only applies in simple linear regression models. ###Code # your turn #reshape the PTRATIO data and instatiate the regression model X = np.array(bos['PTRATIO']).reshape(-1,1) pt_reg = LinearRegression() #fit and predict the model, extracting the first 5 observations pt_reg.fit(X, bos.PRICE) pt_reg.predict(X)[:5] print('Intercept is {}'.format(pt_reg.intercept_)) print('Estimated coefficient is {}'.format(pt_reg.coef_)) #The R2 value is fairly low, indicating a weak predictor print('The R2 value is {}'.format(pt_reg.score(X, bos.PRICE))) #use the summary to find the F-statistic f = ols('PRICE ~ PTRATIO',bos).fit() print(f.summary()) ###Output OLS Regression Results ============================================================================== Dep. Variable: PRICE R-squared: 0.258 Model: OLS Adj. R-squared: 0.256 Method: Least Squares F-statistic: 175.1 Date: Tue, 07 May 2019 Prob (F-statistic): 1.61e-34 Time: 19:39:12 Log-Likelihood: -1764.8 No. Observations: 506 AIC: 3534. Df Residuals: 504 BIC: 3542. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>\|t\| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 62.3446 3.029 20.581 0.000 56.393 68.296 PTRATIO -2.1572 0.163 -13.233 0.000 -2.477 -1.837 ============================================================================== Omnibus: 92.924 Durbin-Watson: 0.725 Prob(Omnibus): 0.000 Jarque-Bera (JB): 191.444 Skew: 1.001 Prob(JB): 2.68e-42 Kurtosis: 5.252 Cond. No. 160. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown Based on this summary and the previous summary from the 'Rooms' model, the t-statistic and the F-statistic seem to be correlated. Part 3 Checkup Exercise Set VFit a linear regression model using three independent variables 'CRIM' (per capita crime rate by town) 'RM' (average number of rooms per dwelling) 'PTRATIO' (pupil-teacher ratio by town)Exercise: Compute or extract the $F$-statistic. What does it tell you about the model?Exercise: Compute or extract the $R^2$ statistic. What does it tell you about the model?Exercise: Which variables in the model are significant in predicting house price? Write a story that interprets the coefficients. ###Code # your turn from sklearn.metrics import r2_score X = bos[['CRIM', 'RM', 'PTRATIO']] reg_3 = LinearRegression() #Fit the three variables against price reg_3.fit(X, bos.PRICE) r2 = r2_score(bos.PRICE, reg_3.predict(X)) r2 X = bos[['CRIM', 'RM', 'PTRATIO']] f_3 = ols('PRICE ~ X', bos).fit() f_3.summary() ###Output _____no_output_____ ###Markdown The model performs better with multiple predictor variables. ###Code #Re-view the coefficients table coef_df ###Output _____no_output_____ ###Markdown The strongest coefficients appear to be RM and NOX. Even though NOX is negative, that information is still useful. Part 4: Comparing Models During modeling, there will be times when we want to compare models to see which one is more predictive or fits the data better. There are many ways to compare models, but we will focus on two. The $F$-Statistic RevisitedThe $F$-statistic can also be used to compare two nested models, that is, two models trained on the same dataset where one of the models contains a subset of the variables of the other model. The full model contains $K$ variables and the reduced model contains a subset of these $K$ variables. This allows us to add additional variables to a base model and then test if adding the variables helped the model fit.$$F = \frac{\left( \frac{RSS_{reduced} - RSS_{full}}{DF_{reduced} - DF_{full}} \right)}{\left( \frac{RSS_{full}}{DF_{full}} \right)}$$where $DF_x = N - K_x - 1$ where $K_x$ is the number of variables in model $x$. Akaike Information Criterion (AIC)Another statistic for comparing two models is AIC, which is based on the likelihood function and takes into account the number of variables in the model.$$AIC = 2 K - 2 \log_e{L}$$where $L$ is the likelihood of the model. AIC is meaningless in the absolute sense, and is only meaningful when compared to AIC values from other models. Lower values of AIC indicate better fitting models.`statsmodels` provides the AIC in its output. Part 4 Checkup ExercisesExercise: Find another variable (or two) to add to the model we built in Part 3. Compute the $F$-test comparing the two models as well as the AIC. Which model is better? ###Code X = bos[['CRIM', 'RM', 'PTRATIO', 'NOX', 'CHAS']] f_5 = ols('PRICE ~ X', bos).fit() f_5.summary() ###Output _____no_output_____ ###Markdown With more variables the F-statistic is lower, but R-squared and AIC indicate a better fit. Part 5: Evaluating the Model via Model Assumptions and Other Issues*Linear regression makes several assumptions. It is always best to check that these assumptions are valid after fitting a linear regression model. Linearity. The dependent variable $Y$ is a linear combination of the regression coefficients and the independent variables $X$. This can be verified with a scatterplot of each $X$ vs. $Y$ and plotting correlations among $X$. Nonlinearity can sometimes be resolved by [transforming](https://onlinecourses.science.psu.edu/stat501/node/318) one or more independent variables, the dependent variable, or both. In other cases, a [generalized linear model](https://en.wikipedia.org/wiki/Generalized_linear_model) or a [nonlinear model](https://en.wikipedia.org/wiki/Nonlinear_regression) may be warranted. Constant standard deviation. The SD of the dependent variable $Y$ should be constant for different values of X. We can check this by plotting each $X$ against $Y$ and verifying that there is no "funnel" shape showing data points fanning out as $X$ increases or decreases. Some techniques for dealing with non-constant variance include weighted least squares (WLS), [robust standard errors](https://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors), or variance stabilizing transformations. Normal distribution for errors. The $\epsilon$ term we discussed at the beginning are assumed to be normally distributed. This can be verified with a fitted values vs. residuals plot and verifying that there is no pattern, and with a quantile plot. $$ \epsilon_i \sim N(0, \sigma^2)$$Sometimes the distributions of responses $Y$ may not be normally distributed at any given value of $X$. e.g. skewed positively or negatively. Independent errors. The observations are assumed to be obtained independently. e.g. Observations across time may be correlated There are some other issues that are important investigate with linear regression models. Correlated Predictors: Care should be taken to make sure that the independent variables in a regression model are not too highly correlated. Correlated predictors typically do not majorly affect prediction, but do inflate standard errors of coefficients making interpretation unreliable. Common solutions are dropping the least important variables involved in the correlations, using regularlization, or, when many predictors are highly correlated, considering a dimension reduction technique such as principal component analysis (PCA). Influential Points:** Data points that have undue influence on the regression model. These points can be high leverage points or outliers. Such points are typically removed and the regression model rerun. Part 5 Checkup ExercisesTake the reduced model from Part 3 to answer the following exercises. Take a look at [this blog post](http://mpastell.com/2013/04/19/python_regression/) for more information on using statsmodels to construct these plots. Exercise: Construct a fitted values versus residuals plot. What does the plot tell you? Are there any violations of the model assumptions?Exercise: Construct a quantile plot of the residuals. What does the plot tell you?Exercise: What are some advantages and disadvantages of the fitted vs. residual and quantile plot compared to each other?Exercise: Identify any outliers (if any) in your model and write a story describing what these outliers might represent.Exercise: Construct a leverage plot and identify high leverage points in the model. Write a story explaining possible reasons for the high leverage points.Exercise: Remove the outliers and high leverage points from your model and run the regression again. How do the results change? ###Code # Your turn. X_3 = bos[['CRIM', 'RM', 'PTRATIO']] f_3 = ols('PRICE ~ X_3', bos).fit() f_3.summary() _ = plt.hist(f_3.resid) _ = plt.title('Residual Histogram', fontsize=16) import scipy.stats as stats stats.probplot(f_3.resid, plot=plt) plt.show() from statsmodels.graphics.regressionplots import * plot_leverage_resid2(f_3) bos_adj = bos.drop(bos.index[[380,418, 405, 367, 373, 365, 373, 369, 371, 372, 368]]) res_adj = ols('PRICE ~ CRIM + RM + PTRATIO', bos_adj).fit() res_adj.summary() ###Output _____no_output_____ ###Markdown Regression in Python**This is a very quick run-through of some basic statistical concepts, adapted from [Lab 4 in Harvard's CS109](https://github.com/cs109/2015lab4) course. Please feel free to try the original lab if you're feeling ambitious :-) The CS109 git repository also has the solutions if you're stuck. Linear Regression Models* Prediction using linear regression* Some re-sampling methods * Train-Test splits * Cross ValidationLinear regression is used to model and predict continuous outcomes while logistic regression is used to model binary outcomes. We'll see some examples of linear regression as well as Train-test splits.The packages we'll cover are: `statsmodels`, `seaborn`, and `scikit-learn`. While we don't explicitly teach `statsmodels` and `seaborn` in the Springboard workshop, those are great libraries to know.* * ###Code # special IPython command to prepare the notebook for matplotlib and other libraries %pylab inline import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt import sklearn import seaborn as sns # special matplotlib argument for improved plots from matplotlib import rcParams sns.set_style("whitegrid") sns.set_context("poster") ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown * Part 1: Linear Regression Purpose of linear regression* Given a dataset $X$ and $Y$, linear regression can be used to: Build a predictive model to predict future values of $X_i$ without a $Y$ value. Model the strength of the relationship between each dependent variable $X_i$ and $Y$ Sometimes not all $X_i$ will have a relationship with $Y$ Need to figure out which $X_i$ contributes most information to determine $Y$ Linear regression is used in so many applications that I won't warrant this with examples. It is in many cases, the first pass prediction algorithm for continuous outcomes. A brief recap (feel free to skip if you don't care about the math)**[Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) is a method to model the relationship between a set of independent variables $X$ (also knowns as explanatory variables, features, predictors) and a dependent variable $Y$. This method assumes the relationship between each predictor $X$ is linearly related to the dependent variable $Y$. $$ Y = \beta_0 + \beta_1 X + \epsilon$$where $\epsilon$ is considered as an unobservable random variable that adds noise to the linear relationship. This is the simplest form of linear regression (one variable), we'll call this the simple model. $\beta_0$ is the intercept of the linear model* Multiple linear regression is when you have more than one independent variable * $X_1$, $X_2$, $X_3$, $\ldots$$$ Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon$$ * Back to the simple model. The model in linear regression is the conditional mean of $Y$ given the values in $X$ is expressed a linear function. $$ y = f(x) = E(Y \| X = x)$$ ![conditional mean](images/conditionalmean.png)http://www.learner.org/courses/againstallodds/about/glossary.html* The goal is to estimate the coefficients (e.g. $\beta_0$ and $\beta_1$). We represent the estimates of the coefficients with a "hat" on top of the letter. $$ \hat{\beta}_0, \hat{\beta}_1 $$* Once you estimate the coefficients $\hat{\beta}_0$ and $\hat{\beta}_1$, you can use these to predict new values of $Y$$$\hat{y} = \hat{\beta}_0 + \hat{\beta}_1 x_1$$* How do you estimate the coefficients? * There are many ways to fit a linear regression model * The method called least squares is one of the most common methods * We will discuss least squares today Estimating $\hat\beta$: Least squares**[Least squares](http://en.wikipedia.org/wiki/Least_squares) is a method that can estimate the coefficients of a linear model by minimizing the difference between the following: $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$where $N$ is the number of observations. We will not go into the mathematical details, but the least squares estimates $\hat{\beta}_0$ and $\hat{\beta}_1$ minimize the sum of the squared residuals $r_i = y_i - (\beta_0 + \beta_1 x_i)$ in the model (i.e. makes the difference between the observed $y_i$ and linear model $\beta_0 + \beta_1 x_i$ as small as possible). The solution can be written in compact matrix notation as$$\hat\beta = (X^T X)^{-1}X^T Y$$ We wanted to show you this in case you remember linear algebra, in order for this solution to exist we need $X^T X$ to be invertible. Of course this requires a few extra assumptions, $X$ must be full rank so that $X^T X$ is invertible, etc. This is important for us because this means that having redundant features in our regression models will lead to poorly fitting (and unstable) models. We'll see an implementation of this in the extra linear regression example.Note: The "hat" means it is an estimate of the coefficient. * Part 2: Boston Housing Data SetThe [Boston Housing data set](https://archive.ics.uci.edu/ml/datasets/Housing) contains information about the housing values in suburbs of Boston. This dataset was originally taken from the StatLib library which is maintained at Carnegie Mellon University and is now available on the UCI Machine Learning Repository. Load the Boston Housing data set from `sklearn`This data set is available in the [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.htmlsklearn.datasets.load_boston) python module which is how we will access it today. ###Code from sklearn.datasets import load_boston boston = load_boston() boston.keys() boston.data.shape # Print column names print boston.feature_names # Print description of Boston housing data set print boston.DESCR ###Output Boston House Prices dataset =========================== Notes ------ Data Set Characteristics: :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive :Median Value (attribute 14) is usually the target :Attribute Information (in order): - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built prior to 1940 - DIS weighted distances to five Boston employment centres - RAD index of accessibility to radial highways - TAX full-value property-tax rate per $10,000 - PTRATIO pupil-teacher ratio by town - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town - LSTAT % lower status of the population - MEDV Median value of owner-occupied homes in $1000's :Missing Attribute Values: None :Creator: Harrison, D. and Rubinfeld, D.L. This is a copy of UCI ML housing dataset. http://archive.ics.uci.edu/ml/datasets/Housing This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter. The Boston house-price data has been used in many machine learning papers that address regression problems. References* - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing) ###Markdown Now let's explore the data set itself. ###Code bos = pd.DataFrame(boston.data) bos.head() ###Output _____no_output_____ ###Markdown There are no column names in the DataFrame. Let's add those. ###Code bos.columns = boston.feature_names bos.head() ###Output _____no_output_____ ###Markdown Now we have a pandas DataFrame called `bos` containing all the data we want to use to predict Boston Housing prices. Let's create a variable called `PRICE` which will contain the prices. This information is contained in the `target` data. ###Code print boston.target.shape bos['PRICE'] = boston.target bos.head() ###Output _____no_output_____ ###Markdown EDA and Summary Statistics*Let's explore this data set. First we use `describe()` to get basic summary statistics for each of the columns. ###Code bos.describe() ###Output _____no_output_____ ###Markdown Scatter plotsLet's look at some scatter plots for three variables: 'CRIM', 'RM' and 'PTRATIO'. What kind of relationship do you see? e.g. positive, negative? linear? non-linear? ###Code plt.scatter(bos.CRIM, bos.PRICE) plt.xlabel("Per capita crime rate by town (CRIM)") plt.ylabel("Housing Price") plt.title("Relationship between CRIM and Price") ###Output _____no_output_____ ###Markdown Your turn: Create scatter plots between RM* and PRICE, and PTRATIO and PRICE. What do you notice? ###Code #your turn: scatter plot between RM and PRICE plt.scatter(bos.RM, bos.PRICE) plt.xlabel("Average Number of Rooms per Dwelling") plt.ylabel("Housing Price") plt.title("Relationship between No. of Rooms and Price"); #your turn: scatter plot between PTRATIO and PRICE plt.scatter(bos.PTRATIO, bos.PRICE) plt.xlabel("Pupil-Teacher Ratio by town") plt.ylabel("Housing Price") plt.title("Relationship between PTRatio and Price"); ###Output _____no_output_____ ###Markdown Your turn: What are some other numeric variables of interest? Plot scatter plots with these variables and PRICE. ###Code #your turn: create some other scatter plots ###Output _____no_output_____ ###Markdown Scatter Plots using Seaborn*[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) is a cool Python plotting library built on top of matplotlib. It provides convenient syntax and shortcuts for many common types of plots, along with better-looking defaults.We can also use [seaborn regplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/regression.htmlfunctions-to-draw-linear-regression-models) for the scatterplot above. This provides automatic linear regression fits (useful for data exploration later on). Here's one example below. ###Code sns.regplot(y="PRICE", x="RM", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Histograms* Histograms are a useful way to visually summarize the statistical properties of numeric variables. They can give you an idea of the mean and the spread of the variables as well as outliers. ###Code plt.hist(bos.CRIM) plt.title("CRIM") plt.xlabel("Crime rate per capita") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Your turn: Plot separate histograms and one for RM, one for PTRATIO. Any interesting observations? ###Code #your turn plt.hist(bos.PTRATIO) plt.title("PTRATIO") plt.xlabel("Pupil-Teacher Ratio") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Linear regression with Boston housing data example*Here, $Y$ = boston housing prices (also called "target" data in python)and$X$ = all the other features (or independent variables)which we will use to fit a linear regression model and predict Boston housing prices. We will use the least squares method as the way to estimate the coefficients. We'll use two ways of fitting a linear regression. We recommend the first but the second is also powerful in its features. Fitting Linear Regression using `statsmodels`[Statsmodels](http://statsmodels.sourceforge.net/) is a great Python library for a lot of basic and inferential statistics. It also provides basic regression functions using an R-like syntax, so it's commonly used by statisticians. While we don't cover statsmodels officially in the Data Science Intensive, it's a good library to have in your toolbox. Here's a quick example of what you could do with it. ###Code # Import regression modules # ols - stands for Ordinary least squares, we'll use this import statsmodels.api as sm from statsmodels.formula.api import ols # statsmodels works nicely with pandas dataframes # The thing inside the "quotes" is called a formula, a bit on that below m = ols('PRICE ~ RM',bos).fit() print m.summary() ###Output OLS Regression Results ============================================================================== Dep. Variable: PRICE R-squared: 0.484 Model: OLS Adj. R-squared: 0.483 Method: Least Squares F-statistic: 471.8 Date: Tue, 04 Jul 2017 Prob (F-statistic): 2.49e-74 Time: 13:38:43 Log-Likelihood: -1673.1 No. Observations: 506 AIC: 3350. Df Residuals: 504 BIC: 3359. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>\|t\| [95.0% Conf. Int.] ------------------------------------------------------------------------------ Intercept -34.6706 2.650 -13.084 0.000 -39.877 -29.465 RM 9.1021 0.419 21.722 0.000 8.279 9.925 ============================================================================== Omnibus: 102.585 Durbin-Watson: 0.684 Prob(Omnibus): 0.000 Jarque-Bera (JB): 612.449 Skew: 0.726 Prob(JB): 1.02e-133 Kurtosis: 8.190 Cond. No. 58.4 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown Interpreting coefficientsThere is a ton of information in this output. But we'll concentrate on the coefficient table (middle table). We can interpret the `RM` coefficient (9.1021) by first noticing that the p-value (under `P>\|t\|`) is so small, basically zero. We can interpret the coefficient as, if we compare two groups of towns, one where the average number of rooms is say $5$ and the other group is the same except that they all have $6$ rooms. For these two groups the average difference in house prices is about $9.1$ (in thousands) so about $\$9,100$ difference. The confidence interval fives us a range of plausible values for this difference, about ($\$8,279, \$9,925$), deffinitely not chump change. `statsmodels` formulasThis formula notation will seem familiar to `R` users, but will take some getting used to for people coming from other languages or are new to statistics.The formula gives instruction for a general structure for a regression call. For `statsmodels` (`ols` or `logit`) calls you need to have a Pandas dataframe with column names that you will add to your formula. In the below example you need a pandas data frame that includes the columns named (`Outcome`, `X1`,`X2`, ...), bbut you don't need to build a new dataframe for every regression. Use the same dataframe with all these things in it. The structure is very simple:`Outcome ~ X1`But of course we want to to be able to handle more complex models, for example multiple regression is doone like this:`Outcome ~ X1 + X2 + X3`This is the very basic structure but it should be enough to get you through the homework. Things can get much more complex, for a quick run-down of further uses see the `statsmodels` [help page](http://statsmodels.sourceforge.net/devel/example_formulas.html). Let's see how our model actually fit our data. We can see below that there is a ceiling effect, we should probably look into that. Also, for large values of $Y$ we get underpredictions, most predictions are below the 45-degree gridlines. Your turn: Create a scatterpot between the predicted prices, available in `m.fittedvalues` and the original prices. How does the plot look? ###Code # your turn plt.scatter(m.fittedvalues, bos.PRICE) plt.xlabel("Predicted Price") plt.ylabel("Original Housing Price") plt.title("Predicted vs. Original Prices"); ###Output _____no_output_____ ###Markdown Fitting Linear Regression using `sklearn` ###Code from sklearn.linear_model import LinearRegression X = bos.drop('PRICE', axis = 1) # This creates a LinearRegression object lm = LinearRegression() lm ###Output _____no_output_____ ###Markdown What can you do with a LinearRegression object? Check out the scikit-learn [docs here](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). We have listed the main functions here. Main functions \| Description--- \| --- `lm.fit()` \| Fit a linear model`lm.predit()` \| Predict Y using the linear model with estimated coefficients`lm.score()` \| Returns the coefficient of determination (R^2). A measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model* What output can you get? ###Code # Look inside lm object #lm. ###Output _____no_output_____ ###Markdown Output \| Description--- \| --- `lm.coef_` \| Estimated coefficients`lm.intercept_` \| Estimated intercept Fit a linear model*The `lm.fit()` function estimates the coefficients the linear regression using least squares. ###Code # Use all 13 predictors to fit linear regression model lm.fit(X, bos.PRICE) ###Output _____no_output_____ ###Markdown Your turn: How would you change the model to not fit an intercept term? Would you recommend not having an intercept? Estimated intercept and coefficientsLet's look at the estimated coefficients from the linear model using `1m.intercept_` and `lm.coef_`. After we have fit our linear regression model using the least squares method, we want to see what are the estimates of our coefficients $\beta_0$, $\beta_1$, ..., $\beta_{13}$: $$ \hat{\beta}_0, \hat{\beta}_1, \ldots, \hat{\beta}_{13} $$ ###Code print 'Estimated intercept coefficient:', lm.intercept_ print 'Number of coefficients:', len(lm.coef_) # The coefficients pd.DataFrame(zip(X.columns, lm.coef_), columns = ['features', 'estimatedCoefficients']) ###Output _____no_output_____ ###Markdown Predict Prices We can calculate the predicted prices ($\hat{Y}_i$) using `lm.predict`. $$ \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_1 + \ldots \hat{\beta}_{13} X_{13} $$ ###Code # first five predicted prices lm.predict(X)[0:5] ###Output _____no_output_____ ###Markdown Your turn:** * Histogram: Plot a histogram of all the predicted prices* Scatter Plot: Let's plot the true prices compared to the predicted prices to see they disagree (we did this with `statsmodels` before). ###Code # your turn plt.hist(lm.predict(X)) plt.title("Predicted Prices") plt.xlabel("Price") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Residual sum of squaresLet's calculate the residual sum of squares $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$ ###Code print np.sum((bos.PRICE - lm.predict(X)) 2) ###Output 11080.2762841 ###Markdown Mean squared errorThis is simple the mean of the residual sum of squares.Your turn:* Calculate the mean squared error and print it. ###Code #your turn print np.mean((bos.PRICE - lm.predict(X)) 2) ###Output 21.8977792177 ###Markdown Relationship between `PTRATIO` and housing priceTry fitting a linear regression model using only the 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code lm = LinearRegression() lm.fit(X[['PTRATIO']], bos.PRICE) #pd.DataFrame(zip(X.columns, lm.coef_), columns = ['features', 'estimatedCoefficients']) print np.mean((bos.PRICE - lm.predict(X[['PTRATIO']])) * 2) msePTRATIO = np.mean((bos.PRICE - lm.predict(X[['PTRATIO']])) 2) print msePTRATIO ###Output 62.6522000138 ###Markdown We can also plot the fitted linear regression line. ###Code plt.scatter(bos.PTRATIO, bos.PRICE) plt.xlabel("Pupil-to-Teacher Ratio (PTRATIO)") plt.ylabel("Housing Price") plt.title("Relationship between PTRATIO and Price") plt.plot(bos.PTRATIO, lm.predict(X[['PTRATIO']]), color='blue', linewidth=3) plt.show() ###Output _____no_output_____ ###Markdown Your turnTry fitting a linear regression model using three independent variables1. 'CRIM' (per capita crime rate by town)2. 'RM' (average number of rooms per dwelling)3. 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code # your turn lm = LinearRegression() lm.fit(X[['CRIM']], bos.PRICE) print np.mean((bos.PRICE - lm.predict(X[['CRIM']])) * 2) plt.scatter(bos.CRIM, bos.PRICE) plt.xlabel("Crime per Capita") plt.ylabel("Housing Price") plt.title("Relationship between CRIM and Price") plt.plot(bos.CRIM, lm.predict(X[['CRIM']]), color='blue', linewidth=3) plt.show() ###Output _____no_output_____ ###Markdown Other important things to think about when fitting a linear regression model* Linearity. The dependent variable $Y$ is a linear combination of the regression coefficients and the independent variables $X$. Constant standard deviation. The SD of the dependent variable $Y$ should be constant for different values of X. e.g. PTRATIO Normal distribution for errors. The $\epsilon$ term we discussed at the beginning are assumed to be normally distributed. $$ \epsilon_i \sim N(0, \sigma^2)$$Sometimes the distributions of responses $Y$ may not be normally distributed at any given value of $X$. e.g. skewed positively or negatively. Independent errors. The observations are assumed to be obtained independently. e.g. Observations across time may be correlated Part 3: Training and Test Data sets Purpose of splitting data into Training/testing sets* Let's stick to the linear regression example: We built our model with the requirement that the model fit the data well. As a side-effect, the model will fit THIS dataset well. What about new data? We wanted the model for predictions, right? One simple solution, leave out some data (for testing) and train the model on the rest This also leads directly to the idea of cross-validation, next section. *One way of doing this is you can create training and testing data sets manually. ###Code X_train = X[:-50] X_test = X[-50:] Y_train = bos.PRICE[:-50] Y_test = bos.PRICE[-50:] print X_train.shape print X_test.shape print Y_train.shape print Y_test.shape X_train.head() ###Output (456, 13) (50, 13) (456,) (50,) ###Markdown Another way, is to split the data into random train and test subsets using the function `train_test_split` in `sklearn.cross_validation`. Here's the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html). ###Code #X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split( # X, bos.PRICE, test_size=0.33, random_state = 5) print X_train.shape print X_test.shape print Y_train.shape print Y_test.shape X_train ###Output (456, 13) (50, 13) (456,) (50,) ###Markdown Your turn:** Let's build a linear regression model using our new training data sets. * Fit a linear regression model to the training set* Predict the output on the test set ###Code # your turn lm = LinearRegression() lm.fit(X_train[['RM']], Y_train) print np.mean((Y_train - lm.predict(X_train[['RM']])) 2) plt.scatter(X_train.RM, Y_train) plt.xlabel("No. of Rooms") plt.ylabel("Housing Price") plt.title("Relationship between No. of Rooms and Price") plt.plot(X_train.RM, lm.predict(X_train[['RM']]), color='blue', linewidth=3) plt.show() ###Output 46.2932242688 ###Markdown Your turn:*Calculate the mean squared error using just the test data* using just the training dataAre they pretty similar or very different? What does that mean? ###Code # your turn lm = LinearRegression() lm.fit(X_test[['RM']], Y_test) print np.mean((Y_test - lm.predict(X_test[['RM']])) 2) plt.scatter(X_test.RM, Y_test) plt.xlabel("No. of Rooms") plt.ylabel("Housing Price") plt.title("Relationship between No. of Rooms and Price") plt.plot(X_test.RM, lm.predict(X_test[['RM']]), color='blue', linewidth=3) plt.show() ###Output 12.9000419523 ###Markdown Residual plots ###Code plt.scatter(lm.predict(X_train), lm.predict(X_train) - Y_train, c='b', s=40, alpha=0.5) plt.scatter(lm.predict(X_test), lm.predict(X_test) - Y_test, c='g', s=40) plt.hlines(y = 0, xmin=0, xmax = 50) plt.title('Residual Plot using training (blue) and test (green) data') plt.ylabel('Residuals') ###Output _____no_output_____ ###Markdown Regression in PythonThis is a very quick run-through of some basic statistical concepts, adapted from [Lab 4 in Harvard's CS109](https://github.com/cs109/2015lab4) course. Please feel free to try the original lab if you're feeling ambitious :-) The CS109 git repository also has the solutions if you're stuck. Linear Regression Models* Prediction using linear regression* Some re-sampling methods * Train-Test splits * Cross ValidationLinear regression is used to model and predict continuous outcomes while logistic regression is used to model binary outcomes. We'll see some examples of linear regression as well as Train-test splits.The packages we'll cover are: `statsmodels`, `seaborn`, and `scikit-learn`. While we don't explicitly teach `statsmodels` and `seaborn` in the Springboard workshop, those are great libraries to know.* * ###Code # special IPython command to prepare the notebook for matplotlib and other libraries %pylab inline import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt import sklearn import seaborn as sns # special matplotlib argument for improved plots from matplotlib import rcParams sns.set_style("whitegrid") sns.set_context("poster") ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown * Part 1: Linear Regression Purpose of linear regression* Given a dataset $X$ and $Y$, linear regression can be used to: Build a predictive model to predict future values of $X_i$ without a $Y$ value. Model the strength of the relationship between each independent variable $X_i$ and $Y$ Sometimes not all $X_i$ will have a relationship with $Y$ Need to figure out which $X_i$ contributes most information to determine $Y$ Linear regression is used in so many applications that I won't warrant this with examples. It is in many cases, the first pass prediction algorithm for continuous outcomes. A brief recap (feel free to skip if you don't care about the math)**[Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) is a method to model the relationship between a set of independent variables $X$ (also knowns as explanatory variables, features, predictors) and a dependent variable $Y$. This method assumes the relationship between each predictor $X$ is linearly related to the dependent variable $Y$. $$ Y = \beta_0 + \beta_1 X + \epsilon$$where $\epsilon$ is considered as an unobservable random variable that adds noise to the linear relationship. This is the simplest form of linear regression (one variable), we'll call this the simple model. $\beta_0$ is the intercept of the linear model* Multiple linear regression is when you have more than one independent variable * $X_1$, $X_2$, $X_3$, $\ldots$$$ Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon$$ * Back to the simple model. The model in linear regression is the conditional mean of $Y$ given the values in $X$ is expressed a linear function. $$ y = f(x) = E(Y \| X = x)$$ ![conditional mean](images/conditionalmean.png)http://www.learner.org/courses/againstallodds/about/glossary.html* The goal is to estimate the coefficients (e.g. $\beta_0$ and $\beta_1$). We represent the estimates of the coefficients with a "hat" on top of the letter. $$ \hat{\beta}_0, \hat{\beta}_1 $$* Once you estimate the coefficients $\hat{\beta}_0$ and $\hat{\beta}_1$, you can use these to predict new values of $Y$$$\hat{y} = \hat{\beta}_0 + \hat{\beta}_1 x_1$$* How do you estimate the coefficients? * There are many ways to fit a linear regression model * The method called least squares is one of the most common methods * We will discuss least squares today Estimating $\hat\beta$: Least squares**[Least squares](http://en.wikipedia.org/wiki/Least_squares) is a method that can estimate the coefficients of a linear model by minimizing the difference between the following: $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$where $N$ is the number of observations. We will not go into the mathematical details, but the least squares estimates $\hat{\beta}_0$ and $\hat{\beta}_1$ minimize the sum of the squared residuals $r_i = y_i - (\beta_0 + \beta_1 x_i)$ in the model (i.e. makes the difference between the observed $y_i$ and linear model $\beta_0 + \beta_1 x_i$ as small as possible). The solution can be written in compact matrix notation as$$\hat\beta = (X^T X)^{-1}X^T Y$$ We wanted to show you this in case you remember linear algebra, in order for this solution to exist we need $X^T X$ to be invertible. Of course this requires a few extra assumptions, $X$ must be full rank so that $X^T X$ is invertible, etc. This is important for us because this means that having redundant features in our regression models will lead to poorly fitting (and unstable) models. We'll see an implementation of this in the extra linear regression example.Note: The "hat" means it is an estimate of the coefficient. * Part 2: Boston Housing Data SetThe [Boston Housing data set](https://archive.ics.uci.edu/ml/datasets/Housing) contains information about the housing values in suburbs of Boston. This dataset was originally taken from the StatLib library which is maintained at Carnegie Mellon University and is now available on the UCI Machine Learning Repository. Load the Boston Housing data set from `sklearn`This data set is available in the [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.htmlsklearn.datasets.load_boston) python module which is how we will access it today. ###Code from sklearn.datasets import load_boston boston = load_boston() boston.keys() boston.data.shape # Print column names print(boston.feature_names) # Print description of Boston housing data set print(boston.DESCR) ###Output Boston House Prices dataset =========================== Notes ------ Data Set Characteristics: :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive :Median Value (attribute 14) is usually the target :Attribute Information (in order): - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built prior to 1940 - DIS weighted distances to five Boston employment centres - RAD index of accessibility to radial highways - TAX full-value property-tax rate per $10,000 - PTRATIO pupil-teacher ratio by town - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town - LSTAT % lower status of the population - MEDV Median value of owner-occupied homes in $1000's :Missing Attribute Values: None :Creator: Harrison, D. and Rubinfeld, D.L. This is a copy of UCI ML housing dataset. http://archive.ics.uci.edu/ml/datasets/Housing This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 1980. N.B. Various transformations are used in the table on pages 244-261 of the latter. The Boston house-price data has been used in many machine learning papers that address regression problems. References* - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing) ###Markdown Now let's explore the data set itself. ###Code bos = pd.DataFrame(boston.data) bos.head() ###Output _____no_output_____ ###Markdown There are no column names in the DataFrame. Let's add those. ###Code bos.columns = boston.feature_names bos.head() ###Output _____no_output_____ ###Markdown Now we have a pandas DataFrame called `bos` containing all the data we want to use to predict Boston Housing prices. Let's create a variable called `PRICE` which will contain the prices. This information is contained in the `target` data. ###Code print(boston.target.shape) bos['PRICE'] = boston.target bos.head() ###Output _____no_output_____ ###Markdown EDA and Summary Statistics*Let's explore this data set. First we use `describe()` to get basic summary statistics for each of the columns. ###Code bos.describe() ###Output _____no_output_____ ###Markdown Scatter plotsLet's look at some scatter plots for three variables: 'CRIM', 'RM' and 'PTRATIO'. What kind of relationship do you see? e.g. positive, negative? linear? non-linear? ###Code plt.scatter(bos.CRIM, bos.PRICE) plt.xlabel("Per capita crime rate by town (CRIM)") plt.ylabel("Housing Price") plt.title("Relationship between CRIM and Price") ###Output _____no_output_____ ###Markdown Your turn: Create scatter plots between RM* and PRICE, and PTRATIO and PRICE. What do you notice? ###Code #your turn: scatter plot between RM and PRICE plt.scatter(bos.RM, bos.PRICE) plt.xlabel("Average number of rooms per dwelling") plt.ylabel("Housing Price") plt.title("Relationship between RM and Price") #your turn: scatter plot between PTRATIO and PRICE sns.regplot(y="PRICE", x="PTRATIO", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Your turn: What are some other numeric variables of interest? Plot scatter plots with these variables and PRICE. ###Code #your turn: create some other scatter plots sns.regplot(y="PRICE", x="TAX", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Scatter Plots using Seaborn*[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) is a cool Python plotting library built on top of matplotlib. It provides convenient syntax and shortcuts for many common types of plots, along with better-looking defaults.We can also use [seaborn regplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/regression.htmlfunctions-to-draw-linear-regression-models) for the scatterplot above. This provides automatic linear regression fits (useful for data exploration later on). Here's one example below. ###Code sns.regplot(y="PRICE", x="RM", data=bos, fit_reg = True) ###Output _____no_output_____ ###Markdown Histograms* Histograms are a useful way to visually summarize the statistical properties of numeric variables. They can give you an idea of the mean and the spread of the variables as well as outliers. ###Code plt.hist(bos.CRIM) plt.title("CRIM") plt.xlabel("Crime rate per capita") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown Your turn: Plot separate histograms and one for RM, one for PTRATIO. Any interesting observations? ###Code #your turn plt.hist(bos.RM) plt.title("Avg Rooms") plt.xlabel("Average number of rooms per dwelling") plt.ylabel("Frequency") plt.show() plt.hist(bos.PTRATIO) plt.title("Pupil-Teacher Ratio") plt.xlabel("Pupil-Teacher Ratio") plt.ylabel("Frequency") plt.show() ###Output _____no_output_____ ###Markdown The distribution of average rooms per dwelling is approximately a gaussian curve distribution whereas the pupil teacher ratio is more of a bimodal distribution. Linear regression with Boston housing data example*Here, $Y$ = boston housing prices (also called "target" data in python)and$X$ = all the other features (or independent variables)which we will use to fit a linear regression model and predict Boston housing prices. We will use the least squares method as the way to estimate the coefficients. We'll use two ways of fitting a linear regression. We recommend the first but the second is also powerful in its features. Fitting Linear Regression using `statsmodels`[Statsmodels](http://statsmodels.sourceforge.net/) is a great Python library for a lot of basic and inferential statistics. It also provides basic regression functions using an R-like syntax, so it's commonly used by statisticians. While we don't cover statsmodels officially in the Data Science Intensive, it's a good library to have in your toolbox. Here's a quick example of what you could do with it. ###Code # Import regression modules # ols - stands for Ordinary least squares, we'll use this import statsmodels.api as sm from statsmodels.formula.api import ols # statsmodels works nicely with pandas dataframes # The thing inside the "quotes" is called a formula, a bit on that below m = ols('PRICE ~ RM',bos).fit() print(m.summary()) ###Output OLS Regression Results ============================================================================== Dep. Variable: PRICE R-squared: 0.484 Model: OLS Adj. R-squared: 0.483 Method: Least Squares F-statistic: 471.8 Date: Fri, 16 Jun 2017 Prob (F-statistic): 2.49e-74 Time: 20:28:10 Log-Likelihood: -1673.1 No. Observations: 506 AIC: 3350. Df Residuals: 504 BIC: 3359. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>\|t\| [95.0% Conf. Int.] ------------------------------------------------------------------------------ Intercept -34.6706 2.650 -13.084 0.000 -39.877 -29.465 RM 9.1021 0.419 21.722 0.000 8.279 9.925 ============================================================================== Omnibus: 102.585 Durbin-Watson: 0.684 Prob(Omnibus): 0.000 Jarque-Bera (JB): 612.449 Skew: 0.726 Prob(JB): 1.02e-133 Kurtosis: 8.190 Cond. No. 58.4 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown Interpreting coefficientsThere is a ton of information in this output. But we'll concentrate on the coefficient table (middle table). We can interpret the `RM` coefficient (9.1021) by first noticing that the p-value (under `P>\|t\|`) is so small, basically zero. We can interpret the coefficient as, if we compare two groups of towns, one where the average number of rooms is say $5$ and the other group is the same except that they all have $6$ rooms. For these two groups the average difference in house prices is about $9.1$ (in thousands) so about $\$9,100$ difference. The confidence interval fives us a range of plausible values for this difference, about ($\$8,279, \$9,925$), deffinitely not chump change. `statsmodels` formulasThis formula notation will seem familiar to `R` users, but will take some getting used to for people coming from other languages or are new to statistics.The formula gives instruction for a general structure for a regression call. For `statsmodels` (`ols` or `logit`) calls you need to have a Pandas dataframe with column names that you will add to your formula. In the below example you need a pandas data frame that includes the columns named (`Outcome`, `X1`,`X2`, ...), bbut you don't need to build a new dataframe for every regression. Use the same dataframe with all these things in it. The structure is very simple:`Outcome ~ X1`But of course we want to to be able to handle more complex models, for example multiple regression is doone like this:`Outcome ~ X1 + X2 + X3`This is the very basic structure but it should be enough to get you through the homework. Things can get much more complex, for a quick run-down of further uses see the `statsmodels` [help page](http://statsmodels.sourceforge.net/devel/example_formulas.html). Let's see how our model actually fit our data. We can see below that there is a ceiling effect, we should probably look into that. Also, for large values of $Y$ we get underpredictions, most predictions are below the 45-degree gridlines. Your turn: Create a scatterpot between the predicted prices, available in `m.fittedvalues` and the original prices. How does the plot look? ###Code # your turn predicted_variables=m.fittedvalues plt.scatter(predicted_variables, bos.PRICE) plt.xlabel("Predicted_Prices") plt.ylabel("Housing Price") plt.title("Predicted vs Original price") ###Output _____no_output_____ ###Markdown Fitting Linear Regression using `sklearn` ###Code from sklearn.linear_model import LinearRegression X = bos.drop('PRICE', axis = 1) # This creates a LinearRegression object lm = LinearRegression() lm ###Output _____no_output_____ ###Markdown What can you do with a LinearRegression object? Check out the scikit-learn [docs here](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). We have listed the main functions here. Main functions \| Description--- \| --- `lm.fit()` \| Fit a linear model`lm.predit()` \| Predict Y using the linear model with estimated coefficients`lm.score()` \| Returns the coefficient of determination (R^2). A measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model* What output can you get? ###Code # Look inside lm object #lm.coef #lm.intercept ###Output _____no_output_____ ###Markdown Output \| Description--- \| --- `lm.coef_` \| Estimated coefficients`lm.intercept_` \| Estimated intercept Fit a linear model*The `lm.fit()` function estimates the coefficients the linear regression using least squares. ###Code # Use all 13 predictors to fit linear regression model lm.fit(X, bos.PRICE) lm.coef_ ###Output _____no_output_____ ###Markdown Your turn: How would you change the model to not fit an intercept term? Would you recommend not having an intercept? ###Code cm=lm = LinearRegression(fit_intercept=False) cm.fit(X, bos.PRICE) ###Output _____no_output_____ ###Markdown You should never assume to say that the intercept passes through the origin and hence it is always recommended to having an intercept. Estimated intercept and coefficientsLet's look at the estimated coefficients from the linear model using `1m.intercept_` and `lm.coef_`. After we have fit our linear regression model using the least squares method, we want to see what are the estimates of our coefficients $\beta_0$, $\beta_1$, ..., $\beta_{13}$: $$ \hat{\beta}_0, \hat{\beta}_1, \ldots, \hat{\beta}_{13} $$ ###Code print('Estimated intercept coefficient:', lm.intercept_) print('Number of coefficients:', len(lm.coef_)) # The coefficients #lm.coef_ #X.columns import pandas as pd LIST1=[lm.coef_] pdata=pd.DataFrame(LIST1 ,columns=X.columns) #pdata.columns= ['features', 'estimatedCoefficients'] pdata ###Output _____no_output_____ ###Markdown Predict Prices We can calculate the predicted prices ($\hat{Y}_i$) using `lm.predict`. $$ \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1 X_1 + \ldots \hat{\beta}_{13} X_{13} $$ ###Code # first five predicted prices lm.predict(X)[0:5] ###Output _____no_output_____ ###Markdown Your turn:** * Histogram: Plot a histogram of all the predicted prices* Scatter Plot: Let's plot the true prices compared to the predicted prices to see they disagree (we did this with `statsmodels` before). ###Code # your turn predicted_prices=lm.predict(X) predicted_prices plt.hist(predicted_prices) plt.title("Predicted prices") plt.xlabel("Predicted prices") plt.ylabel("Frequency") plt.show() plt.scatter(bos.PRICE,predicted_prices) plt.xlabel("Predicted_Prices") plt.ylabel("Housing Price") plt.title("Predicted vs Original price") ###Output _____no_output_____ ###Markdown Residual sum of squaresLet's calculate the residual sum of squares $$ S = \sum_{i=1}^N r_i = \sum_{i=1}^N (y_i - (\beta_0 + \beta_1 x_i))^2 $$ ###Code print(np.sum((bos.PRICE - lm.predict(X)) 2)) ###Output 12231.217345703373 ###Markdown Mean squared errorThis is simple the mean of the residual sum of squares.Your turn:* Calculate the mean squared error and print it. ###Code #your turn print(np.mean((bos.PRICE - lm.predict(X)) 2)) ###Output 24.17236629585647 ###Markdown Relationship between `PTRATIO` and housing priceTry fitting a linear regression model using only the 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code lm = LinearRegression() lm.fit(X[['PTRATIO']], bos.PRICE) msePTRATIO = np.mean((bos.PRICE - lm.predict(X[['PTRATIO']])) * 2) print(msePTRATIO) ###Output 62.65220001376927 ###Markdown We can also plot the fitted linear regression line. ###Code plt.scatter(bos.PTRATIO, bos.PRICE) plt.xlabel("Pupil-to-Teacher Ratio (PTRATIO)") plt.ylabel("Housing Price") plt.title("Relationship between PTRATIO and Price") plt.plot(bos.PTRATIO, lm.predict(X[['PTRATIO']]), color='blue', linewidth=3) plt.show() ###Output _____no_output_____ ###Markdown Your turn*Try fitting a linear regression model using three independent variables1. 'CRIM' (per capita crime rate by town)2. 'RM' (average number of rooms per dwelling)3. 'PTRATIO' (pupil-teacher ratio by town)Calculate the mean squared error. ###Code # your turn lm = LinearRegression() lm.fit(X[['PTRATIO','RM','CRIM']], bos.PRICE) mse_PTRATIO = np.mean((bos.PRICE - lm.predict(X[['PTRATIO','RM','CRIM']])) 2) print(mse_PTRATIO) ###Output 34.32379656468119 ###Markdown Other important things to think about when fitting a linear regression model* Linearity. The dependent variable $Y$ is a linear combination of the regression coefficients and the independent variables $X$. Constant standard deviation. The SD of the dependent variable $Y$ should be constant for different values of X. e.g. PTRATIO Normal distribution for errors. The $\epsilon$ term we discussed at the beginning are assumed to be normally distributed. $$ \epsilon_i \sim N(0, \sigma^2)$$Sometimes the distributions of responses $Y$ may not be normally distributed at any given value of $X$. e.g. skewed positively or negatively. Independent errors. The observations are assumed to be obtained independently. e.g. Observations across time may be correlated Part 3: Training and Test Data sets Purpose of splitting data into Training/testing sets* Let's stick to the linear regression example: We built our model with the requirement that the model fit the data well. As a side-effect, the model will fit THIS dataset well. What about new data? We wanted the model for predictions, right? One simple solution, leave out some data (for testing) and train the model on the rest This also leads directly to the idea of cross-validation, next section. *One way of doing this is you can create training and testing data sets manually. ###Code X_train = X[:-50] X_test = X[-50:] Y_train = bos.PRICE[:-50] Y_test = bos.PRICE[-50:] print(X_train.shape) print(X_test.shape) print(Y_train.shape) print(Y_test.shape) ###Output (456, 13) (50, 13) (456,) (50,) ###Markdown Another way, is to split the data into random train and test subsets using the function `train_test_split` in `sklearn.cross_validation`. Here's the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html). ###Code from sklearn import cross_validation X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split( X, bos.PRICE, test_size=0.33, random_state = 5) print(X_train.shape) print(X_test.shape) print(Y_train.shape) print(Y_test.shape) ###Output (339, 13) (167, 13) (339,) (167,) ###Markdown Your turn:** Let's build a linear regression model using our new training data sets. * Fit a linear regression model to the training set* Predict the output on the test set ###Code # your turn lm = LinearRegression() lm.fit(X_train,Y_train) ###Output _____no_output_____ ###Markdown Your turn:Calculate the mean squared error * using just the test data* using just the training dataAre they pretty similar or very different? What does that mean? ###Code predicted_output1=lm.predict(X_test) train_error = np.mean((Y_train - lm.predict(X_train)) 2) print(train_error) # your turn test_error = np.mean((Y_test - lm.predict(X_test)) 2) print(test_error) ###Output 28.541367275619013 ###Markdown Residual plots ###Code plt.scatter(lm.predict(X_train), lm.predict(X_train) - Y_train, c='b', s=40, alpha=0.5) plt.scatter(lm.predict(X_test), lm.predict(X_test) - Y_test, c='g', s=40) plt.hlines(y = 0, xmin=0, xmax = 50) plt.title('Residual Plot using training (blue) and test (green) data') plt.ylabel('Residuals') ###Output _____no_output_____
module-2/lab-probability-distribution/your_code/main.ipynb	###Markdown Before your start:- Read the README.md file- Comment as much as you can and use the resources (README.md file)- Happy learning! ###Code # Import your libraries ###Output _____no_output_____ ###Markdown Challenge 1 - Generate and Plot Normal Distributions Step 1: Generate samples and test normal distributionUse mean=50, standard_deviation=5, and sample_size=[10, 50, 500, 5000] to generate 4 random samples that are normally distributed. Test your normal distributions with [`scipy.stats.normaltest`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html).Hint: Read the documentation for `scipy.stats.normaltest`. The function does not simply return Yes or No for whether your data is normal distribution. It returns the likelihood. ###Code # Your code here mu, sigma = 50, 5 sample_size = pd.Series([10, 50, 500, 5000, 50000]) dists = sample_size.apply(lambda s: np.random.normal(mu, sigma, s)) tests = dists.apply(lambda d: stats.normaltest(d)) tests ###Output /usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10 "anyway, n=%i" % int(n)) ###Markdown Interpret the normaltest results and make your observations. ###Code # Explain the test results here ###Output _____no_output_____ ###Markdown Step 2: Plot normal distributionsUse matplotlib subplots to plot a histogram of each sample. Hints:- Use subplots to compare your figures side by side.- Your output should look like below:![normal distributions with different sample sizes](ch-1.png) ###Code # Your code here f, ax = plt.subplots(1, 4) f.set_figwidth(15) f.subplots_adjust(wspace=1) for i in range(0, 4): ax[i].set_title('n=%s' % (sample_size[i])) count, bins, ignored = ax[i].hist(dists[i], 20, density=True) ###Output _____no_output_____ ###Markdown Compare the distributions above. What do you observe? Explain with the Central Limit Theorem. ###Code # Your comment and explanation here ###Output _____no_output_____ ###Markdown Challenge 2 - Plot Probability Mass Function (PMF) Background knowledge[PMF](https://en.wikipedia.org/wiki/Probability_mass_function) shows the probability distribution of a discrete random variable. A [discrete random variable](https://en.wikipedia.org/wiki/Random_variableDiscrete_random_variable) has random numeric values that are not continuous. For example, the number of people in a household can only be integers but not floats. Therefore the number of people in a household is a discrete variable. Question: We assume that the probability of clicking an Ad in a Youtube video is 0.15. We have a sample of 5 people who wathched the video and we want to plot the PMF for a binomial distribution.Hint: use binom from `scipy.stats.binom`. Your output should look like below:![binom 10](ch-2.png) ###Code # Your code here from scipy.stats import binom n, p = 5, 0.15 x = range(n) dist = binom(n, p) fig, ax = plt.subplots(1, 1) plt.plot(x, dist.pmf(x)) plt.show() ###Output _____no_output_____ ###Markdown Explain what you observe from the plot above ###Code # Your comment here ###Output _____no_output_____ ###Markdown Now plot PMP with 50, 500, and 5000 visitors. ###Code # Your code here ###Output _____no_output_____ ###Markdown What did you notice from the distribution plots? Comment your findings. ###Code # Your comment here ###Output _____no_output_____ ###Markdown Challenge 3 Reaserch the Poisson distribution. Write about your own understanding of the Poisson distribution. ###Code # Your comment here ###Output _____no_output_____ ###Markdown A website has an average of 300 visits per day. What is the probability of getting 320 visitors in a day?Hint: use `scipy.stats.poisson` ###Code # Your code here ###Output _____no_output_____ ###Markdown What is the probability of getting 60 visits? ###Code # Your code here ###Output _____no_output_____ ###Markdown Plot the distribution of the probability for getting 0-1000 visits.Hints: - Create a list to store the Poisson distribution probabilities for n=0 to 1000. Then plot the probabilities.- Your plot should look like below:![poisson distribution](ch-3.png) ###Code # your code here from scipy.stats import poisson N = np.arange(1000) rv = poisson(300) arr = [] for n in N: arr.append(rv.pmf(n)) plt.plot(arr) ###Output _____no_output_____
notebooks/TimeEval Bechmark Retry1 result analysis.ipynb	###Markdown TimeEval result analysis on the benchmark datasets (retry run 1) ###Code # imports import re import json import warnings import pandas as pd import numpy as np import scipy as sp from IPython.display import display, Markdown, Latex import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = (20, 8) from pathlib import Path from timeeval import Datasets ###Output _____no_output_____ ###Markdown Configuration Define data and results folder: ###Code # constants and configuration data_path = Path("/home/projects/akita/data") / "benchmark-data" / "data-processed" result_root_path = Path("/home/projects/akita/results") experiment_result_folder = "2021-11-16_runtime-benchmark-retry1" # build paths result_paths = [d for d in result_root_path.iterdir() if d.is_dir()] print("Available result directories:") display(result_paths) result_path = result_root_path / experiment_result_folder print("\nSelecting:") print(f"Data path: {data_path.resolve()}") print(f"Result path: {result_path.resolve()}") ###Output Available result directories: ###Markdown Load results and dataset metadata: ###Code # load results print(f"Reading results from {result_path.resolve()}") df = pd.read_csv(result_path / "results.csv") # aggregate runtime df["overall_time"] = df["execute_main_time"].fillna(0) + df["train_main_time"].fillna(0) # add RANGE_PR_AUC if it is not part of the results if "RANGE_PR_AUC" not in df.columns: df["RANGE_PR_AUC"] = np.nan # remove all duplicates (not necessary, but sometimes, we have some) df = df.drop_duplicates() # load dataset metadata dmgr = Datasets(data_path) ###Output Reading results from /home/projects/akita/results/2021-11-16_runtime-benchmark-retry1 ###Markdown Define utility functions ###Code def load_scores_df(algorithm_name, dataset_id, repetition=1): params_id = df.loc[(df["algorithm"] == algorithm_name) & (df["collection"] == dataset_id[0]) & (df["dataset"] == dataset_id[1]), "hyper_params_id"].item() path = ( result_path / algorithm_name / params_id / dataset_id[0] / dataset_id[1] / str(repetition) / "anomaly_scores.ts" ) return pd.read_csv(path, header=None) ###Output _____no_output_____ ###Markdown Define plotting functions: ###Code default_use_plotly = True try: import plotly.offline except ImportError: default_use_plotly = False def plot_scores(algorithm_name, dataset_id, use_plotly: bool = default_use_plotly, kwargs): if not isinstance(algorithm_name, list): algorithms = [algorithm_name] else: algorithms = algorithm_name # deconstruct dataset ID collection_name, dataset_name = dataset_id # load dataset details df_dataset = dmgr.get_dataset_df(dataset_id) # check if dataset is multivariate dataset_dim = df.loc[(df["collection"] == collection_name) & (df["dataset"] == dataset_name), "dataset_input_dimensionality"].unique().item() dataset_dim = dataset_dim.lower() auroc = {} df_scores = pd.DataFrame(index=df_dataset.index) skip_algos = [] algos = [] for algo in algorithms: algos.append(algo) # get algorithm metric results try: auroc[algo] = df.loc[(df["algorithm"] == algo) & (df["collection"] == collection_name) & (df["dataset"] == dataset_name), "ROC_AUC"].item() except ValueError: warnings.warn(f"No ROC_AUC score found! Probably {algo} was not executed on {dataset_id}.") auroc[algo] = -1 skip_algos.append(algo) continue # load scores training_type = df.loc[df["algorithm"] == algo, "algo_training_type"].values[0].lower().replace("_", "-") try: df_scores[algo] = load_scores_df(algo, dataset_id).iloc[:, 0] except (ValueError, FileNotFoundError): warnings.warn(f"No anomaly scores found! Probably {algo} was not executed on {dataset_id}.") df_scores[algo] = np.nan skip_algos.append(algo) algorithms = [a for a in algos if a not in skip_algos] if use_plotly: return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, kwargs) else: return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, kwargs) def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, kwargs): import plotly.offline as py import plotly.graph_objects as go import plotly.figure_factory as ff import plotly.express as px from plotly.subplots import make_subplots # Create plot fig = make_subplots(2, 1) if dataset_dim == "multivariate": for i in range(1, df_dataset.shape[1]-1): fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=df_dataset.columns[i]), 1, 1) else: fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name="timeseries"), 1, 1) fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset["is_anomaly"], name="label"), 2, 1) for algo in algorithms: fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[algo], name=f"{algo}={auroc[algo]:.4f}"), 2, 1) fig.update_xaxes(matches="x") fig.update_layout( title=f"Results of {','.join(np.unique(algorithms))} on {dataset_id}", height=400 ) return py.iplot(fig) def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, *kwargs): import matplotlib.pyplot as plt # Create plot fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8)) if dataset_dim == "multivariate": for i in range(1, df_dataset.shape[1]-1): axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=df_dataset.columns[i]) else: axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f"timeseries") axs[1].plot(df_dataset.index, df_dataset["is_anomaly"], label="label") for algo in algorithms: axs[1].plot(df_scores.index, df_scores[algo], label=f"{algo}={auroc[algo]:.4f}") axs[0].legend() axs[1].legend() fig.suptitle(f"Results of {','.join(np.unique(algorithms))} on {dataset_id}") fig.tight_layout() return fig def plot_boxplot(df, n_show = 20, title="Box plots", ax_label="values", fmt_label=lambda x: x, use_plotly=default_use_plotly): n_show = n_show // 2 title = title + f" (worst {n_show} and best {n_show} algorithms)" if use_plotly: import plotly.offline as py import plotly.graph_objects as go import plotly.figure_factory as ff import plotly.express as px from plotly.subplots import make_subplots fig = go.Figure() for i, c in enumerate(df.columns): fig.add_trace(go.Box( x=df[c], name=fmt_label(c), boxpoints=False, visible=None if i < n_show or i > len(df.columns)-n_show-1 else "legendonly" )) fig.update_layout( title={"text": title, "xanchor": "center", "x": 0.5}, xaxis_title=ax_label, legend_title="Algorithms" ) return py.iplot(fig) else: df_boxplot = pd.concat([df.iloc[:, :n_show], df.iloc[:, -n_show:]]) labels = df_boxplot.columns labels = [fmt_label(c) for c in labels] values = [df_boxplot[c].dropna().values for c in df_boxplot.columns] fig = plt.figure() ax = fig.gca() #ax.boxplot(values, sym="", vert=True, meanline=True, showmeans=True, showfliers=False, manage_ticks=True) ax.boxplot(values, vert=True, meanline=True, showmeans=True, showfliers=True, manage_ticks=True) ax.set_ylabel(ax_label) ax.set_title(title) ax.set_xticklabels(labels, rotation=-45, ha="left", rotation_mode="anchor") # add vline to separate bad and good algos ymin, ymax = ax.get_ylim() ax.vlines([n_show + 0.5], ymin, ymax, colors="black", linestyles="dashed") fig.tight_layout() return fig def plot_algorithm_bars(df, y_name="ROC_AUC", title="Bar chart for algorithms", use_plotly=default_use_plotly): if use_plotly: fig = px.bar(df, x="algorithm", y=y_name) py.iplot(fig) else: fig = plt.figure() ax = fig.gca() ax.bar(df["algorithm"], df[y_name], label=y_name) ax.set_ylabel(y_name) ax.set_title(title) ax.set_xticklabels(df["algorithm"], rotation=-45, ha="left", rotation_mode="anchor") ax.legend() return fig ###Output _____no_output_____ ###Markdown Analyze overall results on the GutenTAG datasets Overview ###Code df[["algorithm", "collection", "dataset", "status", "ROC_AUC", "AVERAGE_PRECISION", "PR_AUC", "RANGE_PR_AUC", "execute_main_time", "hyper_params"]] ###Output _____no_output_____ ###Markdown Algorithm problems grouped by algorithm training type ###Code index_columns = ["algo_training_type", "algo_input_dimensionality", "algorithm"] df_error_counts = df.pivot_table(index=index_columns, columns=["status"], values="repetition", aggfunc="count") df_error_counts = df_error_counts.fillna(value=0).astype(np.int64) df_error_counts = df_error_counts.reset_index().sort_values(by=["algo_input_dimensionality", "Status.ERROR"], ascending=False).set_index(index_columns) df_error_counts["ALL"] = df_error_counts["Status.ERROR"] + df_error_counts["Status.OK"] + df_error_counts["Status.TIMEOUT"] for tpe in ["SEMI_SUPERVISED", "SUPERVISED", "UNSUPERVISED"]: if tpe in df_error_counts.index: print(tpe) if default_use_plotly: py.iplot(ff.create_table(df_error_counts.loc[tpe], index=True)) else: display(df_error_counts.loc[tpe]) ###Output SEMI_SUPERVISED ###Markdown Summary ###Code df_error_summary = pd.DataFrame(df_error_counts.sum(axis=0)) df_error_summary.columns = ["count"] all_count = df_error_summary.loc["ALL", "count"] df_error_summary["percentage"] = df_error_summary / all_count df_error_summary.style.format({"percentage": "{:06.2%}".format}) ###Output _____no_output_____ ###Markdown Inspect errors of a specific algorithm: ###Code ok = "- OK -" oom = "- OOM -" timeout = "- TIMEOUT -" error_mapping = { "TimeoutError": timeout, "status code '137'": oom, "MemoryError: Unable to allocate": oom, "ValueError: Expected 2D array, got 1D array instead": "Wrong shape error", "could not broadcast input array from shape": "Wrong shape error", "not aligned": "Wrong shape error", # shapes (20,) and (19,500) not aligned "array must not contain infs or NaNs": "unexpected Inf or NaN", "contains NaN": "unexpected Inf or NaN", "cannot convert float NaN to integer": "unexpected Inf or NaN", "Error(s) in loading state_dict": "Model loading error", "EOFError": "Model loading error", "Restoring from checkpoint failed": "Model loading error", "RecursionError: maximum recursion depth exceeded in comparison": "Max recursion depth exceeded", "but PCA is expecting": "BROKEN Exathlon DATASETS", # ValueError: X has 44 features, but PCA is expecting 43 features as input. "input.size(-1) must be equal to input_size": "BROKEN Exathlon DATASETS", "ValueError: The condensed distance matrix must contain only finite values.": "LinAlgError", "LinAlgError": "LinAlgError", "NameError: name 'nan' is not defined": "Not converged", "Could not form valid cluster separation": "Not converged", "contamination must be in": "Invariance/assumption not met", "Data must not be constant": "Invariance/assumption not met", "Cannot compute initial seasonals using heuristic method with less than two full seasonal cycles in the data": "Invariance/assumption not met", "ValueError: Anom detection needs at least 2 periods worth of data": "Invariance/assumption not met", "`dataset` input should have multiple elements": "Invariance/assumption not met", "Cannot take a larger sample than population": "Invariance/assumption not met", "num_samples should be a positive integer value": "Invariance/assumption not met", "Cannot use heuristic method to compute initial seasonal and levels with less than periods + 10 datapoints": "Invariance/assumption not met", "ValueError: The window size must be less than or equal to 0": "Invariance/assumption not met", "The window size must be less than or equal to": "Incompatible parameters", "window_size has to be greater": "Incompatible parameters", "Set a higher piecewise_median_period_weeks": "Incompatible parameters", "OutOfBoundsDatetime: cannot convert input with unit 'm'": "Incompatible parameters", "`window_size` must be at least 4": "Incompatible parameters", "elements of 'k' must be between": "Incompatible parameters", "Expected n_neighbors <= n_samples": "Incompatible parameters", "PAA size can't be greater than the timeseries size": "Incompatible parameters", "All window sizes must be greater than or equal to": "Incompatible parameters", "ValueError: __len__() should return >= 0": "Bug", "stack expects a non-empty TensorList": "Bug", "expected non-empty vector": "Bug", "Found array with 0 feature(s)": "Bug", "ValueError: On entry to DLASCL parameter number 4 had an illegal value": "Bug", "Sample larger than population or is negative": "Bug", "ZeroDivisionError": "Bug", "IndexError": "Bug", "status code '139'": "Bug", "replacement has length zero": "Bug", "missing value where TRUE/FALSE needed": "Bug", "invalid subscript type 'list'": "Bug", "subscript out of bounds": "Bug", "invalid argument to unary operator": "Bug", "negative length vectors are not allowed": "Bug", "negative dimensions are not allowed": "Bug", "`std` must be positive": "Bug", "does not have key": "Bug", # State '1' does not have key '1' "Less than 2 uniques breaks left": "Bug", "The encoder for value is invalid": "Bug", "arange: cannot compute length": "Bug", "n_components=3 must be between 0 and min(n_samples, n_features)": "Bug", } def get_folder(index): series = df.loc[index] path = ( result_path / series["algorithm"] / series["hyper_params_id"] / series["collection"] / series["dataset"] / str(series["repetition"]) ) return path def category_from_logfile(logfile): with logfile.open() as fh: log = fh.read() for error in error_mapping: if error in log: return error_mapping[error] #print(log) return "other" def extract_category(series): status = series["status"] msg = series["error_message"] if status == "Status.OK": return ok elif status == "Status.TIMEOUT": return timeout # status is ERROR: elif "DockerAlgorithmFailedError" in msg: path = get_folder(series.name) / "execution.log" if path.exists(): return category_from_logfile(path) return "DockerAlgorithmFailedError" else: m = re.search("^([\w]+)$.$", msg) if m: error = m.group(1) else: error = msg return f"TimeEval:{error}" df["error_category"] = df.apply(extract_category, axis="columns", raw=False) df_error_category_overview = df.pivot_table(index="error_category", columns="algorithm", values="repetition", aggfunc="count") df_error_category_overview.insert(0, "ALL (sum)", df_error_category_overview.sum(axis=1)) with pd.option_context("display.max_rows", None, "display.max_columns", None): display(df_error_category_overview.style.format("{:.0f}", na_rep="")) ###Output _____no_output_____ ###Markdown - SAND Wrong shape error --> no anomaly in dataset (anomaly_window_size=0)- S-H-ESD (Twitter) incompatible parameter errors --> Cannot parse datetime index (would require further analysis- Left STAMPi incompatible parameter errors --> anomaly_window_size > n_init_train --> fixed, but needs re-execution- normal baseline TimeEval:KilledWorker --> is likely the source of these errors (on LTDB dataset) --> try with Docker baseline- TimeEval:ValueError --> all on genesis dataset: had an error in labeling --> fixed, but needs re-execution- Invariance/assumption not met errors: - Dataset Exathlon 1_2_100000_68-16 has too many anomalies (contamination > 0.5) - Left STAMPi has anomaly_window_size > n_init_train; those datasets cannot be processed by this algo - TripleES has error `Cannot compute initial seasonals using heuristic method with less than two full seasonal cycles in the data` --> no idea on how to fix; just bad datasets for this method? ###Code df[ (df["error_category"] == "Incompatible parameters") & (df["algorithm"] == "Left STAMPi") ] df[ #(df["error_category"] == "TimeEval:KilledWorker") & (df["algorithm"] == "VALMOD") ][["algorithm", "collection", "dataset", "status", "error_message", "error_category"]] error_category = "Incompatible parameters" df_invalid_params = df[(df["error_category"] == error_category)].groupby(by="algorithm")[["repetition"]].count().sort_values("repetition", ascending=False) print(f"{error_category} error algorithms:") display(df_invalid_params.T) print(f"{error_category} error datasets:") df_broken_datasets = df[(df["error_category"] == error_category)].groupby(by="dataset")[["repetition"]].count().sort_values("repetition", ascending=False) display(df_broken_datasets.T) ###Output Incompatible parameters error algorithms: ###Markdown Algorithm quality assessment based on ROC_AUC ###Code aggregations = ["min", "mean", "median", "max"] dominant_aggregation = "mean" df_overall_scores = df.pivot_table(index="algorithm", values="ROC_AUC", aggfunc=aggregations) df_overall_scores.columns = aggregations df_overall_scores = df_overall_scores.sort_values(by=dominant_aggregation, ascending=False) df_asl = df.pivot(index="algorithm", columns=["collection", "dataset"], values="ROC_AUC") df_asl = df_asl.dropna(axis=0, how="all").dropna(axis=1, how="all") df_asl[dominant_aggregation] = df_asl.agg(dominant_aggregation, axis=1) df_asl = df_asl.sort_values(by=dominant_aggregation, ascending=True) df_asl = df_asl.drop(columns=dominant_aggregation).T with pd.option_context("display.max_rows", None, "display.max_columns", None): display(df_overall_scores.T) n_show = 20 dataset_count_lut = (df_error_counts["Status.OK"] / df_error_counts["ALL"]).reset_index().set_index("algorithm").drop(columns=["algo_training_type", "algo_input_dimensionality"]).iloc[:, 0] fmt_label = lambda c: f"{c} ({dataset_count_lut[c]:6.2%} of datasets)" fig = plot_boxplot(df_asl, title="AUC_ROC box plots", ax_label="AUC_ROC score", fmt_label=fmt_label, n_show=n_show) ###Output _____no_output_____
Programs/PyProg/HW3_HUA.ipynb	###Markdown homework 3Please write a function that returns N samples from a Normal distribution with a given mean and standard deviation. The only function that you are allowed to use is numpy.random.rand() which returns a random sample from a uniform distribution over [0,1). There are again many ways to doing this. Please implement the code yourself. Don't copy from the interne ###Code %matplotlib inline import numpy as np from numpy.random import rand import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown IntroductionAs we know, the PDF of a 1-D normal distribution is$$f(x)=\frac{1}{{\sigma \sqrt {2\pi } }}e^{-\frac{(x-\mu)^2}{2{\sigma}^2}}$$Therefore, the CDF of this normal distribution should be$$F(x)=\frac{1}{{\sigma \sqrt {2\pi } }}\int_{-\infty}^{x}e^{-\frac{(\xi-\mu)^2}{2{\sigma}^2}}d\xi $$Here, we use 2-D normal distribution whose mean = 0 and standard deviate = 1 to study the CDF.$$ F(x,y)=\frac{1}{{2\pi}} \iint_{-\infty}^{(x,y)} e^{-\frac{\xi^2+\eta^2}{2}}d\xi d\eta $$Transform the equation above into the polar coordinates system, we have$$ F(r)=\frac{1}{{2\pi}} \int_0^{2\pi} \int_0^r \rho e^{-\frac{\rho^2}{2}}d\rho d\theta = 1-e^{-\frac{r^2}{2}}$$Thus, the inverse function of F(r) is$$ R = F^{-1}(z) = \sqrt{-2 ln(1-z)}$$Where z ~ U(0,1).Since $X = R cos(\theta)$, and $Y = R sin(\theta)$, we let $U_1 = 1-z$, and $U_2 = \theta/2\pi$, and we get$$X = \sqrt{-2 ln(U_1)}cos(2\pi U_2), Y = \sqrt{-2 ln(U_1)}sin(2\pi U_2)$$Where X and Y both satisfy the 1-D standard normal distribution.So, $X\sigma+\mu$ and $Y\sigma+\mu$ satisfy the 1-D normal distribution whose mean = $\mu$, and SD = $\sigma$. ###Code def normal_sample(mean, stddev, N): # put your code here. You can only use rand() for creating random numbers #mean,stddev = np.double(mean),np.double(stddev) U1 = np.random.rand(1,N) U2 = np.random.rand(1,N) X = np.sqrt(-2np.log(U1))np.cos(2np.piU2) Y = np.sqrt(-2np.log(U2))np.sin(2np.piU1) X = np.array(stddevX+mean) Y = np.array(stddevY+mean) Norm = np.hstack((X,Y)) Hist = plt.hist([Norm[:]],bins=50) # --------------------verification------------------------- x0 = np.linspace(mean-4stddev,mean+4stddev,200) Gauss = 1.0/stddev/np.sqrt(2np.pi)np.exp(-(x0-mean)*2 / (2stddev*2)) plt.plot(x0,Gauss/max(Gauss)max(Hist[0]),color = 'red',label='Verification') plt.legend(fontsize=10) plt.show() return Hist[0] h = normal_sample(-12,0.5,10000) ###Output _____no_output_____
notebooks/behavior_of_latent_space.ipynb	###Markdown Deblend stamps randomly generated from DC2 data Load 10 DC2 images centred on galaxy. They have been generated using this notebook: https://github.com/BastienArcelin/dc2_img_generation/blob/main/notebooks/dc2_stamps_and_corresponding_parameters.ipynb ###Code data_folder_path = pkg_resources.resource_filename('debvader', "data/") image_path = os.path.join(data_folder_path + 'dc2_imgs/imgs_dc2.npy') images = np.load(image_path, mmap_mode = 'c') ###Output _____no_output_____ ###Markdown Visualize some of the images ###Code fig, axes = plt.subplots(1,3, figsize = (12, 4)) for i in range (3): axes[i].imshow(images[i,:,:,2]) # We plot only r-band here, but the images are multi-bands (ugrizy) ###Output _____no_output_____ ###Markdown Now we can load the deblender ###Code # First, define the parameters of the neural network, for this version of debvader, they are as follow: nb_of_bands = 6 input_shape = (59, 59, nb_of_bands) latent_dim = 32 filters = [32,64,128,256] kernels = [3,3,3,3] # We will load the weights of the network trained on DC2 images survey = "dc2" # Load the network using the load_deblender function net, encoder, decoder, z = load_deblender(survey, input_shape, latent_dim, filters, kernels, return_encoder_decoder_z=True) # We can visualize the network net.summary() ###Output Model: "model_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_3 (InputLayer) [(None, 59, 59, 6)] 0 _________________________________________________________________ model (Model) (None, 560) 3741224 _________________________________________________________________ multivariate_normal_tri_l (M ((None, 32), (None, 32)) 0 _________________________________________________________________ model_1 (Model) (None, 59, 59, 6) 4577228 ================================================================= Total params: 8,318,452 Trainable params: 3,741,212 Non-trainable params: 4,577,240 _________________________________________________________________ ###Markdown We now have everything to do the deblending. Let's use our network to deblend the DC2 images ###Code output_images_mean, output_images_distribution = deblend(net, images) ###Output _____no_output_____ ###Markdown Here the network outputs a distribution over the pixels for each pixel in each filter, this is why both the mean image and the corresponding distribution is outputed.Let's first visualize the mean images outputed by debvader. ###Code fig, axes = plt.subplots(3,3, figsize = (12, 12)) for i in range (3): axes[i,0].imshow(images[i,:,:,2]) # We plot only r-band here, but the images are multi-bands (ugrizy) axes[i,1].imshow(output_images_mean[i,:,:,2]) # We plot only r-band here, but the output images are multi-bands (ugrizy) axes[i,2].imshow(images[i,:,:,2]-output_images_mean[i,:,:,2]) import pandas as pd root_dir = "/pbs/home/b/barcelin/sps_link/data/dc2_test/24.5/test/" images_noiseless = np.load(root_dir+'img_noiseless_sample_2.npy', mmap_mode = 'c') images_noisy = np.load(root_dir+'img_cropped_sample_2.npy', mmap_mode = 'c') data = pd.read_csv(root_dir+'img_noiseless_data_2.csv') latent_space_distribution_noiseless = z(tf.cast(images_noiseless[:1000], tf.float32)) latent_space_distribution_noisy = z(tf.cast(images_noisy[:1000], tf.float32)) #print(latent_space_distribution) plt.plot(np.linspace(0,32, 32), np.mean(latent_space_distribution_noiseless.stddev().numpy() ,axis = 0), '.', color = 'blue', alpha = 0.3) plt.plot(np.linspace(0,32, 32), np.mean(latent_space_distribution_noiseless.stddev().numpy() ,axis = 0), '.', color = 'red', alpha = 0.3) fig, axes = plt.subplots(1,2 ,figsize = (20,5)) for i in range (2): _ = axes[i].hist(np.concatenate(latent_space_distribution_noiseless.stddev().numpy(), axis = 0), bins = 100, alpha = 0.3 , label = 'noiseless') _ = axes[i].hist(np.concatenate(latent_space_distribution_noisy.stddev().numpy(), axis = 0), bins = 100, alpha = 0.3, label = 'blended and noisy') axes[0].legend() axes[1].set_xlim(0, 0.8) axes[1].set_ylim(0, 1000) plt.title('standard deviation of latent space') fig, axes = plt.subplots(1,2 ,figsize = (20,5)) for i in range (2): _ = axes[i].hist(np.concatenate(latent_space_distribution_noiseless.mean().numpy(), axis = 0), bins = 100, alpha = 0.3 , label = 'noiseless') _ = axes[i].hist(np.concatenate(latent_space_distribution_noisy.mean().numpy(), axis = 0), bins = 100, alpha = 0.3, label = 'blended and noisy') axes[0].legend() axes[1].set_xlim(-5, 5) axes[1].set_yscale('log') plt.title('means of latent space') ###Output _____no_output_____ ###Markdown Now let's visualise as a function of blendedness ###Code latent_space_distribution_noiseless.stddev().numpy().shape fig, axes = plt.subplots(1,2 ,figsize = (20,5)) for i in range (2): _ = axes[i].plot(data['blendedness'][:1000], np.mean(latent_space_distribution_noiseless.stddev().numpy(), axis = 1), '.',color = 'blue', alpha = 0.3 , label = 'noiseless') _ = axes[i].plot(data['blendedness'][:1000], np.mean(latent_space_distribution_noisy.stddev().numpy(), axis = 1),'.', color = 'red', alpha = 0.3 , label = 'noisy') axes[0].legend() axes[0].set_xscale('log') axes[0].set_title('mean std in latent space as a function of blendedness') ###Output _____no_output_____ ###Markdown Now we can look at what the images of the standard deviation look like for each example, and how we can sample this distribution in each pixel. ###Code output_uncertainty_mean = output_images_distribution.mean().numpy() # Extract the mean of the distribution. Same image as output_images_mean. output_uncertainty_std = output_images_distribution.stddev().numpy() # Extract the standard deviation of the distribution. output_uncertainty_sample = tf.math.reduce_mean(output_images_distribution.sample(100), axis = 0).numpy() # Sample 100 times the distribution in each pixel and produce a mean image. fig, axes = plt.subplots(3,6, figsize = (28, 12)) for i in range (3): f1 = axes[i,0].imshow(images[i,:,:,2]) f2 = axes[i,1].imshow(output_uncertainty_mean[i,:,:,2]) f3 = axes[i,2].imshow(output_uncertainty_std[i,:,:,2]) f4 = axes[i,3].imshow(output_uncertainty_sample[i,:,:,2]) f5 = axes[i,4].imshow(images[i,:,:,2] - output_uncertainty_mean[i,:,:,2]) f6 = axes[i,5].imshow(images[i,:,:,2] - output_uncertainty_sample[i,:,:,2]) fig.colorbar(f1, ax = axes[i,0]) fig.colorbar(f2, ax = axes[i,1]) fig.colorbar(f3, ax = axes[i,2]) fig.colorbar(f4, ax = axes[i,3]) fig.colorbar(f5, ax = axes[i,4]) fig.colorbar(f6, ax = axes[i,5]) axes[i,0].set_title('Input') axes[i,1].set_title('output mean flux') axes[i,2].set_title('output std of flux per pixel') axes[i,3].set_title('output mean \n of 100 sample') axes[i,4].set_title('target - output mean') axes[i,5].set_title('target - output \n 100 sample') ###Output _____no_output_____
Fashion_Data_Notebook.ipynb	###Markdown CNN With Fashion Data An x layer CNN for fashion (clothing?) type detection trained on an MNIST dataset from Zolando Research ###Code import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg import seaborn as sns %matplotlib inline # what does this ^ mean? np.random.seed(2) # number of epochs? (steps) from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix # look this up import itertools from keras.utils.np_utils import to_categorical # "convert to one-hot-encoding??" from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D #Layers of Network? from keras.optimizers import RMSprop # look this up from keras.preprocessing.image import ImageDataGenerator from keras.callbacks import ReduceLROnPlateau sns.set(style = 'dark', context = 'notebook', palette = 'deep') train = pd.read_csv('fashion-mnist_train.csv') test = pd.read_csv('fashion-mnist_test.csv') # digit recognizer tutorial's 'test' doesn't have a 'label' # column, but this fashion data has a label column on both # the test and train data, need to drop from both when checking # checking for missing or corrupted images Y_train = train['label'] X_train = train.drop(labels = ['label'], axis = 1) Y_test = test['label'] X_test = test.drop(labels = ['label'], axis = 1) del train #freeing space? (taking less time for model training?) del test g = sns.countplot(Y_train) g1 = sns.countplot(Y_test) Y_train.value_counts() Y_test.value_counts() # Is this ^ all supposed to be the same count? # Why is there only one plot? X_train.isnull().any().describe() # checking train for missing data X_test.isnull().any().describe() # checking test for missing data # Missing data returned as True ('top')? X_train = X_train / 255.0 # Can I pick another number besides X_test = X_test / 255.0 # 255.0 for slower convergence when # normalizing the data? X_train = X_train.values.reshape(-1,28,28,1) X_test = X_test.values.reshape(-1,28,28,1) # Reshaping in 3 dimensions, but what is the -1? # Create dictionary of target classes label_dict = { 0: 'Cute_lil_top', 1: 'Hot_pants', 2: 'Hoodie', 3: 'Dress_Up', 4: 'Bundles_up_bitches', 5: 'Toe_flaunters', 6: 'Shirt', 7: 'Sneakaaas', 8: 'Hold_my_purse', 9: 'Booties' } #wtf is label encoding?! Robot binary talk? Y_train = to_categorical(Y_train, num_classes = 10) random_seed = 2 #Split training and make validation sets X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state = random_seed) # ^10% of this data validated, 90% for training g = plt.imshow(X_train[42985][:,:,0]) #Setting CNN Model model = Sequential() model.add(Conv2D(filters = 32, kernel_size = (5,5), padding = 'Same', activation = 'relu', input_shape = (28,28,1))) model.add(Conv2D(filters = 32, kernel_size = (5,5), padding = 'Same', activation = 'relu')) model.add(MaxPool2D(pool_size = (2,2))) model.add(Dropout(0.25)) model.add(Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', activation = 'relu')) model.add(Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', activation = 'relu')) model.add(MaxPool2D(pool_size = (2,2), strides = (2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation = 'relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation = 'softmax')) optimizer = RMSprop(lr = 0.001, rho = 0.9, epsilon = 1e-08, decay = 0.0) model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy']) learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001) epochs = 50 # Changing Epochs doesn't necessarily improve accuracy in this data's case batch_size = 86 # Why 86? # With Data Augmentation datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180) zoom_range = 0.1, # Randomly zoom image width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) height_shift_range=0.1, # randomly shift images vertically (fraction of total height) horizontal_flip=False, # randomly flip images vertical_flip=False) # randomly flip images datagen.fit(X_train) # Fitting Model With Data Augmentation history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size), epochs = epochs, validation_data = (X_val,Y_val), verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size , callbacks=[learning_rate_reduction]) # Without Data Augmentation (Fitting model) history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, validation_data = (X_val, Y_val), verbose = 2) # Confusion Matrix (also needed for error display) def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # Predict the values from the validation dataset Y_pred = model.predict(X_val) # Convert predictions classes to one hot vectors Y_pred_classes = np.argmax(Y_pred,axis = 1) # Convert validation observations to one hot vectors Y_true = np.argmax(Y_val,axis = 1) # compute the confusion matrix confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) # plot the confusion matrix plot_confusion_matrix(confusion_mtx, classes = range(10)) # Display some error results # Errors are difference between predicted labels and true labels errors = (Y_pred_classes - Y_true != 0) Y_pred_classes_errors = Y_pred_classes[errors] Y_pred_errors = Y_pred[errors] Y_true_errors = Y_true[errors] X_val_errors = X_val[errors] def display_errors(errors_index,img_errors,pred_errors, obs_errors): """ This function shows 6 images with their predicted and real labels""" n = 0 nrows = 2 ncols = 3 fig, ax = plt.subplots(nrows,ncols,sharex=True,sharey=True) for row in range(nrows): for col in range(ncols): error = errors_index[n] ax[row,col].imshow((img_errors[error]).reshape((28,28))) ax[row,col].set_title("Predicted label :{}\nTrue label :{}".format(pred_errors[error],obs_errors[error])) n += 1 # Probabilities of the wrong predicted numbers Y_pred_errors_prob = np.max(Y_pred_errors,axis = 1) # Predicted probabilities of the true values in the error set true_prob_errors = np.diagonal(np.take(Y_pred_errors, Y_true_errors, axis=1)) # Difference between the probability of the predicted label and the true label delta_pred_true_errors = Y_pred_errors_prob - true_prob_errors # Sorted list of the delta prob errors sorted_dela_errors = np.argsort(delta_pred_true_errors) # Top 6 errors most_important_errors = sorted_dela_errors[-6:] # Show the top 6 errors display_errors(most_important_errors, X_val_errors, Y_pred_classes_errors, Y_true_errors) # predict results results = model.predict(X_test) # select the index with the maximum probability results = np.argmax(results,axis = 1) results = pd.Series(results,name="Label") ###Output _____no_output_____
notebook/analisi_ultimi_14_giorni_sud.ipynb	###Markdown Analisi dati regioni Italia del sud degli ultimi 14 giorni disponibiliI dati elaborati sono quelli presenti nel file * dpc-covid19-ita-regioni.json * nella directory * dati-json . Elaborazione dei dati:** estrazione del dataset* trovo la data massima contenuta nel dataset* calcolo la data di riferimento per trovare i dati dei 14 giorni precedenti* estraggo dei sotto dataset con i dati delle regioni rilevati per l'analisi in questione relativi al periodo temporale in esame ###Code import pandas as pd pd.plotting.register_matplotlib_converters() dataset = pd.read_json('../dati-json/dpc-covid19-ita-regioni.json') dataset['data'] = pd.to_datetime(dataset['data']) dataset.set_index('data', inplace=True) max_date = dataset.index.max() from datetime import timedelta ref_date = max_date - timedelta(days=14) regioni = ['Abruzzo', 'Basilicata', 'Calabria', 'Campania', 'Molise', 'Puglia', 'Sardegna', 'Sicilia'] data_filter = (dataset.index > ref_date) & (dataset.denominazione_regione.isin(regioni)) filtered_set = dataset[data_filter].loc[:, ['denominazione_regione', 'ricoverati_con_sintomi', 'terapia_intensiva', 'totale_ospedalizzati', 'isolamento_domiciliare', 'totale_positivi', 'variazione_totale_positivi', 'nuovi_positivi', 'dimessi_guariti', 'deceduti', 'casi_da_sospetto_diagnostico', 'casi_da_screening', 'totale_casi', 'tamponi', 'casi_testati']] subsets = [] for r in regioni: subsets.append((r, filtered_set[filtered_set.denominazione_regione == r].sort_index())) import matplotlib.pyplot as plt plt.rc('xtick', labelsize=15) plt.rc('ytick', labelsize=15) default_figsize = (24, 6) default_titlesize = 20 default_padding = 8 ###Output _____no_output_____ ###Markdown Andamento ricoverati con sintomi ###Code plt.figure(figsize=default_figsize) for (r, s) in subsets: plt.plot(s.index, s['ricoverati_con_sintomi'].values, label=r) plt.title('Ricoverati con sintomi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Andamento occupazione delle terapie intensive ###Code plt.figure(figsize=default_figsize) col_name = 'terapia_intensiva' for (r, s) in subsets: plt.plot(s.index, s['terapia_intensiva'].values, label=r) plt.title('Terapia intensiva', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Andamento totale attualmente positiviCalcolati con la formula: $ totale\_ospedalizzati + isolamento\_domiciliare $ ###Code plt.figure(figsize=default_figsize) for (r, s) in subsets: plt.plot(s.index, s['totale_positivi'].values, label=r) plt.title('Totale positivi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Andamento della variazione totale positiviCalcolati con la formula: $ totale\_positivi\ giorno\ corrente - totale\_positivi\ giorno\ precedente $ ###Code plt.figure(figsize=default_figsize) for (r, s) in subsets: plt.plot(s.index, s['variazione_totale_positivi'].values, label=r) plt.title('Variazione totale positivi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Andamento nuovi positiviCalcolati con la formula: $ totale\_casi\ giorno\ corrente - totale\_casi\ giorno\ precedente $ ###Code plt.figure(figsize=default_figsize) for (r, s) in subsets: plt.plot(s.index, s['nuovi_positivi'].values, label=r) plt.title('Nuovi positivi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Andamento decedutiIl dato è cumulativo. ###Code plt.figure(figsize=default_figsize) col_name = 'deceduti' for (r, s) in subsets: plt.plot(s.index, s['deceduti'].values, label=r) plt.title('Deceduti', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Analisi variazione dei dati forniti in modo cumulativo ###Code diff_col = ['ricoverati_con_sintomi', 'terapia_intensiva', 'totale_ospedalizzati', 'isolamento_domiciliare', 'dimessi_guariti', 'deceduti', 'casi_da_sospetto_diagnostico', 'casi_da_screening', 'tamponi', 'casi_testati'] diff_subsets = [] for (r, s) in subsets: diff_subsets.append((r, s.loc[:, diff_col].diff())) ###Output _____no_output_____ ###Markdown Variazione ricoverati con sintomiCalcolata con la formula: $ ricoverati\_con\_sintomi\ giorno\ X - ricoverati\_con\_sintomi\ giorno\ X-1 $ ###Code plt.figure(figsize=default_figsize) for (r, s) in diff_subsets: plt.plot(s.index, s['ricoverati_con_sintomi'].values, label=r) plt.title('Variazione ricoverati con sintomi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Variazione terapie intensiveCalcolata con la formula: $ terapia\_intensiva\ giorno\ X - terapia\_intensiva\ giorno\ X-1 $ ###Code plt.figure(figsize=default_figsize) for (r, s) in diff_subsets: plt.plot(s.index, s['terapia_intensiva'].values, label=r) plt.title('Variazione terapia intensiva', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Variazione decedutiCalcolata con la formula: $ deceduti\ giorno\ X - deceduti\ giorno\ X-1 $ ###Code plt.figure(figsize=default_figsize) for (r, s) in diff_subsets: plt.plot(s.index, s['deceduti'].values, label=r) plt.title('Variazione deceduti', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Variazione tamponiCalcolata con la formula: $ tamponi\ giorno\ X - tamponi\ giorno\ X-1 $ ###Code plt.figure(figsize=default_figsize) for (r, s) in diff_subsets: plt.plot(s.index, s['tamponi'].values, label=r) plt.title('Variazione tamponi', fontsize=default_titlesize, pad=default_padding) plt.legend() plt.show() ###Output _____no_output_____
Chapter_Preprocessing/Wrapper_Methods_backward_SFS.ipynb	###Markdown Chapter: Data Preprocessing Topic: Wrapper Method: Backward SFS ###Code # read data import numpy as np VSdata = np.loadtxt('VSdata.csv', delimiter=',') # separate X and y y = VSdata[:,0] X = VSdata[:,1:] # scale data from sklearn.preprocessing import StandardScaler xscaler = StandardScaler() X_scaled = xscaler.fit_transform(X) yscaler = StandardScaler() y_scaled = yscaler.fit_transform(y[:,None]) # SFS-based variable selection from sklearn.feature_selection import SequentialFeatureSelector from sklearn.linear_model import LinearRegression BSFS = SequentialFeatureSelector(LinearRegression(), n_features_to_select=10, direction='backward', cv=5).fit(X_scaled, y_scaled) # check selected inputs print('Inputs selected: ', BSFS.get_support(indices=True)+1) # returns integer index of the features selected # reduce X to only top relevant inputs X_relevant = BSFS.transform(X) ###Output _____no_output_____
Numpy/More_elaborate_arrays.ipynb	###Markdown More data types Casting “Bigger” type wins in mixed-type operations: ###Code import numpy as np np.array([1, 2, 3]) + 1.5 ###Output _____no_output_____ ###Markdown Assignment never changes the type! ###Code a = np.array([1, 2, 3]) a.dtype a[0] = 1.9 # <-- float is truncated to integer a a[0] = 4 a ###Output _____no_output_____ ###Markdown Forced casts: ###Code a = np.array([1.7, 1.2, 1.6]) b = a.astype(int) # truncates to int b ###Output _____no_output_____ ###Markdown Rounding: ###Code a = np.array([1.7, 1.2, 1.6, 2.2, 3.8]) b= np.around(a) b c = np.around(a).astype(int) c ###Output _____no_output_____ ###Markdown Different data type sizes ###Code np.array([1], dtype=int).dtype np.iinfo(np.int32).max, 231 - 1 np.iinfo(np.int64).max, 263 - 1 ###Output _____no_output_____ ###Markdown Structured data types* sensor_code (4-character string)* position (float)* value (float) ###Code samples = np.zeros((6,), dtype=[('sensor_code', 'S4'), ('position', float), ('value', float)]) samples.ndim samples.shape samples.dtype.names samples[:] = [('ALFA', 1, 0.37), ('BETA', 1, 0.11), ('TAU', 1, 0.13), ('ALFA', 1.5, 0.37), ('ALFA', 3, 0.11), ('TAU', 1.2, 0.13)] samples ###Output _____no_output_____ ###Markdown Field access works by indexing with field names: ###Code samples['sensor_code'] samples['value'] samples[0] samples[0]['sensor_code'] ###Output _____no_output_____ ###Markdown Multiple fields at once: ###Code samples[['position', 'value']] ###Output _____no_output_____ ###Markdown Fancy indexing works, as usual: ###Code samples[samples['sensor_code'] == 'ALFA'] ###Output _____no_output_____ ###Markdown maskedarray: dealing with (propagation of) missing data ** * For floats one could use NaN’s, but masks work for all types: ###Code x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1]) x y = np.ma.array([1, 2, 3, 4], mask=[0, 1, 1, 1]) y x + y ###Output _____no_output_____ ###Markdown * Masking versions of common functions:** ###Code np.ma.sqrt([1, -1, 2, -2]) ###Output _____no_output_____
ML_6_Horse_Classificatiol.ipynb	###Markdown ML Horse ClassificationKapil Nagwanshi ###Code import pandas as pd import numpy as np import matplotlib.pyplot as plt from pandas.plotting import scatter_matrix %matplotlib inline cd 'gdrive/My Drive/Colab Notebooks' animals = pd.read_csv("horse.csv") animals.head() target = animals['outcome'] target.unique() animals =animals.drop(['outcome'], axis =1) category_variables = ['surgery', 'age','temp_of_extremities','peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux' , 'rectal_exam_feces', 'abdomen', 'abdomo_appearance' ,'surgical_lesion', 'cp_data'] for category in category_variables: animals[category]=pd.get_dummies(animals[category]) from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder X,y = animals.values,target.values le = LabelEncoder() y = le.fit_transform(y) xtrain,xtest,ytrain, ytest =train_test_split(X,y,test_size=0.2,random_state=1) from sklearn.tree import DecisionTreeClassifier print(xtrain.shape) from sklearn.impute import SimpleImputer imp = SimpleImputer(missing_values = np.nan,strategy='most_frequent') xtrain = imp.fit_transform(xtrain) xtest = imp.fit_transform(xtest) classifier = DecisionTreeClassifier() classifier.fit(xtrain,ytrain) ypredict = classifier.predict(xtest) from sklearn.metrics import accuracy_score accuracy =accuracy_score(ypredict,ytest) print(accuracy) from sklearn.ensemble import RandomForestClassifier rfc =RandomForestClassifier() rfc.fit(xtrain,ytrain) ypredict = rfc.predict(xtest) print(accuracy_score(ypredict,ytest)) ###Output _____no_output_____
site/zh-cn/guide/eager.ipynb	###Markdown Copyright 2018 The TensorFlow Authors. ###Code #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ###Output _____no_output_____ ###Markdown Eager Execution 在 TensorFlow.org 上查看在 Google Colab 中运行在 GitHub 上查看源代码下载笔记本 TensorFlow 的 Eager Execution 是一种命令式编程环境，可立即评估运算，无需构建计算图：运算会返回具体的值，而非构建供稍后运行的计算图。这样能使您轻松入门 TensorFlow 并调试模型，同时也减少了样板代码。要跟随本指南进行学习，请在交互式 `python` 解释器中运行以下代码示例。Eager Execution 是用于研究和实验的灵活机器学习平台，具备以下特性：- 直观的界面 - 自然地组织代码结构并使用 Python 数据结构。快速迭代小模型和小数据。- 更方便的调试功能 - 直接调用运算以检查正在运行的模型并测试更改。使用标准 Python 调试工具立即报告错误。- 自然的控制流 - 使用 Python 而非计算图控制流，简化了动态模型的规范。Eager Execution 支持大部分 TensorFlow 运算和 GPU 加速。注：启用 Eager Execution 后可能会增加某些模型的开销。我们正在持续改进其性能，如果您遇到问题，请[提交错误报告](https://github.com/tensorflow/tensorflow/issues)并分享您的基准。设置和基本用法 ###Code import os import tensorflow as tf import cProfile ###Output _____no_output_____ ###Markdown 在 Tensorflow 2.0 中，默认启用 Eager Execution。 ###Code tf.executing_eagerly() ###Output _____no_output_____ ###Markdown 现在您可以运行 TensorFlow 运算，结果将立即返回： ###Code x = [[2.]] m = tf.matmul(x, x) print("hello, {}".format(m)) ###Output _____no_output_____ ###Markdown 启用 Eager Execution 会改变 TensorFlow 运算的行为方式 - 现在它们会立即评估并将值返回给 Python。`tf.Tensor` 对象会引用具体值，而非指向计算图中节点的符号句柄。由于无需构建计算图并稍后在会话中运行，可以轻松使用 `print()` 或调试程序检查结果。评估、输出和检查张量值不会中断计算梯度的流程。Eager Execution 可以很好地配合 [NumPy](http://www.numpy.org/) 使用。NumPy 运算接受 `tf.Tensor` 参数。TensorFlow `tf.math` 运算会将 Python 对象和 NumPy 数组转换为 `tf.Tensor` 对象。`tf.Tensor.numpy` 方法会以 NumPy `ndarray` 的形式返回该对象的值。 ###Code a = tf.constant([[1, 2], [3, 4]]) print(a) # Broadcasting support b = tf.add(a, 1) print(b) # Operator overloading is supported print(a * b) # Use NumPy values import numpy as np c = np.multiply(a, b) print(c) # Obtain numpy value from a tensor: print(a.numpy()) # => [[1 2] # [3 4]] ###Output _____no_output_____ ###Markdown 动态控制流Eager Execution 的一个主要优势是，在执行模型时，主机语言的所有功能均可用。因此，编写 [fizzbuzz](https://en.wikipedia.org/wiki/Fizz_buzz) 之类的代码会很容易： ###Code def fizzbuzz(max_num): counter = tf.constant(0) max_num = tf.convert_to_tensor(max_num) for num in range(1, max_num.numpy()+1): num = tf.constant(num) if int(num % 3) == 0 and int(num % 5) == 0: print('FizzBuzz') elif int(num % 3) == 0: print('Fizz') elif int(num % 5) == 0: print('Buzz') else: print(num.numpy()) counter += 1 fizzbuzz(15) ###Output _____no_output_____ ###Markdown 这段代码具有依赖于张量值的条件语句并会在运行时输出这些值。 Eager 训练计算梯度[自动微分](https://en.wikipedia.org/wiki/Automatic_differentiation)对实现机器学习算法（例如用于训练神经网络的[反向传播](https://en.wikipedia.org/wiki/Backpropagation)）十分有用。在 Eager Execution 期间，请使用 `tf.GradientTape` 跟踪运算以便稍后计算梯度。您可以在 Eager Execution 中使用 `tf.GradientTape` 来训练和/或计算梯度。这对复杂的训练循环特别有用。由于在每次调用期间都可能进行不同运算，所有前向传递的运算都会记录到“条带”中。要计算梯度，请反向播放条带，然后丢弃。特定 `tf.GradientTape` 只能计算一个梯度；后续调用会引发运行时错误。 ###Code w = tf.Variable([[1.0]]) with tf.GradientTape() as tape: loss = w * w grad = tape.gradient(loss, w) print(grad) # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32) ###Output _____no_output_____ ###Markdown 训练模型以下示例创建了一个多层模型，该模型会对标准 MNIST 手写数字进行分类。示例演示了在 Eager Execution 环境中构建可训练计算图的优化器和层 API。 ###Code # Fetch and format the mnist data (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32), tf.cast(mnist_labels,tf.int64))) dataset = dataset.shuffle(1000).batch(32) # Build the model mnist_model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu', input_shape=(None, None, 1)), tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) ###Output _____no_output_____ ###Markdown 即使没有训练，也可以在 Eager Execution 中调用模型并检查输出： ###Code for images,labels in dataset.take(1): print("Logits: ", mnist_model(images[0:1]).numpy()) ###Output _____no_output_____ ###Markdown 虽然 Keras 模型有内置训练循环（使用 `fit` 方法），但有时您需要进行更多自定义。下面是一个使用 Eager Execution 实现训练循环的示例： ###Code optimizer = tf.keras.optimizers.Adam() loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) loss_history = [] ###Output _____no_output_____ ###Markdown 注：请在 `tf.debugging` 中使用断言函数检查条件是否成立。这在 Eager Execution 和计算图执行中均有效。 ###Code def train_step(images, labels): with tf.GradientTape() as tape: logits = mnist_model(images, training=True) # Add asserts to check the shape of the output. tf.debugging.assert_equal(logits.shape, (32, 10)) loss_value = loss_object(labels, logits) loss_history.append(loss_value.numpy().mean()) grads = tape.gradient(loss_value, mnist_model.trainable_variables) optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables)) def train(epochs): for epoch in range(epochs): for (batch, (images, labels)) in enumerate(dataset): train_step(images, labels) print ('Epoch {} finished'.format(epoch)) train(epochs = 3) import matplotlib.pyplot as plt plt.plot(loss_history) plt.xlabel('Batch #') plt.ylabel('Loss [entropy]') ###Output _____no_output_____ ###Markdown 变量和优化器`tf.Variable` 对象会存储在训练期间访问的可变、类似于 `tf.Tensor` 的值，以更简单地实现自动微分。变量的集合及其运算方法可以封装到层或模型中。有关详细信息，请参阅[自定义 Keras 层和模型](https://render.githubusercontent.com/view/keras/custom_layers_and_models.ipynb)。层和模型之间的主要区别在于模型添加了如下方法：`Model.fit`、`Model.evaluate` 和 `Model.save`。例如，上面的自动微分示例可以改写为： ###Code class Linear(tf.keras.Model): def __init__(self): super(Linear, self).__init__() self.W = tf.Variable(5., name='weight') self.B = tf.Variable(10., name='bias') def call(self, inputs): return inputs * self.W + self.B # A toy dataset of points around 3 * x + 2 NUM_EXAMPLES = 2000 training_inputs = tf.random.normal([NUM_EXAMPLES]) noise = tf.random.normal([NUM_EXAMPLES]) training_outputs = training_inputs * 3 + 2 + noise # The loss function to be optimized def loss(model, inputs, targets): error = model(inputs) - targets return tf.reduce_mean(tf.square(error)) def grad(model, inputs, targets): with tf.GradientTape() as tape: loss_value = loss(model, inputs, targets) return tape.gradient(loss_value, [model.W, model.B]) ###Output _____no_output_____ ###Markdown 下一步：1. 创建模型。2. 损失函数对模型参数的导数。3. 基于导数的变量更新策略。 ###Code model = Linear() optimizer = tf.keras.optimizers.SGD(learning_rate=0.01) print("Initial loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) steps = 300 for i in range(steps): grads = grad(model, training_inputs, training_outputs) optimizer.apply_gradients(zip(grads, [model.W, model.B])) if i % 20 == 0: print("Loss at step {:03d}: {:.3f}".format(i, loss(model, training_inputs, training_outputs))) print("Final loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy())) ###Output _____no_output_____ ###Markdown 注：变量将一直存在，直至删除对 Python 对象的最后一个引用，并删除该变量。基于对象的保存 `tf.keras.Model` 包括一个方便的 `save_weights` 方法，您可以通过该方法轻松创建检查点： ###Code model.save_weights('weights') status = model.load_weights('weights') ###Output _____no_output_____ ###Markdown 您可以使用 `tf.train.Checkpoint` 完全控制此过程。本部分是[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)的缩略版。 ###Code x = tf.Variable(10.) checkpoint = tf.train.Checkpoint(x=x) x.assign(2.) # Assign a new value to the variables and save. checkpoint_path = './ckpt/' checkpoint.save('./ckpt/') x.assign(11.) # Change the variable after saving. # Restore values from the checkpoint checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path)) print(x) # => 2.0 ###Output _____no_output_____ ###Markdown 要保存和加载模型，`tf.train.Checkpoint` 会存储对象的内部状态，而无需隐藏变量。要记录 `model`、`optimizer` 和全局步骤的状态，请将它们传递到 `tf.train.Checkpoint`： ###Code model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) checkpoint_dir = 'path/to/model_dir' if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") root = tf.train.Checkpoint(optimizer=optimizer, model=model) root.save(checkpoint_prefix) root.restore(tf.train.latest_checkpoint(checkpoint_dir)) ###Output _____no_output_____ ###Markdown 注：在许多训练循环中，会在调用 `tf.train.Checkpoint.restore` 后创建变量。这些变量将在创建后立即恢复，并且可以使用断言来确保检查点已完全加载。有关详细信息，请参阅[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)。面向对象的指标`tf.keras.metrics` 会被存储为对象。可以通过将新数据传递给可调用对象来更新指标，并使用 `tf.keras.metrics.result` 方法检索结果，例如： ###Code m = tf.keras.metrics.Mean("loss") m(0) m(5) m.result() # => 2.5 m([8, 9]) m.result() # => 5.5 ###Output _____no_output_____ ###Markdown 摘要和 TensorBoard[TensorBoard](https://tensorflow.google.cn/tensorboard) 是一种可视化工具，用于了解、调试和优化模型训练过程。它使用在执行程序时编写的摘要事件。您可以在 Eager Execution 中使用 `tf.summary` 记录变量摘要。例如，要每 100 个训练步骤记录一次 `loss` 的摘要，请运行以下代码： ###Code logdir = "./tb/" writer = tf.summary.create_file_writer(logdir) steps = 1000 with writer.as_default(): # or call writer.set_as_default() before the loop. for i in range(steps): step = i + 1 # Calculate loss with your real train function. loss = 1 - 0.001 * step if step % 100 == 0: tf.summary.scalar('loss', loss, step=step) !ls tb/ ###Output _____no_output_____ ###Markdown 自动微分高级主题动态模型`tf.GradientTape` 也可以用于动态模型。下面这个[回溯线搜索](https://wikipedia.org/wiki/Backtracking_line_search)算法示例看起来就像普通的 NumPy 代码，但它的控制流比较复杂，存在梯度且可微分： ###Code def line_search_step(fn, init_x, rate=1.0): with tf.GradientTape() as tape: # Variables are automatically tracked. # But to calculate a gradient from a tensor, you must `watch` it. tape.watch(init_x) value = fn(init_x) grad = tape.gradient(value, init_x) grad_norm = tf.reduce_sum(grad * grad) init_value = value while value > init_value - rate * grad_norm: x = init_x - rate * grad value = fn(x) rate /= 2.0 return x, value ###Output _____no_output_____ ###Markdown 自定义梯度自定义梯度是重写梯度的一种简单方法。在前向函数中，定义相对于输入、输出或中间结果的梯度。例如，下面是在后向传递中裁剪梯度范数的一种简单方法： ###Code @tf.custom_gradient def clip_gradient_by_norm(x, norm): y = tf.identity(x) def grad_fn(dresult): return [tf.clip_by_norm(dresult, norm), None] return y, grad_fn ###Output _____no_output_____ ###Markdown 自定义梯度通常用来为运算序列提供数值稳定的梯度： ###Code def log1pexp(x): return tf.math.log(1 + tf.exp(x)) def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # The gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # However, x = 100 fails because of numerical instability. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 在此例中，`log1pexp` 函数可以通过自定义梯度进行分析简化。下面的实现重用了在前向传递期间计算的 `tf.exp(x)` 值，通过消除冗余计算使其变得更加高效： ###Code @tf.custom_gradient def log1pexp(x): e = tf.exp(x) def grad(dy): return dy * (1 - 1 / (1 + e)) return tf.math.log(1 + e), grad def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # As before, the gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # And the gradient computation also works at x = 100. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 性能在 Eager Execution 期间，计算会自动分流到 GPU。如果想控制计算运行的位置，可将其放在 `tf.device('/gpu:0')` 块（或 CPU 等效块）中： ###Code import time def measure(x, steps): # TensorFlow initializes a GPU the first time it's used, exclude from timing. tf.matmul(x, x) start = time.time() for i in range(steps): x = tf.matmul(x, x) # tf.matmul can return before completing the matrix multiplication # (e.g., can return after enqueing the operation on a CUDA stream). # The x.numpy() call below will ensure that all enqueued operations # have completed (and will also copy the result to host memory, # so we're including a little more than just the matmul operation # time). _ = x.numpy() end = time.time() return end - start shape = (1000, 1000) steps = 200 print("Time to multiply a {} matrix by itself {} times:".format(shape, steps)) # Run on CPU: with tf.device("/cpu:0"): print("CPU: {} secs".format(measure(tf.random.normal(shape), steps))) # Run on GPU, if available: if tf.config.experimental.list_physical_devices("GPU"): with tf.device("/gpu:0"): print("GPU: {} secs".format(measure(tf.random.normal(shape), steps))) else: print("GPU: not found") ###Output _____no_output_____ ###Markdown 可以将 `tf.Tensor` 对象复制到不同设备来执行其运算： ###Code if tf.config.experimental.list_physical_devices("GPU"): x = tf.random.normal([10, 10]) x_gpu0 = x.gpu() x_cpu = x.cpu() _ = tf.matmul(x_cpu, x_cpu) # Runs on CPU _ = tf.matmul(x_gpu0, x_gpu0) # Runs on GPU:0 ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. ###Code #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ###Output _____no_output_____ ###Markdown Eager Execution 在 TensorFlow.org 上查看在 Google Colab 中运行在 GitHub 中查看源代码下载笔记本 TensorFlow 的 Eager Execution 是一种命令式编程环境，可立即评估运算，无需构建计算图：运算会返回具体的值，而非构建供稍后运行的计算图。这样能使您轻松入门 TensorFlow 并调试模型，同时也减少了样板代码。要跟随本指南进行学习，请在交互式 `python` 解释器中运行以下代码示例。Eager Execution 是用于研究和实验的灵活机器学习平台，具备以下特性：- 直观的界面 - 自然地组织代码结构并使用 Python 数据结构。快速迭代小模型和小数据。- 更方便的调试功能 - 直接调用运算以检查正在运行的模型并测试更改。使用标准 Python 调试工具立即报告错误。- 自然的控制流 - 使用 Python 而非计算图控制流，简化了动态模型的规范。Eager Execution 支持大部分 TensorFlow 运算和 GPU 加速。注：启用 Eager Execution 后可能会增加某些模型的开销。我们正在持续改进其性能，如果您遇到问题，请[提交错误报告](https://github.com/tensorflow/tensorflow/issues)并分享您的基准。设置和基本用法 ###Code import os import tensorflow as tf import cProfile ###Output _____no_output_____ ###Markdown 在 Tensorflow 2.0 中，默认启用 Eager Execution。 ###Code tf.executing_eagerly() ###Output _____no_output_____ ###Markdown 现在您可以运行 TensorFlow 运算，结果将立即返回： ###Code x = [[2.]] m = tf.matmul(x, x) print("hello, {}".format(m)) ###Output _____no_output_____ ###Markdown 启用 Eager Execution 会改变 TensorFlow 运算的行为方式—现在它们会立即评估并将值返回给 Python。`tf.Tensor` 对象会引用具体值，而非指向计算图中节点的符号句柄。由于无需构建计算图并稍后在会话中运行，可以轻松使用 `print()` 或调试程序检查结果。评估、输出和检查张量值不会中断计算梯度的流程。Eager Execution 可以很好地配合 [NumPy](http://www.numpy.org/) 使用。NumPy 运算接受 `tf.Tensor` 参数。TensorFlow `tf.math` 运算会将 Python 对象和 NumPy 数组转换为 `tf.Tensor` 对象。`tf.Tensor.numpy` 方法会以 NumPy `ndarray` 的形式返回该对象的值。 ###Code a = tf.constant([[1, 2], [3, 4]]) print(a) # Broadcasting support b = tf.add(a, 1) print(b) # Operator overloading is supported print(a * b) # Use NumPy values import numpy as np c = np.multiply(a, b) print(c) # Obtain numpy value from a tensor: print(a.numpy()) # => [[1 2] # [3 4]] ###Output _____no_output_____ ###Markdown 动态控制流Eager Execution 的一个主要优势是，在执行模型时，主机语言的所有功能均可用。因此，编写 [fizzbuzz](https://en.wikipedia.org/wiki/Fizz_buzz) 之类的代码会很容易： ###Code def fizzbuzz(max_num): counter = tf.constant(0) max_num = tf.convert_to_tensor(max_num) for num in range(1, max_num.numpy()+1): num = tf.constant(num) if int(num % 3) == 0 and int(num % 5) == 0: print('FizzBuzz') elif int(num % 3) == 0: print('Fizz') elif int(num % 5) == 0: print('Buzz') else: print(num.numpy()) counter += 1 fizzbuzz(15) ###Output _____no_output_____ ###Markdown 这段代码具有依赖于张量值的条件语句并会在运行时输出这些值。 Eager 训练计算梯度[自动微分](https://en.wikipedia.org/wiki/Automatic_differentiation)对实现机器学习算法（例如用于训练神经网络的[反向传播](https://en.wikipedia.org/wiki/Backpropagation)）十分有用。在 Eager Execution 期间，请使用 `tf.GradientTape` 跟踪运算以便稍后计算梯度。您可以在 Eager Execution 中使用 `tf.GradientTape` 来训练和/或计算梯度。这对复杂的训练循环特别有用。由于在每次调用期间都可能进行不同运算，所有前向传递的运算都会记录到“条带”中。要计算梯度，请反向播放条带，然后丢弃。特定 `tf.GradientTape` 只能计算一个梯度；后续调用会引发运行时错误。 ###Code w = tf.Variable([[1.0]]) with tf.GradientTape() as tape: loss = w * w grad = tape.gradient(loss, w) print(grad) # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32) ###Output _____no_output_____ ###Markdown 训练模型以下示例创建了一个多层模型，该模型会对标准 MNIST 手写数字进行分类。示例演示了在 Eager Execution 环境中构建可训练计算图的优化器和层 API。 ###Code # Fetch and format the mnist data (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32), tf.cast(mnist_labels,tf.int64))) dataset = dataset.shuffle(1000).batch(32) # Build the model mnist_model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu', input_shape=(None, None, 1)), tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) ###Output _____no_output_____ ###Markdown 即使没有训练，也可以在 Eager Execution 中调用模型并检查输出： ###Code for images,labels in dataset.take(1): print("Logits: ", mnist_model(images[0:1]).numpy()) ###Output _____no_output_____ ###Markdown 虽然 Keras 模型有内置训练循环（使用 `fit` 方法），但有时您需要进行更多自定义。下面是一个使用 Eager Execution 实现训练循环的示例： ###Code optimizer = tf.keras.optimizers.Adam() loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) loss_history = [] ###Output _____no_output_____ ###Markdown 注：请在 `tf.debugging` 中使用断言函数检查条件是否成立。这在 Eager Execution 和计算图执行中均有效。 ###Code def train_step(images, labels): with tf.GradientTape() as tape: logits = mnist_model(images, training=True) # Add asserts to check the shape of the output. tf.debugging.assert_equal(logits.shape, (32, 10)) loss_value = loss_object(labels, logits) loss_history.append(loss_value.numpy().mean()) grads = tape.gradient(loss_value, mnist_model.trainable_variables) optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables)) def train(epochs): for epoch in range(epochs): for (batch, (images, labels)) in enumerate(dataset): train_step(images, labels) print ('Epoch {} finished'.format(epoch)) train(epochs = 3) import matplotlib.pyplot as plt plt.plot(loss_history) plt.xlabel('Batch #') plt.ylabel('Loss [entropy]') ###Output _____no_output_____ ###Markdown 变量和优化器`tf.Variable` 对象会存储在训练期间访问的可变、类似于 `tf.Tensor` 的值，以更简单地实现自动微分。变量的集合及其运算方法可以封装到层或模型中。有关详细信息，请参阅[自定义 Keras 层和模型](https://render.githubusercontent.com/view/keras/custom_layers_and_models.ipynb)。层和模型之间的主要区别在于模型添加了如下方法：`Model.fit`、`Model.evaluate` 和 `Model.save`。例如，上面的自动微分示例可以改写为： ###Code class Linear(tf.keras.Model): def __init__(self): super(Linear, self).__init__() self.W = tf.Variable(5., name='weight') self.B = tf.Variable(10., name='bias') def call(self, inputs): return inputs * self.W + self.B # A toy dataset of points around 3 * x + 2 NUM_EXAMPLES = 2000 training_inputs = tf.random.normal([NUM_EXAMPLES]) noise = tf.random.normal([NUM_EXAMPLES]) training_outputs = training_inputs * 3 + 2 + noise # The loss function to be optimized def loss(model, inputs, targets): error = model(inputs) - targets return tf.reduce_mean(tf.square(error)) def grad(model, inputs, targets): with tf.GradientTape() as tape: loss_value = loss(model, inputs, targets) return tape.gradient(loss_value, [model.W, model.B]) ###Output _____no_output_____ ###Markdown 下一步：1. 创建模型。2. 损失函数对模型参数的导数。3. 基于导数的变量更新策略。 ###Code model = Linear() optimizer = tf.keras.optimizers.SGD(learning_rate=0.01) print("Initial loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) steps = 300 for i in range(steps): grads = grad(model, training_inputs, training_outputs) optimizer.apply_gradients(zip(grads, [model.W, model.B])) if i % 20 == 0: print("Loss at step {:03d}: {:.3f}".format(i, loss(model, training_inputs, training_outputs))) print("Final loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy())) ###Output _____no_output_____ ###Markdown 注：变量将一直存在，直至删除对 Python 对象的最后一个引用，并删除该变量。基于对象的保存 `tf.keras.Model` 包括一个方便的 `save_weights` 方法，您可以通过该方法轻松创建检查点： ###Code model.save_weights('weights') status = model.load_weights('weights') ###Output _____no_output_____ ###Markdown 您可以使用 `tf.train.Checkpoint` 完全控制此过程。本部分是[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)的缩略版。 ###Code x = tf.Variable(10.) checkpoint = tf.train.Checkpoint(x=x) x.assign(2.) # Assign a new value to the variables and save. checkpoint_path = './ckpt/' checkpoint.save('./ckpt/') x.assign(11.) # Change the variable after saving. # Restore values from the checkpoint checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path)) print(x) # => 2.0 ###Output _____no_output_____ ###Markdown 要保存和加载模型，`tf.train.Checkpoint` 会存储对象的内部状态，而无需隐藏变量。要记录 `model`、`optimizer` 和全局步骤的状态，请将它们传递到 `tf.train.Checkpoint`： ###Code model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) checkpoint_dir = 'path/to/model_dir' if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") root = tf.train.Checkpoint(optimizer=optimizer, model=model) root.save(checkpoint_prefix) root.restore(tf.train.latest_checkpoint(checkpoint_dir)) ###Output _____no_output_____ ###Markdown 注：在许多训练循环中，会在调用 `tf.train.Checkpoint.restore` 后创建变量。这些变量将在创建后立即恢复，并且可以使用断言来确保检查点已完全加载。有关详细信息，请参阅[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)。面向对象的指标`tf.keras.metrics` 会被存储为对象。可以通过将新数据传递给可调用对象来更新指标，并使用 `tf.keras.metrics.result` 方法检索结果，例如： ###Code m = tf.keras.metrics.Mean("loss") m(0) m(5) m.result() # => 2.5 m([8, 9]) m.result() # => 5.5 ###Output _____no_output_____ ###Markdown 摘要和 TensorBoard[TensorBoard](https://tensorflow.google.cn/tensorboard) 是一种可视化工具，用于了解、调试和优化模型训练过程。它使用在执行程序时编写的摘要事件。您可以在 Eager Execution 中使用 `tf.summary` 记录变量摘要。例如，要每 100 个训练步骤记录一次 `loss` 的摘要，请运行以下代码： ###Code logdir = "./tb/" writer = tf.summary.create_file_writer(logdir) steps = 1000 with writer.as_default(): # or call writer.set_as_default() before the loop. for i in range(steps): step = i + 1 # Calculate loss with your real train function. loss = 1 - 0.001 * step if step % 100 == 0: tf.summary.scalar('loss', loss, step=step) !ls tb/ ###Output _____no_output_____ ###Markdown 自动微分高级主题动态模型`tf.GradientTape` 也可以用于动态模型。下面这个[回溯线搜索](https://wikipedia.org/wiki/Backtracking_line_search)算法示例看起来就像普通的 NumPy 代码，但它的控制流比较复杂，存在梯度且可微分： ###Code def line_search_step(fn, init_x, rate=1.0): with tf.GradientTape() as tape: # Variables are automatically tracked. # But to calculate a gradient from a tensor, you must `watch` it. tape.watch(init_x) value = fn(init_x) grad = tape.gradient(value, init_x) grad_norm = tf.reduce_sum(grad * grad) init_value = value while value > init_value - rate * grad_norm: x = init_x - rate * grad value = fn(x) rate /= 2.0 return x, value ###Output _____no_output_____ ###Markdown 自定义梯度自定义梯度是重写梯度的一种简单方法。在前向函数中，定义相对于输入、输出或中间结果的梯度。例如，下面是在后向传递中裁剪梯度范数的一种简单方法： ###Code @tf.custom_gradient def clip_gradient_by_norm(x, norm): y = tf.identity(x) def grad_fn(dresult): return [tf.clip_by_norm(dresult, norm), None] return y, grad_fn ###Output _____no_output_____ ###Markdown 自定义梯度通常用来为运算序列提供数值稳定的梯度： ###Code def log1pexp(x): return tf.math.log(1 + tf.exp(x)) def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # The gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # However, x = 100 fails because of numerical instability. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 在此例中，`log1pexp` 函数可以通过自定义梯度进行分析简化。下面的实现重用了在前向传递期间计算的 `tf.exp(x)` 值，通过消除冗余计算使其变得更加高效： ###Code @tf.custom_gradient def log1pexp(x): e = tf.exp(x) def grad(dy): return dy * (1 - 1 / (1 + e)) return tf.math.log(1 + e), grad def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # As before, the gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # And the gradient computation also works at x = 100. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 性能在 Eager Execution 期间，计算会自动分流到 GPU。如果想控制计算运行的位置，可将其放在 `tf.device('/gpu:0')` 块（或 CPU 等效块）中： ###Code import time def measure(x, steps): # TensorFlow initializes a GPU the first time it's used, exclude from timing. tf.matmul(x, x) start = time.time() for i in range(steps): x = tf.matmul(x, x) # tf.matmul can return before completing the matrix multiplication # (e.g., can return after enqueing the operation on a CUDA stream). # The x.numpy() call below will ensure that all enqueued operations # have completed (and will also copy the result to host memory, # so we're including a little more than just the matmul operation # time). _ = x.numpy() end = time.time() return end - start shape = (1000, 1000) steps = 200 print("Time to multiply a {} matrix by itself {} times:".format(shape, steps)) # Run on CPU: with tf.device("/cpu:0"): print("CPU: {} secs".format(measure(tf.random.normal(shape), steps))) # Run on GPU, if available: if tf.config.experimental.list_physical_devices("GPU"): with tf.device("/gpu:0"): print("GPU: {} secs".format(measure(tf.random.normal(shape), steps))) else: print("GPU: not found") ###Output _____no_output_____ ###Markdown 可以将 `tf.Tensor` 对象复制到不同设备来执行其运算： ###Code if tf.config.experimental.list_physical_devices("GPU"): x = tf.random.normal([10, 10]) x_gpu0 = x.gpu() x_cpu = x.cpu() _ = tf.matmul(x_cpu, x_cpu) # Runs on CPU _ = tf.matmul(x_gpu0, x_gpu0) # Runs on GPU:0 ###Output _____no_output_____ ###Markdown Copyright 2018 The TensorFlow Authors. ###Code #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ###Output _____no_output_____ ###Markdown Eager Execution 在 TensorFlow.org 上查看在 Google Colab 中运行在 GitHub 上查看源代码下载笔记本 TensorFlow 的 Eager Execution 是一种命令式编程环境，可立即评估运算，无需构建计算图：运算会返回具体的值，而非构建供稍后运行的计算图。这样能使您轻松入门 TensorFlow 并调试模型，同时也减少了样板代码。要跟随本指南进行学习，请在交互式 `python` 解释器中运行以下代码示例。Eager Execution 是用于研究和实验的灵活机器学习平台，具备以下特性：- 直观的界面 - 自然地组织代码结构并使用 Python 数据结构。快速迭代小模型和小数据。- 更方便的调试功能 - 直接调用运算以检查正在运行的模型并测试更改。使用标准 Python 调试工具立即报告错误。- 自然的控制流 - 使用 Python 而非计算图控制流，简化了动态模型的规范。Eager Execution 支持大部分 TensorFlow 运算和 GPU 加速。注：启用 Eager Execution 后可能会增加某些模型的开销。我们正在持续改进其性能，如果您遇到问题，请[提交错误报告](https://github.com/tensorflow/tensorflow/issues)并分享您的基准。设置和基本用法 ###Code import os import tensorflow as tf import cProfile ###Output _____no_output_____ ###Markdown 在 Tensorflow 2.0 中，默认启用 Eager Execution。 ###Code tf.executing_eagerly() ###Output _____no_output_____ ###Markdown 现在您可以运行 TensorFlow 运算，结果将立即返回： ###Code x = [[2.]] m = tf.matmul(x, x) print("hello, {}".format(m)) ###Output _____no_output_____ ###Markdown 启用 Eager Execution 会改变 TensorFlow 运算的行为方式—现在它们会立即评估并将值返回给 Python。`tf.Tensor` 对象会引用具体值，而非指向计算图中节点的符号句柄。由于无需构建计算图并稍后在会话中运行，可以轻松使用 `print()` 或调试程序检查结果。评估、输出和检查张量值不会中断计算梯度的流程。Eager Execution 可以很好地配合 [NumPy](http://www.numpy.org/) 使用。NumPy 运算接受 `tf.Tensor` 参数。TensorFlow `tf.math` 运算会将 Python 对象和 NumPy 数组转换为 `tf.Tensor` 对象。`tf.Tensor.numpy` 方法会以 NumPy `ndarray` 的形式返回该对象的值。 ###Code a = tf.constant([[1, 2], [3, 4]]) print(a) # Broadcasting support b = tf.add(a, 1) print(b) # Operator overloading is supported print(a * b) # Use NumPy values import numpy as np c = np.multiply(a, b) print(c) # Obtain numpy value from a tensor: print(a.numpy()) # => [[1 2] # [3 4]] ###Output _____no_output_____ ###Markdown 动态控制流Eager Execution 的一个主要优势是，在执行模型时，主机语言的所有功能均可用。因此，编写 [fizzbuzz](https://en.wikipedia.org/wiki/Fizz_buzz) 之类的代码会很容易： ###Code def fizzbuzz(max_num): counter = tf.constant(0) max_num = tf.convert_to_tensor(max_num) for num in range(1, max_num.numpy()+1): num = tf.constant(num) if int(num % 3) == 0 and int(num % 5) == 0: print('FizzBuzz') elif int(num % 3) == 0: print('Fizz') elif int(num % 5) == 0: print('Buzz') else: print(num.numpy()) counter += 1 fizzbuzz(15) ###Output _____no_output_____ ###Markdown 这段代码具有依赖于张量值的条件语句并会在运行时输出这些值。 Eager 训练计算梯度[自动微分](https://en.wikipedia.org/wiki/Automatic_differentiation)对实现机器学习算法（例如用于训练神经网络的[反向传播](https://en.wikipedia.org/wiki/Backpropagation)）十分有用。在 Eager Execution 期间，请使用 `tf.GradientTape` 跟踪运算以便稍后计算梯度。您可以在 Eager Execution 中使用 `tf.GradientTape` 来训练和/或计算梯度。这对复杂的训练循环特别有用。由于在每次调用期间都可能进行不同运算，所有前向传递的运算都会记录到“条带”中。要计算梯度，请反向播放条带，然后丢弃。特定 `tf.GradientTape` 只能计算一个梯度；后续调用会引发运行时错误。 ###Code w = tf.Variable([[1.0]]) with tf.GradientTape() as tape: loss = w * w grad = tape.gradient(loss, w) print(grad) # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32) ###Output _____no_output_____ ###Markdown 训练模型以下示例创建了一个多层模型，该模型会对标准 MNIST 手写数字进行分类。示例演示了在 Eager Execution 环境中构建可训练计算图的优化器和层 API。 ###Code # Fetch and format the mnist data (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32), tf.cast(mnist_labels,tf.int64))) dataset = dataset.shuffle(1000).batch(32) # Build the model mnist_model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu', input_shape=(None, None, 1)), tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) ###Output _____no_output_____ ###Markdown 即使没有训练，也可以在 Eager Execution 中调用模型并检查输出： ###Code for images,labels in dataset.take(1): print("Logits: ", mnist_model(images[0:1]).numpy()) ###Output _____no_output_____ ###Markdown 虽然 Keras 模型有内置训练循环（使用 `fit` 方法），但有时您需要进行更多自定义。下面是一个使用 Eager Execution 实现训练循环的示例： ###Code optimizer = tf.keras.optimizers.Adam() loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) loss_history = [] ###Output _____no_output_____ ###Markdown 注：请在 `tf.debugging` 中使用断言函数检查条件是否成立。这在 Eager Execution 和计算图执行中均有效。 ###Code def train_step(images, labels): with tf.GradientTape() as tape: logits = mnist_model(images, training=True) # Add asserts to check the shape of the output. tf.debugging.assert_equal(logits.shape, (32, 10)) loss_value = loss_object(labels, logits) loss_history.append(loss_value.numpy().mean()) grads = tape.gradient(loss_value, mnist_model.trainable_variables) optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables)) def train(epochs): for epoch in range(epochs): for (batch, (images, labels)) in enumerate(dataset): train_step(images, labels) print ('Epoch {} finished'.format(epoch)) train(epochs = 3) import matplotlib.pyplot as plt plt.plot(loss_history) plt.xlabel('Batch #') plt.ylabel('Loss [entropy]') ###Output _____no_output_____ ###Markdown 变量和优化器`tf.Variable` 对象会存储在训练期间访问的可变、类似于 `tf.Tensor` 的值，以更简单地实现自动微分。变量的集合及其运算方法可以封装到层或模型中。有关详细信息，请参阅[自定义 Keras 层和模型](https://render.githubusercontent.com/view/keras/custom_layers_and_models.ipynb)。层和模型之间的主要区别在于模型添加了如下方法：`Model.fit`、`Model.evaluate` 和 `Model.save`。例如，上面的自动微分示例可以改写为： ###Code class Linear(tf.keras.Model): def __init__(self): super(Linear, self).__init__() self.W = tf.Variable(5., name='weight') self.B = tf.Variable(10., name='bias') def call(self, inputs): return inputs * self.W + self.B # A toy dataset of points around 3 * x + 2 NUM_EXAMPLES = 2000 training_inputs = tf.random.normal([NUM_EXAMPLES]) noise = tf.random.normal([NUM_EXAMPLES]) training_outputs = training_inputs * 3 + 2 + noise # The loss function to be optimized def loss(model, inputs, targets): error = model(inputs) - targets return tf.reduce_mean(tf.square(error)) def grad(model, inputs, targets): with tf.GradientTape() as tape: loss_value = loss(model, inputs, targets) return tape.gradient(loss_value, [model.W, model.B]) ###Output _____no_output_____ ###Markdown 下一步：1. 创建模型。2. 损失函数对模型参数的导数。3. 基于导数的变量更新策略。 ###Code model = Linear() optimizer = tf.keras.optimizers.SGD(learning_rate=0.01) print("Initial loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) steps = 300 for i in range(steps): grads = grad(model, training_inputs, training_outputs) optimizer.apply_gradients(zip(grads, [model.W, model.B])) if i % 20 == 0: print("Loss at step {:03d}: {:.3f}".format(i, loss(model, training_inputs, training_outputs))) print("Final loss: {:.3f}".format(loss(model, training_inputs, training_outputs))) print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy())) ###Output _____no_output_____ ###Markdown 注：变量将一直存在，直至删除对 Python 对象的最后一个引用，并删除该变量。基于对象的保存 `tf.keras.Model` 包括一个方便的 `save_weights` 方法，您可以通过该方法轻松创建检查点： ###Code model.save_weights('weights') status = model.load_weights('weights') ###Output _____no_output_____ ###Markdown 您可以使用 `tf.train.Checkpoint` 完全控制此过程。本部分是[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)的缩略版。 ###Code x = tf.Variable(10.) checkpoint = tf.train.Checkpoint(x=x) x.assign(2.) # Assign a new value to the variables and save. checkpoint_path = './ckpt/' checkpoint.save('./ckpt/') x.assign(11.) # Change the variable after saving. # Restore values from the checkpoint checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path)) print(x) # => 2.0 ###Output _____no_output_____ ###Markdown 要保存和加载模型，`tf.train.Checkpoint` 会存储对象的内部状态，而无需隐藏变量。要记录 `model`、`optimizer` 和全局步骤的状态，请将它们传递到 `tf.train.Checkpoint`： ###Code model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16,[3,3], activation='relu'), tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(10) ]) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) checkpoint_dir = 'path/to/model_dir' if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") root = tf.train.Checkpoint(optimizer=optimizer, model=model) root.save(checkpoint_prefix) root.restore(tf.train.latest_checkpoint(checkpoint_dir)) ###Output _____no_output_____ ###Markdown 注：在许多训练循环中，会在调用 `tf.train.Checkpoint.restore` 后创建变量。这些变量将在创建后立即恢复，并且可以使用断言来确保检查点已完全加载。有关详细信息，请参阅[检查点训练指南](https://render.githubusercontent.com/view/checkpoint.ipynb)。面向对象的指标`tf.keras.metrics` 会被存储为对象。可以通过将新数据传递给可调用对象来更新指标，并使用 `tf.keras.metrics.result` 方法检索结果，例如： ###Code m = tf.keras.metrics.Mean("loss") m(0) m(5) m.result() # => 2.5 m([8, 9]) m.result() # => 5.5 ###Output _____no_output_____ ###Markdown 摘要和 TensorBoard[TensorBoard](https://tensorflow.google.cn/tensorboard) 是一种可视化工具，用于了解、调试和优化模型训练过程。它使用在执行程序时编写的摘要事件。您可以在 Eager Execution 中使用 `tf.summary` 记录变量摘要。例如，要每 100 个训练步骤记录一次 `loss` 的摘要，请运行以下代码： ###Code logdir = "./tb/" writer = tf.summary.create_file_writer(logdir) steps = 1000 with writer.as_default(): # or call writer.set_as_default() before the loop. for i in range(steps): step = i + 1 # Calculate loss with your real train function. loss = 1 - 0.001 * step if step % 100 == 0: tf.summary.scalar('loss', loss, step=step) !ls tb/ ###Output _____no_output_____ ###Markdown 自动微分高级主题动态模型`tf.GradientTape` 也可以用于动态模型。下面这个[回溯线搜索](https://wikipedia.org/wiki/Backtracking_line_search)算法示例看起来就像普通的 NumPy 代码，但它的控制流比较复杂，存在梯度且可微分： ###Code def line_search_step(fn, init_x, rate=1.0): with tf.GradientTape() as tape: # Variables are automatically tracked. # But to calculate a gradient from a tensor, you must `watch` it. tape.watch(init_x) value = fn(init_x) grad = tape.gradient(value, init_x) grad_norm = tf.reduce_sum(grad * grad) init_value = value while value > init_value - rate * grad_norm: x = init_x - rate * grad value = fn(x) rate /= 2.0 return x, value ###Output _____no_output_____ ###Markdown 自定义梯度自定义梯度是重写梯度的一种简单方法。在前向函数中，定义相对于输入、输出或中间结果的梯度。例如，下面是在后向传递中裁剪梯度范数的一种简单方法： ###Code @tf.custom_gradient def clip_gradient_by_norm(x, norm): y = tf.identity(x) def grad_fn(dresult): return [tf.clip_by_norm(dresult, norm), None] return y, grad_fn ###Output _____no_output_____ ###Markdown 自定义梯度通常用来为运算序列提供数值稳定的梯度： ###Code def log1pexp(x): return tf.math.log(1 + tf.exp(x)) def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # The gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # However, x = 100 fails because of numerical instability. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 在此例中，`log1pexp` 函数可以通过自定义梯度进行分析简化。下面的实现重用了在前向传递期间计算的 `tf.exp(x)` 值，通过消除冗余计算使其变得更加高效： ###Code @tf.custom_gradient def log1pexp(x): e = tf.exp(x) def grad(dy): return dy * (1 - 1 / (1 + e)) return tf.math.log(1 + e), grad def grad_log1pexp(x): with tf.GradientTape() as tape: tape.watch(x) value = log1pexp(x) return tape.gradient(value, x) # As before, the gradient computation works fine at x = 0. grad_log1pexp(tf.constant(0.)).numpy() # And the gradient computation also works at x = 100. grad_log1pexp(tf.constant(100.)).numpy() ###Output _____no_output_____ ###Markdown 性能在 Eager Execution 期间，计算会自动分流到 GPU。如果想控制计算运行的位置，可将其放在 `tf.device('/gpu:0')` 块（或 CPU 等效块）中： ###Code import time def measure(x, steps): # TensorFlow initializes a GPU the first time it's used, exclude from timing. tf.matmul(x, x) start = time.time() for i in range(steps): x = tf.matmul(x, x) # tf.matmul can return before completing the matrix multiplication # (e.g., can return after enqueing the operation on a CUDA stream). # The x.numpy() call below will ensure that all enqueued operations # have completed (and will also copy the result to host memory, # so we're including a little more than just the matmul operation # time). _ = x.numpy() end = time.time() return end - start shape = (1000, 1000) steps = 200 print("Time to multiply a {} matrix by itself {} times:".format(shape, steps)) # Run on CPU: with tf.device("/cpu:0"): print("CPU: {} secs".format(measure(tf.random.normal(shape), steps))) # Run on GPU, if available: if tf.config.experimental.list_physical_devices("GPU"): with tf.device("/gpu:0"): print("GPU: {} secs".format(measure(tf.random.normal(shape), steps))) else: print("GPU: not found") ###Output _____no_output_____ ###Markdown 可以将 `tf.Tensor` 对象复制到不同设备来执行其运算： ###Code if tf.config.experimental.list_physical_devices("GPU"): x = tf.random.normal([10, 10]) x_gpu0 = x.gpu() x_cpu = x.cpu() _ = tf.matmul(x_cpu, x_cpu) # Runs on CPU _ = tf.matmul(x_gpu0, x_gpu0) # Runs on GPU:0 ###Output _____no_output_____
docs/notebooks/inference-example.ipynb	###Markdown Crop Lungs from datasetImagine we created a deep learning network which analyzes a specific lung disease. We want to apply the network now on a new dataset. To preprocess the dataset, we need to do two things: 1. Filter all volumes, where the lungs are present2. Crop the lungs from the volumeIn this tutorial we will go through the preprocessing, and it will be explained how the bpreg package can help us. 1. Download DataFor this tutorial, a subset of the [TCGA-KIRC ](https://wiki.cancerimagingarchive.net/display/Public/TCGA-KIRC580038695f8cd691bda43dda71b4093c69c7318)dataset from the TCIA is used. The DICOM files were transformed to nifti files. To save the information in the DICOM metadata, an additional meta-data file was created with the metadata inside. Moreover, to reduce the size of the dataset and to remove CT scans with few slices, nifti files with a size greater than 35 MB and smaller than 5 MB were removed. Please download the data from Zenodo: https://zenodo.org/record/5141950.YQFiMNaxWEA 2. Analyze datasetFirs, we want to analyze the dataset from Zenodo. ###Code input_path = "data/TCGA-KIRC/TCGA-KIRC_nifti" meta_data = "data/TCGA-KIRC/TCGA-KIRC_meta_data.xlsx" output_path = "data/TCGA-KIRC/TCGA-KIRC_json" if not os.path.exists(output_path): os.mkdir(output_path) print(f"The dataset contains: {len(os.listdir(input_path))} CT volumes.") ###Output The dataset contains: 291 CT volumes. ###Markdown 2.1 Analyze DICOM tag BodyPartExaminedOne approach to obtain the Examined Body Part from a CT volume is to analyze the DICOM tag BodyPartExamined. Unfortunately, the tag is not very precise and often misleading or empty. ###Code df = pd.read_excel(meta_data) plot_dicomexamined_distribution(df, column="BodyPartExamined", count_column="filepath") ###Output _____no_output_____ ###Markdown 3. Body Part Regression InferenceWe can create for every nifti file a corresponding json output file. The json file contains metadata to the examined body part. Moreover, in the output path an additional `README.md` file is saved, where the metadata inside the json-files is explained. ###Code bpreg_inference(input_path, output_path) ###Output JSON-file already exists. Skip file: TCGA-B8-4154_07-27-2003-CT-ABDOMEN-PELVIS-W-CONT-78543_2.000000-AbdPelvis--5.0--B31f-92442_.nii.gz JSON-file already exists. Skip file: TCGA-B8-5551_02-18-2004-CT-RENAL-Mass-79313_3.000000-Arterial--5.0--B31f-51058_.nii.gz JSON-file already exists. Skip file: TCGA-B8-4151_05-19-2003-CT-RENAL-Mass-57015_2.000000-Unenhanced---5.0--B30f-16470_.nii.gz JSON-file already exists. Skip file: TCGA-B8-5162_10-06-2003-CT-RENAL-Mass-58198_3.000000-Arterial-Phase--5.0--B30f-49138_.nii.gz JSON-file already exists. Skip file: TCGA-CW-6096_04-02-1995-Abdomen020APRoutine-Adult-68078_2.000000-kids-wo--5.0--B40s-64573_.nii.gz JSON-file already exists. 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Skip file: TCGA-CW-5587_04-20-1997-Abdomen020APRoutineAbdomenPelvis-Adult-51162_2.000000-AP-Routine--5.0--B40f-94102_.nii.gz JSON-file already exists. Skip file: TCGA-B8-4148_04-02-2003-CT-RENAL-Mass-68197_603.000000-cor-thin-mip-ss-19517_.nii.gz JSON-file already exists. Skip file: TCGA-CW-6096_04-25-1997-Abdomen020AbdomenRoutine-Adult-75027_2.000000-AP-Routine--5.0--B20f-52705_.nii.gz JSON-file already exists. Skip file: TCGA-BP-4351_07-11-1990-FORFILE-CT-CHABPEL---CD-48554_2.000000-ROUTINE-CHEST-59786_.nii.gz JSON-file already exists. Skip file: TCGA-B0-5085_06-18-1987-e1-AP-18854_2.000000-69293_.nii.gz JSON-file already exists. Skip file: TCGA-CW-6088_01-16-1993-Abdomen020APRoutine-Adult-99778_2.000000-AP-wcont--5.0--B40f-49970_.nii.gz Create body-part meta data file: TCGA-B0-5115_06-17-1992-AP-82538_350.000000-Reformatted-34987_.json ###Markdown 4. AnalyzeLet's analyze the json metadata files. In each json file, several tags are saved. 4.1 Slice ScoresWe can for example plot the slice scores. The slice scores increase monotonously with slice height. With the help of the `look-up table` the slice scores can be mapped to concrete anatomies. 4.2 Body Part Examined tagThe body part examined tag is based on the predicted slice scores. For each volume, one tag is calculated. If the slope of the slice scores seem unrealistic, the tag is equal to NONE. ###Code json_filepaths = [os.path.join(output_path, f) for f in os.listdir(output_path) if f.endswith(".json")] nifti_filepaths = [os.path.join(input_path, f.replace(".json", ".nii.gz")) for f in os.listdir(output_path)] x = load_json(json_filepaths[0]) x.keys() plot_scores_interactive(json_filepaths, nifti_filepaths) dftags = get_updated_bodypartexamined_from_json_files(output_path) plot_dicomexamined_distribution(dftags, fontsize=12) ###Output _____no_output_____ ###Markdown Based on the Body Part Examined tag, we are able to filter all CT volumes, where the CHEST is present. ###Code # Filter files which include the chest dfchest = dftags[dftags.tag.isin(["CHEST", "CHEST-ABDOMEN-PELVIS", "CHEST-ABDOMEN"])] dfchest.reset_index(drop=False, inplace=True) dfchest = dfchest.rename({"index": "json"}, axis=1) dfchest = dfchest.drop(["count"], axis=1) print(f"Files which include the chest: {dfchest.shape[0]}") dfchest.sample(5) ###Output Files which include the chest: 29 ###Markdown 5. Crop the lungsFor the remaining CT volumes, we can crop the lungs by finding the expected scores for the landmark `lung_start` and `lung_end`. With these score boundaries, the appropriate region can be cropped in the CT volumes based on the slice score curves. ###Code x = load_json(json_filepaths[0]) lookuptable = pd.DataFrame(x["look-up table"]).T start_score = x["look-up table"]["lung_start"]["mean"] end_score = x["look-up table"]["lung_end"]["mean"] lookuptable.sort_values(by="mean")[["mean"]] plot_tailored_volumes_interactive(dfchest, start_score, end_score, json_base_path=output_path, nifti_base_path=input_path) ###Output _____no_output_____
04_data_integration/3. data_profiling_notebooks_ipynb/02_fd_check/fd_discovery_wines2016.ipynb	###Markdown Name: Rachel Fanti Coelho Lima Date: 12/2021 Subject: LM-18 73005B - Data Profiling - AY 2021-22 Task: Implementation of FD. ###Code %matplotlib inline # pip install PyQt5==5.9.2 import time import pandas as pd import networkx as nx import matplotlib.pyplot as plt import seaborn import sys #pip install pandas_profiling --user #pip install PyQt5==5.9.2 from pandas_profiling import ProfileReport from pandas_profiling.utils.cache import cache_file ###Output _____no_output_____ ###Markdown Input Data ###Code ''' df = pd.DataFrame([ [ 'C', '3', 'X', 722, 112 ], [ 'A', '1', 'X', 289, 553 ], [ 'A', '1', 'Y', 189, 583 ], [ 'B', '1', 'X', 289, 513 ], [ 'C', '1', 'X', 289, 553 ] ], columns = [a for a in 'abcde']) ''' df = pd.read_csv('./../00_data/wines2016.csv', sep = ",", index_col=False) df.columns=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p'] #df.columns = [x for x in range (0, len(df.columns))] plot_lattice = True schema = set(a for a in df.columns) df.head() schema ###Output _____no_output_____ ###Markdown Layout functions ###Code def layout_lattice(basket): from itertools import combinations from scipy.special import binom G = nx.DiGraph() len_max, pos = binom(len(basket), len(basket)/2), {} for k in range(1, len(basket)+1): sorted_nodes_at_level = sort([set(e) for e in combinations(basket, k)]) G.add_nodes_from(sorted_nodes_at_level) for p, n in enumerate(sorted_nodes_at_level): pos[n] = (p + 0.5 * (len_max - len(sorted_nodes_at_level)), k-len(basket)) nx.draw_networkx_nodes(G, pos, nodelist=sorted_nodes_at_level, node_color='tab:grey') return G, pos def mark_as_visited_sup(E, F): if not plot_lattice: return nx.draw_networkx_nodes(viz[0], viz[1], nodelist=sort(E), node_color='tab:red') nx.draw_networkx_nodes(viz[0], viz[1], nodelist=sort(F), node_color='tab:green') nx.draw_networkx_labels(viz[0], viz[1], font_size=8, font_color="whitesmoke") plt.show() plt.pause(1) def mark_as_visited_gen(f, s): if not plot_lattice: return edges = [(sort_single(f), sort_single(s))] viz[0].add_edges_from(edges) nx.draw_networkx_edges(viz[0], viz[1], edgelist=edges, arrows=False) ###Output _____no_output_____ ###Markdown Helper functions ###Code def sort_single(s): return ''.join(sorted(list(s))) def sort(F): return sorted(sort_single(s) for s in F) # original to keep def pli_single(a, D): pli = {} for row in range(D.shape[0]): key = D.at[row, a] if key in pli: pli[key].add(row) else: pli[key] = { row } return list(v for v in pli.values() if len(v) > 1) # original to keep def pli_intersect(p1, p2): pli, prob = {}, {} for c, s in enumerate(p1): for row in s: prob[row] = c for c, s in enumerate(p2): for row in s: if row in prob: key = (c, prob[row]) if key in pli: pli[key].add(row) else: pli[key] = { row } return list(v for v in pli.values() if len(v) > 1) def intersection_sets (set_1, set_2): '''Finding the common chars for both strings ''' result = set() for e in set_1: if e in set_2: result.add(e) return result #rachel def set_intersect(X, C): ''' e.g. Input: X = {'b', 'a'} C = {'d': {'a', 'b', 'c', 'd', 'e'}, 'e': {'a', 'b', 'c', 'd', 'e'}, 'b': {'a', 'b', 'c'}, 'c': {'a', 'b', 'c', 'd', 'e'}, 'a': {'a', 'c', 'e'}} set_intersect(X, C) Output: {'ab': {'a', c'}} ''' intersection = {} X = sort_single(X) for key, value in C.items(): if all (k in X for k in key): if X in intersection: intersection[X]= intersection_sets (intersection[X], value) else: intersection[X] = value return intersection #rachel def get_cardinality (list_of_sets): '''calculates the cardinality of a list of sets e.g. Input list_of_sets = [{0, 4}, {1, 2}] get_cardinality (list_of_sets) Output 4 ''' count = 0 for s in list_of_sets: #for each set in list of sets for e in s: # for each element in set count += 1 return count #rachel def is_valid(X, A, pli): ''' X\A->A - Check if is a funtional dependency Use key-error and pli to infer FDs (key error can be used with pli and stripped-pli): X->y pli(X)=pli(XY) cr(X)=Cr(XY) Therefore: X\A->A Cr(X\A) = Cr(X) e.g. Input: X = {'b', 'd'} A = 'b' pli = {'d': [{1, 3, 4}], 'e': [{1, 4}], 'b': [{1, 2, 3, 4}], 'c': [{0, 1, 3, 4}], 'a': [{0, 4}, {1, 2}], 'ab': [{1, 2}], 'ac': [{0, 4}], 'ad': [], 'ae': [], 'bc': [{1, 3, 4}], 'bd': [{1, 3, 4}], 'be': [{1, 4}], 'cd': [{1, 3, 4}], 'ce': [{1, 4}], 'de': [{1, 4}]} is_valid(X, A, pli) Output: True ''' pli_X_wth_A = pli[sort_single(X).replace(A, '')] key_error_X_wth_A = get_cardinality(pli_X_wth_A) - len(pli_X_wth_A) pli_X = pli[sort_single(X)] key_error_X = get_cardinality(pli_X) - len(pli_X) if key_error_X_wth_A == key_error_X: return True else: return False #rachel def is_key(X, pli): ''' Check if X is a (super)key verify if key_error_X = 0 ''' pli_X = pli[sort_single(X)] key_error_X = get_cardinality(pli_X) - len(pli_X) if key_error_X == 0: return True else: return False ###Output _____no_output_____ ###Markdown Funtional dependency discovery algorithm ###Code #rachel def apriori_fd_discovery(schema, table): ''' Ej: candidates LHSs [of size j?] Fj: validated LHSs [of size j?] Cj: candidates RHSs for LHSs [of size j?] Format: E: [{'c', 'b', 'a'}, {'d', 'c', 'b'}, {'e', 'c', 'b'}, {'d', 'b', 'e'}, {'d', 'c', 'e'}] pli: {'ad': [], 'ae': [], 'abc': [], 'bcd': [{1, 3, 4}], 'bce': [{1, 4}], 'bde': [{1, 4}], 'cde': [{1, 4}]} C = {'d': {'a', 'b', 'c', 'd', 'e'}, 'e': {'a', 'b', 'c', 'd', 'e'}, 'b': {'a', 'b', 'c', 'd', 'e'}, 'c': {'a', 'b', 'c', 'd', 'e'}, 'a': {'a', 'b', 'c', 'd', 'e'}} ''' k=1 U = [] E, pli = [set(a) for a in schema], {a: pli_single(a, table) for a in schema} # single attribute sets and PLIs C = {sort_single(a): schema for a in schema} # canidates sets for single attributes F, U = prune(E, pli, C) # determine the non-unique ones # print('\n','E:', E, '\n', 'pli:', pli, '\n', 'C:', C, '\n', 'F:', F) while len(F)!=0: # while there are non-dependent LHSs E, pli_next = candidates(F, pli) # create candidate LHSs with one attribute more and PLIs pli.update(pli_next) C, U_next_1 = dependencies (E, pli, C) # create candidate RHSs for new candidate LHSs # print('\n','E:', E, '\n', 'pli:', pli, '\n', 'C:', C) F, U_next_2 = prune(E, pli, C) # and determine the non-dependent LHSs # increment again the LHSs size U = U + U_next_1 + U_next_2 U = sorted(U, key = lambda i: (len(i[0]), i[0])) # print('\n', 'U:', U) # print('\n', 'F:', F) print('--> done with level', k, ' - FDs: ', end ="") print(['%s -> %s' % (sort_single(set(u[0])),u[1]) for u in U if len(u[0]) == k]), k=k+1 return U # return the functional dependencies # original to keep def candidates(F, pli): ''' Format: E: [{'b', 'a'}, {'c', 'a'}, {'d', 'a'}, {'e', 'a'}, {'c', 'b'}] pli: {'c': [{0, 1, 3, 4}], 'e': [{1, 4}], 'b': [{1, 2, 3, 4}], 'd': [{1, 3, 4}], 'ab': [{1, 2}], 'ac': [{0, 4}], 'ad': [], 'ae': [], 'bc': [{1, 3, 4}]} ''' pli_next = pli.copy() Fs, E_next = sort(F), [] for i1 in range(len(Fs)): for i2 in range(i1+1, len(Fs)): if Fs[i1][:-1] == Fs[i2][:-1] and Fs[i1][-1] < Fs[i2][-1]: f = set(fs for fs in Fs[i1]).union({Fs[i2][-1]}) superset = len(f) for i in f: if f.difference({i}) in F: superset -= 1 if not superset: E_next.append(f) pli_next[sort_single(f)] = pli_intersect(pli_next[sort_single(Fs[i1])], pli_next[sort_single(Fs[i2])]) mark_as_visited_gen(f, set(s for s in Fs[i1])) del pli[sort_single(Fs[i1])] return E_next, pli_next #rachel def dependencies(E, pli, C): ''' INPUTs: E = [{'b', 'a'}, {'c', 'a'}, {'d', 'a'}, {'e', 'a'}, {'c', 'b'}, {'d', 'b'}, {'e', 'b'}, {'d', 'c'}, {'c', 'e'}, {'d', 'e'}] pli = {'d': [{1, 3, 4}], 'e': [{1, 4}], 'b': [{1, 2, 3, 4}], 'c': [{0, 1, 3, 4}], 'a': [{0, 4}, {1, 2}], 'ab': [{1, 2}], 'ac': [{0, 4}], 'ad': [], 'ae': [], 'bc': [{1, 3, 4}], 'bd': [{1, 3, 4}], 'be': [{1, 4}], 'cd': [{1, 3, 4}], 'ce': [{1, 4}], 'de': [{1, 4}]} C = {'d': {'a', 'b', 'c', 'd', 'e'}, 'e': {'a', 'b', 'c', 'd', 'e'}, 'b': {'a', 'b', 'c', 'd', 'e'}, 'c': {'a', 'b', 'c', 'd', 'e'}, 'a': {'a', 'b', 'c', 'd', 'e'}} dependencies(E, pli, C) E.g. LHS = left hand size = X = {'b', 'c', 'e'} RHS = right hand size = X-A = {'c', 'e'} A = {'b'} ''' Cd = {} # initialize the set of rhs candidates U = [] for X in E: # traverse the lhs candidates rhs = set_intersect(X, C) # Add rhs candidates from previous level Cd.update(rhs) for X in E: # traverse the lhs candidates (change for x, pli!!) for A in sort_single(X): if A in Cd[sort_single(X)]: # traverse the rhs candidates if is_valid (X, A, pli): # check if valid using key error and pli # print ('*FD', X,'/',A, '-->', A) # then is a minimal funtional dependency [C1] U_A = [x for x in X if x != A] # U-A U.append((sort(U_A), A)) Cd[sort_single(X)] = Cd[sort_single(X)]-{A} # remove from RHS candidates [C1] # print ('C1 - C[', X, ']:', Cd[sort_single(X)]) for B in (schema-X): # traverse the candidates Cd[sort_single(X)] = Cd[sort_single(X)]-{B} # remove from RHS candidates [C2] # print ('C2 - C[', X, ']:', Cd[sort_single(X)]) return Cd, U #return the pruned RHS candidates sets and the funtional dependencies #rachel def prune(E, pli, C): ''' Formats: E = [{'b', 'a'}, {'c', 'a'}, {'d', 'a'}, {'e', 'a'}, {'c', 'b'}, {'d', 'b'}, {'e', 'b'}, {'d', 'c'}, {'c', 'e'}, {'d', 'e'}] pli = {'a': [{0, 4}, {1, 2}], 'c': [{0, 1, 3, 4}], 'd': [{1, 3, 4}], 'b': [{1, 2, 3, 4}], 'e': [{1, 4}], 'ad': [], 'ae': [], 'ab': [{1, 2}], 'ac': [{0, 4}], 'bc': [{1, 3, 4}], 'bd': [{1, 3, 4}], 'be': [{1, 4}], 'cd': [{1, 3, 4}], 'ce': [{1, 4}], 'de': [{1, 4}], 'abc': [], 'bcd': [{1, 3, 4}], 'bce': [{1, 4}], 'bde': [{1, 4}], 'cde': [{1, 4}]} C = {'ab': {'a', 'b', 'c', 'd', 'e'}, 'ac': {'a', 'b', 'c', 'd', 'e'}, 'ad': {'a', 'b', 'c', 'd', 'e'}, 'ae': {'a', 'b', 'c', 'd', 'e'}, 'bc': '{'a', 'b', 'c', 'd', 'e'}, 'bd': {'d'}, 'be': {'e'}, 'cd': {'d'}, 'ce': {'e'}, 'de': {'e'}} X = {'d', 'a'} U = [] ''' U=[] F = E.copy() # initialize the set of LHSs candidates for X in E: # traverse the candidates if len(C[sort_single(X)])==0: # if a LHS there is no RHS candidate F.remove(X) # remove the LHS form the prefix tree if is_key(X, pli): # check if is a (super)key using key-error and pli print (sort_single(X) ,'is key') for A in ([x for x in C[sort_single(X)] if x not in sort_single(X)]): # traverse the RHSs candidates of X [and iterate in each candidate C[X] - X keys_XA_B = [sort_single(X.union(A)).replace(B,"") for B in sort_single(X)] #C[X+A-B] if RHS in all C of augmented LHS subsets [C3] # print ('keys_XA_B', keys_XA_B) if all (key in C for key in keys_XA_B): # [Check if exist all augmented LHS subsets] # print ('all keys present') if all(A in C[k] for k in keys_XA_B): #if RHS in all C of augmented LHS subsets [C3] # print ('FD', sort_single(X), "-->", A, 'Minimal') # then it is a minimal FD U.append((sort(list(X)), A)) F.remove(X) # remove the LHS from the prefix tree (everything after this will not be minimal) [key-pruning] mark_as_visited_sup(E, F) return F, U # return the pruned set of candidates and the funtional dependencies ###Output _____no_output_____ ###Markdown Run ###Code if plot_lattice: viz = layout_lattice(schema) plt.ion() # run it print(df.head()) print('') print('Funtional dependency discovery on', sort_single(schema)) ''' profile = ProfileReport(table, title="dataset", explorative=True) profile.to_file("./report.html") ''' fd_start = time.time() fd = apriori_fd_discovery(schema, df) # print it print('\n\ndiscovered', len(fd), 'minimal FDs in', time.time() - fd_start, ' seconds') print('\n**All FDs:') print(['%s -> %s' % (sort_single(set(u[0])),u[1]) for u in fd]), if plot_lattice: plt.ioff() plt.show() ###Output a b c d e f g h i \ 0 NaN NaN NaN NaN Torre Gaia NaN NaN NaN NaN 1 NaN NaN NaN NaN Terre Stregate NaN NaN NaN NaN 2 NaN NaN NaN NaN Cantine Terranera NaN NaN NaN NaN 3 NaN NaN NaN NaN Santimartini NaN NaN NaN NaN 4 NaN NaN NaN NaN Donnachiara NaN NaN NaN NaN j k l \ 0 Via Boscocupo 11 Dugenta Benevento 1 C.da Santa Lucia SS87 Guardia Sanframondi Benevento 2 Via Sandro Pertini Grottolella Avellino 3 via Bebiana, 107/A Solopaca Benevento 4 Via Stazione Montefalcione Avellino m n \ 0 41.122790, 14.462032 http://www.tenutatorregaia.it/code/home.php 1 41.249499, 14.572098 http://www.terrestregate.it/it/ 2 40.967256, 14.790662 http://www.cantineterranera.it/terranera/index... 3 41.194201, 14.528736 http://www.santimartini.it/index.php?lang=it 4 40.955245, 14.878085 http://www.donnachiara.com/ o p 0 0824 906054 NaN 1 0824 817857 NaN 2 0825 671455 NaN 3 0824 971254 NaN 4 0825 977135 NaN Funtional dependency discovery on abcdefghijklmnop
Image_to_Sketch.ipynb	###Markdown LetsGrowMore(VIP) January 2022TASK-I Beginner LevelImage manipulation using OpenCV ,Converting a Image into Sketch ###Code import cv2 import matplotlib.pyplot as plt from google.colab import files uploaded=files.upload() image = cv2.imread("Dog.jpeg") image plt.imshow(image[:,:,::-1]) plt.axis(False) plt.show() plt.imshow(image) plt.axis(False) plt.show() RGB_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) plt.imshow(RGB_image) plt.axis(False) plt.show() grey_image=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) plt.imshow(grey_image) plt.axis(False) plt.show() invert_image=cv2.bitwise_not(grey_image) plt.imshow(invert_image) plt.axis(False) plt.show() blur_image=cv2.GaussianBlur(invert_image, (111,111),0) plt.imshow(blur_image) plt.axis(False) plt.show() invblur_image=cv2.bitwise_not(blur_image) plt.imshow(invblur_image) plt.axis(False) plt.show() sketch_image=cv2.divide(grey_image,invblur_image, scale=256.0) plt.imshow(sketch_image) plt.axis(False) plt.show() cv2.imwrite('Dog.jpeg', sketch_image) rgb_sketch=cv2.cvtColor(sketch_image, cv2.COLOR_BGR2RGB) plt.imshow(rgb_sketch) plt.axis('off') plt.show() plt.figure(figsize=(14,8)) plt.subplot(1,2,1) plt.title('Original image', size=18) plt.imshow(RGB_image) plt.axis('off') plt.subplot(1,2,2) plt.title('Sketch', size=18) rgb_sketch=cv2.cvtColor(sketch_image, cv2.COLOR_BGR2RGB) plt.imshow(rgb_sketch) plt.axis('off') plt.show() ###Output _____no_output_____
11 Naive Bayes/homework/Gruen_Gianna_11_1.ipynb	###Markdown 2. Use the LabelEncoder to convert the group names to numeric labels ###Code from sklearn import preprocessing le = preprocessing.LabelEncoder() le.fit(newsgroups_df['group']) le.transform(newsgroups_df['group']) newsgroups_df['newsgroups_label'] = le.transform(newsgroups_df['group']) newsgroups_df.head(3) newsgroups_df.tail(3) ###Output _____no_output_____ ###Markdown 3. Pick out 10 words or phrases to use as manually created features. Doing an 80/20 train/test split, how well does a Naive Bayes classifier do? atheismgraphicsmotorcyclesbaseballhockeycryptmedspacegunsmideast ###Code newsgroups_df['has_atheism'] = newsgroups_df['content'].str.contains('atheism') newsgroups_df['has_graphics'] = newsgroups_df['content'].str.contains('graphics') newsgroups_df['has_motorcycles'] = newsgroups_df['content'].str.contains('motocycles') newsgroups_df['has_baseball'] = newsgroups_df['content'].str.contains('baseball') newsgroups_df['has_hockey'] = newsgroups_df['content'].str.contains('hockey') newsgroups_df['has_crypt'] = newsgroups_df['content'].str.contains('crypt') newsgroups_df['has_med'] = newsgroups_df['content'].str.contains('med') newsgroups_df['has_space'] = newsgroups_df['content'].str.contains('space') newsgroups_df['has_guns'] = newsgroups_df['content'].str.contains('guns') newsgroups_df['has_mideast'] = newsgroups_df['content'].str.contains('mideast') newsgroups_df.columns from sklearn.cross_validation import train_test_split nbx_train, nbx_test, nby_train, nby_test = train_test_split( newsgroups_df[['has_atheism', 'has_graphics', 'has_motorcycles', 'has_baseball', 'has_hockey', 'has_crypt', 'has_med', 'has_space', 'has_guns', 'has_mideast']], # the first is our FEATURES newsgroups_df['newsgroups_label'], # the second parameter is the LABEL (0-16, southern us, brazilian, anything really) test_size=0.2) # 80% training, 20% testing from sklearn import naive_bayes clf = naive_bayes.BernoulliNB() clf.fit(nbx_train, nby_train) clf.score(nbx_train, nby_train) clf.score(nbx_test, nby_test) ###Output _____no_output_____ ###Markdown 4. Use a CountVectorizer to automatically create your list of features. Doing an 80/20 train/test split, how well can a Naive Bayes classifier do? ###Code from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer(max_features=1000) #kernel dies without max features vectorizer.fit(newsgroups_df['content']) all_word_features = vectorizer.transform(newsgroups_df['content']) all_word_features x_train, x_test, y_train, y_test = train_test_split( all_word_features, newsgroups_df['newsgroups_label'], test_size=0.2) clf = naive_bayes.BernoulliNB() clf.fit(x_train, y_train) clf.score(x_train, y_train) clf.score(x_test, y_test) clf.predict(x_test) # So Naive Bayes did better with the manually selected features. ###Output _____no_output_____ ###Markdown 5. PUSH THAT SCORE UP! You can adjust ngrams, max_features and any other options of the vectorizer, or try a decision tree or any other type of classifier. bc I couldn't run the Count Vectorizer without max_features and ngrams don't make sense here, bc it is more about single words(?), I would like to try Random Forests ###Code from sklearn.ensemble import RandomForestClassifier tree_clf = RandomForestClassifier() tree_clf.fit(x_train, y_train) print("Training score:", tree_clf.score(x_train, y_train)) print("Testing score:", tree_clf.score(x_test, y_test)) #Same results for Naive Bayes and Random forest? ###Output _____no_output_____ ###Markdown 6. Write 15 sentences that, when run against the predictor, are put in 15 separate newsgroups (list the names of the newsgroups). ###Code sentences = [ "I believe atheism is also a religion", "I love graphics", "We rode a motorcycle on the weekend", "I never played or watched baseball", "Who likes hockey, really?!" "You would want to encrypt your emails", "I really love the pharma industry for all the medicine provided", "How does medical care in space work out?", "Do you have a collection of guns?", "What's your favourite country in the mideast?", "The US should pass a anti-gun law to control weapon sales", "You should teach how to crypt in schools", "Have you ever fired guns?", "These are really cool graphics. How did you make them?", "Mideast politics are really complicated." ] sentences_words_features = vectorizer.transform(sentences) sentences_words_features clf = naive_bayes.BernoulliNB() clf.fit(vectorizer.transform(newsgroups_df['content']), newsgroups_df['newsgroups_label']) predictions = clf.predict(sentences_words_features) predictions le.inverse_transform(predictions) # This is predicting badly, but it is also with the training data, so it is consistent in a way. # I'd assume that this is a case for improving training data, to aslo improve the resule # (ie go for word combinations instead of single words.) ###Output _____no_output_____
Homework/my_solutions/HW3/hw3.ipynb	###Markdown Problem 1 In order to get a start on the problem, I wanted to simply plot out some terms of the sequence to see what was going on ###Code def sequence(func, x0, num_terms, p): seq = [x0] for i in range(num_terms): if (seq[-1] == p): return np.array(seq) seq.append(func(seq[-1])) return np.array(seq) ###Output _____no_output_____ ###Markdown Part A ###Code func = lambda x : -16 + 6x + (12/x) p = 2 plt.plot(p - sequence(func, 1, 20, p)) plt.plot(p - sequence(func, 1.5, 20, p)) plt.plot(p - sequence(func, 1.6, 5, p)) plt.xlabel('n') plt.ylabel('xn') plt.show() ###Output _____no_output_____ ###Markdown Part B ###Code func = lambda x : (2/3)x + (1/x2) p = 3(1/3) plt.plot(p - sequence(func, 1, 20, p)) plt.plot(p - sequence(func, 1.5, 20, p)) plt.plot(p - sequence(func, 2, 5, p)) plt.xlabel('n') plt.ylabel('xn') plt.show() ###Output _____no_output_____ ###Markdown Part C ###Code func = lambda x : 12/(1+x) p = 3 plt.plot(p - sequence(func, 1, 20, p)) plt.plot(p - sequence(func, 1.5, 20, p)) plt.plot(p - sequence(func, 2, 20, p)) plt.plot(p - sequence(func, 4, 20, p)) plt.xlabel('n') plt.ylabel('xn') plt.show() ###Output _____no_output_____ ###Markdown Problem 2 ###Code def newtons_method(func, func_prime, x0, max_iter=100, tol=1e-8): history_x = [] history_fx = [] x = x0 fx = func(x) history_x.append(x) history_fx.append(fx) for i in range(max_iter): fx_prime = func_prime(x) if (fx_prime == 0): return x, history_x, history_fx x -= (fx / fx_prime) fx = func(x) history_x.append(x) history_fx.append(fx) if np.abs(fx) <= tol or np.isinf(fx): return x, history_x, history_fx return x, history_x, history_fx ###Output _____no_output_____ ###Markdown Part A ###Code function = lambda x : (x - 3) * (x - 2) function_prime = lambda x : 2x - 5 xs = np.linspace(1, 4, 100) plt.plot(xs, function_prime(xs)) plt.plot(xs[:-1], np.diff(function(xs)) / (xs[1] - xs[0])) plt.xlabel('x') plt.ylabel('y') plt.title('f(x) = (x-3) (x-2)') plt.legend(['Derivative', 'np.diff']) plt.show() ###Output _____no_output_____ ###Markdown The two derivative methods seem close enough Part B ###Code function = lambda x : (x - 3) * (x - 2) function_prime = lambda x : 2x - 5 root, x_his, fx_his = newtons_method(function, function_prime, 10) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - 3)) plt.xlabel('n') plt.ylabel('\|x_n - 3\|') plt.title('Newtons method for f(x) = (x-3) (x-2)') plt.legend(['Error']) plt.show() ###Output _____no_output_____ ###Markdown The error does decay to 0, and it does decay at the rate I would think (quadrtic convergence). This is because the root is simple, so Newton's Method guarantees at least quadtratic convergence. Part C ###Code function = lambda x : (x-3)*2 function_prime = lambda x : 2(x-3) root, x_his, fx_his = newtons_method(function, function_prime, 10) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - 3)) plt.xlabel('n') plt.ylabel('\|x_n - 3\|') plt.title('Newtons method for f(x) = (x-3)^2') plt.legend(['Error']) plt.show() ###Output _____no_output_____ ###Markdown This error decays slightly more slowly than before, which is what I would expect, since the derivative has a smaller magnitude for x > 3, so each step is getting closer to the root, but at a slower rate than before. The convergence is still quadtratic, since it is a well-defined Newton's method. In adition, the root is not simple, so the convergance is slower. Part D ###Code # (i) 3(x-3) function_prime = lambda x : 3(x-3) root, x_his, fx_his = newtons_method(function, function_prime, 10) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - 3)) # (ii) 2(x-3.1) function_prime = lambda x : 2(x-3.1) root, x_his, fx_his = newtons_method(function, function_prime, 10) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - 3)) plt.xlabel('n') plt.ylabel('\|x_n - 3\|') plt.title('Newtons method for f(x) = (x-3)^2') plt.legend(['Error for f\' = 3(x-3)', 'Error for f\' = 2(x-3.1)']) plt.show() ###Output _____no_output_____ ###Markdown The error for both incorrect derivatives eventually converge, but it is interesting to note that the error for 3\(x-3) smoothly converges, while the 2\(x-3.1) case has a slight hitch, i.e. it breifly increases after about 5 iterations. Part E ###Code function = lambda x : (x-3)*2 + 1 function_prime = lambda x : 2(x-3) root, x_his, fx_his = newtons_method(function, function_prime, 10) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - 3)) plt.xlabel('n') plt.ylabel('\|x_n - 3\|') plt.title('Newtons method for f(x) = (x-3)^2') plt.legend(['Error']) plt.show() xs = np.arange(0, 10) plt.plot(xs, function(xs)) plt.xlabel('x') plt.ylabel('f(x)') plt.title('(x-3)^2 + 1') plt.show() ###Output _____no_output_____ ###Markdown By plotting the function, it is clear that Newton's method is getting to the closest value to a root (3), and then going up the other side, oscillating back and forth near the minimum. AS expected, there is no root, so the error never converges. Problem 3 Part A ###Code function = lambda x : (x - (1/3))*5 function_prime = lambda x : 5((x - (1/3))*4) root, x_his, fx_his = newtons_method(function, function_prime, 0.4, tol=MACHINE_EPSILON) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - (1/3))) plt.xlabel('n') plt.ylabel('\|x_n + 1/3\|') plt.title('Newtons method for f(x) = (x-1/3)^5') plt.legend(['Error']) plt.show() ###Output _____no_output_____ ###Markdown The error decays slowly, as expected, since the root has a multiplicity of $5$ Part B By hand, it is clear that $\mu(x) = \frac{(x-1/3)^5}{5(x-1/3)^4}$ which simplifies to $\frac{1}{5}(x-\frac{1}{3})$. Thus $\mu'(x) = \frac{1}{5}$ ###Code mu = lambda x : (1/5)(x-1/3) mu_prime = lambda x : 1/5 root, x_his, fx_his = newtons_method(mu, mu_prime, 0.4, tol=MACHINE_EPSILON) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - (1/3))) plt.xlabel('n') plt.ylabel('\|x_n - 1/3\|') plt.title('Newtons method for mu(x) = 1/5(x-1/3)') plt.legend(['Error']) plt.show() with np.printoptions(precision=20, suppress=True): print(np.abs(x_his - (1/3))[:20]) print(root) ###Output [0.06666666666666671 0. ] 0.3333333333333333 ###Markdown The error converges in one step! This is expected since modified Newton's is meant to speed up the convergence Part C ###Code f_coeffs = np.poly(5[1/3]) f_prime_coeffs = np.polyder(f_coeffs) f_prime_prime_coeffs = np.polyder(f_prime_coeffs) f = lambda x : np.polyval(f_coeffs, x) f_prime = lambda x : np.polyval(f_prime_coeffs, x) f_prime_prime = lambda x : np.polyval(f_prime_prime_coeffs, x) mu = lambda x : f(x) / f_prime(x) mu_prime = lambda x : (f_prime(x)*2 - f(x)f_prime_prime(x)) / (f_prime(x))*2 root, x_his, fx_his = newtons_method(mu, mu_prime, 0.4, tol=MACHINE_EPSILON) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - (1/3))) plt.xlabel('n') plt.yscale('log') plt.ylabel('\|x_n - 1/3\| (log)') plt.title('Newtons method for mu(x) = f(x) / f\'(x) (numpy poly edition)') plt.legend(['Error']) plt.show() with np.printoptions(precision=20, suppress=True): print(np.abs(x_his - (1/3))[:20]) ###Output [0.06666666666666671 0.00000000000007799317 0.12152777777785578 0.00000000000006511458 0.1956521739131086 0.0000000000000039968 0.20833333333333734 0.00000000000000460743 0.7499999999999953 0.00000000000000160982 0.08333333333333492 0.00000000000027738922 0.0833333333336107 0.00000000000031574743 0.2083333333336491 0.00000000000000272005 0.10500000000000273 0.00000000000016892043 0.08522727272744163 0.00000000000036876058] ###Markdown The function here never converges. It is probably because of subtractive cancellation, where the terms will never be quite 0, even though they should be. The error gets really close to $0$, and then bounces back up. Part D ###Code mu = lambda x : (1/5)(x-1/2) mu_prime = lambda x : 1/5 root, x_his, fx_his = newtons_method(mu, mu_prime, 0.4, tol=MACHINE_EPSILON) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - (1/2))) plt.xlabel('n') plt.ylabel('\|x_n - 1/2\|') plt.title('Newtons method for mu(x) = 1/5(x-1/2)') plt.legend(['Error']) plt.show() with np.printoptions(precision=20, suppress=True): print(np.abs(x_his - (1/2))[:20]) f_coeffs = np.poly(5[1/2]) f_prime_coeffs = np.polyder(f_coeffs) f_prime_prime_coeffs = np.polyder(f_prime_coeffs) f = lambda x : np.polyval(f_coeffs, x) f_prime = lambda x : np.polyval(f_prime_coeffs, x) f_prime_prime = lambda x : np.polyval(f_prime_prime_coeffs, x) mu = lambda x : 0 if f_prime(x) == 0 else f(x) / f_prime(x) # protect against divide by zero mu_prime = lambda x : (f_prime(x)*2 - f(x)f_prime_prime(x)) / (f_prime(x))2 root, x_his, fx_his = newtons_method(mu, mu_prime, 0.4, tol=MACHINE_EPSILON) x_his = np.array(x_his) plt.plot(np.arange(len(x_his)), np.abs(x_his - (1/2))) plt.xlabel('n') plt.ylabel('\|x_n - 1/2\|') plt.title('Newtons method for mu(x) = f(x) / f\'(x) (numpy poly edition)') plt.legend(['Error']) plt.show() with np.printoptions(precision=20, suppress=True): print(np.abs(x_his - (1/2))[:20]) ###Output [0.09999999999999998 0.00000000000009431345] ###Markdown Here, the error converges in both implementations. This is probably due to the fact that $\frac{1}{3}$ is irrational, whereas $\frac{1}{2}$ is not. Therefore, the precision requried is finite, as 0.5 can be represented exactly in a computer Problem 4 ###Code happiness = lambda dog : dog2 - 0.5(np.exp(dog) - 1) xs = np.linspace(0, 3) plt.plot(xs, happiness(xs)) plt.xlabel('# dogs') plt.ylabel('Happiness') plt.title('Trying to achieve happiness using man\'s best friend') plt.show() ###Output _____no_output_____ ###Markdown A little sad, but understandable, that between owning 0.001 and 0.5ish of a dog, happiness is actually negative! What does negative happiness even look like? Could 2020 have reached this level?? To maximize this function, we can simply find the root of the derivative. If only we had a function to do this... oh wait! Recalling all the way back to the last problem, we have Newton's Method! So we need to find the first derivative and the second derivative, which is easy enough to do by hand ###Code happiness_prime = lambda dog : 2dog - 0.5np.exp(dog) happiness_double_prime = lambda dog : 2 - 0.5np.exp(dog) root, x_his, _ = newtons_method(happiness_prime, happiness_double_prime, 1.5, tol=MACHINE_EPSILON) with np.printoptions(precision=20, suppress=True): print(np.array(x_his)[:20]) plt.plot(xs, happiness(xs)) plt.plot(root, happiness(root), 'o', markersize=15) plt.xlabel('# dogs') plt.ylabel('Happiness') plt.title('The quest to get maximum utility out of dogs') plt.legend(['Happiness', 'Maximum happiness']) plt.show() print("Time to go buy %s dogs!"%root) ###Output Time to go buy 2.1532923641103494 dogs!
adhoc/predict_calculated_fields.ipynb	###Markdown Predict calculated fieldsIdentify the most important features in predicting key calculated features, using random forest variable importance. Setup Imports ###Code import pandas as pd import numpy as np from sklearn import ensemble from matplotlib import pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Columns ###Code COLS = [ 'DSI', 'E00200', 'E00300', 'E00400', 'E00600', 'E00650', 'E00700', 'E00800', 'E00900', 'E01100', 'E01200', 'E01400', 'E01500', 'E01700', 'E02000', 'E02100', 'E02300', 'E02400', 'E03150', 'E03210', 'E03220', 'E03230', 'E03240', 'E03270', 'E03290', 'E03300', 'E03400', 'E03500', 'E07240', 'E07260', 'E07300', 'E07400', 'E07600', 'E09700', 'E09800', 'E09900', 'E11200', 'E17500', 'E18400', 'E18500', 'E19200', 'E19800', 'E20100', 'E20400', 'E24515', 'E24518', 'E26270', 'E27200', 'E32800', 'E58990', 'E62900', 'E87521', 'E87530', 'EIC', 'F2441', 'F6251', 'FDED', 'MARS', 'MIDR', 'N24', 'P08000', 'P22250', 'P23250', 'S006', 'XTOT'] CALCULATED_COLS = [ 'E00100', 'E04600', 'P04470', 'E04800', 'E62100', 'E05800', 'E08800', 'E59560', 'E26190' ] AGG_RECIDS = [999996, 999997, 999998, 999999] ###Output _____no_output_____ ###Markdown Load ###Code puf = pd.read_csv('~/puf2011.csv', usecols=COLS + CALCULATED_COLS + ['RECID']) puf = puf[~puf.RECID.isin(AGG_RECIDS)].drop('RECID', axis=1) ###Output _____no_output_____ ###Markdown Predict ###Code rf = ensemble.RandomForestRegressor(n_estimators=50, random_state=0) importances_list = [] %%time for col in CALCULATED_COLS: print('Analyzing ' + col + '...') rf.fit(puf[COLS], puf[col]) importance = pd.DataFrame({ 'x': COLS, 'y': col, 'importance': rf.feature_importances_ }) importances_list.append(importance) importances = pd.concat(importances_list) importances.sort_values('importance', ascending=False).head(10) importances['importance_rank'] = importances.groupby('y').importance.rank(ascending=False) max_imp = importances.groupby('x').importance_rank.max() mean_imp = importances.groupby('x').importance_rank.mean() imp_pivot = importances.pivot_table( index='x', columns='y', values='importance_rank').loc[ mean_imp.sort_values(ascending=True).index.values] imp_pivot.head(10) sns.heatmap(imp_pivot.head(5), annot=True) plt.title('Feature importance rank of raw Xs predicting calculated Xs') plt.show() ###Output _____no_output_____ ###Markdown Again without `S006`Since we're not using the synthesized version anyway. ###Code cols_no_s006 = set(COLS) - set(['S006']) importances_list2 = [] %%time for col in CALCULATED_COLS: print('Analyzing ' + col + '...') rf.fit(puf[COLS], puf[col]) importance = pd.DataFrame({ 'x': COLS, 'y': col, 'importance': rf.feature_importances_ }) importances_list2.append(importance) importances2 = pd.concat(importances_list2) importances2.sort_values('importance', ascending=False).head(10) importances2['importance_rank'] = importances2.groupby('y').importance.rank(ascending=False) max_imp2 = importances2.groupby('x').importance_rank.max() mean_imp2 = importances2.groupby('x').importance_rank.mean() imp_pivot2 = importances2.pivot_table( index='x', columns='y', values='importance_rank').loc[ mean_imp2.sort_values(ascending=True).index.values] imp_pivot2.head(10) sns.heatmap(imp_pivot2.head(5), annot=True) plt.title('Feature importance rank of raw Xs predicting calculated Xs') plt.show() ###Output _____no_output_____
Data-Science-Virtual-Machine/Samples/Notebooks/SQLDW_Explorations.ipynb	###Markdown License Information ###Code This sample Jupyter Notebook is shared by Microsoft under the MIT license. Please check the LICENSE.txt file in the directory where this Jupyter Notebook is stored for license information and additional details. ###Output _____no_output_____ ###Markdown NYC Data wrangling using Jupyter Notebook and SQL Data Warehouse This notebook demonstrates data exploration and feature generation using Python and SQL queries for data stored in Azure SQL Data Warehouse. We start with reading a sample of the data into a Pandas data frame and visualizing and exploring the data. We show how to use Python to execute SQL queries against the data and manipulate data directly within the Azure SQL Data Warehouse.This IPNB is accompanying material to the data Azure Data Science in Action walkthrough document (https://azure.microsoft.com/en-us/documentation/articles/machine-learning-data-science-process-sql-walkthrough/) and uses a sample of the New York City Taxi dataset (http://www.andresmh.com/nyctaxitrips/). Read data in Pandas frame for visualizations We start with loading a sample of the data in a Pandas data frame and performing some explorations on the sample. We assume that the nyctaxi table have been created and loaded from the taxi dataset mentioned earlier. If you haven't done this already please refer to the "LoadDataintoDW" notebook. Import required packages in this experiment ###Code import pandas as pd from pandas import Series, DataFrame import numpy as np import matplotlib.pyplot as plt from time import time import pyodbc import os import tables import time ###Output _____no_output_____ ###Markdown Set plot inline ###Code %matplotlib inline ###Output _____no_output_____ ###Markdown Initialize Database Credentials ###Code SERVER_NAME = 'ENTER SERVER_NAME ' DATABASE_NAME = 'ENTER DATABASE_NAME' USERID = 'ENTER USERID' PASSWORD = 'ENTER PASSWORD' DB_DRIVER = 'SQL Server Native Client 11.0' NYCTAXITABLE_NAME = "nyctaxi" NYCTAXISCHEMA = "nyctaxinb" ###Output _____no_output_____ ###Markdown Create Database Connection ###Code driver = 'DRIVER={' + DB_DRIVER + '}' server = 'SERVER=' + SERVER_NAME database = 'DATABASE=' + DATABASE_NAME uid = 'UID=' + USERID pwd = 'PWD=' + PASSWORD CONNECTION_STRING = ';'.join([driver,server,database,uid,pwd, 'Encrypt=yes;TrustServerCertificate=no']) print CONNECTION_STRING conn = pyodbc.connect(CONNECTION_STRING) ###Output _____no_output_____ ###Markdown Report number of rows and columns in table ###Code nrows = pd.read_sql('''SELECT SUM(rows) FROM sys.partitions WHERE object_id = OBJECT_ID('nyctaxinb.nyctaxi')''', conn) print 'Total number of rows = %d' % nrows.iloc[0,0] ncols = pd.read_sql('''SELECT count() FROM information_schema.columns WHERE table_name = ('nyctaxi') AND table_schema = ('nyctaxinb')''', conn) print 'Total number of columns = %d' % ncols.iloc[0,0] ###Output Total number of rows = 170399 Total number of columns = 23 ###Markdown Read-in data from SQL Data Warehouse ###Code t0 = time.time() #load only a small percentage of the data for some quick visuals df1 = pd.read_sql('''select top 10000 from nyctaxinb.nyctaxi t ''', conn) t1 = time.time() print 'Time to read the sample table is %f seconds' % (t1-t0) print 'Number of rows and columns retrieved = (%d, %d)' % (df1.shape[0], df1.shape[1]) ###Output Time to read the sample table is 1.036000 seconds Number of rows and columns retrieved = (10000, 23) ###Markdown Descriptive Statistics Now we can explore the sample data. We start with looking at descriptive statistics for trip distance: ###Code df1['trip_distance'].describe() ###Output _____no_output_____ ###Markdown Box Plot Next we look at the box plot for trip distance to visualize quantiles ###Code df1.boxplot(column='trip_distance',return_type='dict') ###Output _____no_output_____ ###Markdown Distribution Plot ###Code fig = plt.figure() ax1 = fig.add_subplot(1,2,1) ax2 = fig.add_subplot(1,2,2) df1['trip_distance'].plot(ax=ax1,kind='kde', style='b-') df1['trip_distance'].hist(ax=ax2, bins=100, color='k') ###Output _____no_output_____ ###Markdown Binning trip_distance ###Code trip_dist_bins = [0, 1, 2, 4, 10, 1000] df1['trip_distance'] trip_dist_bin_id = pd.cut(df1['trip_distance'], trip_dist_bins) trip_dist_bin_id ###Output _____no_output_____ ###Markdown Bar and Line Plots The distribution of the trip distance values after binning looks like the following: ###Code pd.Series(trip_dist_bin_id).value_counts() ###Output _____no_output_____ ###Markdown We can plot the above bin distribution in a bar or line plot as below ###Code pd.Series(trip_dist_bin_id).value_counts().plot(kind='bar') pd.Series(trip_dist_bin_id).value_counts().plot(kind='line') ###Output _____no_output_____ ###Markdown We can also use bar plots for visualizing the sum of passengers for each vendor as follows ###Code vendor_passenger_sum = df1.groupby('vendor_id').passenger_count.sum() print vendor_passenger_sum vendor_passenger_sum.plot(kind='bar') ###Output _____no_output_____ ###Markdown Scatterplot We plot a scatter plot between trip_time_in_secs and trip_distance to see if there is any correlation between them. ###Code plt.scatter(df1['trip_time_in_secs'], df1['trip_distance']) ###Output _____no_output_____ ###Markdown To further drill down on the relationship we can plot distribution side by side with the scatter plot (while flipping independentand dependent variables) as follows ###Code df1_2col = df1[['trip_time_in_secs','trip_distance']] pd.scatter_matrix(df1_2col, diagonal='hist', color='b', alpha=0.7, hist_kwds={'bins':100}) ###Output _____no_output_____ ###Markdown Similarly we can check the relationship between rate_code and trip_distance using a scatter plot ###Code plt.scatter(df1['passenger_count'], df1['trip_distance']) ###Output _____no_output_____ ###Markdown Correlation Pandas 'corr' function can be used to compute the correlation between trip_time_in_secs and trip_distance as follows: ###Code df1[['trip_time_in_secs', 'trip_distance']].corr() ###Output _____no_output_____ ###Markdown Sub-Sampling the Data in SQL In this section we used a sampled table we pregenerated by joining Trip and Fare data and taking a sub-sample of the full dataset. The sample data table named '' has been created and the data is loaded when you run the PowerShell script. Report number of rows and columns in the sampled table ###Code nrows = pd.read_sql('''SELECT SUM(rows) FROM sys.partitions WHERE object_id = OBJECT_ID('nyctaxinb.nyctaxi')''', conn) print 'Number of rows in sample = %d' % nrows.iloc[0,0] ncols = pd.read_sql('''SELECT count() FROM information_schema.columns WHERE table_name = ('nyctaxi') AND table_schema = ('nyctaxinb')''', conn) print 'Number of columns in sample = %d' % ncols.iloc[0,0] ###Output Number of rows in sample = 170399 Number of columns in sample = 23 ###Markdown We show some examples of exploring data using SQL in the sections below. We also show some useful visualizatios that you can use below. Note that you can read the sub-sample data in the table above in Azure Machine Learning directly using the SQL code in the reader module. Exploration in SQL In this section, we would be doing some explorations using SQL on the 1% sample data (that we created above). Tipped/Not Tipped Distribution ###Code query = ''' SELECT tipped, count() AS tip_freq FROM nyctaxinb.nyctaxi GROUP BY tipped ''' pd.read_sql(query, conn) ###Output _____no_output_____ ###Markdown Tip Class Distribution ###Code query = ''' SELECT tip_class, count() AS tip_freq FROM nyctaxinb.nyctaxi GROUP BY tip_class ''' tip_class_dist = pd.read_sql(query, conn) tip_class_dist ###Output _____no_output_____ ###Markdown Plot the tip distribution by class ###Code tip_class_dist['tip_freq'].plot(kind='bar') ###Output _____no_output_____ ###Markdown Daily distribution of trips ###Code query = ''' SELECT CONVERT(date, dropoff_datetime) as date, count() as c from nyctaxinb.nyctaxi group by CONVERT(date, dropoff_datetime) ''' pd.read_sql(query,conn) ###Output _____no_output_____ ###Markdown Trip distribution per medallion ###Code query = '''select medallion,count() as c from nyctaxinb.nyctaxi group by medallion''' pd.read_sql(query,conn) ###Output _____no_output_____ ###Markdown Trip distribution by medallion and hack license ###Code query = '''select medallion, hack_license,count() from nyctaxinb.nyctaxi group by medallion, hack_license''' pd.read_sql(query,conn) ###Output _____no_output_____ ###Markdown Trip time distribution ###Code query = '''select trip_time_in_secs, count() from nyctaxinb.nyctaxi group by trip_time_in_secs order by count() desc''' pd.read_sql(query,conn) ###Output _____no_output_____ ###Markdown Trip distance distribution ###Code query = '''select floor(trip_distance/5)5 as tripbin, count() from nyctaxinb.nyctaxi group by floor(trip_distance/5)5 order by count() desc''' pd.read_sql(query,conn) ###Output _____no_output_____ ###Markdown Payment type distribution ###Code query = '''select payment_type,count() from nyctaxinb.nyctaxi group by payment_type''' pd.read_sql(query,conn) query = '''select TOP 10 from nyctaxinb.nyctaxi''' pd.read_sql(query,conn) ###Output _____no_output_____
jupyter_notebook/humam_sr_ch2.ipynb	###Markdown Statistical Rethinking Second EditionIni adalah kesimpulan yang didapatkan dari buku Statistical Rethinking tulisan Professor Richard McElreath. Buku ini berisi tentang pendekatan statistik untuk penelitian dengan cara Bayesian. Dalam buku ini, dapat diketemukan beberapa contoh kasus dan penerapan-nya dalam kode R yang akan kami terjemahkan ke dalam python. ###Code import numpy as np import itertools ###Output _____no_output_____ ###Markdown Chapter 2: Small Worlds and Large WorldsPada bab ini, penulis berargumen bahwa dalam setiap penelitian selalu ada dua buah dunia. Pertama adalah dunia nyata dimana kasus terjadi dan yang kedua adalah dunia tempat penelitian dijalankan. Beberapa penelitian dalam buku ini adalah membentuk model yang juga menjadi representasi dari small word. Dalam upaya mengambil beberapa bagian dari dunia nyata dan memodelkannya dalam ruang isolasi, dibutuhkan asumsi-asumsi yang berkaitan dengan upaya tersebut. Asumsi ini ada karena small world tidak mungkin mereplikasi apa yang sebenernya terjadi--entah karena memang tidak ada model yang cocok atau membutuhkan usaha komputasi yang besar--dan memudahkan perhitungan untuk beberapa kasus. Soal 1 Ada empat buah kelereng bewarna biru dan putih dan sebuah kantung. Tidak diketahui berapa jumlah kelerang yang biru dan yang putih. Estimasi jumlah kelereng berwarna biru dan putih berdasarkan hasil yang ditarik (dan dikembalikan lagi) dari kantung. ###Code collection = itertools.combinations_with_replacement("WB", 4) for num, val in enumerate(collection): print(num, val) ###Output 0 ('W', 'W', 'W', 'W') 1 ('W', 'W', 'W', 'B') 2 ('W', 'W', 'B', 'B') 3 ('W', 'B', 'B', 'B') 4 ('B', 'B', 'B', 'B') ###Markdown Setidaknya ada 5 kemungkinan bagaimana komposisi kelereng dalam kantung. Dalam buku ini, penulis mengatakan ini adalah dugaan dari komposisi dalam kantung. Tugas kami adalah mencari mana yang paling masuk akal. Asumsilah bahwa penarikan kelereng menghasilkan Biru, Putih, dan Biru.Mari kami telaah satu dari lima dugaan yaitu nomor 1. Dalam dugaan nomor 1, ada 4 kemungkinan yang terjadi saat pengambilan kelereng. dimana ada 3 warna putih dan satu warna biru. Pada pengambilan kelereng yang kedua, masing-masing kemungkinan akan memiliki 4 kemungkinan juga sehingga total kemungkinan menjadi $4^2=16$. Pada pengambilan ketiga masing masing dari 16 kemungikinan ini akan memiliki 4 kemungkinan lagi sehingga total kemungkinan adalah $4^3=64$ ###Code collection1 = itertools.product('WWWB', repeat=1) print("First draw") for num, val in enumerate(collection1): print(num, val) collection2 = itertools.product('WWWB', repeat=2) print("Second draw") for num, val in enumerate(collection2): print(num, val) collection3 = itertools.product('WWWB', repeat=3) print("Third draw") for num, val in enumerate(collection3): print(num, val) ###Output First draw 0 ('W',) 1 ('W',) 2 ('W',) 3 ('B',) Second draw 0 ('W', 'W') 1 ('W', 'W') 2 ('W', 'W') 3 ('W', 'B') 4 ('W', 'W') 5 ('W', 'W') 6 ('W', 'W') 7 ('W', 'B') 8 ('W', 'W') 9 ('W', 'W') 10 ('W', 'W') 11 ('W', 'B') 12 ('B', 'W') 13 ('B', 'W') 14 ('B', 'W') 15 ('B', 'B') Third draw 0 ('W', 'W', 'W') 1 ('W', 'W', 'W') 2 ('W', 'W', 'W') 3 ('W', 'W', 'B') 4 ('W', 'W', 'W') 5 ('W', 'W', 'W') 6 ('W', 'W', 'W') 7 ('W', 'W', 'B') 8 ('W', 'W', 'W') 9 ('W', 'W', 'W') 10 ('W', 'W', 'W') 11 ('W', 'W', 'B') 12 ('W', 'B', 'W') 13 ('W', 'B', 'W') 14 ('W', 'B', 'W') 15 ('W', 'B', 'B') 16 ('W', 'W', 'W') 17 ('W', 'W', 'W') 18 ('W', 'W', 'W') 19 ('W', 'W', 'B') 20 ('W', 'W', 'W') 21 ('W', 'W', 'W') 22 ('W', 'W', 'W') 23 ('W', 'W', 'B') 24 ('W', 'W', 'W') 25 ('W', 'W', 'W') 26 ('W', 'W', 'W') 27 ('W', 'W', 'B') 28 ('W', 'B', 'W') 29 ('W', 'B', 'W') 30 ('W', 'B', 'W') 31 ('W', 'B', 'B') 32 ('W', 'W', 'W') 33 ('W', 'W', 'W') 34 ('W', 'W', 'W') 35 ('W', 'W', 'B') 36 ('W', 'W', 'W') 37 ('W', 'W', 'W') 38 ('W', 'W', 'W') 39 ('W', 'W', 'B') 40 ('W', 'W', 'W') 41 ('W', 'W', 'W') 42 ('W', 'W', 'W') 43 ('W', 'W', 'B') 44 ('W', 'B', 'W') 45 ('W', 'B', 'W') 46 ('W', 'B', 'W') 47 ('W', 'B', 'B') 48 ('B', 'W', 'W') 49 ('B', 'W', 'W') 50 ('B', 'W', 'W') 51 ('B', 'W', 'B') 52 ('B', 'W', 'W') 53 ('B', 'W', 'W') 54 ('B', 'W', 'W') 55 ('B', 'W', 'B') 56 ('B', 'W', 'W') 57 ('B', 'W', 'W') 58 ('B', 'W', 'W') 59 ('B', 'W', 'B') 60 ('B', 'B', 'W') 61 ('B', 'B', 'W') 62 ('B', 'B', 'W') 63 ('B', 'B', 'B') ###Markdown Tentu saja tidak semua kemungkinan diatas didukung oleh data yang kami miliki. Oleh karena itu, hasil yang terakhir yang berjumlah 64 akan disaring dan akan dikumpulkan mana yang cocok dengan data yang dimiliki. Muncul 3 buah kemungkinan yang didukung oleh data. Ini hanya satu dari banyak lima dugaan yang ada. Mari kami periksa bagaimana dugaan-dugaan yang kami miliki. ###Code def conjecture_fitting(conjecture, data): collection = itertools.product(conjecture, repeat=len(data)) arr_buff = [] for num, val in enumerate(collection): arr = list(val) if arr == data: arr_buff.append(1) return len(arr_buff) print('Conjecture WWWW ',conjecture_fitting('WWWW', ['B', 'W', 'B'])) print('Conjecture WWWB ',conjecture_fitting('WWWB', ['B', 'W', 'B'])) print('Conjecture WWBB ',conjecture_fitting('WWBB', ['B', 'W', 'B'])) print('Conjecture WBBB ',conjecture_fitting('WBBB', ['B', 'W', 'B'])) print('Conjecture BBBB ',conjecture_fitting('BBBB', ['B', 'W', 'B'])) ###Output Conjecture WWWW 0 Conjecture WWWB 3 Conjecture WWBB 8 Conjecture WBBB 9 Conjecture BBBB 0 ###Markdown Menurut perhitungan yang kami buat, dugaan yang paling sesuai dengan data adalah dugaan ke 3 karena memiliki jumlah kemungkinan kombinasi yang sama dengan data paling tinggi dibandingkan dugaan lain. Oleh karena itu, memilih komposisi kelereng dugaan ke 3 adalah hal yang paling masuk akal. Asumsilah kami mengambil satu buah kelereng dan warananya adalah biru. Kita dapat menghitung ulang menggunakan cara yang sama dengan total kemungkinan adalah $4^4= 256$. Kita akan membandingkan dua cara, yaitu mengulang ulang perhitungan untuk 4 buah penarikan dan mengalikan kemungkinan sebelumnya (saat hanya memiliki data 3 buah penarikan) dengan kemungkinan yang baru (3 + 1 penarikan). ###Code print("Recounting") data = ['B', 'W', 'B', 'B'] print('Conjecture WWWW ',conjecture_fitting('WWWW', data)) print('Conjecture WWWB ',conjecture_fitting('WWWB', data)) print('Conjecture WWBB ',conjecture_fitting('WWBB', data)) print('Conjecture WBBB ',conjecture_fitting('WBBB', data)) print('Conjecture BBBB ',conjecture_fitting('BBBB', data)) print("\nUpdate Counting P N T") prev_data = ['B', 'W', 'B'] next_data = ['B'] def update_conjecture(conjecture, prev_data, next_data): prev_con = conjecture_fitting(conjecture, prev_data) next_con = conjecture_fitting(conjecture, next_data) return prev_con, next_con prev_con, next_con = update_conjecture('WWWW', prev_data, next_data) print('Conjecture WWWW ', prev_con, next_con, prev_con * next_con) prev_con, next_con = update_conjecture('WWWB', prev_data, next_data) print('Conjecture WWWB ', prev_con, next_con, prev_con * next_con) prev_con, next_con = update_conjecture('WWBB', prev_data, next_data) print('Conjecture WWBB ', prev_con, next_con, prev_con * next_con) prev_con, next_con = update_conjecture('WBBB', prev_data, next_data) print('Conjecture WBBB ', prev_con, next_con, prev_con * next_con) prev_con, next_con = update_conjecture('BBBB', prev_data, next_data) print('Conjecture BBBB ', prev_con, next_con, prev_con * next_con) ###Output Recounting Conjecture WWWW 0 Conjecture WWWB 3 Conjecture WWBB 16 Conjecture WBBB 27 Conjecture BBBB 0 Update Counting P N T Conjecture WWWW 0 0 0 Conjecture WWWB 3 1 3 Conjecture WWBB 8 2 16 Conjecture WBBB 9 3 27 Conjecture BBBB 0 4 0 ###Markdown Ternyata hasil dari menggunakan perhitungan ulang dan memperbarui perhitungan menggunakan perhitungan sebelumnya menghasilkan hasil yang sama. Secara komputasi, menggukanan metode memperbarui perhitungan lebih unggul karena tidak terbentur kompleksitas $4^n$ yang dimiliki oleh perhitungan ulang. Ditanyakan proporsi antara air dan tanah pada suatu bola bumi. Informasi yang diberikan adalah hasil dari melempar bola bumi tersebut dan menangkapnya. Diambil letak jari manis pada tangan kanan untuk menentukan apakah hasil lemparan ini mendarat pada tanah atau air. Ini sebenernya mirip dengan kasus pelemparan koin, hanya saja untuk pelemparan koin khalayak umum biasanya mengasumsi koin jujur--kemungkinan muncul angka dan gambar adalah sama--sedangkan untuk bola bumi khalayak umum hanya memiliki asumsi bahwa air lebih banyak daripada tanah. Kita akan coba memodelkan pelemparan ini sambil menerjemahkan kode penulis dari R menjadi Python.Jumlah pelemparan yang menghasilkan air $W$ dilambangkan dengan proporsi $p$ sedangkan yang menghasilkan tanah $L$ dilambangkan dengan $1-p$. Dikarenakan ini adalah contoh kasus binomial, maka akan diterapkan distribusi binomial yang dapat dijabarkan sebagai$$Pr(W, L\|p) = \frac{(W+L)!}{W!L!}p^W(1-p)^L$$Dibawah ini adalah class dari pelemparan yang sudah ada metode metode untuk membantu memudahkan pengolahan. ###Code import numpy as np from scipy.stats import binom, norm, beta from sklearn.preprocessing import MinMaxScaler from scipy.special import comb import matplotlib.pyplot as plt class globe_toss: def __init__(self): self.toss_history = [] self.probability_land = 0.5 self.probability_water = 0.5 self.prior_function = None def toss(self, number_toss): result = np.random.choice(["W", "L"], number_toss, p=[self.probability_water, self.probability_land]) self.toss_history = np.append(self.toss_history, result) return def reset_history(self): self.toss_history = [] return def get_water_total(self): return np.count_nonzero(self.toss_history == "W") def get_land_total(self): return np.count_nonzero(self.toss_history == "L") def get_binom_toss(self): k = self.get_water_total() n = len(self.toss_history) p = self.probability_water return binom.pmf(k, n, p) def get_binom_grid(self, x_grid): k = self.get_water_total() n = len(self.toss_history) return [binom.pmf(k, n, p) for p in x_grid] def analytical_solution(self, n_grid=100): a = self.get_water_total() b = self.get_land_total() return [beta.pdf(i, a + 1, b + 1) for i in np.linspace(0,1,n_grid)] def grid_calculate_posterior(self, n_grid=100): x_grid = np.linspace(0, 1, n_grid) if self.prior_function is None: y_prior = np.ones(n_grid) else: y_prior = np.array([self.prior_function(i) for i in x_grid]) likelihood = self.get_binom_grid(x_grid) posterior = np.multiply(y_prior, likelihood) / np.sum(np.multiply(y_prior, likelihood)) return x_grid, self.normalize_array(posterior) def normalize_array(self, array): array = np.array(array) arr_max = np.max(array) arr_min = np.min(array) interval = arr_max - arr_min result = (array - arr_min) / interval return result def register_prior_function(self, prior_function): self.prior_function = prior_function def grid_approx_plot(self): plt.figure(1, figsize = (11,6)) plt.title("Posterior Distribution (Grid Approximation)") axes = plt.gca() axes.set_xlim([-0.1, 1.1]) axes.set_xlabel("Probability of Water") axes.set_ylabel("Posterior") axes.grid() x_grid, posterior = self.grid_calculate_posterior(5) plt.plot(x_grid, posterior, label='5 Approx Normalized') x_grid, posterior = self.grid_calculate_posterior(10) plt.plot(x_grid, posterior, label='10 Approx Normalized') x_grid, posterior = self.grid_calculate_posterior(25) plt.plot(x_grid, posterior, label='25 Approx Normalized') x_grid, posterior = self.grid_calculate_posterior(100) plt.plot(x_grid, posterior, label='100 Approx Normalized') x_grid = np.linspace(0,1,100) plt.plot( x_grid, self.normalize_array(np.array(self.analytical_solution(100))), label='Analytical Normalized' ) plt.legend() def quadratic_approximation_plot(self): prior = 1 plt.figure(1, figsize = (11,6)) plt.title("Posterior Distribution (Quadratic Approximation)") axes = plt.gca() axes.set_xlim([-0.1, 1.1]) axes.set_xlabel("Probability of Water") axes.set_ylabel("Posterior") axes.grid() plt.plot(x_grid, posterior) toss1 = globe_toss() # Override the history so it match with book's example toss1.toss_history = np.array(['W', 'L', 'W', 'W', 'W', 'L', 'W', 'L', 'W']) toss1.get_binom_toss() ###Output _____no_output_____ ###Markdown Dalam ilmu probabilistik kemungkinan terjadinya dua kejadian pada saat yang sama dapat dituliskan sebagai$$Pr(a, b) = Pr(a\|b) Pr(b)$$> Kemungkinan terjadinya kejadian $a$ dan $b$ adalah hasil perkalian dari kemungkinan kejadian $a$ apabila $b$ terjadi dan kemungkinan $b$ terjadi.Dalam kasus yang kami miliki $a$ sama dengan $W, L$ atau proporsi dari darat dan laut--dapat digabungkan karena apabila $W$ diketahui maka $L$ juga dapat diketahui--dan $b$ adalah kemungkinan mendaratkan air $p$. Yang kami cari disini adalah probablitas dari pendaratan di air $p$ apabila jumlah $W$ dan $L$ diketahui. Sehingga dapat ditulis$$\begin{align} &Pr(W, L, p) = Pr(W, L\|p) Pr(p) \\ &Pr(W, L, p) = Pr(p \| W, L) Pr(W, L) \\ &Pr(W, L\|p) Pr(p) = Pr(p \| W, L) Pr(W, L) \\ &Pr(p\|W, L) = \frac{Pr(W, L\| p) Pr(p)}{Pr(W, L)} \\\end{align}$$Ada empat komponen dalam persamaan diatas- $Pr(W, L\|p)$ adalah apa yang disebut dengan likelihood data yang dimiliki untuk saat ini- $Pr(p)$ adalah apa yang disebut dengan prior yaitu probabilitas yang awal sebelum diperbarui- $Pr(W, L)$ adalah averaging agar probabilitas tidak lebih dari satu- $Pr(p\|W, L)$ adalah posterior probabilitas yang didapatkan setelah memperbarui dengan data yang tersediaBerbekal pengetahuan ini kita dapat mengestimasi probabilitas dari pendaratan di air. Ada tiga metode numerik yang dapat diterapkan dalam estimasi ini yaitu Grid Approximation, Quadratic Approximation, dan Markov Chain Monte Carlo. Dimulai dengan Grid Approximation dahulu karena cukup intuitif. ###Code toss1.grid_approx_plot() ###Output _____no_output_____ ###Markdown Grid approximation ini adalah memberikan sebuah vector dengan ukuran $n$ dan memiliki batas $[0, 1]$ dan memasukan masing-masing vector ke persamaan. Semakin tinggi bentuk vectorisasi-nya, maka akan semakin akurat juga.> Ini kenapa kalo ngga di normalized semakin gede aproksimasinya semakin merunduk ya grafiknya? Quadratic approximation adalah menggunakan distribusi normal untuk mencari bentuk distribusi normal mana yang paling mirip dengan posterior. Ini dapat dikerjakan dengan mencari rerata dan standar deviasi yang paling mewakili. Kami mencari rerata dengan mencari puncak dari estimasi. Puncak tersebut akan menjadi rerata dari distribusi normal. Chapter 2 Practice2M1 ###Code toss2 = globe_toss() toss2.toss_history = np.array(['W', 'W', 'W']) toss2.grid_approx_plot() toss3 = globe_toss() toss3.toss_history = np.array(['W', 'W', 'W', 'L']) toss3.grid_approx_plot() toss4 = globe_toss() toss4.toss_history = np.array(['L', 'W', 'W', 'L', 'W', 'W', 'L']) toss4.grid_approx_plot() ###Output _____no_output_____ ###Markdown Note Mengapa nilai grid approksimasi dengan nominal kecil seperti 5 memiliki puncak berbeda dengan aproksimasi yang memiliki nominal lebih besar? Ini terjadi karena adanya normalisasi sehingga setiap puncak harus berada pada titik 1.2M2 ###Code def step_function(x): if x < 0.5: return 0 elif x >= 0.5: return 1 toss5 = globe_toss() toss5.register_prior_function(step_function) toss5.toss_history = np.array(['W', 'W', 'W', 'L']) toss5.grid_approx_plot() ###Output _____no_output_____ ###Markdown Seperti yang dapat dilihat pada hasil aproksimasi, ada nilai cutoff pada 0.5 dikarenakan prior dengan nilai yang lebih rendah dari 0.5 nilainya 0 dan menghasilkan plot yang flat 0 pada nilai sebelum 0.52M3. Berdasarkan deskripsi diatas dapat dipetakan menjadi dua probabilitas, yang pertama adalah probabilitas dari mana yang akan dilemparkan, bumi atau mars, yang sama sama bernilai 0.5. Selanjutnya adalah kejadian tempat mendaratnya, apakah mendarat pada tanah atau air. Untuk bumi kemungkinan mendarat pada tanah adalah 0.3 dan pada air adalah 0.7. Untuk mars, mendarat pada air adalah 0 dan mendarat pada darat adalah 1.$Pr(Earth\|land)$ dapat dihasilkan dari $\frac{Pr(Earth, land)}{Pr(land)}$. $Pr(land)$ dapat dihasilkan dari menjumlahkan kemungkinan land dan dibagi dengan asalnya $$Pr_{land} = \frac{Pr_{land}^{mars} + Pr_{land}^{earth}}{2} = \frac{1 + 0.3}{2} = 0.65$$$$Pr(Earth, land) = Pr(Earth) Pr_{land}^{earth} = 0.15 $$$$Pr(Earth\|land) = \frac{0.15}{0.65} = 0.23$$ 2M3 Ada 6 kemungkinan yang bisa didapatkan dari penjumlahan kartu. Ini hasil dari perkalian jumlah kartu yang dimiliki dan sisi yang dimiliki setiap kartu. Yang ditanyakan adalah berapa kemungkinan sisi bawah kartu berwarna hitam apabila sisi atas kartu berwarna hitam.Kemungkinan mendapatkan kartu hitam adalah $0.5$. Tapi dikarenakan yang ditanyakan adalah kemungkinan kartu sisi bawah adalah hitam menjadikan sampel kartu hanya 2--yang keduanya hitam dan yang memiliki sisi hitam dan putih.$$\begin{align}Pr(Black, Black) &= Pr(Black \| Black) Pr(Black) \\\frac{1}{3} &= Pr(Black \| Black) \frac{1}{2} \\Pr(Black \| Black) &=\frac{1}{3}2 \\ Pr(Black \| Black) &=\frac{2}{3} \\ \end{align}$$ ###Code import itertools, pprint import numpy as np pp = pprint.PrettyPrinter(indent=4) possible_draw = list(itertools.product('BW', repeat=2)) first_draw_possibilities = [ np.count_nonzero(np.array(i)[0] == 'B') for i in possible_draw] second_draw_possibilities = [ np.count_nonzero(np.array(i)[1] == 'B') for i in possible_draw] for num, i in enumerate(possible_draw): print(num, i, first_draw_possibilities[num], second_draw_possibilities[num]) ###Output _____no_output_____
tf/examples/Bonsai/bonsai_example.ipynb	###Markdown Bonsai in TensorflowThis is a simple notebook that illustrates the usage of Tensorflow implementation of Bonsai. We are using the USPS dataset. Please refer to `fetch_usps.py` and run it for downloading and cleaning up the dataset. ###Code # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT license. import helpermethods import tensorflow as tf import numpy as np import sys import os sys.path.insert(0, '../../') #Provide the GPU number to be used os.environ['CUDA_VISIBLE_DEVICES'] ='' #Bonsai imports from edgeml.trainer.bonsaiTrainer import BonsaiTrainer from edgeml.graph.bonsai import Bonsai # Fixing seeds for reproducibility tf.set_random_seed(42) np.random.seed(42) ###Output _____no_output_____ ###Markdown USPS DataIt is assumed that the USPS data has already been downloaded and set up with the help of [fetch_usps.py](fetch_usps.py) and is present in the `./usps10` subdirectory. ###Code #Loading and Pre-processing dataset for Bonsai dataDir = "usps10/" (dataDimension, numClasses, Xtrain, Ytrain, Xtest, Ytest) = helpermethods.preProcessData(dataDir) print("Feature Dimension: ", dataDimension) print("Num classes: ", numClasses) ###Output Feature Dimension: 257 Num classes: 10 ###Markdown Model ParametersNote that Bonsai is designed for low-memory setting and the best results are obtained when operating in that setting. Use the sparsity, projection dimension and tree depth to vary the model size. ###Code sigma = 1.0 #Sigmoid parameter for tanh depth = 3 #Depth of Bonsai Tree projectionDimension = 28 #Lower Dimensional space for Bonsai to work on #Regularizers for Bonsai Parameters regZ = 0.0001 regW = 0.001 regV = 0.001 regT = 0.001 totalEpochs = 100 learningRate = 0.01 outFile = None #Sparsity for Bonsai Parameters. x => 100x % are non-zeros sparZ = 0.2 sparW = 0.3 sparV = 0.3 sparT = 0.62 batchSize = np.maximum(100, int(np.ceil(np.sqrt(Ytrain.shape[0])))) useMCHLoss = True #only for Multiclass cases True: Multiclass-Hing Loss, False: Cross Entropy. #Bonsai uses one classier for Binary, thus this condition if numClasses == 2: numClasses = 1 ###Output _____no_output_____ ###Markdown Placeholders for Data feeding during training and infernece ###Code X = tf.placeholder("float32", [None, dataDimension]) Y = tf.placeholder("float32", [None, numClasses]) ###Output _____no_output_____ ###Markdown Creating a directory for current model in the datadirectory using timestamp ###Code currDir = helpermethods.createTimeStampDir(dataDir) helpermethods.dumpCommand(sys.argv, currDir) ###Output _____no_output_____ ###Markdown Bonsai Graph ObjectInstantiating the Bonsai Graph which will be used for training and inference. ###Code bonsaiObj = Bonsai(numClasses, dataDimension, projectionDimension, depth, sigma) ###Output _____no_output_____ ###Markdown Bonsai Trainer ObjectInstantiating the Bonsai Trainer which will be used for 3 phase training. ###Code bonsaiTrainer = BonsaiTrainer(bonsaiObj, regW, regT, regV, regZ, sparW, sparT, sparV, sparZ, learningRate, X, Y, useMCHLoss, outFile) ###Output /home/t-dodenn/.virtualenvs/tfsource/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:98: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " ###Markdown Session declaration and variable initialization. Interactive Session doesn't clog the entire GPU. ###Code sess = tf.InteractiveSession() sess.run(tf.global_variables_initializer()) ###Output _____no_output_____ ###Markdown Bonsai Training RoutineThe method to to run the 3 phase training, followed by giving out the best early stopping model, accuracy along with saving of the parameters. ###Code bonsaiTrainer.train(batchSize, totalEpochs, sess, Xtrain, Xtest, Ytrain, Ytest, dataDir, currDir) ###Output Epoch Number: 0 ***************** Dense Training Phase Started **************** Train Loss: 6.389298193984562 Train accuracy: 0.6244444446638227 Test accuracy 0.724464 MarginLoss + RegLoss: 1.4452395 + 3.6491284 = 5.094368 Epoch Number: 1 Train Loss: 3.688335908783807 Train accuracy: 0.8611111144224802 Test accuracy 0.769307 MarginLoss + RegLoss: 0.9957626 + 2.7783642 = 3.7741268 Epoch Number: 2 Train Loss: 2.6678174800342984 Train accuracy: 0.9186111175351672 Test accuracy 0.760339 MarginLoss + RegLoss: 0.8745117 + 2.0955048 = 2.9700165 Epoch Number: 3 Train Loss: 1.9926944921414058 Train accuracy: 0.941527778075801 Test accuracy 0.776283 MarginLoss + RegLoss: 0.7323152 + 1.5899123 = 2.3222275 Epoch Number: 4 Train Loss: 1.5220159557130601 Train accuracy: 0.9556944477889273 Test accuracy 0.809666 MarginLoss + RegLoss: 0.58971727 + 1.2277353 = 1.8174525 Epoch Number: 5 Train Loss: 1.1967213302850723 Train accuracy: 0.9623611138926612 Test accuracy 0.839063 MarginLoss + RegLoss: 0.49087143 + 0.9732404 = 1.4641118 Epoch Number: 6 Train Loss: 0.9660082765751414 Train accuracy: 0.9694444487492243 Test accuracy 0.858495 MarginLoss + RegLoss: 0.4301353 + 0.79288167 = 1.223017 Epoch Number: 7 Train Loss: 0.8048144280910492 Train accuracy: 0.9725000088413557 Test accuracy 0.884405 MarginLoss + RegLoss: 0.34885734 + 0.6637592 = 1.0126165 Epoch Number: 8 Train Loss: 0.6851460528042581 Train accuracy: 0.9747222305999862 Test accuracy 0.899851 MarginLoss + RegLoss: 0.3091187 + 0.5690259 = 0.87814456 Epoch Number: 9 Train Loss: 0.5977631327178743 Train accuracy: 0.979305564529366 Test accuracy 0.899851 MarginLoss + RegLoss: 0.28436783 + 0.49890098 = 0.7832688 Epoch Number: 10 Train Loss: 0.535357097370757 Train accuracy: 0.9794444549414847 Test accuracy 0.910314 MarginLoss + RegLoss: 0.2635972 + 0.44721544 = 0.7108126 Epoch Number: 11 Train Loss: 0.48886746995978886 Train accuracy: 0.9802777866522471 Test accuracy 0.919781 MarginLoss + RegLoss: 0.23250574 + 0.40637112 = 0.63887686 Epoch Number: 12 Train Loss: 0.4487115616599719 Train accuracy: 0.9806944512658649 Test accuracy 0.923767 MarginLoss + RegLoss: 0.22620481 + 0.37376344 = 0.59996825 Epoch Number: 13 Train Loss: 0.41764777070946163 Train accuracy: 0.9812500104308128 Test accuracy 0.923269 MarginLoss + RegLoss: 0.22121286 + 0.34504575 = 0.5662586 Epoch Number: 14 Train Loss: 0.3893965221941471 Train accuracy: 0.9837500088744693 Test accuracy 0.924763 MarginLoss + RegLoss: 0.21379846 + 0.3230402 = 0.53683865 Epoch Number: 15 Train Loss: 0.3666431142224206 Train accuracy: 0.9838888976309035 Test accuracy 0.921276 MarginLoss + RegLoss: 0.21161106 + 0.30337086 = 0.5149819 Epoch Number: 16 Train Loss: 0.3501228694286611 Train accuracy: 0.9830555650922987 Test accuracy 0.926258 MarginLoss + RegLoss: 0.19876844 + 0.28749332 = 0.48626176 Epoch Number: 17 Train Loss: 0.32852235808968544 Train accuracy: 0.9845833430687586 Test accuracy 0.929746 MarginLoss + RegLoss: 0.18692228 + 0.27008235 = 0.45700464 Epoch Number: 18 Train Loss: 0.3183121085166931 Train accuracy: 0.9823611204822859 Test accuracy 0.931241 MarginLoss + RegLoss: 0.18857315 + 0.25766617 = 0.44623932 Epoch Number: 19 Train Loss: 0.3019753938747777 Train accuracy: 0.9845833430687586 Test accuracy 0.930244 MarginLoss + RegLoss: 0.1913568 + 0.24436466 = 0.43572146 Epoch Number: 20 Train Loss: 0.2879051696509123 Train accuracy: 0.9852777851952447 Test accuracy 0.926258 MarginLoss + RegLoss: 0.19720516 + 0.23309413 = 0.43029928 Epoch Number: 21 Train Loss: 0.2760823153787189 Train accuracy: 0.9848611222373115 Test accuracy 0.929746 MarginLoss + RegLoss: 0.19013962 + 0.22110444 = 0.41124406 Epoch Number: 22 Train Loss: 0.265699431921045 Train accuracy: 0.9843055639002058 Test accuracy 0.927255 MarginLoss + RegLoss: 0.18394011 + 0.21136439 = 0.3953045 Epoch Number: 23 Train Loss: 0.2586811340103547 Train accuracy: 0.9841666767994562 Test accuracy 0.932237 MarginLoss + RegLoss: 0.18472062 + 0.20268525 = 0.38740587 Epoch Number: 24 Train Loss: 0.2515812249233325 Train accuracy: 0.9834722330172857 Test accuracy 0.932237 MarginLoss + RegLoss: 0.17988203 + 0.19471233 = 0.37459436 Epoch Number: 25 Train Loss: 0.2439375292095873 Train accuracy: 0.9819444533851411 Test accuracy 0.936223 MarginLoss + RegLoss: 0.17650211 + 0.1861265 = 0.3626286 Epoch Number: 26 Train Loss: 0.23371089560290179 Train accuracy: 0.9833333450886939 Test accuracy 0.932735 MarginLoss + RegLoss: 0.18389018 + 0.17848735 = 0.36237752 Epoch Number: 27 Train Loss: 0.22481733912395108 Train accuracy: 0.983611119290193 Test accuracy 0.93423 MarginLoss + RegLoss: 0.16752453 + 0.1700884 = 0.33761293 Epoch Number: 28 Train Loss: 0.21649042848083708 Train accuracy: 0.9833333442608515 Test accuracy 0.931241 MarginLoss + RegLoss: 0.18024354 + 0.16205509 = 0.34229863 Epoch Number: 29 Train Loss: 0.2109515176465114 Train accuracy: 0.983194451365206 Test accuracy 0.932735 MarginLoss + RegLoss: 0.18075311 + 0.15641537 = 0.33716848 Epoch Number: 30 Train Loss: 0.20864862451950708 Train accuracy: 0.9813889016707739 Test accuracy 0.930742 MarginLoss + RegLoss: 0.18594784 + 0.15273505 = 0.3386829 Epoch Number: 31 Train Loss: 0.21935068257153034 Train accuracy: 0.975833343134986 Test accuracy 0.940708 MarginLoss + RegLoss: 0.17358617 + 0.15079822 = 0.3243844 Epoch Number: 32 Train Loss: 0.20754448076089224 Train accuracy: 0.9801388954122862 Test accuracy 0.93722 MarginLoss + RegLoss: 0.17546712 + 0.14778407 = 0.3232512 Epoch Number: 33 **************** IHT Phase Started ****************** Train Loss: 0.2142104934900999 Train accuracy: 0.9783333390951157 Test accuracy 0.92576 MarginLoss + RegLoss: 0.19405848 + 0.11986786 = 0.31392634 Epoch Number: 34 Train Loss: 0.1957869732545482 Train accuracy: 0.9804166762365235 Test accuracy 0.933732 MarginLoss + RegLoss: 0.17486349 + 0.12441108 = 0.29927456 Epoch Number: 35 Train Loss: 0.20117665910058552 Train accuracy: 0.9798611212107871 Test accuracy 0.933234 MarginLoss + RegLoss: 0.18556419 + 0.12760702 = 0.3131712 Epoch Number: 36 Train Loss: 0.19235002787576783 Train accuracy: 0.9818055654565493 Test accuracy 0.932237 MarginLoss + RegLoss: 0.18422216 + 0.12556252 = 0.30978468 Epoch Number: 37 Train Loss: 0.18417591125600868 Train accuracy: 0.9818055654565493 Test accuracy 0.933234 MarginLoss + RegLoss: 0.18428192 + 0.122980185 = 0.3072621 Epoch Number: 38 Train Loss: 0.18028576651381123 Train accuracy: 0.9831944563322597 Test accuracy 0.933732 MarginLoss + RegLoss: 0.17935511 + 0.12061933 = 0.29997444 Epoch Number: 39 Train Loss: 0.1745260620696677 Train accuracy: 0.9833333442608515 Test accuracy 0.932735 MarginLoss + RegLoss: 0.18099774 + 0.11807041 = 0.29906815 Epoch Number: 40 Train Loss: 0.17143954140030676 Train accuracy: 0.9840277888708644 Test accuracy 0.93722 MarginLoss + RegLoss: 0.17120871 + 0.11562464 = 0.28683335 Epoch Number: 41 Train Loss: 0.16799314061386716 Train accuracy: 0.9834722355008125 Test accuracy 0.93423 MarginLoss + RegLoss: 0.1756582 + 0.113046676 = 0.28870487 Epoch Number: 42 Train Loss: 0.16445335994164148 Train accuracy: 0.9845833447244432 Test accuracy 0.936223 MarginLoss + RegLoss: 0.17149687 + 0.11167378 = 0.28317064 Epoch Number: 43 Train Loss: 0.1617109359552463 Train accuracy: 0.983194457160102 Test accuracy 0.938216 MarginLoss + RegLoss: 0.16902651 + 0.109498546 = 0.27852505 Epoch Number: 44 Train Loss: 0.1589122601888246 Train accuracy: 0.9848611214094691 Test accuracy 0.93722 MarginLoss + RegLoss: 0.16804701 + 0.10741738 = 0.2754644 Epoch Number: 45 Train Loss: 0.1585660283971164 Train accuracy: 0.9852777885066138 Test accuracy 0.937718 MarginLoss + RegLoss: 0.17146519 + 0.10648606 = 0.27795124 Epoch Number: 46 Train Loss: 0.15569559753768974 Train accuracy: 0.9843055663837327 Test accuracy 0.941206 MarginLoss + RegLoss: 0.16175063 + 0.104837134 = 0.26658776 Epoch Number: 47 Train Loss: 0.1528543213175403 Train accuracy: 0.985138900578022 Test accuracy 0.940209 MarginLoss + RegLoss: 0.1619776 + 0.10374366 = 0.26572126 Epoch Number: 48 Train Loss: 0.1517944024461839 Train accuracy: 0.9856944547759162 Test accuracy 0.93722 MarginLoss + RegLoss: 0.16866311 + 0.10247567 = 0.2711388 ###Markdown Bonsai in TensorflowThis is a simple notebook that illustrates the usage of Tensorflow implementation of Bonsai. We are using the USPS dataset. Please refer to `fetch_usps.py` and run it for downloading and cleaning up the dataset. ###Code # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT license. import helpermethods import tensorflow as tf import numpy as np import sys import os #Provide the GPU number to be used os.environ['CUDA_VISIBLE_DEVICES'] ='' #Bonsai imports from edgeml.trainer.bonsaiTrainer import BonsaiTrainer from edgeml.graph.bonsai import Bonsai # Fixing seeds for reproducibility tf.set_random_seed(42) np.random.seed(42) ###Output _____no_output_____ ###Markdown USPS DataIt is assumed that the USPS data has already been downloaded and set up with the help of [fetch_usps.py](fetch_usps.py) and is present in the `./usps10` subdirectory. ###Code #Loading and Pre-processing dataset for Bonsai dataDir = "usps10/" (dataDimension, numClasses, Xtrain, Ytrain, Xtest, Ytest, mean, std) = helpermethods.preProcessData(dataDir, isRegression=False) print("Feature Dimension: ", dataDimension) print("Num classes: ", numClasses) ###Output Feature Dimension: 257 Num classes: 10 ###Markdown Model ParametersNote that Bonsai is designed for low-memory setting and the best results are obtained when operating in that setting. Use the sparsity, projection dimension and tree depth to vary the model size. ###Code sigma = 1.0 #Sigmoid parameter for tanh depth = 3 #Depth of Bonsai Tree projectionDimension = 28 #Lower Dimensional space for Bonsai to work on #Regularizers for Bonsai Parameters regZ = 0.0001 regW = 0.001 regV = 0.001 regT = 0.001 totalEpochs = 100 learningRate = 0.01 outFile = None #Sparsity for Bonsai Parameters. x => 100x % are non-zeros sparZ = 0.2 sparW = 0.3 sparV = 0.3 sparT = 0.62 batchSize = np.maximum(100, int(np.ceil(np.sqrt(Ytrain.shape[0])))) useMCHLoss = True #only for Multiclass cases True: Multiclass-Hing Loss, False: Cross Entropy. #Bonsai uses one classier for Binary, thus this condition if numClasses == 2: numClasses = 1 ###Output _____no_output_____ ###Markdown Placeholders for Data feeding during training and infernece ###Code X = tf.placeholder("float32", [None, dataDimension]) Y = tf.placeholder("float32", [None, numClasses]) ###Output _____no_output_____ ###Markdown Creating a directory for current model in the datadirectory using timestamp ###Code currDir = helpermethods.createTimeStampDir(dataDir) helpermethods.dumpCommand(sys.argv, currDir) ###Output _____no_output_____ ###Markdown Bonsai Graph ObjectInstantiating the Bonsai Graph which will be used for training and inference. ###Code bonsaiObj = Bonsai(numClasses, dataDimension, projectionDimension, depth, sigma) ###Output _____no_output_____ ###Markdown Bonsai Trainer ObjectInstantiating the Bonsai Trainer which will be used for 3 phase training. ###Code bonsaiTrainer = BonsaiTrainer(bonsaiObj, regW, regT, regV, regZ, sparW, sparT, sparV, sparZ, learningRate, X, Y, useMCHLoss, outFile) ###Output C:\Users\t-vekusu\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " ###Markdown Session declaration and variable initialization. Interactive Session doesn't clog the entire GPU. ###Code sess = tf.InteractiveSession() sess.run(tf.global_variables_initializer()) ###Output _____no_output_____ ###Markdown Bonsai Training RoutineThe method to to run the 3 phase training, followed by giving out the best early stopping model, accuracy along with saving of the parameters. ###Code bonsaiTrainer.train(batchSize, totalEpochs, sess, Xtrain, Xtest, Ytrain, Ytest, dataDir, currDir) ###Output Epoch Number: 0 ***************** Dense Training Phase Started **************** Classification Train Loss: 6.388934433460236 Training accuracy (Classification): 0.6250000005174015 Test accuracy 0.726956 MarginLoss + RegLoss: 1.4466879 + 3.6487768 = 5.0954647 Epoch Number: 1 Classification Train Loss: 3.6885906954606376 Training accuracy (Classification): 0.8623611107468605 Test accuracy 0.758346 MarginLoss + RegLoss: 1.0173264 + 2.778634 = 3.7959604 Epoch Number: 2 Classification Train Loss: 2.667721450328827 Training accuracy (Classification): 0.9184722271230485 Test accuracy 0.7429 MarginLoss + RegLoss: 0.92546654 + 2.095467 = 3.0209336 Epoch Number: 3 Classification Train Loss: 1.9921080254846149 Training accuracy (Classification): 0.941944446000788 Test accuracy 0.767314 MarginLoss + RegLoss: 0.7603649 + 1.5889603 = 2.3493252 Epoch Number: 4 Classification Train Loss: 1.5233625107341342 Training accuracy (Classification): 0.9563888907432556 Test accuracy 0.791231 MarginLoss + RegLoss: 0.6496898 + 1.2271981 = 1.8768879 Epoch Number: 5 Classification Train Loss: 1.1950715631246567 Training accuracy (Classification): 0.9650000035762787 Test accuracy 0.810164 MarginLoss + RegLoss: 0.54003507 + 0.97295314 = 1.5129882 Epoch Number: 6 Classification Train Loss: 0.9672323316335678 Training accuracy (Classification): 0.968333340353436 Test accuracy 0.855007 MarginLoss + RegLoss: 0.44149697 + 0.79325426 = 1.2347512 Epoch Number: 7 Classification Train Loss: 0.8014380658666292 Training accuracy (Classification): 0.9722222313284874 Test accuracy 0.874938 MarginLoss + RegLoss: 0.37062877 + 0.6628879 = 1.0335166 Epoch Number: 8 Classification Train Loss: 0.684503066043059 Training accuracy (Classification): 0.976111119820012 Test accuracy 0.899851 MarginLoss + RegLoss: 0.3099702 + 0.5688073 = 0.8787775 Epoch Number: 9 Classification Train Loss: 0.5987317487597466 Training accuracy (Classification): 0.9794444565971693 Test accuracy 0.907324 MarginLoss + RegLoss: 0.2689218 + 0.49965328 = 0.7685751 Epoch Number: 10 Classification Train Loss: 0.5343128165437115 Training accuracy (Classification): 0.9804166778922081 Test accuracy 0.9143 MarginLoss + RegLoss: 0.24538836 + 0.44663915 = 0.6920275 Epoch Number: 11 Classification Train Loss: 0.48874612069792217 Training accuracy (Classification): 0.9801388987236552 Test accuracy 0.916293 MarginLoss + RegLoss: 0.23703864 + 0.40629783 = 0.6433365 Epoch Number: 12 Classification Train Loss: 0.44733552055226433 Training accuracy (Classification): 0.98097223126226 Test accuracy 0.918286 MarginLoss + RegLoss: 0.23851919 + 0.37269312 = 0.6112123 Epoch Number: 13 Classification Train Loss: 0.4165669356783231 Training accuracy (Classification): 0.9822222317258517 Test accuracy 0.917289 MarginLoss + RegLoss: 0.23061273 + 0.345445 = 0.57605773 Epoch Number: 14 Classification Train Loss: 0.39181090601616436 Training accuracy (Classification): 0.9812500087751282 Test accuracy 0.92277 MarginLoss + RegLoss: 0.2121576 + 0.32245666 = 0.53461426 Epoch Number: 15 Classification Train Loss: 0.36949437111616135 Training accuracy (Classification): 0.9820833446251022 Test accuracy 0.926258 MarginLoss + RegLoss: 0.19854721 + 0.30341443 = 0.50196165 Epoch Number: 16 Classification Train Loss: 0.3469446731938256 Training accuracy (Classification): 0.9831944538487328 Test accuracy 0.927255 MarginLoss + RegLoss: 0.19628116 + 0.28535655 = 0.48163772 Epoch Number: 17 Classification Train Loss: 0.329777576857143 Training accuracy (Classification): 0.984166675971614 Test accuracy 0.92277 MarginLoss + RegLoss: 0.20166817 + 0.26965213 = 0.4713203 Epoch Number: 18 Classification Train Loss: 0.317672994815641 Training accuracy (Classification): 0.9815277879436811 Test accuracy 0.925262 MarginLoss + RegLoss: 0.20086277 + 0.2559616 = 0.45682436 Epoch Number: 19 Classification Train Loss: 0.3000084459781647 Training accuracy (Classification): 0.9843055655558904 Test accuracy 0.931739 MarginLoss + RegLoss: 0.18073215 + 0.24324338 = 0.42397553 Epoch Number: 20 Classification Train Loss: 0.2897499371320009 Training accuracy (Classification): 0.9827777867515882 Test accuracy 0.921276 MarginLoss + RegLoss: 0.20172484 + 0.23221089 = 0.43393573 Epoch Number: 21 Classification Train Loss: 0.2821065636558665 Training accuracy (Classification): 0.9812500096029706 Test accuracy 0.928749 MarginLoss + RegLoss: 0.18990344 + 0.22147894 = 0.41138238 Epoch Number: 22 Classification Train Loss: 0.2660716378854381 Training accuracy (Classification): 0.9844444559680091 Test accuracy 0.928251 MarginLoss + RegLoss: 0.17955597 + 0.21111046 = 0.39066643 Epoch Number: 23 Classification Train Loss: 0.2567368100086848 Training accuracy (Classification): 0.9852777885066138 Test accuracy 0.928251 MarginLoss + RegLoss: 0.18770447 + 0.20248988 = 0.39019436 Epoch Number: 24 Classification Train Loss: 0.25224825532899964 Training accuracy (Classification): 0.9823611204822859 Test accuracy 0.932735 MarginLoss + RegLoss: 0.18552671 + 0.19460817 = 0.38013488 Epoch Number: 25 Classification Train Loss: 0.24661735258996487 Training accuracy (Classification): 0.9804166762365235 Test accuracy 0.931241 MarginLoss + RegLoss: 0.18796808 + 0.18610859 = 0.37407666 Epoch Number: 26 Classification Train Loss: 0.23342499737110403 Training accuracy (Classification): 0.9829166763358645 Test accuracy 0.932735 MarginLoss + RegLoss: 0.17906994 + 0.17793566 = 0.3570056 Epoch Number: 27 Classification Train Loss: 0.22210048822065195 Training accuracy (Classification): 0.9851388972666528 Test accuracy 0.934728 MarginLoss + RegLoss: 0.17679122 + 0.16876754 = 0.34555876 Epoch Number: 28 Classification Train Loss: 0.2189549288402001 Training accuracy (Classification): 0.9831944538487328 Test accuracy 0.932237 MarginLoss + RegLoss: 0.19115414 + 0.16296963 = 0.35412377 Epoch Number: 29 Classification Train Loss: 0.21842483865718046 Training accuracy (Classification): 0.9805555658208 Test accuracy 0.936722 MarginLoss + RegLoss: 0.17462157 + 0.15921564 = 0.3338372 Epoch Number: 30 Classification Train Loss: 0.21449942576388517 Training accuracy (Classification): 0.9804166754086813 Test accuracy 0.939711 MarginLoss + RegLoss: 0.17741902 + 0.15273981 = 0.33015883 Epoch Number: 31 Classification Train Loss: 0.20739994280868107 Training accuracy (Classification): 0.9825000100665622 Test accuracy 0.933732 MarginLoss + RegLoss: 0.17381513 + 0.1498537 = 0.32366884 Epoch Number: 32 Classification Train Loss: 0.20110303929282558 Training accuracy (Classification): 0.9840277888708644 Test accuracy 0.93423 MarginLoss + RegLoss: 0.18619148 + 0.14583017 = 0.33202165 Epoch Number: 33 **************** IHT Phase Started ****************** Classification Train Loss: 0.21433907147083017 Training accuracy (Classification): 0.9801388987236552 Test accuracy 0.927255 MarginLoss + RegLoss: 0.19979775 + 0.12088289 = 0.32068065 Epoch Number: 34 Classification Train Loss: 0.1990115779141585 Training accuracy (Classification): 0.980694454577234 Test accuracy 0.933234 MarginLoss + RegLoss: 0.17835513 + 0.12438774 = 0.30274287 Epoch Number: 35 Classification Train Loss: 0.20429682172834873 Training accuracy (Classification): 0.9788888974322213 Test accuracy 0.929248 MarginLoss + RegLoss: 0.19013074 + 0.12853864 = 0.31866938 Epoch Number: 36 Classification Train Loss: 0.19357945707937083 Training accuracy (Classification): 0.9816666767001152 Test accuracy 0.932735 MarginLoss + RegLoss: 0.18534705 + 0.12509713 = 0.31044418 Epoch Number: 37 Classification Train Loss: 0.18653404754069117 Training accuracy (Classification): 0.9818055638008647 Test accuracy 0.929746 MarginLoss + RegLoss: 0.18708317 + 0.12236847 = 0.30945164 Epoch Number: 38 Classification Train Loss: 0.18141362298693922 Training accuracy (Classification): 0.9815277871158388 Test accuracy 0.933234 MarginLoss + RegLoss: 0.18262453 + 0.11991154 = 0.30253607 Epoch Number: 39 Classification Train Loss: 0.17729416727605793 Training accuracy (Classification): 0.9820833429694176 Test accuracy 0.932735 MarginLoss + RegLoss: 0.1798804 + 0.11748926 = 0.29736966
examples/notebooks/cartoee_projections.ipynb	###Markdown Uncomment the following line to install [geemap](https://geemap.org) if needed. ###Code # !pip install geemap ###Output _____no_output_____ ###Markdown Working with projections in cartoee ###Code import ee from geemap import cartoee import cartopy.crs as ccrs %pylab inline ee.Initialize() ###Output _____no_output_____ ###Markdown Plotting an image on a mapHere we are going to show another example of creating a map with EE results. We will use global sea surface temperature data for Jan-Mar 2018. ###Code # get an earth engine image of ocean data for Jan-Mar 2018 ocean = ( ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') .filter(ee.Filter.date('2018-01-01', '2018-03-01')) .median() .select(["sst"],["SST"]) ) # set parameters for plotting # will plot the Sea Surface Temp with specific range and colormap visualization = {'bands':"SST",'min':-2,'max':30} # specify region to focus on bbox = [-180,-88,180,88] fig = plt.figure(figsize=(10,7)) # plot the result with cartoee using a PlateCarre projection (default) ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=bbox) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Mapping with different projectionsYou can specify what ever projection is available within `cartopy` to display the results from Earth Engine. Here are a couple examples of global and regions maps using the sea surface temperature example. Please refer to the [`cartopy` projection documentation](https://scitools.org.uk/cartopy/docs/latest/crs/projections.html) for more examples with different projections. ###Code fig = plt.figure(figsize=(10,7)) # create a new Mollweide projection centered on the Pacific projection = ccrs.Mollweide(central_longitude=-180) # plot the result with cartoee using the Mollweide projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Mollweide projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new Goode homolosine projection centered on the Pacific projection = ccrs.InterruptedGoodeHomolosine(central_longitude=-180) # plot the result with cartoee using the Goode homolosine projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Goode homolosine projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new orographic projection focused on the Pacific projection = ccrs.Orthographic(-130,-10) # plot the result with cartoee using the orographic projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma', orientation='vertical') ax.set_title("Orographic projection") ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Warping artifactsOften times global projections are not needed so we use specific projection for the map that provides the best view for the geographic region of interest. When we use these, sometimes image warping effects occur. This is because `cartoee` only requests data for region of interest and when mapping with `cartopy` the pixels get warped to fit the view extent as best as possible. Consider the following example where we want to map SST over the south pole: ###Code fig = plt.figure(figsize=(10,7)) # Create a new region to focus on spole = [-180,-88,180,0] projection = ccrs.SouthPolarStereo() # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') plt.show() ###Output _____no_output_____ ###Markdown As you can see from the result there are warping effects on the plotted image. There is really no way of getting aound this (other than requesting a larger extent of data which may not alway be the case). So, what we can do is set the extent of the map to a more realistic view after plotting the image as in the following example: ###Code fig = plt.figure(figsize=(10,7)) # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') # get bounding box coordinates of a zoom area zoom = spole zoom[-1] = -20 # convert bbox coordinate from [W,S,E,N] to [W,E,S,N] as matplotlib expects zoom_extent = cartoee.bbox_to_extent(zoom) # set the extent of the map to the zoom area ax.set_extent(zoom_extent,ccrs.PlateCarree()) plt.show() ###Output _____no_output_____ ###Markdown Uncomment the following line to install [geemap](https://geemap.org) if needed. ###Code # !pip install geemap ###Output _____no_output_____ ###Markdown Working with projections in cartoee ###Code import ee from geemap import cartoee import cartopy.crs as ccrs %pylab inline ee.Initialize() ###Output _____no_output_____ ###Markdown Plotting an image on a mapHere we are going to show another example of creating a map with EE results. We will use global sea surface temperature data for Jan-Mar 2018. ###Code # get an earth engine image of ocean data for Jan-Mar 2018 ocean = ( ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') .filter(ee.Filter.date('2018-01-01', '2018-03-01')) .median() .select(["sst"],["SST"]) ) # set parameters for plotting # will plot the Sea Surface Temp with specific range and colormap visualization = {'bands':"SST",'min':-2,'max':30} # specify region to focus on bbox = [-180,-88,180,88] fig = plt.figure(figsize=(10,7)) # plot the result with cartoee using a PlateCarre projection (default) ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=bbox) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Mapping with different projectionsYou can specify what ever projection is available within `cartopy` to display the results from Earth Engine. Here are a couple examples of global and regions maps using the sea surface temperature example. Please refer to the [`cartopy` projection documentation](https://scitools.org.uk/cartopy/docs/latest/crs/projections.html) for more examples with different projections. ###Code fig = plt.figure(figsize=(10,7)) # create a new Mollweide projection centered on the Pacific projection = ccrs.Mollweide(central_longitude=-180) # plot the result with cartoee using the Mollweide projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Mollweide projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new Goode homolosine projection centered on the Pacific projection = ccrs.InterruptedGoodeHomolosine(central_longitude=-180) # plot the result with cartoee using the Goode homolosine projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Goode homolosine projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new orographic projection focused on the Pacific projection = ccrs.Orthographic(-130,-10) # plot the result with cartoee using the orographic projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma', orientation='vertical') ax.set_title("Orographic projection") ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Warping artifactsOften times global projections are not needed so we use specific projection for the map that provides the best view for the geographic region of interest. When we use these, sometimes image warping effects occur. This is because `cartoee` only requests data for region of interest and when mapping with `cartopy` the pixels get warped to fit the view extent as best as possible. Consider the following example where we want to map SST over the south pole: ###Code fig = plt.figure(figsize=(10,7)) # Create a new region to focus on spole = [-180,-88,180,0] projection = ccrs.SouthPolarStereo() # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') plt.show() ###Output _____no_output_____ ###Markdown As you can see from the result there are warping effects on the plotted image. There is really no way of getting aound this (other than requesting a larger extent of data which may not always be the case). So, what we can do is set the extent of the map to a more realistic view after plotting the image as in the following example: ###Code fig = plt.figure(figsize=(10,7)) # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') # get bounding box coordinates of a zoom area zoom = spole zoom[-1] = -20 # convert bbox coordinate from [W,S,E,N] to [W,E,S,N] as matplotlib expects zoom_extent = cartoee.bbox_to_extent(zoom) # set the extent of the map to the zoom area ax.set_extent(zoom_extent,ccrs.PlateCarree()) plt.show() ###Output _____no_output_____ ###Markdown Uncomment the following line to install [geemap](https://geemap.org) if needed. ###Code # !pip install geemap ###Output _____no_output_____ ###Markdown Working with projections in cartoee ###Code import ee from geemap import cartoee import cartopy.crs as ccrs %pylab inline ee.Initialize() ###Output _____no_output_____ ###Markdown Plotting an image on a mapHere we are going to show another example of creating a map with EE results. We will use global sea surface temperature data for Jan-Mar 2018. ###Code # get an earth engine image of ocean data for Jan-Mar 2018 ocean = ( ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') .filter(ee.Filter.date('2018-01-01', '2018-03-01')) .median() .select(["sst"], ["SST"]) ) # set parameters for plotting # will plot the Sea Surface Temp with specific range and colormap visualization = {'bands': "SST", 'min': -2, 'max': 30} # specify region to focus on bbox = [-180, -88, 180, 88] fig = plt.figure(figsize=(10, 7)) # plot the result with cartoee using a PlateCarre projection (default) ax = cartoee.get_map(ocean, cmap='plasma', vis_params=visualization, region=bbox) cb = cartoee.add_colorbar(ax, vis_params=visualization, loc='right', cmap='plasma') ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Mapping with different projectionsYou can specify what ever projection is available within `cartopy` to display the results from Earth Engine. Here are a couple examples of global and regions maps using the sea surface temperature example. Please refer to the [`cartopy` projection documentation](https://scitools.org.uk/cartopy/docs/latest/crs/projections.html) for more examples with different projections. ###Code fig = plt.figure(figsize=(10, 7)) # create a new Mollweide projection centered on the Pacific projection = ccrs.Mollweide(central_longitude=-180) # plot the result with cartoee using the Mollweide projection ax = cartoee.get_map( ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection ) cb = cartoee.add_colorbar( ax, vis_params=visualization, loc='bottom', cmap='plasma', orientation='horizontal' ) ax.set_title("Mollweide projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10, 7)) # create a new Goode homolosine projection centered on the Pacific projection = ccrs.InterruptedGoodeHomolosine(central_longitude=-180) # plot the result with cartoee using the Goode homolosine projection ax = cartoee.get_map( ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection ) cb = cartoee.add_colorbar( ax, vis_params=visualization, loc='bottom', cmap='plasma', orientation='horizontal' ) ax.set_title("Goode homolosine projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10, 7)) # create a new orographic projection focused on the Pacific projection = ccrs.Orthographic(-130, -10) # plot the result with cartoee using the orographic projection ax = cartoee.get_map( ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection ) cb = cartoee.add_colorbar( ax, vis_params=visualization, loc='right', cmap='plasma', orientation='vertical' ) ax.set_title("Orographic projection") ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Warping artifactsOften times global projections are not needed so we use specific projection for the map that provides the best view for the geographic region of interest. When we use these, sometimes image warping effects occur. This is because `cartoee` only requests data for region of interest and when mapping with `cartopy` the pixels get warped to fit the view extent as best as possible. Consider the following example where we want to map SST over the south pole: ###Code fig = plt.figure(figsize=(10, 7)) # Create a new region to focus on spole = [-180, -88, 180, 0] projection = ccrs.SouthPolarStereo() # plot the result with cartoee focusing on the south pole ax = cartoee.get_map( ocean, cmap='plasma', vis_params=visualization, region=spole, proj=projection ) cb = cartoee.add_colorbar(ax, vis_params=visualization, loc='right', cmap='plasma') ax.coastlines() ax.set_title('The South Pole') plt.show() ###Output _____no_output_____ ###Markdown As you can see from the result there are warping effects on the plotted image. There is really no way of getting aound this (other than requesting a larger extent of data which may not always be the case). So, what we can do is set the extent of the map to a more realistic view after plotting the image as in the following example: ###Code fig = plt.figure(figsize=(10, 7)) # plot the result with cartoee focusing on the south pole ax = cartoee.get_map( ocean, cmap='plasma', vis_params=visualization, region=spole, proj=projection ) cb = cartoee.add_colorbar(ax, vis_params=visualization, loc='right', cmap='plasma') ax.coastlines() ax.set_title('The South Pole') # get bounding box coordinates of a zoom area zoom = spole zoom[-1] = -20 # convert bbox coordinate from [W,S,E,N] to [W,E,S,N] as matplotlib expects zoom_extent = cartoee.bbox_to_extent(zoom) # set the extent of the map to the zoom area ax.set_extent(zoom_extent, ccrs.PlateCarree()) plt.show() ###Output _____no_output_____ ###Markdown Working with projections in cartoee ###Code import ee from geemap import cartoee import cartopy.crs as ccrs %pylab inline ee.Initialize() ###Output _____no_output_____ ###Markdown Plotting an image on a mapHere we are going to show another example of creating a map with EE results. We will use global sea surface temperature data for Jan-Mar 2018. ###Code # get an earth engine image of ocean data for Jan-Mar 2018 ocean = ( ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') .filter(ee.Filter.date('2018-01-01', '2018-03-01')) .median() .select(["sst"],["SST"]) ) # set parameters for plotting # will plot the Sea Surface Temp with specific range and colormap visualization = {'bands':"SST",'min':-2,'max':30} # specify region to focus on bbox = [-180,-88,180,88] fig = plt.figure(figsize=(10,7)) # plot the result with cartoee using a PlateCarre projection (default) ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=bbox) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Mapping with different projectionsYou can specify what ever projection is available within `cartopy` to display the results from Earth Engine. Here are a couple examples of global and regions maps using the sea surface temperature example. Please refer to the [`cartopy` projection documentation](https://scitools.org.uk/cartopy/docs/latest/crs/projections.html) for more examples with different projections. ###Code fig = plt.figure(figsize=(10,7)) # create a new Mollweide projection centered on the Pacific projection = ccrs.Mollweide(central_longitude=-180) # plot the result with cartoee using the Mollweide projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Mollweide projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new Goode homolosine projection centered on the Pacific projection = ccrs.InterruptedGoodeHomolosine(central_longitude=-180) # plot the result with cartoee using the Goode homolosine projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='bottom',cmap='plasma', orientation='horizontal') ax.set_title("Goode homolosine projection") ax.coastlines() plt.show() fig = plt.figure(figsize=(10,7)) # create a new orographic projection focused on the Pacific projection = ccrs.Orthographic(-130,-10) # plot the result with cartoee using the orographic projection ax = cartoee.get_map(ocean,vis_params=visualization,region=bbox, cmap='plasma',proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma', orientation='vertical') ax.set_title("Orographic projection") ax.coastlines() plt.show() ###Output _____no_output_____ ###Markdown Warping artifactsOften times global projections are not needed so we use specific projection for the map that provides the best view for the geographic region of interest. When we use these, sometimes image warping effects occur. This is because `cartoee` only requests data for region of interest and when mapping with `cartopy` the pixels get warped to fit the view extent as best as possible. Consider the following example where we want to map SST over the south pole: ###Code fig = plt.figure(figsize=(10,7)) # Create a new region to focus on spole = [-180,-88,180,0] projection = ccrs.SouthPolarStereo() # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') plt.show() ###Output _____no_output_____ ###Markdown As you can see from the result there are warping effects on the plotted image. There is really no way of getting aound this (other than requesting a larger extent of data which may not alway be the case). So, what we can do is set the extent of the map to a more realistic view after plotting the image as in the following example: ###Code fig = plt.figure(figsize=(10,7)) # plot the result with cartoee focusing on the south pole ax = cartoee.get_map(ocean,cmap='plasma',vis_params=visualization,region=spole,proj=projection) cb = cartoee.add_colorbar(ax,vis_params=visualization,loc='right',cmap='plasma') ax.coastlines() ax.set_title('The South Pole') # get bounding box coordinates of a zoom area zoom = spole zoom[-1] = -20 # convert bbox coordinate from [W,S,E,N] to [W,E,S,N] as matplotlib expects zoom_extent = cartoee.bbox_to_extent(zoom) # set the extent of the map to the zoom area ax.set_extent(zoom_extent,ccrs.PlateCarree()) plt.show() ###Output _____no_output_____
beginner/sympy_in_10_minutes.ipynb	###Markdown Tutorial - SymPy in 10 minutes IntroductionIn this tutorial we will learn the basics of [Jupyter](http://jupyter.org/) and [SymPy](https://es.wikipedia.org/wiki/SymPy). SymPy is a Python library for symbolic computation. It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as [SymPy Live](http://live.sympy.org/) or [SymPy Gamma](http://www.sympygamma.com/). Sympy is similar to other CAS (Computer Algebra Software) like Mathematica, Maple or Maxima.A more complete tutorial can be found at [http://docs.sympy.org/latest/tutorial/index.html](http://docs.sympy.org/latest/tutorial/index.html).Before using SymPy we should load it, like any other Python libary. We will use```pythoninit_session()```to make some imports, this will help us in its interactive use. For scripting it would be better to do the imports diffferently, for example```pythonimport sympy as sym```and then call the functions from SymPy in the following manner```pythonx = sym.Symbols("x")expr = sym.cos(x)*2 + 3xderiv = expr.diff(x)```where we computed the derivative of $\cos^2(x) + 3x$, that should be $-2\sin(x)\cos(x) + 3$.For further information on the Jupyter Notebook you can refer to the [User Manual](https://athena.brynmawr.edu/jupyter/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb). ###Code %matplotlib notebook import numpy as np import matplotlib.pyplot as plt from sympy import * init_session() plt.style.use("seaborn-notebook") plt.rcParams["figure.figsize"] = 6, 4 ###Output _____no_output_____ ###Markdown Let us start with some simple calculations. Below we have a _code cell_ with an addition. Place the cursor on it and press SHIFT + ENTER to evaluate it. ###Code 1 + 3 ###Output _____no_output_____ ###Markdown Let us make some calculations ###Code factorial(5) Out[6]* 10 1 // 3 1 / 3 S(1) / 3 ###Output _____no_output_____ ###Markdown We can evaluate an expression to its floating point version ###Code sqrt(2pi) float(Out[11]) ###Output _____no_output_____ ###Markdown In the previous line we used the expression Out[11]* that stores the output from the evaluation in cell 11 (In[11]). We can also store expressions as variables, just as any Python variable. ###Code radius = 10 height = 100 area = pi * radius*2 volume = area height volume float(volume) ###Output _____no_output_____ ###Markdown So far, we have just used SymPy as a calculator. Let us try more advanced calculations Definite and indefinite integrals ###Code integrate(sin(x), x) integrate(sin(x), (x, 0, pi)) ###Output _____no_output_____ ###Markdown We can define a function and integrate it ###Code f = lambda x: x2 + 5 f(5) integrate(f(z), z) integrate(1/(x2 + y), x) ###Output _____no_output_____ ###Markdown If we assume that the denominator is positive, the expression can be factorized further ###Code a = symbols("a", positive=True) integrate(1/(x2 + a), x) ###Output _____no_output_____ ###Markdown We just learnt the basics, we can try some examples now.Note:** If you want to know more about a specific function you can use ``help()`` or the IPython magic command ``??`` ###Code help(integrate) integrate?? ###Output _____no_output_____ ###Markdown Examples Solving algebraic equationsRight now, there are two main options for solution of algebraic systems, namely: [``solveset`` and ``solve``](http://docs.sympy.org/latest/tutorial/solvers.html).The preferred method is ``solveset`` (see this[explanation](http://docs.sympy.org/latest/modules/solvers/solveset.html), althoughthere are systems that can be solved using ``solve`` and not ``solveset``.To solve equations using ``solveset``: ###Code a, b, c = symbols("a b c") solveset(ax2 + bx + c, x) ###Output _____no_output_____ ###Markdown We should enter the equations as equated to 0, or as an equation ###Code solveset(Eq(ax2 + bx, -c), x) ###Output _____no_output_____ ###Markdown Currently solveset is not capable of solving the following types of equations:- Non-linear multivariate system- Equations solvable by LambertW (Transcendental equation solver).solve can be used for such cases: ###Code solve([xy - 1, x - 2], x, y) solve(xexp(x) - 1, x ) ###Output _____no_output_____ ###Markdown Linear AlgebraWe use ``Matrix`` to create matrices. Matrices can contain expressions. And we use the method ``.inv()`` to compute the inverse and ```` to multiply matrices. ###Code A = Matrix([ [1, -1], [1, sin(c)] ]) display(A) B = A.inv() display(B) A B ###Output _____no_output_____ ###Markdown This should be the identity matrix, let us simplify the expression. There are several simplification functions, and ``simplify`` is the most general one. Simplifying is a complicated matter... if you are uncertain; use ``simplify``. ###Code simplify(A * B) ###Output _____no_output_____ ###Markdown PlottingWe can make 2D and 3D plots ###Code from sympy.plotting import plot3d plot(sin(x), (x, -pi, pi)); monkey_saddle = x*3 - 3xy2 p = plot3d(monkey_saddle, (x, -2, 2), (y, -2, 2)) ###Output _____no_output_____ ###Markdown Derivatives and differential equationsWe can use the function ``diff`` or the method ``.diff()`` to compute derivatives. ###Code f = lambda x: x2 diff(f(x), x) f(x).diff(x) g = lambda x: sin(x) diff(g(f(x)), x) ###Output _____no_output_____ ###Markdown Yes, SymPy knows about the chain rule.To finish, let us solve a second order ODE$$ u''(t) + \omega^2 u(t) = 0$$ ###Code u = symbols("u", cls=Function) omega = symbols("omega", positive=True) ode = u(t).diff(t, 2) + omega2 u(t) dsolve(ode, u(t)) ###Output _____no_output_____ ###Markdown Turning Sympy expression into evaluable functions``lambdify`` provides convenient functions to transform sympy expressions to lambda functions which can be used to calculate numerical values very fast.Let's try a first example ###Code f = lambdify(x, x*2, "numpy") f(3) f(np.array([1, 2, 3])) ###Output _____no_output_____ ###Markdown We can try a more difficult one ###Code fun = diff(sin(x)cos(x**3) - sin(x)/x, x) fun fun_numpy = lambdify(x, fun, "numpy") ###Output _____no_output_____ ###Markdown and then evaluate it for some points in, let's say, $[0, 5]$ ###Code pts = np.linspace(0, 5, 1000) fun_pts = fun_numpy(pts + 1e-6) # To avoid division by 0 plt.figure() plt.plot(pts, fun_pts); ###Output _____no_output_____ ###Markdown References- Žiga Lenarčič. ["10 minute (wx)Maxima tutorial:"](https://andrejv.github.io/wxmaxima/tutorials/10minute.zip), (2008). Accessed on: July 25, 2017.- Borland, David, and Russell M. Taylor II. ["Rainbow color map (still) considered harmful."](https://data3.mprog.nl/course/15%20Readings/40%20Reading%204/Borland_Rainbow_Color_Map.pdf) IEEE computer graphics and applications 27.2 (2007): 14-17. The following cell change the style of the notebook. ###Code from IPython.core.display import HTML def css_styling(): styles = open('../styles/sympy.css', 'r').read() return HTML(styles) css_styling() ###Output _____no_output_____
ykbp_model_comparison_to_hipparcos.ipynb	###Markdown Introduction [Yamaguchi et al 2017](http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1710.09839) estimated the number of binaries with a black hole and a stellar companion which will be detected by the [Gaia mission](http://sci.esa.int/gaia/). In this notebook we want to check whether their model reproduces the non detection of such binaries by the [hipparcos mission](http://sci.esa.int/hipparcos/). Loading libraries and setting up environment ###Code import sympy import numpy sympy.init_printing() ###Output _____no_output_____ ###Markdown Reproducing Paper Results As a first step, we try to reproduce the results in the paper, specifically, table 2. We start with the integrand of the huge integral in equation 15. ###Code f_bin = sympy.Symbol('f_bin') # Binary fraction Gamma_0 = sympy.Symbol('Gamma_0') # Slope of binary separation pdf Phi = sympy.Symbol('Phi') # Mass ratio distribution t_L1 = sympy.Symbol(r't_{L,1}') # Lifetime of the star that collapsed to a black hole t_L2 = sympy.Symbol(r't_{L,2}') # Lifetime of the companion A_bar = sympy.Symbol('\overline{A}') # Separation between stars b = sympy.Symbol('b') # Viewing angle relative to galactic plane D = sympy.Symbol('D') # Distance from Earth rho_d = sympy.Symbol('rho_d') # Star formation number density eqn_15_integrand_raw = (2f_bin/(1-f_bin))Gamma_0Phi(t_L2-t_L1)(1/A_bar)sympy.cos(b)D2rho_d eqn_15_integrand_raw ###Output _____no_output_____ ###Markdown Spatial distribution of stars Bahcall Soniera star formation number density (equation 11) ###Code Psi = sympy.Symbol('Psi') rho_d0 = sympy.Symbol(r'\rho_{d,0}') # Normalisation factor z = sympy.Symbol('z') # Distance from the galactic plane h_z = sympy.Symbol('h_z', positive=True) # Scale height of the galactic disc x = sympy.Symbol('x') # Distance from the rotational axis of the galaxy r_0 = sympy.Symbol('r_0') # Distance between the sun and the galactic centre h_r = sympy.Symbol('h_r', positive=True) # Scale radius of the galaxy eqn_11 = sympy.Eq(rho_d,Psirho_d0sympy.exp(-z/h_z-(x-r_0)/h_r)) eqn_11 ###Output _____no_output_____ ###Markdown Definition of the normalisation (equation 12) ###Code x_max = sympy.Symbol(r'x_{\max}') z_max = sympy.Symbol(r'z_{\max}') temp = eqn_11.rhs/ Psi temp = sympy.integrate(4sympy.pixtemp,(z,0,z_max),(x,0,x_max)).simplify() eqn_12 = sympy.Eq(rho_d0,sympy.solve(temp-1,rho_d0)[0]) eqn_12 ###Output _____no_output_____ ###Markdown Actually, the bounds $x_{\max}$ and $z_{\max}$ are not defined in the paper, so I assume $x_{\max}, z_{\max} \rightarrow \infty$ ###Code temp = eqn_11.rhs/Psi4sympy.pix temp = sympy.integrate(temp, (z, 0, sympy.oo)) temp = sympy.integrate(temp, (x, 0, sympy.oo)) temp = sympy.solve(temp-1,rho_d0)[0] eqn_12_var = sympy.Eq(rho_d0, temp) eqn_12_var ###Output _____no_output_____ ###Markdown Geocentric coordinates ###Code l = sympy.Symbol('l') # Viewing angle relative to the rotation axis of the galaxy eqn_13 = sympy.Eq(x,sympy.sqrt(r_02+D2sympy.cos(b)2-2Dr_0sympy.cos(b)sympy.cos(l))) eqn_13 eqn_14 = sympy.Eq(z, Dsympy.sin(b)) eqn_14 ###Output _____no_output_____ ###Markdown Initial mass function ###Code Psi_1a = sympy.Symbol(r'\Psi_{1,a}') # Coefficient Psi_1b = sympy.Symbol(r'\Psi_{1,b}') # Coefficient M_sol = sympy.Symbol('M_{\odot}') M_1_bar = sympy.Symbol(r'\overline{M}_1') m_1_bar = sympy.Symbol(r'\overline{m}_1') # M_1_bar divided by a solar mass temp = sympy.Piecewise((Psi_1am_1_bar(-1.3), sympy.And(m_1_bar < 0.5, m_1_bar>0.08)), (Psi_1bm_1_bar*(-2.3), sympy.And(m_1_bar>0.5, m_1_bar<100)), (0,True)) eqn_1 = sympy.Eq(Psi, temp) eqn_1 Psi_2a = sympy.Symbol(r'\Psi_{2,a}') # Coefficient Psi_2b = sympy.Symbol(r'\Psi_{2,b}') # Coefficient Psi_2c = sympy.Symbol(r'\Psi_{2,c}') # Coefficient temp = sympy.Piecewise((Psi_2am_1_bar*(-1.3), sympy.And(m_1_bar < 0.5, m_1_bar>0.08)), (Psi_2bm_1_bar*(-2.2), sympy.And(m_1_bar>0.5, m_1_bar<1)), (Psi_2cm_1_bar*(-2.7), sympy.And(m_1_bar>1.0, m_1_bar<100)), (0,True)) eqn_2 = sympy.Eq(Psi,temp) eqn_2 ###Output _____no_output_____ ###Markdown The coefficients can be determined by continuity and normalisation condition ###Code cond_1 = sympy.Eq(eqn_1.rhs.subs(m_1_bar,0.5-1e-6),eqn_1.rhs.subs(m_1_bar,0.5+1e-6)) cond_1 Upsilon = sympy.Symbol('Upsilon') # Star formation rate in the entire galaxy, roughly 3.5 M_sol/year temp = sympy.integrate(eqn_1.rhsm_1_bar,m_1_bar) temp = ((temp.subs(m_1_bar, 0.5-1e-6) - temp.subs(m_1_bar, 0.08+1e-6))+ (temp.subs(m_1_bar, 100-1e-6) - temp.subs(m_1_bar, 0.5+1e-6))) cond_2 = sympy.Eq(temp, Upsilon) cond_2 eqn_1_coefficients = sympy.solve([cond_1, cond_2], [Psi_1a, Psi_1b]) eqn_1_coefficients cond_3 = sympy.Eq(eqn_2.rhs.subs(m_1_bar,0.5-1e-6), eqn_2.rhs.subs(m_1_bar, 0.5+1e-6)) cond_3 cond_4 = sympy.Eq(eqn_2.rhs.subs(m_1_bar,1-1e-6), eqn_2.rhs.subs(m_1_bar, 1+1e-6)) cond_4 temp = sympy.integrate(eqn_2.rhsm_1_bar, m_1_bar) temp = ((temp.subs(m_1_bar,0.5-1e-6) - temp.subs(m_1_bar,0.08+1e-6))+ (temp.subs(m_1_bar,1.0-1e-6) - temp.subs(m_1_bar,0.5+1e-6))+ (temp.subs(m_1_bar,100-1e-6) - temp.subs(m_1_bar,1+1e-6))) cond_5 = sympy.Eq(temp, Upsilon) cond_5 eqn_2_coefficients = sympy.solve([cond_3, cond_4, cond_5], [Psi_2a, Psi_2b, Psi_2c]) eqn_2_coefficients ###Output _____no_output_____ ###Markdown Distribution of mass ratios ###Code M_min_bar = sympy.Symbol(r'\overline{M}_{\min}') eqn_3 = sympy.Eq(Phi, 1/(1-M_min_bar/M_1_bar)) eqn_3 ###Output _____no_output_____ ###Markdown Distribution of semi major axes Minimum semimajor axis ###Code A_bar_min = sympy.Symbol(r'\overline{A}_{\min}') q = sympy.Symbol('q', positive=True) # Mass ratio R_1 = sympy.Symbol('R_1') # Radios of primary eqn_7 = sympy.Eq(A_bar_min, (0.6qsympy.Rational(-2,3)+sympy.log(1+qsympy.Rational(-1,3)))R_1/(0.49q*sympy.Rational(-2,3))) eqn_7 ###Output _____no_output_____ ###Markdown Mass raius relation, from [Demircan and Kahraman 1991](http://adsabs.harvard.edu/abs/1991Ap%26SS.181..313D) ###Code R_sol = sympy.Symbol('R_{\odot}') # Solar radius mass_radius_relation = 1.61m_1_bar*0.83R_sol ###Output _____no_output_____ ###Markdown Determination of semimajor axes distribution ###Code A_bar_max = sympy.Symbol(r'\overline{A}_{\max}') temp = Gamma_0/A_bar temp = sympy.integrate(temp, (A_bar, A_bar_min, A_bar_max)) temp = temp.subs(A_bar_max, 1e5R_sol).subs(eqn_7.lhs, eqn_7.rhs).subs(R_1, mass_radius_relation) Gamma_0_expr = sympy.solve(temp-1,Gamma_0)[0] Gamma_0_expr ###Output _____no_output_____ ###Markdown Lifetimes ###Code tau_r = sympy.Symbol('tau_r') # Reference lifetime (10 Gy) lifetime_mass_relation = tau_rm_1_bar*(-2.5) lifetime_mass_relation ###Output _____no_output_____ ###Markdown Putting it all together Carrying out the integral in equation 15 ###Code solar_radius_in_pc = 2.25e-8 D_max = sympy.Symbol(r'D_{\max}') temp = eqn_15_integrand_raw.subs(eqn_11.lhs, eqn_11.rhs).subs(eqn_12_var.lhs, eqn_12_var.rhs) temp = temp.subs(eqn_13.lhs, eqn_13.rhs) temp = temp.subs(eqn_14.lhs, eqn_14.rhs) temp = temp.subs(eqn_3.lhs, eqn_3.rhs) temp = temp.subs(Gamma_0, Gamma_0_expr) temp = temp.subs(Psi, eqn_1.rhs.args[1][0]) temp = temp.subs(eqn_1_coefficients) temp = temp.subs(t_L1,lifetime_mass_relation) temp = temp.subs(t_L2,lifetime_mass_relation.subs(m_1_bar,m_1_barq)) temp = temp.subs(M_min_bar, 0.08M_sol) temp = temp.subs(M_1_bar, m_1_barM_sol) temp = temp.subs(h_z, 250) temp = temp.subs(h_r, 3500) temp = temp.subs(r_0, 8000) temp = temp.subs(b, 1) temp = temp.subs(l, sympy.Rational(1,2)) temp = temp.subs(D, 100) temp = temp.subs(f_bin, 0.5) temp = temp.subs(q, 0.1) temp = temp.subs(R_sol, solar_radius_in_pc) temp = temp.subs(m_1_bar, 30) temp.n() ###Output _____no_output_____ ###Markdown Step by Step Integration In this section we break up the integral in equation 15 into different categories, and consider each category separately to obtain insight to the problem. We begin by eliminating the units. From the star formation rate $3.5 \, M_{\odot} / \rm year$, a typical timescale $\tau_r = 10 \, \rm Gyr$ and a typical mass $M_{\odot}$ we obtain the total number of stars $3.5 \cdot 10^{10}$. From this point, each integral filters out the irrelevant stars and reduces this number, Primary massIn this section we filter out all primaries below the necessary mass needed to collapse to a black hole ###Code def integrand_func_mk_1(m1): if m1<20: return 0 return 3.5e100.22m1*(-2.3) m_range = numpy.linspace(20,100) # Primary mass range dm = m_range[1] - m_range[0] numpy.sum([integrand_func_mk_1(m1) for m1 in m_range])dm ###Output _____no_output_____ ###Markdown Mass ratio We filter out large companion masses because their lifetime is too short, and low masses because they are too dim ###Code def integrand_func_mk_2(m1, q): m2min = 0.4 if m2min/m1>q: return 0 return integrand_func_mk_1(m1)/(1-m2min/m1)m1-2.5(q*-2.5-1) m_range = numpy.linspace(20,100) # Range of primary masses q_range = numpy.linspace(0, 1) # Range of mass ratios dm = m_range[1] - m_range[0] # Mass interval dq = q_range[1] - q_range[0] # Mass ratio interval numpy.sum([[integrand_func_mk_2(m1, q) for m1 in m_range] for q in q_range])dmdq ###Output _____no_output_____ ###Markdown Semi major axis ###Code def integrand_func_mk_3(m1,q,lnA): A_max_val = 1e5 D_ref = 4e10 # Reference distance, 1 kpc in solar radii uas = 5e-12 # micro arcsecond in radius gaia_angular_res = 300uas # Gaia angular sensitivity A_min_val = 0.66m10.83(3.0+5q0.67numpy.log(1+q*0.33)) A_period = 2000 # http://www.wolframalpha.com/input/?i=((solar+mass)(gravitation+constant)(5+year)%5E2)%5E(1%2F3)%2F(solar+radius) if lnA>numpy.log10(max(A_period, A_max_val)) or lnA<numpy.log10(A_min_val) or lnA<numpy.log10(gaia_angular_res)+numpy.log10(D_ref): return 0 G0_val = 1.0/numpy.log10(A_max_val/A_min_val) return integrand_func_mk_2(m1,q)G0_val m_range = numpy.linspace(20,100,10) # Range of primary masses q_range = numpy.linspace(0, 1, 10) # Range of mass ratios lnA_range = numpy.linspace(-1, 5, 10) # Rand of semi - major axis dm = m_range[1] - m_range[0] # Mass interval dq = q_range[1] - q_range[0] # Mass ratio interval dlnA = lnA_range[1] - lnA_range[0] # semi major axis interval numpy.sum([[[integrand_func_mk_3(m1, q, lnA) for m1 in m_range] for q in q_range] for lnA in lnA_range])dmdq*dlnA ###Output _____no_output_____
doc/app/evo-model-with-tree.ipynb	###Markdown Apply a non-stationary nucleotide model to an alignment with a treeWe analyse an alignment with sequences from 6 primates. ###Code from cogent3.app import io reader = io.load_aligned(format="fasta", moltype="dna") aln = reader("../data/primate_brca1.fasta") aln.names ###Output _____no_output_____ ###Markdown Specify the tree via a tree instance ###Code from cogent3 import load_tree from cogent3.app import evo tree = load_tree("../data/primate_brca1.tree") gn = evo.model("GN", tree=tree) gn ###Output _____no_output_____ ###Markdown Specify the tree via a path. ###Code gn = evo.model("GN", tree="../data/primate_brca1.tree") gn ###Output _____no_output_____ ###Markdown Apply the model to an alignment ###Code fitted = gn(aln) fitted ###Output _____no_output_____ ###Markdown In the above, no value is shown for `unique_Q`. This can happen because of numerical precision issues.NOTE: in the display of the `lf` below, the "length" parameter is not the ENS. It is, instead, just a scalar. ###Code fitted.lf ###Output _____no_output_____ ###Markdown Apply a non-stationary nucleotide model to an alignment with a treeWe analyse an alignment with sequences from 6 primates. ###Code from cogent3.app import io reader = io.load_aligned(format="fasta", moltype="dna") aln = reader("../data/primate_brca1.fasta") aln.names ###Output _____no_output_____ ###Markdown Specify the tree via a tree instance ###Code from cogent3 import load_tree from cogent3.app import evo tree = load_tree("../data/primate_brca1.tree") gn = evo.model("GN", tree=tree) gn ###Output _____no_output_____ ###Markdown Specify the tree via a path. ###Code gn = evo.model("GN", tree="../data/primate_brca1.tree") gn ###Output _____no_output_____ ###Markdown Apply the model to an alignment ###Code fitted = gn(aln) fitted ###Output _____no_output_____ ###Markdown In the above, no value is shown for `unique_Q`. This can happen because of numerical precision issues.NOTE: in the display of the `lf` below, the "length" parameter is not the ENS. It is, instead, just a scalar. ###Code fitted.lf ###Output _____no_output_____ ###Markdown Apply a non-stationary nucleotide model to an alignment with a treeWe analyse an alignment with sequences from 6 primates. ###Code from cogent3.app import io reader = io.load_aligned(format="fasta", moltype="dna") aln = reader("../data/primate_brca1.fasta") aln.names ###Output _____no_output_____ ###Markdown Specify the tree via a tree instance ###Code from cogent3 import load_tree from cogent3.app import evo tree = load_tree("../data/primate_brca1.tree") gn = evo.model("GN", tree=tree) gn ###Output _____no_output_____ ###Markdown Specify the tree via a path. ###Code gn = evo.model("GN", tree="../data/primate_brca1.tree") gn ###Output _____no_output_____ ###Markdown Apply the model to an alignment ###Code fitted = gn(aln) fitted ###Output _____no_output_____ ###Markdown In the above, no value is shown for `unique_Q`. This can happen because of numerical precision issues.NOTE: in the display of the `lf` below, the "length" parameter is not the ENS. It is, instead, just a scalar. ###Code fitted.lf ###Output _____no_output_____
bacteria_archaea/marine/cell_num/.ipynb_checkpoints/marine_prokaryote_cell_number-checkpoint.ipynb	###Markdown Estimating the total number of marine bacteria and archaea This notebook details the procedure for estimating the total number of marine bacteria and archaea.The estimate is based on three data sources:[Aristegui et al.](http://dx.doi.org/10.4319/lo.2009.54.5.1501),[Buitenhuis et al.](http://dx.doi.org/10.5194/essd-4-101-2012), and[Lloyd et al.](http://dx.doi.org/10.1128/AEM.02090-13) ###Code import pandas as pd import numpy as np from scipy.stats import gmean pd.options.display.float_format = '{:,.1e}'.format import sys sys.path.insert(0, '../../../statistics_helper') from CI_helper import * # Genaral parameters used in the estimate ocean_area = 3.6e14 liters_in_m3 = 1e3 ml_in_m3 = 1e6 # Load the datasets buitenhuis = pd.read_excel('marine_prok_cell_num_data.xlsx','Buitenhuis') aristegui = pd.read_excel('marine_prok_cell_num_data.xlsx','Aristegui') aristegui[['Cell abundance (cells m-2)','SE']] = aristegui[['Cell abundance (cells m-2)','SE']].astype(float) lloyd = pd.read_excel('marine_prok_cell_num_data.xlsx','Lloyd') ###Output _____no_output_____ ###Markdown Here are samples from the data in Aristegui et al.: ###Code aristegui.head() ###Output _____no_output_____ ###Markdown From the data in Buitenhuis et al.: ###Code buitenhuis.head() ###Output _____no_output_____ ###Markdown And from Llyod et al.: ###Code lloyd.head() ###Output _____no_output_____ ###Markdown For Aristegui et al. we estimate the total number of cells by multiplying each layer by the surface area of the ocean ###Code aristegui_total = (aristegui['Cell abundance (cells m-2)']ocean_area).sum() print('Total number of cells based on Aristegui et al.: %.1e' % aristegui_total) ###Output Total number of cells based on Aristegui et al.: 1.7e+29 ###Markdown For Buitenhuis et al. we bin the data along 100 meter depth bins, and estimate the average cell abundance in each bin. We then multiply the total number of cells per liter by the volume at each depth and sum across layers. ###Code # Define depth range every 100 m from 0 to 4000 meters depth_range = np.linspace(0,4000,41) #Bin data along depth bins buitenhuis['Depth_bin'] = pd.cut(buitenhuis['Depth'], depth_range) #For each bin, calculate the average number of cells per liter buitenhuis_bins = buitenhuis.groupby(['Depth_bin']).mean()['Bact/L'] #Multiply each average concentration by the total volume at each bin: 100 meters depth time the surface area of the oceac buitenhuis_bins = 100ocean_arealiters_in_m3 #Sum across all bins to get the total estimate for the number of cells of marine prokaryotes buitenhuis_total = buitenhuis_bins.sum() print('Total number of cells based on Buitenhuis et al.: %.1e' % buitenhuis_total) ###Output Total number of cells based on Buitenhuis et al.: 1.3e+29 ###Markdown For Lloyd et al., we rely on the sum of the total number of bacteria and archaea. The estimate for the number of bacteria and archaea is based on the regression of the concentration of bacteria and archaea with depth. We use the equations reported in Lloyd et al. to extrapolate the number of cells of bacteria and archaea across the average ocean depth of 4000 km. ###Code # Define the regression equation for the number of bacteria in the top 64 m: def bac_surf(depth): result = np.zeros_like(depth) for i,x in enumerate(depth): if x==0 : result[i] = 5.54 else: result[i] = np.log10(x)0.08+5.54 return 10result # Define the regression equation for the number of bacteria in water deeper than 64 m: bac_deep = lambda x: 10(np.log10(x)-1.09+7.66) # Define the regression equation for the number of bacteria in the top 389 m: def arch_surf(depth): result = np.zeros_like(depth) for i,x in enumerate(depth): if x==0 : result[i] = 4.1 else: result[i] = np.log10(x)0.1+4.1 return 10result # Define the regression equation for the number of bacteria in water below 389 m: arch_deep = lambda x: 10(np.log10(x)-0.8+6.43) # Estimate the total number of bacteria in the top 64 m by first estimating the concentration using the # regression equation, multiplying by the volume at each depth, which is 1 m^3 times the surface # Area of the ocean, and finally summing across different depths total_bac_surf = (bac_surf(np.linspace(0,64,65))ml_in_m3ocean_area).sum() # We repeat the same procedure for the total number of bacteria in waters deeper than 64 m, and for the total # Number of archaea total_bac_deep = (bac_deep(np.linspace(65,4000,4000-65+1))ml_in_m3ocean_area).sum() total_arch_surf = (arch_surf(np.linspace(0,389,390))ml_in_m3ocean_area).sum() total_arch_deep = (arch_deep(np.linspace(390,4000,4000-390+1))ml_in_m3ocean_area).sum() # Sum across bacteria and archaea to get the estimate for the total number of bacteria and archaea in the ocean lloyd_total = total_bac_surf+total_bac_deep+total_arch_surf+total_arch_deep print('Total number of cells based on Lloyd et al.: %.1e' % lloyd_total) ###Output Total number of cells based on Lloyd et al.: 6.2e+28 ###Markdown The estimate of the total number of cells in Lloyd et al. is based on FISH measurements, but in general not all cells which are DAPI positive are also stained with FISH. To correct for this effect, we estimate the average FISH yield across samples, and divide our estimate from the FISH measurements by the average FISH yield. ###Code fish_yield = lloyd['FISH yield'].dropna() # Values which are not feasible are turned to the maximal value. We do not use 1 because of numerical reasons fish_yield[fish_yield >=1] = 0.999 # calculate the statistics on the fish_visible/fish_invisible value and not the # fish_visible/(fish_visible+fish_invisible) value because the first is not bound by 0 and 1 # We transform the values to log space to calculate the geometric mean alpha_fish_yield = np.log10(1./(1./fish_yield[fish_yield<1]-1.)) mean_alpha_yield = np.average(-alpha_fish_yield.dropna()) mean_yield = 1./(1.+10mean_alpha_yield) print('The mean yield of FISH is %.1f' % mean_yield) lloyd_total /= mean_yield print('After correcting for FISH yield, the estimate for the total number of bacteria and archaea based on Lloyd et al is %.1e' % lloyd_total) ###Output The mean yield of FISH is 0.8 After correcting for FISH yield, the estimate for the total number of bacteria and archaea based on Lloyd et al is 8.1e+28 ###Markdown Our best estimate for the total number of marine bacteria and archaea is the geometric mean of the estimates from Aristegui et al., Buitenhuis et al. and Lloyd et al. ###Code estimates = [aristegui_total,buitenhuis_total,lloyd_total] best_estimate = 10(np.log10(estimates).mean()) print('Our best estimate for the total number of marine bacteria and archaea is %.1e' %best_estimate) ###Output Our best estimate for the total number of marine bacteria and archaea is 1.2e+29 ###Markdown Uncertainty analysisTo calculate the uncertainty associated with the estimate for the total number of of bacteria and archaea, we first collect all available uncertainties and then take the largest value as our best projection for the uncertainty. Intra-study uncertainties We first survey the uncertainties reported in each of the studies. Aristegui et al. report a sandard error of ≈10% for the average cell concentration per unit area. Buitenhuis et al. and Lloyd et al. do not report uncertainties. Interstudy uncertaintiesWe estimate the 95% multiplicative error of the geometric mean of the values from the three studies. ###Code mul_CI = geo_CI_calc(estimates) print('The interstudy uncertainty is about %.1f' % mul_CI) ###Output The interstudy uncertainty is about 1.5 ###Markdown We thus take the highest uncertainty from our collection which is ≈1.4-fold.Our final parameters are: ###Code print('Total number of marine bacteria and archaea: %.1e' % best_estimate) print('Uncertainty associated with the total number of marine bacteria and archaea: %.1f-fold' % mul_CI) old_results = pd.read_excel('../marine_prok_biomass_estimate.xlsx') result = old_results.copy() result.loc[0] = pd.Series({ 'Parameter': 'Total number of marine bacteria and archaea', 'Value': int(best_estimate), 'Units': 'Cells', 'Uncertainty': "{0:.1f}".format(mul_CI) }) result.to_excel('../marine_prok_biomass_estimate.xlsx',index=False) ###Output Total number of marine bacteria and archaea: 1.2e+29 Uncertainty associated with the total number of marine bacteria and archaea: 1.5-fold
Introduction to Tensorflow for AI ML and DL/Week 2/Exercise2_Question.ipynb	###Markdown Exercise 2In the course you learned how to do classification using Fashion MNIST, a data set containing items of clothing. There's another, similar dataset called MNIST which has items of handwriting -- the digits 0 through 9.Write an MNIST classifier that trains to 99% accuracy or above, and does it without a fixed number of epochs -- i.e. you should stop training once you reach that level of accuracy.Some notes:1. It should succeed in less than 10 epochs, so it is okay to change epochs to 10, but nothing larger2. When it reaches 99% or greater it should print out the string "Reached 99% accuracy so cancelling training!"3. If you add any additional variables, make sure you use the same names as the ones used in the classI've started the code for you below -- how would you finish it? ###Code # YOUR CODE SHOULD START HERE # YOUR CODE SHOULD END HERE import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() # YOUR CODE SHOULD START HERE class myCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if(logs.get('accuracy') >= 0.99): print("\nReached 99% accuracy so cancelling training!") self.model.stop_training = True x_train = x_train / 255.0 x_test = x_test / 255.0 callbacks = myCallback() # YOUR CODE SHOULD END HERE model = tf.keras.models.Sequential([ # YOUR CODE SHOULD START HERE tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(512, activation=tf.nn.relu), tf.keras.layers.Dense(10, activation=tf.nn.softmax) # YOUR CODE SHOULD END HERE ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # YOUR CODE SHOULD START HERE model.fit(x_train, y_train, epochs=10, callbacks=[callbacks]) # YOUR CODE SHOULD END HERE ###Output _____no_output_____
2_Curso/Laboratorio/SAGE-noteb/IPYNB/CRIPT/82-CRIPT-cesar.ipynb	###Markdown Cifra de César ###Code def encriptar_cesar(C,k): L = list(C) L1 = map(ord,L) L2 = [(item+k)%256 for item in L1] C1 = join(map(chr,L2),sep = "") return C1 encriptar_cesar(texto,5) encriptar_cesar(encriptar_cesar(texto,5),251) encriptar_cesar(limpiar(texto,alfb),5) encriptar_cesar(encriptar_cesar(limpiar(texto,alfb),5),251) ###Output _____no_output_____ ###Markdown Análisis de frecuencias ###Code def analisis_frec(T): frecuencias = {} N = len(T) for letra in T: if letra in frecuencias: frecuencias[letra] += (1/N).n() else: frecuencias[letra]=(1/N).n() return frecuencias dicc = analisis_frec(encriptar_cesar(limpiar(texto,alfb),5));dicc def invertir(dicc): dict_inv = {} for key in dicc: dict_inv[dicc[key]] = ord(key) return dict_inv dicc2 = invertir(dicc);dicc2 L = dicc2.items();L L.sort(reverse = True);L ord('E') ###Output _____no_output_____
notebook/ontune-neural.ipynb	###Markdown 하루 ###Code df = pd.read_csv( "../input/10min/ontune2016.csv", usecols=["date", "value"], parse_dates=["date"] ) print(df.shape) df.head() df.rename(columns={"date": "ds", "value": "y"}, inplace=True) df.tail() train = df.iloc[:int(df.shape[0] * 0.8)] valid = df.iloc[int(df.shape[0] * 0.2):] valid["days"] = valid["ds"].dt.date valid = valid.groupby("days")["y"].agg("mean") def objective(trial: Trial) -> float: params = { "epochs": trial.suggest_categorical("epochs", [50, 100, 300, 500]), "batch_size": trial.suggest_categorical("batch_size", [32, 64, 128, 256]), "num_hidden_layers": trial.suggest_int("num_hidden_layers", 0, 5), "learning_rate": trial.suggest_float("learning_rate", 1e-3, 0.1), "changepoints_range": trial.suggest_discrete_uniform( "changepoints_range", 0.8, 0.95, 0.001 ), "n_changepoints": trial.suggest_int("n_changepoints", 20, 35), "seasonality_mode": "additive", "yearly_seasonality": False, "weekly_seasonality": True, "daily_seasonality": True, "loss_func": "MSE", } # fit_model m = NeuralProphet(params) m.fit(train, freq="1D") future = m.make_future_dataframe(train, periods=len(valid), n_historic_predictions=True) forecast = m.predict(future) valid_forecast = forecast[forecast.y.isna()] val_rmse = mean_squared_error(valid_forecast.yhat1, valid, squared=False) return val_rmse study = optuna.create_study(direction="minimize", sampler=optuna.samplers.TPESampler(seed=42)) study.optimize(objective, n_trials=20) prophet_params = study.best_params prophet_params["batch_size"] = 64 prophet_params["seasonality_mode"] = "additive" prophet_params["loss_func"] = "MSE" prophet_params["weekly_seasonality"] = True prophet_params["daily_seasonality"] = True prophet_params["yearly_seasonality"] = False # model = NeuralProphet() if you're using default variables below. model = NeuralProphet(prophet_params) metrics = model.fit(train, freq="1D") future = model.make_future_dataframe(train, periods=len(valid), n_historic_predictions=True) forecast = model.predict(future) fig, ax = plt.subplots(figsize=(14, 10)) model.plot(forecast, ax=ax) plt.show() # model = NeuralProphet() if you're using default variables below. model = NeuralProphet(**prophet_params) metrics = model.fit(df, freq="1D") future = model.make_future_dataframe(df, periods=144, n_historic_predictions=True) forecast = model.predict(future) fig, ax = plt.subplots(figsize=(14, 10)) model.plot(forecast, ax=ax) plt.show() ###Output _____no_output_____
study_roadmaps/4_image_classification_zoo/Classifier - Monkey Species.ipynb	###Markdown Table of contents Install Monk Using pretrained model for classifying Monkey specie types Training a classifier from scratch Install Monk Using pip (Recommended) - colab (gpu) - All bakcends: `pip install -U monk-colab` - kaggle (gpu) - All backends: `pip install -U monk-kaggle` - cuda 10.2 - All backends: `pip install -U monk-cuda102` - Gluon bakcned: `pip install -U monk-gluon-cuda102` - Pytorch backend: `pip install -U monk-pytorch-cuda102` - Keras backend: `pip install -U monk-keras-cuda102` - cuda 10.1 - All backend: `pip install -U monk-cuda101` - Gluon bakcned: `pip install -U monk-gluon-cuda101` - Pytorch backend: `pip install -U monk-pytorch-cuda101` - Keras backend: `pip install -U monk-keras-cuda101` - cuda 10.0 - All backend: `pip install -U monk-cuda100` - Gluon bakcned: `pip install -U monk-gluon-cuda100` - Pytorch backend: `pip install -U monk-pytorch-cuda100` - Keras backend: `pip install -U monk-keras-cuda100` - cuda 9.2 - All backend: `pip install -U monk-cuda92` - Gluon bakcned: `pip install -U monk-gluon-cuda92` - Pytorch backend: `pip install -U monk-pytorch-cuda92` - Keras backend: `pip install -U monk-keras-cuda92` - cuda 9.0 - All backend: `pip install -U monk-cuda90` - Gluon bakcned: `pip install -U monk-gluon-cuda90` - Pytorch backend: `pip install -U monk-pytorch-cuda90` - Keras backend: `pip install -U monk-keras-cuda90` - cpu - All backend: `pip install -U monk-cpu` - Gluon bakcned: `pip install -U monk-gluon-cpu` - Pytorch backend: `pip install -U monk-pytorch-cpu` - Keras backend: `pip install -U monk-keras-cpu` Install Monk Manually (Not recommended) Step 1: Clone the library - git clone https://github.com/Tessellate-Imaging/monk_v1.git Step 2: Install requirements - Linux - Cuda 9.0 - `cd monk_v1/installation/Linux && pip install -r requirements_cu90.txt` - Cuda 9.2 - `cd monk_v1/installation/Linux && pip install -r requirements_cu92.txt` - Cuda 10.0 - `cd monk_v1/installation/Linux && pip install -r requirements_cu100.txt` - Cuda 10.1 - `cd monk_v1/installation/Linux && pip install -r requirements_cu101.txt` - Cuda 10.2 - `cd monk_v1/installation/Linux && pip install -r requirements_cu102.txt` - CPU (Non gpu system) - `cd monk_v1/installation/Linux && pip install -r requirements_cpu.txt` - Windows - Cuda 9.0 (Experimental support) - `cd monk_v1/installation/Windows && pip install -r requirements_cu90.txt` - Cuda 9.2 (Experimental support) - `cd monk_v1/installation/Windows && pip install -r requirements_cu92.txt` - Cuda 10.0 (Experimental support) - `cd monk_v1/installation/Windows && pip install -r requirements_cu100.txt` - Cuda 10.1 (Experimental support) - `cd monk_v1/installation/Windows && pip install -r requirements_cu101.txt` - Cuda 10.2 (Experimental support) - `cd monk_v1/installation/Windows && pip install -r requirements_cu102.txt` - CPU (Non gpu system) - `cd monk_v1/installation/Windows && pip install -r requirements_cpu.txt` - Mac - CPU (Non gpu system) - `cd monk_v1/installation/Mac && pip install -r requirements_cpu.txt` - Misc - Colab (GPU) - `cd monk_v1/installation/Misc && pip install -r requirements_colab.txt` - Kaggle (GPU) - `cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt` Step 3: Add to system path (Required for every terminal or kernel run) - `import sys` - `sys.path.append("monk_v1/");` Used trained classifier for demo ###Code #Using mxnet-gluon backend # When installed using pip from monk.gluon_prototype import prototype # When installed manually (Uncomment the following) #import os #import sys #sys.path.append("monk_v1/"); #sys.path.append("monk_v1/monk/"); #from monk.gluon_prototype import prototype # Download trained weights ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1E6WQFx9OVgbIQl2AkrjrLVZ--J7KYMRa' -O- \| sed -rn 's/.confirm=([0-9A-Za-z_]+)./\1\n/p')&id=1E6WQFx9OVgbIQl2AkrjrLVZ--J7KYMRa" -O cls_monkey_trained.zip && rm -rf /tmp/cookies.txt ! unzip -qq cls_monkey_trained.zip ls workspace/Project-Monkey-Species/ # Load project in inference mode gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet161", eval_infer=True); #Other trained models - uncomment #gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169", eval_infer=True); #gtf.Prototype("Project-Monkey-Species", "Gluon-densenet201", eval_infer=True); # Infer img_name = "workspace/test/1.jpg" predictions = gtf.Infer(img_name=img_name); from IPython.display import Image Image(filename=img_name) img_name = "workspace/test/2.jpg" predictions = gtf.Infer(img_name=img_name); from IPython.display import Image Image(filename=img_name) ###Output Prediction Image name: workspace/test/2.jpg Predicted class: n1 Predicted score: 0.9997491836547852 ###Markdown Training custom classifier from scratch Dataset - Credits: https://www.kaggle.com/slothkong/10-monkey-species Download ###Code ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1AgjZP8UGCabVgyw5GcFUF8ZhR9yADYjd' -O- \| sed -rn 's/.confirm=([0-9A-Za-z_]+)./\1\n/p')&id=1AgjZP8UGCabVgyw5GcFUF8ZhR9yADYjd" -O 10-monkey-species.zip && rm -rf /tmp/cookies.txt ! mkdir dataset ! mv 10-monkey-species.zip dataset ! cd dataset && unzip -qq 10-monkey-species.zip ###Output _____no_output_____ ###Markdown Training ###Code # Using mxnet-gluon backend from monk.gluon_prototype import prototype # For pytorch backend #from monk.pytorch_prototype import prototype # For Keras backend #from monk.keras_prototype import prototype # Create Project and Experiment gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169"); gtf.Default(dataset_path="dataset/training/training", model_name="densenet169", freeze_base_network=False, num_epochs=2); ###Output _____no_output_____ ###Markdown How to change hyper parameters and models - Docs - https://github.com/Tessellate-Imaging/monk_v14 - Examples - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/1_getting_started_roadmap ###Code #Start Training gtf.Train(); #Read the training summary generated once you run the cell and training is completed ###Output _____no_output_____ ###Markdown Validating on the same dataset ###Code # Using mxnet-gluon backend from monk.gluon_prototype import prototype # For pytorch backend #from monk.pytorch_prototype import prototype # For Keras backend #from monk.keras_prototype import prototype # Create Project and Experiment gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169", eval_infer=True); # Load dataset for validaion gtf.Dataset_Params(dataset_path="dataset/validation/validation"); gtf.Dataset(); # Run validation accuracy, class_based_accuracy = gtf.Evaluate(); ###Output _____no_output_____ ###Markdown Table of contents Install Monk Using pretrained model for classifying Monkey specie types Training a classifier from scratch Install Monk - git clone https://github.com/Tessellate-Imaging/monk_v1.git - cd monk_v1/installation/Linux && pip install -r requirements_cu9.txt (Select the requirements file as per OS and CUDA version) ###Code ! git clone https://github.com/Tessellate-Imaging/monk_v1.git # If using Colab install using the commands below ! cd monk_v1/installation/Misc && pip install -r requirements_colab.txt # If using Kaggle uncomment the following command #! cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt # Select the requirements file as per OS and CUDA version when using a local system or cloud #! cd monk_v1/installation/Linux && pip install -r requirements_cu9.txt ###Output _____no_output_____ ###Markdown Used trained classifier for demo ###Code # Monk import os import sys sys.path.append("monk_v1/monk/"); # Download trained weights ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1E6WQFx9OVgbIQl2AkrjrLVZ--J7KYMRa' -O- \| sed -rn 's/.confirm=([0-9A-Za-z_]+)./\1\n/p')&id=1E6WQFx9OVgbIQl2AkrjrLVZ--J7KYMRa" -O cls_monkey_trained.zip && rm -rf /tmp/cookies.txt ! unzip -qq cls_monkey_trained.zip ls workspace/Project-Monkey-Species/ # Gluon project from gluon_prototype import prototype # Load project in inference mode gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet161", eval_infer=True); #Other trained models - uncomment #gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169", eval_infer=True); #gtf.Prototype("Project-Monkey-Species", "Gluon-densenet201", eval_infer=True); # Infer img_name = "workspace/test/1.jpg" predictions = gtf.Infer(img_name=img_name); from IPython.display import Image Image(filename=img_name) img_name = "workspace/test/2.jpg" predictions = gtf.Infer(img_name=img_name); from IPython.display import Image Image(filename=img_name) ###Output _____no_output_____ ###Markdown Training custom classifier from scratch Dataset - Credits: https://www.kaggle.com/slothkong/10-monkey-species Download ###Code ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1AgjZP8UGCabVgyw5GcFUF8ZhR9yADYjd' -O- \| sed -rn 's/.confirm=([0-9A-Za-z_]+)./\1\n/p')&id=1AgjZP8UGCabVgyw5GcFUF8ZhR9yADYjd" -O 10-monkey-species.zip && rm -rf /tmp/cookies.txt ! mkdir dataset ! mv 10-monkey-species.zip dataset ! cd dataset && unzip -qq 10-monkey-species.zip ###Output _____no_output_____ ###Markdown Training ###Code # Monk import os import sys sys.path.append("monk_v1/monk/"); # Using mxnet-gluon backend from gluon_prototype import prototype # For pytorch backend #from pytorch_prototype import prototype # For Keras backend #from keras_prototype import prototype # Create Project and Experiment gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169"); gtf.Default(dataset_path="dataset/training/training", model_name="densenet169", freeze_base_network=False, num_epochs=2); ###Output _____no_output_____ ###Markdown How to change hyper parameters and models - Docs - https://github.com/Tessellate-Imaging/monk_v14 - Examples - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/1_getting_started_roadmap ###Code #Start Training gtf.Train(); #Read the training summary generated once you run the cell and training is completed ###Output _____no_output_____ ###Markdown Validating on the same dataset ###Code # Import monk import os import sys sys.path.append("monk_v1/monk/"); # Using mxnet-gluon backend from gluon_prototype import prototype # For pytorch backend #from pytorch_prototype import prototype # For Keras backend #from keras_prototype import prototype # Create Project and Experiment gtf = prototype(verbose=1); gtf.Prototype("Project-Monkey-Species", "Gluon-densenet169", eval_infer=True); # Load dataset for validaion gtf.Dataset_Params(dataset_path="dataset/validation/validation"); gtf.Dataset(); # Run validation accuracy, class_based_accuracy = gtf.Evaluate(); ###Output _____no_output_____
notebooks/plot_OT_2D_samples.ipynb	###Markdown 2D Optimal transport between empirical distributionsIllustration of 2D optimal transport between discributions that are weightedsum of diracs. The OT matrix is plotted with the samples. ###Code # Author: Remi Flamary <[email protected]> # Kilian Fatras <[email protected]> # # License: MIT License import numpy as np import matplotlib.pylab as pl import ot import ot.plot ###Output _____no_output_____ ###Markdown Generate data------------- ###Code #%% parameters and data generation n = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix M = ot.dist(xs, xt) M /= M.max() ###Output _____no_output_____ ###Markdown Plot data--------- ###Code #%% plot samples pl.figure(1) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') pl.figure(2) pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') ###Output _____no_output_____ ###Markdown Compute EMD----------- ###Code #%% EMD G0 = ot.emd(a, b, M) pl.figure(3) pl.imshow(G0, interpolation='nearest') pl.title('OT matrix G0') pl.figure(4) ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix with samples') ###Output _____no_output_____ ###Markdown Compute Sinkhorn---------------- ###Code #%% sinkhorn # reg term lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd) pl.figure(5) pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix sinkhorn') pl.figure(6) ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() ###Output _____no_output_____ ###Markdown Emprirical Sinkhorn---------------- ###Code #%% sinkhorn # reg term lambd = 1e-3 Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) pl.figure(7) pl.imshow(Ges, interpolation='nearest') pl.title('OT matrix empirical sinkhorn') pl.figure(8) ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn from samples') pl.show() ###Output Warning: numerical errors at iteration 0 ###Markdown 2D Optimal transport between empirical distributionsIllustration of 2D optimal transport between discributions that are weightedsum of diracs. The OT matrix is plotted with the samples. ###Code # Author: Remi Flamary <[email protected]> # # License: MIT License import numpy as np import matplotlib.pylab as pl import ot ###Output _____no_output_____ ###Markdown Generate data------------- ###Code #%% parameters and data generation n = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix M = ot.dist(xs, xt) M /= M.max() ###Output _____no_output_____ ###Markdown Plot data--------- ###Code #%% plot samples pl.figure(1) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') pl.figure(2) pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') ###Output _____no_output_____ ###Markdown Compute EMD----------- ###Code #%% EMD G0 = ot.emd(a, b, M) pl.figure(3) pl.imshow(G0, interpolation='nearest') pl.title('OT matrix G0') pl.figure(4) ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix with samples') ###Output _____no_output_____ ###Markdown Compute Sinkhorn---------------- ###Code #%% sinkhorn # reg term lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd) pl.figure(5) pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix sinkhorn') pl.figure(6) ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() ###Output _____no_output_____ ###Markdown 2D Optimal transport between empirical distributionsIllustration of 2D optimal transport between discributions that are weightedsum of diracs. The OT matrix is plotted with the samples. ###Code # Author: Remi Flamary <[email protected]> # # License: MIT License import numpy as np import matplotlib.pylab as pl import ot import ot.plot ###Output _____no_output_____ ###Markdown Generate data------------- ###Code #%% parameters and data generation n = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix M = ot.dist(xs, xt) M /= M.max() ###Output _____no_output_____ ###Markdown Plot data--------- ###Code #%% plot samples pl.figure(1) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') pl.figure(2) pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') ###Output _____no_output_____ ###Markdown Compute EMD----------- ###Code #%% EMD G0 = ot.emd(a, b, M) pl.figure(3) pl.imshow(G0, interpolation='nearest') pl.title('OT matrix G0') pl.figure(4) ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix with samples') ###Output _____no_output_____ ###Markdown Compute Sinkhorn---------------- ###Code #%% sinkhorn # reg term lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd) pl.figure(5) pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix sinkhorn') pl.figure(6) ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() ###Output _____no_output_____
3.4_notebook_quizz_objects.ipynb	###Markdown You will need the class Car for the next exercises. The class Car has four data attributes: make, model, colour and number of owners (owner_number). The method car_info() prints out the data attributes and the method sell() increments the number of owners. ###Code class Car(object): def __init__(self,make,model,color): self.make=make; self.model=model; self.color=color; self.owner_number=0 def car_info(self): print("make: ",self.make) print("model:", self.model) print("color:",self.color) print("number of owners:",self.owner_number) def sell(self): self.owner_number=self.owner_number+1 ###Output _____no_output_____ ###Markdown Create a Car object Create a Car object my_car with the given data attributes: ###Code make="BMW" model="M3" color="red" ###Output _____no_output_____ ###Markdown Data Attributes Use the method car_info() to print out the data attributes Methods Call the method sell() in the loop, then call the method car_info() again ###Code for i in range(5): ###Output _____no_output_____ ###Markdown You will need the class Car for the next exercises. The class Car has four data attributes: make, model, colour and number of owners (owner_number). The method car_info() prints out the data attributes and the method sell() increments the number of owners. ###Code class Car(object): def __init__(self,make,model,color): self.make=make; self.model=model; self.color=color; self.owner_number=0 def car_info(self): print("make: ",self.make) print("model:", self.model) print("color:",self.color) print("number of owners:",self.owner_number) def sell(self): self.owner_number=self.owner_number+1 ###Output _____no_output_____ ###Markdown Create a Car object Create a Car object my_car with the given data attributes: ###Code make="BMW" model="M3" color="red" my_car = Car(make, model, color) print(my_car.make) ###Output BMW ###Markdown Data Attributes Use the method car_info() to print out the data attributes ###Code print(my_car.car_info()) ###Output make: BMW model: M3 color: red number of owners: 0 None ###Markdown Methods Call the method sell() in the loop, then call the method car_info() again ###Code for i in range(5): my_car.sell() print(my_car.car_info()) ###Output make: BMW model: M3 color: red number of owners: 5 None
02-Crystallography.ipynb	###Markdown _Reference textbook / figure credits: Charles Kittel, "Introduction to solid-state physics", Chapter 1_ Unit cells, fractional coordinates, latticesA periodic structure is defined by a lattice and an a-periodic repeat unit. The lattice is a periodic set of points generated by all integer combinations of three unit cell vectors $\mathbf{a}_{1,2,3}$, i.e. $$\mathbf{T} = u_1 \mathbf{a}_1 + u_2 \mathbf{a}_2 + u_3 \mathbf{a}_3, \quad u_{1,2,3} \in \mathbb{Z}$$The repeat unit, or basis is defined by the coordinates $\{x_i, y_i, z_i\}$ of a (usually small) number of atoms that sit in arbitrary positions within the unit cell. These are often given in fractional coordinates $\{s_{i1}, s_{i2}, s_{i3}\}$, such that the set of all atoms in the crystal is generated by $$(u_1+s_{i1}) \mathbf{a}_1 + (u_2+s_{i2}) \mathbf{a}_2 + (u_3+s_{i3})$$with the $u_{1,2,3}$ ranging over all positive and negative integers, and $i$ ranging over the number of atoms in the basis.There are 14 types of lattices known as _Bravais lattices_ in three dimensions, that are distinguished by the symmetry group of the lattice points. If you need to refresh your fundamentals of crystallography, and don't remember what a _face centered cubic_ or _body centered cubic_ lattice is, the [wikipedia page](https://en.wikipedia.org/wiki/Bravais_lattice) is excellent. Note also that the crystal structure (lattice+basis) can have more complicated symmetries, forming the 230 [space groups](https://en.wikipedia.org/wiki/Space_group) Now, consider the structure below. This is just a finite set of atoms, and coordinates are listed individually. You can click on the atoms in the viewer and see its coordinates by clicking on the info panel below the viewer. ###Code positions = np.array( [[x,y,z] for x,y,z in itertools.product([0, 5, 10, 15],[0, 5, 10, 15],[0, 5, 10, 15]) ]) ase_cube = ase.Atoms("Ga64", positions=positions) properties = {} properties["index"] = { "target": "atom", "values": list(range(1, len(ase_cube)+1)), } properties["coordinates"] = { "target": "atom", "values": positions, } cs = chemiscope.show([ase_cube], properties=properties, mode="structure", environments=chemiscope.all_atomic_environments([ase_cube], cutoff=40), settings={"structure":[{"spaceFilling": False, "environments": {"cutoff": 40}}]} ) display(cs) ###Output _____no_output_____ ###Markdown 01a Write a function that returns the lattice vectors and a basis that generates the periodic structure that continues to infinity the motif above. _NB: the validation code only tests for the primitive cell - you can come up with correct structures that will be marked as incorrect. Trust your own judgment (and look carefully at the visualization)_ ###Code ex01_wci = WidgetCodeInput( function_name="sc_lattice_basis", function_parameters="", docstring=""" Returns the lattice vectors and the basis (in fractional coordinates) that generates a simple cubic structure. :return: Three lattice vectors and a list of basis coordinates """, function_body=""" # Write your solution, then click on the button below to update the plotter # and check against the reference value a1 = [] a2 = [] a3 = [] basis = [[]] return a1, a2, a3, basis """ ) def reciprocal_lattice_vectors(a1, a2): # compute it in obfuscated way reciprocal_lattice = 2np.piase.Atoms(cell=[[a1[0], a1[1], 0], [a2[0], a2[1], 0], [0,0,1]]).cell.reciprocal() return reciprocal_lattice[0][:2], reciprocal_lattice[1][:2] data_dump.register_field("ex01-function", ex01_wci, "function_body") def ex01_updater(): a1, a2, a3, basis = ex01_wci.get_function_object()() positions = np.asarray(basis) h = np.asarray([a1,a2,a3]) structure = ase.Atoms('Ga'+str(len(basis)), [email protected], cell=[a1, a2, a3]) display(chemiscope.show(frames = [structure], mode="structure", settings={"structure":[{"unitCell":True,"supercell":{"0":3,"1":3,"2":3}}]} )) def match_lattice(a, b): a1, a2, a3, basis = b; return np.allclose([a1,a2,a3], np.eye(3)5) and np.asarray(basis).shape==(1,3) """ref_values = { (): ase.Atoms("CNH6", positions=[[ 1., -0., -0.01], [ 2.52, -0.01, 0. ], [ 0.6 , 1.02, -0. ], [ 0.59, -0.52, 0.88], [ 0.6 , -0.51, -0.9 ], [ 2.92, 0.5 , 0.89], [ 2.93, 0.51, -0.88], [ 2.92, -1.03, -0. ]]) }, ref_match = match_structure, """ ex01_wcc = WidgetCodeCheck(ex01_wci, ref_values = { (): ([5,0,0],[0,5,0],[0,0,5],[[0,0,0]]) }, ref_match = match_lattice, demo=WidgetUpdater(updater=ex01_updater)) display(ex01_wcc) ###Output _____no_output_____ ###Markdown 01b* Try to shift rigidly the basis atom(s). Does the resulting crystal change in a significant way? ###Code ex01_txt = Textarea("enter any additional comment", layout=Layout(width="100%")) data_dump.register_field("ex01-answer", ex01_txt, "value") display(ex01_txt) ###Output _____no_output_____ ###Markdown Primitive cell, supercells, conventional cellsIt is always possible to give different descriptions to the same crystal structure. For instance, for a given cell and basis one can always specify a _supercell_ i.e. a multiple of repeat units of the original cell, with a correspondingly larger basis. This basis contains "hidden" symmetries, meaning that it would be possible to describe the same structure with a smaller unit cell. The smallest possible cell is called a _primitive_ (or minimal) cell. For instance, the figure below shows three different choices of unit cell for a face-centered cubic lattice. All cells describe the same structure! The first structure is the primitive cell, and the third the conventional cells that reveals more clearly the origin of the name of this lattice. The following visualizer shows a collection of 9 crystal structures, containing either one or two chemical species. Look at them, and try to understand what type of lattice they belong to. You can visualize the environment of each atom within an adjusable cutoff, which may help you appreciate the 3D nature of the structure. ###Code ase_crystals = read('data/crystals.xyz',":") properties = {} properties["lattice vectors a1"] = { "target": "structure", "values": np.asarray([str(list(a.cell[0])) for a in ase_crystals]), } properties["lattice vectors a2"] = { "target": "structure", "values": np.asarray([str(list(a.cell[1])) for a in ase_crystals]), } properties["lattice vectors a3"] = { "target": "structure", "values": np.asarray([str(list(a.cell[2])) for a in ase_crystals]), } properties["basis"] = { "target": "atom", "values": np.vstack([a.positions for a in ase_crystals]), } cs_crystal = chemiscope.show(ase_crystals, properties=properties, mode="structure", environments=chemiscope.all_atomic_environments(ase_crystals), settings={"structure":[{"bonds":True, "unitCell":True,"supercell":{"0":3,"1":3,"2":3}, "environments": {"cutoff": 6}}]} ) def update_co(change): cs_crystal.settings={"structure": [{"environments": {"cutoff": pb_crystal.value['co']}}]} pb_crystal = WidgetParbox(onchange=update_co, co=(6.,1,12,0.1, r"environment cutoff / Å")) display(VBox([pb_crystal,cs_crystal])) ###Output _____no_output_____ ###Markdown 02 Each of the structures above are either face-centered (fcc), body-centered (bcc) or simple cubic (sc). Write down in the box below what is the Bravais lattice for each structure, and the size of the basis used to describe the structure in each frame. _NB: pay attention: (1) you are asked about the symmetry of the lattice: if you just look at the cell, you may be misled; (2) the number of atoms included in the basis depend on the choice of cell._ ###Code ex02_txt = Textarea("structure 1: lattice: XXX, basis size: YYY\n ....", layout=Layout(width="100%")) data_dump.register_field("ex02-answer", ex02_txt, "value") display(ex02_txt) ###Output _____no_output_____ ###Markdown Using a supercell is not only a way of obtaining a more convenient/intuitive view of a crystal structure. It can also be useful to represent real materials, in which the ideal periodicity of the crystal is broken, e.g. because there are defects, interfaces, or simply disorder. In these cases, one often starts from a small unit cells and explicitly repeats it many times. _This is different from just replicating the unit cell for visualization purposes: the cell is also enlarged, and the coordinates of all atoms inside the unit cell can be adjusted independently_ASE provides convenient utilities to replicate a structure. If `structure` is an `ase.Atoms` object, then`structure.repeat((nx, ny, nz))` will replicate the structure the given number of times along each of the axes. You can learn more about the function using `help(ase.Atoms.repeat)`. ###Code help(ase.Atoms.repeat) ###Output _____no_output_____ ###Markdown 03 Write code to generate a supercell for _fcc_ aluminum, using the number of repeat units as an argument, and a lattice parameter of 4Å. You can use the `repeat` ASE command, but if you feel adventurous you may as well write a replicate function yourself. _NB: the self-test function can generate false errors because it expects a specific order of the basis atoms. Don't worry too much if it tells you one test has not passed!_ ###Code ex03_wci = WidgetCodeInput( function_name="al_supercell", function_parameters="nrep", docstring=""" Returns an ASE object describing a supercell built by replicating `nrep` times along each direction a conventional (4-atoms) fcc unit cell for aluminum. Use a lattice parameter of 4 Å. :return: The replicated-cell ASE object """, function_body=""" # Write your solution, then click on the button below to update the chemiscope viewer import ase al4 = ase.Atoms("Al4", pbc=True) al4.cell = [ ... ] # lattice parameters of the conventional unit cell al4.positions = [[atom1x, atom1y, atom1z], [atom2x, atom2y, atom2z], [atom3x, atom3y, atom3z], [atom4x, atom4y, atom4z]] # positions of the 4 atoms in the conventional fcc cell # empty atom al_replicated = ... return al_replicated """ ) data_dump.register_field("ex03-function", ex03_wci, "function_body") def ex03_updater(): al_multi = ex03_wci.get_function_object()(ex03_wp.value['nrep']) display(VBox([ex03_wp,chemiscope.show(frames = [al_multi], mode="structure", settings={"structure":[{"unitCell":True}]} )]) ) def match_lattice(a, b): return np.allclose(b.cell, a[0]) and (True if a[1] is None else np.allclose(b.positions-b.positions[0], a[1])) ex03_wp = WidgetParbox(nrep=(2, 1, 4, 1, r"$n_{\mathrm{repeat}}$ (must click on update button!)")) ex03_wcc = WidgetCodeCheck(ex03_wci, ref_values = { (1,): ([[4,0,0],[0,4,0],[0,0,4]], [[0,0,0],[2,2,0],[2,0,2],[0,2,2]]), (2,): ([[8,0,0],[0,8,0],[0,0,8]], None) }, ref_match = match_lattice, demo=WidgetUpdater(updater=ex03_updater)) display(ex03_wcc) ###Output _____no_output_____ ###Markdown Lattice planes and surfaces The general notation used to define lattice planes is somewhat contrived (because it actually relates to the reciprocal lattice!). A plane is defined by three points, so one can define a lattice plane by picking three points that are multiples of the unit cell vectors, $(n_1 \mathbf{a_1}, n_2 \mathbf{a_2}, n_3 \mathbf{a_3})$. The plane is determined by the reciprocals of the $n_{1,2,3}$, multiplied by their least common multiple so as to obtain three integer indices $(h k l)$. For instance, a plane that intercepts the lattice axes with $n_{1,2,3} = (4,3,1)$ has Miller indices $(3,4,12)$. _Only in a cubic lattice_ the plane identified by (h k l) has $h \mathbf{a_1} + k \mathbf{a_2} + l \mathbf{a_3}$ as a plane normal. If you need a more detailed discussion, see Chapter 1 of _Kittel_, or the [Wikipedia page on Miller indices](https://en.wikipedia.org/wiki/Miller_index) that has some good figures. In the widget below, you can see a large supercell of _fcc_ aluminum. You can select different Miller indices, and see the atoms that belong to one of the lattice planes highlighted in a different color. Experiment a bit and note how e.g. `(2,2,4)` is equivalent to `(1,1,2)`, etc. Note also how the density of atoms on low-index planes is higher than on high-index planes. ###Code fcc_al = ase.lattice.cubic.FaceCenteredCubic('Al') al_supercell = fcc_al.repeat((4,4,4)) normal_plane = np.array([1,0,0]) origin = np.diagonal(al_supercell.cell)/2 distances = np.abs((al_supercell.positions-origin) @ normal_plane) / np.linalg.norm(normal_plane) atoms_on_plane = np.where( distances < 1e-5 )[0] al_supercell.numbers[atoms_on_plane] = 12 cs_fcc = chemiscope.show([al_supercell], mode="structure", settings={"structure":[{"spaceFilling": False, "unitCell":True}]} ) def update_surface_cut(change): al_supercell.numbers[:] = 13 normal_plane = np.array([pb_fcc.value['h'],pb_fcc.value['k'],pb_fcc.value['l']]) if np.linalg.norm(normal_plane) != 0: origin = np.diagonal(al_supercell.cell)/2 distances = np.abs((al_supercell.positions-origin) @ normal_plane) / np.linalg.norm(normal_plane) atoms_on_plane = np.where( distances < 1e-6 )[0] al_supercell.numbers[atoms_on_plane] = 12 global cs_fcc settings = cs_fcc.settings cs_fcc.close() cs_fcc = chemiscope.show([al_supercell], mode="structure", settings=cs_fcc.settings ) display(cs_fcc) #cs.settings={"structure": [{"environments": {"cutoff": pb.value['co']}}]} pb_fcc = WidgetParbox(onchange=update_surface_cut, h=(1,0,3,1,r"h"), k=(0,0,3,1,r"k"), l=(0,0,3,1,r"l")) display(VBox([pb_fcc,cs_fcc])) ###Output _____no_output_____ ###Markdown 04 Activate the visualization of multiple replicas in the visualizer above (e.g. $2\times2\times2$). Are the planes continuous across the edges of the supercell, if you pick a `(1,0,0)` plane? And what happens if you pick `(1,1,1)`? ###Code ex04_txt = Textarea("enter your answer", layout=Layout(width="100%")) data_dump.register_field("ex04-answer", ex04_txt, "value") display(ex04_txt) ###Output _____no_output_____ ###Markdown Now let's create a supercell with an actual surface. While there are tools to do this for more complicated scenarios (see e.g. [here](https://wiki.fysik.dtu.dk/ase/ase/build/surface.htmlcreate-specific-non-common-surfaces) for some examples using ASE), we will do this manually to understand better what is going on. If the surface is aligned with one of the lattice parameters, it is sufficient to first replicate the unit cell a number of times, and then artificially enlarge one of the edges of the unit cell. This will leave some empty space between the top and bottom layers of the structure, effectively leaving some atoms in contact with vacuum. Note you always create _two_ surfaces, because you are still dealing with a periodic structure. This is usually referred to as a _slab geometry_. Note also that for complicated crystals with a basis, the unit cell can be cut at different positions, so that there can be multiple different surfaces corresponding to the same lattice direction, depending on the position of the cut. 05 Write a function that creates a supercell for _fcc_ aluminum with 4x4x2 replicas along the three directions. Use a conventional unit cell and a lattice parameter of 4Å. Modify the `cell` of the resulting ASE structure to add 20Å of vacuum along the $\mathbf{a}_3$ direction, creating a slab geometry with a surface along `(0,0,1)` _NB: you can copy most of the code from exercise 03._ ###Code ex05_wci = WidgetCodeInput( function_name="al_slab", function_parameters="nrep, nslab, vacuum", docstring=""" Returns an ASE object describing Al (100) surfaces using a slab geometry with nrep x nrep x nslab cells, and a vacuum region along z. Use a lattice parameter of 4 Å. :return: The ASE object describing the slab geometry """, function_body=""" # Write your solution, then click on the button below to update the chemiscope viewer import ase al4 = ase.Atoms("Al4", pbc=True) al4.cell = [ ... ] # lattice parameters of the conventional unit cell al4.positions = [[, ,], [, ,], [, ,], [, ,]] # positions of the 4 atoms in the conventional fcc cell # empty atom al_replicated = ... # create gap return al_replicated """ ) data_dump.register_field("ex05-function", ex05_wci, "function_body") def ex05_updater(): al_multi = ex05_wci.get_function_object()(ex05_wp.value['nrep'], ex05_wp.value['nslab'], ex05_wp.value['vacuum']) display(VBox([ex05_wp,chemiscope.show(frames = [al_multi], mode="structure", settings={"structure":[{"unitCell":True}]} )]) ) def match_lattice(a, b): return np.allclose(b.cell, a[0]) and (True if a[1] is None else np.allclose(b.positions-b.positions[0], a[1])) ex05_wp = WidgetParbox( nrep=(4, 1, 6, 1, r"$n_{\mathrm{rep}}$ (click on button to update!)"), nslab=(2, 1, 6, 1, r"$n_{\mathrm{slab}}$"), vacuum=(20., 0, 40, 0.5, r"$L_{\mathrm{vacuum}}$") ) ex05_wcc = WidgetCodeCheck(ex05_wci, ref_values = { (1, 1, 10): ([[4,0,0],[0,4,0],[0,0,14]], [[0,0,0],[2,2,0],[2,0,2],[0,2,2]]), (4, 2, 11): ([[16,0,0],[0,16,0],[0,0,19]], None) }, ref_match = match_lattice, demo=WidgetUpdater(updater=ex05_updater)) display(ex05_wcc) ###Output _____no_output_____ ###Markdown Creating a `(1,1,1)` surface is somewhat more complicated, because the conventional unit cell is not aligned properly. One way to create a slab with the appropriate orientation is to create a _primitive_ face-centered cell and to create the slab by _extending_ one of the cell vectors. 06 Write a function that creates a supercell for _fcc_ aluminum, starting with the primitive cell replicated as 8x8x2 along the lattice vectors. Then _multiply_ by four $\mathbf{a}_3$. Visualize the resulting structure ###Code ex06_wci = WidgetCodeInput( function_name="al_slab_111", function_parameters="nrep, nslab, zscale", docstring=""" Returns an ASE object describing Al (111) surfaces using a slab geometry with nrep x nrep x nslab copies of the primitive cell. Vacuum must be created by elongating one of the lattice vectors by a factor zscale. Use a lattice parameter of 4 Å. :return: The ASE object describing the slab geometry """, function_body=""" # Write your solution, then click on the button below to update the chemiscope viewer import ase al = ase.Atoms("Al", pbc=True) al.cell = [ ... ] # lattice parameters of the primitive unit cell al.positions = [[0,0,0]] # just one-atom basis! # empty atom al_replicated = ... # create gap return al_replicated """ ) data_dump.register_field("ex06-function", ex06_wci, "function_body") def ex06_updater(): al_multi = ex06_wci.get_function_object()(ex06_wp.value['nrep'], ex06_wp.value['nslab'], ex06_wp.value['zscale']) display(VBox([ex06_wp,chemiscope.show(frames = [al_multi], mode="structure", settings={"structure":[{"unitCell":True}]} )]) ) def match_lattice(a, b): return np.allclose(b.cell, a[0]) and (True if a[1] is None else np.allclose(b.positions-b.positions[0], a[1])) ex06_wp = WidgetParbox( nrep=(4, 1, 6, 1, r"$n_{\mathrm{rep}}$ (click on button to update!)"), nslab=(2, 1, 6, 1, r"$n_{\mathrm{slab}}$"), zscale=(4., 1., 10, 0.5, r"$z_{\mathrm{scale}}$") ) ex06_wcc = WidgetCodeCheck(ex06_wci, ref_values = { (1, 1, 1): ([[2,2,0],[2,0,2],[0,2,2]], [[0,0,0]]), (1, 2, 2): ([[2,2,0],[2,0,2],[0,8,8]], None) }, ref_match = match_lattice, demo=WidgetUpdater(updater=ex06_updater)) display(ex06_wcc) ###Output _____no_output_____ ###Markdown 07 What is the direction of the primitive lattice parameters relative to the conventional _fcc_ index system? Is the surface normal parallel to the $\mathbf{a}_3$ cell vector? In order to create an easier to visualize structure, one often builds a unit cell that is orthorhombic and has one axis that is parallel to the surface normal. Can you come up with three low-Miller-indices directions that are mutually orthogonal and could be used to define an appropriate unit cell?_NB: The last point is not entirely trivial, think about it but don't get too stressed if you don't manage to create the appropriate cell. If you are really into these geometric problems, you can also try to derive an actual unit cell, including also the basis._ ###Code ex07_txt = Textarea("write your answer", layout=Layout(width="100%")) data_dump.register_field("ex07-answer", ex07_txt, "value") display(ex07_txt) ###Output _____no_output_____ ###Markdown Reciprocal lattice in 2D From here on, we work exclusively with 2D lattices,$$\mathbf{T} = u_1 \mathbf{a}_1 + u_2 \mathbf{a}_2, \quad \mathbf{a}_{1,2} \in \mathbb{R}^2, u_{1,2} \in Z$$because they allow for simpler visualization of the core concepts. Most of the concepts we develop and demonstrate, however, apply equally well to actual 3D systems. The idea of the reciprocal lattice is deeply linked to the idea of scatterig, and constructive interference. A plane waves $e^{\mathrm{i} \mathbf{k}\cdot \mathbf{x}}$ originating at the origin will be in phase with similar waves originating at all other lattice points if its wavevector $\mathbf{k}$ satisfy the phase relation $\mathbf{k}\cdot \mathbf{T} = 2\pi n$, where $n$ is an integer, for each and every lattice vector. We can build a _reciprocal lattice_ that consist of integer combinations of basis vectors $\mathbf{b}_{1,2}$$$\mathbf{G} = v_1 \mathbf{b}_1 + v_2 \mathbf{b}_2, \quad \mathbf{b}_{1,2} \in \mathbb{R}^2, v_{1,2} \in Z$$such that every reciprocal lattice point is a wavevector that results in a complete in-phase scattering from the direct lattice. To do this, we need relationships to hold between the lattice vectors: $\mathbf{b}_{1,2} \cdot \mathbf{a}_{1,2} = 2\pi$, $\mathbf{b}_{1,2}\cdot \mathbf{a}_{2,1} = 0$. Thus, $\mathbf{b}_1$ must be orthogonal to $\mathbf{a}_2$ and $\mathbf{b}_2$ must be orthogonal to $\mathbf{a}_1$.We can achieve this (including also normalization that ensure the correct scaling $\mathbf{b}_{1,2} \cdot \mathbf{a}_{1,2} = 2\pi$, with the definition$$ \mathbf{b}_1 = 2\pi\frac{\mathbf{R}\mathbf{a}_2}{\mathbf{a}_1\cdot\mathbf{R}\mathbf{a}_2}, \quad \mathbf{b}_2 = 2\pi\frac{\mathbf{R}\mathbf{a}_1}{\mathbf{a}_2\cdot\mathbf{R}\mathbf{a}_1},\quad\quad\mathbf{R} = \begin{bmatrix}0 & -1\\ 1 & 0\end{bmatrix}$$where $\mathbf{R}$ is a $\pi/2$ rotation. 08 What are the reciprocal lattice vectors for a 2D rectangular Bravais lattice with lattice vectors $(1,0)$ and $(0,2)$? ###Code ex08_txt = Textarea("The reciprocal primitive vectors are:\n b_1 = ... , b_2 = ...", layout=Layout(width="100%")) data_dump.register_field("ex08-answer", ex08_txt, "value") display(ex08_txt) ###Output _____no_output_____ ###Markdown 09 Write a function that computes the reciprocal lattice vectors given $\mathbf{a}_{1,2}$. Use the demo widget to experiment and get an intuitive understanding of what's going on. ###Code # TODO AG: make an exercise where they build b1, b2 starting from # a1, a2, and have a demo box that shows two panels, one with the direct lattice # and one with the reciprocal lattice ex09_wci = WidgetCodeInput( function_name="reciprocal_lattice", function_parameters="a1, a2", docstring=""" Return the 2D reciprocal unit cell vectors. :param a1: unit cell vector a1 :param a2: unit cell vector a2 :return: reciprocal lattice unit cell vectors """, function_body=""" import numpy as np from numpy import pi # this is just to accept all sort of sequence inputs a1 = np.asarray(a1) a2 = np.asarray(a2) # matrix-matrix and matrix-vector multiplication can be achieved with the @ operator # For example `R @ a` multiplies the matrix R with the vector a R = np.array([[0,-1],[1,0]]) # <- this converts a nested list to a numpy array # change to the correct expression b1 = 2pia1 b2 = 2pia2 return np.asarray(b1), np.asarray(b2) # this is just to return a standard format even if you define b1 and b2 as another type of iterable """ ) data_dump.register_field("ex09-function", ex09_wci, "function_body") def plot_lattice(ax, a1, a2, basis=None, alphas=None, s=20, c='red', lattice_size = 60, head_length = 0.5, head_width= 0.2, width=0.05): if basis is None: basis = np.array([[0,0]]) A = np.array([a1, a2]) # each atom in the basis gets a different basis alpha value when plotted if alphas is None: alphas = np.linspace(1, 0.3, len(basis)) for i in range(len(basis)): lattice = (np.mgrid[:lattice_size,:lattice_size].T @ A + basis[i]).reshape(-1, 2) lattice -= (np.array([lattice_size//2,lattice_size//2]) @ A).reshape(-1, 2) ax.scatter(lattice[:,0], lattice[:,1], color=c, s=s, alpha=alphas[i]) ax.fill([0,a1[0],(a1+a2)[0],a2[0]], [0,a1[1],(a1+a2)[1],a2[1]], color=c, alpha=0.2) ax.arrow(0,0, a1[0], a1[1],width=width, length_includes_head=True, fc=c, ec='black') ax.arrow(0,0, a2[0], a2[1],width=width, length_includes_head=True, fc=c, ec='black') def plot_reciprocal_and_real_lattice(axes, a11, a12, a21, a22, basis=None): if basis is None: basis = np.array([[0,0]]) a1 = np.array([a11, a12]) a2 = np.array([a21, a22]) b1, b2 = ex09_wci.get_function_object()(a1, a2) plot_lattice(axes[0], a1, a2, basis, s=20, c='red') plot_lattice(axes[1], b1/(2np.pi), b2/(2np.pi), None, s=20, c='blue') axes[0].set_title('real space') axes[0].set_xlim(-5,5) axes[0].set_ylim(-5,5) axes[0].set_xlabel("$x$ / Å") axes[0].set_ylabel("$y$ / Å") axes[1].set_title('reciprocal space') axes[1].set_xlim(-5,5) axes[1].set_ylim(-5,5) axes[1].set_xlabel("$k_x/2\pi$ / Å$^{-1}$") axes[1].set_ylabel("$k_y/2\pi$ / Å$^{-1}$") fig_ax = plt.subplots(1, 2, figsize=(7.5,3.8), tight_layout=True) ex09_wp = WidgetPlot(plot_reciprocal_and_real_lattice, WidgetParbox(a11 = (1., -4, 4, 0.1, r'$a_{11} / Å$'), a12 = (0., -4, 4, 0.1, r'$a_{12} / Å$'), a21 = (0., -4, 4, 0.1, r'$a_{21} / Å$'), a22 = (2., -4, 4, 0.1, r'$a_{22} / Å$') ), fig_ax=fig_ax) ex09_wcc = WidgetCodeCheck(ex09_wci, ref_values= { aa: reciprocal_lattice_vectors(np.asarray(aa[0]), np.asarray(aa[1]) ) for aa in [((0,1), (1,0)), ((1,1), (1,-1)), ((0,2), (2,1))] } , demo=ex09_wp) display(ex09_wcc) ###Output _____no_output_____ ###Markdown Diffraction from a lattice To understand diffraction, you need to consider that scattering is a process involving an incoming wave, $e^{\mathrm{i} \mathbf{k}_{\mathrm{in}} \cdot \mathbf{x}}$. Each lattice point will be hit by the wave with a different phase $e^{\mathrm{i} \mathbf{k}_{\mathrm{in}} \cdot \mathbf{T}}$ and act as a spherical scatterer. The detector will be in a given position $\mathbf{R}_d$, and if it is sufficiently far it pick up a plane wave in that direction, with wavevector $\mathbf{k}_{\mathrm{out}}$. Depending on the position of the scattering point, it will accumulate a further phase $e^{-\mathrm{i} \mathbf{k}_{\mathrm{out}} \cdot \mathbf{T}}$ (the minus coming from the fact the distance traveled will be $~(\mathbf{R}_d - \mathbf{T})$. The scheme below, drawn for arbitrary position of the scattering centers, applies clearly also to the case when scattering occurs from lattice pointsThe condition to have constructive interference is therefore to have $\mathbf{k}=\mathbf{k}_\mathrm{in}-\mathbf{k}_\mathrm{out}$ be a vector of the reciprocal lattice. For typical diffraction experiments, the scattering process is elastic, meaning that the modulus of the wavevector does not change during the interaction. Furthermore, the modulus of the wavevector depends on the source of radiation used in the experiment. 10 what are the smallest and largest reciprocal lattice vectors that can be observed with electromagnetic radiation with wavelength $\lambda$? Think of the geometry of the experiment, and take the modulus of the wavevector to be $2\pi/\lambda$. ###Code ex10_txt = Textarea("Write the answer here", layout=Layout(width="100%")) data_dump.register_field("ex10-answer", ex10_txt, "value") display(ex10_txt) ###Output _____no_output_____ ###Markdown The widget below allows you to experiment with the geometry of a scattering experiment. You can define a direct lattice, the wavelength of the incoming light, and the angle between incoming and outgoing beam. The scheme that is drawn around the origin is the construction for the Ewald circle (2D equivalent of the Ewald sphere). There may be scattering only if the scattering vector $\mathbf{k}$ coincides with a point of the reciprocal lattice. ###Code def plot_braggs_reflection(axes, a11, a12, a21, a22, phi, wavelength, two_theta): def rot2d(angle): return np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]]) rot_phi = rot2d(phi) a1 = np.array([a11, a12]) @ rot_phi a2 = np.array([a21, a22]) @ rot_phi # reuse plot_reciprocal_and_real_lattice(axes, a1[0], a1[1], a2[0], a2[1]) head_length = 0.5 head_width = 0.2 width = 0.05 lw = 1 alpha_unit_cell_vectors = 0.8 k_in = 2np.pinp.array([-1,0])/wavelength /(2np.pi) #<- scale by 2pi so it is consistent with reciprocal lattice plot k_out = rot2d(two_theta)@k_in k = k_in - k_out scatter_origin = np.array([0,0]) axes[1].arrow(scatter_origin[0]+k_in[0], scatter_origin[1]+k_in[1], -k_in[0], -k_in[1], lw=lw, head_width=head_width, head_length=head_length, width=0.05, length_includes_head=True, fc='red', ec='red', label='$k_{in}}$') axes[1].arrow(scatter_origin[0]+k_in[0], scatter_origin[1]+k_in[1], -k_out[0], -k_out[1], lw=lw, head_width=head_width, head_length=head_length, width=0.05, length_includes_head=True, fc='orange', ec='orange', label='$k_{out}$') axes[1].arrow(scatter_origin[0], scatter_origin[1], k[0], k[1], lw=lw, head_width=head_width, head_length=head_length, width=0.05, length_includes_head=True, fc='black', ec='black', label='$k$') circle_theta = np.linspace(-np.pi, np.pi, 200) r = 2np.pi/wavelength/(2np.pi) axes[1].plot(np.sin(circle_theta)r + scatter_origin[0]+k_in[0], np.cos(circle_theta)r + scatter_origin[1]+k_in[1], color='red', label="Ewald circle") r = np.linalg.norm(k) axes[1].plot(np.sin(circle_theta)r + scatter_origin[0], np.cos(circle_theta)r + scatter_origin[1], color='black', label="$\|\mathbf{k}\|$ circle") axes[1].legend() fig_ax = plt.subplots(1, 2, figsize=(7.5,3.8), tight_layout=True) ex10_wp = WidgetPlot(plot_braggs_reflection, WidgetParbox(a11 = (1., -4, 4, 0.1, r'$a_{11} / Å$'), a12 = (0., -4, 4, 0.1, r'$a_{12} / Å$'), a21 = (0., -4, 4, 0.1, r'$a_{21} / Å$'), a22 = (1., -4, 4, 0.1, r'$a_{22} / Å$'), phi = (0., 0., 2np.pi, 0.05, r'$\phi$'), wavelength = (1., 0.2, 2, 0.05, r'$\lambda$ / Å'), two_theta = (1., 0.1, np.pi, 0.05, r'$2\theta$') ), fig_ax=fig_ax) # we need to reload the widget when the solution of ex09 # computing the reciprocal unit cell vectors has been updated button = Button(description="Update") button.on_click(ex10_wp.update) display(button) display(ex10_wp) ###Output _____no_output_____ ###Markdown You will notice that it is not easy, with a fixed position of the incoming light, to orient the detector so that $\mathbf{k}$ corresponds to a reciprocal lattice vector. The widget above allows you to change the orientation of the direct lattice. Note how, by changing the orientation of the crystal, you can bring any of the reciprocal lattice point that lie on the circle with radius $k$ in coincidence with the scattering wavevector $\mathbf{k}$. 11 What happens if you consider scattering from a collection of crystals with different, random orientations (a powder sample)? Does diffraction depend on the orientation of the sample? ###Code ex11_txt = Textarea("Write the answer here", layout=Layout(width="100%")) data_dump.register_field("ex11-answer", ex11_txt, "value") display(ex11_txt) ###Output _____no_output_____ ###Markdown Diffraction from an atomic structure An actual crystalline structure involves both a lattice and of an a-periodic basis $\{\mathbf{s}_i\}$ of atoms. Each will scatter with a form factor $f_i$ (that depends on the modulus of $\mathbf{k}$ but we will take to be a constant proportional to the atomic charge $Z_i$). The scattered amplitude can be written as $$F(\mathbf{k}) = \frac{1}{N} \sum^N_{j \textrm{ atom}} f_j\exp(-\mathrm{i}\mathbf{k}\cdot\mathbf{r}_j)\textrm{, with }\mathbf{r}_j\textrm{ position of atom }j$$by breaking the position of atoms into lattice vector and basis positions ($\mathbf{r}_j=\mathbf{T}+\mathbf{s}_m$), we can write $$F(\mathbf{k}) = \frac{1}{n_\mathrm{cell} n_\mathrm{basis}} \sum^{n_\mathrm{cell}}_{\mathbf{T}} \exp(-\mathrm{i}\mathbf{k}\cdot \mathbf{T}) \sum_m^{n_\mathrm{basis}} f_m \exp(-\mathrm{i}\mathbf{k}\cdot\mathbf{s}_m).$$One sees that the sum over lattice vectors selects the diffracted wavevectors that correspond to a reciprocal lattice vector $\mathbf{G}$, while the sum over the atomic basis modulates the amplitude of the peak through a _structure factor_$$F_\mathbf{G} = \sum_m^{n_\mathrm{basis}} f_m \exp(-\mathrm{i}\mathbf{G}\cdot\mathbf{s}_m).$$ In the scattering geometry above, one can determine the conditions for scattering in terms of the scattering angle $2\theta$, as follows. If $k$ is the modulus of both $\mathbf{k}_\mathrm{in}$ and $\mathbf{k}_\mathrm{out}$, and $\mathbf{G} = \mathbf{k}_\mathrm{in}-\mathbf{k}_\mathrm{out}$ must hold, a first necessary condition is$$G^2 = \|\mathbf{k}_\mathrm{in}-\mathbf{k}_\mathrm{out}\|^2 = 2k^2 (1-\cos 2\theta) = 4k^2 sin^2 \theta$$hence, $\sin\theta=G/2k$. To determine the orientation of $\mathbf{G}$ a second condition is needed. We can consider the angle between $\mathbf{G}$ and the incoming vector $\phi$, that can be set by changing the orientation of the crystal. Thus, by writing $\|\mathbf{G}-\mathbf{k}_\mathrm{in}\|^2 = \|\mathbf{k}_\mathrm{out}\|^2$ we can easily get $G = 2 k \cos\phi$, and hence $\cos\phi = \sin\theta$. If the sample is formed by uniformly oriented grains, the second condition is always satisfied by some crystals, and so the diffraction pattern is just a sequence of peaks at particular values of the angle $2\theta$. The intensity of each peak is given by the square modulus of the structure factor, $\|F_\mathbf{G}\|^2$. This widget below computes the powder (rotationally averaged) diffraction pattern for a crystal with a basis of two atoms. The function computes the list of diffraction peaks, with the corresponding structure factor. It is not entirely trivial, and so it is already implemented: you don't have to change it, but you can read it and understand what it does. Experiment with the widget, and then move to the exercises below, that will ask you to comment on what you observe in different scenarios. Parameters are as follows:* $a_{ij}$: components of the lattice vectors* $\phi$: rigid rotation of the lattice (does it have an impact on the diffraction?)* $s_{1,2}$: fractional coordinates of the second atom of the basis (first atom is in (0,0))* $f_{1,2}$: atomic form factors for the two atoms in the basis (roughly take it to be the atomic number)* $\lambda$: wavelength of the scattering radiation ###Code # set upt the code widget window ex12_wci = WidgetCodeInput( function_name="diffraction_peaks", function_parameters="basis, atomic_ff, reciprocal_b1, reciprocal_b2, wavelength", docstring=""" Computes the list of peaks for a lattice with a given (real-space) basis and reciprocal lattice, and for a given wavelength of the incoming radiation. :param basis: list of N 2D vectors corresponding to the (real space!) position of the basis atoms :param atomic_form_factors: atomic form factors of atoms in lattice, array of length N :param reciprocal_b1, reciprocal_b2: reciprocal lattice vectors :param wavelength: wavelength of the incoming radiation :return: The list of diffraction peaks, as [h, l, theta, intensity] """, function_body=""" import numpy as np def compute_absolute_structure_factor(sj, fj, G): # sj: atomic basis (n_basis x 2) # fj: form factors (n_basis) # G: reciprocal lattice vectors (2D) return np.abs(fj @ np.exp(-1j * sj @ G)) # wave number (modulus of the incoming wavevector) k = np.pi2/wavelength # determine the range of reciprocal lattice vectors that could give rise to permissible reflections n1 = int((k2)/np.sqrt(reciprocal_b1@reciprocal_b1))+1 n2 = int((k2)/np.sqrt(reciprocal_b2@reciprocal_b2))+1 # allocated space for the list of peaks lpeaks = [] for v1 in range(-n1,n1+1): for v2 in range(-n2,n2+1): # reciprocal lattice vector G = reciprocal_b1v1 + reciprocal_b2v2 # theta (from 2theta geometry) sin_theta = np.sqrt(G@G)/(2k) if sin_theta > 1: # discards reflections that fall outside of the permissible range continue theta = np.arcsin(sin_theta) # structure factor absolute_structure_factor = compute_absolute_structure_factor(basis, atomic_ff, G) lpeaks.append([v1, v2, theta, absolute_structure_factor*2]) return np.asarray(lpeaks) """ ) data_dump.register_field("ex12-function", ex12_wci, "function_body") def plot_diffraction(axes, a11, a12, a21, a22, s1, s2, f1, f2, phi, wavelength): def rot2d(angle): return np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]]) rot_phi = rot2d(phi) a1 = np.array([a11, a12]) @ rot_phi a2 = np.array([a21, a22]) @ rot_phi basis = np.asarray([0a1,s1a1+s2a2]) plot_lattice(axes[0], a1, a2, basis=basis, alphas=[f1/40, f2/40], c='red') axes[0].set_title('real space') axes[0].set_xlim(-5,5) axes[0].set_ylim(-5,5) axes[0].set_xlabel("$x$ / Å") axes[0].set_ylabel("$y$ / Å") b1, b2 = reciprocal_lattice_vectors(a1, a2) dpeaks = ex12_wci.get_function_object()( basis, np.asarray([f1, f2]), b1, b2, wavelength ) twotheta_grid = np.linspace(0, 180, 720) dp_grid = np.zeros(len(twotheta_grid)) for _, _, t, f2 in dpeaks: dp_grid += np.exp(-(twotheta_grid-2t180/np.pi)*2/0.5)f2 axes[1].clear() # we plot two theta axes[1].plot(twotheta_grid, dp_grid, 'b-') axes[1].set_xlim(0,180) axes[1].set_xlabel("$2\\theta$ / degree °") axes[1].set_title('Diffraction pattern') axes[1].set_ylabel("Intensity $\|F(\mathbf{k}(\\theta))\|$") axes[0].set_aspect('equal') axes[1].set_aspect(150/np.max(dp_grid)) header = """<tr> <th>v<sub>1</sub> / Å <span style="padding-left:150px"></th> <th>v<sub>2</sub> / Å <span style="padding-left:150px"></th> <th>2θ / degree ° <span style="padding-left:150px"></th> <th>\|F<sub>G</sub>\|<sup>2</sup> <span style="padding-left:150px"></th> </tr>""" # cleans up peak info for displaying tpeaks = [] for d in dpeaks[np.argsort(dpeaks[:,2])]: tpeaks.append( [ int(d[0]), int(d[1]), np.round(2d[2]180/np.pi,2), np.round(d[3],1) ]) reflection_table_html.value = array_to_html_table(tpeaks, header) def array_to_html_table(numpy_array, header): rows = "" for i in range(len(numpy_array)): rows += "<tr>" + functools.reduce(lambda x,y: x+y, map(lambda x: "<td>" + str(x) + "</td>", numpy_array[i]) ) + "</tr>" return "<table>" + header + rows + "</table>" reflection_table_html = HTML( value=f"dpeaks") reflection_table = HBox(layout=Layout(height='250px', overflow_y='auto')) reflection_table.children += (reflection_table_html,) fig_ax = plt.subplots(1, 2, figsize=(9,4.2), tight_layout=True) ex12_wp = WidgetPlot(plot_diffraction, WidgetParbox(a11 = (1., -4, 4, 0.1, r'$a_{11} / Å$'), a12 = (0., -4, 4, 0.1, r'$a_{12} / Å$'), a21 = (0., -4, 4, 0.1, r'$a_{21} / Å$'), a22 = (1.5, -4, 4, 0.1, r'$a_{22} / Å$'), phi = (0., 0., 2np.pi, 0.1, r'$\phi$'), s1 = (0.25, 0.01, 0.99, 0.01, r'$s_1$'), s2 = (0.75, 0.01, 0.99, 0.01, r'$s_2$'), f1 = (10., 1., 40., 1, r'$f_{1}$'), f2 = (30., 0, 40., 1, r'$f_{2}$'), wavelength = (0.1, 1.0, 2, 0.05, r'$\lambda$') ), fig_ax=fig_ax) ex12_wcc = WidgetCodeCheck(ex12_wci, ref_values = {}, demo = (ex12_wp, reflection_table)) display(ex12_wcc) ###Output _____no_output_____ ###Markdown 12a* Set $f_2$ to zero (so that effectively this becomes a lattice with a single atom) and set the unit cell to be a $1\times 1.2$ rectangle. Set the wavelength to 1. Observe how the position and intensity of the peaks change when you change the dimensions of the lattice. What happens if you set the off-diagonal term $a_{12}$ to be (slightly) different from zero? What happens if you set the two lattice vectors to be orthogonal and equal in length? ###Code ex12a_txt = Textarea("Write the answer here", layout=Layout(width="100%")) data_dump.register_field("ex12a-answer", ex12a_txt, "value") display(ex12a_txt) ###Output _____no_output_____ ###Markdown 12b Set $f_1=30$, $f_2=10$, and set the unit cell to be a $1\times 1.2$ rectangle. Set the fractional coordinates of the second atom to be $(0.5,0.5)$. Set the wavelength to 1. Observe how the position and intensity of the peaks change when you change the form factor of the second atom from $0$ to be equal to $f_2$. Do the peak positions change? How do you explain the change in intensity?What happens if (keeping the form factors equal) you change one of the fractional coordinates to be different from $0.5$? And if you change both coordinates? How do you explain this observation? ###Code ex12b_txt = Textarea("Write the answer here", layout=Layout(width="100%")) data_dump.register_field("ex12b-answer", ex12b_txt, "value") display(ex12b_txt) ###Output _____no_output_____ ###Markdown 12c Set $f_1=30$, $f_2=10$, and set the unit cell to be a $1\times 1$ square. Set the fractional coordinates of the second atom to be $(0.5,0.5)$. Set the wavelength to 1. Observe how the position and intensity of the peaks change when you change the form factor of the second atom from $0$ to be $f_2=f_1=30$. How many peaks are left? How do you explain this?What lattice parameters should you use to obtain the exact same diffraction pattern with $f_2=0$? ###Code ex12c_txt = Textarea("Write the answer here", layout=Layout(width="100%")) data_dump.register_field("ex12c-answer", ex12c_txt, "value") display(ex12c_txt) ###Output _____no_output_____
venv/3Keras/keras.ipynb	###Markdown here we are creating 500 points and then we are scattering in the plane such that the above area contain 500 points distrubuted with a center deviation of 2. similarly in the bottom region 500 points randomly distributed at a sd of 2.We have transposed the points because by this only we can print all the points in x and y Then we are stacking the two variables and then plotting it. ###Code n_pts = 500 np.random.seed(0) Xa = np.array([np.random.normal(13,2, n_pts), np.random.normal(12,2, n_pts)]).T Xb = np.array([np.random.normal(8,2, n_pts), np.random.normal(6,2, n_pts)]).T X = np.vstack((Xa,Xb)) Y = np.matrix(np.append(np.zeros(n_pts), np.ones(n_pts))).T plt.scatter(X[:n_pts,0], X[:n_pts,1]) plt.scatter(X[n_pts:, 0], X[n_pts:,1]) #IMPLEMENTING AND CREATING THE NEURAL NETWORK model = Sequential() model.add(Dense(units =1, input_shape=(2,), activation ='sigmoid' ))#by this we are adding the layers in neural network """units = 1 means that single output input_shape means how many input layers we wanna take activation function is what type of function is required""" adam = Adam(lr = 0.1) """adam is a optimizer which is just mean adaptive learning method algorithm combination of two extensions a stochastic gradient descent read about in details hERE WE SET ATOM IS EQUAL TO AN INSTANCE OF ATOM WHICH WILL MINIMIZE THE ERROR WITH A LEARNING RATE LR """ model.compile(adam, loss='binary_crossentropy', metrics=['accuracy']) """ here we need to find what type of optimizer we're going to use what kind of error function and tend to == ADAM loss == it calculate the cross entropy value for a binary classification problems for miltiple we use multiple_crossentropy metrices == metrice """ h = model.fit(x=X,y=Y, verbose=1, batch_size=50, epochs =500, shuffle='true') """fit function == that we use to start treating our perceptron to start trainig and model that perfectly classify our data """ ###Output Epoch 1/500 450/1000 [============>.................] - ETA: 1:10 - loss: 1.8885 - acc: 0.5511 ###Markdown the sequential class is a linear stack of layers in Dense every network is connected to the preciddingg layer which is the case for the fully connected neural network. ###Code plt.plot(h.history['acc']) plt.title('accuracy') plt.xlabel('epoch') plt.legend(['accuracy']) """X refers to the data that we created earlias 500 above nd below y refers to the matrix whuch contained datasets oone for the botton and above the last model refers to the sequential model that refers to the sequential model that contain all the data that we trained earlier containing all our neural network data """ def plot_decision_boundary(X, y, model): x_span = np.linspace(min(X[:,0])-1, max(X[:,0]) + 1) y_span = np.linespace(min(X[:,1])-1, max(X[:,1] + 1)) xx, yy = np.meshgrid(x_span, y_span) xx_,yy_= xx.ravel(), yy.ravel() grid = np.c_[xx_, yy_] pred_func = model.predict(grid) z = pred_func.reshape(xx.shape) plt.contourf(xx, yy, z) plot_decision_boundary(X, y, model) plt.scatter(X[:n_pts,0], X[:n_pts,1]) plt.scatter(X[n_pts:,0], X[n_pts: ,1]) ###Output _____no_output_____
Untitled10.ipynb	###Markdown ###Code import pandas as pd ###Output _____no_output_____ ###Markdown Considering a dataframe with 12 rows to Just Understanding the case ###Code data = {'AGE': ['', '','','','','','','','','','',''], 'SEX': ['', '','','','','','','','','','',''], 'GI illness': ['Y', 'Y','N','N','Y','Y','N','Y','Y','Y','U','Y'], 'INCUBATION': ['', '','','','','','','','','','',''], 'LENGTH OF': ['', '','','','','','','','','','',''], 'SALAD': ['Y', 'Y','N','Y','Y','Y','Y','Y','N','Y','N','Y'], 'RIGATONI': ['Y', 'Y','Y','Y','Y','N','N','N','Y','Y','U','Y'], 'ROAST BEEF': ['Y','Y','N','Y','Y','Y','Y','N','Y','Y','Y','Y'], 'CHICKEN': ['Y','Y','Y','Y','Y','Y','N','N','Y','Y','U','Y'], 'POTATOES': ['Y','Y','N','Y','Y','Y','Y','Y','N','Y','U','Y'], 'GREEN BEANS': ['Y', 'Y','Y','Y','Y','Y','Y','Y','Y','Y','U','Y'], 'CAKE': ['Y','Y','Y','Y','Y','Y','Y','Y','Y','Y','U','Y'], 'COFFEE': ['Y', 'N','Y','Y','Y','Y','Y','N','Y','Y','U','Y'], 'WATER': ['Y', 'N','N','N','N','Y','N','N','N','Y','U','Y'], 'SODA': ['Y', 'Y','N','N','N','N','N','Y','Y','Y','U','Y'] } df = pd.DataFrame (data, columns = ['AGE','SEX','GI illness','INCUBATION', 'LENGTH OF','SALAD','RIGATONI','ROAST BEEF','CHICKEN','POTATOES', 'GREEN BEANS','CAKE','COFFEE','WATER','SODA']) df.head(13) # we dont need AGE, SEX,INCUBATION, LENGTH OF so we select only required columns from dataframe df_res = pd.DataFrame(df, columns = ['GI illness','SALAD','RIGATONI','ROAST BEEF','CHICKEN','POTATOES', 'GREEN BEANS','CAKE','COFFEE','WATER','SODA']) df_res.head() #We are to find out the guests who consumed each food and drinks item at the picnic df_eachfood=df_res.loc[(df['SALAD']=='Y') & (df['RIGATONI']=='Y') & (df['ROAST BEEF']=='Y') & (df['CHICKEN']=='Y') & (df['POTATOES']=='Y')& (df['GREEN BEANS']=='Y')& (df['CAKE']=='Y') & (df['COFFEE']=='Y') & (df['WATER']=='Y') & (df['SODA']=='Y')] df_eachfood.head() number_of_ill_each = df_eachfood['GI illness'][df_eachfood['GI illness']=='Y'].count() total_numbereach= df_eachfood.shape[0] print(number_of_ill_each) print(total_numbereach) Attack_Rate_EachFood= number_of_ill_each/total_numbereach print(Attack_Rate_EachFood) #We are to find out the guests who didn't consumed each food and drinks item at the picnic df_noteach=df_res.drop(df_eachfood.index,axis=0) df_noteach #This dataframe contains the guests who didn't consumed each food and drinks item at the picnic number_of_ill_noteach = df_noteach['GI illness'][df_noteach['GI illness']=='Y'].count() total_numbernoteach= df_noteach.shape[0] Attack_Rate_NotEachFood= number_of_ill_noteach/total_numbernoteach print(Attack_Rate_NotEachFood) if ###Output _____no_output_____ ###Markdown ###Code ### install necessary packages if in colab def run_subprocess_command(cmd): process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE) for line in process.stdout: print(line.decode().strip()) import sys, subprocess IN_COLAB = "google.colab" in sys.modules colab_requirements = ["pip install tf-nightly-gpu-2.0-preview==2.0.0.dev20190513"] if IN_COLAB: for i in colab_requirements: run_subprocess_command(i) import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from tqdm.autonotebook import tqdm %matplotlib inline from IPython import display import pandas as pd print(tf.__version__) TRAIN_BUF=60000 BATCH_SIZE=512 TEST_BUF=10000 DIMS = (28,28,1) N_TRAIN_BATCHES =int(TRAIN_BUF/BATCH_SIZE) N_TEST_BATCHES = int(TEST_BUF/BATCH_SIZE) # load dataset (train_images, _), (test_images, _) = tf.keras.datasets.fashion_mnist.load_data() # split dataset train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype( "float32" ) / 255.0 test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype("float32") / 255.0 # batch datasets train_dataset = ( tf.data.Dataset.from_tensor_slices(train_images) .shuffle(TRAIN_BUF) .batch(BATCH_SIZE) ) test_dataset = ( tf.data.Dataset.from_tensor_slices(test_images) .shuffle(TEST_BUF) .batch(BATCH_SIZE) ) class GAN(tf.keras.Model): """ a basic GAN class Extends: tf.keras.Model """ def __init__(self, *kwargs): super(GAN, self).__init__() self.__dict__.update(kwargs) self.gen = tf.keras.Sequential(self.gen) self.disc = tf.keras.Sequential(self.disc) def generate(self, z): return self.gen(z) def discriminate(self, x): return self.disc(x) def compute_loss(self, x): """ passes through the network and computes loss """ # generating noise from a uniform distribution z_samp = tf.random.normal([x.shape[0], 1, 1, self.n_Z]) # run noise through generator x_gen = self.generate(z_samp) # discriminate x and x_gen logits_x = self.discriminate(x) logits_x_gen = self.discriminate(x_gen) ### losses # losses of real with label "1" disc_real_loss = gan_loss(logits=logits_x, is_real=True) # losses of fake with label "0" disc_fake_loss = gan_loss(logits=logits_x_gen, is_real=False) disc_loss = disc_fake_loss + disc_real_loss # losses of fake with label "1" gen_loss = gan_loss(logits=logits_x_gen, is_real=True) return disc_loss, gen_loss def compute_gradients(self, x): """ passes through the network and computes loss """ ### pass through network with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape: disc_loss, gen_loss = self.compute_loss(x) # compute gradients gen_gradients = gen_tape.gradient(gen_loss, self.gen.trainable_variables) disc_gradients = disc_tape.gradient(disc_loss, self.disc.trainable_variables) return gen_gradients, disc_gradients def apply_gradients(self, gen_gradients, disc_gradients): self.gen_optimizer.apply_gradients( zip(gen_gradients, self.gen.trainable_variables) ) self.disc_optimizer.apply_gradients( zip(disc_gradients, self.disc.trainable_variables) ) @tf.function def train(self, train_x): gen_gradients, disc_gradients = self.compute_gradients(train_x) self.apply_gradients(gen_gradients, disc_gradients) def gan_loss(logits, is_real=True): """Computes standard gan loss between logits and labels """ if is_real: labels = tf.ones_like(logits) else: labels = tf.zeros_like(logits) return tf.compat.v1.losses.sigmoid_cross_entropy( multi_class_labels=labels, logits=logits ) N_Z = 64 generator = [ tf.keras.layers.Dense(units=7 7 * 64, activation="relu"), tf.keras.layers.Reshape(target_shape=(7, 7, 64)), tf.keras.layers.Conv2DTranspose( filters=64, kernel_size=3, strides=(2, 2), padding="SAME", activation="relu" ), tf.keras.layers.Conv2DTranspose( filters=32, kernel_size=3, strides=(2, 2), padding="SAME", activation="relu" ), tf.keras.layers.Conv2DTranspose( filters=1, kernel_size=3, strides=(1, 1), padding="SAME", activation="sigmoid" ), ] discriminator = [ tf.keras.layers.InputLayer(input_shape=DIMS), tf.keras.layers.Conv2D( filters=32, kernel_size=3, strides=(2, 2), activation="relu" ), tf.keras.layers.Conv2D( filters=64, kernel_size=3, strides=(2, 2), activation="relu" ), tf.keras.layers.Flatten(), tf.keras.layers.Dense(units=1, activation=None), ] # optimizers gen_optimizer = tf.keras.optimizers.Adam(0.001, beta_1=0.5) disc_optimizer = tf.keras.optimizers.RMSprop(0.005)# train the model # model model = GAN( gen = generator, disc = discriminator, gen_optimizer = gen_optimizer, disc_optimizer = disc_optimizer, n_Z = N_Z ) # exampled data for plotting results def plot_reconstruction(model, nex=8, zm=2): samples = model.generate(tf.random.normal(shape=(BATCH_SIZE, N_Z))) fig, axs = plt.subplots(ncols=nex, nrows=1, figsize=(zm * nex, zm)) for axi in range(nex): axs[axi].matshow( samples.numpy()[axi].squeeze(), cmap=plt.cm.Greys, vmin=0, vmax=1 ) axs[axi].axis('off') plt.show() # a pandas dataframe to save the loss information to losses = pd.DataFrame(columns = ['disc_loss', 'gen_loss']) n_epochs = 50 for epoch in range(n_epochs): # train for batch, train_x in tqdm( zip(range(N_TRAIN_BATCHES), train_dataset), total=N_TRAIN_BATCHES ): model.train(train_x) # test on holdout loss = [] for batch, test_x in tqdm( zip(range(N_TEST_BATCHES), test_dataset), total=N_TEST_BATCHES ): loss.append(model.compute_loss(train_x)) losses.loc[len(losses)] = np.mean(loss, axis=0) # plot results display.clear_output() print( "Epoch: {} \| disc_loss: {} \| gen_loss: {}".format( epoch, losses.disc_loss.values[-1], losses.gen_loss.values[-1] ) ) plot_reconstruction(model) ###Output _____no_output_____ ###Markdown ###Code from google.colab import files uploaded = files.upload() import io import pandas as pd df = pd.read_csv(io.StringIO(uploaded['중앙선_죽령터널(노면온도).csv'].decode('cp949'))) df.head() def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): n_vars = 1 if type(data) is list else data.shape[1] df = DataFrame(data) cols = list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) # put it all together agg = concat(cols, axis=1) # drop rows with NaN values if dropnan: agg.dropna(inplace=True) return agg.values def walk_forward_validation(data, n_test): predictions = list() # split dataset train, test = train_test_split(data, n_test) # seed history with training dataset history = [x for x in train] # step over each time-step in the test set for i in range(len(test)): # split test row into input and output columns testX, testy = test[i, :-1], test[i, -1] # fit model on history and make a prediction yhat = random_forest_forecast(history, testX) # store forecast in list of predictions predictions.append(yhat) # add actual observation to history for the next loop history.append(test[i]) # summarize progress print('>expected=%.1f, predicted=%.1f' % (testy, yhat)) # estimate prediction error error = mean_absolute_error(test[:, -1], predictions) return error, test[:, 1], predictions def random_forest_forecast(train, testX): # transform list into array train = asarray(train) # split into input and output columns trainX, trainy = train[:, :-1], train[:, -1] # fit model model = RandomForestRegressor(n_estimators=1000) model.fit(trainX, trainy) # make a one-step prediction yhat = model.predict([testX]) return yhat[0] df.head() df.수집일시 index = df.index columns = df.columns data = df.values index columns data type(index) type(columns) type(data) issubclass(pd.RangeIndex, pd.Index) index.values columns.values df.dtypes df.노선 type(df['노선']) director = df['노선'] director.name director.to_frame() s_attr_methonds = set(dir(pd.Series)) len(s_attr_methonds) df_attr_methods = set(dir(pd.DataFrame)) len(df_attr_methods) len(s_attr_methonds & df_attr_methods ) ###Output _____no_output_____ ###Markdown ###Code #Import scikit-learn dataset library from sklearn import datasets #load datasets wine=datasets.load_wine() #print the names of the 13 features print("Features: ", wine.feature_names) #print the label type of wine(class_0, class_1, class_2) print("Labels: ", wine.target_names) #Import train_test_split from sklearn.model_selection import train_test_split X = wine.data y = wine.target X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.3, random_state=109) # 70% training and 30% test #Import Gaussian Naive Bayes model from sklearn.naive_bayes import GaussianNB #Create a Gaussian Classifier gnb=GaussianNB() #Train the model using the training sets gnb.fit(X_train, y_train) #Predict the response for test dataset y_pred = gnb.predict(X_test) #Import scikit-learn metrics module for accuracy calculation from sklearn import metrics #Model Accuracy, how often is the classifier correct? print("Accuracy:", metrics.accuracy_score(y_test, y_pred)) ###Output Accuracy: 0.9074074074074074 ###Markdown ###Code !pip install python-docx !pip install beautifulsoup4 !pip install mammoth import mammoth from bs4 import BeautifulSoup with open("PwC Brief.docx", "rb") as docx_file: result = mammoth.convert_to_html(docx_file,ignore_empty_paragraphs=False) html = result.value soup = BeautifulSoup(html, 'lxml') strong html from bs4 import BeautifulSoup soup = BeautifulSoup(html, 'lxml') strong import spacy, re from spacy.matcher import Matcher from spacy.tokenizer import Tokenizer from spacy import displacy def custom_tokenizer(nlp, infix_reg): """ Function to return a customized tokenizer based on the infix regex PARAMETERS ---------- nlp : Language A Spacy language object with loaded model infix_reg : relgular expression object The infix regular expression object based on which the tokenization is to be carried out. RETURNS ------- Tokenizer : Tokenizer object The Spacy tokenizer obtained based on the infix regex. """ return Tokenizer(nlp.vocab, infix_finditer = infix_reg.finditer) #!python -m spacy download de_core_news_md #import de_core_news_md import spacy from spacy import displacy from spacy.scorer import Scorer from spacy.pipeline import EntityRuler import re spacy.prefer_gpu() # or spacy.require_gpu() nlp = spacy.blank('de') infix_re = re.compile(r'''[-/,.]''') nlp.tokenizer = custom_tokenizer(nlp, infix_re) #nlp = de_core_news_md.load() #test doc ruler = EntityRuler(nlp) patternd = [{'IS_ALPHA': True},{"IS_SPACE": False, "OP": "?"},{"LOWER": 'platz'.lower()}] patterns = [{"label": "DATE", "pattern": [{'LIKE_NUM': True},{'ORTH': '/' ,'OP': '?'}, {'ORTH': '.', 'OP': '?'},{'ORTH': '-', 'OP': '?'}, {'LIKE_NUM': True}, {'ORTH': '/' ,'OP': '?'}, {'ORTH': '.' ,'OP': '?'},{'ORTH': '-' ,'OP': '?'},{'LIKE_NUM': True}]}, {"label":"LOC", "pattern":[{"LOWER":{"REGEX": "straße([a-zA-Z])"}}]}, {"label":"LOC", "pattern":[{"LOWER":{"REGEX": "platz([a-zA-Z])"}}]}, {"label":"LOC", "pattern":[{"LOWER":{"REGEX": "gasse([a-zA-Z])"}}]}, {"label":"LOC", "pattern":[{"LOWER":{"REGEX": "stadt([a-zA-Z])"}}]}, {"label":"LOC", "pattern":[{"LOWER":{"REGEX": "bundesland([a-zA-Z])"}}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "mag([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "dipl([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "dr([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "ing([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "habil([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "frau([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, {"label":"PER", "pattern":[{"LOWER":{"REGEX": "herr([a-zA-Z/.])"}},{'ORTH': '.', 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'},{"TEXT":{"REGEX": "[A-Z]"}, 'OP': '?'}]}, ] ruler.add_patterns(patterns) nlp.add_pipe(ruler) !pip install docx2txt html_in.replace("</br></br></br>","").replace("</br></br>","</br>") import docx2txt # extract text text = docx2txt.process("PwC Brief.docx") doc = nlp(text) html_in = displacy.render(doc,style='ent') import IPython IPython.display.HTML(html_in.replace("</br></br>","</br>").replace("</br></br>","</br>")) # Write HTML String to file.html #with open("PwC Brief.html", "w") as file: #file.write(html_in) text html_in import docx import mammoth doc_name = 'PwC Brief.docx' doc = docx.Document(doc_name) indexes =[] right_alig_ind =[] bold_ind =[] html_in = '' for para_id, para in enumerate(doc.paragraphs): # make a sentence #if len(para.text)>0: indexes.append(para_id) if para.alignment: print(para_id, para, para.alignment) right_alig_ind.append(para) html_in += para.text #doc = nlp(para.text) #html_in += displacy.render(doc,style='ent') html_in += '\n' else: #for rn in para.runs: #doc = nlp(para.text) html_in += para.text #html_in += displacy.render(doc,style='ent') html_in += '\n' print(para_id, para.style.name) if para.style.name == "UNamec": bold_ind.append(para) #for para_id, para in enumerate(doc.sections): # make a sentence #if len(para.text)>0: # print(para_id, para) doc = nlp(html_in) html_out = displacy.render(doc,style='ent',page= True) #html_out = html_out.replace("</br></br>","</br>") #result = HTML_WRAPPER.format(html_out) bold_ind[5].text import IPython hto = html_out.replace("</br></br>","</br>").replace('</br>\t','</br>                          ') IPython.display.HTML(hto) import mammoth from bs4 import BeautifulSoup with open("PwC Brief.docx", "rb") as docx_file: result = mammoth.convert_to_html(docx_file,ignore_empty_paragraphs=False) html = result.value soup = BeautifulSoup(html, 'lxml') list_of_bold = soup.findAll('strong') for pr in right_alig_ind: hto = hto.replace(pr.text, '<div style="text-align: right">{}</div>'.format(pr.text),1) for pr in list_of_bold: hto = hto.replace(pr.text, '<strong>{}</strong>'.format(pr.text), 1) import xml.etree.ElementTree as ET import zipfile as zf import re z = zf.ZipFile("PwC Brief2.docx") f = z.open("word/document.xml") # a file-like object nsmap = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'} def qn(tag): """ Stands for 'qualified name', a utility function to turn a namespace prefixed tag name into a Clark-notation qualified tag name for lxml. For example, ``qn('p:cSld')`` returns ``'{http://schemas.../main}cSld'``. Source: https://github.com/python-openxml/python-docx/ """ prefix, tagroot = tag.split(':') uri = nsmap[prefix] return '{{{}}}{}'.format(uri, tagroot) def xml2text(xml): """ A string representing the textual content of this run, with content child elements like ``<w:tab/>`` translated to their Python equivalent. Adapted from: https://github.com/python-openxml/python-docx/ """ check_next = False text = u'' root = ET.fromstring(xml) for child in root.iter(): if child.tag == qn('w:b'): check_next = True print('check') #for it in child.items(): print(''.join(child.iter().itertext())) if child.tag == qn('w:t'): #if check_next: #print(child.text) #check_next = False t_text = child.text text += t_text if t_text is not None else '' elif child.tag == qn('w:tab'): text += '\t' elif child.tag in (qn('w:br'), qn('w:cr')): text += '\n' elif child.tag == qn("w:p"): text += '\n\n' return text header = u'' # unzip the docx in memory zipf = zf.ZipFile("PwC Brief5.docx") filelist = zipf.namelist() # get header text # there can be 3 header files in the zip header_xmls = 'word/header[0-9].xml' for fname in filelist: if re.match(header_xmls, fname): header += xml2text(zipf.read(fname)) # get main text text = u'' doc_xml = 'word/document.xml' text += xml2text(zipf.read(doc_xml)) text #header qn('w:jc') IPython.display.HTML(header.replace('\n\n\n\n\n\n\n\n\n\n','').replace('\n','</br>') + '</br>' + hto+ '</br>') !pip install docx2python import mammoth from docx import Document from docx2python import docx2python from docx2python.iterators import iter_paragraphs # one header one footer doc = docx2python("PwC Brief.docx") header = ''.join(iter_paragraphs(doc.header)) footer = ''.join(iter_paragraphs(doc.footer)) print("header: ", header) print("footer: ", footer) header.encode('utf-8') header for header in header: file.write(header.encode('utf-8')) file.write('\n'.encode('utf-8')) file.write(text) """ add footers """ for footer in footer3: file.write(footer.encode('utf-8')) file.write('\n'.encode('utf-8')) from bs4 import BeautifulSoup #html = document.value soup = BeautifulSoup(hto,"html") import IPython IPython.display.HTML(str(soup)) str(soup) !pip install docx2python from docx2python import docx2python # one header one footer doc3 = docx2python("PwC Brief.docx",html=True) doc3.header docx2python.iterators.get_html_map(doc3.document) import IPython IPython.display.HTML(soup.prettify()) import IPython IPython.display.HTML(html_out.replace("</br></br>","</br>")) print(result) # Write HTML String to file.html with open("PwC Brief.html", "w") as file: file.write(result) from bs4 import BeautifulSoup #html = document.value soup = BeautifulSoup(html_out, 'lxml') print(html_out) import IPython IPython.display.HTML(text) def label_par(par,html_out): soup_S = BeautifulSoup(html_out) p1 = soup_S.find('body') s = p1.find_next('div') labels = s.find_all('mark') newcont = str(par) print(labels) if len(labels) >0: for lb in labels: for c in par.contents: if lb.contents[0].strip() in c: #c.(lb.contents[0].strip(),lb) print(lb.contents[0].strip()) newcont = newcont.replace(lb.contents[0].strip(), str(lb)) newconthtml = BeautifulSoup(newcont).find('body').contents[0] par.string = '' par.append(newconthtml) return par #options = get_entity_options() for par_i, par in enumerate(soup.findAll('p')): print(par_i,par.getText()) doc = nlp(par.getText()) html_out = displacy.render(doc, style='ent') par = label_par(par,html_out) #print(html_out) #par.replace_with() #IPython.display.HTML(html_out) #soup_S = BeautifulSoup(html_out) #for match in soup_S.findAll('div'): #match.replaceWithChildren() ###Output _____no_output_____ ###Markdown ###Code import torch ###Output _____no_output_____ ###Markdown Table of Contents ###Code import geopandas ###Output _____no_output_____ ###Markdown Data Exploration ###Code import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB import sklearn.metrics as metrics from sklearn.linear_model import PassiveAggressiveClassifier from sklearn.datasets import make_classification import itertools from sklearn.feature_extraction.text import HashingVectorizer from scipy.sparse import csr_matrix # Import `fake_or_real_news.csv` df = pd.read_csv("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/fake_or_real_news.csv") # Inspect shape of `df` df.shape # Print first lines of `df` df.head() # Set index df = df.set_index("Unnamed: 0") # Print first lines of `df` df.head() ###Output _____no_output_____ ###Markdown Extracting the training data ###Code # Set `y` y = df.label # Drop the `label` column df.drop("label", axis=1) # Make training and test sets X_train, X_test, y_train, y_test = train_test_split(df['text'], y, test_size=0.33, random_state=53) ###Output _____no_output_____ ###Markdown Building Vectorizer Classifiers* bold textNow that you have your training and testing data, you can build your classifiers. To get a good idea if the words and tokens in the articles had a significant impact on whether the news was fake or real, you begin by using CountVectorizer and TfidfVectorizer.You’ll see the example has a max threshhold set at .7 for the TF-IDF vectorizer tfidf_vectorizer using the max_df argument. This removes words which appear in more than 70% of the articles. Also, the built-in stop_words parameter will remove English stop words from the data before making vectors.There are many more parameters available and you can read all about them in the scikit-learn documentation for TfidfVectorizer and CountVectorizer. ###Code # Initialize the `count_vectorizer` count_vectorizer = CountVectorizer(stop_words='english') # Fit and transform the training data count_train = count_vectorizer.fit_transform(X_train) # Transform the test set count_test = count_vectorizer.transform(X_test) # Initialize the `tfidf_vectorizer` tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7) # Fit and transform the training data tfidf_train = tfidf_vectorizer.fit_transform(X_train) # Transform the test set tfidf_test = tfidf_vectorizer.transform(X_test) ###Output _____no_output_____ ###Markdown Now that you have vectors, you can then take a look at the vector features, stored in count_vectorizer and tfidf_vectorizer.Are there any noticable issues? (Yes!)There are clearly comments, measurements or other nonsensical words as well as multilingual articles in the dataset that you have been using. Normally, you would want to spend more time preprocessing this and removing noise, but as this tutorial just showcases a small proof of concept, you will see if the model can overcome the noise and properly classify despite these issues. ###Code # Get the feature names of `tfidf_vectorizer` print(tfidf_vectorizer.get_feature_names()[-10:]) # Get the feature names of `count_vectorizer` print(count_vectorizer.get_feature_names()[:10]) ###Output ['حلب', 'عربي', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade'] ['00', '000', '0000', '00000031', '000035', '00006', '0001', '0001pt', '000ft', '000km'] ###Markdown Intermezzo: Count versus TF-IDF FeaturesI was curious if my count and TF-IDF vectorizers had extracted different tokens. To take a look and compare features, you can extract the vector information back into a DataFrame to use easy Python comparisons.As you can see by running the cells below, both vectorizers extracted the same tokens, but obviously have different weights. Likely, changing the max_df and min_df of the TF-IDF vectorizer could alter the result and lead to different features in each. ###Code count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names()) tfidf_df = pd.DataFrame(tfidf_train.A, columns=tfidf_vectorizer.get_feature_names()) difference = set(count_df.columns) - set(tfidf_df.columns) difference print(count_df.equals(tfidf_df)) count_df.head() tfidf_df.head() ###Output _____no_output_____ ###Markdown Comparing ModelsNow it's time to train and test your models.Here, you'll begin with an NLP favorite, MultinomialNB. You can use this to compare TF-IDF versus bag-of-words. My intuition was that bag-of-words (aka CountVectorizer) would perform better with this model. (For more reading on multinomial distribution and why it works best with integers, check out this fairly succinct explanation from a UPenn statistics course).I personally find Confusion Matrices easier to compare and read, so I used the scikit-learn documentation to build some easily-readable confusion matrices (thanks open source!). A confusion matrix shows the proper labels on the main diagonal (top left to bottom right). The other cells show the incorrect labels, often referred to as false positives or false negatives. Depending on your problem, one of these might be more significant. For example, for the fake news problem, is it more important that we don't label real news articles as fake news? If so, we might want to eventually weight our accuracy score to better reflect this concern. ###Code def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ See full source and example: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') clf = MultinomialNB() clf.fit(tfidf_train, y_train) pred = clf.predict(tfidf_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL']) plot_confusion_matrix(cm, classes=['FAKE', 'REAL']) clf = MultinomialNB() clf.fit(count_train, y_train) pred = clf.predict(count_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL']) plot_confusion_matrix(cm, classes=['FAKE', 'REAL']) linear_clf = PassiveAggressiveClassifier() linear_clf.fit(tfidf_train, y_train) pred = linear_clf.predict(tfidf_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL']) plot_confusion_matrix(cm, classes=['FAKE', 'REAL']) clf = MultinomialNB(alpha=0.1) last_score = 0 for alpha in np.arange(0,1,.1): nb_classifier = MultinomialNB(alpha=alpha) nb_classifier.fit(tfidf_train, y_train) pred = nb_classifier.predict(tfidf_test) score = metrics.accuracy_score(y_test, pred) if score > last_score: clf = nb_classifier print("Alpha: {:.2f} Score: {:.5f}".format(alpha, score)) def most_informative_feature_for_binary_classification(vectorizer, classifier, n=100): """ See: https://stackoverflow.com/a/26980472 Identify most important features if given a vectorizer and binary classifier. Set n to the number of weighted features you would like to show. (Note: current implementation merely prints and does not return top classes.) """ class_labels = classifier.classes_ feature_names = vectorizer.get_feature_names() topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n] topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:] for coef, feat in topn_class1: print(class_labels[0], coef, feat) print() for coef, feat in reversed(topn_class2): print(class_labels[1], coef, feat) most_informative_feature_for_binary_classification(tfidf_vectorizer, linear_clf, n=30) feature_names = tfidf_vectorizer.get_feature_names() ### Most real sorted(zip(clf.coef_[0], feature_names), reverse=True)[:20] ### Most fake sorted(zip(clf.coef_[0], feature_names))[:20] tokens_with_weights = sorted(list(zip(feature_names, clf.coef_[0]))) hash_vectorizer = HashingVectorizer(stop_words='english') hash_train = hash_vectorizer.fit_transform(X_train) hash_test = hash_vectorizer.transform(X_test) clf = PassiveAggressiveClassifier() clf.fit(hash_train, y_train) pred = clf.predict(hash_test) score = metrics.accuracy_score(y_test, pred) print("accuracy: %0.3f" % score) cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL']) plot_confusion_matrix(cm, classes=['FAKE', 'REAL']) ###Output _____no_output_____ ###Markdown ###Code class Usuario(): _nomeUsuario = "" def settNomeUsuario(self, nomeUsuario): self._nomeUsuario = nomeUsuario def getNomeUsuario(self): return self._nomeUsuario class Admin(Usuario): def escrevaNome(self): return "ADM" def digaOla(self): return "Ola ADM, "+ self.getNomeUsuario() admin1 = Admin() admin1.settNomeUsuario("Baltazar") print(admin1.digaOla()) ###Output Ola ADM, Baltazar
docker/65896179-get-exposed-port-from-within-docker-compose/DOCS.ipynb	###Markdown Question [[link](https://stackoverflow.com/questions/65896179/get-exposed-port-from-within-docker-compose65896179)] Get exposed port from within docker-compose Answer ###Code !cat dockerfile !cat docker-compose.yml ###Output version: "3.9" services: server: build: . ports: - "8086" environment: - PORT=8086 ###Markdown [!]No `HOST_PORT` has been provided in the `docker-compose.yml` file above. ###Code !docker-compose up ###Output Building with native build. Learn about native build in Compose here: https://docs.docker.com/go/compose-native-build/ Building server Sending build context to Docker daemon 38.4kB Step 1/10 : FROM python:3.8 ---> 4d53664a7025 Step 2/10 : RUN python -m pip install --upgrade pip ---> Using cache ---> 9a549e8b7b16 Step 3/10 : RUN pip install pipenv ---> Using cache ---> a6144725fda4 Step 4/10 : COPY Pipfile* /app/ ---> Using cache ---> dee26770a0c6 Step 5/10 : RUN cd /app && pipenv lock --keep-outdated --requirements > requirements.txt ---> Using cache ---> 6ec6eef050f8 Step 6/10 : RUN pip install -r /app/requirements.txt ---> Using cache ---> 91e654cb1802 Step 7/10 : COPY . /app/src ---> aa2a5a9da395 Step 8/10 : WORKDIR /app/src ---> Running in dab674cdaa8d Removing intermediate container dab674cdaa8d ---> 9ca4edcc70cf Step 9/10 : EXPOSE ${PORT} ---> Running in fe3270892dcf Removing intermediate container fe3270892dcf ---> 1ccbbad053cf Step 10/10 : CMD uwsgi --http :${PORT} --wsgi-file server.py --callable main ---> Running in a66c32531bdd Removing intermediate container a66c32531bdd ---> 6914fdab68cb Successfully built 6914fdab68cb Successfully tagged 65896179-get-exposed-port-from-within-docker-compose_server:latest [33mWARNING[0m: Image for service server was built because it did not already exist. To rebuild this image you must use `docker-compose build` or `docker-compose up --build`. Creating 65896179-get-exposed-port-from-within-docker-compose_server_1 ... [1BAttaching to 65896179-get-exposed-port-from-within-docker-compose_server_12mdone[0m [36mserver_1 \|[0m * Starting uWSGI 2.0.19.1 (64bit) on [Tue Jan 26 06:12:47 2021] * [36mserver_1 \|[0m compiled with version: 8.3.0 on 26 January 2021 06:08:33 [36mserver_1 \|[0m os: Linux-5.4.0-64-generic #72-Ubuntu SMP Fri Jan 15 10:27:54 UTC 2021 [36mserver_1 \|[0m nodename: de0120c61c1e [36mserver_1 \|[0m machine: x86_64 [36mserver_1 \|[0m clock source: unix [36mserver_1 \|[0m pcre jit disabled [36mserver_1 \|[0m detected number of CPU cores: 16 [36mserver_1 \|[0m current working directory: /app/src [36mserver_1 \|[0m detected binary path: /usr/local/bin/uwsgi [36mserver_1 \|[0m uWSGI running as root, you can use --uid/--gid/--chroot options [36mserver_1 \|[0m * WARNING: you are running uWSGI as root !!! (use the --uid flag) * [36mserver_1 \|[0m * WARNING: you are running uWSGI without its master process manager * [36mserver_1 \|[0m your processes number limit is 126513 [36mserver_1 \|[0m your memory page size is 4096 bytes [36mserver_1 \|[0m detected max file descriptor number: 1048576 [36mserver_1 \|[0m lock engine: pthread robust mutexes [36mserver_1 \|[0m thunder lock: disabled (you can enable it with --thunder-lock) [36mserver_1 \|[0m uWSGI http bound on :8086 fd 4 [36mserver_1 \|[0m spawned uWSGI http 1 (pid: 9) [36mserver_1 \|[0m uwsgi socket 0 bound to TCP address 127.0.0.1:33619 (port auto-assigned) fd 3 [36mserver_1 \|[0m uWSGI running as root, you can use --uid/--gid/--chroot options [36mserver_1 \|[0m * WARNING: you are running uWSGI as root !!! (use the --uid flag) * [36mserver_1 \|[0m Python version: 3.8.7 (default, Jan 12 2021, 17:06:28) [GCC 8.3.0] [36mserver_1 \|[0m * Python threads support is disabled. You can enable it with --enable-threads * [36mserver_1 \|[0m Python main interpreter initialized at 0x55a43c32d5c0 [36mserver_1 \|[0m uWSGI running as root, you can use --uid/--gid/--chroot options [36mserver_1 \|[0m * WARNING: you are running uWSGI as root !!! (use the --uid flag) * [36mserver_1 \|[0m your server socket listen backlog is limited to 100 connections [36mserver_1 \|[0m your mercy for graceful operations on workers is 60 seconds [36mserver_1 \|[0m mapped 72920 bytes (71 KB) for 1 cores [36mserver_1 \|[0m * Operational MODE: single process * [36mserver_1 \|[0m WSGI app 0 (mountpoint='') ready in 0 seconds on interpreter 0x55a43c32d5c0 pid: 8 (default app) [36mserver_1 \|[0m uWSGI running as root, you can use --uid/--gid/--chroot options [36mserver_1 \|[0m * WARNING: you are running uWSGI as root !!! (use the --uid flag) * [36mserver_1 \|[0m * uWSGI is running in multiple interpreter mode * [36mserver_1 \|[0m spawned uWSGI worker 1 (and the only) (pid: 8, cores: 1) ^C Gracefully stopping... (press Ctrl+C again to force) Stopping 65896179-get-exposed-port-from-within-docker-compose_server_1 ... ###Markdown [!]As seen above, docker-compose can build the container just fine.But as shown below, the container is assigned a random `HOST_PORT`, in this case `49157`. ###Code !docker ps !cat docker-compose.yml ###Output version: "3.9" services: server: build: . ports: - "8086:8086" environment: - PORT=8086 ###Markdown [!]We can solve this by providing a `HOST_PORT` as described in https://docs.docker.com/compose/networking/ ###Code !docker-compose up !docker ps !curl localhost:8086 ###Output Hello World! ###Markdown [!]Alternatively, if the `--scale` flag is to be used and multiple ports have to be chosen one can use a range of ports as `HOST_PORT` such as `8000-8004:8` ###Code !docker-compose up --scale server=4 !docker ps !curl localhost:8000 !curl localhost:8001 !curl localhost:8002 !curl localhost:8003 ###Output Hello World!
diploma/Poissons on the original distrib.ipynb	###Markdown Outlier Factors for Device Profiling ###Code import pandas as pd import numpy as np %matplotlib inline time_epoch = 60 epochs_per_batch = 50 # hard coded nrows df_all = pd.read_csv('../../../diploma/multi-source-syber-security-events/flows.txt', header=None, nrows=500000) df_all.columns = ['time', 'duration', 'source computer', 'source port', 'destination computer', 'destination port', 'protocol', 'packet count', 'byte count'] df = df_all[df_all['time'] <= epochs_per_batch * time_epoch] df.index = df['time'] df.drop(columns=['time'],inplace=True) df.head() # get all the host in the buckets we are interested in hosts = np.array(list(set(df_all[df_all['time'] <= epochs_per_batch * time_epoch * 2]['source computer'].values))) def group_scale_data(df, size_of_bin_seconds=60, addZeros=True, hosts=None, verbose=0): """ :param size_of_bin_seconds: the time period of each bin, assumes the dataframe has a column names 'source computer' and a name 'byte count' :param addZeros: add values (0, 0) where no data has been received for this bucket :return: a dictionary containing for each host the features, the hosts """ if hosts is None: hosts = np.array(list(set(df['source computer'].values))) bins = np.arange(df.index.min(), df.index.max() + size_of_bin_seconds + 1, size_of_bin_seconds) groups = df[['byte count','source computer']].groupby([np.digitize(df.index, bins),'source computer']) data = groups.count() data.columns = ['number of flows'] data['mean(byte count)'] = groups.mean().values data_reset = data.reset_index() if verbose > 0: print('A total of', len(bins) - 1, 'time epochs have been encountered') len_hosts = len(hosts) intervals = int(len_hosts / 20) i = 0 if addZeros: for host in hosts: if verbose > 0 and i % intervals == 0: print('Done with', i, 'hosts out of', len_hosts) i += 1 for bin_i in range(1,len(bins)): if (bin_i, host) not in data.index: new_row = [bin_i, host, 0.0, 0.0] data_reset = data_reset.append(pd.DataFrame([new_row], columns=data_reset.columns), ignore_index=True ) groupped_data = pd.DataFrame(data_reset.values[:,2:], columns=['number of flows', 'mean(byte count)']) groupped_data['epoch'] = data_reset['level_0'] groupped_data['source computer'] = data_reset['source computer'] # set parameters for next acquisition of data parameters = {} parameters['size_of_bin_seconds'] = size_of_bin_seconds parameters['hosts'] = hosts groupped_data = groupped_data.sample(frac=1) groupped_data['mean(byte count)'] = groupped_data['mean(byte count)'].values.astype(int) groupped_data['number of flows'] = groupped_data['number of flows'].values.astype(int) return groupped_data.sort_values(by=['epoch']), hosts, parameters groupped_data, hosts, parameters = group_scale_data(df, size_of_bin_seconds=time_epoch, addZeros=True, verbose=1, hosts=hosts) groupped_data.head(5) print(np.max(groupped_data['number of flows'].values)) print(np.max(groupped_data['mean(byte count)'].values)) from emClustering import OnlineEM from plots import plot_points, plot_results, plot_category, plot_all_categories from kplusplus import KPlusPlus import numpy as np from sklearn.externals import joblib #joblib.dump(groupped_data,'groupped_data_60_50.pkl') groupped_data = joblib.load('groupped_data_60_50.pkl') print(np.sum(groupped_data['mean(byte count)'] == 0)) print(len(groupped_data['mean(byte count)'])) print(np.max(groupped_data['mean(byte count)'])) from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0,1000)) scaler.fit([[x] for x in groupped_data['mean(byte count)'].values]) groupped_data['mean(byte count)'] = scaler.transform([[x] for x in groupped_data['mean(byte count)'].values]).astype(int) np.sum(groupped_data['mean(byte count)'] == 0) from pylab import rcParams from matplotlib import pyplot as plt import numpy as np from collections import Counter import matplotlib.patches as mpatches colors = ['blue', 'red', 'green', 'yellow'] styles = ['-','--',':','-.'] rcParams['font.size'] = 16 def plot_points(data, em=None): rcParams['figure.figsize'] = 16, 9 data_hashable = [tuple(x) for x in data] total_points = len(data_hashable) values = np.vstack([list(x) for x in list(Counter(data_hashable).keys())]) counts = np.array(list(Counter(data_hashable).values())) for i in range(len(values)): plt.scatter(values[i][0], values[i][1], s=20, color='blue') if em: for i, lambda_i in enumerate(em.lambdas): plt.scatter(lambda_i[0], lambda_i[1], s=em.gammas[i]1000, linewidth=4, color='red', marker='x') blue_patch = mpatches.Patch(color='blue', label='Data points') red_patch = mpatches.Patch(color='red', label='Centers of Distribution') plt.legend(handles=[red_patch, blue_patch], fontsize=18) else: blue_patch = mpatches.Patch(color='blue', label='Data points') plt.legend(handles=[blue_patch], fontsize=18) plt.ylabel('average number of bytes') plt.xlabel('number of flows') plt.show() plot_points(groupped_data.values[:, :2]) test = groupped_data[groupped_data['number of flows'] > 1].copy() small_points = test[(test['mean(byte count)'] < 1000) & (test['number of flows'] < 30)].values[:, :2] plot_points(small_points) len(small_points) import scipy.stats.distributions from math import log import sys import numpy as np def poisson(x, l): return_value = 1 for x_i, l_i in zip(x, l): return_value = scipy.stats.distributions.poisson.pmf(x_i, l_i) if return_value == 0: return sys.float_info.epsilon return return_value class EM: def __init__(self, lambdas, gammas, x, convergence_error=0.0001, verbose=0): self.convergence_error = convergence_error self.lambdas = np.vstack(lambdas) self.gammas = np.array(gammas) self.m = len(gammas) self.x = x def _calculate_probabilities(self): for i, x_i in enumerate(self.x): self.probabilities[i] = np.sum(self.gammas * np.array([poisson(x_i, lambda_i) for lambda_i in self.lambdas])) def _calculate_likelihood(self): # naive implementation for likelihood calculation new_likelihood = 0 for x in self.x: total_x = np.sum(self.gammas * np.array([poisson(x, lambda_i) for lambda_i in self.lambdas])) new_likelihood = new_likelihood + log(total_x) return new_likelihood def _calculate_participation(self): f = np.zeros(shape=(len(self.x), len(self.lambdas))) for i, x_i in enumerate(self.x): participation = self.gammas * np.array([poisson(x_i, lambda_i) for lambda_i in self.lambdas]) total_x = np.sum(participation) if total_x == 0: participation = np.array([1/self.m] * self.m) total_x = 1 f[i] = participation / total_x return f def run(self): previous_likelihood = self._calculate_likelihood() while True: f = self._calculate_participation() temp_sum = f.sum(axis=0) self.gammas = temp_sum / len(self.x) temp = np.zeros(shape=(len(self.lambdas), len(self.lambdas[0]))) for i, x_i in enumerate(self.x): temp = temp + np.vstack([x_i * f_i for f_i in f[i]]) self.lambdas = np.vstack([temp[i] / temp_i for i, temp_i in enumerate(temp_sum)]) new_likelihood = self._calculate_likelihood() convergence = new_likelihood / previous_likelihood - 1 print('convergence', convergence) if - self.convergence_error < convergence < self.convergence_error: break previous_likelihood = new_likelihood em = EM([[0,0],[20, 500]], [0.6, 0.4] , small_points[:1000], convergence_error=0.1) em.run() print(em.gammas) print(em.lambdas) def real_poisson(x, l): return_value = 1 for x_i, l_i in zip(x, l): return_value = scipy.stats.distributions.poisson.logpmf(x_i, l_i) return return_value print(real_poisson([100, 200], [0, 0])) print(real_poisson([100, 200], [25, 2000])) print(real_poisson([100, 200], [75, 2000])) print(real_poisson([100, 200], [25, 2100000])) print(real_poisson([100, 200], [75, 2100000])) print(real_poisson([100, 2100000], [100, 2000000])) scipy.stats.distributions.poisson.pmf(100, 1000) mixtures = 3 kplusplus = KPlusPlus(mixtures, groupped_data.values[:, :2], stochastic=True, stochastic_n_samples=10000) kplusplus.init_centers(verbose=1) kplusplus.centers test = int(len(set(groupped_data['source computer'].values))) # random initialization onlineEM = OnlineEM([1/mixtures]mixtures, kplusplus.centers, test, n_clusters=8, verbose=1, update_power=0.5) plot_points(groupped_data.values[:, :2], onlineEM) data = groupped_data.values[:,[0,1,3]] onlineEM.fit(data) from pylab import rcParams from matplotlib import pyplot as plt import numpy as np from collections import Counter import matplotlib.patches as mpatches colors = ['blue', 'red', 'green', 'yellow'] styles = ['-','--',':','-.'] rcParams['font.size'] = 16 def plot_points(data, em=None): rcParams['figure.figsize'] = 16, 9 data_hashable = [tuple(x) for x in data] total_points = len(data_hashable) values = np.vstack([list(x) for x in list(Counter(data_hashable).keys())]) counts = np.array(list(Counter(data_hashable).values())) for i in range(len(values)): plt.scatter(values[i][0], values[i][1], s=20, color='blue') if em: for i, lambda_i in enumerate(em.lambdas): plt.scatter(lambda_i[0], lambda_i[1], s=em.gammas[i]*1000, linewidth=4, color='red', marker='x') blue_patch = mpatches.Patch(color='blue', label='Data points') red_patch = mpatches.Patch(color='red', label='Centers of Distribution') plt.legend(handles=[red_patch, blue_patch], fontsize=18) else: blue_patch = mpatches.Patch(color='blue', label='Data points') plt.legend(handles=[blue_patch], fontsize=18) plt.ylabel('average number of bytes') plt.xlabel('number of flows') plt.xlim([-5,35]) plt.ylim([-10000,1000000]) plt.show() plot_points(groupped_data.values[:, :2], onlineEM) print(onlineEM.lambdas) print(onlineEM.gammas) total = 0 for i in range(1): total += np.sum(groupped_data['number of flows'] == i) print(total) np.sum(groupped_data['number of flows'] == 0) ###Output _____no_output_____
mapping/zillow_data.ipynb	###Markdown Ok so the want here is to do a copule of things with the map. Specifically:- Bring in the Zillow data- Map rents across NYC- Layer on a subway map? ###Code import pandas as pd import os import numpy as np import matplotlib.pyplot as plt # Helps plot import numpy as np # Numerical operations import pyarrow as pa import pyarrow.parquet as pq import matplotlib.colors as colors #import fiona # Needed for geopandas to run import geopandas as gpd # this is the main geopandas #from shapely.geometry import Point, Polygon # also needed from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes from mpl_toolkits.axes_grid1.inset_locator import mark_inset os.getcwd() + "\\zillow\\zillow_med_price" df = pd.read_csv(os.getcwd() + "\\zillow\\Zip_ZriPerSqft_AllHomes.csv",encoding = "ISO-8859-1") df.head() ###Output _____no_output_____ ###Markdown One thing about the zillow dataset is that is what I would call "wide" in the sense that there are many columns, and most of these reflect the same obsrvation unit, but just different time periods. For a lot of reasons, it makes for sense to make this dataframe "long" in the sense that each row reflects a observation (place,time,value).Below is one way to do this by "melting it" ###Code df = df.melt(id_vars = ["RegionID","RegionName","City","State","Metro","CountyName","SizeRank" ]) df.head() ###Output _____no_output_____ ###Markdown Now what we have is a "long data set" where each row is a place, time, value. This facilitaties our ability to select on stuff ###Code nyc_price = df[(df.City == "New York") & (df.variable == "2019-01")].copy() nyc_price.shape nyc_price.head() nyc_price["ZIPCODE"] = nyc_price.RegionName.astype(int) new_name_dict = {"value": "price"} nyc_price.rename(columns= new_name_dict, inplace=True) nyc_price["log_price"] = np.log(nyc_price.price) cwd = os.getcwd() regions_shape = cwd + "\\shapefile\\ZIP_CODE_040114.shx" regions_shape nyc_map = gpd.read_file(regions_shape) nyc_map.ZIPCODE = nyc_map.ZIPCODE.astype(int) # we want these to look like numbers nyc_map = nyc_map.merge(nyc_price, on='ZIPCODE', how = "left", indicator = True) nyc_map.price.replace(np.nan,0.0, inplace = True) nyc_map.tail() fig, ax = plt.subplots(figsize = (10,8)) plt.tight_layout() # First create the map for the urban share nyc_map.plot(ax = ax, edgecolor='tab:grey', column='price', # THIS IS NEW, it says color it based on this column cmap='RdBu_r', # This is the color map scheme https://matplotlib.org #/examples/color/colormaps_reference.html alpha = 0.75, vmin=0, vmax=1.1nyc_price.price.max(), legend=True) #subway.plot(ax = ax, color = 'k', alpha = 0.5) #ax.get_xaxis().set_visible(False) #ax.get_yaxis().set_visible(False) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) ax.spines["left"].set_visible(False) ax.spines["bottom"].set_visible(False) fig.suptitle("Rental Price Per Square Foot", fontsize = 15) ######################################################################################## axins = zoomed_inset_axes(ax, # The original ax 4, # zoom level loc=2, # location borderpad=2) # space around it relative to figure nyc_map.plot(ax = axins, column='price', cmap='RdBu_r', vmin=0, vmax=1.1nyc_price.price.max()) # Then create the map in the "insice ax" or axins. Note, that you do not # need to keep the colering or the income, you could have the inset # be population or what ever else. # then the stuff below picks the box for the inset to cover. I # I kind of just eyballed this untill I zoomed into what I wanted # Note the "axins" object really just works like the ax x1, x2, y1, y2 = 975000, 987000, 190000, 210000 axins.set_xlim(x1, x2) axins.set_ylim(y1, y2) axins.set_title("Downtown NYC") # Make a title. mark_inset(ax, axins, loc1=3, loc2=1, alpha = 0.15) # This then creates the lines that marks where the inset comes from # Make it look nice axins.spines["right"].set_visible(False) axins.spines["top"].set_visible(False) axins.spines["left"].set_visible(False) axins.spines["bottom"].set_visible(False) #axins.Tick.remove() axins.get_xaxis().set_visible(False) axins.get_yaxis().set_visible(False) plt.show() cwd = os.getcwd() subway_shape = cwd + "\\shapefile\\geo_export_ff76e398-04a7-40d7-b073-109c7324632d.shx" regions_shape subway = gpd.read_file(subway_shape) subway.head() fig, ax = plt.subplots(figsize = (10,8)) plt.tight_layout() # First create the map for the urban share subway.plot(ax = ax, color = 'k', alpha = 0.5) subway.crs nyc_map.crs test = nyc_map.to_crs({'init': 'epsg:4326'}) test.COUNTY test_nyc_map = nyc_map.to_crs({'init': 'epsg:3395'}) test_subway = subway.to_crs({'init': 'epsg:3395'}) # This converts the geometry to a mercator projection. We had two problems, one was just_man = test_nyc_map[test_nyc_map.COUNTY == "New York"] fig, ax = plt.subplots(figsize = (10,8)) plt.tight_layout() # First create the map for the urban share just_man.plot(ax = ax, edgecolor='tab:grey', column='price', # THIS IS NEW, it says color it based on this column cmap='RdBu_r', # This is the color map scheme https://matplotlib.org #/examples/color/colormaps_reference.html alpha = 0.75, vmin=0, vmax=1.1*test.price.max(), legend=True) test_subway.plot(ax = ax, color = 'k', alpha = 0.5) x1, x2, y1, y2 = -8245000, -8225000, 4939000, 4965000 ax.set_xlim(x1, x2) ax.set_ylim(y1, y2) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) ax.spines["left"].set_visible(False) ax.spines["bottom"].set_visible(False) #fig.suptitle("Rental Price Per Square Foot", fontsize = 15) ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) ######################################################################################## plt.show() ###Output _____no_output_____
assessment/spm_sr15_figure_3a_global_emissions_pathways.ipynb	###Markdown IPCC SR15 scenario assessment Global emission pathway characteristics Figure 3a of the Summary for PolicymakersThis notebook extracts the emissions pathways for Figure 3a in the Summary for Policymakersof the IPCC's _"Special Report on Global Warming of 1.5°C"_.The scenario data used in this analysis can be accessed and downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer). DisclaimerThe figures shown in this notebook are NOT the same figures as used in Figure 3a of the SPM.They are simplified figures included here only for reference. Load `pyam` package and other dependencies ###Code import pandas as pd import numpy as np import io import itertools import yaml import math import matplotlib.pyplot as plt plt.style.use('style_sr15.mplstyle') %matplotlib inline import pyam ###Output _____no_output_____ ###Markdown Import scenario data, categorization and specifications filesThe metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer). Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook. ###Code sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx') sr1p5.load_meta('sr15_metadata_indicators.xlsx') with open("sr15_specs.yaml", 'r') as stream: specs = yaml.load(stream, Loader=yaml.FullLoader) rc = pyam.run_control() for item in specs.pop('run_control').items(): rc.update({item[0]: item[1]}) cats = specs.pop('cats') cats_15 = specs.pop('cats_15') cats_15_no_lo = specs.pop('cats_15_no_lo') marker= specs.pop('marker') ###Output _____no_output_____ ###Markdown Downselect scenario ensemble to categories of interest for this assessmentThis figure only includes scenarios where the 2010 Kyoto GHG emissions are in line with the valid range as determined by the Second Assessment Report. ###Code sr1p5.meta.rename(columns={'Kyoto-GHG\|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True) df = sr1p5.filter(category=cats_15, kyoto_ghg_2010='in range') df.set_meta(meta='1.5C limited overshoot', name='supercategory', index=df.filter(category=cats_15_no_lo)) rc.update({'color': {'supercategory': {'1.5C limited overshoot': 'xkcd:bluish'}}}) ###Output _____no_output_____ ###Markdown Set specifications for filters and initialize data list ###Code filter_args = dict(df=df, category=cats, marker=None, join_meta=True) data = [] ###Output _____no_output_____ ###Markdown Plot different emissions pathways by category Net carbon dioxide emissions for all pathways limiting global warming to 1.5°C by the end of the century ###Code co2 = ( df.filter(variable='Emissions\|CO2') .convert_unit('Mt CO2/yr', 'Gt CO2/yr') ) data.append(('Net carbon dioxide', co2)) _co2 = co2.filter(category=cats_15, year=range(2010, 2101, 5)) fig, ax = plt.subplots() _co2.filter(year=[2010]).line_plot(ax=ax, color='category', linewidth=2) _co2.line_plot(ax=ax, color='category', linewidth=0.1, fill_between=True, final_ranges=True) _co2.filter(marker=marker).line_plot(ax=ax, color='category') ###Output _____no_output_____ ###Markdown Emissions of methane, black carbon and nitrous oxide for 1.5°C pathways with limited overshootThe figures below are shown as reduction relative to 2010. ###Code def plot_relative(data, baseyear=2010): _data = data.timeseries() _data_rel = pd.DataFrame() for y in range(2010, 2101, 5): _data_rel[y] = _data[y] / _data[2010] _data_rel.reset_index(inplace=True) _data_rel['unit'] = 'relative to {}'.format(baseyear) _df = pyam.IamDataFrame(_data_rel) _df.set_meta(meta='1.5C limited overshoot', name='supercategory') _df.filter(supercategory='1.5C limited overshoot', year=range(2010, 2101, 5))\ .line_plot(color='supercategory', linewidth=0.1, fill_between=True, legend=False) ch4 = df.filter(variable='Emissions\|CH4') data.append(('Methane', ch4)) plot_relative(ch4) bc = df.filter(variable='Emissions\|BC') data.append(('Black carbon', bc)) plot_relative(bc) n2o = df.filter(variable='Emissions\|N2O') n2o.convert_unit('kt N2O/yr', 'Mt N2O/yr', factor=1/1000, inplace=True) data.append(('Nitrous oxide', n2o)) plot_relative(n2o) ###Output _____no_output_____ ###Markdown Save timeseries data to `xlsx` ###Code writer = pd.ExcelWriter('output/spm_sr15_figure3a_data_table.xlsx') for (name, _df) in data: pyam.utils.write_sheet(writer, name, pyam.filter_by_meta(_df.timeseries(), *filter_args), index=True) writer.save() ###Output _____no_output_____ ###Markdown IPCC SR15 scenario assessment* Global emission pathway characteristics Figure 3a of the Summary for PolicymakersThis notebook extracts the emissions pathways for Figure 3a in the Summary for Policymakersof the IPCC's _"Special Report on Global Warming of 1.5°C"_.The scenario data used in this analysis can be accessed and downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer). DisclaimerThe figures shown in this notebook are NOT the same figures as used in Figure 3a of the SPM.They are simplified figures included here only for reference. Load `pyam` package and other dependencies ###Code import pandas as pd import numpy as np import warnings import io import itertools import yaml import math import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt plt.style.use('style_sr15.mplstyle') %matplotlib inline import pyam ###Output _____no_output_____ ###Markdown Import scenario data, categorization and specifications filesThe metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository. If the snapshot file has been updated, make sure that you rerun the categorization notebook.The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook. ###Code sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx') #sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx') sr1p5.load_metadata('../data/sr15_metadata_indicators.xlsx') with open("sr15_specs.yaml", 'r') as stream: specs = yaml.load(stream, Loader=yaml.FullLoader) rc = pyam.run_control() for item in specs.pop('run_control').items(): rc.update({item[0]: item[1]}) cats = specs.pop('cats') cats_15 = specs.pop('cats_15') cats_15_no_lo = specs.pop('cats_15_no_lo') marker= specs.pop('marker') ###Output _____no_output_____ ###Markdown Downselect scenario ensemble to categories of interest for this assessmentThis figure only includes scenarios where the 2010 Kyoto GHG emissions are in line with the valid range as determined by the Second Assessment Report. ###Code sr1p5.meta.rename(columns={'Kyoto-GHG\|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True) df = sr1p5.filter(category=cats_15, kyoto_ghg_2010='in range') df.set_meta(meta='1.5C limited overshoot', name='supercategory', index=df.filter(category=cats_15_no_lo)) rc.update({'color': {'supercategory': {'1.5C limited overshoot': 'xkcd:bluish'}}}) ###Output _____no_output_____ ###Markdown Set specifications for filters and initialize data list ###Code filter_args = dict(df=df, category=cats, marker=None, join_meta=True) data = [] ###Output _____no_output_____ ###Markdown Plot different emissions pathways by category Net carbon dioxide emissions for all pathways limiting global warming to 1.5°C by the end of the century ###Code co2 = ( df.filter(variable='Emissions\|CO2') .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)}) ) data.append(('Net carbon dioxide', co2)) _co2 = co2.filter(category=cats_15, year=range(2010, 2101, 5)) fig, ax = plt.subplots() _co2.filter(year=[2010]).line_plot(ax=ax, color='category', linewidth=2) _co2.line_plot(ax=ax, color='category', linewidth=0.1, fill_between=True, final_ranges=True) _co2.filter(marker=marker).line_plot(ax=ax, color='category') type(_co2) ###Output _____no_output_____ ###Markdown Emissions of methane, black carbon and nitrous oxide for 1.5°C pathways with limited overshootThe figures below are shown as reduction relative to 2010. ###Code def plot_relative(data, baseyear=2010): _data = data.timeseries() _data_rel = pd.DataFrame() for y in range(2010, 2101, 5): _data_rel[y] = _data[y] / _data[2010] _data_rel.reset_index(inplace=True) _data_rel['unit'] = 'relative to {}'.format(baseyear) _df = pyam.IamDataFrame(_data_rel) _df.set_meta(meta='1.5C limited overshoot', name='supercategory') _df.filter(supercategory='1.5C limited overshoot', year=range(2010, 2101, 5))\ .line_plot(color='supercategory', linewidth=0.1, fill_between=True, legend=False) ch4 = df.filter(variable='Emissions\|CH4') data.append(('Methane', ch4)) plot_relative(ch4) bc = df.filter(variable='Emissions\|BC') data.append(('Black carbon', bc)) plot_relative(bc) n2o = df.filter(variable='Emissions\|N2O') n2o.convert_unit({'kt N2O/yr': ('Mt N2O/yr', 0.001)}, inplace=True) data.append(('Nitrous oxide', n2o)) plot_relative(n2o) ###Output _____no_output_____ ###Markdown Save timeseries data to `xlsx` ###Code writer = pd.ExcelWriter('output/spm_sr15_figure3a_data_table.xlsx') for (name, _df) in data: pyam.utils.write_sheet(writer, name, pyam.filter_by_meta(_df.timeseries(), *filter_args), index=True) writer.save() type(data) len(data) data ###Output _____no_output_____ ###Markdown IPCC SR15 scenario assessment* Global emission pathway characteristics Figure 3a of the Summary for PolicymakersThis notebook extracts the emissions pathways for Figure 3a in the Summary for Policymakersof the IPCC's _"Special Report on Global Warming of 1.5°C"_.The scenario data used in this analysis can be accessed and downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer). DisclaimerThe figures shown in this notebook are NOT the same figures as used in Figure 3a of the SPM.They are simplified figures included here only for reference. Load `pyam` package and other dependencies ###Code import pandas as pd import numpy as np import warnings import io import itertools import yaml import math import matplotlib.pyplot as plt plt.style.use('style_sr15.mplstyle') %matplotlib inline import pyam ###Output _____no_output_____ ###Markdown Import scenario data, categorization and specifications filesThe metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository. If the snapshot file has been updated, make sure that you rerun the categorization notebook.The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook. ###Code sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx') sr1p5.load_metadata('sr15_metadata_indicators.xlsx') with open("sr15_specs.yaml", 'r') as stream: specs = yaml.load(stream, Loader=yaml.FullLoader) rc = pyam.run_control() for item in specs.pop('run_control').items(): rc.update({item[0]: item[1]}) cats = specs.pop('cats') cats_15 = specs.pop('cats_15') cats_15_no_lo = specs.pop('cats_15_no_lo') marker= specs.pop('marker') ###Output _____no_output_____ ###Markdown Downselect scenario ensemble to categories of interest for this assessmentThis figure only includes scenarios where the 2010 Kyoto GHG emissions are in line with the valid range as determined by the Second Assessment Report. ###Code sr1p5.meta.rename(columns={'Kyoto-GHG\|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True) df = sr1p5.filter(category=cats_15, kyoto_ghg_2010='in range') df.set_meta(meta='1.5C limited overshoot', name='supercategory', index=df.filter(category=cats_15_no_lo)) rc.update({'color': {'supercategory': {'1.5C limited overshoot': 'xkcd:bluish'}}}) ###Output _____no_output_____ ###Markdown Set specifications for filters and initialize data list ###Code filter_args = dict(df=df, category=cats, marker=None, join_meta=True) data = [] ###Output _____no_output_____ ###Markdown Plot different emissions pathways by category Net carbon dioxide emissions for all pathways limiting global warming to 1.5°C by the end of the century ###Code co2 = ( df.filter(variable='Emissions\|CO2') .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)}) ) data.append(('Net carbon dioxide', co2)) _co2 = co2.filter(category=cats_15, year=range(2010, 2101, 5)) fig, ax = plt.subplots() _co2.filter(year=[2010]).line_plot(ax=ax, color='category', linewidth=2) _co2.line_plot(ax=ax, color='category', linewidth=0.1, fill_between=True, final_ranges=True) _co2.filter(marker=marker).line_plot(ax=ax, color='category') ###Output _____no_output_____ ###Markdown Emissions of methane, black carbon and nitrous oxide for 1.5°C pathways with limited overshootThe figures below are shown as reduction relative to 2010. ###Code def plot_relative(data, baseyear=2010): _data = data.timeseries() _data_rel = pd.DataFrame() for y in range(2010, 2101, 5): _data_rel[y] = _data[y] / _data[2010] _data_rel.reset_index(inplace=True) _data_rel['unit'] = 'relative to {}'.format(baseyear) _df = pyam.IamDataFrame(_data_rel) _df.set_meta(meta='1.5C limited overshoot', name='supercategory') _df.filter(supercategory='1.5C limited overshoot', year=range(2010, 2101, 5))\ .line_plot(color='supercategory', linewidth=0.1, fill_between=True, legend=False) ch4 = df.filter(variable='Emissions\|CH4') data.append(('Methane', ch4)) plot_relative(ch4) bc = df.filter(variable='Emissions\|BC') data.append(('Black carbon', bc)) plot_relative(bc) n2o = df.filter(variable='Emissions\|N2O') n2o.convert_unit({'kt N2O/yr': ('Mt N2O/yr', 0.001)}, inplace=True) data.append(('Nitrous oxide', n2o)) plot_relative(n2o) ###Output _____no_output_____ ###Markdown Save timeseries data to `xlsx` ###Code writer = pd.ExcelWriter('output/spm_sr15_figure3a_data_table.xlsx') for (name, _df) in data: pyam.utils.write_sheet(writer, name, pyam.filter_by_meta(_df.timeseries(), **filter_args), index=True) writer.save() ###Output _____no_output_____
Assignment3_Panganiban.ipynb	###Markdown Linear Algebra for ChE Assignment 3: Matrices ObjectivesAt the end of this activity you will be able to:1. Be familiar with matrices and their relation to linear equations.2. Perform basic matrix operations.3. Program and translate matrix equations and operations using Python. ###Code import numpy as np import matplotlib.pyplot as plt import scipy.linalg as la %matplotlib inline ###Output _____no_output_____ ###Markdown MATRICES The notation and use of matrices is probably one of the fundamentals of modern computing. Matrices are also handy representations of complex equations or multiple inter-related equations from 2-dimensional equations to even hundreds and thousands of them. Let's say for example you have $A$ and $B$ as system of equation. $$A = \left\{ \begin{array}\ x + y \\ 4x - 10y \end{array}\right. \\B = \left\{ \begin{array}\ x+y+z \\ 3x -2y -z \\ -x + 4y +2z \end{array}\right. $$ We could see that $A$ is a system of 2 equations with 2 parameters. While $B$ is a system of 3 equations with 3 parameters. We can represent them as matrices: $$A=\begin{bmatrix} 1 & 1 \\ 4 & {-10}\end{bmatrix} \\B=\begin{bmatrix} 1 & 1 & 1 \\ 3 & -2 & -1 \\ -1 & 4 & 2\end{bmatrix}$$ Declaring Matrices Just like our previous laboratory activity, we'll represent system of linear equations as a matrix. The entities or numbers in matrices are called the elements of a matrix. These elements are arranged and ordered in rows and columns which form the list/array-like structure of matrices. And just like arrays, these elements are indexed according to their position with respect to their rows and columns. This can be represent just like the equation below. Whereas, $A$ is a matrix consisting of elements denoted by Aij. Denoted by i is the number of rows in the matrix while $j$ stands for the number of columns. Do note that the $size$ of a matrix is $i$ x $j$ $$A=\begin{bmatrix}a_{(0,0)}&a_{(0,1)}&\dots&a_{(0,j-1)}\\a_{(1,0)}&a_{(1,1)}&\dots&a_{(1,j-1)}\\\vdots&\vdots&\ddots&\vdots&\\a_{(i-1,0)}&a_{(i-1,1)}&\dots&a_{(i-1,j-1)}\end{bmatrix}$$ We already gone over some of the types of matrices but we'll further discuss them in this laboratory activity. Since you already know how to describe vectors using shape, dimension, and size attributes of the matrices, we will do it in this platform. ###Code ## Since we'll keep on describing matrices. Let's make a function. def describe_mat(matrix): print(f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\n') ## Declaring a 2 x 2 matrix A = np.array([ [1, 2], [3,1] ]) describe_mat(A) G = np.array([ [4,1], [2,2] ]) describe_mat(G) ## Declaring a 3 x 2 matrix B = np.array([ [8,2], [5,4], [1,1] ]) describe_mat (B) H = np.array([1, 2, 3, 4, 5,]) describe_mat(H) ###Output Matrix: [1 2 3 4 5] Shape: (5,) Rank: 1 ###Markdown Categorizing Matrices There are several ways of classifying matrices. Once could be according to their shape and another is according to their element values. We'll try to go through them. ROW AND COLUMN MATRICES Row and column matrices are common in vector and matrix computations. They can also represent row and column spaces of a bigger vector space. Row and column matrices are represented by a single column or single row. So with that being, the shape of row matrices would be $1$ x $j$ and column matrices would $i$ x $1$ ###Code ## Declaring a Row Matrix row_mat_1D = np.array([ 1, 3, 2 ]) ## this is a 1-D Matrix with a shape of (3,), it's not really considered as a row matrix row_mat_2D = np.array([ [1,2,3] ]) ## this is a 2-D Matrix with a shape of (3,1) describe_mat(row_mat_1D) describe_mat(row_mat_2D) ## Declaring a Column Matrix col_mat = np.array([ [1], [2], [5] ]) ## this is a 2-D matrix with a shape of (3,1) describe_mat(col_mat) ###Output Matrix: [[1] [2] [5]] Shape: (3, 1) Rank: 2 ###Markdown SQUARE MATRICES Square matrices are matrices that have the same row and column sizes. We could say a matrix is square if $i=j$. We can tweak our matrix descriptor function to determine square matrices. ###Code def describe_mat(matrix): is_square = True if matrix.shape[0] == matrix.shape[1] else False print (f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\nIs Square: {is_square}\n') square_mat = np.array([ [1,2,5], [3,3,8], [6,1,2] ]) non_square_mat = np.array([ [1,2,5], [3,3,8] ]) describe_mat(square_mat) describe_mat(non_square_mat) ###Output Matrix: [[1 2 5] [3 3 8] [6 1 2]] Shape: (3, 3) Rank: 2 Is Square: True Matrix: [[1 2 5] [3 3 8]] Shape: (2, 3) Rank: 2 Is Square: False ###Markdown *According to element values* A *Null Matrix* is a matrix that has no elements. It is always a subspace of any vector or matrix. ###Code def describe_mat(matrix): if matrix.size > 0: is_square = True if matrix.shape[0] == matrix.shape[1] else False print (f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\nIs Square: {is_square}\n') else: print('Matrix is Null') null_mat = np.array([]) describe_mat(null_mat) ###Output Matrix is Null ###Markdown A *zero matrix* can be any rectangular matrix but with all elements having a value of 0. ###Code zero_mat_row = np.zeros((1,2)) zero_mat_sqr = np.zeros((2,2)) zero_mat_rct = np.zeros((3,2)) zero_4x4 = np.zeros((4,4)) print(f'Zero Row Matrix: \n{zero_mat_row}') print(f'Zero Square Matrix: \n{zero_mat_sqr}') print(f'Zero Rectangular Matrix: \n{zero_mat_rct}') print(f'Zero Matrix 4x4: \n{zero_4x4}') ###Output Zero Row Matrix: [[0. 0.]] Zero Square Matrix: [[0. 0.] [0. 0.]] Zero Rectangular Matrix: [[0. 0.] [0. 0.] [0. 0.]] Zero Matrix 4x4: [[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]] ###Markdown A *ones matrix, just like the zero matrix, can be any rectangular matrix but all of its elements are 1s instead of 0s. ###Code ones_mat_row = np.ones((1,2)) ones_mat_sqr = np.ones((2,2)) ones_mat_rct = np.ones((3,2)) print(f'Ones Row Matrix: \n{ones_mat_row}') print(f'Ones square Matrix: \n{ones_mat_sqr}') print(f'Ones Rectangular Matrix: \n{ones_mat_rct}') ###Output Ones Row Matrix: [[1. 1.]] Ones square Matrix: [[1. 1.] [1. 1.]] Ones Rectangular Matrix: [[1. 1.] [1. 1.] [1. 1.]] ###Markdown A diagonal matrix* is a square matrix that has values only at the diagonal of the matrix. ###Code np.array([ [2,0,0], [0,3,0], [0,0,5] ]) # a[1,1], a[2,2], a[3,3], ... a[n-1,n-1] d = np.diag([2,3,5,7]) d ###Output _____no_output_____ ###Markdown An *identity matrix* is a special diagonal matrix in which the values at the diagonal are ones. ###Code np.eye(5) np.identity(5) ###Output _____no_output_____ ###Markdown An *upper triangular matrix* is a matrix that has no values below the diagonal. ###Code np.array([ [1,2,3], [0,3,1], [0,0,5] ]) ###Output _____no_output_____ ###Markdown A *lower triangular matrix* is a matrix that has no values above the diagonal. ###Code np.array([ [1,0,0], [5,3,0], [7,8,5] ]) ###Output _____no_output_____ ###Markdown PRACTICE 1.) $$\theta = 5x + 3y - z$$ ###Code theta = np.array([ [5,3,-1] ]) describe_mat(theta) ###Output Matrix: [[ 5 3 -1]] Shape: (1, 3) Rank: 2 Is Square: False ###Markdown 2.) $$A = \left\{\begin{array}5x_1 + 2x_2 +x_3\\4x_2 - x_3\\10x_3\end{array}\right.$$ ###Code A = np.array([ [1,2,1], [0,4,-1], [10,0,0] ]) describe_mat(A) ###Output Matrix: [[ 1 2 1] [ 0 4 -1] [10 0 0]] Shape: (3, 3) Rank: 2 Is Square: True ###Markdown 3.) Given the matrix below, express it as a linear combination in a markdown. ###Code G = np.array([ [1,7,8], [2,2,2], [4,6,7] ]) ###Output _____no_output_____ ###Markdown G = \begin{bmatrix} 1 & 7 & 8 \\ 2 & 2 & 2 \\ 4 & 6 & 7\end{bmatrix} \\$$G = \left\{ \begin{array}\ x + 7x_2 + 8x_3 \\ 2x + 2x_2 + 2x_3 \\ 4x + 6x_2 + 7x_3 \end{array}\right. \\$$ 4.) Given the matrix below, display the output as a LaTeX markdown also express it as a system of linear combinations. ###Code H = np.tril(G) H ###Output _____no_output_____ ###Markdown MATRIX ALGEBRA Addition ###Code A = np.array([ [1,2], [2,3], [4,1] ]) B = np.array([ [2,2], [0,0], [1,1] ]) A+B 2+A ##Broadcasting # 2np.ones(A.shape)+A ###Output _____no_output_____ ###Markdown Subtraction ###Code A-B 3-B == 3np.ones(B.shape)-B ###Output _____no_output_____ ###Markdown Element-wise Multiplication ###Code AB np.multiply(A,B) 2A ##A@B alpha=10-10 A/(alpha+B) np.add(A,B) ###Output _____no_output_____ ###Markdown ACTIVITY** Task 1Create a function named `mat_desc()` that througouhly describes a matrix, it should: 1. Displays the shape, size, and rank of the matrix. 2. Displays whether the matrix is square or non-square. 3. Displays whether the matrix is an empty matrix. 4. Displays if the matrix is an identity, ones, or zeros matrix Use 5 sample matrices in which their shapes are not lower than $(3,3)$.In your methodology, create a flowchart discuss the functions and methods you have done. Present your results in the results section showing the description of each matrix you have declared. ###Code def mat_desc(matrix): print(f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\n') ###Output _____no_output_____ ###Markdown SHAPE, SIZE, & RANK ###Code X = np.array([ [7,3,6,2], [2,1,4,3], [1,9,2,5] ]) mat_desc(X) Y = np.array([ [4,7,8,2,9], [9,3,1,1,3], [7,6,2,5,4], [1,3,6,5,8] ]) mat_desc(Y) ###Output Matrix: [[4 7 8 2 9] [9 3 1 1 3] [7 6 2 5 4] [1 3 6 5 8]] Shape: (4, 5) Rank: 2 ###Markdown *FLOWCHART![image.png](data:image/png;base64,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) SQUARE & NON-SQUARE MATRICES* ###Code def mat_desc(matrix): is_square = True if matrix.shape[0] == matrix.shape[1] else False print (f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\nIs Square: {is_square}\n') square_mat = np.array([ [6,8,2,9], [3,7,4,6], [7,2,6,3], [8,9,1,1] ]) non_square_mat = np.array([ [7,3,8], [9,1,2], [4,4,3], [6,5,6] ]) mat_desc(square_mat) mat_desc(non_square_mat) ###Output Matrix: [[6 8 2 9] [3 7 4 6] [7 2 6 3] [8 9 1 1]] Shape: (4, 4) Rank: 2 Is Square: True Matrix: [[7 3 8] [9 1 2] [4 4 3] [6 5 6]] Shape: (4, 3) Rank: 2 Is Square: False ###Markdown *FLOWCHART![image.png](data:image/png;base64,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) EMPTY MATRIX* ###Code def mat_desc(matrix): if matrix.size > 0: is_square = True if matrix.shape[0] == matrix.shape[1] else False print (f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\nIs Square: {is_square}\n') else: print('Matrix is Null') null_mat = np.array([]) mat_desc(null_mat) ###Output Matrix is Null ###Markdown *FLOWCHART![image.png](data:image/png;base64,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) IDENTITY, ONES, AND ZEROS* ###Code ##IDENTITY MATRIX np.eye(12) np.identity(8) ###Output _____no_output_____ ###Markdown *FLOWCHART![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAR0AAAI8CAYAAADMcn91AAAgAElEQVR4Ae2d/c8lyXXX939arEBICIIYJX5Bi2Q5UYAxJI6EE0gCCX4Tnmwm9gIajJNo7bBY8WgTx+NY3nWwBbHDovGLZKMMAZyIOLElYyL/lIC0P4D840Xffp5z59t1q7qqTvU9T9+nv1fq6e6qOl2nTp3z6VN9b8/z1KHj8+qrr3a0vmoaIfOtb31rk3pBqYjxox/ZQDaI8oNRn36qJ1pHO2vtq7cfBZwCLirg0E+vf0bKRMTC6PgFnVYSytkmS406XKu5Pf1EBFwkQLZqA49eLCPotEaBoCPoXPsKB1Cr+0TJRIB3dCyCTqvXCDqCjqCzig8IOoJOhwX0PAPGGr3Ttxrc048ynaAJijC0nO0qVDyBECUjP4j5QmF0PpXptN52ggDqhZsCLibgvPMzGqitbhrhB6NjeQoXuPQNhr70MYzqLxu8Ov1WadSOly5/CX6gTKf1FqJMZ7IUgrL3EyUTcZfH2KPG4+knwgYevVhG0OmIIDZcq1iUTISzKeCuZj1qTj39RPiBRy+WEXRa6aE73GQpdp5W00XJRAQcxhw1Hk8/ETbw6MUygk5r5MjZBJ1rX+EAanWfKBlBJyhQIwwN54pyHE8/soG+vYKPRviBxz9ZRplO661K0Jksxc7TaroomYiAw5ijxuPpJ8IGHr1YRtBpjRw5m6Bz7SscQK3uEyUj6AQFaoSh4VxRjuPpRzaIWVrID8bjQJlO661K0Jks5QFilIzAGwPe0fkUdASdDguM3+VaO/M4tqAj6ITdGeVsMc6GCfXAIEpGfhDjB6PzqUyn9dargAu7iXjhJuhcCHRArUvf4GyXPoZR/WUDvfAJH7oEP1Cmo0ynwwJaXnmzMACh9+ORicj2PHqxjKDT4QlsuFaxKJkIZ8OYo8bj6Uc2uJDlVWvwbNnh5GwxzrZlH4Bu8oMYG3huCCyjTKeDumy4VrEoGQVcTMAJvOPZrqDTSg8tLSZLRUHU04/AGwNez9ywjKAj6HRYYPwu19oZO2mrjKAj6Ey+4nGeXhk5W4yzYUJ75yZSRn4Q4wejPqBMp/U2qoALu4l4QSXoCDphTipni3E2LwxG74yTIzX8Iz+I8YPR+VSm0+DM1mTU2Had2t7TjwIuJuAwd575iZKJ8IPRsQg6NQJQ/aix6VKLh55+IpwNSnt0i5KRDWLAOzqfgs5i+M8rR409v1r5zNOPAi4m4DBrnvmJkonwg9Gx6C983oIXXuEEcDbs0+3x48cH2x4+fHjg7d69ewdsd+7cWdysne35GnZt3qc6RJ2XbBDV/xb6uQQbKNMpJxwnNXCq3s+5Zb72ta8dXn755cODBw8Ozz333LTVIBJVb/oASNCz5+OxW8RdHmPw6BYlE2GD0bEIOh2RMGrs1q6W+gFgsCGgW+Dxjmffe8D2gRc/Mds+/sWvHrB94dvfadqsve35etYH70u6GYgAoSUQLdmgZMeIgBN0xqEr6JQ8OFPuCYQRGctiAJlSEDNQeiDSCpuRdgYog1FpDAYijNNA5LGboBPzXMszNywj6GTgUipiw5XapOW9Mgg8PDMpBahlGFsDTCucDEQYB2CUGycghOdHBqDUpqVzQUfQmXyjN+gg1Ctz6c4G0OSyGc5iWoP6EtsZiHIZkWVBLQC6dD9IYdobB5CPsIFHL5ZRppPO9MI5G26h2ayqJFMCjWUylwiPtXQGhDwAigg4TG5pTmcTn5xEyUTYYHQsgk7iHEuno8bGtXMZjUBTfpi9BKA0+4kIOMzhGn6w5GdW5+knwgYevVhG0LEZbtiz4RqaT01MJoWNLZ3Wyg72cJ0cgLD8MvhEBBwm1ea01QciZSJsMDp+QafDczzGTh8KI6vZAyDOPUYDkD2IBnwePXrUMZtXTT1zumUZQSforhBh6N67VZrZaAlVXkKNACoHH8t8Wgi0ZYB4dIuIBY9eLKNMp8Uzr9uw4ZbEGDiCzXlgk4IqhQ/moOXTOqd8rS3LCDo7y3QYNkj7tZSKAQ4DCDbnJVct69kyQDy6XQR0MLBL32Domx4DP7sRbOJhw+BJsx7MzU37R1T/W4iF2li1vOK8uXIMY+Y+eIhpd1cB52aBw/DhrKe03CrNaW6erWzLMheR6ZghW/ZbNXaEoWGf3PgNOPgKnB1ex9uATw08uTmtxcKWZSJiYXT8ynRqHkb1qbEFnG2ApQZ4LLcsE00znnROabqLh1uWEXQK2UFxNq8reic1wtBQjfUScC4DOAakEnh4Tmt+afVblomIhdHxK9MxT2rYm7HtW6pLXVJ95LNfPthmQbnm/pzXHtGTl1r2rZbNacP0H5tsWUbQSbKD46xVDnonNcLQUBl6wVktVcfdcyQI1pT95O99/XD/xZcO77r/fHb70KdeOXzmD7896Ys2Tz/99NRuTR3sWm9+y1un69v5lvYGHmSqNqcVdzyp7vXPyH4iYmF0/Mp0TlyqXABj27IKzrulYHr9M2+aAh0wKW0/+lM/t3voYM6QoeLGgYx1NIDK3jKviepH0LllmQ7+r18469aWVVjOADTIMGxpk+45++BMhzMjyPA5HyOLQjZloH3p8TeybXEN68vk333/g5NeJnvTe8t2MJdRMIjqR9C5ZdCxv5ywpWUVAhiBbtApBbRBCPWAQZoNGbDScj5HNmXX53I+Zuik5Sa7hb1lO/jhYO8nCiCefgSdWwadLT7LQQAzdAwu6d6e56C9QQf7tF3p3GRQb8clect00mttATamg2U7gs78W9kWAHtgyDJ6ptNi5cPh+AB5a0srC6KWZzo/9Z73LT7TSSHB5waaFDrWP+8NOly2tWP7Ch3Za++HA6hVNkpGmc4tynTsW6utQqf27RWg9H1/7a9XocNLotzxbYEOIGiZays4rF0UQDz9XAR0oKS2ug3wH0TBSbf2rVVrBsHZh2Ut2LM8gALQoC3q0g11txE68v+6/69pIy2v7PZV2Vums0XoMCwYInzcCx2WxbGB6jZCpzL1J9WeDCRKBnDo/fTq1tse+rCMoNM4QwadLS6vPvsnfz4tnXLLIS4DeACQT/3+N7PfXqHuta97w0kdX6MFOv/kF/7lyTUgl4Lsps+1vLpyfgZCSzj0thd0rq3qMZw56U0HS67/X/rYZw5ve/t7pqUR4JJuyFTwq2ST/de/+e9O2qDuhU8/WryOQQfXxzXterxHm194/sHs+luDjh4kP8FLbyz0thd0BqBjv9PZ4hKLg17H9ZdR7Xc6+MFn72c06Fr78/Sj5VWyljuXsSMMDd3tfwcUdOpBvXXwWdYq6Myft7TEqAeGLKNnOi1Wvm4Dw5mzbj2opF8ZjLhpYB7xHh0HQ6srbFkm4gY8On5Bp9XTrrM2+28tlO2Ug3rLwLNnOYAOvhwYDaBW94nqR9C5ZcsrcxzLdgSeywOPPcux/0HQ5rQVHmi3ZRlBJ2iCIgzNzmZfnwM+As/lgIeXVQaZLQPEo1tELHj0Yhktr8z7GvZsOFtmATxbe+t8y8ubm9LNgGPLKptunlMrq+23LCPo3NJMx5ySwaOMZ7sZTwk4mMctA8Sjm6ATNKkRhi45qMCzXdggq1oCTmlO7aZS2ntgECUTEQujY3kKF7j0DYa+yTHY73eQum/xNYmbWtLcZL8MG/yoE7/HuUkfier7pmOhZZx6plO6nWXKYdDShx8u6wHzzWY/DBz7lqo0b0tzeokyF5HplAybK9/qBEUYGvZoGb/9x+2W9eghcxyAGDawfw04rXOaxkKLH9yUTEQsjI5fmU7qHQvnrcZG1iP4bBs2Ns2tc2rtsd+yjKATNEERhvY4Gz9kVuazPoSQ2Xiym0sBiAduEbHg0YtllOmwB1aO2XCVpsdqyAg+6wKnBJuIgMPEev3g6BSNB55+Imzg0YtlBJ1GB1jD2XLwQQDpuU8dSjnQpM9tIgJuDT9odTkO1FaZCBt49GIZQad1Nle8wwE+/MzHll74ul0AegKgJdDAhuknIuDQJwdQqkPpPEomwgajYxF0Sl6SKR81dnpJe+AsAD0BDaAL2ADEvAEyOdCwTSMCDv2t7Qc8Bj729BNhA49eLCPo8CxXjtlwlabH6laZGoBsGXabMiEDDMZmb38zaADjGmiOhj4cpr9qwuctx63zw9fasoygE3RXiDA0nC7K2fDrWQRcmgFxQCJILw1E0LcEGBubgcZj69vmB1u1gUcvllGmw7ewyjEbrtL0WD0qgwwIG+74LSCy50LIImyLeB3B+uLspRUwuWzGYzdBJybb88wNywg6RzzUD9hw9dZXLc4l0woiyyB4DzDxZhkI9gwPO+Z6luNrthwDLtige+3jsZugcyHQweRe+gZnu/QxjOoPG2BZhg0voOIlR95aoOBtw/2gb94iX7SUH7w6Pdca9aVzyyvTqd1yqR6T0fuJkum9yyPbABCwt6zJMhHAx56tWJntTabHDlu1AcYQpVtUP71+4LHB6FgEnY7oGTV2a1eeftZ0NgNOTl+PblEya9ogN3YrixqPp58IG3j0YhlBxzypYc+Ga2g+NYmSWdPZBJ3l2Y2aU08/a/pByQoevVhG0ClZNlPOhstUZ4uiZNZ0NkEnO5XHwqg59fSzph8cB5wcePRiGUEnMejSKRtuqR3XRcms6WyCDs/g6XHUnHr6WdMPTkd+VeLRi2UEnZJlM+VsuEx1tihKZk1nE3SyU3ksjJpTTz9r+sFxwMmBRy+WEXQSgy6dsuGW2nFdlMyazibo8AyeHkfNqaefNf3gdORXJR69WEbQKVk2U86Gy1Rni6Jk1nQ2QSc7lcfCqDn19LOmHxwHnBx49GIZQScx6NIpG26pHddFyazpbIIOz+DpcdScevpZ0w9OR35V4tGLZQSdkmUz5Wy4THW2KEpmTWcTdLJTeSyMmlNPP2v6wXHAyYFHL5YRdBKDLp2y4ZbacV2UzJrOJujwDJ4eR82pp581/eB05FclHr1YRtApWTZTzobLVGeLomTWdDZBJzuVx8KoOfX0s6YfHAecHHj0Yhn9hc9b8MIrJhTOhr1345c07QVOK4t8adOr/xo2GOl7K7KjfhAxDmU6CcWXTjEhvZ8omdE7HLKb0lvm/F9RRI3H08+oDVrn1qNblEyEDUbHIui0etrG30gedTa8RS7otDnDaNC19eJ7A37UD1p0Gx2/oNNi5es2o8Zu7crTzxrOloMOMiD+eHSLklnDBjzW0nHUeDz9RNjAoxfLCDolz8qUs+Ey1dmiKJk1nC23xOKlFQYYNR5PP2vYIDuJSaFHtyiZCBuMjkXQSRxq6XTU2EvX5jpPP2s4W26JJejwzFwde+YnSmYNPzgd8bxkdCyCztyei2ejxl68OFV6+lnL2XiJlS6toKJHtyiZtWxAU5E9jBqPp58IG3j0YhlBJ+tW+UI2XL7FaWmUzFrOxkusNMvB6KLG4+lnLRuczuK8xKNblEyEDUbHIujM/WnxbNTYixenSk8/azkbL7EEHZoUOvTMT5TMWn5Awz05HB2LoHNi0nLBqLHLV57XePpZy9kAGiyxcksraOnRLUpmLRvMZ+P0LGo8nn4ibODRi2UEnVOfKpaw4YqNkooomTWdDdBBxpP7RI3H08+aNsiN3co8ukXJRNhgdCyCjnlSw37U2A1dTE08/azpbABObmkF5Ty6RcmsaYOluYoaj6efCBt49GIZQWfJu5I6NhyqEJgI0KUN7y8t1efqPDIPHjwI6cejW5TMTdigBOfEdcJgfRHQQSBd+gZDR44BQcRfLev4zu7tAZ+I9MFSX9GxUNJjqVyZTnpLWjiHIZGZGGTwd73573zr+BO7sof9XXfzhwXXUaZDL0sLOkuektThv3gwBwNgvvDt72iTDSbQwi9wQyp9cMPq/XhkLmJ51WMIjxEiZCIMDTvh/5mBcwk4gi3fcOAPgs4ySZgDynSWbXWstd+vIKVmh9OxAASfAHSWHipz0B2dqnLgkYm4AXv0YhlBpzLxVi3oCC6lG4ygY1FS3gs6jt+b2DtJH//iV5Xp6DnO0QfgD8hySr/gtjDkoLOy2t4jo0zHEdyYiF5jRxgajoWtdLdT+T4zIUGnhs6reo5pLa8abKal1T6B0nIjaVlaeW6kXpmIGzADpCF8piYsI+g0WM1+m6NvrQSfFESWAdfciIOu1tbqPTKCjmOpBIP3GvvchtbzHMEmhQ3OW5dWHp/2ypw7Frx6cUwr04EVKx+7m+UcT2X7BZKgUwkcqp5BB2TUVrbBo0ePpgfI+n3OfuFSurHY8xz4iGKoHEOpbZTpEI1zh1paCTYl6FgGnPObtIzv9Gld6dwjgwDv/fT209se+rCMoFOZIUFH0MlBp2dplQZdxeWO1Ryox8LKgaCTEK5ir2N1r7HPZWj7qly/zxF4UvDY0mrpJc+jQwfFAfo7VyysORZlOmzN5Nigo+c5gk4JOvCRlk/vjRTX9MgIOk7D9Rr7XIbW0kqwSWFj5z3Pc7wA6Y0D9HOuWGCwevRiGWU6bM3kWNARdAwyvO99ngO34qBL3Kx46pERdIKMfQ5Da2kl4DBo+Nie57QurQSdOXSV6RTuM4KOoMOg4WNBZ+x/QRR0CtDR0krQYdDYsWdppUxHmc6Emdp62R4UmrNpLwjBBwSdOUAK9+yTYo43ZTon5rn6e1aAjr4qF2jSm41naQUX46DLuFy2yCNzjuebqXIevVhG0Ektejgc/8yM/isLQSeFjmXAGbdZLOKgW2xIlR6Zi4AOBnbpGwy95hjsrz4glU6dTuf7BZEtreAfa/rbmtdaOxbW1M2upUyH7ix2aHczAWa/gMnNPUPHfKV1j4Dr/XhkLiLT6TGExwgRMmsaWl+VCzQ54KDMnufgjy72fiLiADqtGQulMY6ORZlOYll9VS7olKBjGfBo0CUuVzz19CPoBD21X9PQgo6gk4OOLa3gHx4YRMmsGQslGo6ORZkOWdaWVrij5RxPZfsFki2t8F9ZjAYdudzioacfQefCMh2Djn6fs1+4lG4sBh34iAcGUTKCzoVBR0urU9h85LNfPmArBeNeyu15DlKTKIB4+hF0giZoLUNvDToI9nff/+CNBv2b3/LWA7YWuHz433/p8K9/89916YsxPvdvf/Pwwmc+39RHix5rt+HnOYLOOHT1TOd6Vb3FpRUC8umnn+4K4rUDrgc6r33dG7r1tTFCdm3d17oeL60EHUFnQsYamc4lQOdd958/lLaPfv6/H4O21Abl1s7a5AKT6ww6VlbKvAwe2OOa2JtMur//4kuHT/7e14/6prI5nW6yTNC5vjNf7zzLPpZRpnNtyK0trSxwOdPB8dL2wqcfTYG81AZ1aAcQ4DgXzFwH6KTXM7CwbAoOO09l7fwH3vjM4ZVvvnoEFMpz1+U+buI4XVrBXTiA5uFYPouSWeMGXB7FVc3oWASdawvbg8KbcOxSnxa4FowITAAhbW/trK6lHYMlvR7XWaaTtknPTQfTNT3n9mldes5tb/pY0DlF0DB0cIFL30D3kTHgZ+2Azta+Kk+DsQUmCNKWdgyWNLC5bu/QsaUVfGTEx6JkR2MhQk9lOhv+rywEndOv8FNAnvvcMmC+3yMwez9RMlpeBa1/Rw29xec5CCZB52ahk1taATZRAPH0MxoLLTD16MUyynQOh2lphTvaue+avdfPQQffHqXXsXZWp+XVOrASdPIIYoDkW5yWsszuobPFr8oNKgYT7FEGmBhYrA321s7q0O71z7xp+lGfPZOxPerwAz5sOLZy3qP8mR/6kanPn3znsyftTB/WwfTDdVBvOuXach2OIYM+0+vd9Lk9z4GP8IcDiMuXjqNklOkEpaIjht7q0goBZwFpgYvgNLBwQFo7qyvBBPL/4tc+fvidP/6zacMvgVGWbniQ/KFPvTJBANe++8sfnrUxfVgHHEPuztt++ggdXDfX1vTFHts/fPe97Ldy6fWjz3PPcwCWKIB4+hmJhSVocp1HL5bZfaazZeh4gwzQAQC88pJb/qsPHEAcjEvHUTKCTtBdwWtoW1pt8XnOSOALOuPPdGxphf/KIv1EAcTTjzcW0jEunXv0YpldZzoGna39PmcEOJAVdNaDTvo8B8HIAbQUnFwXJSPoBE2Q19C3cWk1CizJXwGr9DxH0BmH7q4zHUFnPCO4jZAqfVVumUtU1uLpx3sDtrG17D16scxuobPVVx9uYxBf2pjseU5uaaVMR5nOBGYP3QUdZTklGAo6y/kOZy3LLZ/UssxTOLn0DdDpHYP+iqegk4OOLa22/Fc8l3zdEwtL1ztH3W6XV/agMOd4KtsvkAw6eN5X+iAQez9RMp6sv1e33vawFcvsEjq39atywXIclrWlVRpArfDhoDunjKCTEO5cxu41NH7whUznAy9+Qr/a/fZ4oN4m2FkGvOSrUQDx9NMbCxhnbz+97dM+dpnp6KtygSYHypalVRpAS3DiutFA5WstHQs6DorCoL0T1Gtou5vlHE9l+wWSoLOEsyd1vfGZxvTuMh09z9kvVGo3lJbnOWkAPQnF5aPRQF2++pPa3hswJHt1622f9rE76GhpJeiU4GMZ8JMQzh+NBl3+qqelnn4EHQdFYfpeY/cYWtARdHLQaV1aefwzUqYnFgxzvfHW2z4d/64yHVta4Y6WczyV7RdItrTK/VcWFpy2Hw06u05t7+lH0HFkLZiIXmO3GtqgAwcTYPYLmNzcG3TgI7VPr396fNor0xoLPMbe8fS2T8eyq0xHSyuBJgcclLU+z0kDiIN36Xg0UJeuzXWCjiNrgQF7J6jV0IKOoJODTs/zHI9/Rsq0xgKDqjfeetun49/NC596q1zAyQEHZba0upS/4omgL22ATqluK+W7WV7peY6gU4NOy/Oc9K7NGcPSMQK+9+ORuYhMp8cQHiNEyLQYWksrQScHnd6lFeIlwqe9/bTEQhrzvePpbZ+OZTeZjj0ozDmeyvYLJEGnH6KCzuFwqNFdS6v9QqV2Q7HnOa1Lq/SunWYNpfPRQC1dNy2vxULaHue9uvW2T/vYRaaj/8pC0CnBxzLgXDCWykaDrnTdtNzTj6DjoCgM32vsmqH1PEfQyUHHs7Ty+GekTC0WUrB5dOuNz7SPXWQ6djfLOZ7K9gskQecKQb0Q6W2/O+joec5+oVK7oXie56QBdBW29X9HA7Xew1ULZTqOpRJM1ztBS4bW0krQKcHHMuDWgLZ2vf7p8WmvzFIsmP7pvnc8ve3Tsdz65ZWgI+jkoGNLK/ypmd7PaNC19ufpR9BxZC0pFVsmaMnQgo6gk4OOLa3w6kPvxwODKJmlWCiNs1e33vbol2Vufaajr8sFHYYO/gKIAQc3JA6GUlCm5VuWuQjoQMnbvtnaHXs4nLZ92oD94O7du7fe77ca17c+08FdCt9g2TKLHU/Hd47/j8yebMH/O+CWsxaPbheR6aSp49K5xwgRMj2GBoCwYS1vx637Lcs8evSoaTyACwC8RxvkfDvCP9FvVD89sWD26NWtt306/l1kOmZc3o8ajq+1dBzVT6uzATp2p4/SLaqfVhvwfEXpFtVPhA1GxyLosAdWjkeNXbn8sdrTT6uzCTpHM08HHltvWabVD9gKvePpbY++WEbQYetXjtlwlabH6iiZVmcTdI5TMx1EzU9UP61+wFbo1a23PfpiGUGHrV85ZsNVmh6ro2RanU3QOU7NdBA1P1H9tPoBW6FXt9726ItlBB22fuWYDVdpeqyOkml1NkHnODXTQdT8RPXT6gdshV7detujL5YRdNj6lWM2XKXpsTpKptXZBJ3j1EwHUfMT1U+rH7AVenXrbY++WEbQYetXjtlwlabH6iiZVmcTdI5TMx1EzU9UP61+wFbo1a23PfpiGUGHrV85ZsNVmh6ro2RanU3QOU7NdBA1P1H9tPoBW6FXt9726ItlBB22fuWYDVdpeqyOkml1NkHnODXTQdT8RPXT6gdshV7detujL5YRdNj6lWM2XKXpsTpKptXZBJ3j1EwHUfMT1U+rH7AVenXrbY++WGY3f+ETg77NG5ytZXyAzsOHD5vatlxvS21abbAlndfW5RJsoEyHkV85hoP0fqJkWu9wynTmMxg1P1H9tPoBW6FXt9726ItlBB22fuWYDVdpeqyOkml1NkHnODXTQdT8RPXT6gdshV7detujL5YRdNj6lWM2XKXpsTpKptXZBJ3j1EwHUfMT1U+rH7AVenXrbY++WEbQYetXjtlwlabH6iiZVmcTdI5TMx1EzU9UP61+wFbo1a23PfpiGUGHrV85ZsNVmh6ro2RanU3QOU7NdBA1P1H9tPoBW6FXt9726ItlBB22fuWYDVdpeqyOkml1NkHnODXTQdT8RPXT6gdshV7detujL5YRdNj6lWM2XKXpsTpKptXZBJ3j1EwHUfMT1U+rH7AVenXrbY++WEbQYetXjtlwlabH6iiZVmcTdI5TMx1EzU9UP61+wFbo1a23PfpiGUGHrV85ZsNVmh6ro2RanU3QOU7NdBA1P1H9tPoBW6FXt9726ItlBB22fuWYDVdpeqyOkml1NkHnODXTQdT8RPXT6gdshV7detujL5YRdNj6lWM2XKXpsTpKptXZBJ3j1EwHUfMT1U+rH7AVenXrbY++WEbQYetXjtlwlabH6iiZVmcTdI5TMx1EzU9UP61+wFbo1a23PfpiGb3weUteBIWzYWJrG6CjFz7rdqrZcav1rX5wk/or02HkV44xUb2fKJnWO5wynfkMRs1PVD+tfsBW6NWttz36YhlBh61fOWbDVZoeq6NklpwNoClt+EufPZ+o8Xj6WbJBaYyefrYsE2GD0fELOiVvzJSPGjtzyWyRp58lZ8Nf9BR0sqae3YHzLU5LPfMTJbPkB6cjuSrp1a23PXphGUGnNBOZcjZcpjpbFCWz5Gwl6Ny7dy+r81Jh1Hg8/SzZoDQmTz9blomwwej4BZ2SN2bKR42duWS2yNNPzdlymc7jx4+z/S8VenSLkqnZIDeuKN2i+omwwehYBJ2cJxbKRl2nb9QAACAASURBVI1duOxJsaefmrPlsh1BZ572n0xEocAzP1EyNT/IDalXt9726JNlBJ3cLBTK2HCFJifFUTI1Z0uh89xzz80c4UTxQkHUeDz91GyQG5Knny3LRNhgdPyCTs4TC2Wjxi5c9qTY00+Ls/ESC99aefrZskyLDVJjb3k8Ht0ibODRi2UEndQLF87ZcAvNZlVRMi3OxtmOoHM1TVHzE9VPix/MHDRZ+qR1ufPRsQg6OasWykaNXbjsSbGnnxZnM+hgaYWPp58ty7TYIDX2lsfj0S3CBh69WEbQSb1w4ZwNt9BsVhUl0+psWGIJOk+mKGp+ovpp9YMnFui/+YyORdBh61eOR41dufyx2tNPq7Mh27FfIXv62bJMqw2Ohla2N5mid05726MTltELnw0vScJgW98QcC064mvylnaX2KbVBpc4tladL8EGF5/p4M599+7d4s/8+RsbHZffwZJtTm2DZagtRS8lO4rI9gDA3g/LXDR07MGoBcw7nn3vQZtssJYPmF+l4OEAag2+KBlBJ1nLrTlBeC5hTvGBFz9x+MK3v6NNNljVBz7+xa9ONzH4GW5w9okCiKcfQeeM0MHdB84g4Ai2577h2M1N0LmygAeGLHOxyyuDDu5G53Y6XX/fYMONDeC5hG/9lOkEZDoCwr6BEDH/go7leMp0prtPhNOpj32DTdARdCYL2PJKQNg3ECLmX9ARdAQdfUsV+hxP0BF0BB1BR9CZc+B4pgfJepAcGhwRS4099qFM58i06YC//p7XlM9Y5uK/Mt9jEGjMsc+xBJ05TBgg85ryGctc7Auf+EsG+O2EAjA2APdob4POJbwsi+UVAnzLmzIdPR8RuCs+YNDRjwOvMhnOWsq5zbyGZQSdisPt8c6uMc+zR0GnDJB5TflM0BFolN10+ICgM4cJA2ReUz5jGWU6Hc6nDGCeAezFHoLOHCYMkHlN+YxlBB1BR1lPxQcEnTlMGCDzmvIZywg6FYfby91c4yxncYLOHCYMkHlN+YxlBB1BR5lOxQcEnTlMGCDzmvIZywg6FYdTBlDOAPZiG0FnDhMGyLymfMYygo6go0yn4gOCzhwmDJB5TfmMZQSdisPt5W6ucZYzOkFnDhMGyLymfMYygo6go0yn4gOCzhwmDJB5TfmMZQSdisMpAyhnAHuxjaAzhwkDZF5TPmOZp/CC2CVu9gf29uL4GufNwc+g8+jRo4uMla3FtzIdZTpaXlV8wKCjFz6vMhnOWsq5zbyGZQSdisMpw7i5DGMrthd0ygCZ15TPBB2BRtlNhw8IOnOYMEDmNeUzllGm0+F8W7nzSo/Y7EvQmcOEATKvKZ+xjKCzEnQ+8tkvH7DdJBA+9fvfPHzkc1+p6oA2aLu2rp/9kz+f+seer216oZyPuc2WjwWdOUwYIPOa8hnL7A46AMO7739wdUA8/fTTh3fdf34WbNGB9O5/9cED9Kj1izZoW2vXWw/b4topfE2vyfbXOqZtevqarnO/T3+T8cBW0JnDhAEyrymfscwuoZMLjB6nz7XFNW8aOlMW0ZBtRUPH9EKmwwDK2bFWBnhAf2y1tlxv/b7xTT/cJYdrCDpzmDBA5jXlM5YRdCrLq09/9U8Pv/Jb/2ECCqDC20uPv3F0YIMO6pFJlQCEAER9aXv4pT+crtnajgPL7uYomwK80A9DB/2VdIEOuFZNF2tnQHjb299zvOb9F1+aMh/0MV3nOtPhNpBj3XlMOOY6g4ct4ayuNAZrh+uYbHr92rmgM4cJA2ReUz5jGUGnAp07b/vp453V7rC8t4DjMjvOOTOCxOpz++/5K391AkZrO+6Dgwp39Nz1rQxtoTv6s7J0jzpcv0WXUrs33/mxY7BPgLiGDvcFXVh3HhOOuY6PuY6vx8ec2aSyaT+lc0FnDhMGyLymfMYygk4FOnDgXNZigWh1pXapI5vc85/83WOWZG2sDns7rrUzWew5qKAPzrkex7jzW531gX3aDv2iHevibWd6QZ6PuU8r5zI75jo+Rr2dc0aTk+O2Vt+6F3TmMGGAzGvKZywj6DRCx4KP9wwaPl5yZshbMKftuI6Pl9pxnQUgytAHzrnejq3O+vjFX31x+tYJ32rZhjLT09phb9ewPdfxsdVjb3qhno9zbbjMjk2Gr5WrszLbs1xO1trV9oLOHCYMkHlN+YxlBJ0G6CD4Sps30/EGMAKkFtxoY2DJBZTV2XVKY0M52lg77NPrcR0fczsLftTzca4Nl9mxyeCcj3PnJpOrS2W57dKxoDOHCQNkXlM+Y5nd/YXPUmCUnA6BB7AsbZC1dqXrWPlS/1zHxyZr+1IdBxX0wbnJ8N7q7Dr/4J/+s6kt2qcbnvtYO+z5OjjmOj7mdqYX6vk414bL7NhkcM7HuXOTydWlstx26digo7/wuc5fDlWm05DpWDaz5JiXDJ0cTHisJZigDdfxMctbsKOej3NtuMyOTQbnfJw7N5lcXSrLbZeODTp64fMqk+GspZzbzGtYZrfQefNb3nqobXi+8cwP/cjh9c+8qdjWgJRrl3PkUmCiLdfZ8Wtf94aTvlEGyCEL+cfP/supHvIcVH/5e7/vkJO1b7XQFl+X4zooK9kCbUwX7NMxcV3uetDP9ELbXBt8rW4PrnO62HjRN9qazumYU92sX5PDdWCXtF3tXNApA2ReUz7bPXRKAZaWI0g+9PJ/PPzUe95XDEqDTq5dzpkRdPgaGfu0nussmP8mgHDnx042BB/kJ+jc+bHpGGVoi3K7Vk4WZSZvMqV2uI5dC/slnVGXXg/6WZnJAwbcn+mSllub7//B10+gsb6tHfdndby3frmd6cDtaseCzhwmDJB5TfmMZXaX6dQcbCv1Bh3st6LTTenBGctN6CDozGHCAJnXlM9YRtCpPNO5CSdHn3uFTu6XxchAsaS6qbkQdOYwYYDMa8pnLCPoCDo3Fsw5iAAuuc2WYCYDKEdlgYLOHCYMkHlN+YxlBJ2NQseCa8t7ZCU1/QAGtFsbEACTPU+r6TBaL+jMYcIAmdeUz1hG0BF0quDIBe3P3P3nE0xydVwG2AAQgs6r5Ygs1HCgFpqcFOM/Ye/99PbT2x76sIygszJ0EFy4A+c2LBE++XtfPwb5i688zrZD0KId2qfXwfWROaTldo763/3G/5n6sDKGgB0v1Vmb0v7nf+XXJpD80sc+cxxLqS306YHO2m/1l/TqKVemM8cYA2ReUz5jGUHnDNDJPZOwsh944zOHV7756uGFTz/KPrtAu0f/6/8e0M5keI8gtgerXM7HP/e+908wQBngkguwpbpceysDaCD7C88/yF7X2tm+Fzprv9VveozsBZ05TBgg85ryGcsIOgXoIFgsG8jtUd/ryByAuCaCN3cN/OgPdZ4+7LdGuO4SWJbqcjqhDPp813f9xcPP/uL9rN45OR5zrj4tK+ll1zGIltql11vjXNCZw4QBMq8pn7GMoLMAHTh2aSsBAeVLG66H+iXoYGll7XJBs3T9c0EHOv2N173x8NZ//I5m4EB36Lo0lnR8aAvb5MZodZDh4/Qaa58LOnOYMEDmNeUzltndC59rO2R6PQsyBEVpQ5sl6PzWl/9oMVBryyvUQ6+lwFyqS8eEZ0T4dfCb/u7fPz4vStuUzs0e2JfacHnJZlZ+k5mOXvjUC5+HO3fuNDkyO3XrMYLEsobcvhREFmQIjtyG4EGbJeh87Et/UIUOdMpd33TFOJfAslSX2gjZDd5b4ofgaZvSudmjZK9UzvTKjc3KIGPtUvlznCvTmWcwnLXMa8pnLKPl1cLyygI4ty8F0VKQcd0SdD76+f/WBJ1cgJmutcBsDVo8v/kLr3nNBMpcf7UyHnOtbU1nlm/Vn2W8x4LOHCYMkHlN+YxlBJ0CdLwOiq98v/t7vreYJSFYEIhL0Pn1//RfFqHzk+98dqo3wOT20B9vvqO/XH1r0KLd+z/62+6M0qCT0yEtO8db/d55ZDlBZw4TBsi8pnzGMoLOytCBsyJI04Dic4MOyti57fjB5/7zJI92VsZ7lN/95Q8v9oH2H/rUKxPcuG87boUO+uG+e4+hq/VZ26Nt7m19lgOsp7Fl3v7v1a21vaAzhwkDZF5TPmMZQecM0Gl15pts1wqdm9RxK30LOnOYMEDmNeUzlhF0bjl07OFrugd03vfCbwxlMVuBwrn1EHTmMGGAzGvKZywj6OwAOgBMuv3EO3/+wH8s8NyBe8nXF3TmMGGAzGvKZywj6Fw4dPDrZc9b3B6ZSwbHiO6CzhwmDJB5TfmMZQSdC4eO/f/B+N/1egLLMh/Pf9/Z089taCvozGHCAJnXlM9YZlfQsawAd/ncZgHY2i4NqH/16y9nr/uLv/riEQh4czz9D6nsOtDJ6vAXK3M6ooy/1QI8GDglmVQO10hlTQ/vHtdMnx3ZOcbFPy68pDfsBZ05TBgg85ryGcvsCjoWaHaXT/cjf0ccQZVej8/xt6UQzPafjecCG+1Rjzr7qw18DT42QKKMocNtcscAKq5vtmDZnE49ZXbNXL8ou9Q37AWdOUwYIPOa8hnL7BI6tb8PbsFTa8cB+Zaf+JkJOlxmxwwaPrZ62yMwDTo4zgHBdLO6tF16btc2uXsfusq67NyuY+2wR51lKLk96rl9y7H1Z9eGnjk5QBF1nj7s9zy4Lq4B3XN9LNXl2gs6c5gwQOY15TOW2dULn+z4qXNxHR8vteM6wGIKFvp74PZ3wae/5XSdwfRA521vf8/xb4vbtbBUQz8GCz6GPum56WhjMrn03Nphb3W4Vm5DPbe3Y5QvbbgW6gEDHJsc77f4hr1BRy986oXP7hc+4fDm+OzoOOY6Pl5qx3UGnVyQoswymB7olK6FcoMHH0Of9Nx0tDGZXHpu7Ub2ds0lvdFmCTpbe8Me9jDo6C98XmUynLWUc5t5DcvscnkFx0+DywIGez5easd1Bh0EdW6zB8Q90EHb3LVQZmNIIZOem442JsiiLD23dlZnS5Xc3vpmGZND/7klmYEGsnacyuN8a2/YQydBpwyQeU35TNA5I3RygcRlPdAxQLB8epxCJj239ilk0nNrhz3qcrCxMtRzezu2a+bquW4JOlt6w97GJejMYcIAmdeUz1hGmc7173Q4KOwYv4GxQLO9/S4GDzz574jjAS0C3tqle7SFE+OBM74lS+txPslfP/sp/S1yk7PMydrZeSt07Nsx+xbMAmxkf9vesDdbCDpzmDBA5jXlM5bZFXQQYMg0coHGdQadnr8jjt/V4Gtxy2TSvUEH7QCetB7nDB3TJ9cOZQYZ6MrnfGxBg71dz+TQriWT4mu0HN+mN+xtvILOHCYMkHlN+YxldgUdc6La3qCDfa3tmvUMnTWvq2t9ZzaPsDOWeK12EXTmMGGAzGvKZywj6GRegzg3dEq/eEYw2I8IWwNC7eZAYXsALLkNdu55w17QmcOEATKvKZ+xjKBzA9AxqMH5efv+H3x9dunHgaTjMmRS29gDa7YxjnvfsBd05jBhgMxrymcsI+hkoJM6r87bA/022krQmcOEATKvKZ+xjKAj6DQ/27iNQGkZk6AzhwkDZF5TPmMZQUfQEXQqPiDozGHCAJnXlM9YRtCpOFzLnVBtbvfyS9CZw4QBMq8pn7HMrl74FBxuNxzONb8GHb3wqRc+u1/4PJdT6rq3G2YGHb3weZXJcNZSzm3mNSyj5ZWWV3qmU/EBQacMkHlN+UzQqTiZMpfbnbn0zq+gM4cJA2ReUz5jGWU6ApAynYoPCDpzmDBA5jXlM5YRdCoO13tXVPvblyUJOnOYMEDmNeUzlhF0BB1lOhUfEHTmMGGAzGvKZywj6FQcTpnL7ctceudU0JnDhAEyrymfsYygI+go06n4gKAzhwkDZF5TPmMZQaficL13RbW/fZmRoDOHCQNkXlM+YxlBR9BRplPxAUFnDhMGyLymfMYygk7F4ZS53L7MpXdOBZ05TBgg85ryGcsIOoKOMp2KDwg6c5gwQOY15TOWeepb3/rW4RK3u3fv6t2rSrD03tHVPp/VGXQePXp0kbGytfhWpqPAVaZT8QGDjl74vMpkOGsp5zbzGpYRdCoOp7t//u6/J7sIOmWAzGvKZ4KOQKPspsMHBJ05TBgg85ryGcso0+lwvj3d3TXWJxmeoDOHCQNkXlM+YxlBR9BR1lPxAUFnDhMGyLymfMYygk7F4XTHf3LH36stBJ05TBgg85ryGcsIOoKOMp2KDwg6c5gwQOY15TOWEXQqDrfXu7vG/STDe8ez751+E6avzK+gwgApY2ZewzIXC52XX355cgTchRQgTwJEtljfFoJOGSDzmvLZrYIOHEKBtn6gyaZXNrWl1Z07d44RxQF0LKwcRMng18e9n17dettDH5a52EwHA3nuueembAfggXN8/Itf1SYbrOID8CcGDjJr+3AAWVltHyUj6CSEq02M1fdMkIEHdyJtssE5fICBAx/t8U+PT4/IXAR0YMBL3/Ai3sOHD3e9PXjw4Dj+e/fuTQDem03YBmuN/dJiA9DZus4XvbyyO0IE3S/pDodvWXDHt29bzE7pHs7Z+9myjPzgML0Ff+45HfUBQadjhkaN3dqVpx8OOEGn1dLbXiqN+kGrFXr76W0PPVhG0GmdmcRwrWJs7HPKCDoxd/k0gM45px7fYT84l24evVhG0GmdGUFnshQ7T6vpomQiAg5jjhqPp58IG3j0YhlBpzVyLsjZtLxqn1QOhlapLcsIOkGBGmHoS7rDCTqt+Nh21uKBW0QsePRiGWU67f55MWm1oNM+qRwMrVJblhF0lOmEgYqdTdBpxYcyHViqF6K97dM+lOm0+2f35KTGbu3KM6mCjr69gn+xH5zL3zz+yTKCTuvMOO4IuDQbu7Urjww7mzKdVkvHzY9nTj0y7AetVujtp7d9GgeCTuvMBALEM6nsbIJO+6R6bL1lGfaDViv0jqe3/Ql0cIFL32DoSx/DqP5sg8ePH0+vQWA/et1LkmcbXJLea+p6CTZQptN6O1CmM1kKAdL7iZJBwPV+onSL6ifCBqNjEXQ6vHTU2K1defphZ9PyqtXSeqYDS/X6W2/7tA9Bp90/uycnNXZrV55JFXRivrmJnNNRPziXv3n0YhlBp3VmHHeESAcVdAQd+Bv7QatrMxBaZHrbp3Eg6LRY+brNqLFbu/L0w86m5VWrpfuXFriyZ36iZNgPWq3Qq1tv+9Rmgk7rzFyQswk67ZM6GkCtPUX1I+gEBWqEoVNab9nZBJ3W2dl21uIBVUQsePRiGWU67f55MWm1oNM+qRwMrVJblhF0lOmEgYqdTdBpxYcyHViqF6K97dM+lOm0+2f35KTGbu3KM6mCTsw3N5FzOuoH5/I3j14sI+i0zozjjhDpoIKOoAN/Yz9odW0GQotMb/s0DgSdFitftxk1dmtXnn7Y2bS8arV0/9ICV/bMT5QM+0GrFXp1622f2uwpXODSNxj60scwqj/bQC98Xr5Pe/2B/cB7jXPLKdNpvR1c0B1OmU77pCLAej9blrmITKfH4Fs1doShYaetjh+64U8r429u24a/8Im/827nuXne8ng8uskP9Exn8nOP8/TKyNmunA2gKW3IftJPr50hv2UZ+YGgI+gEBikCDplNDjooz322DBCPboKOoCPoBEMHS6kcdHJZDibHE9hblhF0BB1BJzCwLeAEnVxOVy7bMkQ9upkflEd8WtPbT2979Mgy+vbqdA6KJWy4YqOkIkrGnC1dYpWWVlAzSreofswGyRQsnkbpFtVPhA1GxyLoLLrkvHLU2POrlc88/ZizpUus0tIKvXv62bKM2aBs2dOaLY/Ho1uEDTx6sYygc+qHxRI2XLFRUhElw87GSyxBJ5mQ5DRqfqL6YT9Ihlo87dWttz06ZhlBpzgVpxVsuNPafEmUDDubLbGWllbQNkq3qH7YBvnZOC2N0i2qnwgbjI5F0Dn1w2LJqLGLF04qPP2ws9kSaynLQZeefrYswzZITFo83fJ4PLpF2MCjF8sIOkV3PK1gw53W5kuiZFJnwxJL0MnPCZdGzU9UP6kf8FhLx7269bZHvyyjFz5vwQuvmFA4G/a23bt373hsZbd9n9rgto83N75LsIEynRL+M+WY5NaPvfP08OHD4/tPVlbbe2QePHgw6wfPc87Rj0e3FhlkZZyZ9dja5iTiLo++PLpFyUTYYHQsgo55bMO+xdgIdP72SMfl98FytrGH3y22TqcsIuAEnXHoCjqp5y6cLwUC7tIcRB948RMHbe02eMez7z1ggw0B7iVbl6ZI0NFrEJNveJynV2YLzmZfUwM0X/j2d7Q5bWDgxn9E1vvZgh+UdO71aVzHIxNhA49eLKNMp+QlmXI2HFdbloM7tYAzBlzLdgQdQWeKsVLQcQCmxxEyEXTHuEpjsSzn41/8qqDjzHAM1oLOkwgq+duTFqdHEbHg0YtllOmczluxhA3HjWxJYIGjvS/bAbRhS0C8ZGu2e3ocEXDo06NblEyEDUbHIuiknrtwnjO2llY+wOTALOjMnS/nb/MWp2eCTtBdIcLQmN6cE9hX5HqAPA4fW1oB5Dlbn4bYvOQm/WCuyemZZzwemQgbePRiGWU6p/5RLGHDWSM9zxmHjWU9tkyFbXO2NpuX9hEB59XNMx6PTIQNPHqxjKBT8uBMORvOqi1QLHC090GIl1bewI4IOK9uOd8xHyrtPTIRNvDoxTKCTmnGM+VsOFTreY4PMDkwY3kKgGO5ik9q68x0nBRFBJxXN894PDIRNvDoxTJPQUltPhvcvXt3ChR9VT4OH3ueg7/fJX/0+eOl2E2Zzsn9slzAtEYrPc8Zh41lPbZMNeuntrbypT2Crvfj6WfLMhE2GB2/oNPhpWxsLa3WA076PAdTwrZunaKIgPPq5hmPRybCBh69WEbQafXoJBAEnfWgY0sr2NQ+7KRWVttHBBx08OgWJRNhg9GxCDo1T6Z6NraWVoIOuUb1kH2n2vi6gUdG0Am6K0QYGn7ATiDorAMdW1rhmQ5/2NZcvnR8E36wpA/XecbjkYmwgUcvllGmw55ROTbDaWm1DnDwENmgA4jzx2zNZbXjiICDDh7domQibDA6FkGn5slUb8YWdNaDTu55jjewIwLOq5v5DrlT9dAjE2EDj14sI+hUp/5JAzOcllaCzhOvaDsy32lrfdXKIyPoBKWiEYaGG5gT2G9K7Dcm2vsgVFpasa17gjTaD3p0M985t0yEDUbHokynwwtgbC2tfIDJgVnQWXY+T3ALOpQdLJt3Xttr7AhDQ0Popf/KYj3olJ7nmK3nXlE/i/SDujbzFr0+DWmPTIQNPHqxjDKduW8snsFwep6zHnRsmZozOjtprj5XFhFw6NejW5RMhA1Gx6K/8El/FRPGrG0WKLnlgsragWRLqzX/EikCrjZ/t73+EmygTCd3yyyU4S8UADpYFmwVMB/57JcP2Hr088j0XD/XNv2vLFKTAw69n4i7PHTy6BYlE2GD0bEIOh2ejbsyoIO7dC6QvGUI+nfdf35xu//iS4ff+eM/q/aL6zz99NPVdqzrm9/y1gM2Ljv38dLzHG9gRwScV7fRQG110wgbjI5F0GmdzcPhcE7oABS1rQUMnqzlJqBjy9SS+T2OHRFwgs54pifolLw+KT/nV+UABYCDfSnDsAzG2rzw6UcnmdG773/wgA1tcZ2l7OmFz3z+8Lmv/++pnUHH2uMa1k9Jn5Fye56TvvrAJhd0fMEdAV7P3LCMoMOevnC8Geh87isTTHJZEeABYKAOUMB5rp2V/dz73l9sd07o1JZWmAZ20oVpmVVFBJxXN894PDIRNvDoxTKCzsxtyyfn/KocAQ4QLAW6weQjn/vK1BZAyWUb1s6gU2qHcqvj49w11y4TdMp+xjUcqFy+dCzoOO9YvcaOMHQEdN729vcUl0Q/+lM/N8HmY1/8g4uGji2t8Exn6dPrA7hWhB+gH49uUTIRNhgdizKdJc+/rjvn0gpZhGU6tuzJ7V/7ujccn9Wg3rKUNAvZeqZj0Fl6nuMN7IiA8+o2GqgNbjo1ibDB6FgEnYbZjILO0vKK4XLJ0GlZWnkDOyLgvLqNBmqDm05NImwwOhZBp2E2z7m04kxH0HkyGR7Hjgg4aOjRLUomwgajYxF0nvh58ch+U8LZxprHtry6Kej85DufPT4nsofKrbr02KF1aeUN7IiA8+o2GqhF50wqImwwOhZBJ5m09PTcSyvLdBDsrYFuYMgFPJ7p2POepXZch35/4fkHk5yVt+qS06FUJuik3rV87gnui4AOBnbpGwx9rjE8fPhwevUB7wqVgknlbS962vMcvMN2jvk6px+cQ99zXPMSbKBMZ/lmo//K4tttQGkBry1TKyafqhGQvZ+Iuzx08ugWJRNhg9GxCDoVz7ZAaQkqtSkDqmdp5Q3siIDz6jYaqBU3PVZH2GB0LILOcbpODyKe5+wFVLX/yiK1vsexIwIOenp0i5KJsMHoWASd1Nvp/Nxfle8FOBinPc8ByFs+HseOCDjo7tEtSibCBqNjEXQWIkDQKS+XeoFpy9QFc8+qPI4dEXCCzjh0BZ2Zqz85WXtpha+g7b+OSPf4D7o++XtfP3479uIrj7NtEehoh/bpNXB9fF2elts56n/3G/9n6sPKcuBYqsu1bynrfZ7jDWxBJ+b9M88NgWUEnSecmR2dAzq5d6qs7Afe+MzhlW++esD/k2Nl6f7R//q/B7RLy3EOqOA3Nrk6K7P/ygLngEsOGEt1ufYtZb1LK0HnyhU5UGfOuXASAV6PXiwj6BQmMHJpBWAg2LEHDHCcC+ZP/f43j+1y9Utl9qM/tFkCy1Ld0vWX6gSd8SVJwU1PigWdoIdu5zD02tABUJa2FuhgaWXtckG+dP2bgo4trfBMp+fDd8ZWuXP4Qa5vj25RMhE2GB2LMp2MV629tAIgAAQAY2lDm6VM57e+/EeL0Kktr1APXaBD1PLKoAOI93w8jh0RcBiDR7co7QW61wAAFNFJREFUmQgbjI5F0MlEwjmhg2DPbZbBLEHnY1+6+g+8AKdcpmPZTO76VhcNHc/SyhvYEQHn1W00UDNumi2KsMHoWASdzNStvbTiTCcHDMuCapnORz//36qZjmUzKZQEncxEbzxr8QT3RUAHA7v0DYZecwz2m5I0cEfOP/3VPz189/d87+xNbgMB9i2Zzq//p/9ybJfTJfdfVHAfOIbcMz/0I9N10jrTA5lS7vq9Zba0WvOveC7N89p+sNTXVusuwQbKdJIb4DmWVhas7//obxehg4C3TMfgYHK2f/C5/zzJ57IltEH53V/+8GIfaPehT70yLfEioZOYuXqKoO79RNzloZNHtyiZCBuMjkXQSTz75Zdf3vV/ZbH0kNng17q35zn4ryx6Px7Hjgg4QWccuoJOEg3neJ7TGqSR7XIPm+0h9vte+I1Vlle2TPUAxCMj6OgXyVM4e5ynV2ZNZ7NAiQTATfRlgEFmw9tPvPPnDy89/sYwdOx5DiDeO5/ebGJNP0juRbPTqPF4+omwgUcvllGmQ+50zuc5NwGWm+yT/ysLdjgy9+KhRyYi4KC0R7comQgbjI5F0CHX38vSKgJG9jwHIB91UpqixcOIgBN0xqEr6JAbCzrn+a8sBJ3xQCU3XTyMAO/ofAo611OopdV6wOHnOTDvqJMuRhlVRgRc5Hg8douwgUcvlhF0rp1W0FkPOry0igzSiICLHA8H6rWbVncRNvDoxTKCzvU0amkl6FQj+roBB9DWZASdoNR6DUMLOutAx5ZW/F9ZRAXpGn7QApGo8Xj6ibCBRy+WUaZzOBy0tFoHOPhWzKADiNuHHc7KanuPTETAQW+PblEyETYYHctTuMClbzD0yBjwM33cmfEsIuLr5Nvchz3POddf8Vya51E/WLr2pdRdgg2U6RwO+iueK/4VT4MOskf7IGB7Px6ZiLs8xuHRLUomwgajYxF0Docpy0Gmc5szkIix5ZZWkUEaEXCR4/EEd4QNPHqxzO6ho+c5532eExmkEQEXOR4OVPTb8omwgUcvltk9dPb+X1msmQHlllYIFHa4lsDxykQEnFe322SD0bHsHjr6qny9TMfe0E/BMuqk6fVK54KO/muLyTciHG7E2SxQ1rzj7/Fapec5cIIIH0A/I35QAlmuPGo8nn4ibODRi2V2nenoec56WQ7/VxZpoLLDpXWlc49MRMBBX49uUTIRNhgdy66ho6XVetApPc+JDNKIgIscjye4I2zg0YtlBJ07d6Zf0e5xSbTmmG2Zmstc2OFy9bkyj0xEwAk645nebqGjXyGvl+UsPc+JDFJBJ+a5lueGwDKCjl59GP5R5NLSStC5yuM46K5K6v96ZCLA69GLZQQdQWcIOpblYHlV+rDDldqk5R6ZiICDnh7domQibDA6lqeg5B63R48eHV9/QOCs+XxjL9eyb6wAnAcPHuzSj/YYO6Nj3m2mA1rbr5ERNFgiaGu3gT04xh52XPqM3hmXrs11CIbeT5RuUf1E2GB0LLuGDhwUX5vbV+ccSDq+c8wES7YAbGrAgY1HnbQVJBEBFzkej90ibODRi2V2D51Wh96ys9mPHHvGsuXxeHWLCDivbhx0rfPkkYmwgUcvlhF0Wj0g8I7NE9SinqBzZaWIgENPvfMTKRNhg9HxCzotUX3dZtTYrV319iPoXFk2IuDQU+/8RMpE2GB0/IJOKwk27GyCjqBjbizoBAVqhKEj71a9dxJBR9ARdMwCgk5IKi7oCDoWchE34N6bInRjGS2vbLYa9my4huZTkwgZQedqNiICDj1FzKm3nwgbjI5f0Lny16Z/R43d1InDqQWdK8tGBBx62qofQLcIG4yOX9BpJcGGnU3QEXTMjQWdoECNMDQmdZTw5hi1fW8/gs6VRffuB7BChA16/RN6sYz+wuct+Aun9n8DYWL3vCHg9jx+jP0SbKDlVS3toHpMau8nQkaZztWsRNzl0VPEnHr7ibDB6PgFnQ6KjBq7tavefgSdK8tGBJwXBr1z6u0nwgajYxF0Wkmw4TucoCPomBsLOkGBGmFo751n9K5gzrS0F3SurLN3P4AVImww6tPKdJaiOakbNXZyueJpbz+CzpUpIwIOPfXOT6RMhA1Gxy/oFEP/tGLU2KdXzJf09iPoXNkxIuDQU+/8RMpE2GB0/IJOPu6zpaPGzl40U9jbj6BzZcSIgENPvfMTKRNhg9HxCzqZoC8VjRq7dN20vLcfQefKghEBh5565ydSJsIGo+MXdNKIXzgfNfbCpWdVvf0IOlfmiwg49NQ7P5EyETYYHb+gMwv35ZNRYy9f/Ultbz+CzpXtIgIOPfXOT6RMhA1Gxy/oPIn16tGosasdXDfo7UfQuTJcRMChp975iZSJsMHo+AWd60Bv2Y0au6UPj4MKOleWjQg4z/xEykTYYDQO9MLnLXhJUi98Xr3oioBDQOx5uwQbKNO5ukk2/TtK+KZOHOm7Mp0ry0bc5dHTVv0AukXYYHT8gk4rCTbsbIKOoGNuLOgEBWqEoTGpo4Q3x6jte/sRdK4sunc/gBUibNDrn9CLZZTp1AhA9Ww4Kl48PJcM/oZ46W+Moxwgqn3OpVvab1Q/EQGHsUWNx9NPhA08erGMoJNGyMI5G26h2azqnDIl6Dz33HMzHUon59SN+4zqJyLgMK6o8Xj6ibCBRy+WEXQ4OirHbLhK02P1OWVK2Y6gczR/9eCc88OdR/Uj6ATdFSIMDQeKcpzWfkrQaVlabXE8o0G6Vz9gu0XYoNU/WS+WUabDlqkcs+EqTY/V55ZJl1itWQ4UPLduZoSofiICbut2i7DB6HwKOhYZDftRYzd0MTXp6SfNdgSdVitfteuxtV15yzKCTtDdNMLQcLgtOlsKndal1VbHMxLYe/YDs1uEDUbjQJmOzVbDftTYDV1MTXr7sSVWT5aDjnr72bpMRMDJBuN+I+hMYd72z1aD1LIdQedbbRNJrbY6p1DRo1sEeD16sYxe+LwFLwg+fPhw+qEgXvzE5O51Q8Dtdew27kuwgTIduuvVDjGxvZ8oGSyxej9RukX1E3GXh42jxuPpJ8IGHr1YRtDpiFQ2XKtYlMyDBw9aVTq2i9Itqp+IgIPxosbj6SfCBh69WEbQOYZg/YANV2991cIrg2+hbMMzG9vw3MY2e4DMe6uzvclhb9ezPZZjvR/veCL6iQg4jGPvNhgdv6DTEQ2jxuauDAYGB+wZHjdxbLoYlFhfO17TBnbN3N7Tj6Cjt8wnX/I4T6/M1p0NQQzItILlHc++92DbB178xIG3j3/xqwdsX/j2d4qbtbE9y9t1eb8EOAORZUq9cwMniJLZuh/k4LpU5rFbhA08erGMMp2lWU/q2HBJ1fHUAIMgLQUzAt5AYGBYgkhUneliQCrpn4LoOPjCQYvdUlGPTETAQU+PblEyETYYHYugk3r7wnnJ2LUshgETBZC1+jEQYQyA0RKIANzcp2S3XFsr88hEBBz08+gWJRNhg9GxCDrm5Q17NjZAk8tmOItZK/C3dp0aiJAJMYDYbg1mnpp4ZCICDsp5dIuSibDB6FgEndYoOBwO+BFeDjSWyWwNDlH6GIRymRAAFPUtWUTACTrj0BV0GqAj0JQfWqdgKwEozX5qZvfcTQUdfXs1+ZXHeXplzuVsKWxs6ZQGms7LUAKE0gwIdq19en0A1zuXH6S6enSLkomwwehYlOmkHnU4nCyhsHwSWMpgabFNL3w8jh0RcHAXj25RMhE2GB2LXvikFyTtxUn7hkawGQNNDkY5+MDucOTRDQE3eo1Ll78EGyjTUWZzI1lcCp/0mQ+Cv/cTcZdXpjOe6e0eOvzcRpnN+plNLtvhshQ+9rxH0PEFdwR4PXPDMruFDr7Gxd1VS6l40DB07BjAt7nAvLCTtmY8EQGnTMcHQ57PXUIHP1wzB8c3K+b42t8sgDjruXfv3uwHhi3gEXRivsFjgLTMSwrq3UFHy6mbBUsL2Pkrdv5lc83BBR1BZ/KRUSrWHA31rc7GGY6e32wbPrzcagVPqx+wT0X4J/qL6ifCBqNj2U2mI+BsGzK5DMjAg2c8LZ+IgIsEiCe4I2zg0YtldgMde2isDOey4GPgsW+1luATEXCCznjWtgvo2HMcPTS+LOBY9mMP/WvgEXTaHzUwvDkL4fLScW/7FNS3Hjq8rMK3I+bIW9p/5LNfPrzr/vPVDe247W//1/+ZHY9dK21v5en+/osvHX7nj/8se60t2MmyHcBn6SPoCDqTf4xSccnJrG7J2WxZteUsB3B4+umnq5tBxNr+0N/78cPn//T/ncDizW9563SttL3J5faQ2QJgSjoYeJaynSU/MF9J9xH+iT6j+omwwehYbn2mY6n5VrMcBJlBB4FvoFjaAxoGlp9458+fwMLq7BrW3s7TPbcvBf1Nlxt0lh4qRwRcJEA8wR1hA49eLPMUlLyt26NHj6YfAW45y0mhUwtuAMMg8trXvWE6fs8HXpiBhyHC7UvX5valNlsotxsI5vW2+uwexnWrMx17gIy75BaCpqQDgyHNQuzcZLntB1/63cNfeM1rJvC8/6O/fRwjQ4Tb2zXSPbdP67Z0btlOaYmFgO398B24VXbLMhE2GB3/rYaOPc/Z8tIKQW1gyD1rsazGgt/aAhQoe++/+fUJOn/xL3334cP//ktTGUMkbW/X4T235/KtHQs6dSwKOkEP0EqGvjToIPjTb5bs3ACQg8jP/uL9CTyvf+ZNh0985WvH5z1om2tv17L9pUAHNw8ssZTplOFTioWyRP9DbmU6C69BXCJ0DASlfQkiP/qPfnYCz9/+8Z8UdJYiLKkbDaDkcsXTqH4EnRvOdOyZzqUsr5BxlGBj5SXofO7rf374Wz/8dybwcOZSam/Xw57bc/nWjrW8KjLtWCHobAQ6l/IgufRMx8oBkCWI/NaX/8fh+/7690/ggQy3t2uU9vgmbGuQSfWxt89LL4BGBByiOypr8fQTYQOPXixzqx8k26+RL+Erc2Qbtc0gYu3SoMT5C59+dLxO2t7k0v3b3v6eSS53vS2V2Vfmx9t6chARcILOOHRvNXTgIOaoWwoe6dL/Dpg9RNaPAxPSJqcR4OWsJem+eMoytx469lxn60ssgWgZRLWlFbw9IuDQDwdQMcqSiiiZCBuMjuXWQwdzb9mOwLMc2FsFnz1AXspyMM8RAYd+RoMu4VHx1NNPhA08erHMLqBj2Q7gs9XAkl55INqyCnNXeoBsURsRcILOOHR3AR04ioFn6w+VBZ85fGxZhfmrfQSdmGyPs5banFg9y+zqL3ziLwzgjqll1jywtwo6Aw7mDU5b2wCdWpvbXn8JNthNpmPEtec7yni2DR4DTu05js0r9sp0YmwAcPd+WGZ30MFzAXs9Ao699V8rbzULOZde/AynBziCzhUGIsDLAGmFD8vsDjpmJFtqIfMReLaR9di3VJiThw8f2lQ17yMCDspwALUqFyUTYYPRsewWOjCcPVyGk2u5dbPgYeBgXjyOHRFwgs44dHcNHTiQvSphz3qU9cTCh2GDObCvxQUdX3BHgNczNyyze+hYemzPeSzrEXzOC58UNulX4uykNke1fUTAQQePblEyETYYHYugQ57MD5kFn/NAJwebFDjewI4IOK9uo4FKbrp4GGGD0bEIOpkpFHzWB04rbGw6PI4dEXDQz6NblEyEDUbHIuiYl2f2KXyU/fTBCKDphY1Ng8exIwJO0BmHrqBjXr6wz8FHAMoDKAca2ApLqNwyqmR2QccX3BHg9cwNywg6Ja/PlD9+/Hj6doUfOiOg9g4gPHRPMxoGDTtcxqzZIo9MRMBBWY9uUTIRNhgdi6CTdfl8IRsb2c9SBoQgRDDetm/BDDAYn72qYODFHkBOMxq2W96yp6UemYiAg6Ye3aJkImwwOpZdvfAJY51jQwaEjX/lzIGIYwToJYIIOpcAY2PEuPEL4nPYtueaCLie9rex7SXYQJnO6U22WAInbfkgA8LdHnf9Gojs/a+byoqsX4OL7Q0o6R7jseczGGfLp9VufC2PTMRdHjp6dIuSibDB6FgEHfb0yvGIsXk5lnsmlAa3nQNKvBkUsDdg5PbcDsd8Dbt2yz4FzIgNKuadVXv6iQg4KOnRLUomwgajYxF0Zq6+fDJq7PTqKYgAox4gtUBjqQ2yFuvT4IL9Ugaztg1Sm9i5p5+IgIN+Ht2iZCJsMDoWQce8vGE/auyGLqYmaT8GJ9szIAwad+/ePQIEZdzG5FKYpP206LdlmYiAg432boPR8Qs6LZF23WbU2K1defpRwMX8B1aCzjh0BZ1WEugON1nKA8QoGYE3Bryj8ynoCDodFhi/y7V25nFsQUfQCbszytlinA0T6oFBlIz8IMYPRudTmU7rrVcBF3YT8cJN0BF0wpxUzhbjbF4YjN4ZJ0dq+Ed+EOMHo/OpTKfBma3JqLHtOrW9px8FXEzAYe488xMlE+EHo2PRu1dneh8LExO5wdki+9tiX7LBq9Pf/tri3LBOynRqaQfVw3C9nyiZiDscxh41Hk8/skFMtueZG5YRdDoowoZrFYuSUcDFBJzAO37jEXRa6aG7/GSpKIh6+hF4Y8DrmRuWEXQEnQ4LjN/lWjtjJ22VEXQEnclXPM7TKyNni3E2TGjv3ETKyA9i/GDUB5TptN5GFXBhNxEvqAQdQSfMSeVsMc7mhcHonXFypIZ/5AcxfjA6n8p0GpzZmowa265T23v6UcDFBBzmzjM/UTIRfjA6FkGnRgCqHzU2XWrx0NNPhLNBaY9uUTKyQQx4R+dT0FkM/3nlqLHnVyufefpRwMUEHGbNMz9RMhF+MDoWQacc+yc1o8Y+uWChwNNPhLNBXY9uUTKyQQx4R+dT0CkEfq541Ni5a+bKPP0o4GICDvPlmZ8omQg/GB2LXvgMfjETE3aODc52jute0jVlA73wOd3E4bS9n16ZCLrrDnc1i71zE2k3+UFMtjfqA1pedRBx1NitXXn6UcDFBFwkRLfqBx69WEbQaSWB1vKTpdh5Wk0XJSPwxoB3dD4FndbIEXQEnWtfGQ26Vpfz9BMBXo9eLCPotHqAoCPoCDqr+ICgI+h0WEBfF8NYfNduNV6UjDKdoAmKMLSc7Sq8ooLH04/84DKe6fx/O5n6fkXF0TYAAAAASUVORK5CYII=) ###Code ##ONES MATRIX ones_mat_row = np.ones((5,4)) ones_mat_sqr = np.ones((4,6)) ones_mat_rct = np.ones((9,5)) print(f'Ones Row Matrix: \n{ones_mat_row}') print(f'Ones square Matrix: \n{ones_mat_sqr}') print(f'Ones Rectangular Matrix: \n{ones_mat_rct}') ###Output Ones Row Matrix: [[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]] Ones square Matrix: [[1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1.]] Ones Rectangular Matrix: [[1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.]] ###Markdown FLOWCHART* 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) ###Code ##ZEROS MATRIX zero_mat_row = np.zeros((4,8)) zero_mat_sqr = np.zeros((6,6)) zero_mat_rct = np.zeros((5,9)) print(f'Zero Row Matrix: \n{zero_mat_row}') print(f'Zero Square Matrix: \n{zero_mat_sqr}') print(f'Zero Rectangular Matrix: \n{zero_mat_rct}') ###Output Zero Row Matrix: [[0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0.]] Zero Square Matrix: [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] Zero Rectangular Matrix: [[0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0.]] ###Markdown *FLOWCHART![image.png](data:image/png;base64,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) Task 2Create a function named `mat_operations()` that takes in two matrices a input parameters it should: 1. Determines if the matrices are viable for operation and returns your own error message if they are not viable. 2. Returns the sum of the matrices. 3. Returns the difference of the matrices. 4. Returns the element-wise multiplication of the matrices. 5. Returns the element-wise division of the matrices.Use 5 sample matrices in which their shapes are not lower than $(3,3)$.In your methodology, create a flowchart discuss the functions and methods you have done. Present your results in the results section showing the description of each matrix you have declared. ###Code def mat_operations(matrix): print(f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\t{matrix.ndim}\n') ## Determines if the matrices are viable for operation and returns your own error message if they are not viable.* print ("Yes, the matrices are viable.") ###Output Yes, the matrices are viable. ###Markdown 2. ADDITION ###Code A = np.array([ [8,2,3], [3,9,6], [7,5,9], [6,6,1], [1,4,5] ]) B = np.array([ [2,4,4], [5,8,1], [9,3,7], [3,5,9], [1,2,3] ]) A + B 15+A ###Output _____no_output_____ ###Markdown *FLOWCHART![image.png](data:image/png;base64,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) 3. SUBTRACTION* ###Code A-B 9-A ###Output _____no_output_____
EficienciaCódigos.ipynb	###Markdown ###Code from time import time def ejemplo2( n ): contador = 0 start_time = time() for i in range( n ) : for j in range( n ) : contador += 1 elapsed_time = time() - start_time print("Tiempo transcurrido: %0.10f segundos." % elapsed_time) return contador def ejemplo3( n ): start_time = time() x = n * 2 y = 0 for m in range (100): y = x - n elapsed_time = time() - start_time print("Tiempo transcurrido: %0.10f segundos." % elapsed_time) return y def ejemplo4( n ): start_time = time() x = 3 * 3.1416 + n y = x + 3 * 3 - n z = x + y elapsed_time = time() - start_time print("Tiempo transcurrido: %0.10f segundos." % elapsed_time) return z def ejemplo5( x ): start_time = time() n = 10 for j in range( 0 , x , 1 ): n = j + n elapsed_time = time() - start_time print("Tiempo transcurrido: %0.10f segundos." % elapsed_time) return n def ejemplo6( n ): start_time = time() data=[[[1 for x in range(n)] for x in range(n)] for x in range(n)] suma = 0 for d in range(n): for r in range(n): for c in range(n): suma += data[d][r][c] elapsed_time = time() - start_time print("Tiempo transcurrido: %0.10f segundos." % elapsed_time) return suma def main(): vuelta = 1 for h in range(100,1100,100): print(vuelta) print("Ejemplo 2: ",ejemplo2(h)) print("Ejemplo 3: ",ejemplo3(h)) print("Ejemplo 4: ",ejemplo4(h)) print("Ejemplo 5: ",ejemplo5(h)) print("Ejemplo 6: ",ejemplo6(h)) vuelta += 1 main() ###Output 1 Tiempo transcurrido: 0.0004897118 segundos. Ejemplo 2: 10000 Tiempo transcurrido: 0.0000069141 segundos. Ejemplo 3: 100 Tiempo transcurrido: 0.0000009537 segundos. Ejemplo 4: 127.84960000000001 Tiempo transcurrido: 0.0000097752 segundos. Ejemplo 5: 4960 Tiempo transcurrido: 0.1384265423 segundos. Ejemplo 6: 1000000 2 Tiempo transcurrido: 0.0019879341 segundos. Ejemplo 2: 40000 Tiempo transcurrido: 0.0000078678 segundos. Ejemplo 3: 200 Tiempo transcurrido: 0.0000014305 segundos. Ejemplo 4: 227.8496 Tiempo transcurrido: 0.0000226498 segundos. Ejemplo 5: 19910 Tiempo transcurrido: 1.1131577492 segundos. Ejemplo 6: 8000000 3 Tiempo transcurrido: 0.0041046143 segundos. Ejemplo 2: 90000 Tiempo transcurrido: 0.0000097752 segundos. Ejemplo 3: 300 Tiempo transcurrido: 0.0000016689 segundos. Ejemplo 4: 327.8496 Tiempo transcurrido: 0.0000259876 segundos. Ejemplo 5: 44860 Tiempo transcurrido: 3.6521036625 segundos. Ejemplo 6: 27000000 4 Tiempo transcurrido: 0.0084984303 segundos. Ejemplo 2: 160000 Tiempo transcurrido: 0.0000102520 segundos. Ejemplo 3: 400 Tiempo transcurrido: 0.0000011921 segundos. Ejemplo 4: 427.8496 Tiempo transcurrido: 0.0000369549 segundos. Ejemplo 5: 79810 Tiempo transcurrido: 8.7559375763 segundos. Ejemplo 6: 64000000 5 Tiempo transcurrido: 0.0135233402 segundos. Ejemplo 2: 250000 Tiempo transcurrido: 0.0000107288 segundos. Ejemplo 3: 500 Tiempo transcurrido: 0.0000014305 segundos. Ejemplo 4: 527.8496 Tiempo transcurrido: 0.0000417233 segundos. Ejemplo 5: 124760 Tiempo transcurrido: 17.6952991486 segundos. Ejemplo 6: 125000000 6 Tiempo transcurrido: 0.0185880661 segundos. Ejemplo 2: 360000 Tiempo transcurrido: 0.0000095367 segundos. Ejemplo 3: 600 Tiempo transcurrido: 0.0000014305 segundos. Ejemplo 4: 627.8496 Tiempo transcurrido: 0.0000524521 segundos. Ejemplo 5: 179710 Tiempo transcurrido: 32.6443607807 segundos. Ejemplo 6: 216000000 7 Tiempo transcurrido: 0.0260484219 segundos. Ejemplo 2: 490000 Tiempo transcurrido: 0.0000107288 segundos. Ejemplo 3: 700 Tiempo transcurrido: 0.0000011921 segundos. Ejemplo 4: 727.8496 Tiempo transcurrido: 0.0000498295 segundos. Ejemplo 5: 244660 Tiempo transcurrido: 53.7953050137 segundos. Ejemplo 6: 343000000 8 Tiempo transcurrido: 0.0394110680 segundos. Ejemplo 2: 640000 Tiempo transcurrido: 0.0000271797 segundos. Ejemplo 3: 800 Tiempo transcurrido: 0.0000016689 segundos. Ejemplo 4: 827.8496 Tiempo transcurrido: 0.0000572205 segundos. Ejemplo 5: 319610 Tiempo transcurrido: 78.4677960873 segundos. Ejemplo 6: 512000000 9 Tiempo transcurrido: 0.0429062843 segundos. Ejemplo 2: 810000 Tiempo transcurrido: 0.0000107288 segundos. Ejemplo 3: 900 Tiempo transcurrido: 0.0000016689 segundos. Ejemplo 4: 927.8496 Tiempo transcurrido: 0.0000705719 segundos. Ejemplo 5: 404560 Tiempo transcurrido: 109.4635345936 segundos. Ejemplo 6: 729000000 10 Tiempo transcurrido: 0.0521612167 segundos. Ejemplo 2: 1000000 Tiempo transcurrido: 0.0000112057 segundos. Ejemplo 3: 1000 Tiempo transcurrido: 0.0000014305 segundos. Ejemplo 4: 1027.8496 Tiempo transcurrido: 0.0000815392 segundos. Ejemplo 5: 499510 Tiempo transcurrido: 152.6276922226 segundos. Ejemplo 6: 1000000000
stock_data.ipynb	###Markdown Getting microsoft Historical data from IEX ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import datetime as dt web.DataReader("MSFT","iex","2014-1-1","2018-9-24") ###Output 5y ###Markdown DOWNLOADING MICROSOFT HISTORICAL DATA(CLEAN)FROM YAHOO ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("MSFT",start,end) display (web) #web=yf.download("MSFT","2014-1-1","2018-10-17") #web=yf.download("AAPL","2014-1-1","2018-10-17")# downloading APPL historical stock data web ###Output _____no_output_____ ###Markdown LINE PLOT OF ADJ CLOSE OF MICROSOFT STOCK OF ALL THE YEARS ###Code import matplotlib.pyplot as plt %matplotlib inline %pylab inline pylab.rcParams['figure.figsize'] = (20, 9) web["Adj Close"].plot(grid = True) ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown https://www.pivotaltracker.com/story/show/160775317 ###Code import numpy as np import matplotlib.pyplot as plt %matplotlib inline %pylab inline pylab.rcParams['figure.figsize'] = (20, 9) web["Adj Close"]["2018-01-01":].plot(grid = True) ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown https://www.pivotaltracker.com/story/show/160800230 SIMPLE MOVING AVERAGE OF CLOSE PRICE (20 days and 50 days) ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt %matplotlib inline %pylab inline start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("MSFT",start,end) display (web) web_close=pd.DataFrame(web.Close) web_close["MA_20"]=web_close.Close.rolling(20).mean() web_close["MA_50"]=web_close.Close.rolling(50).mean() pylab.rcParams['figure.figsize'] = (20, 9) plt.grid(True) plt.plot(web_close['Close'],label='Close') plt.plot(web_close["MA_20"],label="MA 20 days") plt.plot(web_close["MA_50"],label="MA 50 days") plt.legend(loc=2) ###Output Populating the interactive namespace from numpy and matplotlib [*******************100%********************] 1 of 1 downloaded ###Markdown https://www.pivotaltracker.com/story/show/160695607 BOLLINGER BAND ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt %matplotlib inline %pylab inline start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("AAPL",start,end) web_close=pd.DataFrame(web.Close) # assigning close prices to web_ close web_close["MA_20"]=web_close.Close.rolling(20).mean() # moving average of close prices of 20 days web["20 Day STD"] = web.Close.rolling(20).std() web["Upper Band"] = web_close["MA_20"] + (web['20 Day STD'] 2) # adding 20 day moving average to 20 day standard deviation web["Lower Band"] = web_close["MA_20"] - (web['20 Day STD'] * 2) # for lower band subtracting 20 day moving average and 20 day standard deviation pylab.rcParams['figure.figsize'] = (20, 9) plt.plot(web_close["Close"],label='Close') plt.plot(web_close["MA_20"],label="MA 20 days") plt.plot(web["Upper Band"], label="Upperband") plt.plot(web["Lower Band"], label="Lowerband") plt.legend(loc=2) ###Output Populating the interactive namespace from numpy and matplotlib [*******************100%********************] 1 of 1 downloaded ###Markdown https://www.pivotaltracker.com/story/show/160696264 CCI https://www.pivotaltracker.com/story/show/160699188 ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt %matplotlib inline %pylab inline start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("MSFT",start,end) web_close=pd.DataFrame(web.Close) web_high=pd.DataFrame(web.High) web_low=pd.DataFrame(web.Low) web_close["MA_20"]=web.Close.rolling(20).mean() web["20 Day STD"] = web.Close.rolling(20).std() TP=(web.High+web.Low+web.Close)/3 CCI = pd.Series((TP -web_close["MA_20"]) / (0.015web["20 Day STD"])) pylab.rcParams['figure.figsize'] = (20, 9) print (CCI) plt.plot(CCI,label='CCI') plt.legend(loc=2) ###Output Populating the interactive namespace from numpy and matplotlib [*******************100%********************] 1 of 1 downloaded Date 2017-09-01 NaN 2017-09-05 NaN 2017-09-06 NaN 2017-09-07 NaN 2017-09-08 NaN 2017-09-11 NaN 2017-09-12 NaN 2017-09-13 NaN 2017-09-14 NaN 2017-09-15 NaN 2017-09-18 NaN 2017-09-19 NaN 2017-09-20 NaN 2017-09-21 NaN 2017-09-22 NaN 2017-09-25 NaN 2017-09-26 NaN 2017-09-27 NaN 2017-09-28 NaN 2017-09-29 -3.996477 2017-10-02 25.599563 2017-10-03 3.322109 2017-10-04 -10.993418 2017-10-05 107.340255 2017-10-06 105.774882 2017-10-09 119.063751 2017-10-10 112.930350 2017-10-11 95.931636 2017-10-12 117.760421 2017-10-13 132.861846 ... 2018-10-02 74.006028 2018-10-03 77.358163 2018-10-04 -1.897045 2018-10-05 -56.343274 2018-10-08 -125.291161 2018-10-09 -68.977777 2018-10-10 -170.732654 2018-10-11 -163.012137 2018-10-12 -80.635641 2018-10-15 -98.799314 2018-10-16 -41.079862 2018-10-17 -34.432299 2018-10-18 -65.505431 2018-10-19 -52.545248 2018-10-22 -42.982536 2018-10-23 -81.224092 2018-10-24 -119.974506 2018-10-25 -44.757032 2018-10-26 -59.621239 2018-10-29 -89.901345 2018-10-30 -120.707458 2018-10-31 -34.211038 2018-11-01 -40.414796 2018-11-02 -37.867501 2018-11-05 -11.432448 2018-11-06 10.524711 2018-11-07 98.340627 2018-11-08 96.521523 2018-11-09 53.258507 2018-11-12 -4.019879 Length: 302, dtype: float64 ###Markdown RSI INDICATOR https://www.pivotaltracker.com/story/show/160699122 ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like # Used as a patch for pandas data reader to avoid error from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("MSFT",start,end) import numpy as np import matplotlib.pyplot as plt import matplotlib.mlab as mlab import matplotlib.dates as mdates import matplotlib.font_manager as font_manager %matplotlib inline %pylab inline web_close=pd.DataFrame(web.Close) print (len(web_close)) matplotlib.rcParams.update({'font.size': 9}) def rsiFunc(prices, n=14): deltas = np.diff(prices,axis=0) seed = deltas[:n+1] up = seed[seed>=0].sum()/n #moving average of last 14 days prices down = -seed[seed<0].sum()/n rs = (up/down) rsi= np.zeros_like(prices) rsi[:n] = 100. - (100./(1.+rs)) for i in range(n, len(prices)): delta = deltas[i-1] # cause the diff is 1 shorter if delta>0: upval = delta downval = 0. else: upval = 0. downval = -delta up = (up(n-1) + upval)/n down = (down(n-1) + downval)/n rs = abs(up/down) rsi[i] = 100. -(100./(1.+rs)) return rsi #Reset the index to remove Date column from index web_ohlc = web.reset_index() #Naming columns web_ohlc.columns = ["Date","Open","High",'Low',"Close","Adj Close","Volume"] #Converting dates column to float values web_ohlc['Date'] = web_ohlc['Date'].map(mdates.date2num) prices=web_close rsi=rsiFunc(prices) print (rsi)# PRINTING THR RSI VALUES #plotting RSI INDICATOR pylab.rcParams['figure.figsize'] = (20, 9)#this function makes the graph bigger and presentable ax= plt.gca() ax.xaxis_date() plt.xlabel("Date") plt.plot(web_ohlc['Date'],rsi) plt.ylim(0,100) plt.yticks([30, 70]) plt.show() ###Output [******************100%*******************] 1 of 1 downloaded Populating the interactive namespace from numpy and matplotlib 302 [[45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [45.29085351] [46.87568171] [39.74616755] [39.74616755] [44.74642706] [44.91335474] [49.95912221] [50.89664254] [48.06804218] [51.62508435] [60.33456731] [60.51399817] [62.28990422] [62.28990422] [63.15149278] [67.46250188] [69.49403192] [70.35600846] [69.56216975] [69.68497518] [71.53944178] [76.23599659] [76.32949526] [76.47895254] [72.68935016] [73.48897734] [88.08395307] [88.19482924] [80.99212126] [80.99212126] [82.96877643] [83.16381837] [83.89226009] [81.58824359] [82.34536902] [76.83102173] [74.32221381] [74.56613444] [75.07611537] [62.95486975] [64.23346779] [56.58514107] [57.47131258] [64.59552134] [59.12772126] [60.02382938] [63.52599065] [68.45381018] [56.02475427] [60.21729809] [60.6553468 ] [42.74617832] [45.52418142] [51.44436966] [50.01780167] [57.35205565] [61.27311308] [62.48802053] [61.13081976] [57.28591337] [65.03653219] [62.38395121] [59.33422274] [57.62425368] [57.50912049] [57.55480095] [56.83117862] [58.41784946] [58.47088699] [57.0603605 ] [59.4593277 ] [61.70709022] [65.60872545] [70.24788837] [70.60374245] [70.00262147] [65.96998093] [67.28905328] [73.7058229 ] [62.79621691] [69.71024282] [69.39985496] [68.57780754] [73.93170909] [74.76574067] [74.06177724] [75.63663197] [80.05941873] [78.81254734] [69.05101596] [75.37079949] [70.26554332] [56.61065322] [42.91986842] [53.57222193] [48.53401802] [38.18980898] [46.63137446] [48.88439098] [50.54124827] [52.84566505] [56.92574928] [55.09422901] [56.72977711] [53.16721204] [53.77717993] [59.31692871] [62.16710553] [58.2263151 ] [56.85824271] [53.93819617] [54.48544497] [56.14082326] [54.97299076] [56.61316642] [58.33828371] [64.0385044 ] [64.6069576 ] [54.9998324 ] [52.98636351] [54.05379925] [55.44047147] [48.96141867] [49.84729965] [47.44538714] [39.05757394] [32.96760702] [52.94708327] [43.77193653] [43.62083342] [48.15047313] [42.7410585 ] [45.58965461] [51.3305872 ] [51.4358866 ] [46.75142188] [48.03159729] [52.80607398] [50.39572035] [54.19276638] [52.92450289] [55.37624007] [59.35043145] [60.09574079] [59.05572838] [55.57200477] [56.44447731] [49.74191905] [47.533957 ] [52.94864782] [56.7905582 ] [50.27281334] [53.9363336 ] [49.94681682] [51.40175628] [54.19284014] [56.79168791] [55.48052953] [58.33557728] [60.66738907] [59.8859433 ] [60.74168497] [57.88076188] [57.18624676] [53.25928818] [53.89203167] [58.10003806] [57.64315661] [61.43200339] [59.69685899] [59.8712411 ] [57.98021181] [61.49753261] [60.85556343] [67.36025892] [69.79922189] [71.17013909] [71.96090962] [62.11414013] [64.54780124] [61.27003481] [62.19676164] [59.48477247] [61.71258155] [54.41945499] [57.48167283] [57.48167283] [61.61916639] [57.28050563] [53.24327149] [44.00138407] [47.36243396] [41.39124442] [46.52965081] [46.44919692] [52.62554043] [48.49515198] [51.52541331] [56.90902685] [59.30771183] [60.24040761] [59.47914668] [66.64482882] [69.86489808] [66.94618391] [69.67485113] [65.05886326] [61.2669557 ] [66.69617533] [70.7149685 ] [69.07802501] [75.35945847] [69.55168213] [61.38349298] [53.34932714] [55.28645933] [55.84268931] [59.35485694] [60.58492295] [60.82937259] [62.89468556] [64.53266293] [65.02331882] [61.60695685] [57.75382129] [62.11444557] [53.71149275] [53.62922837] [53.36524364] [50.21514014] [46.50901231] [51.21438396] [53.26411432] [56.56577933] [60.82362492] [62.97354099] [68.0143675 ] [67.62005876] [68.68153946] [64.94091906] [49.77786238] [50.73955296] [48.61423764] [53.26782122] [59.54081161] [60.96663227] [64.41478351] [65.66683625] [59.62567234] [62.82926719] [56.06803226] [61.58182266] [63.40682097] [64.48635157] [63.40543783] [61.0510985 ] [62.4257564 ] [62.20581021] [66.18337863] [63.51298237] [63.58177098] [51.20726883] [48.39442702] [43.41374627] [49.57077329] [32.89504951] [32.4137851 ] [45.08147331] [40.66357663] [49.80609119] [49.11102163] [44.06429373] [44.50890215] [47.24645583] [43.5933147 ] [33.16163262] [47.23092689] [44.94772872] [40.10236641] [39.92353869] [46.51617127] [44.98014016] [45.50276172] [48.46787702] [48.93332745] [57.31617941] [56.81869242] [51.79296194] [46.32737144]] ###Markdown https://www.pivotaltracker.com/story/show/160780916 Candle Stick Depiction of Stock Price ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt import matplotlib.ticker as mticker from mpl_finance import candlestick_ohlc import matplotlib.dates as mdates import datetime as dt %matplotlib inline %pylab inline start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d')# retrives data on a daily basis /or web=yf.download("MSFT",start,end) #Reset the index to remove Date column from index web_ohlc = web.reset_index() #Naming columns web_ohlc.columns = ["Date","Open","High",'Low',"Close","Adj Close","Volume"] #Converting dates column to float values web_ohlc['Date'] = web_ohlc['Date'].map(mdates.date2num) pylab.rcParams['figure.figsize'] = (20, 9) ax1 = plt.subplot2grid((8,1), (0,0), rowspan=8, colspan=1) plt.tight_layout() ax1.xaxis_date() plt.xlabel("Date") print(web_ohlc) candlestick_ohlc(ax1,web_ohlc.values,width=1.5, colorup='g', colordown='r',alpha=0.75) plt.ylabel("Price") plt.legend(loc=2)# shows label in the left most postion of the graph plt.show() ###Output Populating the interactive namespace from numpy and matplotlib [*****************100%*******************] 1 of 1 downloaded Date Open High Low Close Adj Close \ 0 736573.0 74.709999 74.739998 73.639999 73.940002 72.631020 1 736577.0 73.339996 73.889999 72.980003 73.610001 72.306847 2 736578.0 73.739998 74.040001 73.349998 73.400002 72.100578 3 736579.0 73.680000 74.599998 73.599998 74.339996 73.023933 4 736580.0 74.330002 74.440002 73.839996 73.980003 72.670311 5 736583.0 74.309998 74.940002 74.309998 74.760002 73.436501 6 736584.0 74.760002 75.239998 74.370003 74.680000 73.357918 7 736585.0 74.930000 75.230003 74.550003 75.209999 73.878525 8 736586.0 75.000000 75.489998 74.519997 74.769997 73.446312 9 736587.0 74.830002 75.389999 74.070000 75.309998 73.976761 10 736590.0 75.230003 75.970001 75.040001 75.160004 73.829422 11 736591.0 75.209999 75.709999 75.010002 75.440002 74.104462 12 736592.0 75.349998 75.550003 74.309998 74.940002 73.613312 13 736593.0 75.110001 75.239998 74.110001 74.209999 72.896225 14 736594.0 73.989998 74.510002 73.849998 74.410004 73.092697 15 736597.0 74.089996 74.250000 72.919998 73.260002 71.963051 16 736598.0 73.669998 73.809998 72.989998 73.260002 71.963051 17 736599.0 73.550003 74.169998 73.169998 73.849998 72.542610 18 736600.0 73.540001 73.970001 73.309998 73.870003 72.562256 19 736601.0 73.940002 74.540001 73.879997 74.489998 73.171272 20 736604.0 74.709999 75.010002 74.300003 74.610001 73.289154 21 736605.0 74.669998 74.879997 74.190002 74.260002 72.945351 22 736606.0 74.089996 74.720001 73.709999 74.690002 73.367737 23 736607.0 75.220001 76.120003 74.959999 75.970001 74.625076 24 736608.0 75.669998 76.029999 75.540001 76.000000 74.654549 25 736611.0 75.970001 76.550003 75.860001 76.290001 74.939415 26 736612.0 76.330002 76.629997 76.139999 76.290001 74.939415 27 736613.0 76.360001 76.459999 75.949997 76.419998 75.067108 28 736614.0 76.489998 77.290001 76.370003 77.120003 75.754723 29 736615.0 77.589996 77.870003 77.290001 77.489998 76.118172 .. ... ... ... ... ... ... 272 736969.0 115.300003 115.839996 114.440002 115.150002 115.150002 273 736970.0 115.419998 116.180000 114.930000 115.169998 115.169998 274 736971.0 114.610001 114.760002 111.629997 112.790001 112.790001 275 736972.0 112.629997 113.169998 110.639999 112.129997 112.129997 276 736975.0 111.660004 112.029999 109.339996 110.849998 110.849998 277 736976.0 111.139999 113.080002 110.800003 112.260002 112.260002 278 736977.0 111.239998 111.500000 105.790001 106.160004 106.160004 279 736978.0 105.349998 108.930000 104.199997 105.910004 105.910004 280 736979.0 109.010002 111.239998 107.120003 109.570000 109.570000 281 736982.0 108.910004 109.480003 106.949997 107.599998 107.599998 282 736983.0 109.540001 111.410004 108.949997 111.000000 111.000000 283 736984.0 111.680000 111.809998 109.550003 110.709999 110.709999 284 736985.0 110.099998 110.529999 107.830002 108.500000 108.500000 285 736986.0 108.930000 110.860001 108.209999 108.660004 108.660004 286 736989.0 109.320000 110.540001 108.239998 109.629997 109.629997 287 736990.0 107.769997 108.970001 105.110001 108.099998 108.099998 288 736991.0 108.410004 108.489998 101.589996 102.320000 102.320000 289 736992.0 106.550003 109.269997 106.150002 108.300003 108.300003 290 736993.0 105.690002 108.750000 104.760002 106.959999 106.959999 291 736996.0 108.110001 108.699997 101.629997 103.849998 103.849998 292 736997.0 103.660004 104.379997 100.110001 103.730003 103.730003 293 736998.0 105.440002 108.139999 105.389999 106.809998 106.809998 294 736999.0 107.050003 107.320000 105.529999 105.919998 105.919998 295 737000.0 106.480003 107.320000 104.980003 106.160004 106.160004 296 737003.0 106.370003 107.739998 105.900002 107.510002 107.510002 297 737004.0 107.379997 108.839996 106.279999 107.720001 107.720001 298 737005.0 109.440002 112.239998 109.400002 111.959999 111.959999 299 737006.0 111.800003 112.209999 110.910004 111.750000 111.750000 300 737007.0 110.849998 111.449997 108.760002 109.570000 109.570000 301 737010.0 109.419998 109.959999 106.099998 106.870003 106.870003 Volume 0 21736200 1 21556000 2 16535800 3 17471200 4 14703800 5 17910400 6 14394900 7 13380800 8 15733900 9 38578400 10 23307000 11 16093300 12 21587900 13 19186100 14 14111400 15 24149200 16 18019600 17 19565100 18 10883800 19 17079100 20 15304800 21 12190400 22 13317700 23 21195300 24 13959800 25 11386500 26 13944500 27 15388900 28 16876500 29 15335700 .. ... 272 20787200 273 16648000 274 34821700 275 29068900 276 29640600 277 26198600 278 61376300 279 63904300 280 47742100 281 32068100 282 31610200 283 26548200 284 32506200 285 32785500 286 26545600 287 43770400 288 63897800 289 61646800 290 55523100 291 55162000 292 65350900 293 51062400 294 33384200 295 37680200 296 27922100 297 24340200 298 37901700 299 25644100 300 32039200 301 33598300 [302 rows x 7 columns] ###Markdown https://www.pivotaltracker.com/story/show/161293103 OHLC GRAPH WITH SIMPLE LINE PLOTTING ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt import matplotlib.ticker as mticker from mpl_finance import candlestick_ohlc import matplotlib.dates as mdates import datetime as dt %matplotlib inline %pylab inline start=dt.datetime(2017,9,1) end=dt.datetime.today().strftime('%Y-%m-%d') web=yf.download("MSFT",start,end) web_ohlc = web.reset_index() #Naming columns web_ohlc.columns = ["Date","Open","High",'Low',"Close","Adj Close","Volume"] #Converting dates column to float values web_ohlc['Date'] = web_ohlc['Date'].map(mdates.date2num) pylab.rcParams['figure.figsize'] = (20, 9) ax= plt.gca() #used to set "get current axes".Current here means that it provides a handle to the last active axes. If there is no axes yet, an axes will be created. If you create two subplots, the subplot that is created last is the current one. ax.xaxis_date() plt.plot(web_ohlc['Date'],web_ohlc.Close) plt.plot(web_ohlc['Date'],web_ohlc.Open) plt.plot(web_ohlc['Date'],web_ohlc.High) plt.plot(web_ohlc['Date'],web_ohlc.Low) plt.ylabel("Price") plt.xlabel("Dates") plt.legend(loc=2) ###Output Populating the interactive namespace from numpy and matplotlib [*****************100%*********************] 1 of 1 downloaded ###Markdown MACD https://www.pivotaltracker.com/story/show/160699005 ###Code import pandas as pd pd.core.common.is_list_like=pd.api.types.is_list_like from pandas_datareader import data as web import fix_yahoo_finance as yf import datetime as dt import matplotlib.pyplot as plt import matplotlib.ticker as mticker from mpl_finance import candlestick_ohlc import matplotlib.dates as mdates import datetime as dt %matplotlib inline %pylab inline #start=dt.datetime(2017,9,1) #end=dt.datetime.today().strftime('%Y-%m-%d') #web=yf.download("MSFT",start,end) #web_close=pd.DataFrame(web.Close) display(web_close) web['26 ema'] = web_close.ewm(com=26).mean() web['12 ema'] = web_close.ewm(com=12).mean() #print (web['26 ema'],web['12 ema']) web['MACD'] = (web['12 ema'] - web['26 ema']) print(web['MACD']) pylab.rcParams['figure.figsize'] = (20, 9) plt.plot(web['MACD'],label='MACD') plt.legend(loc=2) ###Output Populating the interactive namespace from numpy and matplotlib
Lessons/Lesson06b_Matplotlib_supplement.ipynb	###Markdown "Geo Data Science with Python" Notebook Lesson 6b Matplotlib - Supplement--- Import Modules and Data ###Code import matplotlib.pyplot as plt import numpy as np # Temperature converter def tempF2C(temp): return (temp - 32.0) / 1.8 # read datasets from file to numpy arrays fp = './Kumpula-June-2016-w-metadata.txt' data = np.genfromtxt(fp, skip_header=9, delimiter=',') dates = data[:, 0] temp = data[:, 1] temp_max = data[:, 2] temp_min = data[:, 3] # Convert temperature into celcius temp_celsius = tempF2C(temp) temp_max_celsius = tempF2C(temp_max) temp_min_celsius = tempF2C(temp_min) ###Output _____no_output_____ ###Markdown CODE EXAMPLES 1Add dates to your plot with datetime objects ###Code # convert dates into integer numbers, then strings dtStr = (dates.astype(int)).astype(str) # convert dates from strings to datetime date objects from datetime import datetime datesDt = [ datetime.strptime(d, '%Y%m%d') for d in dtStr ] # plot the dataset plt.plot(datesDt, temp_celsius) # add x-label plt.xlabel('Time') # x-label # use the plt.ticks() function to rotate the dates locs = plt.xticks(rotation=30, ha='right') ###Output _____no_output_____ ###Markdown --- CODE EXAMPLES 2Plot the OOP way! ###Code # create figure and axes objects fig, ax = plt.subplots() # plot the dataset ax.plot(datesDt, temp_celsius, 'r') # add x-label ax.set_xlabel('Time') # use the autofmt_xdate() method to rotate the dates fig.autofmt_xdate() ###Output _____no_output_____ ###Markdown --- CODE EXAMPLES 3Create subplotsdef tempF2C(temp): (temp - 32.0) / 1.8 ###Code # Create the figure and subplots fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(6,8)) # Rename the axes for ease of use ax1 = axes[0] ax2 = axes[1] # Set the plotted line width line_width = 1.5 # Plot data ax1.plot(dates, temp_celsius, c='red', lw=line_width) ax2.plot(dates, temp_max_celsius, c='blue', lw=line_width) # Set y-axis limits min_temp = temp_celsius.min() - 2 max_temp = temp_max_celsius.max() + 2 ax1.set_ylim(min_temp, max_temp) ax2.set_ylim(min_temp, max_temp) # Turn plot grids on, for each subplot ax1.grid() ax2.grid() # Figure title fig.suptitle('Temperature observations - Kumpula June 2016') # Axis labels ax1.set_ylabel('Temperature [C]') ax2.set_xlabel('Date') ###Output _____no_output_____
notebooks/birdsong/cassins_vireo_example/.ipynb_checkpoints/2.0-CAVI-UMAP-HDBSCAN-Clustering-checkpoint.ipynb	###Markdown Reduce dimensionality of dataset using UMAP, then cluster the dataset using HDBSCAN - Compare the clustering of the dataset using HDBSCAN to the hand-labels Import packages ###Code from IPython.display import clear_output import matplotlib.pyplot as plt import numpy as np import seaborn as sns %matplotlib inline import os from tqdm import tqdm_notebook as tqdm import pandas as pd import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import hdbscan import seaborn as sns import umap from avgn.network_analysis.network_analysis import * import avgn.network.convnet_model as conv from avgn.network.training import * ###Output _____no_output_____ ###Markdown Define data parameters ###Code dims = [128, 128, 1] # first dimension of input data batch_size = 16 # size of batches to use (per GPU) ###Output _____no_output_____ ###Markdown Load the dataset ###Code from glob import glob bird_name = 'CAVI' hdf_locs = glob('../../../data/CAVI_wavs/_'+str(dims[0])+'.hdf5') hdf_locs[:3] # What information is stored in the HDF5 file to_load = ['spectrograms', 'lengths', 'start', 'wav_file', 'syll_start_rel_wav', 'symbols'] all_content = load_from_hdf5(hdf_locs, to_load, min_ex=2500, verbose=True) num_examples = len(all_content['name']) ###Output ../../../data/CAVI_wavs/GRA_128.hdf5 3045 ../../../data/CAVI_wavs/YAW_128.hdf5 3149 ../../../data/CAVI_wavs/AGBk_128.hdf5 8185 ../../../data/CAVI_wavs/AGO_128.hdf5 3869 ../../../data/CAVI_wavs/WABk_128.hdf5 6430 ../../../data/CAVI_wavs/BuRA_128.hdf5 5527 ../../../data/CAVI_wavs/AOBu_128.hdf5 7638 ../../../data/CAVI_wavs/ORA_128.hdf5 2805 ../../../data/CAVI_wavs/RYA_128.hdf5 3565 ###Markdown Plot a few examples ###Code nex=32 fig, ax = plt.subplots(nrows=1,ncols=nex, figsize=(nex,1)) for i in range(nex): ax[i].matshow(all_content['spectrograms'][i].reshape((dims[0],dims[1])), cmap=plt.cm.viridis, interpolation='nearest', origin='lower') ax[i].axis('off') ###Output _____no_output_____ ###Markdown Reduce image size for clustering ###Code from skimage.transform import resize x = all_content['spectrograms'] x_small = [resize(i, [32,32]) for i in tqdm(x)] x_small = np.array(x_small).reshape((len(x_small), np.prod(np.shape(x_small)[1:]))) ###Output _____no_output_____ ###Markdown UMAP embedding ###Code breakme x_small[0] x_small = [(i255).astype('uint8') for i in x_small] fig, ax = plt.subplots(ncols=2, figsize=(8,4)) ax[0].matshow(x[0].reshape([128,128])) ax[1].matshow(x_small[0].reshape([32,32])) clusterable_embedding = umap.UMAP( n_neighbors=30, #min_dist=0.0, n_components=2, random_state=42, ).fit_transform(x_small) np.unique(all_content['symbols']) plt.plot(clusterable_embedding[:,0], clusterable_embedding[:,1]) unique_sylls = np.unique(all_content['symbols']) len(unique_sylls) ###Output _____no_output_____ ###Markdown Cluster UMAP representations ###Code # we set the minimum cluster size at 0.25% of the dataset () cluster_pct = 0.0025 min_cluster_size = int(len(clusterable_embedding)cluster_pct) min_cluster_size clustered_labels = cluster_data(np.array(list(clusterable_embedding)), hdbscan.HDBSCAN, (), {'min_cluster_size':min_cluster_size, 'min_samples':1}, verbose = True) # check how many syllables were labelled pct_unlabelled = np.sum(clustered_labels == -1)/len(clustered_labels) print(str(round(pct_unlabelled100,1))+ '% of syllables went unlabelled') ###Output 1.1% of syllables went unlabelled ###Markdown Compare hand labels to UMAP embeddings ###Code def plot_with_labels(data, labels, title = '', ax = None, figsize = (9,9)): palette = sns.color_palette('husl', len(np.unique(labels))) labs_to_numbers_dict = {l:i for i,l in enumerate(np.unique(labels))} np.random.shuffle(palette) colors = [palette[labs_to_numbers_dict[x]] if x >= 0 else (0.75, 0.75, 0.75) for x in np.array(labels)] if not ax: fig, ax= plt.subplots(nrows=1,ncols=1,figsize=figsize) ax.scatter(data.T[0], data.T[1], color=colors, alpha = 1, linewidth= 0, s=1) ax.axis('off') ax.set_title(title) if not ax: plt.show() def compareLabellingSchemes(lab1, lab2, z, figsize=(24,24)): # get palette for first labelling palette = sns.color_palette('husl', len(np.unique(lab1))) labs_to_numbers_dict = {l:i for i,l in enumerate(np.unique(lab1))} np.random.shuffle(palette) colors = [palette[labs_to_numbers_dict[x]] if int(x) >= 0 else (0.75, 0.75, 0.75) for x in np.array(lab1)] # plot first labelling fig, ax= plt.subplots(nrows=1,ncols=1,figsize=figsize) ax.scatter(z.T[0], z.T[1], color=colors, alpha = 1, linewidth= 0, s=10) ax.axis('off') plt.show() # compare labels used_labs = [] unchanged_labs = [] for lab in np.unique(lab2): lab1_labs = lab1[lab2 == lab] closest_lab = np.unique(lab1_labs)[np.argmax([list(lab1_labs).count(i) for i in np.unique(lab1_labs)])] if closest_lab not in used_labs: used_labs.append(closest_lab) lab2[lab2 == lab] = closest_lab else: unchanged_labs.append(lab) # get palette for second labelling palette = palette + sns.hls_palette(len(np.unique(unchanged_labs)), l=.5, s=.8) ul = len(np.unique(lab1)) for ilab, lab in enumerate(unchanged_labs): labs_to_numbers_dict[lab] = ilab + ul colors = [palette[labs_to_numbers_dict[x]] for x in np.array(lab2)] fig, ax= plt.subplots(nrows=1,ncols=1,figsize=figsize) ax.scatter(z.T[0], z.T[1], color=colors, alpha = 1, linewidth= 0, s=10) ax.axis('off') plt.show() lab1 = clustered_labels.astype('str') lab2 = all_content['symbols'].astype('str') compareLabellingSchemes(lab1, lab2, x) plot_with_labels(clusterable_embedding, clustered_labels, figsize=(16,16)) ###Output _____no_output_____
14 Text und Machine Learning/2.4 Classifying Text.ipynb	###Markdown Document classifier Daten- Wir brauchen zuerst daten um unser Modell zu trainieren ###Code from textblob.classifiers import NaiveBayesClassifier train = [ ('I love this sandwich.', 'pos'), ('This is an amazing place!', 'pos'), ('I feel very good about these beers.', 'pos'), ('This is my best work.', 'pos'), ("What an awesome view", 'pos'), ('I do not like this restaurant', 'neg'), ('I am tired of this stuff.', 'neg'), ("I can't deal with this", 'neg'), ('He is my sworn enemy!', 'neg'), ('My boss is horrible.', 'neg') ] test = [ ('The beer was good.', 'pos'), ('I do not enjoy my job', 'neg'), ("I ain't feeling dandy today.", 'neg'), ("I feel amazing!", 'pos'), ('Gary is a friend of mine.', 'pos'), ("I can't believe I'm doing this.", 'neg') ] ###Output _____no_output_____ ###Markdown Training ###Code cl = NaiveBayesClassifier(train) ###Output _____no_output_____ ###Markdown Test- Wie gut performed unser Modell bei Daten die es noch nie gesehen hat? ###Code cl.accuracy(test) ###Output _____no_output_____ ###Markdown - Zu 80% korrekt, ok für mich :) Features- Welche wörter sorgen am meisten dafür dass etwas positiv oder negativ klassifiziert wird? ###Code cl.show_informative_features(5) ###Output Most Informative Features contains(this) = True neg : pos = 2.3 : 1.0 contains(this) = False pos : neg = 1.8 : 1.0 contains(This) = False neg : pos = 1.6 : 1.0 contains(an) = False neg : pos = 1.6 : 1.0 contains(I) = True neg : pos = 1.4 : 1.0 ###Markdown Er ist der meinung wenn "this" vorkommt ist es eher positiv, was natürlich quatsch ist, aber das hat er nun mal so gelernt, deswegen braucht ihr gute trainingsdaten. Klassifizierung ###Code cl.classify("Their burgers are amazing") # "pos" cl.classify("I don't like their pizza.") # "neg" ###Output _____no_output_____ ###Markdown Klassizierung nach Sätzen ###Code from textblob import TextBlob blob = TextBlob("The beer was amazing. " "But the hangover was horrible. My boss was not happy.", classifier=cl) for sentence in blob.sentences: print(("%s (%s)") % (sentence,sentence.classify())) ###Output The beer was amazing. (pos) But the hangover was horrible. (neg) My boss was not happy. (neg) ###Markdown Mit schweizer Songtexten Kommentare klassifizieren ###Code import os,glob from nltk.tokenize import sent_tokenize from nltk.tokenize import word_tokenize from io import open train = [] countries = ["schweiz", "deutschland"] for country in countries: out = [] folder_path = 'songtexte/%s' % country for filename in glob.glob(os.path.join(folder_path, '.txt')): with open(filename, 'r') as f: text = f.read() words = word_tokenize(text) words=[word.lower() for word in words if word.isalpha()] for word in words: out.append(word) out = set(out) for word in out: train.append((word,country)) #print (filename) #print (len(text)) train from textblob.classifiers import NaiveBayesClassifier c2 = NaiveBayesClassifier(train) c2.classify("Ich gehe durch den Wald") # "deutsch" c2.classify("Häsch es guet") # "deutsch" ###Output _____no_output_____ ###Markdown Hardcore Beispiel mit Film-review daten mit NLTK- https://www.nltk.org/book/ch06.html- Wir nutzen nur noch die 100 häufigsten Wörter in den Texten und schauen ob sie bei positiv oder negativ vorkommen ###Code review = (" ").join(train[0][0]) print(review) import random import nltk nltk.download('movie_reviews') from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) (" ").join(documents[0][0]) all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features[0:10] all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains({})'.format(word)] = (word in document_words) return features print(document_features(movie_reviews.words('pos/cv957_8737.txt'))) featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) classifier.classify(document_features("a movie with bad actors".split(" "))) classifier.classify(document_features("an uplifting movie with russel crowe".split(" "))) classifier.show_most_informative_features(10) ###Output Most Informative Features contains(unimaginative) = True neg : pos = 8.2 : 1.0 contains(shoddy) = True neg : pos = 6.9 : 1.0 contains(turkey) = True neg : pos = 6.7 : 1.0 contains(singers) = True pos : neg = 6.4 : 1.0 contains(suvari) = True neg : pos = 6.2 : 1.0 contains(mena) = True neg : pos = 6.2 : 1.0 contains(atrocious) = True neg : pos = 6.1 : 1.0 contains(stretched) = True neg : pos = 6.1 : 1.0 contains(schumacher) = True neg : pos = 6.1 : 1.0 contains(unravel) = True pos : neg = 5.8 : 1.0 ###Markdown Document classifier Daten- Wir brauchen zuerst daten um unser Modell zu trainieren ###Code from textblob.classifiers import NaiveBayesClassifier train = [ ('I love this sandwich.', 'pos'), ('This is an amazing place!', 'pos'), ('I feel very good about these beers.', 'pos'), ('This is my best work.', 'pos'), ("What an awesome view", 'pos'), ('I do not like this restaurant', 'neg'), ('I am tired of this stuff.', 'neg'), ("I can't deal with this", 'neg'), ('He is my sworn enemy!', 'neg'), ('My boss is horrible.', 'neg') ] test = [ ('The beer was good.', 'pos'), ('I do not enjoy my job', 'neg'), ("I ain't feeling dandy today.", 'neg'), ("I feel amazing!", 'pos'), ('Gary is a friend of mine.', 'pos'), ("I can't believe I'm doing this.", 'neg') ] ###Output _____no_output_____ ###Markdown Training ###Code a = NaiveBayesClassifier(train) ###Output _____no_output_____ ###Markdown Test- Wie gut performed unser Modell bei Daten die es noch nie gesehen hat? ###Code a.accuracy(test) ###Output _____no_output_____ ###Markdown - Zu 80% korrekt, ok für mich :) Features- Welche wörter sorgen am meisten dafür dass etwas positiv oder negativ klassifiziert wird? ###Code cl.show_informative_features(5) ###Output Most Informative Features contains(this) = True neg : pos = 2.3 : 1.0 contains(this) = False pos : neg = 1.8 : 1.0 contains(This) = False neg : pos = 1.6 : 1.0 contains(an) = False neg : pos = 1.6 : 1.0 contains(I) = False pos : neg = 1.4 : 1.0 ###Markdown Er ist der meinung wenn "this" vorkommt ist es eher positiv, was natürlich quatsch ist, aber das hat er nun mal so gelernt, deswegen braucht ihr gute trainingsdaten. Klassifizierung ###Code cl.classify("Their burgers are amazing") # "pos" cl.classify("I don't like their pizza.") # "neg" a.classify("I love my job.") ###Output _____no_output_____ ###Markdown Klassizierung nach Sätzen ###Code from textblob import TextBlob blob = TextBlob("The beer was amazing. " "But the hangover was horrible. My boss was not happy.", classifier=a) for sentence in blob.sentences: print(("%s (%s)") % (sentence,sentence.classify())) ###Output The beer was amazing. (pos) But the hangover was horrible. (neg) My boss was not happy. (neg) ###Markdown Mit schweizer Songtexten Kommentare klassifizieren- http://www.falleri.ch ###Code import os,glob from nltk.tokenize import sent_tokenize from nltk.tokenize import word_tokenize from io import open train = [] countries = ["schweiz", "deutschland"] for country in countries: out = [] folder_path = 'songtexte/%s' % country for filename in glob.glob(os.path.join(folder_path, '.txt')): with open(filename, 'r') as f: text = f.read() words = word_tokenize(text) words=[word.lower() for word in words if word.isalpha()] for word in words: out.append(word) out = set(out) for word in out: train.append((word,country)) #print (filename) #print (len(text)) train from textblob.classifiers import NaiveBayesClassifier c2 = NaiveBayesClassifier(train) c2.classify("Ich gehe durch den Wald") # "deutsch" c2.classify("Häsch es guet") # "deutsch" c2.classify("Ich fahre mit meinem Porsche auf der Autobahn.") c2.show_informative_features(5) ###Output Most Informative Features contains(ich) = True schwei : deutsc = 1.2 : 1.0 contains(im) = True schwei : deutsc = 1.2 : 1.0 contains(dir) = True schwei : deutsc = 1.2 : 1.0 contains(zur) = True schwei : deutsc = 1.2 : 1.0 contains(wer) = True schwei : deutsc = 1.2 : 1.0 ###Markdown Orakel Ihr könnt natürlich jetzt Euer eigenes Orakel bauen wie hier: - http://home.datacomm.ch/cgi-heeb/dialect/chochi.pl?Hand=Hand&nicht=net&heute=hit&Fenster=Feischter&gestern=gescht&Abend=Abend&gehorchen=folge&Mond=Manat&jeweils=abe&Holzsplitter=Schepfa&Senden=Jetzt+analysieren%21 Hardcore Beispiel mit Film-review daten mit NLTK- https://www.nltk.org/book/ch06.html- Wir nutzen nur noch die 100 häufigsten Wörter in den Texten und schauen ob sie bei positiv oder negativ vorkommen ###Code import random import nltk nltk.download("movie_reviews") from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) (" ").join(documents[1][0]) all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] word_features[0:10] all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains({})'.format(word)] = (word in document_words) return features print(document_features(movie_reviews.words('pos/cv957_8737.txt'))) featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) classifier.classify(document_features("a movie with bad actors".split(" "))) classifier.classify(document_features("an uplifting movie with russel crowe".split(" "))) classifier.show_most_informative_features(10) ###Output Most Informative Features contains(sans) = True neg : pos = 8.9 : 1.0 contains(uplifting) = True pos : neg = 8.7 : 1.0 contains(mediocrity) = True neg : pos = 7.6 : 1.0 contains(fabric) = True pos : neg = 6.4 : 1.0 contains(overwhelmed) = True pos : neg = 6.4 : 1.0 contains(topping) = True pos : neg = 5.8 : 1.0 contains(sunny) = True pos : neg = 5.8 : 1.0 contains(wits) = True pos : neg = 5.8 : 1.0 contains(lang) = True pos : neg = 5.8 : 1.0 contains(ugh) = True neg : pos = 5.7 : 1.0 ###Markdown Document classifier Daten- Wir brauchen zuerst daten um unser Modell zu trainieren ###Code from textblob.classifiers import NaiveBayesClassifier train = [ ('I love this sandwich.', 'pos'), ('This is an amazing place!', 'pos'), ('I feel very good about these beers.', 'pos'), ('This is my best work.', 'pos'), ("What an awesome view", 'pos'), ('I do not like this restaurant', 'neg'), ('I am tired of this stuff.', 'neg'), ("I can't deal with this", 'neg'), ('He is my sworn enemy!', 'neg'), ('My boss is horrible.', 'neg') ] test = [ ('The beer was good.', 'pos'), ('I do not enjoy my job', 'neg'), ("I ain't feeling dandy today.", 'neg'), ("I feel amazing!", 'pos'), ('Gary is a friend of mine.', 'pos'), ("I can't believe I'm doing this.", 'neg') ] ###Output _____no_output_____ ###Markdown Training ###Code cl = NaiveBayesClassifier(train) ###Output _____no_output_____ ###Markdown Test- Wie gut performed unser Modell bei Daten die es noch nie gesehen hat? ###Code cl.accuracy(test) ###Output _____no_output_____ ###Markdown - Zu 80% korrekt, ok für mich :) Features- Welche wörter sorgen am meisten dafür dass etwas positiv oder negativ klassifiziert wird? ###Code cl.show_informative_features(5) ###Output Most Informative Features contains(this) = True neg : pos = 2.3 : 1.0 contains(this) = False pos : neg = 1.8 : 1.0 contains(This) = False neg : pos = 1.6 : 1.0 contains(an) = False neg : pos = 1.6 : 1.0 contains(I) = False pos : neg = 1.4 : 1.0 ###Markdown Er ist der meinung wenn "this" vorkommt ist es eher positiv, was natürlich quatsch ist, aber das hat er nun mal so gelernt, deswegen braucht ihr gute trainingsdaten. Klassifizierung ###Code cl.classify("Their burgers are amazing") # "pos" cl.classify("I don't like their pizza.") # "neg" cl.classify("I hate cars.") cl.classify("Zurich is beautiful.") cl.classify("Zurich") ###Output _____no_output_____ ###Markdown Klassizierung nach Sätzen ###Code from textblob import TextBlob blob = TextBlob("The beer was amazing. " "But the hangover was horrible. My boss was not happy.", classifier=cl) for sentence in blob.sentences: print(("%s (%s)") % (sentence,sentence.classify())) ###Output The beer was amazing. (pos) But the hangover was horrible. (neg) My boss was not happy. (neg) ###Markdown Mit schweizer Songtexten Kommentare klassifizieren ###Code import os,glob from nltk.tokenize import sent_tokenize from nltk.tokenize import word_tokenize from io import open train = [] countries = ["schweiz", "deutschland"] for country in countries: out = [] folder_path = 'songtexte/%s' % country for filename in glob.glob(os.path.join(folder_path, '*.txt')):#alle Dateien einlesen with open(filename, 'r') as f: text = f.read() words = word_tokenize(text) words=[word.lower() for word in words if word.isalpha()] for word in words: out.append(word) out = set(out) for word in out: train.append((word,country)) #print (filename) #print (len(text)) train from textblob.classifiers import NaiveBayesClassifier c2 = NaiveBayesClassifier(train) c2.classify("Ich gehe durch den Wald") # "deutsch" c2.classify("Häsch es guet") # "deutsch" c2.classify("Wötsch da?") c2.show_informative_features(5) ###Output Most Informative Features contains(zur) = True schwei : deutsc = 1.3 : 1.0 contains(froh) = True schwei : deutsc = 1.3 : 1.0 contains(wer) = True schwei : deutsc = 1.3 : 1.0 contains(das) = True schwei : deutsc = 1.3 : 1.0 contains(macht) = True schwei : deutsc = 1.3 : 1.0 ###Markdown Hardcore Beispiel mit Film-review daten mit NLTK- https://www.nltk.org/book/ch06.html- Wir nutzen nur noch die 100 häufigsten Wörter in den Texten und schauen ob sie bei positiv oder negativ vorkommen ###Code import random import nltk nltk.download('movie_reviews') review = (" ").join(train[0][0]) print(review) from nltk.corpus import movie_reviews documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) (" ").join(documents[0][0]) (" ").join(documents[1][1]) #ist hier ein Zwischenschritt all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] #wir nehmen die 2000 häufigsten Wörter word_features all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = list(all_words)[:2000] #wir nehmen die 2000 häufigsten Wörter def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains({})'.format(word)] = (word in document_words) return features print(document_features(movie_reviews.words('pos/cv957_8737.txt'))) featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) classifier.classify(document_features("a movie with bad actors".split(" "))) classifier.classify(document_features("an uplifting movie with russel crowe".split(" "))) #split: er nimmt nur Wörtelisten classifier.show_most_informative_features(10) ###Output _____no_output_____
Clusterer_interface.ipynb	###Markdown Example ###Code filename = "data/airbnb.csv" df = pd.read_csv(filename).dropna() df = df.groupby(['neighbourhood','city']).mean() df = df[df.columns.difference(['id','longitude','latitude'])] df = df._get_numeric_data() print(df.shape) df.head(5) ###Output (610, 8) ###Markdown K-means Elbow method ###Code c.elbow_k_means(df) ###Output _____no_output_____ ###Markdown Silhouette plot ###Code c.silhouette_clusters_K_Means(df) ###Output For n_clusters = 2 The average silhouette_score is : 0.546679819262802 ###Markdown Clustering ###Code n_clusters = 4 clusters = c.k_means(df, n_clusters = n_clusters) ###Output Reduced inertia: 2738.632031981707 Clusters centers: ###Markdown Visualization ###Code c.plot_cluster_2D(df, clusters) c.plot_cluster_3D(df, clusters, False) ###Output _____no_output_____ ###Markdown Hierarchical clustering Clustering ###Code clusters = c.hierarchical_clusters(df, k=4) ###Output _____no_output_____ ###Markdown Visualization ###Code c.plot_cluster_2D(df, clusters) c.plot_cluster_3D(df, clusters) ###Output _____no_output_____ ###Markdown Gaussian Mixture Models Clustering with BIC ###Code clusters = c.best_GMM_clusters(df, criterion='bic') c.plot_cluster_2D(df, clusters) c.plot_cluster_3D(df, clusters) ###Output _____no_output_____
11-2-Time-Series.ipynb	###Markdown Chapter 11 - Time Series 11.2 - Time Series Basics ###Code from datetime import datetime as dt from dateutil.parser import parse import pandas as pd import numpy as np ###Output _____no_output_____ ###Markdown The basic time series object in `pandas` is a `Series` index. ###Code qs1 = [] # Instantiate a time series: First day of each Q in the year 2019. for i in range(1, 13, 3): t = dt(2019, i, 1) qs1.append(t) display(qs1) # Use the time series as the index to a Series s1 = pd.Series([10,20,30,40], index=qs1) display(s1) ###Output _____no_output_____ ###Markdown Under the hood, the `datetime` objects are stored as a `DateTimeIndex`. ###Code display(s1.index) print(type(s1.index)) ###Output _____no_output_____ ###Markdown Like other `Series`, arithmetic operations on two `Series` objects are aligned on the dates. ###Code # Instantiate a new Series with Q1 - Q3 dates qs2 = [parse('2019-01-01'), parse('2019-04-01'), parse('2019-07-01')] s2 = pd.Series([25,45,65], index=qs2) display(s2) # Add s1 and s2 Series objects display(s1+s2) # There is no value for 2019-10-01 so resultant of sum is NaN ###Output _____no_output_____ ###Markdown Indexing, Selection, Subsetting You can use the date as the index to pull out its value. Different date formats are permitted. ###Code display(s2) print(s2['20190101']) print(s2['07/01/2019']) ###Output _____no_output_____ ###Markdown Use `pd.date_range` to automatically generate dates with a start date and number of periods. ###Code d1 = pd.Series(range(0,20), pd.date_range('2018/07/01', periods=20)) display(d1.head(10)) d2 = pd.Series(range(0,400), pd.date_range('20190701', periods=400)) display(d2.head(5)) print('...') display(d2.tail(3)) ###Output _____no_output_____ ###Markdown Now, filtering can be performed on the `DateTimeIndex`. ###Code display(d2['2019'].iloc[:5]) # Filter by year display(d2['2019-09'].iloc[:5]) # Filter by year & month display(d2[dt(2019,10, 3):].iloc[:5]) # Filter by datetime ###Output _____no_output_____ ###Markdown Filtering can also be done using truncating ###Code # Truncate (remove) all values before 1 Aug 2019 d2.truncate(before='2019-08-01').iloc[:10] # Truncate (Remove) all values after 5 Jul d2.truncate(after='2019-07-05') ###Output _____no_output_____ ###Markdown Time Series with Duplicate Indices Sometimes, there may be multiple data observations falling on the same timestamp. ###Code dup_dates = pd.DatetimeIndex(['2020-07-28', '2020-07-29', '2020-07-29', '2020-07-30', '2020-07-31']) s3 = pd.Series(np.arange(5), index=dup_dates) display(s3) # Checking index.is_unique can validate that the index contains duplicates print(s3.index.is_unique) # Indexing to this time series produces a slice if there are duplicates print(s3['20200729']) # If there are no duplicates, this produces a scalar print(s3['20200728']) print(s3['20200731']) # To aggregate having non-unique timestamps, level=0 needs to be specified print(s3.groupby(level=0).sum()) print(s3.groupby(level=0).mean()) ###Output 2020-07-28 0 2020-07-29 3 2020-07-30 3 2020-07-31 4 dtype: int64 2020-07-28 0.0 2020-07-29 1.5 2020-07-30 3.0 2020-07-31 4.0 dtype: float64
ImageCollection/metadata.ipynb	###Markdown View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine APIInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The geehydro Python package builds on the [folium](https://github.com/python-visualization/folium) package and implements several methods for displaying Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, `Map.centerObject()`, and `Map.setOptions()`.The following script checks if the geehydro package has been installed. If not, it will install geehydro, which automatically install its dependencies, including earthengine-api and folium. ###Code import subprocess try: import geehydro except ImportError: print('geehydro package not installed. Installing ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geehydro']) ###Output _____no_output_____ ###Markdown Import libraries ###Code import ee import folium import geehydro ###Output _____no_output_____ ###Markdown Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. ###Code try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `ESRI`. ###Code Map = folium.Map(location=[40, -100], zoom_start=4) Map.setOptions('HYBRID') ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output Collection: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1581772792877035, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'oli_tirs', 'lc8', 'c1', 't1', 'l8', 'tier1', 'radiance'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1581206400000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'oli_tirs', 'lc8', 'c1', 't1', 'l8', 'tier1', 'radiance'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1581772792877035, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': 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'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140708_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.6486486196517944, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.33314895629883, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1404845155330, 'RADIANCE_ADD_BAND_5': -29.57748031616211, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.355649948120117, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.479249954223633, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.74198913574219, 'RADIANCE_ADD_BAND_2': -62.200538635253906, 'RADIANCE_ADD_BAND_3': -57.31726837158203, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.69982147216797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.559550285339355, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043141_00057', 'EARTH_SUN_DISTANCE': 1.016627550125122, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488642210000, 'SCENE_CENTER_TIME': '18:45:55.3336140Z', 'SUN_ELEVATION': 65.8777847290039, 'BPF_NAME_OLI': 'LO8BPF20140708182239_20140708190335.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'N', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 506, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 187}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 465592.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}], 'version': 1581772792877035, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724', 'properties': {'RADIANCE_MULT_BAND_5': 0.005925099831074476, 'RADIANCE_MULT_BAND_6': 0.0014735000440850854, 'RADIANCE_MULT_BAND_3': 0.011482000350952148, 'RADIANCE_MULT_BAND_4': 0.00968219991773367, 'RADIANCE_MULT_BAND_1': 0.01216800045222044, 'RADIANCE_MULT_BAND_2': 0.01245999988168478, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.76979738859893, 38.12703313441971], [-120.77158274982695, 38.12754115630432], [-121.42134145512932, 38.25446242506864], [-122.22152328015193, 38.40525302827857], [-122.89018639673915, 38.5266157865438], [-122.89242341654155, 38.526617857573015], [-122.89466073063308, 38.519621963160894], [-123.38187286142927, 36.80872337128997], [-123.38186259045791, 36.806647917217006], [-123.35901116184863, 36.80244946066433], [-122.88546161915531, 36.71490670011608], [-121.3092309147788, 36.40846418437395], [-121.30782254819886, 36.40844420946354], [-121.08696039686296, 37.12150541970936], [-121.06667332030511, 37.186300761679455], [-120.9265815780102, 37.63183285571133], [-120.88231915679422, 37.77176559071555], [-120.83617669320071, 37.917319414649754], [-120.8201155519523, 37.96798246241547], [-120.7756360373179, 38.10840000147115], [-120.76979738859893, 38.12703313441971]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 126.32495880126953, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.4832499027252197, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.84012985229492, 'RADIANCE_ADD_BAND_2': -62.301029205322266, 'RADIANCE_ADD_BAND_3': -57.40987014770508, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.78820037841797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.57822036743164, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043005_00057', 'EARTH_SUN_DISTANCE': 1.0158073902130127, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488621091000, 'SCENE_CENTER_TIME': '18:45:57.2197370Z', 'SUN_ELEVATION': 63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}]} Count: 9 Date range: {'type': 'Date', 'value': 1395168392050} {'type': 'Date', 'value': 1406227557220} Sun elevation statistics: {'type': 'DataDictionary', 'values': {'max': 67.10252380371094, 'mean': 61.01998392740885, 'min': 46.471065521240234, 'sample_sd': 7.251804209519804, 'sample_var': 52.58866429320915, 'sum': 549.1798553466797, 'sum_sq': 33931.65526085679, 'total_count': 9, 'total_sd': 6.837066576518139, 'total_var': 46.74547937174147, 'valid_count': 9, 'weight_sum': 9, 'weighted_sum': 549.1798553466797}} Least cloudy image: {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1581772792877035, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}} Recent images: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1581772792877035, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'oli_tirs', 'lc8', 'c1', 't1', 'l8', 'tier1', 'radiance'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1581206400000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'oli_tirs', 'lc8', 'c1', 't1', 'l8', 'tier1', 'radiance'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 465592.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}], 'version': 1581772792877035, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724', 'properties': {'RADIANCE_MULT_BAND_5': 0.005925099831074476, 'RADIANCE_MULT_BAND_6': 0.0014735000440850854, 'RADIANCE_MULT_BAND_3': 0.011482000350952148, 'RADIANCE_MULT_BAND_4': 0.00968219991773367, 'RADIANCE_MULT_BAND_1': 0.01216800045222044, 'RADIANCE_MULT_BAND_2': 0.01245999988168478, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.76979738859893, 38.12703313441971], [-120.77158274982695, 38.12754115630432], [-121.42134145512932, 38.25446242506864], [-122.22152328015193, 38.40525302827857], [-122.89018639673915, 38.5266157865438], [-122.89242341654155, 38.526617857573015], [-122.89466073063308, 38.519621963160894], [-123.38187286142927, 36.80872337128997], [-123.38186259045791, 36.806647917217006], [-123.35901116184863, 36.80244946066433], [-122.88546161915531, 36.71490670011608], [-121.3092309147788, 36.40846418437395], [-121.30782254819886, 36.40844420946354], [-121.08696039686296, 37.12150541970936], [-121.06667332030511, 37.186300761679455], [-120.9265815780102, 37.63183285571133], [-120.88231915679422, 37.77176559071555], [-120.83617669320071, 37.917319414649754], [-120.8201155519523, 37.96798246241547], [-120.7756360373179, 38.10840000147115], [-120.76979738859893, 38.12703313441971]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 126.32495880126953, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 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63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 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'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}}]} ###Markdown Display Earth Engine data layers ###Code Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True) Map ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The geemap Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemapdependencies), including earthengine-api, folium, and ipyleaflet.Important note: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). ###Code # Installs geemap package import subprocess try: import geemap except ImportError: print('geemap package not installed. Installing ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap']) # Checks whether this notebook is running on Google Colab try: import google.colab import geemap.eefolium as emap except: import geemap as emap # Authenticates and initializes Earth Engine import ee try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map The default basemap is `Google Satellite`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/geemap.pyL13) can be added using the `Map.add_basemap()` function. ###Code Map = emap.Map(center=[40,-100], zoom=4) Map.add_basemap('ROADMAP') # Add Google Map Map ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Add Earth Engine dataset # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output _____no_output_____ ###Markdown Display Earth Engine data layers ###Code Map.addLayerControl() # This line is not needed for ipyleaflet-based Map. Map ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The geemap Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemapdependencies), including earthengine-api, folium, and ipyleaflet.Important note: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). ###Code # Installs geemap package import subprocess try: import geemap except ImportError: print('geemap package not installed. Installing ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap']) # Checks whether this notebook is running on Google Colab try: import google.colab import geemap.eefolium as emap except: import geemap as emap # Authenticates and initializes Earth Engine import ee try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map The default basemap is `Google Satellite`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/geemap.pyL13) can be added using the `Map.add_basemap()` function. ###Code Map = emap.Map(center=[40,-100], zoom=4) Map.add_basemap('ROADMAP') # Add Google Map Map ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Add Earth Engine dataset # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output _____no_output_____ ###Markdown Display Earth Engine data layers ###Code Map.addLayerControl() # This line is not needed for ipyleaflet-based Map. Map ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The geemap Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemapdependencies), including earthengine-api, folium, and ipyleaflet. ###Code # Installs geemap package import subprocess try: import geemap except ImportError: print('Installing geemap ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap']) import ee import geemap ###Output _____no_output_____ ###Markdown Create an interactive map The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. ###Code Map = geemap.Map(center=[40,-100], zoom=4) Map ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Add Earth Engine dataset # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output _____no_output_____ ###Markdown Display Earth Engine data layers ###Code Map.addLayerControl() # This line is not needed for ipyleaflet-based Map. Map ###Output _____no_output_____ ###Markdown Pydeck Earth Engine IntroductionThis is an introduction to using [Pydeck](https://pydeck.gl) and [Deck.gl](https://deck.gl) with [Google Earth Engine](https://earthengine.google.com/) in Jupyter Notebooks. If you wish to run this locally, you'll need to install some dependencies. Installing into a new Conda environment is recommended. To create and enter the environment, run:```conda create -n pydeck-ee -c conda-forge python jupyter notebook pydeck earthengine-api requests -ysource activate pydeck-eejupyter nbextension install --sys-prefix --symlink --overwrite --py pydeckjupyter nbextension enable --sys-prefix --py pydeck```then open Jupyter Notebook with `jupyter notebook`. Now in a Python Jupyter Notebook, let's first import required packages: ###Code from pydeck_earthengine_layers import EarthEngineLayer import pydeck as pdk import requests import ee ###Output _____no_output_____ ###Markdown AuthenticationUsing Earth Engine requires authentication. If you don't have a Google account approved for use with Earth Engine, you'll need to request access. For more information and to sign up, go to https://signup.earthengine.google.com/. If you haven't used Earth Engine in Python before, you'll need to run the following authentication command. If you've previously authenticated in Python or the command line, you can skip the next line.Note that this creates a prompt which waits for user input. If you don't see a prompt, you may need to authenticate on the command line with `earthengine authenticate` and then return here, skipping the Python authentication. ###Code try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create MapNext it's time to create a map. Here we create an `ee.Image` object ###Code # Initialize objects ee_layers = [] view_state = pdk.ViewState(latitude=37.7749295, longitude=-122.4194155, zoom=10, bearing=0, pitch=45) # %% # Add Earth Engine dataset # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output _____no_output_____ ###Markdown Then just pass these layers to a `pydeck.Deck` instance, and call `.show()` to create a map: ###Code r = pdk.Deck(layers=ee_layers, initial_view_state=view_state) r.show() ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine APIInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The geehydro Python package builds on the [folium](https://github.com/python-visualization/folium) package and implements several methods for displaying Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, `Map.centerObject()`, and `Map.setOptions()`.The magic command `%%capture` can be used to hide output from a specific cell. Uncomment these lines if you are running this notebook for the first time. ###Code # %%capture # !pip install earthengine-api # !pip install geehydro ###Output _____no_output_____ ###Markdown Import libraries ###Code import ee import folium import geehydro ###Output _____no_output_____ ###Markdown Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. Uncomment the line `ee.Authenticate()` if you are running this notebook for the first time or if you are getting an authentication error. ###Code # ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `ESRI`. ###Code Map = folium.Map(location=[40, -100], zoom_start=4) Map.setOptions('HYBRID') ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output Collection: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1580563126058134, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'tier1', 'lc8', 'c1', 'oli_tirs', 't1', 'l8', 'radiance'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1579910400000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'tier1', 'lc8', 'c1', 'oli_tirs', 't1', 'l8', 'radiance'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, 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'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.4832499027252197, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.84012985229492, 'RADIANCE_ADD_BAND_2': -62.301029205322266, 'RADIANCE_ADD_BAND_3': -57.40987014770508, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.78820037841797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.57822036743164, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043005_00057', 'EARTH_SUN_DISTANCE': 1.0158073902130127, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488621091000, 'SCENE_CENTER_TIME': '18:45:57.2197370Z', 'SUN_ELEVATION': 63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}]} Count: 9 Date range: {'type': 'Date', 'value': 1395168392050} {'type': 'Date', 'value': 1406227557220} Sun elevation statistics: {'type': 'DataDictionary', 'values': {'max': 67.10252380371094, 'mean': 61.01998392740885, 'min': 46.471065521240234, 'sample_sd': 7.251804209519804, 'sample_var': 52.58866429320915, 'sum': 549.1798553466797, 'sum_sq': 33931.65526085679, 'total_count': 9, 'total_sd': 6.837066576518139, 'total_var': 46.74547937174147, 'valid_count': 9, 'weight_sum': 9, 'weighted_sum': 549.1798553466797}} Least cloudy image: {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1580563126058134, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}} Recent images: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1580563126058134, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'tier1', 'lc8', 'c1', 'oli_tirs', 't1', 'l8', 'radiance'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1579910400000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'tier1', 'lc8', 'c1', 'oli_tirs', 't1', 'l8', 'radiance'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 465592.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}], 'version': 1580563126058134, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724', 'properties': {'RADIANCE_MULT_BAND_5': 0.005925099831074476, 'RADIANCE_MULT_BAND_6': 0.0014735000440850854, 'RADIANCE_MULT_BAND_3': 0.011482000350952148, 'RADIANCE_MULT_BAND_4': 0.00968219991773367, 'RADIANCE_MULT_BAND_1': 0.01216800045222044, 'RADIANCE_MULT_BAND_2': 0.01245999988168478, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.76979738859893, 38.12703313441971], [-120.77158274982695, 38.12754115630432], [-121.42134145512932, 38.25446242506864], [-122.22152328015193, 38.40525302827857], [-122.89018639673915, 38.5266157865438], [-122.89242341654155, 38.526617857573015], [-122.89466073063308, 38.519621963160894], [-123.38187286142927, 36.80872337128997], [-123.38186259045791, 36.806647917217006], [-123.35901116184863, 36.80244946066433], [-122.88546161915531, 36.71490670011608], [-121.3092309147788, 36.40846418437395], [-121.30782254819886, 36.40844420946354], [-121.08696039686296, 37.12150541970936], [-121.06667332030511, 37.186300761679455], [-120.9265815780102, 37.63183285571133], [-120.88231915679422, 37.77176559071555], [-120.83617669320071, 37.917319414649754], [-120.8201155519523, 37.96798246241547], [-120.7756360373179, 38.10840000147115], [-120.76979738859893, 38.12703313441971]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 126.32495880126953, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.4832499027252197, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.84012985229492, 'RADIANCE_ADD_BAND_2': -62.301029205322266, 'RADIANCE_ADD_BAND_3': -57.40987014770508, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.78820037841797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.57822036743164, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043005_00057', 'EARTH_SUN_DISTANCE': 1.0158073902130127, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488621091000, 'SCENE_CENTER_TIME': '18:45:57.2197370Z', 'SUN_ELEVATION': 63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 464392.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}], 'version': 1580563126058134, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140708', 'properties': {'RADIANCE_MULT_BAND_5': 0.005915500223636627, 'RADIANCE_MULT_BAND_6': 0.0014711000258103013, 'RADIANCE_MULT_BAND_3': 0.011463000439107418, 'RADIANCE_MULT_BAND_4': 0.00966660026460886, 'RADIANCE_MULT_BAND_1': 0.012148000299930573, 'RADIANCE_MULT_BAND_2': 0.012439999729394913, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.05901267311019, 38.1813072066471], [-122.67195531509144, 38.48483894042248], [-122.90730152252529, 38.52707032726991], [-122.90792970602998, 38.52608585671175], [-122.91360364873563, 38.50706451546646], [-123.39734192537979, 36.8083407130841], [-123.39733405223458, 36.80681601889492], [-123.39114513279036, 36.8055936364345], [-123.34317991176952, 36.79681686843965], [-122.28073257380132, 36.59717466111698], [-121.36957092975639, 36.417575938065966], [-121.32540815303872, 36.40869214276654], [-121.32304292059108, 36.40865900248354], [-121.19650818732099, 36.81902664136925], [-121.07109421952906, 37.221019713169355], [-120.94367715094019, 37.62606705102397], [-120.90082928429048, 37.761553141330744], [-120.84740670701625, 37.93009641124127], [-120.82257019700445, 38.00830842766878], [-120.78499155821282, 38.12676852456719], [-120.78581606001764, 38.12745169022067], [-121.05901267311019, 38.1813072066471]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 122.4483642578125, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-08', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 537.4625854492188, 'google:registration_offset_y': 10.817861557006836, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0023119000252336264, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004958499921485782, 'RADIANCE_MULT_BAND_8': 0.010940000414848328, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 39.97999954223633, 'GEOMETRIC_RMSE_VERIFY': 2.5929999351501465, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 12.0600004196167, 'GEOMETRIC_RMSE_MODEL': 5.275000095367432, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014189LGN01', 'WRS_PATH': 44, 'google:registration_count': 96, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.4619998931884766, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 3.9800000190734863, 'system:asset_size': 1303038285, 'system:index': 'LC08_044034_20140708', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140708181845_20140708190428.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140708_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.6486486196517944, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.33314895629883, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1404845155330, 'RADIANCE_ADD_BAND_5': -29.57748031616211, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.355649948120117, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.479249954223633, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.74198913574219, 'RADIANCE_ADD_BAND_2': 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3.2160000801086426, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 31.59000015258789, 'GEOMETRIC_RMSE_MODEL': 6.959000110626221, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014093LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15581, 'GEOMETRIC_RMSE_MODEL_Y': 4.63700008392334, 'REFLECTIVE_LINES': 7791, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 5.188000202178955, 'system:asset_size': 1208697743, 'system:index': 'LC08_044034_20140403', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140403182815_20140403190449.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140403_20170306_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -49.95764923095703, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1396550776290, 'RADIANCE_ADD_BAND_5': -30.571590423583984, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.602880001068115, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.562580108642578, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -62.78356170654297, 'RADIANCE_ADD_BAND_2': -64.29113006591797, 'RADIANCE_ADD_BAND_3': -59.24372863769531, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -56.53831100463867, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.94806957244873, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7791, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063782_00025', 'EARTH_SUN_DISTANCE': 0.9999619126319885, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488829355000, 'SCENE_CENTER_TIME': '18:46:16.2881730Z', 'SUN_ELEVATION': 52.549800872802734, 'BPF_NAME_OLI': 'LO8BPF20140403183209_20140403190356.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'N', 'SATURATION_BAND_2': 'N', 'SATURATION_BAND_3': 'N', 'SATURATION_BAND_4': 'N', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 385, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 98}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1580563126058134, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}}]} ###Markdown Display Earth Engine data layers ###Code Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True) Map ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine APIInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The geehydro Python package builds on the [folium](https://github.com/python-visualization/folium) package and implements several methods for displaying Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, `Map.centerObject()`, and `Map.setOptions()`.The magic command `%%capture` can be used to hide output from a specific cell. ###Code # %%capture # !pip install earthengine-api # !pip install geehydro ###Output _____no_output_____ ###Markdown Import libraries ###Code import ee import folium import geehydro ###Output _____no_output_____ ###Markdown Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. Uncomment the line `ee.Authenticate()` if you are running this notebook for this first time or if you are getting an authentication error. ###Code # ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `ESRI`. ###Code Map = folium.Map(location=[40, -100], zoom_start=4) Map.setOptions('HYBRID') ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output Collection: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1580044754541352, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'tier1', 'oli_tirs', 'c1', 'radiance', 'lc8', 'l8', 't1'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1578700800000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'tier1', 'oli_tirs', 'c1', 'radiance', 'lc8', 'l8', 't1'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1580044754541352, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15581], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7791], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1580044754541352, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140403', 'properties': {'RADIANCE_MULT_BAND_5': 0.00611429987475276, 'RADIANCE_MULT_BAND_6': 0.0015206000534817576, 'RADIANCE_MULT_BAND_3': 0.011849000118672848, 'RADIANCE_MULT_BAND_4': 0.009991499595344067, 'RADIANCE_MULT_BAND_1': 0.012556999921798706, 'RADIANCE_MULT_BAND_2': 0.01285799965262413, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.8473141778081, 38.05593855929062], [-120.8399593728871, 38.079323071287384], [-120.82522434534502, 38.126298845124154], [-120.82517062317932, 38.12810935862697], [-120.8677905264658, 38.13653674526281], [-121.37735830917396, 38.23574890955089], [-122.92397603591857, 38.5218201625494], [-122.94540185152168, 38.52557313562304], [-122.94781508421401, 38.52557420469068], [-122.9538620955667, 38.50519466790785], [-123.43541566635548, 36.80572425461524], [-123.43388775775958, 36.8051169737102], [-121.36103157158686, 36.408726677230895], [-121.3601864919046, 36.410036730606365], [-121.3547960201613, 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1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.559550285339355, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043141_00057', 'EARTH_SUN_DISTANCE': 1.016627550125122, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488642210000, 'SCENE_CENTER_TIME': '18:45:55.3336140Z', 'SUN_ELEVATION': 65.8777847290039, 'BPF_NAME_OLI': 'LO8BPF20140708182239_20140708190335.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'N', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 506, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 187}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 465592.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}], 'version': 1580044754541352, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724', 'properties': {'RADIANCE_MULT_BAND_5': 0.005925099831074476, 'RADIANCE_MULT_BAND_6': 0.0014735000440850854, 'RADIANCE_MULT_BAND_3': 0.011482000350952148, 'RADIANCE_MULT_BAND_4': 0.00968219991773367, 'RADIANCE_MULT_BAND_1': 0.01216800045222044, 'RADIANCE_MULT_BAND_2': 0.01245999988168478, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.76979738859893, 38.12703313441971], [-120.77158274982695, 38.12754115630432], [-121.42134145512932, 38.25446242506864], [-122.22152328015193, 38.40525302827857], [-122.89018639673915, 38.5266157865438], [-122.89242341654155, 38.526617857573015], [-122.89466073063308, 38.519621963160894], [-123.38187286142927, 36.80872337128997], [-123.38186259045791, 36.806647917217006], [-123.35901116184863, 36.80244946066433], [-122.88546161915531, 36.71490670011608], [-121.3092309147788, 36.40846418437395], [-121.30782254819886, 36.40844420946354], [-121.08696039686296, 37.12150541970936], [-121.06667332030511, 37.186300761679455], [-120.9265815780102, 37.63183285571133], [-120.88231915679422, 37.77176559071555], [-120.83617669320071, 37.917319414649754], [-120.8201155519523, 37.96798246241547], [-120.7756360373179, 38.10840000147115], [-120.76979738859893, 38.12703313441971]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 126.32495880126953, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.4832499027252197, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.84012985229492, 'RADIANCE_ADD_BAND_2': -62.301029205322266, 'RADIANCE_ADD_BAND_3': -57.40987014770508, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.78820037841797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.57822036743164, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043005_00057', 'EARTH_SUN_DISTANCE': 1.0158073902130127, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488621091000, 'SCENE_CENTER_TIME': '18:45:57.2197370Z', 'SUN_ELEVATION': 63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}]} Count: 9 Date range: {'type': 'Date', 'value': 1395168392050} {'type': 'Date', 'value': 1406227557220} Sun elevation statistics: {'type': 'DataDictionary', 'values': {'max': 67.10252380371094, 'mean': 61.01998392740885, 'min': 46.471065521240234, 'sample_sd': 7.251804209519804, 'sample_var': 52.58866429320915, 'sum': 549.1798553466797, 'sum_sq': 33931.65526085679, 'total_count': 9, 'total_sd': 6.837066576518139, 'total_var': 46.74547937174147, 'valid_count': 9, 'weight_sum': 9, 'weighted_sum': 549.1798553466797}} Least cloudy image: {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15321, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 460792.5, 0, -15, 4264207.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7661, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 460785, 0, -30, 4264215]}], 'version': 1580044754541352, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318', 'properties': {'RADIANCE_MULT_BAND_5': 0.006170900072902441, 'RADIANCE_MULT_BAND_6': 0.001534600043669343, 'RADIANCE_MULT_BAND_3': 0.011958000250160694, 'RADIANCE_MULT_BAND_4': 0.010084000416100025, 'RADIANCE_MULT_BAND_1': 0.012672999873757362, 'RADIANCE_MULT_BAND_2': 0.012977000325918198, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-121.3637119499993, 36.41016684133052], [-121.35905784815819, 36.42528989660049], [-121.2315833015866, 36.840374852891664], [-121.09978718573184, 37.26438246506325], [-121.00571062336425, 37.564795515259384], [-120.98453376062118, 37.632161601008896], [-120.95100979452299, 37.73864548098522], [-120.90277241165228, 37.89149086576169], [-120.8836409072059, 37.951976016520376], [-120.85713152433351, 38.03584247073611], [-120.82804345546616, 38.12789513604401], [-122.38148159443172, 38.42337450676813], [-122.9500220192271, 38.525813632077686], [-122.95103687833704, 38.52422133103557], [-122.9569591344694, 38.504384836247866], [-123.43853932998316, 36.805122381748035], [-123.18722447462653, 36.759167415189125], [-121.5105534682754, 36.43765126135182], [-121.36447385999617, 36.408418528930035], [-121.3637119499993, 36.41016684133052]]}, 'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}} Recent images: {'type': 'ImageCollection', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}}], 'id': 'LANDSAT/LC08/C01/T1_TOA', 'version': 1580044754541352, 'properties': {'system:visualization_0_min': '0.0', 'type_name': 'ImageCollection', 'visualization_1_bands': 'B5,B4,B3', 'thumb': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_thumb.png', 'visualization_1_max': '30000.0', 'description': '<p>Landsat 8 Collection 1 Tier 1\n calibrated top-of-atmosphere (TOA) reflectance.\n Calibration coefficients are extracted from the image metadata. See<a href="http://www.sciencedirect.com/science/article/pii/S0034425709000169">\n Chander et al. (2009)</a> for details on the TOA computation.</p></p>\n<p><b>Revisit Interval</b>\n<br>\n 16 days\n</p>\n<p><b>Bands</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Resolution</th>\n<th scope="col">Wavelength</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>B1</td>\n<td>\n 30 meters\n</td>\n<td>0.43 - 0.45 µm</td>\n<td><p>Coastal aerosol</p></td>\n</tr>\n<tr>\n<td>B2</td>\n<td>\n 30 meters\n</td>\n<td>0.45 - 0.51 µm</td>\n<td><p>Blue</p></td>\n</tr>\n<tr>\n<td>B3</td>\n<td>\n 30 meters\n</td>\n<td>0.53 - 0.59 µm</td>\n<td><p>Green</p></td>\n</tr>\n<tr>\n<td>B4</td>\n<td>\n 30 meters\n</td>\n<td>0.64 - 0.67 µm</td>\n<td><p>Red</p></td>\n</tr>\n<tr>\n<td>B5</td>\n<td>\n 30 meters\n</td>\n<td>0.85 - 0.88 µm</td>\n<td><p>Near infrared</p></td>\n</tr>\n<tr>\n<td>B6</td>\n<td>\n 30 meters\n</td>\n<td>1.57 - 1.65 µm</td>\n<td><p>Shortwave infrared 1</p></td>\n</tr>\n<tr>\n<td>B7</td>\n<td>\n 30 meters\n</td>\n<td>2.11 - 2.29 µm</td>\n<td><p>Shortwave infrared 2</p></td>\n</tr>\n<tr>\n<td>B8</td>\n<td>\n 15 meters\n</td>\n<td>0.52 - 0.90 µm</td>\n<td><p>Band 8 Panchromatic</p></td>\n</tr>\n<tr>\n<td>B9</td>\n<td>\n 15 meters\n</td>\n<td>1.36 - 1.38 µm</td>\n<td><p>Cirrus</p></td>\n</tr>\n<tr>\n<td>B10</td>\n<td>\n 30 meters\n</td>\n<td>10.60 - 11.19 µm</td>\n<td><p>Thermal infrared 1, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>B11</td>\n<td>\n 30 meters\n</td>\n<td>11.50 - 12.51 µm</td>\n<td><p>Thermal infrared 2, resampled from 100m to 30m</p></td>\n</tr>\n<tr>\n<td>BQA</td>\n<td>\n</td>\n<td></td>\n<td><p>Landsat Collection 1 QA Bitmask (<a href="https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band">See Landsat QA page</a>)</p></td>\n</tr>\n<tr>\n<td colspan=100>\n Bitmask for BQA\n<ul>\n<li>\n Bit 0: Designated Fill\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bit 1: Terrain Occlusion\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 2-3: Radiometric Saturation\n<ul>\n<li>0: No bands contain saturation</li>\n<li>1: 1-2 bands contain saturation</li>\n<li>2: 3-4 bands contain saturation</li>\n<li>3: 5 or more bands contain saturation</li>\n</ul>\n</li>\n<li>\n Bit 4: Cloud\n<ul>\n<li>0: No</li>\n<li>1: Yes</li>\n</ul>\n</li>\n<li>\n Bits 5-6: Cloud Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 7-8: Cloud Shadow Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 9-10: Snow / Ice Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n<li>\n Bits 11-12: Cirrus Confidence\n<ul>\n<li>0: Not Determined / Condition does not exist.</li>\n<li>1: Low, (0-33 percent confidence)</li>\n<li>2: Medium, (34-66 percent confidence)</li>\n<li>3: High, (67-100 percent confidence)</li>\n</ul>\n</li>\n</ul>\n</td>\n</tr>\n</table>\n<p><b>Image Properties</b>\n<table class="eecat">\n<tr>\n<th scope="col">Name</th>\n<th scope="col">Type</th>\n<th scope="col">Description</th>\n</tr>\n<tr>\n<td>BPF_NAME_OLI</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain OLI bands.</p></td>\n</tr>\n<tr>\n<td>BPF_NAME_TIRS</td>\n<td>STRING</td>\n<td><p>The file name for the Bias Parameter File (BPF) used to generate the product, if applicable. This only applies to products that contain TIRS bands.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>CLOUD_COVER_LAND</td>\n<td>DOUBLE</td>\n<td><p>Percentage cloud cover over land, -1 = not calculated.</p></td>\n</tr>\n<tr>\n<td>COLLECTION_CATEGORY</td>\n<td>STRING</td>\n<td><p>Tier of scene. (T1 or T2)</p></td>\n</tr>\n<tr>\n<td>COLLECTION_NUMBER</td>\n<td>DOUBLE</td>\n<td><p>Number of collection.</p></td>\n</tr>\n<tr>\n<td>CPF_NAME</td>\n<td>STRING</td>\n<td><p>Calibration parameter file name.</p></td>\n</tr>\n<tr>\n<td>DATA_TYPE</td>\n<td>STRING</td>\n<td><p>Data type identifier. (L1T or L1G)</p></td>\n</tr>\n<tr>\n<td>DATE_ACQUIRED</td>\n<td>STRING</td>\n<td><p>Image acquisition date. "YYYY-MM-DD"</p></td>\n</tr>\n<tr>\n<td>DATUM</td>\n<td>STRING</td>\n<td><p>Datum used in image creation.</p></td>\n</tr>\n<tr>\n<td>EARTH_SUN_DISTANCE</td>\n<td>DOUBLE</td>\n<td><p>Earth sun distance in astronomical units (AU).</p></td>\n</tr>\n<tr>\n<td>ELEVATION_SOURCE</td>\n<td>STRING</td>\n<td><p>Elevation model source used for standard terrain corrected (L1T) products.</p></td>\n</tr>\n<tr>\n<td>ELLIPSOID</td>\n<td>STRING</td>\n<td><p>Ellipsoid used in image creation.</p></td>\n</tr>\n<tr>\n<td>EPHEMERIS_TYPE</td>\n<td>STRING</td>\n<td><p>Ephemeris data type used to perform geometric correction. (Definitive or Predictive)</p></td>\n</tr>\n<tr>\n<td>FILE_DATE</td>\n<td>DOUBLE</td>\n<td><p>File date in milliseconds since epoch.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL</td>\n<td>DOUBLE</td>\n<td><p>Combined Root Mean Square Error (RMSE) of the geometric residuals\n(metres) in both across-track and along-track directions\nmeasured on the GCPs used in geometric precision correction.\nNot present in L1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_X</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the X direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GEOMETRIC_RMSE_MODEL_Y</td>\n<td>DOUBLE</td>\n<td><p>RMSE of the Y direction geometric residuals (in metres) measured\non the GCPs used in geometric precision correction. Not present in\nL1G products.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_PANCHROMATIC</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_REFLECTIVE</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the reflective band.</p></td>\n</tr>\n<tr>\n<td>GRID_CELL_SIZE_THERMAL</td>\n<td>DOUBLE</td>\n<td><p>Grid cell size used in creating the image for the thermal band.</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_MODEL</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used. Not used in L1GT products.\nValues: 0 - 999 (0 is used for L1T products that have used\nMulti-scene refinement).</p></td>\n</tr>\n<tr>\n<td>GROUND_CONTROL_POINTS_VERSION</td>\n<td>DOUBLE</td>\n<td><p>The number of ground control points used in the verification of\nthe terrain corrected product. Values: -1 to 1615 (-1 = not available)</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY</td>\n<td>DOUBLE</td>\n<td><p>Image quality, 0 = worst, 9 = best, -1 = quality not calculated</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_OLI</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the OLI bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>IMAGE_QUALITY_TIRS</td>\n<td>DOUBLE</td>\n<td><p>The composite image quality for the TIRS bands. Values: 9 = Best. 1 = Worst. 0 = Image quality not calculated. This parameter is only present if OLI bands are present in the product.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K1_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K1 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 10 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>K2_CONSTANT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Calibration K2 constant for Band 11 radiance to temperature conversion.</p></td>\n</tr>\n<tr>\n<td>LANDSAT_PRODUCT_ID</td>\n<td>STRING</td>\n<td><p>The naming convention of each Landsat Collection 1 Level-1 image based\non acquisition parameters and processing parameters.</p>\n<p>Format: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager,\nT = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>SS = Satellite (08 = Landsat 8)</li>\n<li>LLLL = Processing Correction Level (L1TP = precision and terrain,\nL1GT = systematic terrain, L1GS = systematic)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYYMMDD = Acquisition Date expressed in Year, Month, Day</li>\n<li>yyyymmdd = Processing Date expressed in Year, Month, Day</li>\n<li>CC = Collection Number (01)</li>\n<li>TX = Collection Category (RT = Real Time, T1 = Tier 1, T2 = Tier 2)</li>\n</ul></td>\n</tr>\n<tr>\n<td>LANDSAT_SCENE_ID</td>\n<td>STRING</td>\n<td><p>The Pre-Collection naming convention of each image is based on acquisition\nparameters. This was the naming convention used prior to Collection 1.</p>\n<p>Format: LXSPPPRRRYYYYDDDGSIVV</p>\n<ul>\n<li>L = Landsat</li>\n<li>X = Sensor (O = Operational Land Imager, T = Thermal Infrared Sensor, C = Combined OLI/TIRS)</li>\n<li>S = Satellite (08 = Landsat 8)</li>\n<li>PPP = WRS Path</li>\n<li>RRR = WRS Row</li>\n<li>YYYY = Year of Acquisition</li>\n<li>DDD = Julian Day of Acquisition</li>\n<li>GSI = Ground Station Identifier</li>\n<li>VV = Version</li>\n</ul></td>\n</tr>\n<tr>\n<td>MAP_PROJECTION</td>\n<td>STRING</td>\n<td><p>Projection used to represent the 3-dimensional surface of the earth for the Level-1 product.</p></td>\n</tr>\n<tr>\n<td>NADIR_OFFNADIR</td>\n<td>STRING</td>\n<td><p>Nadir or Off-Nadir condition of the scene.</p></td>\n</tr>\n<tr>\n<td>ORIENTATION</td>\n<td>STRING</td>\n<td><p>Orientation used in creating the image. Values: NOMINAL = Nominal Path, NORTH_UP = North Up, TRUE_NORTH = True North, USER = User</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the panchromatic band.</p></td>\n</tr>\n<tr>\n<td>PANCHROMATIC_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the panchromatic bands.</p></td>\n</tr>\n<tr>\n<td>PROCESSING_SOFTWARE_VERSION</td>\n<td>STRING</td>\n<td><p>Name and version of the processing software used to generate the L1 product.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 1.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 10.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 11.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 2.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 3.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 4.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 5.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 6.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 7.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 8.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated DN to radiance for Band 9.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 1 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_10</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 10 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_11</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 11 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 2 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 3 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 4 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 5 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 6 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 7 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 8 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>RADIANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative rescaling factor used to convert calibrated Band 9 DN to radiance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Additive rescaling factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_ADD_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Minimum achievable spectral reflectance value for Band 8.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_1</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 1 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_2</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 2 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_3</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 3 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_4</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 4 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_5</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 5 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_6</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 6 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_7</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 7 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_8</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 8 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTANCE_MULT_BAND_9</td>\n<td>DOUBLE</td>\n<td><p>Multiplicative factor used to convert calibrated Band 9 DN to reflectance.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REFLECTIVE_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the reflective bands.</p></td>\n</tr>\n<tr>\n<td>REQUEST_ID</td>\n<td>STRING</td>\n<td><p>Request id, nnnyymmdd0000_0000</p>\n<ul>\n<li>nnn = node number</li>\n<li>yy = year</li>\n<li>mm = month</li>\n<li>dd = day</li>\n</ul></td>\n</tr>\n<tr>\n<td>RESAMPLING_OPTION</td>\n<td>STRING</td>\n<td><p>Resampling option used in creating the image.</p></td>\n</tr>\n<tr>\n<td>RLUT_FILE_NAME</td>\n<td>STRING</td>\n<td><p>The file name for the Response Linearization Lookup Table (RLUT) used to generate the product, if applicable.</p></td>\n</tr>\n<tr>\n<td>ROLL_ANGLE</td>\n<td>DOUBLE</td>\n<td><p>The amount of spacecraft roll angle at the scene center.</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_1</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 1 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_10</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 10 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_11</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 11 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_2</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 2 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_3</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 3 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_4</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 4 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_5</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 5 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_6</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 6 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_7</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 7 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_8</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 8 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SATURATION_BAND_9</td>\n<td>STRING</td>\n<td><p>Flag indicating saturated pixels for band 9 ('Y'/'N')</p></td>\n</tr>\n<tr>\n<td>SCENE_CENTER_TIME</td>\n<td>STRING</td>\n<td><p>Scene center time of acquired image. HH:MM:SS.SSSSSSSZ</p>\n<ul>\n<li>HH = Hour (00-23)</li>\n<li>MM = Minutes</li>\n<li>SS.SSSSSSS = Fractional seconds</li>\n<li>Z = "Zulu" time (same as GMT)</li>\n</ul></td>\n</tr>\n<tr>\n<td>SENSOR_ID</td>\n<td>STRING</td>\n<td><p>Sensor used to capture data.</p></td>\n</tr>\n<tr>\n<td>SPACECRAFT_ID</td>\n<td>STRING</td>\n<td><p>Spacecraft identification.</p></td>\n</tr>\n<tr>\n<td>STATION_ID</td>\n<td>STRING</td>\n<td><p>Ground Station/Organisation that received the data.</p></td>\n</tr>\n<tr>\n<td>SUN_AZIMUTH</td>\n<td>DOUBLE</td>\n<td><p>Sun azimuth angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>SUN_ELEVATION</td>\n<td>DOUBLE</td>\n<td><p>Sun elevation angle in degrees for the image center location at the image centre acquisition time.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 path to the line-of-sight scene center of the image.</p></td>\n</tr>\n<tr>\n<td>TARGET_WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Nearest WRS-2 row to the line-of-sight scene center of the image. Rows 880-889 and 990-999 are reserved for the polar regions where it is undefined in the WRS-2.</p></td>\n</tr>\n<tr>\n<td>THERMAL_LINES</td>\n<td>DOUBLE</td>\n<td><p>Number of product lines for the thermal band.</p></td>\n</tr>\n<tr>\n<td>THERMAL_SAMPLES</td>\n<td>DOUBLE</td>\n<td><p>Number of product samples for the thermal band.</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_MODEL</td>\n<td>STRING</td>\n<td><p>Due to an anomalous condition on the Thermal Infrared\nSensor (TIRS) Scene Select Mirror (SSM) encoder electronics,\nthis field has been added to indicate which model was used to process the data.\n(Actual, Preliminary, Final)</p></td>\n</tr>\n<tr>\n<td>TIRS_SSM_POSITION_STATUS</td>\n<td>STRING</td>\n<td><p>TIRS SSM position status.</p></td>\n</tr>\n<tr>\n<td>TIRS_STRAY_LIGHT_CORRECTION_SOURCE</td>\n<td>STRING</td>\n<td><p>TIRS stray light correction source.</p></td>\n</tr>\n<tr>\n<td>TRUNCATION_OLI</td>\n<td>STRING</td>\n<td><p>Region of OLCI truncated.</p></td>\n</tr>\n<tr>\n<td>UTM_ZONE</td>\n<td>DOUBLE</td>\n<td><p>UTM zone number used in product map projection.</p></td>\n</tr>\n<tr>\n<td>WRS_PATH</td>\n<td>DOUBLE</td>\n<td><p>The WRS orbital path number (001 - 251).</p></td>\n</tr>\n<tr>\n<td>WRS_ROW</td>\n<td>DOUBLE</td>\n<td><p>Landsat satellite WRS row (001-248).</p></td>\n</tr>\n</table>\n<style>\n table.eecat {\n border: 1px solid black;\n border-collapse: collapse;\n font-size: 13px;\n }\n table.eecat td, tr, th {\n text-align: left; vertical-align: top;\n border: 1px solid gray; padding: 3px;\n }\n td.nobreak { white-space: nowrap; }\n</style>', 'source_tags': ['landsat', 'usgs'], 'visualization_1_name': 'Near Infrared (543)', 'visualization_0_max': '30000.0', 'title': 'USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance', 'visualization_0_gain': '500.0', 'system:visualization_2_max': '30000.0', 'product_tags': ['global', 'toa', 'tier1', 'oli_tirs', 'c1', 'radiance', 'lc8', 'l8', 't1'], 'visualization_1_gain': '500.0', 'provider': 'USGS/Google', 'visualization_1_min': '0.0', 'system:visualization_2_name': 'Shortwave Infrared (753)', 'visualization_0_min': '0.0', 'system:visualization_1_bands': 'B5,B4,B3', 'system:visualization_1_max': '30000.0', 'visualization_0_name': 'True Color (432)', 'date_range': [1365638400000, 1578700800000], 'visualization_2_bands': 'B7,B5,B3', 'visualization_2_name': 'Shortwave Infrared (753)', 'period': 0, 'system:visualization_2_min': '0.0', 'system:visualization_0_bands': 'B4,B3,B2', 'visualization_2_min': '0.0', 'visualization_2_gain': '500.0', 'provider_url': 'http://landsat.usgs.gov/', 'sample': 'https://mw1.google.com/ges/dd/images/LANDSAT_TOA_sample.png', 'system:visualization_1_name': 'Near Infrared (543)', 'tags': ['landsat', 'usgs', 'global', 'toa', 'tier1', 'oli_tirs', 'c1', 'radiance', 'lc8', 'l8', 't1'], 'system:visualization_0_max': '30000.0', 'visualization_2_max': '30000.0', 'system:visualization_2_bands': 'B7,B5,B3', 'system:visualization_1_min': '0.0', 'system:visualization_0_name': 'True Color (432)', 'visualization_0_bands': 'B4,B3,B2'}, 'features': [{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [15341, 15601], 'crs': 'EPSG:32610', 'crs_transform': [15, 0, 465592.5, 0, -15, 4264507.5]}, {'id': 'B9', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B10', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'B11', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}, {'id': 'BQA', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 65535}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 465585, 0, -30, 4264515]}], 'version': 1580044754541352, 'id': 'LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140724', 'properties': {'RADIANCE_MULT_BAND_5': 0.005925099831074476, 'RADIANCE_MULT_BAND_6': 0.0014735000440850854, 'RADIANCE_MULT_BAND_3': 0.011482000350952148, 'RADIANCE_MULT_BAND_4': 0.00968219991773367, 'RADIANCE_MULT_BAND_1': 0.01216800045222044, 'RADIANCE_MULT_BAND_2': 0.01245999988168478, 'K2_CONSTANT_BAND_11': 1201.1441650390625, 'K2_CONSTANT_BAND_10': 1321.078857421875, 'system:footprint': {'type': 'LinearRing', 'coordinates': [[-120.76979738859893, 38.12703313441971], [-120.77158274982695, 38.12754115630432], [-121.42134145512932, 38.25446242506864], [-122.22152328015193, 38.40525302827857], [-122.89018639673915, 38.5266157865438], [-122.89242341654155, 38.526617857573015], [-122.89466073063308, 38.519621963160894], [-123.38187286142927, 36.80872337128997], [-123.38186259045791, 36.806647917217006], [-123.35901116184863, 36.80244946066433], [-122.88546161915531, 36.71490670011608], [-121.3092309147788, 36.40846418437395], [-121.30782254819886, 36.40844420946354], [-121.08696039686296, 37.12150541970936], [-121.06667332030511, 37.186300761679455], [-120.9265815780102, 37.63183285571133], [-120.88231915679422, 37.77176559071555], [-120.83617669320071, 37.917319414649754], [-120.8201155519523, 37.96798246241547], [-120.7756360373179, 38.10840000147115], [-120.76979738859893, 38.12703313441971]]}, 'REFLECTIVE_SAMPLES': 7671, 'SUN_AZIMUTH': 126.32495880126953, 'CPF_NAME': 'LC08CPF_20140701_20140930_01.01', 'DATE_ACQUIRED': '2014-07-24', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 407.9683837890625, 'google:registration_offset_y': -124.7548828125, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.00231559993699193, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0004966499982401729, 'RADIANCE_MULT_BAND_8': 0.010958000086247921, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.3199999928474426, 'GEOMETRIC_RMSE_VERIFY': 2.700000047683716, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.23000000417232513, 'GEOMETRIC_RMSE_MODEL': 5.454999923706055, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014205LGN01', 'WRS_PATH': 44, 'google:registration_count': 10, 'PANCHROMATIC_SAMPLES': 15341, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 3.703000068664551, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.00600004196167, 'system:asset_size': 1201420225, 'system:index': 'LC08_044034_20140724', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140724181847_20140724190430.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140724_20170304_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0.3333333432674408, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -48.41123962402344, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1406227557220, 'RADIANCE_ADD_BAND_5': -29.625259399414062, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.36752986907959, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.4832499027252197, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -60.84012985229492, 'RADIANCE_ADD_BAND_2': -62.301029205322266, 'RADIANCE_ADD_BAND_3': -57.40987014770508, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -54.78820037841797, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -11.57822036743164, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703043005_00057', 'EARTH_SUN_DISTANCE': 1.0158073902130127, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488621091000, 'SCENE_CENTER_TIME': '18:45:57.2197370Z', 'SUN_ELEVATION': 63.77280807495117, 'BPF_NAME_OLI': 'LO8BPF20140724182241_20140724190337.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': 0, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 568, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7671, 'GROUND_CONTROL_POINTS_VERIFY': 213}}, {'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B6', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 464385, 0, -30, 4264515]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'float'}, 'dimensions': [7671, 7801], 'crs': 'EPSG:32610', 'crs_transform': [30, 0, 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'REFLECTIVE_SAMPLES': 7661, 'SUN_AZIMUTH': 146.2395782470703, 'CPF_NAME': 'LC08CPF_20140101_20140331_01.01', 'DATE_ACQUIRED': '2014-03-18', 'ELLIPSOID': 'WGS84', 'google:registration_offset_x': 0, 'google:registration_offset_y': 0, 'STATION_ID': 'LGN', 'RESAMPLING_OPTION': 'CUBIC_CONVOLUTION', 'ORIENTATION': 'NORTH_UP', 'WRS_ROW': 34, 'RADIANCE_MULT_BAND_9': 0.0024117000866681337, 'TARGET_WRS_ROW': 34, 'RADIANCE_MULT_BAND_7': 0.0005172499804757535, 'RADIANCE_MULT_BAND_8': 0.0114120002835989, 'IMAGE_QUALITY_TIRS': 9, 'TRUNCATION_OLI': 'UPPER', 'CLOUD_COVER': 0.05999999865889549, 'GEOMETRIC_RMSE_VERIFY': 3.249000072479248, 'COLLECTION_CATEGORY': 'T1', 'GRID_CELL_SIZE_REFLECTIVE': 30, 'CLOUD_COVER_LAND': 0.10000000149011612, 'GEOMETRIC_RMSE_MODEL': 6.78000020980835, 'COLLECTION_NUMBER': 1, 'IMAGE_QUALITY_OLI': 9, 'LANDSAT_SCENE_ID': 'LC80440342014077LGN01', 'WRS_PATH': 44, 'google:registration_count': 0, 'PANCHROMATIC_SAMPLES': 15321, 'PANCHROMATIC_LINES': 15601, 'GEOMETRIC_RMSE_MODEL_Y': 4.747000217437744, 'REFLECTIVE_LINES': 7801, 'TIRS_STRAY_LIGHT_CORRECTION_SOURCE': 'TIRS', 'GEOMETRIC_RMSE_MODEL_X': 4.841000080108643, 'system:asset_size': 1105511852, 'system:index': 'LC08_044034_20140318', 'REFLECTANCE_ADD_BAND_1': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_2': -0.10000000149011612, 'DATUM': 'WGS84', 'REFLECTANCE_ADD_BAND_3': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_4': -0.10000000149011612, 'RLUT_FILE_NAME': 'LC08RLUT_20130211_20150302_01_11.h5', 'REFLECTANCE_ADD_BAND_5': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_6': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_7': -0.10000000149011612, 'REFLECTANCE_ADD_BAND_8': -0.10000000149011612, 'BPF_NAME_TIRS': 'LT8BPF20140318182855_20140318190505.01', 'GROUND_CONTROL_POINTS_VERSION': 4, 'DATA_TYPE': 'L1TP', 'UTM_ZONE': 10, 'LANDSAT_PRODUCT_ID': 'LC08_L1TP_044034_20140318_20170307_01_T1', 'REFLECTANCE_ADD_BAND_9': -0.10000000149011612, 'google:registration_ratio': 0, 'GRID_CELL_SIZE_PANCHROMATIC': 15, 'RADIANCE_ADD_BAND_4': -50.419559478759766, 'REFLECTANCE_MULT_BAND_7': 1.9999999494757503e-05, 'system:time_start': 1395168392050, 'RADIANCE_ADD_BAND_5': -30.854249954223633, 'REFLECTANCE_MULT_BAND_6': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_6': -7.67317008972168, 'REFLECTANCE_MULT_BAND_9': 1.9999999494757503e-05, 'PROCESSING_SOFTWARE_VERSION': 'LPGS_2.7.0', 'RADIANCE_ADD_BAND_7': -2.5862700939178467, 'REFLECTANCE_MULT_BAND_8': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_1': -63.364051818847656, 'RADIANCE_ADD_BAND_2': -64.88555908203125, 'RADIANCE_ADD_BAND_3': -59.79148864746094, 'REFLECTANCE_MULT_BAND_1': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_8': -57.06106185913086, 'REFLECTANCE_MULT_BAND_3': 1.9999999494757503e-05, 'RADIANCE_ADD_BAND_9': -12.058540344238281, 'REFLECTANCE_MULT_BAND_2': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_5': 1.9999999494757503e-05, 'REFLECTANCE_MULT_BAND_4': 1.9999999494757503e-05, 'THERMAL_LINES': 7801, 'TIRS_SSM_POSITION_STATUS': 'NOMINAL', 'GRID_CELL_SIZE_THERMAL': 30, 'NADIR_OFFNADIR': 'NADIR', 'RADIANCE_ADD_BAND_11': 0.10000000149011612, 'REQUEST_ID': '0501703063989_00019', 'EARTH_SUN_DISTANCE': 0.9953709244728088, 'TIRS_SSM_MODEL': 'ACTUAL', 'FILE_DATE': 1488849349000, 'SCENE_CENTER_TIME': '18:46:32.0535800Z', 'SUN_ELEVATION': 46.471065521240234, 'BPF_NAME_OLI': 'LO8BPF20140318183249_20140318190412.01', 'RADIANCE_ADD_BAND_10': 0.10000000149011612, 'ROLL_ANGLE': -0.0010000000474974513, 'K1_CONSTANT_BAND_10': 774.8853149414062, 'SATURATION_BAND_1': 'Y', 'SATURATION_BAND_2': 'Y', 'SATURATION_BAND_3': 'Y', 'SATURATION_BAND_4': 'Y', 'SATURATION_BAND_5': 'Y', 'MAP_PROJECTION': 'UTM', 'SATURATION_BAND_6': 'Y', 'SENSOR_ID': 'OLI_TIRS', 'SATURATION_BAND_7': 'Y', 'K1_CONSTANT_BAND_11': 480.8883056640625, 'SATURATION_BAND_8': 'N', 'SATURATION_BAND_9': 'N', 'TARGET_WRS_PATH': 44, 'RADIANCE_MULT_BAND_11': 0.00033420001273043454, 'RADIANCE_MULT_BAND_10': 0.00033420001273043454, 'GROUND_CONTROL_POINTS_MODEL': 527, 'SPACECRAFT_ID': 'LANDSAT_8', 'ELEVATION_SOURCE': 'GLS2000', 'THERMAL_SAMPLES': 7661, 'GROUND_CONTROL_POINTS_VERIFY': 164}}]} ###Markdown Display Earth Engine data layers ###Code Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True) Map ###Output _____no_output_____ ###Markdown View source on GitHub Notebook Viewer Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The geemap Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemapdependencies), including earthengine-api, folium, and ipyleaflet.Important note: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). ###Code # Installs geemap package import subprocess try: import geemap except ImportError: print('geemap package not installed. Installing ...') subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap']) # Checks whether this notebook is running on Google Colab try: import google.colab import geemap.eefolium as geemap except: import geemap # Authenticates and initializes Earth Engine import ee try: ee.Initialize() except Exception as e: ee.Authenticate() ee.Initialize() ###Output _____no_output_____ ###Markdown Create an interactive map The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. ###Code Map = geemap.Map(center=[40,-100], zoom=4) Map ###Output _____no_output_____ ###Markdown Add Earth Engine Python script ###Code # Add Earth Engine dataset # Load a Landsat 8 ImageCollection for a single path-row. collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \ .filter(ee.Filter.eq('WRS_PATH', 44)) \ .filter(ee.Filter.eq('WRS_ROW', 34)) \ .filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection.getInfo()) # Get the number of images. count = collection.size() print('Count: ', count.getInfo()) # Get the date range of images in the collection. range = collection.reduceColumns(ee.Reducer.minMax(), ["system:time_start"]) print('Date range: ', ee.Date(range.get('min')).getInfo(), ee.Date(range.get('max')).getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection.aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats.getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee.Image(collection.sort('CLOUD_COVER').first()) print('Least cloudy image: ', image.getInfo()) # Limit the collection to the 10 most recent images. recent = collection.sort('system:time_start', False).limit(10) print('Recent images: ', recent.getInfo()) ###Output _____no_output_____ ###Markdown Display Earth Engine data layers ###Code Map.addLayerControl() # This line is not needed for ipyleaflet-based Map. Map ###Output _____no_output_____
.ipynb_checkpoints/Lipidtools-checkpoint.ipynb	###Markdown LipidtoolsUtility classes for creating a new topology (.itp) file, making a sample minimal membrane containing that species, minimizing that system, generating and showing an image of that speciesAdd the following to the top of any Notebook to be able to use these tools >%run Lipidtools.ipynb Class lipid(name, head, link, alpha, beta).write(fileName) : lipid method lipid.create(verbose) : lipid method lipid.description(descr) : lipid method lipid.appendTo(fileName) : lipid method lipid.buildMembrane(name) : lipid method lipid.minimize(verbose,parameterFile) : lipid method lipid.show(x,y,z) : lipid * name: Martini notation for the lipid, eg: "POPC"* head: Martini notation for the head group, eg: "C P", "P E", or "PI"* link: Martini notation for the linking groups, eg: "G G" or "A A" * alpha: Martini notation for the alpha acyl tail, eg: "CDCC" or oleic * beta: Martini notation for the beta acyl tail, eg: "CCCC" for palmitic* methods * create creates a topology, depending on "verbose" outputs to the stream a summary or the output of that process, and returns an instance to this lipid * description set the description for the species, and returns an instance to this lipid * write(fileName) writes the topology to fileName.itp, returns an instance to this lipid * append(fileName) appends the topology to fileName.itp (removing any previous instance of this name), returns an instance to this lipid * buildMembrane(Name) creates a minimal membrane system name.gro containing this species * minimize(verbose,parameterFile) if a membrane system doesn't exist it makes one, and then minimzes it using the parameter (.mdp) file and writes a summary (if verbose=false) or the complete output in the stream, returns an instance to this lipid * method show(x,y,z) writes to output stream an image of the species using x,y,z to translate (move) the image in the camera field * fields * topology : string * success : boolean - whether minimize completed all stepsUSAGE: lipid("POPC","C P","G G","CDCC","CCCC").description("A general model phosphatidylcholine (PC) lipid corresponding to atomistic e.g. C16:0/18:1 1-palmitoyl-2-oleoyl (POPC) tails.").appendTo("./martini.ff/myLipids.itp").minimize(false,"Test.mdp").show().success() Will add the lipid to myLipids.itp, print out the summary result of minimizing a membrane containing this lipid using Test.mdp, and an image of the lipid, and return if the creation/embeding/minimization was successfull. ###Code # import libraries used in these utilities import os # Operating system specific commands import re # Regular expression library import datetime # Date and Time routines import weakref # Object lifetime managment import tempfile # Temporary paths and files import shutil # Shell utilities #defaults defaultpath="./" defaultmdp = os.path.join(defaultpath,"test.mdp") defaultmartini = os.path.join(defaultpath,"martini.ff") defaultinsane = os.path.join(defaultpath,"insane+SF.py") #verify defaults exist if not os.path.isfile(defaultmdp): print("WARNING: default MDP [{}] missing".format(defaultmdp)) if not os.path.isfile(defaultinsane): print("WARNING: default insane [{}] missing".format(defaultinsane)) if not os.path.isdir(defaultmartini): print("WARNING: default martini path [{}] missing".format(defaultmartini)) #Exeception class for an imediate stop class StopExecution(Exception): def _render_traceback_(self): pass class lipid: def __init__(self,name,head,link,alpha,beta): if not name: print("usage lipids(name,head,link,alpha,beta)") self.name=name self.head=head self.link=link self.alpha = alpha self.beta = beta self.topology = "" self.success = False self.parameterization = "; This topology follows the standard Martini 2.0 lipid definitions and building block rules.\n; Reference(s): \n; S.J. Marrink, A.H. de Vries, A.E. Mark. Coarse grained model for semi-quantitative lipid simulations. JPC-B, 108:750-760, \n; 2004. doi:10.1021/jp036508g \n; S.J. Marrink, H.J. Risselada, S. Yefimov, D.P. Tieleman, A.H. de Vries. The MARTINI force field: coarse grained model for \n; biomolecular simulations. JPC-B, 111:7812-7824, 2007. doi:10.1021/jp071097f \n; T.A. Wassenaar, H.I. Ingolfsson, R.A. Bockmann, D.P. Tieleman, S.J. Marrink. Computational lipidomics with insane: a versatile \n; tool for generating custom membranes for molecular simulations. JCTC, 150410125128004, 2015. doi:10.1021/acs.jctc.5b00209\n; Created: "+datetime.datetime.now().strftime("%Y.%m.%d") startingdir = os.getcwd() self.temp = tempfile.mkdtemp() def create(self,verbose): return self def description(self,descr): self.description = descr return self def appendTo(self,filename): return self def buildMembrane(self,name): return self def minimize(self,parameterFile,verbose): return self def show(self,x=0,y=0,z=0): return self def success(self): return self.created def __repr__(self): return "Lipid ({})".format(self.name) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): shutil.rmtree(tmpdirname) os.chdir(startingdir) return self ###Output _____no_output_____ ###Markdown Tests NB: these will only run when the notebook is loaded directly and run, they will not run when the notebook is loaded as a %run module from other notebooks Run this notebook stand alone to run all tests ###Code with lipid("POPC","C P","G G","CDCC","CCCC") as Lipid: Lipid.description("phosphatidylcholine Lipid") Lipid.appendTo("./martini.ff/myLipids.itp") Lipid.minimize("Test.mdp",False) Lipid.show() import datetime lipidname = "OIPC" tail = "CDDC CDCC" link = "G G" head = "C P" description = "; A general model phosphatidylcholine (PC) lipid \n; C18:1(9c) oleic acid, and C18:2(9c,12c) linoleic acid\n" modeledOn="; This topology follows the standard Martini 2.0 lipid definitions and building block rules.\n; Reference(s): \n; S.J. Marrink, A.H. de Vries, A.E. Mark. Coarse grained model for semi-quantitative lipid simulations. JPC-B, 108:750-760, \n; 2004. doi:10.1021/jp036508g \n; S.J. Marrink, H.J. Risselada, S. Yefimov, D.P. Tieleman, A.H. de Vries. The MARTINI force field: coarse grained model for \n; biomolecular simulations. JPC-B, 111:7812-7824, 2007. doi:10.1021/jp071097f \n; T.A. Wassenaar, H.I. Ingolfsson, R.A. Bockmann, D.P. Tieleman, S.J. Marrink. Computational lipidomics with insane: a versatile \n; tool for generating custom membranes for molecular simulations. JCTC, 150410125128004, 2015. doi:10.1021/acs.jctc.5b00209\n; Created: " now = datetime.datetime.now() membrane="testmembrane" insane="../insane+SF.py" mdparams="../test.mdp" martinipath="../martini.ff/" ITPCatalogue="./epithelial.cat" ITPMasterFile="martini_v2_epithelial.itp" modeledOn+= now.strftime("%Y.%m.%d")+"\n" # Cleaning up intermediate files from previous runs !rm -f # !rm -f step !rm -f {membrane}* import fileinput import os.path print("Create itp") !python {martinipath}/lipid-martini-itp-v06.py -o {lipidname}.itp -alname {lipidname} -name {lipidname} -alhead '{head}' -allink '{link}' -altail '{tail}' #update description and parameters with fileinput.FileInput(lipidname+".itp", inplace=True) as file: for line in file: if line == "; This is a ...\n": print(description, end='') elif line == "; Was modeled on ...\n": print(modeledOn, end='') else: print(line, end='') #Add this ITP file to the catalogue file if not os.path.exists(ITPCatalogue): ITPCatalogueData = [] else: with open(ITPCatalogue, 'r') as file : ITPCatalogueData = file.read().splitlines() ITPCatalogueData = [x for x in ITPCatalogueData if not x==lipidname+".itp"] ITPCatalogueData.append(lipidname+".itp") with open(ITPCatalogue, 'w') as file : file.writelines("%s\n" % item for item in ITPCatalogueData) #build ITPFile with open(martinipath+ITPMasterFile, 'w') as masterfile: for ITPfilename in ITPCatalogueData: with open(ITPfilename, 'r') as ITPfile : for line in ITPfile: masterfile.write(line) print("Done") # build a simple membrane to visualize this species !python2 {insane} -o {membrane}.gro -p {membrane}.top -d 0 -x 3 -y 3 -z 3 -sol PW -center -charge 0 -orient -u {lipidname}:1 -l {lipidname}:1 -itpPath {martinipath} import os #Operating system specific commands import re #Regular expression library print("Test") print("Grompp") grompp = !gmx grompp -f {mdparams} -c {membrane}.gro -p {membrane}.top -o {membrane}.tpr success=True for line in grompp: if re.search("ERROR", line): success=False if re.search("Fatal error", line): success=False #if not success: print(line) if success: print("Run") !export GMX_MAXCONSTRWARN=-1 !export GMX_SUPPRESS_DUMP=1 run = !gmx mdrun -v -deffnm {membrane} summary="" logfile = membrane+".log" if not os.path.exists(logfile): print("no log file") print("== === ====") for line in run: print(line) else: try: file = open(logfile, "r") fe = False for line in file: if fe: success=False summary=line elif re.search("^Steepest Descents.*converge", line): success=True summary=line break elif re.search("Fatal error", line): fe = True except IOError as exc: sucess=False; summary=exc; if success: print("Success") else: print(summary) ###Output Test Grompp :-) GROMACS - gmx grompp, 2018.1 (-: GROMACS is written by: Emile Apol Rossen Apostolov Paul Bauer Herman J.C. Berendsen Par Bjelkmar Aldert van Buuren Rudi van Drunen Anton Feenstra Gerrit Groenhof Aleksei Iupinov Christoph Junghans Anca Hamuraru Vincent Hindriksen Dimitrios Karkoulis Peter Kasson Jiri Kraus Carsten Kutzner Per Larsson Justin A. Lemkul Viveca Lindahl Magnus Lundborg Pieter Meulenhoff Erik Marklund Teemu Murtola Szilard Pall Sander Pronk Roland Schulz Alexey Shvetsov Michael Shirts Alfons Sijbers Peter Tieleman Teemu Virolainen Christian Wennberg Maarten Wolf and the project leaders: Mark Abraham, Berk Hess, Erik Lindahl, and David van der Spoel Copyright (c) 1991-2000, University of Groningen, The Netherlands. Copyright (c) 2001-2017, The GROMACS development team at Uppsala University, Stockholm University and the Royal Institute of Technology, Sweden. check out http://www.gromacs.org for more information. GROMACS is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. GROMACS: gmx grompp, version 2018.1 Executable: /usr/bin/gmx Data prefix: /usr Working dir: /home/richard/projects/epithelial/buildITP Command line: gmx grompp -f ../test.mdp -c testmembrane.gro -p testmembrane.top -o testmembrane.tpr Ignoring obsolete mdp entry 'title' Setting the LD random seed to -913580286 Generated 1 of the 741 non-bonded parameter combinations Excluding 1 bonded neighbours molecule type 'OIPC' Excluding 1 bonded neighbours molecule type 'OIPC' Excluding 1 bonded neighbours molecule type 'PW' Removing all charge groups because cutoff-scheme=Verlet Number of degrees of freedom in T-Coupling group System is 1149.00 GROMACS reminds you: "Harvard makes mistakes too, you know. Kissinger taught there." (Woody Allen) Analysing residue names: There are: 32 Other residues Analysing residues not classified as Protein/DNA/RNA/Water and splitting into groups... This run will generate roughly 0 Mb of data Run Success
archive/logistic/logistic.ipynb	###Markdown Experimenting with random data ###Code N = 1000 D = 2 X = np.random.randn(N,D) ones = np.ones((N,1)) print(X.shape, ones.shape) n=500 X[:n,:] = X[:n,:] - 2np.ones((n,D)) X[n:,:] = X[n:,:] + 2np.ones((n,D)) Xb = np.concatenate((ones,X), axis=1) T = np.array([0]n + [1](N-n)) print(Xb.shape) w = np.random.randn(D+1) print(w.shape) z = Xb.dot(w) print(z.shape) w_sol = np.array([0,4,4]) plt.scatter(X[:,0],X[:,1], c=T, alpha=0.5) learning_rate = 0.1 lambda_reg = 0.1 for i in range(10000): Y = sigmoid(Xb.dot(w)) w += learning_rate(np.dot((T-Y).T,Xb) - lambda_regw) if i%1000==0: print(cross_entropy(T,Y)) print('Final w = ', w) xx, yy = np.mgrid[-6:6:.01, -6:6:.01] grid = np.c_[xx.ravel(), yy.ravel()] ones = np.ones((grid.shape[0],1)) grid_b = np.concatenate((ones,grid), axis=1) probs = forward(grid, w[1:3], w[0]) zi = griddata(xx, yy, probs, xi, yi, interp='linear') f, ax = plt.subplots(figsize=(8, 6)) contour = ax.contourf(xx, yy, probs, 25, cmap="RdBu", vmin=0, vmax=1) print(np.sum(probs>1e-5)) ###Output 1103335 ###Markdown With e-commerce data ###Code file_name = "../ann_logistic_extra/ecommerce_data.csv" from process import get_binary_data, get_data X, Y = get_binary_data(file_name) print(X.shape) np.savetxt("foo.csv", X, delimiter=",") from sklearn.utils import shuffle X, Y = shuffle(X,Y) D = X.shape[1] W = np.random.randn(D) b = 0 X_train = X[:-100,:] Y_train = Y[:-100] X_test = X[-100:,:] Y_test = Y[-100:] train_costs = [] test_costs = [] learning_rate = 0.1 lambda_reg = 0.1 for i in range(50000): pYtrain = forward(X_train, W, b) pYtest = forward(X_test, W, b) ctrain = cross_entropy(Y_train,pYtrain) ctest = cross_entropy(Y_test,pYtest) W -= learning_rate(X_train.T.dot(pYtrain-Y_train) + lambda_regW) b -= learning_rate(pYtrain-Y_train).sum() if i%1000==0: print(i, ": ", ctrain, ", ", ctest) train_costs.append(ctrain) test_costs.append(ctest) print("Final training classification rate :", classification_rate(P=np.round(pYtrain), Y=Y_train)) print("Final test classification rate :", classification_rate(P=np.round(pYtest), Y=Y_test)) plt.plot(train_costs, label='train_costs') plt.plot(test_costs, label='test_costs') print("Final weights: ", W) tmp = np.array(test_costs) np.savetxt('foo.csv', tmp, delimiter=',') train_costs = [] test_costs = [] learning_rate = 0.1 lambda_reg = 0.5 for i in range(10000): pYtrain = forward(X_train, W, b) pYtest = forward(X_test, W, b) ctrain = cross_entropy(Y_train,pYtrain) ctest = cross_entropy(Y_test,pYtest) W += learning_rate(X_train.T.dot(pYtrain-Y_train)) b += learning_rate(pYtrain-Y_train).sum() if i%500==0: print(i, ": ", ctrain, ", ", ctest) train_costs.append(ctrain) test_costs.append(ctest) print("Final training classification rate :", classification_rate(P=np.round(pYtrain), Y=Y_train)) print("Final test classification rate :", classification_rate(P=np.round(pYtest), Y=Y_test)) plt.plot(train_costs, label='train_costs') plt.plot(test_costs, label='test_costs') print("Final weights: ", W) ###Output 0 : 0.491100161491 , 0.439036971167 500 : 0.172261416722 , 0.222558414698 1000 : 0.17181989045 , 0.220833733962 1500 : 0.172182938304 , 0.222286570665 2000 : 0.179050113087 , 0.237655150202 2500 : 0.189640473069 , 0.254723850106 3000 : 0.258387382684 , 0.323268906003 3500 : 0.171939474022 , 0.22136977376 4000 : 0.173798197528 , 0.226658992562 4500 : 0.171621051307 , 0.219243114577 5000 : 0.172631989486 , 0.223710152074 5500 : 0.172343768005 , 0.222827960732 6000 : 0.171683149903 , 0.22005159866 6500 : 0.17347333421 , 0.225969366835 7000 : 0.171620503577 , 0.219349648486 7500 : 0.334022244668 , 0.41909865773 8000 : 0.194753784851 , 0.261478134258 8500 : 0.17450304299 , 0.228419301815 9000 : 0.683330281243 , 0.69135591332 9500 : 0.171646713793 , 0.21948867148 Final training classification rate : 0.973154362416 Final test classification rate : 0.96 Final weights: [ 2.57661175 10.89220217 1.03284006 3.24934831 0.71448471 -0.15681118 -0.32330776 -1.5218886 ] ###Markdown L1 regularization ###Code N = 50 D = 50 # uniformly distributed numbers between -5, +5 X = (np.random.random((N, D)) - 0.5)10 # true weights - only the first 3 dimensions of X affect Y true_w = np.array([1, 0.5, -0.5] + [0](D - 3)) print(true_w) # generate Y - add noise with variance 0.5 Y = np.round(sigmoid(X.dot(true_w) + np.random.randn(N)0.5)) print(Y) costs = [] # keep track of squared error cost w = np.random.randn(D) / np.sqrt(D) # randomly initialize w learning_rate = 0.001 l1 = 3.0 # try different values - what effect does it have on w? for t in range(5000): # update w Yhat = sigmoid(X.dot(w)) delta = Yhat - Y w -= learning_rate(X.T.dot(delta) + l1np.sign(w)) # find and store the cost cost = -(Ynp.log(Yhat) + (1-Y)np.log(1 - Yhat)).mean() + l1np.abs(w).mean() costs.append(cost) plt.plot(costs, label='cost') plt.plot(true_w, label='true w') plt.plot(w, label='w_map') ###Output _____no_output_____ ###Markdown Donut ###Code N = 1000 D = 2 R_inner = 5 R_outer = 10 # distance from origin is radius + random normal # angle theta is uniformly distributed between (0, 2pi) R1 = np.random.randn(N//2) + R_inner theta = 2np.pinp.random.random(N//2) X_inner = np.concatenate([[R1 np.cos(theta)], [R1 * np.sin(theta)]]).T R2 = np.random.randn(N//2) + R_outer theta = 2np.pinp.random.random(N//2) X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T X = np.concatenate([ X_inner, X_outer ]) T = np.array([0](N//2) + [1](N//2)) # labels: first 50 are 0, last 50 are 1 plt.scatter(X[:,0], X[:,1], c=T) plt.show() ones = np.ones((N,1)) r = np.sum(np.sqrt(XX), axis=1).reshape((N,1)) Xb = np.concatenate((ones, r, X), axis=1) # print(X.shape, r.shape, ones.shape) w = np.random.randn(D+2) error = [] for i in range(5000): Y = sigmoid(Xb.dot(w)) e = cross_entropy(T, Y) error.append(e) if i%1000==0: print("error: ", e) w += 0.0001(Xb.T.dot(T-Y) - 0.1w) plt.plot(error) plt.title("Cross-entropy error") print("Final weights: ", w) print("Classification rate: ", 1 - np.abs(np.round(Y)-T).sum()/N) ###Output error: 3.81394995915 error: 0.174204332976 error: 0.12143722358 error: 0.101130008159 error: 0.0900940518642 Final weights: [ -1.12938022e+01 1.23940316e+00 -1.00532830e-02 -8.89319354e-03] Classification rate: 0.979 ###Markdown XOR ###Code N = 4 D = 2 # XOR X = np.array([ [0, 0], [0, 1], [1, 0], [1, 1], ]) T = np.array([0, 1, 1, 0]) # add a column of ones # ones = np.array([[1]N]).T ones = np.ones((N, 1)) # add a column of xy = xy xy = (X[:,0] X[:,1]).reshape(N, 1) Xb = np.concatenate((ones, xy, X), axis=1) print(Xb) plt.scatter(Xb[1,:],Xb[2,:],Xb[3,:]) ###Output _____no_output_____
chapter09.ipynb	###Markdown 9. Overview of time-domain EEG analyses ###Code import numpy as np import scipy.io from matplotlib import pyplot as plt ###Output _____no_output_____ ###Markdown Figure 9.1a ###Code data = scipy.io.loadmat('sampleEEGdata') #get all the data we need from the eeg file. Working with .mat files like this is not ideal, as you can clearly see below. #A better way to access this data would be to re-save the sampleEEGdata.mat file as v-7.3 in matlab, or convert it to hdf5, #then open it in python using h5py or pytables. Since I'd rather not mess with the batteries-included-ness of this book, #I'll keep the data as-is and extract what we'll need. eeg_data = data["EEG"][0,0]["data"] eeg_pts = data["EEG"][0,0]["pnts"][0,0] #number of points in EEG data eeg_times = data["EEG"][0,0]["times"][0] eeg_rate = float(data["EEG"][0,0]["srate"][0]) #make float for division purposes later eeg_trials = data["EEG"][0,0]["trials"][0,0] eeg_epoch=data["EEG"][0,0]["epoch"][0] which_channel_to_plot = 'FCz' #specify label of channel to plot eeg_chan_locs_labels=data["EEG"][0,0]["chanlocs"][0]["labels"] channel_index = (eeg_chan_locs_labels == which_channel_to_plot) #specify index (channel number) of label x_axis_limit = (-200, 1000) #in milliseconds num_trials2plot = 12 plt.figure(figsize=(10, 6)) # pick a random trials using random.choice (from numpy.random) random_trial_to_plot = np.random.choice(np.arange(eeg_trials), num_trials2plot) # figure out how many subplots we need n_rows = np.ceil(num_trials2plot/np.ceil(np.sqrt(num_trials2plot))).astype(int) n_cols = np.ceil(np.sqrt(num_trials2plot)).astype(int) fig, ax = plt.subplots(n_rows, n_cols, sharex='all') for ii in range(num_trials2plot): idx = np.unravel_index(ii, (n_rows, n_cols)) #plot trial and specify x-axis and title ax[idx].plot(eeg_times, np.squeeze(eeg_data[channel_index,:,random_trial_to_plot[ii] - 1])) ax[idx].set(title=f"Trial {random_trial_to_plot[ii]}", yticks=[]) fig.tight_layout() ###Output _____no_output_____ ###Markdown Figure 9.1b ###Code #plot all trials plt.plot(eeg_times,np.squeeze(eeg_data[channel_index,:,:]),'y') #plot the event-related potential (ERP), i.e. the average time-domain signal plt.plot(eeg_times,np.squeeze(np.mean(eeg_data[channel_index,:,:],axis=2)),'k',linewidth=2) _=plt.title("All EEG traces, and their average") #now plot only the ERP plt.plot(eeg_times,np.squeeze(np.mean(eeg_data[channel_index,:,:],axis=2))) #axis=2 specifies which axis to compute the mean along plt.vlines(0,-10,10,linestyles='dashed') plt.hlines(0,-1000,1500) plt.axis([-300,1000,-10,10]) plt.xlabel("Time from stimlulus onset (ms)") plt.ylabel(r'$ \mu V $') #latex interpreter looks for dollar signs plt.title("ERP (average of " + str(eeg_trials) + " trials) from electrode " + eeg_chan_locs_labels[channel_index][0][0]) plt.gca().invert_yaxis() #EEG convention to flip y axis ###Output _____no_output_____ ###Markdown Figure 9.2To my knowledge, Python (specifically, scipy) does not have a function that is completely analgous to MATLAB's firls(). A very close approximation that I will use instead is an n-th order Butterworth bandpass filter. TODO ###Code import scipy.signal as sig chan2plot = "P7" channel_index = eeg_chan_locs_labels == chan2plot #specify index (channel number) of label erp = np.squeeze(np.mean(eeg_data[channel_index,:,:],axis=2)) nyquist = eeg_rate/2. transition_width = 0.15 #low-pass filter data #we'll look at filtering in detail in chapter 14 #filter form 0-40 filter_high = 40 #Hz; high cut off b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to40 = sig.filtfilt(b, a, erp, padlen=150) #use filfilt (filters forwards and backwards to eliminate phase shift) #next, filter from 0-10 filter_high = 10 #Hz b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to10 = sig.filtfilt(b, a, erp, padlen=150) #next, filter from 5-15 filter_low = 5 # Hz filter_high = 15 # Hz b, a = sig.butter(5, np.array([filter_low(1-transition_width), filter_high(1+transition_width)])/nyquist,btype="bandpass") erp_5to15 = sig.filtfilt(b, a, erp, padlen=150) plt.figure() plt.plot(eeg_times,erp,'k') plt.plot(eeg_times,erp_0to40,'c') plt.plot(eeg_times,erp_0to10,'r') plt.plot(eeg_times,erp_5to15,'m') plt.xlim([-200,1200]) plt.gca().invert_yaxis() plt.xlabel("time (ms)") plt.ylabel("voltage " + r"$(\mu V)$") plt.title("Raw and filtered signal") _=plt.legend(['raw','0-40 Hz','0-10Hz','5-15Hz']) ###Output _____no_output_____ ###Markdown Figure 9.3 ###Code plt.figure() plt.subplot(211) plt.plot(eeg_times,np.squeeze(eeg_data.mean(axis=0))) plt.xlim([-200, 1000]) plt.gca().invert_yaxis() #flip for EEG conventions plt.title("ERP from all sensors") #topographical variance plot plt.subplot(212) plt.plot(eeg_times,np.squeeze(eeg_data.mean(axis=0).var(axis=1))) plt.xlim([-200,1000]) plt.xlabel("Time (ms)") plt.ylabel("var "+r'$ (\mu V) $') plt.title("Topographical variance") plt.tight_layout() ###Output _____no_output_____ ###Markdown Figures 9.4-9.5 use the function topoplot from MATLAB toolbox EEGlabTODO Figure 9.6 ###Code use_rts = True #or false #get RTs from each trial to use for sorting trials. In this experiment, #the RT was always the first event after the stimulus (the time=0 event). #Normally, you should build in exceptions in case there was no response or #another event occured between the stimulus and response. This was already #done for the current dataset. rts = np.zeros(len(eeg_epoch)) for ei in range(len(eeg_epoch)): #first, find the index at which time = 0 event occurs time0event = eeg_epoch[ei]["eventlatency"][0] == 0 #bool array of where time=0 occurs time0event = np.where(time0event == time0event.max())[0][0] # find the index of the True value in this array rts[ei] = eeg_epoch[ei]["eventlatency"][0][time0event+1] if use_rts: rts_idx=np.argsort(rts) else: rts_idx = np.argsort(np.squeeze(eeg_data[46,333,:])) #plot the trials for one channel, in (un)sorted order plt.imshow(np.squeeze(eeg_data[46,:,rts_idx]), extent=[eeg_times[0], eeg_times[-1], 1, eeg_trials], aspect="auto", cmap=plt.get_cmap("jet"), origin="lower", interpolation="none") plt.xlabel("time from stim onset (ms)") plt.ylabel("trial number") plt.clim([-30,30]) plt.colorbar(label=r"$\mu V$") plt.axis([-200,1200,1,99]) plt.grid(False) if use_rts: rtplot=plt.plot(rts[rts_idx],np.arange(1,eeg_trials+1),'k',linewidth=3, label= "Reaction time") plt.legend(bbox_to_anchor=[1.5,1]) #put the legend outside of the image ###Output _____no_output_____ ###Markdown 第9章: RNN, CNN 80. ID番号への変換**問題51で構築した学習データ中の単語にユニークなID番号を付与したい．学習データ中で最も頻出する単語に$1$，2番目に頻出する単語に$2$，……といった方法で，学習データ中で2回以上出現する単語にID番号を付与せよ．そして，与えられた単語列に対して，ID番号の列を返す関数を実装せよ．ただし，出現頻度が2回未満の単語のID番号はすべて$0$とせよ． ###Code !wget https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip !unzip NewsAggregatorDataset.zip !wc -l ./newsCorpora.csv !head -10 ./newsCorpora.csv # 読込時のエラー回避のためダブルクォーテーションをシングルクォーテーションに置換 !sed -e 's/"/'\''/g' ./newsCorpora.csv > ./newsCorpora_re.csv import pandas as pd from sklearn.model_selection import train_test_split # データの読込 df = pd.read_csv('./newsCorpora_re.csv', header=None, sep='\t', names=['ID', 'TITLE', 'URL', 'PUBLISHER', 'CATEGORY', 'STORY', 'HOSTNAME', 'TIMESTAMP']) # データの抽出 df = df.loc[df['PUBLISHER'].isin(['Reuters', 'Huffington Post', 'Businessweek', 'Contactmusic.com', 'Daily Mail']), ['TITLE', 'CATEGORY']] # データの分割 train, valid_test = train_test_split(df, test_size=0.2, shuffle=True, random_state=123, stratify=df['CATEGORY']) valid, test = train_test_split(valid_test, test_size=0.5, shuffle=True, random_state=123, stratify=valid_test['CATEGORY']) train.reset_index(drop=True, inplace=True) valid.reset_index(drop=True, inplace=True) test.reset_index(drop=True, inplace=True) # 事例数の確認 print('【学習データ】') print(train['CATEGORY'].value_counts()) print('【検証データ】') print(valid['CATEGORY'].value_counts()) print('【評価データ】') print(test['CATEGORY'].value_counts()) from collections import defaultdict import string # 単語の頻度集計 d = defaultdict(int) table = str.maketrans(string.punctuation, ' 'len(string.punctuation)) # 記号をスペースに置換するテーブル for text in train['TITLE']: for word in text.translate(table).split(): d[word] += 1 d = sorted(d.items(), key=lambda x:x[1], reverse=True) # 単語ID辞書の作成 word2id = {word: i + 1 for i, (word, cnt) in enumerate(d) if cnt > 1} # 出現頻度が2回以上の単語を登録 print(f'ID数: {len(set(word2id.values()))}\n') print('---頻度上位20語---') for key in list(word2id)[:20]: print(f'{key}: {word2id[key]}') import torch def tokenizer(text, word2id=word2id, unk=0): """ 入力テキストをスペースで分割しID列に変換(辞書になければunkで指定した数字を設定)""" table = str.maketrans(string.punctuation, ' 'len(string.punctuation)) return [word2id.get(word, unk) for word in text.translate(table).split()] # 確認 text = train.iloc[1, train.columns.get_loc('TITLE')] print(f'テキスト: {text}') print(f'ID列: {tokenizer(text)}') ###Output テキスト: Amazon Plans to Fight FTC Over Mobile-App Purchases ID列: [169, 539, 1, 683, 1237, 82, 279, 1898, 4199] ###Markdown 81. RNNによる予測ID番号で表現された単語列$\boldsymbol{x} = (x_1, x_2, \dots, x_T)$がある．ただし，$T$は単語列の長さ，$x_t \in \mathbb{R}^{V}$は単語のID番号のone-hot表記である（$V$は単語の総数である）．再帰型ニューラルネットワーク（RNN: Recurrent Neural Network）を用い，単語列$\boldsymbol{x}$からカテゴリ$y$を予測するモデルとして，次式を実装せよ．$\overrightarrow h_0 = 0, \\\overrightarrow h_t = {\rm \overrightarrow{RNN}}(\mathrm{emb}(x_t), \overrightarrow h_{t-1}), \\y = {\rm softmax}(W^{(yh)} \overrightarrow h_T + b^{(y)})$ただし，$\mathrm{emb}(x) \in \mathbb{R}^{d_w}$は単語埋め込み（単語のone-hot表記から単語ベクトルに変換する関数），$\overrightarrow h_t \in \mathbb{R}^{d_h}$は時刻$t$の隠れ状態ベクトル，${\rm \overrightarrow{RNN}}(x,h)$は入力$x$と前時刻の隠れ状態$h$から次状態を計算するRNNユニット，$W^{(yh)} \in \mathbb{R}^{L \times d_h}$は隠れ状態ベクトルからカテゴリを予測するための行列，$b^{(y)} \in \mathbb{R}^{L}$はバイアス項である（$d_w, d_h, L$はそれぞれ，単語埋め込みの次元数，隠れ状態ベクトルの次元数，ラベル数である）．RNNユニット${\rm \overrightarrow{RNN}}(x,h)$には様々な構成が考えられるが，典型例として次式が挙げられる．${\rm \overrightarrow{RNN}}(x,h) = g(W^{(hx)} x + W^{(hh)}h + b^{(h)})$ただし，$W^{(hx)} \in \mathbb{R}^{d_h \times d_w}，W^{(hh)} \in \mathbb{R}^{d_h \times d_h}, b^{(h)} \in \mathbb{R}^{d_h}$はRNNユニットのパラメータ，$g$は活性化関数（例えば$\tanh$やReLUなど）である．なお，この問題ではパラメータの学習を行わず，ランダムに初期化されたパラメータで$y$を計算するだけでよい．次元数などのハイパーパラメータは，$d_w = 300, d_h=50$など，適当な値に設定せよ（以降の問題でも同様である）． ###Code import torch from torch import nn class RNN(nn.Module): def __init__(self, vocab_size, emb_size, padding_idx, output_size, hidden_size): super().__init__() self.hidden_size = hidden_size self.emb = nn.Embedding(vocab_size, emb_size, padding_idx=padding_idx) self.rnn = nn.RNN(emb_size, hidden_size, nonlinearity='tanh', batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): self.batch_size = x.size()[0] hidden = self.init_hidden() # h0のゼロベクトルを作成 emb = self.emb(x) # emb.size() = (batch_size, seq_len, emb_size) out, hidden = self.rnn(emb, hidden) # out.size() = (batch_size, seq_len, hidden_size) out = self.fc(out[:, -1, :]) # out.size() = (batch_size, output_size) return out def init_hidden(self): hidden = torch.zeros(1, self.batch_size, self.hidden_size) return hidden from torch.utils.data import Dataset class CreateDataset(Dataset): def __init__(self, X, y, tokenizer): self.X = X self.y = y self.tokenizer = tokenizer def __len__(self): # len(Dataset)で返す値を指定 return len(self.y) def __getitem__(self, index): # Dataset[index]で返す値を指定 text = self.X[index] inputs = self.tokenizer(text) return { 'inputs': torch.tensor(inputs, dtype=torch.int64), 'labels': torch.tensor(self.y[index], dtype=torch.int64) } # ラベルベクトルの作成 category_dict = {'b': 0, 't': 1, 'e':2, 'm':3} y_train = train['CATEGORY'].map(lambda x: category_dict[x]).values y_valid = valid['CATEGORY'].map(lambda x: category_dict[x]).values y_test = test['CATEGORY'].map(lambda x: category_dict[x]).values # Datasetの作成 dataset_train = CreateDataset(train['TITLE'], y_train, tokenizer) dataset_valid = CreateDataset(valid['TITLE'], y_valid, tokenizer) dataset_test = CreateDataset(test['TITLE'], y_test, tokenizer) print(f'len(Dataset)の出力: {len(dataset_train)}') print('Dataset[index]の出力:') for var in dataset_train[1]: print(f' {var}: {dataset_train[1][var]}') # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 HIDDEN_SIZE = 50 # モデルの定義 model = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE) # 先頭10件の予測値取得 for i in range(10): X = dataset_train[i]['inputs'] print(torch.softmax(model(X.unsqueeze(0)), dim=-1)) ###Output tensor([[0.3273, 0.2282, 0.2454, 0.1992]], grad_fn=<SoftmaxBackward>) tensor([[0.1324, 0.4295, 0.2220, 0.2162]], grad_fn=<SoftmaxBackward>) tensor([[0.4091, 0.2159, 0.1736, 0.2014]], grad_fn=<SoftmaxBackward>) tensor([[0.2081, 0.3668, 0.2390, 0.1861]], grad_fn=<SoftmaxBackward>) tensor([[0.2383, 0.3205, 0.2695, 0.1717]], grad_fn=<SoftmaxBackward>) tensor([[0.3224, 0.1460, 0.1993, 0.3324]], grad_fn=<SoftmaxBackward>) tensor([[0.2012, 0.2345, 0.3660, 0.1982]], grad_fn=<SoftmaxBackward>) tensor([[0.2072, 0.2365, 0.2525, 0.3038]], grad_fn=<SoftmaxBackward>) tensor([[0.2681, 0.3235, 0.1889, 0.2195]], grad_fn=<SoftmaxBackward>) tensor([[0.1969, 0.3336, 0.3064, 0.1631]], grad_fn=<SoftmaxBackward>) ###Markdown 82. 確率的勾配降下法による学習確率的勾配降下法（SGD: Stochastic Gradient Descent）を用いて，問題81で構築したモデルを学習せよ．訓練データ上の損失と正解率，評価データ上の損失と正解率を表示しながらモデルを学習し，適当な基準（例えば10エポックなど）で終了させよ． ###Code from torch.utils.data import DataLoader import time from torch import optim def calculate_loss_and_accuracy(model, dataset, device=None, criterion=None): """損失・正解率を計算""" dataloader = DataLoader(dataset, batch_size=1, shuffle=False) loss = 0.0 total = 0 correct = 0 with torch.no_grad(): for data in dataloader: # デバイスの指定 inputs = data['inputs'].to(device) labels = data['labels'].to(device) # 順伝播 outputs = model(inputs) # 損失計算 if criterion != None: loss += criterion(outputs, labels).item() # 正解率計算 pred = torch.argmax(outputs, dim=-1) total += len(inputs) correct += (pred == labels).sum().item() return loss / len(dataset), correct / total def train_model(dataset_train, dataset_valid, batch_size, model, criterion, optimizer, num_epochs, collate_fn=None, device=None): """モデルの学習を実行し、損失・正解率のログを返す""" # デバイスの指定 model.to(device) # dataloaderの作成 dataloader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True, collate_fn=collate_fn) dataloader_valid = DataLoader(dataset_valid, batch_size=1, shuffle=False) # スケジューラの設定 scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, num_epochs, eta_min=1e-5, last_epoch=-1) # 学習 log_train = [] log_valid = [] for epoch in range(num_epochs): # 開始時刻の記録 s_time = time.time() # 訓練モードに設定 model.train() for data in dataloader_train: # 勾配をゼロで初期化 optimizer.zero_grad() # 順伝播 + 誤差逆伝播 + 重み更新 inputs = data['inputs'].to(device) labels = data['labels'].to(device) outputs = model.forward(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 評価モードに設定 model.eval() # 損失と正解率の算出 loss_train, acc_train = calculate_loss_and_accuracy(model, dataset_train, device, criterion=criterion) loss_valid, acc_valid = calculate_loss_and_accuracy(model, dataset_valid, device, criterion=criterion) log_train.append([loss_train, acc_train]) log_valid.append([loss_valid, acc_valid]) # チェックポイントの保存 torch.save({'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict()}, f'checkpoint{epoch + 1}.pt') # 終了時刻の記録 e_time = time.time() # ログを出力 print(f'epoch: {epoch + 1}, loss_train: {loss_train:.4f}, accuracy_train: {acc_train:.4f}, loss_valid: {loss_valid:.4f}, accuracy_valid: {acc_valid:.4f}, {(e_time - s_time):.4f}sec') # 検証データの損失が3エポック連続で低下しなかった場合は学習終了 if epoch > 2 and log_valid[epoch - 3][0] <= log_valid[epoch - 2][0] <= log_valid[epoch - 1][0] <= log_valid[epoch][0]: break # スケジューラを1ステップ進める scheduler.step() return {'train': log_train, 'valid': log_valid} def visualize_logs(log): fig, ax = plt.subplots(1, 2, figsize=(15, 5)) ax[0].plot(np.array(log['train']).T[0], label='train') ax[0].plot(np.array(log['valid']).T[0], label='valid') ax[0].set_xlabel('epoch') ax[0].set_ylabel('loss') ax[0].legend() ax[1].plot(np.array(log['train']).T[1], label='train') ax[1].plot(np.array(log['valid']).T[1], label='valid') ax[1].set_xlabel('epoch') ax[1].set_ylabel('accuracy') ax[1].legend() plt.show() # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 HIDDEN_SIZE = 50 LEARNING_RATE = 1e-3 BATCH_SIZE = 1 NUM_EPOCHS = 10 # モデルの定義 model = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train) _, acc_test = calculate_loss_and_accuracy(model, dataset_test) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown 83. ミニバッチ化・GPU上での学習問題82のコードを改変し，$B$事例ごとに損失・勾配を計算して学習を行えるようにせよ（$B$の値は適当に選べ）．また，GPU上で学習を実行せよ． ###Code class Padsequence(): """Dataloaderからミニバッチを取り出すごとに最大系列長でパディング""" def __init__(self, padding_idx): self.padding_idx = padding_idx def __call__(self, batch): sorted_batch = sorted(batch, key=lambda x: x['inputs'].shape[0], reverse=True) sequences = [x['inputs'] for x in sorted_batch] sequences_padded = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=self.padding_idx) labels = torch.LongTensor([x['labels'] for x in sorted_batch]) return {'inputs': sequences_padded, 'labels': labels} # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 HIDDEN_SIZE = 50 LEARNING_RATE = 5e-2 BATCH_SIZE = 32 NUM_EPOCHS = 10 # モデルの定義 model = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # デバイスの指定 #device = torch.device('cuda') device = torch.device('cpu') # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train, device) _, acc_test = calculate_loss_and_accuracy(model, dataset_test, device) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown 84. 単語ベクトルの導入事前学習済みの単語ベクトル（例えば，Google Newsデータセット（約1,000億単語）での学習済み単語ベクトル）で単語埋め込み$emb(x)$を初期化し，学習せよ． ###Code # 学習済み単語ベクトルのダウンロード FILE_ID = "0B7XkCwpI5KDYNlNUTTlSS21pQmM" FILE_NAME = "GoogleNews-vectors-negative300.bin.gz" !wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=$FILE_ID' -O- \| sed -rn 's/.confirm=([0-9A-Za-z_]+)./\1\n/p')&id=$FILE_ID" -O $FILE_NAME && rm -rf /tmp/cookies.txt from gensim.models import KeyedVectors # 学習済みモデルのロード model = KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin.gz', binary=True) # 学習済み単語ベクトルの取得 VOCAB_SIZE = len(set(word2id.values())) + 1 EMB_SIZE = 300 weights = np.zeros((VOCAB_SIZE, EMB_SIZE)) words_in_pretrained = 0 for i, word in enumerate(word2id.keys()): try: weights[i] = model[word] words_in_pretrained += 1 except KeyError: weights[i] = np.random.normal(scale=0.4, size=(EMB_SIZE,)) weights = torch.from_numpy(weights.astype((np.float32))) print(f'学習済みベクトル利用単語数: {words_in_pretrained} / {VOCAB_SIZE}') print(weights.size()) class RNN(nn.Module): def __init__(self, vocab_size, emb_size, padding_idx, output_size, hidden_size, num_layers, emb_weights=None, bidirectional=False): super().__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.num_directions = bidirectional + 1 # 単方向：1、双方向：2 if emb_weights != None: # 指定があれば埋め込み層の重みをemb_weightsで初期化 self.emb = nn.Embedding.from_pretrained(emb_weights, padding_idx=padding_idx) else: self.emb = nn.Embedding(vocab_size, emb_size, padding_idx=padding_idx) self.rnn = nn.RNN(emb_size, hidden_size, num_layers, nonlinearity='tanh', bidirectional=bidirectional, batch_first=True) self.fc = nn.Linear(hidden_size self.num_directions, output_size) def forward(self, x): self.batch_size = x.size()[0] hidden = self.init_hidden() # h0のゼロベクトルを作成 emb = self.emb(x) # emb.size() = (batch_size, seq_len, emb_size) out, hidden = self.rnn(emb, hidden) # out.size() = (batch_size, seq_len, hidden_size * num_directions) out = self.fc(out[:, -1, :]) # out.size() = (batch_size, output_size) return out def init_hidden(self): hidden = torch.zeros(self.num_layers * self.num_directions, self.batch_size, self.hidden_size) return hidden # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 HIDDEN_SIZE = 50 NUM_LAYERS = 1 LEARNING_RATE = 5e-2 BATCH_SIZE = 32 NUM_EPOCHS = 10 # モデルの定義 model = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE, NUM_LAYERS, emb_weights=weights) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # デバイスの指定 #device = torch.device('cuda') device = torch.device('cpu') # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train, device) _, acc_test = calculate_loss_and_accuracy(model, dataset_test, device) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown 85. 双方向RNN・多層化*順方向と逆方向のRNNの両方を用いて入力テキストをエンコードし，モデルを学習せよ．$\overleftarrow h_{T+1} = 0, \\\overleftarrow h_t = {\rm \overleftarrow{RNN}}(\mathrm{emb}(x_t), \overleftarrow h_{t+1}), \\y = {\rm softmax}(W^{(yh)} [\overrightarrow h_T; \overleftarrow h_1] + b^{(y)})$ただし，$\overrightarrow h_t \in \mathbb{R}^{d_h}, \overleftarrow h_t \in \mathbb{R}^{d_h}$はそれぞれ，順方向および逆方向のRNNで求めた時刻$t$の隠れ状態ベクトル，${\rm \overleftarrow{RNN}}(x,h)$は入力$x$と次時刻の隠れ状態$h$から前状態を計算するRNNユニット，$W^{(yh)} \in \mathbb{R}^{L \times 2d_h}$は隠れ状態ベクトルからカテゴリを予測するための行列，$b^{(y)} \in \mathbb{R}^{L}$はバイアス項である．また，$[a; b]$はベクトル$a$と$b$の連結を表す。さらに，双方向RNNを多層化して実験せよ． ###Code # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 HIDDEN_SIZE = 50 NUM_LAYERS = 2 LEARNING_RATE = 5e-2 BATCH_SIZE = 32 NUM_EPOCHS = 10 # モデルの定義 model = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE, NUM_LAYERS, emb_weights=weights, bidirectional=True) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # デバイスの指定 #device = torch.device('cuda') device = torch.device('cpu') # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train, device) _, acc_test = calculate_loss_and_accuracy(model, dataset_test, device) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown 86. 畳み込みニューラルネットワーク (CNN)ID番号で表現された単語列$\boldsymbol x = (x_1, x_2, \dots, x_T)$がある．ただし，$T$は単語列の長さ，$x_t \in \mathbb{R}^{V}$は単語のID番号のone-hot表記である（$V$は単語の総数である）．畳み込みニューラルネットワーク（CNN: Convolutional Neural Network）を用い，単語列$\boldsymbol x$からカテゴリ$y$を予測するモデルを実装せよ．ただし，畳み込みニューラルネットワークの構成は以下の通りとする．+ 単語埋め込みの次元数: $d_w$+ 畳み込みのフィルターのサイズ: 3 トークン+ 畳み込みのストライド: 1 トークン+ 畳み込みのパディング: あり+ 畳み込み演算後の各時刻のベクトルの次元数: $d_h$+ 畳み込み演算後に最大値プーリング（max pooling）を適用し，入力文を$d_h$次元の隠れベクトルで表現すなわち，時刻$t$の特徴ベクトル$p_t \in \mathbb{R}^{d_h}$は次式で表される．$p_t = g(W^{(px)} [\mathrm{emb}(x_{t-1}); \mathrm{emb}(x_t); \mathrm{emb}(x_{t+1})] + b^{(p)})$]ただし，$W^{(px)} \in \mathbb{R}^{d_h \times 3d_w}, b^{(p)} \in \mathbb{R}^{d_h}$はCNNのパラメータ，$g$は活性化関数（例えば$\tanh$やReLUなど），$[a; b; c]$はベクトル$a, b, c$の連結である．なお，行列$W^{(px)}$の列数が$3d_w$になるのは，3個のトークンの単語埋め込みを連結したものに対して，線形変換を行うためである．最大値プーリングでは，特徴ベクトルの次元毎に全時刻における最大値を取り，入力文書の特徴ベクトル$c \in \mathbb{R}^{d_h}$を求める．$c[i]$でベクトル$c$の$i$番目の次元の値を表すことにすると，最大値プーリングは次式で表される．$c[i] = \max_{1 \leq t \leq T} p_t[i]$ 最後に，入力文書の特徴ベクトル$c$に行列$W^{(yc)} \in \mathbb{R}^{L \times d_h}$とバイアス項$b^{(y)} \in \mathbb{R}^{L}$による線形変換とソフトマックス関数を適用し，カテゴリ$y$を予測する．$y = {\rm softmax}(W^{(yc)} c + b^{(y)})$なお，この問題ではモデルの学習を行わず，ランダムに初期化された重み行列で$y$を計算するだけでよい． ###Code from torch.nn import functional as F class CNN(nn.Module): def __init__(self, vocab_size, emb_size, padding_idx, output_size, out_channels, kernel_heights, stride, padding, emb_weights=None): super().__init__() if emb_weights != None: # 指定があれば埋め込み層の重みをemb_weightsで初期化 self.emb = nn.Embedding.from_pretrained(emb_weights, padding_idx=padding_idx) else: self.emb = nn.Embedding(vocab_size, emb_size, padding_idx=padding_idx) self.conv = nn.Conv2d(1, out_channels, (kernel_heights, emb_size), stride, (padding, 0)) self.drop = nn.Dropout(0.3) self.fc = nn.Linear(out_channels, output_size) def forward(self, x): # x.size() = (batch_size, seq_len) emb = self.emb(x).unsqueeze(1) # emb.size() = (batch_size, 1, seq_len, emb_size) conv = self.conv(emb) # conv.size() = (batch_size, out_channels, seq_len, 1) act = F.relu(conv.squeeze(3)) # act.size() = (batch_size, out_channels, seq_len) max_pool = F.max_pool1d(act, act.size()[2]) # max_pool.size() = (batch_size, out_channels, 1) -> seq_len方向に最大値を取得 out = self.fc(self.drop(max_pool.squeeze(2))) # out.size() = (batch_size, output_size) return out # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 # 辞書のID数 + パディングID EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 OUT_CHANNELS = 100 KERNEL_HEIGHTS = 3 STRIDE = 1 PADDING = 1 # モデルの定義 model = CNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, OUT_CHANNELS, KERNEL_HEIGHTS, STRIDE, PADDING, emb_weights=weights) # 先頭10件の予測値取得 for i in range(10): X = dataset_train[i]['inputs'] print(torch.softmax(model(X.unsqueeze(0)), dim=-1)) ###Output tensor([[0.2147, 0.2028, 0.3156, 0.2669]], grad_fn=<SoftmaxBackward>) tensor([[0.2341, 0.2047, 0.2999, 0.2612]], grad_fn=<SoftmaxBackward>) tensor([[0.2225, 0.2443, 0.2795, 0.2538]], grad_fn=<SoftmaxBackward>) tensor([[0.2110, 0.2021, 0.3058, 0.2811]], grad_fn=<SoftmaxBackward>) tensor([[0.2178, 0.2192, 0.2740, 0.2890]], grad_fn=<SoftmaxBackward>) tensor([[0.2105, 0.1714, 0.3283, 0.2899]], grad_fn=<SoftmaxBackward>) tensor([[0.2456, 0.2161, 0.2943, 0.2440]], grad_fn=<SoftmaxBackward>) tensor([[0.2146, 0.1642, 0.3159, 0.3053]], grad_fn=<SoftmaxBackward>) tensor([[0.2119, 0.2169, 0.3154, 0.2557]], grad_fn=<SoftmaxBackward>) tensor([[0.2306, 0.2179, 0.2853, 0.2662]], grad_fn=<SoftmaxBackward>) ###Markdown 87. 確率的勾配降下法によるCNNの学習確率的勾配降下法（SGD: Stochastic Gradient Descent）を用いて，問題86で構築したモデルを学習せよ．訓練データ上の損失と正解率，評価データ上の損失と正解率を表示しながらモデルを学習し，適当な基準（例えば10エポックなど）で終了させよ． ###Code # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 EMB_SIZE = 300 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 OUT_CHANNELS = 100 KERNEL_HEIGHTS = 3 STRIDE = 1 PADDING = 1 LEARNING_RATE = 5e-2 BATCH_SIZE = 64 NUM_EPOCHS = 10 # モデルの定義 model = CNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, OUT_CHANNELS, KERNEL_HEIGHTS, STRIDE, PADDING, emb_weights=weights) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE) # デバイスの指定 device = torch.device('cuda') # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train, device) _, acc_test = calculate_loss_and_accuracy(model, dataset_test, device) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown 88. パラメータチューニング問題85や問題87のコードを改変し，ニューラルネットワークの形状やハイパーパラメータを調整しながら，高性能なカテゴリ分類器を構築せよ． ###Code !pip install optuna from torch.nn import functional as F class textCNN(nn.Module): def __init__(self, vocab_size, emb_size, padding_idx, output_size, out_channels, conv_params, drop_rate, emb_weights=None): super().__init__() if emb_weights != None: # 指定があれば埋め込み層の重みをemb_weightsで初期化 self.emb = nn.Embedding.from_pretrained(emb_weights, padding_idx=padding_idx) else: self.emb = nn.Embedding(vocab_size, emb_size, padding_idx=padding_idx) self.convs = nn.ModuleList([nn.Conv2d(1, out_channels, (kernel_height, emb_size), padding=(padding, 0)) for kernel_height, padding in conv_params]) self.drop = nn.Dropout(drop_rate) self.fc = nn.Linear(len(conv_params) out_channels, output_size) def forward(self, x): # x.size() = (batch_size, seq_len) emb = self.emb(x).unsqueeze(1) # emb.size() = (batch_size, 1, seq_len, emb_size) conv = [F.relu(conv(emb)).squeeze(3) for i, conv in enumerate(self.convs)] # conv[i].size() = (batch_size, out_channels, seq_len + padding * 2 - kernel_height + 1) max_pool = [F.max_pool1d(i, i.size(2)) for i in conv] # max_pool[i].size() = (batch_size, out_channels, 1) -> seq_len方向に最大値を取得 max_pool_cat = torch.cat(max_pool, 1) # max_pool_cat.size() = (batch_size, len(conv_params) * out_channels, 1) -> フィルター別の結果を結合 out = self.fc(self.drop(max_pool_cat.squeeze(2))) # out.size() = (batch_size, output_size) return out import optuna def objective(trial): # チューニング対象パラメータのセット emb_size = int(trial.suggest_discrete_uniform('emb_size', 100, 400, 100)) out_channels = int(trial.suggest_discrete_uniform('out_channels', 50, 200, 50)) drop_rate = trial.suggest_discrete_uniform('drop_rate', 0.0, 0.5, 0.1) learning_rate = trial.suggest_loguniform('learning_rate', 5e-4, 5e-2) momentum = trial.suggest_discrete_uniform('momentum', 0.5, 0.9, 0.1) batch_size = int(trial.suggest_discrete_uniform('batch_size', 16, 128, 16)) # 固定パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 CONV_PARAMS = [[2, 0], [3, 1], [4, 2]] NUM_EPOCHS = 30 # モデルの定義 model = textCNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, out_channels, CONV_PARAMS, drop_rate, emb_weights=weights) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum) # デバイスの指定 device = torch.device('cuda') # モデルの学習 log = train_model(dataset_train, dataset_valid, batch_size, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # 損失の算出 loss_valid, _ = calculate_loss_and_accuracy(model, dataset_valid, device, criterion=criterion) return loss_valid # 最適化 study = optuna.create_study() study.optimize(objective, timeout=7200) # 結果の表示 print('Best trial:') trial = study.best_trial print(' Value: {:.3f}'.format(trial.value)) print(' Params: ') for key, value in trial.params.items(): print(' {}: {}'.format(key, value)) # パラメータの設定 VOCAB_SIZE = len(set(word2id.values())) + 1 EMB_SIZE = int(trial.params['emb_size']) PADDING_IDX = len(set(word2id.values())) OUTPUT_SIZE = 4 OUT_CHANNELS = int(trial.params['out_channels']) CONV_PARAMS = [[2, 0], [3, 1], [4, 2]] DROP_RATE = trial.params['drop_rate'] LEARNING_RATE = trial.params['learning_rate'] BATCH_SIZE = int(trial.params['batch_size']) NUM_EPOCHS = 30 # モデルの定義 model = textCNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, OUT_CHANNELS, CONV_PARAMS, DROP_RATE, emb_weights=weights) print(model) # 損失関数の定義 criterion = nn.CrossEntropyLoss() # オプティマイザの定義 optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=0.9) # デバイスの指定 device = torch.device('cuda') # モデルの学習 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, collate_fn=Padsequence(PADDING_IDX), device=device) # ログの可視化 visualize_logs(log) # 正解率の算出 _, acc_train = calculate_loss_and_accuracy(model, dataset_train, device) _, acc_test = calculate_loss_and_accuracy(model, dataset_test, device) print(f'正解率（学習データ）：{acc_train:.3f}') print(f'正解率（評価データ）：{acc_test:.3f}') ###Output _____no_output_____ ###Markdown Import necessary libraries and functions. ###Code import numpy as np, cmath,scipy as sp import scipy.io from matplotlib import pyplot as plt from numpy import pi, sin, cos, exp, sqrt, log,log10, random, convolve#import basic functions from numpy that we'll need from numpy.fft import fft, ifft %matplotlib inline ###Output _____no_output_____ ###Markdown Import optional library for pretty plotting. ###Code import seaborn as sns sns.set_palette('muted') sns.set_style('darkgrid') ###Output _____no_output_____ ###Markdown Figure 9.1a ###Code data = scipy.io.loadmat('sampleEEGdata') #get all the data we need from the eeg file. Working with .mat files like this is not ideal, as you can clearly see below. #A better way to access this data would be to re-save the sampleEEGdata.mat file as v-7.3 in matlab, or convert it to hdf5, #then open it in python using h5py or pytables. Since I'd rather not mess with the batteries-included-ness of this book, #I'll keep the data as-is and extract what we'll need. EEGdata = data["EEG"][0,0]["data"] EEGpnts = data["EEG"][0,0]["pnts"][0,0] #number of points in EEG data EEGtimes = data["EEG"][0,0]["times"][0] EEGsrate = float(data["EEG"][0,0]["srate"][0]) #make float for division purposes later EEGtrials = data["EEG"][0,0]["trials"][0,0] EEGepoch=data["EEG"][0,0]["epoch"][0] which_channel_to_plot = 'FCz'; #specify label of channel to plot EEGchanlocslabels=data["EEG"][0,0]["chanlocs"][0]["labels"] channel_index = EEGchanlocslabels == which_channel_to_plot #specify index (channel number) of label x_axis_limit = [-200,1000] #in milliseconds num_trials2plot = 12 plt.figure(figsize=(10,6)) #pick a random trials using random.choice (from numpy.random) random_trial_to_plot = random.choice(xrange(EEGtrials),num_trials2plot) for ii in xrange(num_trials2plot): #figure out how many subplots we need plt.subplot(np.ceil(num_trials2plot/np.ceil(sqrt(num_trials2plot))),np.ceil(sqrt(num_trials2plot)),ii+1) #plot trial and specify x-axis and title plt.plot(EEGtimes,np.squeeze(EEGdata[channel_index,:,random_trial_to_plot[ii] - 1])) plt.title("Trial " + str(random_trial_to_plot[ii])) plt.yticks([]) plt.xlim(x_axis_limit) _=plt.tight_layout() ###Output _____no_output_____ ###Markdown Figure 9.1b ###Code #plot all trials plt.plot(EEGtimes,np.squeeze(EEGdata[channel_index,:,:]),'y') #plot the event-related potential (ERP), i.e. the average time-domain signal plt.plot(EEGtimes,np.squeeze(np.mean(EEGdata[channel_index,:,:],axis=2)),'k',linewidth=2) _=plt.title("All EEG traces, and their average") #now plot only the ERP plt.plot(EEGtimes,np.squeeze(np.mean(EEGdata[channel_index,:,:],axis=2))) #axis=2 specifies which axis to compute the mean along plt.vlines(0,-10,10,linestyles='dashed') plt.hlines(0,-1000,1500) plt.axis([-300,1000,-10,10]) plt.xlabel("Time from stimlulus onset (ms)") plt.ylabel(r'$ \mu V $') #latex interpreter looks for dollar signs plt.title("ERP (average of " + str(EEGtrials) + " trials) from electrode " + EEGchanlocslabels[channel_index][0][0]) plt.gca().invert_yaxis() #EEG convention to flip y axis ###Output _____no_output_____ ###Markdown Figure 9.2To my knowledge, Python (specifically, scipy) does not have a function that is completely analgous to MATLAB's firls(). A very close approximation that I will use instead is an n-th order Butterworth bandpass filter. TODO ###Code import scipy.signal as sig chan2plot = "P7" channel_index = EEGchanlocslabels == chan2plot #specify index (channel number) of label erp = np.squeeze(np.mean(EEGdata[channel_index,:,:],axis=2)) nyquist = EEGsrate/2. transition_width = 0.15 #low-pass filter data #we'll look at filtering in detail in chapter 14 #filter form 0-40 filter_high = 40 #Hz; high cut off b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to40 = sig.filtfilt(b, a, erp, padlen=150) #use filfilt (filters forwards and backwards to eliminate phase shift) #next, filter from 0-10 filter_high = 10 #Hz b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to10 = sig.filtfilt(b, a, erp, padlen=150) #next, filter from 5-15 filter_low = 5 #Hz filter_high = 15 #Hz b, a = sig.butter(5, np.array([filter_low(1-transition_width), filter_high(1+transition_width)])/nyquist,btype="bandpass") erp_5to15 = sig.filtfilt(b, a, erp, padlen=150) plt.figure() plt.plot(EEGtimes,erp,'k') plt.plot(EEGtimes,erp_0to40,'c') plt.plot(EEGtimes,erp_0to10,'r') plt.plot(EEGtimes,erp_5to15,'m') plt.xlim([-200,1200]) plt.gca().invert_yaxis() plt.xlabel("time (ms)") plt.ylabel("voltage " + r"$(\mu V)$") plt.title("Raw and filtered signal") _=plt.legend(['raw','0-40 Hz','0-10Hz','5-15Hz']) ###Output _____no_output_____ ###Markdown Figure 9.3 ###Code fig=plt.figure() plt.subplot(211) plt.plot(EEGtimes,np.squeeze(EEGdata.mean(axis=0))) plt.xlim([-200, 1000]) plt.gca().invert_yaxis() #flip for EEG conventions plt.title("ERP from all sensors") #topographical variance plot plt.subplot(212) plt.plot(EEGtimes,np.squeeze(EEGdata.mean(axis=0).var(axis=1))) plt.xlim([-200,1000]) plt.xlabel("Time (ms)") plt.ylabel("var "+r'$ (\mu V) $') plt.title("Topographical variance") plt.tight_layout() ###Output _____no_output_____ ###Markdown Figures 9.4-9.5 use the function topoplot from MATLAB toolbox EEGlab TODO Figure 9.6 ###Code useRTs = True #or false #get RTs from each trial to use for sorting trials. In this experiment, #the RT was always the first event after the stimulus (the time=0 event). #Normally, you should build in exceptions in case there was no response or #another event occured between the stimulus and response. This was already #done for the current dataset. rts = np.zeros(len(EEGepoch)) for ei in xrange(len(EEGepoch)): #first, find the index at which time = 0 event occurs time0event = EEGepoch[ei]["eventlatency"][0] == 0 #bool array of where time=0 occurs time0event = np.where(time0event == time0event.max())[0][0] # find the index of the True value in this array rts[ei] = EEGepoch[ei]["eventlatency"][0][time0event+1] if useRTs: rts_idx=np.argsort(rts) else: rts_idx = np.argsort(np.squeeze(EEGdata[46,333,:])) #plot the trials for one channel, in (un)sorted order plt.imshow(np.squeeze(EEGdata[46,:,rts_idx]), extent=[EEGtimes[0], EEGtimes[-1], 1, EEGtrials], aspect="auto", cmap=plt.get_cmap("jet"), origin="lower", interpolation="none") plt.xlabel("time from stim onset (ms)") plt.ylabel("trial number") plt.clim([-30,30]) plt.colorbar(label=r"$\mu V$") plt.axis([-200,1200,1,99]) plt.grid(False) if useRTs: rtplot=plt.plot(rts[rts_idx],np.arange(1,EEGtrials+1),'k',linewidth=3, label= "Reaction time") plt.legend(bbox_to_anchor=[1.5,1]) #put the legend outside of the image ###Output _____no_output_____ ###Markdown 9. Overview of time-domain EEG analyses Imports ###Code import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable import mne from mne.externals.pymatreader import read_mat ###Output _____no_output_____ ###Markdown Load data ###Code # load data using MNE data_in = read_mat('C:/users/micha/analyzing_neural_time_series/sampleEEGdata.mat') EEG = data_in['EEG'] ###Output _____no_output_____ ###Markdown Figure 9.1a ###Code # Choose example channel to plot which_channel_to_plot = 'FCz' #specify label of channel to plot channel_index = (np.asarray(EEG["chanlocs"]["labels"]) == which_channel_to_plot) #specify index (channel number) of label chan_idx = int(np.linspace(0,len(channel_index)-1,len(channel_index))[channel_index]) # set plotting parameters x_axis_limit = (-200, 1000) #in milliseconds num_trials2plot = 12 # pick a random trials using random.choice (from numpy.random) random_trial_to_plot = np.random.choice(np.arange(EEG['trials']), num_trials2plot) # figure out how many subplots we need n_rows = np.ceil(num_trials2plot/np.ceil(np.sqrt(num_trials2plot))).astype(int) n_cols = np.ceil(np.sqrt(num_trials2plot)).astype(int) fig, ax = plt.subplots(n_rows, n_cols, sharex='all', figsize=(10, 6)) for ii in range(num_trials2plot): idx = np.unravel_index(ii, (n_rows, n_cols)) #plot trial and specify x-axis and title ax[idx].plot(EEG['times'], np.squeeze(EEG['data'][channel_index,:,random_trial_to_plot[ii] - 1])) ax[idx].set(title=f"Trial {random_trial_to_plot[ii]}", yticks=[]) fig.tight_layout(); ###Output _____no_output_____ ###Markdown Figure 9.1b ###Code #plot all trials fig, ax = plt.subplots( figsize=(8,6)) ax.plot(EEG['times'],np.squeeze(EEG['data'][channel_index,:,:]),'y') #plot the event-related potential (ERP), i.e. the average time-domain signal ax.plot(EEG['times'],np.squeeze(np.mean(EEG['data'][channel_index,:,:],axis=2)),'k',linewidth=2) ax.set_title("All EEG traces, and their average") plt.show() #now plot only the ERP fig, ax = plt.subplots(figsize=(8,6)) ax.plot(EEG['times'],np.squeeze(np.mean(EEG['data'][channel_index],axis=2))) #axis=2 specifies which axis to compute the mean along ax.vlines(0,-10,10,linestyles='dashed') ax.hlines(0,-1000,1500) ax.axis([-300,1000,-10,10]) ax.set_xlabel("Time from stimlulus onset (ms)") ax.set_ylabel(r'$ \mu V $') #latex interpreter looks for dollar signs ax.set(title="ERP (average of " + str(EEG["trials"]) + " trials) from electrode " + EEG["chanlocs"]["labels"][chan_idx]) ax.invert_yaxis() #EEG convention to flip y axis plt.show() ###Output _____no_output_____ ###Markdown Figure 9.2To my knowledge, Python (specifically, scipy) does not have a function that is completely analgous to MATLAB's firls(). A very close approximation that I will use instead is an n-th order Butterworth bandpass filter. TODO ###Code import scipy.signal as sig # pick example hannle to plot chan2plot = "P7" channel_index = np.asarray(EEG["chanlocs"]["labels"]) == chan2plot #specify index (channel number) of label erp = np.squeeze(np.mean(EEG['data'][channel_index],axis=2)) # filter parameters nyquist = EEG['srate'] / 2. transition_width = 0.15 #low-pass filter data #we'll look at filtering in detail in chapter 14 #filter form 0-40 filter_high = 40 #Hz; high cut off b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to40 = sig.filtfilt(b, a, erp, padlen=150) #use filfilt (filters forwards and backwards to eliminate phase shift) #next, filter from 0-10 filter_high = 10 #Hz b, a = sig.butter(5, np.array([filter_high(1+transition_width)])/nyquist,btype="lowpass") erp_0to10 = sig.filtfilt(b, a, erp, padlen=150) #next, filter from 5-15 filter_low = 5 # Hz filter_high = 15 # Hz b, a = sig.butter(5, np.array([filter_low(1-transition_width), filter_high(1+transition_width)])/nyquist,btype="bandpass") erp_5to15 = sig.filtfilt(b, a, erp, padlen=150) # plot results fig, ax = plt.subplots(figsize=[8,6]) ax.plot(EEG['times'],erp,'k') ax.plot(EEG['times'],erp_0to40,'c') ax.plot(EEG['times'],erp_0to10,'r') ax.plot(EEG['times'],erp_5to15,'m') # label plot ax.set_xlim([-200,1200]) ax.invert_yaxis() ax.set_xlabel("time (ms)") ax.set_ylabel("voltage " + r"$(\mu V)$") ax.set(title="Raw and filtered signal") ax.legend(['raw','0-40 Hz','0-10Hz','5-15Hz']) plt.show() ###Output _____no_output_____ ###Markdown Figure 9.3 ###Code fig, (ax_1, ax_2) = plt.subplots(nrows=2, figsize=[12,6], tight_layout=True) ax_1.plot(EEG['times'],np.squeeze(EEG['data'].mean(axis=2)).T) ax_1.set_xlim([-200, 1000]) ax_1.invert_yaxis() #flip for EEG conventions ax_1.set(title="ERP from all sensors") #topographical variance plot ax_2.plot(EEG['times'], np.squeeze(EEG['data'].mean(axis=2)).var(axis=0)) ax_2.set_xlim([-200,1000]) ax_2.set_xlabel("Time (ms)") ax_2.set_ylabel("var "+r'$ (\mu V) $') ax_2.set(title="Topographical variance") ###Output _____no_output_____ ###Markdown Figure 9.4 ###Code # create mne Evoked object # create channel montage chan_labels = EEG['chanlocs']['labels'] coords = np.vstack([EEG['chanlocs']['Y'],EEG['chanlocs']['X'],EEG['chanlocs']['Z']]).T montage = mne.channels.make_dig_montage(ch_pos=dict(zip(chan_labels, coords)), coord_frame='head') # create MNE Info and Evoked object info = mne.create_info(chan_labels, EEG['srate'] ,ch_types='eeg') evoked = mne.EvokedArray(EEG['data'].mean(axis=2), info, tmin=EEG['xmin']) evoked.set_montage(montage); # topoplot with colored dots vs. interpolated surface # average voltage over trials for a given timepoint TOI = 300 # ms c = EEG['data'].mean(axis=2)[:, np.argmin(abs(EEG['times'] - TOI))] # create figure fig = plt.figure(figsize=(12,4), tight_layout=True) # plot topomap without interpolation, 3D clim = np.max(np.abs(c)) ax_1 = fig.add_subplot(131, projection='3d') ax_1.scatter(EEG['chanlocs']['Y'], EEG['chanlocs']['X'], EEG['chanlocs']['Z'], s=50, c=c, cmap='coolwarm', vmin=-clim, vmax=clim) ax_1.set(title='no interpolation, 3D') # plot topomap without interpolation ax_2 = fig.add_subplot(132) ax_2.scatter(EEG['chanlocs']['Y'], EEG['chanlocs']['X'], s=50, c=c, cmap='coolwarm', vmin=-clim, vmax=clim) ax_2.set(title='no interpolation, 2D') ax_2.set_xlim([-120,120]) ax_2.set_ylim([-100,100]) # plot interpolated data # make colorbar axis ax_3 = fig.add_subplot(133) divider = make_axes_locatable(ax_3) cax = divider.append_axes("right", size="5%", pad=0.05) ax_3.set(title='interpolated') evoked.plot_topomap(times=TOI/1000, axes=(ax_3, cax), time_format=''); ###Output _____no_output_____ ###Markdown Figure 9.5 ###Code # plot topomap, interpolated surface, 50 ms intervals evoked.plot_topomap(times=np.linspace(-100,600,15)/1000, nrows=3); ###Output _____no_output_____ ###Markdown Figure 9.6 ###Code use_rts = True #or false #get RTs from each trial to use for sorting trials. In this experiment, #the RT was always the first event after the stimulus (the time=0 event). #Normally, you should build in exceptions in case there was no response or #another event occured between the stimulus and response. This was already #done for the current dataset. rts = np.zeros(EEG['trials']) for ei in range(EEG['trials']): #first, find the index at which time = 0 event occurs time0event = np.asarray(EEG['epoch']["eventlatency"][ei]) == 0 #bool array of where time=0 occurs time0event = np.where(time0event == time0event.max())[0][0] # find the index of the True value in this array rts[ei] = EEG['epoch']["eventlatency"][ei][time0event+1] if use_rts: rts_idx=np.argsort(rts) else: rts_idx = np.argsort(np.squeeze(EEG['data'][46,333,:])) #plot the trials for one channel, in (un)sorted order fig, ax = plt.subplots(figsize=[8,6]) im = ax.imshow(np.squeeze(EEG['data'][46,:,rts_idx]), extent=[EEG['times'][0], EEG['times'][-1], 1, EEG['trials']], aspect="auto", cmap=plt.get_cmap("jet"), origin="lower", interpolation="none") # add colorbar fig.colorbar(im, label=r"$\mu V$") im.set_clim([-30,30]) # label fig ax.set_xlabel("time from stim onset (ms)") ax.set_ylabel("trial number") ax.axis([-200,1200,1,99]) # add legend if use_rts: rtplot=plt.plot(rts[rts_idx],np.arange(1,EEG['trials']+1),'k',linewidth=3, label= "Reaction time") ax.legend(bbox_to_anchor=[1.5,1]) #put the legend outside of the image ###Output _____no_output_____ ###Markdown chapter 09 튜플과 레인지 09-1. 튜플 ###Code lst = [1, 2, 3] lst lst = [1, 2, 3] lst[0] type(lst) tpl = (1, 2, 3) tpl tpl = (1, 2, 3) tpl[0] type(tpl) lst = [1, 2, 3] lst.append(3) lst lst = [1, 2, 3] lst[0] = 7 lst tpl = (1, 2, 3) tpl.append(3) tpl tpl = (1, 2, 3) tpl[0] = 7 tpl ###Output _____no_output_____ ###Markdown 09-2. 튜플을 어디다 쓸 것인가? ###Code frns = [['동수', 131120], ['진우', 130312], ['선영', 130904]] frns frns[2] frns = [('동수', 131120), ('진우', 130312), ('선영', 130904)] frns frns[2] ###Output _____no_output_____ ###Markdown 09-3. 튜플 관련 함수와 연산들 ###Code nums = (3, 2, 5, 7, 1) len(nums) # 값의 개수는? max(nums) # 최댓값은? min(nums) # 최솟값은? nums = (1, 2, 3, 1, 2) nums.count(2) # 2가 몇 번 등장해? nums.index(1) # 가장 앞에(왼쪽에) 저장된 1의 인덱스 값은? nums = (1, 2, 3) 3 in nums # nums에 3이 있니? 2 not in nums # num에 2가 없니? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플과 (4, 5) 를 합한 새로운 튜플 생성 ###Code nums + (4, 5) # num에 (4, 5)를 덧붙인 결과는? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플 두 개를 이어 놓은 새로운 튜플 생성 ###Code nums * 2 # nums를 두 개 덧붙인 결과는? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플의 일부로만 이뤄진 새로운 튜플 생성 ###Code nums[0:3] # nums[0] ~ num[2]을 꺼내면? for i in (1, 3, 5, 7, 9): print(i, end = ' ') ###Output _____no_output_____ ###Markdown 09-4. 말이 나온 김에 리스트 안에 저장된 데이터를 바꿔보자 ###Code frns = [('동수', 131120), ('진우', 130312), ('선영', 130904)] frns[0][0] frns[0][1] frns[0][0] = '동준' frns[0][1] = 123456 frns[0] = ('동준', 123456) frns ###Output _____no_output_____ ###Markdown 09-5. 범위를 지정하는 레인지 ###Code for i in range(1, 11): print(i, end = ' ') r = range(1, 10) type(r) r = range(1, 10) 9 in r 10 not in r list((1, 2, 3)) # 튜플을 리스트로 list(range(1, 5)) # 레인지를 리스트로 list("Hello") # 문자열을 리스트로 tuple([1, 2, 3]) # 리스트를 튜플로 tuple(range(1, 5)) # 레인지를 튜플로 tuple("Hello") # 문자열을 튜플로 lst = list(range(1, 16)) lst tpl = tuple(range(1, 16)) tpl range(1, 10, 2) # 1부터 10 이전까지 2씩 증가하는 레인지 range(1, 10, 3) # 1부터 10 이전까지 3씩 증가하는 레인지 list(range(1, 10, 2)) # 1부터 10 이전까지 2씩 증가하는 리스트 만들기 list(range(1, 10, 3)) # 1부터 10 이전까지 3씩 증가하는 리스트 만들기 ###Output _____no_output_____ ###Markdown 09-6. 레인지 범위 거꾸로 지정하기 ###Code list(range(2, 10)) list(range(2, 10, 1)) list(range(10, 2)) list(range(10, 2, 1)) list(range(10, 2, -1)) # 10부터 1씩 감소하여 3까지 이르는 정수들 list(range(10, 2, -2)) # 10부터 2씩 감소하여 3까지 이르는 정수들 list(range(10, 2, -3)) # 10부터 3씩 감소하여 3까지 이르는 정수들 ###Output _____no_output_____ ###Markdown 과제 - 레인지도 할 줄 알게 되었으니 백준 문제를 오지게 풀어야겠지요? ###Code ###Output _____no_output_____ ###Markdown chapter 09 튜플과 레인지 - 정수형 : int- 소수형 : float- 문자형 : str(string)- 부울형 : bool(boolean)- 리스트형 : list- 튜플 : tpl(tuple) - 리스트와 유사(곧 배열) - 리스트는 값을 추가, 제거, 변경할 수 있는데 반해 튜플은 값을 읽기만 가능(튜플의 인자는 무조건 상수) 09-1. 튜플 ###Code lst = [1, 2, 3] lst lst = [1, 2, 3] lst[0] type(lst) tpl = (1, 2, 3) tpl tpl = (1, 2, 3) tpl[0] type(tpl) lst = [1, 2, 3] lst.append(3) lst lst = [1, 2, 3] lst[0] = 7 lst tpl = (1, 2, 3) tpl.append(3) tpl tpl = (1, 2, 3) tpl[0] = 7 tpl ###Output _____no_output_____ ###Markdown 09-2. 튜플을 어디다 쓸 것인가? ###Code frns = [['동수', 131120], ['진우', 130312], ['선영', 130904]] frns frns[2] frns = [('동수', 131120), ('진우', 130312), ('선영', 130904)] frns frns[2] ###Output _____no_output_____ ###Markdown 09-3. 튜플 관련 함수와 연산들 ###Code nums = (3, 2, 5, 7, 1) len(nums) # 값의 개수는? max(nums) # 최댓값은? min(nums) # 최솟값은? nums = (1, 2, 3, 1, 2) nums.count(2) # 2가 몇 번 등장해? nums.index(1) # 가장 앞에(왼쪽에) 저장된 1의 인덱스 값은? nums = (1, 2, 3) 3 in nums # nums에 3이 있니? 2 not in nums # num에 2가 없니? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플과 (4, 5) 를 합한 새로운 튜플 생성 ###Code nums + (4, 5) # num에 (4, 5)를 덧붙인 결과는? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플 두 개를 이어 놓은 새로운 튜플 생성 ###Code nums * 2 # nums를 두 개 덧붙인 결과는? ###Output _____no_output_____ ###Markdown - nums에 저장된 튜플의 일부로만 이뤄진 새로운 튜플 생성 ###Code nums[0:3] # nums[0] ~ num[2]을 꺼내면? for i in (1, 3, 5, 7, 9): print(i, end = ' ') ###Output _____no_output_____ ###Markdown 09-4. 말이 나온 김에 리스트 안에 저장된 데이터를 바꿔보자 ###Code frns = [('동수', 131120), ('진우', 130312), ('선영', 130904)] frns[0][0] frns[0][1] frns[0][0] = '동준' frns[0][1] = 123456 frns[0] = ('동준', 123456) frns ###Output _____no_output_____ ###Markdown 09-5. 범위를 지정하는 레인지 ###Code for i in range(1, 11): print(i, end = ' ') r = range(1, 10) type(r) r = range(1, 10) 9 in r 10 not in r list((1, 2, 3)) # 튜플을 리스트로 list(range(1, 5)) # 레인지를 리스트로 list("Hello") # 문자열을 리스트로 tuple([1, 2, 3]) # 리스트를 튜플로 tuple(range(1, 5)) # 레인지를 튜플로 tuple("Hello") # 문자열을 튜플로 lst = list(range(1, 16)) lst tpl = tuple(range(1, 16)) tpl range(1, 10, 2) # 1부터 10 이전까지 2씩 증가하는 레인지 range(1, 10, 3) # 1부터 10 이전까지 3씩 증가하는 레인지 list(range(1, 10, 2)) # 1부터 10 이전까지 2씩 증가하는 리스트 만들기 list(range(1, 10, 3)) # 1부터 10 이전까지 3씩 증가하는 리스트 만들기 ###Output _____no_output_____ ###Markdown 09-6. 레인지 범위 거꾸로 지정하기 ###Code list(range(2, 10)) list(range(2, 10, 1)) list(range(10, 2)) list(range(10, 2, 1)) list(range(10, 2, -1)) # 10부터 1씩 감소하여 3까지 이르는 정수들 list(range(10, 2, -2)) # 10부터 2씩 감소하여 3까지 이르는 정수들 list(range(10, 2, -3)) # 10부터 3씩 감소하여 3까지 이르는 정수들 ###Output _____no_output_____ ###Markdown 과제 - 레인지도 할 줄 알게 되었으니 백준 문제를 오지게 풀어야겠지요? ###Code ###Output _____no_output_____
Transfer_Learning_Lab.ipynb	###Markdown Lab: Transfer Learningtraining a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers.使用ImageNet预先训练的权值作为基础来训练网络，但是要添加您自己的额外网络层。你也会看到在所有层面上使用冻结权重和在所有层上训练的区别。冻结权重当只对模型进行微调时，通常使用冻结权值，因为在训练期间，反向传播和权值更新不会应用于任何冻结层。如果你有一个ImageNet pre-trained模型,大部分的网络可能适用于你的情况,所以你可能只需要切断顶部全层,冻结所有其他层,最后添加一个或多个层不冻执行一些微调。还有一个不冻结权重的选项，它将在ImageNet预训练的权重(如果适用)上启动您的模型，然后从那里执行进一步的训练。冻结的一个额外好处的权重也会在形式的内存使用情况,训练速度——VGG等更大的网络,有一个大大大内存使用和较慢的速度,当它需要执行反向传播和重量更新所有层上而不是一小部分(可能性较小)层。注意:如果您遇到问题，可以通过单击工作区左上角的Jupyter徽标找到一个解决方案笔记本。 ###Code # Set a couple flags for training - you can ignore these for now freeze_flag = True # `True` to freeze layers, `False` for full training weights_flag = 'imagenet' # 'imagenet' or None preprocess_flag = True # Should be true for ImageNet pre-trained typically # Loads in InceptionV3 from keras.applications.inception_v3 import InceptionV3 # We can use smaller than the default 299x299x3 input for InceptionV3 # which will speed up training. Keras v2.0.9 supports down to 139x139x3 input_size = 139 # Using Inception with ImageNet pre-trained weights inception = InceptionV3(weights=weights_flag, include_top=False, input_shape=(input_size,input_size,3)) ###Output Using TensorFlow backend. ###Markdown We'll use Inception V3 for this lab, although you can use the same techniques with any of the models in [Keras Applications](https://keras.io/applications/). Do note that certain models are only available in certain versions of Keras; this workspace uses Keras v2.0.9, for which you can see the available models [here](https://faroit.github.io/keras-docs/2.0.9/applications/).In the above, we've set Inception to use an `input_shape` of 139x139x3 instead of the default 299x299x3. This will help us to speed up our training a bit later (and we'll actually be upsampling from smaller images, so we aren't losing data here). In order to do so, we also must set `include_top` to `False`, which means the final fully-connected layer with 1,000 nodes for each ImageNet class is dropped, as well as a Global Average Pooling layer. Pre-trained with frozen weightsTo start, we'll see how an ImageNet pre-trained model with all weights frozen in the InceptionV3 model performs. We will also drop the end layer and append new layers onto it, although you could do this in different ways (not drop the end and add new layers, drop more layers than we will here, etc.).首先，我们将看到在InceptionV3模型中冻结了所有权重的ImageNet预训练模型是如何执行的。我们还将删除结束层并在其上添加新层，尽管您可以使用不同的方法(不是删除结束层并添加新层，而是删除比这里更多的层，等等)。You can freeze layers by setting `layer.trainable` to False for a given `layer`. Within a `model`, you can get the list of layers with `model.layers`. ###Code if freeze_flag == True: ## TODO: Iterate through the layers of the Inception model ## loaded above and set all of them to have trainable = False for layer in inception.layers: layer.trainable = False ###Output _____no_output_____ ###Markdown Dropping layersYou can drop layers from a model with `model.layers.pop()`. Before you do this, you should check out what the actual layers of the model are with Keras's `.summary()` function. ###Code ## TODO: Use the model summary function to see all layers in the ## loaded Inception model inception.summary ###Output _____no_output_____ ###Markdown In a normal Inception network, you would see from the model summary that the last two layers were a global average pooling layer, and a fully-connected "Dense" layer. However, since we set `include_top` to `False`, both of these get dropped. If you otherwise wanted to drop additional layers, you would use:```inception.layers.pop()```Note that `pop()` works from the end of the model backwards. It's important to note two things here:1. How many layers you drop is up to you, typically. We dropped the final two already by setting `include_top` to False in the original loading of the model, but you could instead just run `pop()` twice to achieve similar results. (Note: Keras requires us to set `include_top` to False in order to change the `input_shape`.) Additional layers could be dropped by additional calls to `pop()`.2. If you make a mistake with `pop()`, you'll want to reload the model. If you use it multiple times, the model will continue to drop more and more layers, so you may need to check `model.summary()` again to check your work. Adding new layersNow, you can start to add your own layers. While we've used Keras's `Sequential` model before for simplicity, we'll actually use the [Model API](https://keras.io/models/model/) this time. This functions a little differently, in that instead of using `model.add()`, you explicitly tell the model which previous layer to attach to the current layer. This is useful if you want to use more advanced concepts like [skip layers](https://en.wikipedia.org/wiki/Residual_neural_network), for instance (which were used heavily in ResNet).For example, if you had a previous layer named `inp`:```x = Dropout(0.2)(inp)```is how you would attach a new dropout layer `x`, with it's input coming from a layer with the variable name `inp`.We are going to use the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which consists of 60,000 32x32 images of 10 classes. We need to use Keras's `Input` function to do so, and then we want to re-size the images up to the `input_size` we specified earlier (139x139). ###Code from keras.layers import Input, Lambda import tensorflow as tf # Makes the input placeholder layer 32x32x3 for CIFAR-10 cifar_input = Input(shape=(32,32,3)) # Re-sizes the input with Kera's Lambda layer & attach to cifar_input resized_input = Lambda(lambda image: tf.image.resize_images( image, (input_size, input_size)))(cifar_input) # Feeds the re-sized input into Inception model # You will need to update the model name if you changed it earlier! inp = inception(resized_input) # Imports fully-connected "Dense" layers & Global Average Pooling from keras.layers import Dense, GlobalAveragePooling2D ## TODO: Setting `include_top` to False earlier also removed the ## GlobalAveragePooling2D layer, but we still want it. ## Add it here, and make sure to connect it to the end of Inception x = GlobalAveragePooling2D()(inp) ## TODO: Create two new fully-connected layers using the Model API ## format discussed above. The first layer should use `out` ## as its input, along with ReLU activation. You can choose ## how many nodes it has, although 512 or less is a good idea. ## The second layer should take this first layer as input, and ## be named "predictions", with Softmax activation and ## 10 nodes, as we'll be using the CIFAR10 dataset. #x = Dense(512, activation = 'relu')(x) predictions = Dense(10, activation = 'softmax')(x) ###Output _____no_output_____ ###Markdown We're almost done with our new model! Now we just need to use the actual Model API to create the full model. ###Code # Imports the Model API from keras.models import Model # Creates the model, assuming your final layer is named "predictions" model = Model(inputs=cifar_input, outputs=predictions) # Compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) # Check the summary of this new model to confirm the architecture model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_5 (InputLayer) (None, 32, 32, 3) 0 _________________________________________________________________ lambda_4 (Lambda) (None, 139, 139, 3) 0 _________________________________________________________________ inception_v3 (Model) (None, 3, 3, 2048) 21802784 _________________________________________________________________ global_average_pooling2d_3 ( (None, 2048) 0 _________________________________________________________________ dense_5 (Dense) (None, 10) 20490 ================================================================= Total params: 21,823,274 Trainable params: 20,490 Non-trainable params: 21,802,784 _________________________________________________________________ ###Markdown Great job creating a new model architecture from Inception! Notice how this method of adding layers before InceptionV3 and appending to the end of it made InceptionV3 condense down into one line in the summary; if you use the Inception model's normal input (which you could gather from `inception.layers.input`), it would instead show all the layers like before.Most of the rest of the code in the notebook just goes toward loading our data, pre-processing it, and starting our training in Keras, although there's one other good point to make here - Keras callbacks.从一开始就创建一个新的模型体系结构是一项伟大的工作!请注意，在InceptionV3之前添加层并将其追加到末尾的方法是如何使InceptionV3在摘要中浓缩为一行的;如果您使用Inception模型的普通输入(您可以从init .layers.input中收集它)，它将会像以前一样显示所有的层。笔记本中剩下的大部分代码只是用来加载数据、对其进行预处理，并开始在Keras中进行培训，不过这里还有一个很好的地方需要说明——Keras回调。 Keras CallbacksKeras [callbacks](https://keras.io/callbacks/) allow you to gather and store additional information during training, such as the best model, or even stop training early if the validation accuracy has stopped improving. These methods can help to avoid overfitting, or avoid other issues.Keras回调允许您在培训期间收集和存储额外的信息，例如最佳模型，或者如果验证精度停止提高，甚至可以提前停止培训。这些方法可以帮助避免过拟合，或避免其他问题。There's two key callbacks to mention here, `ModelCheckpoint` and `EarlyStopping`. As the names may suggest, model checkpoint saves down the best model so far based on a given metric, while early stopping will end training before the specified number of epochs if the chosen metric no longer improves after a given amount of time.这里要提到两个关键的回调，ModelCheckpoint和early stop。顾名思义，模型检查点根据给定的指标保存到目前为止最好的模型，而如果所选的指标在给定的时间后不再改善，那么早期停止将在指定的时间点之前结束培训。To set these callbacks, you could do the following:要设置这些回调，可以执行以下操作:```checkpoint = ModelCheckpoint(filepath=save_path, monitor='val_loss', save_best_only=True)```This would save a model to a specified `save_path`, based on validation loss, and only save down the best models. If you set `save_best_only` to `False`, every single epoch will save down another version of the model.这将根据验证损失将模型保存到指定的save_path，并且只保存最好的模型。如果您将save_best_only设置为False，那么每个历元都将保存模型的另一个版本。```stopper = EarlyStopping(monitor='val_acc', min_delta=0.0003, patience=5)```This will monitor validation accuracy, and if it has not decreased by more than 0.0003 from the previous best validation accuracy for 5 epochs, training will end early.这将监控验证的准确性，如果它没有比之前5个时期的最佳验证精度下降超过0.0003，那么培训将提前结束。You still need to actually feed these callbacks into `fit()` when you train the model (along with all other relevant data to feed into `fit`):当您训练模型时(连同所有其他相关数据一起)，您仍然需要实际地将这些回调输入fit():```model.fit(callbacks=[checkpoint, stopper])``` ###Code from sklearn.utils import shuffle from sklearn.preprocessing import LabelBinarizer from keras.datasets import cifar10 (X_train, y_train), (X_val, y_val) = cifar10.load_data() # One-hot encode the labels label_binarizer = LabelBinarizer() y_one_hot_train = label_binarizer.fit_transform(y_train) y_one_hot_val = label_binarizer.fit_transform(y_val) # Shuffle the training & test data X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train) X_val, y_one_hot_val = shuffle(X_val, y_one_hot_val) # We are only going to use the first 10,000 images for speed reasons # And only the first 2,000 images from the test set X_train = X_train[:2000] y_one_hot_train = y_one_hot_train[:2000] X_val = X_val[:400] y_one_hot_val = y_one_hot_val[:400] ###Output _____no_output_____ ###Markdown You can check out Keras's [ImageDataGenerator documentation](https://faroit.github.io/keras-docs/2.0.9/preprocessing/image/) for more information on the below - you can also add additional image augmentation through this function, although we are skipping that step here so you can potentially explore it in the upcoming project. ###Code # Use a generator to pre-process our images for ImageNet from keras.preprocessing.image import ImageDataGenerator from keras.applications.inception_v3 import preprocess_input if preprocess_flag == True: datagen = ImageDataGenerator(preprocessing_function=preprocess_input) val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) else: datagen = ImageDataGenerator() val_datagen = ImageDataGenerator() # Train the model batch_size = 32 epochs = 5 # Note: we aren't using callbacks here since we only are using 5 epochs to conserve GPU time model.fit_generator(datagen.flow(X_train, y_one_hot_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, verbose=1, validation_data=val_datagen.flow(X_val, y_one_hot_val, batch_size=batch_size), validation_steps=len(X_val)/batch_size) ###Output Epoch 1/5 63/62 [==============================] - 233s - loss: 1.7256 - acc: 0.4137 - val_loss: 1.1955 - val_acc: 0.6650 Epoch 2/5 63/62 [==============================] - 228s - loss: 1.1022 - acc: 0.6409 - val_loss: 1.1578 - val_acc: 0.6200 Epoch 3/5 63/62 [==============================] - 226s - loss: 0.9580 - acc: 0.6835 - val_loss: 1.0928 - val_acc: 0.6500 Epoch 4/5 63/62 [==============================] - 226s - loss: 0.8361 - acc: 0.7187 - val_loss: 1.0487 - val_acc: 0.6675 Epoch 5/5 63/62 [==============================] - 225s - loss: 0.7097 - acc: 0.7684 - val_loss: 1.1868 - val_acc: 0.6425 ###Markdown Lab: Transfer LearningWelcome to the lab on Transfer Learning! Here, you'll get a chance to try out training a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers. GPU usageIn our previous examples in this lesson, we've avoided using GPU, but this time around you'll have the option to enable it. You do not need it on to begin with, but make sure anytime you switch from non-GPU to GPU, or vice versa, that you save your notebook! If not, you'll likely be reverted to the previous checkpoint. We also suggest only using the GPU when performing the (mostly minor) training below - you'll want to conserve GPU hours for your Behavioral Cloning project coming up next! ###Code # Set a couple flags for training - you can ignore these for now freeze_flag = True # `True` to freeze layers, `False` for full training weights_flag = None # 'imagenet' or None preprocess_flag = True # Should be true for ImageNet pre-trained typically # Loads in InceptionV3 from keras.applications.inception_v3 import InceptionV3 # We can use smaller than the default 299x299x3 input for InceptionV3 # which will speed up training. Keras v2.0.9 supports down to 139x139x3 input_size = 139 # Using Inception with ImageNet pre-trained weights inception = InceptionV3(weights=weights_flag, include_top=False, input_shape=(input_size,input_size,3)) ###Output Using TensorFlow backend. ###Markdown We'll use Inception V3 for this lab, although you can use the same techniques with any of the models in [Keras Applications](https://keras.io/applications/). Do note that certain models are only available in certain versions of Keras; this workspace uses Keras v2.0.9, for which you can see the available models [here](https://faroit.github.io/keras-docs/2.0.9/applications/).In the above, we've set Inception to use an `input_shape` of 139x139x3 instead of the default 299x299x3. This will help us to speed up our training a bit later (and we'll actually be upsampling from smaller images, so we aren't losing data here). In order to do so, we also must set `include_top` to `False`, which means the final fully-connected layer with 1,000 nodes for each ImageNet class is dropped, as well as a Global Average Pooling layer. Pre-trained with frozen weightsTo start, we'll see how an ImageNet pre-trained model with all weights frozen in the InceptionV3 model performs. We will also drop the end layer and append new layers onto it, although you could do this in different ways (not drop the end and add new layers, drop more layers than we will here, etc.).You can freeze layers by setting `layer.trainable` to False for a given `layer`. Within a `model`, you can get the list of layers with `model.layers`. ###Code if freeze_flag == True: ## TODO: Iterate through the layers of the Inception model ## loaded above and set all of them to have trainable = False for layer in inception.layers: layer.trainable = False ###Output _____no_output_____ ###Markdown Dropping layersYou can drop layers from a model with `model.layers.pop()`. Before you do this, you should check out what the actual layers of the model are with Keras's `.summary()` function. ###Code ## TODO: Use the model summary function to see all layers in the ## loaded Inception model print(inception.summary()) ###Output __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 139, 139, 3) 0 __________________________________________________________________________________________________ conv2d_1 (Conv2D) (None, 69, 69, 32) 864 input_1[0][0] __________________________________________________________________________________________________ batch_normalization_1 (BatchNor (None, 69, 69, 32) 96 conv2d_1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 69, 69, 32) 0 batch_normalization_1[0][0] __________________________________________________________________________________________________ conv2d_2 (Conv2D) (None, 67, 67, 32) 9216 activation_1[0][0] __________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 67, 67, 32) 96 conv2d_2[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 67, 67, 32) 0 batch_normalization_2[0][0] __________________________________________________________________________________________________ conv2d_3 (Conv2D) (None, 67, 67, 64) 18432 activation_2[0][0] __________________________________________________________________________________________________ batch_normalization_3 (BatchNor (None, 67, 67, 64) 192 conv2d_3[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 67, 67, 64) 0 batch_normalization_3[0][0] __________________________________________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 33, 33, 64) 0 activation_3[0][0] __________________________________________________________________________________________________ conv2d_4 (Conv2D) (None, 33, 33, 80) 5120 max_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_4 (BatchNor (None, 33, 33, 80) 240 conv2d_4[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 33, 33, 80) 0 batch_normalization_4[0][0] __________________________________________________________________________________________________ conv2d_5 (Conv2D) (None, 31, 31, 192) 138240 activation_4[0][0] __________________________________________________________________________________________________ batch_normalization_5 (BatchNor (None, 31, 31, 192) 576 conv2d_5[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 31, 31, 192) 0 batch_normalization_5[0][0] __________________________________________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 15, 15, 192) 0 activation_5[0][0] __________________________________________________________________________________________________ conv2d_9 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_9 (BatchNor (None, 15, 15, 64) 192 conv2d_9[0][0] __________________________________________________________________________________________________ activation_9 (Activation) (None, 15, 15, 64) 0 batch_normalization_9[0][0] __________________________________________________________________________________________________ conv2d_7 (Conv2D) (None, 15, 15, 48) 9216 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_10 (Conv2D) (None, 15, 15, 96) 55296 activation_9[0][0] __________________________________________________________________________________________________ batch_normalization_7 (BatchNor (None, 15, 15, 48) 144 conv2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_10 (BatchNo (None, 15, 15, 96) 288 conv2d_10[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 15, 15, 48) 0 batch_normalization_7[0][0] __________________________________________________________________________________________________ activation_10 (Activation) (None, 15, 15, 96) 0 batch_normalization_10[0][0] __________________________________________________________________________________________________ average_pooling2d_1 (AveragePoo (None, 15, 15, 192) 0 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_6 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_8 (Conv2D) (None, 15, 15, 64) 76800 activation_7[0][0] __________________________________________________________________________________________________ conv2d_11 (Conv2D) (None, 15, 15, 96) 82944 activation_10[0][0] __________________________________________________________________________________________________ conv2d_12 (Conv2D) (None, 15, 15, 32) 6144 average_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_6 (BatchNor (None, 15, 15, 64) 192 conv2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_8 (BatchNor (None, 15, 15, 64) 192 conv2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_11 (BatchNo (None, 15, 15, 96) 288 conv2d_11[0][0] __________________________________________________________________________________________________ batch_normalization_12 (BatchNo (None, 15, 15, 32) 96 conv2d_12[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 15, 15, 64) 0 batch_normalization_6[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 15, 15, 64) 0 batch_normalization_8[0][0] __________________________________________________________________________________________________ activation_11 (Activation) (None, 15, 15, 96) 0 batch_normalization_11[0][0] __________________________________________________________________________________________________ activation_12 (Activation) (None, 15, 15, 32) 0 batch_normalization_12[0][0] __________________________________________________________________________________________________ mixed0 (Concatenate) (None, 15, 15, 256) 0 activation_6[0][0] activation_8[0][0] activation_11[0][0] activation_12[0][0] __________________________________________________________________________________________________ conv2d_16 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ batch_normalization_16 (BatchNo (None, 15, 15, 64) 192 conv2d_16[0][0] __________________________________________________________________________________________________ activation_16 (Activation) (None, 15, 15, 64) 0 batch_normalization_16[0][0] __________________________________________________________________________________________________ conv2d_14 (Conv2D) (None, 15, 15, 48) 12288 mixed0[0][0] __________________________________________________________________________________________________ conv2d_17 (Conv2D) (None, 15, 15, 96) 55296 activation_16[0][0] __________________________________________________________________________________________________ batch_normalization_14 (BatchNo (None, 15, 15, 48) 144 conv2d_14[0][0] __________________________________________________________________________________________________ batch_normalization_17 (BatchNo (None, 15, 15, 96) 288 conv2d_17[0][0] __________________________________________________________________________________________________ activation_14 (Activation) (None, 15, 15, 48) 0 batch_normalization_14[0][0] __________________________________________________________________________________________________ activation_17 (Activation) (None, 15, 15, 96) 0 batch_normalization_17[0][0] __________________________________________________________________________________________________ average_pooling2d_2 (AveragePoo (None, 15, 15, 256) 0 mixed0[0][0] __________________________________________________________________________________________________ conv2d_13 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ conv2d_15 (Conv2D) (None, 15, 15, 64) 76800 activation_14[0][0] __________________________________________________________________________________________________ conv2d_18 (Conv2D) (None, 15, 15, 96) 82944 activation_17[0][0] __________________________________________________________________________________________________ conv2d_19 (Conv2D) (None, 15, 15, 64) 16384 average_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_13 (BatchNo (None, 15, 15, 64) 192 conv2d_13[0][0] __________________________________________________________________________________________________ batch_normalization_15 (BatchNo (None, 15, 15, 64) 192 conv2d_15[0][0] __________________________________________________________________________________________________ batch_normalization_18 (BatchNo (None, 15, 15, 96) 288 conv2d_18[0][0] __________________________________________________________________________________________________ batch_normalization_19 (BatchNo (None, 15, 15, 64) 192 conv2d_19[0][0] __________________________________________________________________________________________________ activation_13 (Activation) (None, 15, 15, 64) 0 batch_normalization_13[0][0] __________________________________________________________________________________________________ activation_15 (Activation) (None, 15, 15, 64) 0 batch_normalization_15[0][0] __________________________________________________________________________________________________ activation_18 (Activation) (None, 15, 15, 96) 0 batch_normalization_18[0][0] __________________________________________________________________________________________________ activation_19 (Activation) (None, 15, 15, 64) 0 batch_normalization_19[0][0] __________________________________________________________________________________________________ mixed1 (Concatenate) (None, 15, 15, 288) 0 activation_13[0][0] activation_15[0][0] activation_18[0][0] activation_19[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ batch_normalization_23 (BatchNo (None, 15, 15, 64) 192 conv2d_23[0][0] __________________________________________________________________________________________________ activation_23 (Activation) (None, 15, 15, 64) 0 batch_normalization_23[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 15, 15, 48) 13824 mixed1[0][0] __________________________________________________________________________________________________ conv2d_24 (Conv2D) (None, 15, 15, 96) 55296 activation_23[0][0] __________________________________________________________________________________________________ batch_normalization_21 (BatchNo (None, 15, 15, 48) 144 conv2d_21[0][0] __________________________________________________________________________________________________ batch_normalization_24 (BatchNo (None, 15, 15, 96) 288 conv2d_24[0][0] __________________________________________________________________________________________________ activation_21 (Activation) (None, 15, 15, 48) 0 batch_normalization_21[0][0] __________________________________________________________________________________________________ activation_24 (Activation) (None, 15, 15, 96) 0 batch_normalization_24[0][0] __________________________________________________________________________________________________ average_pooling2d_3 (AveragePoo (None, 15, 15, 288) 0 mixed1[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 15, 15, 64) 76800 activation_21[0][0] __________________________________________________________________________________________________ conv2d_25 (Conv2D) (None, 15, 15, 96) 82944 activation_24[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 15, 15, 64) 18432 average_pooling2d_3[0][0] __________________________________________________________________________________________________ batch_normalization_20 (BatchNo (None, 15, 15, 64) 192 conv2d_20[0][0] __________________________________________________________________________________________________ batch_normalization_22 (BatchNo (None, 15, 15, 64) 192 conv2d_22[0][0] __________________________________________________________________________________________________ batch_normalization_25 (BatchNo (None, 15, 15, 96) 288 conv2d_25[0][0] __________________________________________________________________________________________________ batch_normalization_26 (BatchNo (None, 15, 15, 64) 192 conv2d_26[0][0] __________________________________________________________________________________________________ activation_20 (Activation) (None, 15, 15, 64) 0 batch_normalization_20[0][0] __________________________________________________________________________________________________ activation_22 (Activation) (None, 15, 15, 64) 0 batch_normalization_22[0][0] __________________________________________________________________________________________________ activation_25 (Activation) (None, 15, 15, 96) 0 batch_normalization_25[0][0] __________________________________________________________________________________________________ activation_26 (Activation) (None, 15, 15, 64) 0 batch_normalization_26[0][0] __________________________________________________________________________________________________ mixed2 (Concatenate) (None, 15, 15, 288) 0 activation_20[0][0] activation_22[0][0] activation_25[0][0] activation_26[0][0] __________________________________________________________________________________________________ conv2d_28 (Conv2D) (None, 15, 15, 64) 18432 mixed2[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 15, 15, 64) 192 conv2d_28[0][0] __________________________________________________________________________________________________ activation_28 (Activation) (None, 15, 15, 64) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ conv2d_29 (Conv2D) (None, 15, 15, 96) 55296 activation_28[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 15, 15, 96) 288 conv2d_29[0][0] __________________________________________________________________________________________________ activation_29 (Activation) (None, 15, 15, 96) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ conv2d_27 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0] __________________________________________________________________________________________________ conv2d_30 (Conv2D) (None, 7, 7, 96) 82944 activation_29[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 7, 7, 384) 1152 conv2d_27[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 7, 7, 96) 288 conv2d_30[0][0] __________________________________________________________________________________________________ activation_27 (Activation) (None, 7, 7, 384) 0 batch_normalization_27[0][0] __________________________________________________________________________________________________ activation_30 (Activation) (None, 7, 7, 96) 0 batch_normalization_30[0][0] __________________________________________________________________________________________________ max_pooling2d_3 (MaxPooling2D) (None, 7, 7, 288) 0 mixed2[0][0] __________________________________________________________________________________________________ mixed3 (Concatenate) (None, 7, 7, 768) 0 activation_27[0][0] activation_30[0][0] max_pooling2d_3[0][0] __________________________________________________________________________________________________ conv2d_35 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0] __________________________________________________________________________________________________ activation_35 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ conv2d_36 (Conv2D) (None, 7, 7, 128) 114688 activation_35[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0] __________________________________________________________________________________________________ activation_36 (Activation) (None, 7, 7, 128) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ conv2d_32 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ conv2d_37 (Conv2D) (None, 7, 7, 128) 114688 activation_36[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_32[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_37[0][0] __________________________________________________________________________________________________ activation_32 (Activation) (None, 7, 7, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ activation_37 (Activation) (None, 7, 7, 128) 0 batch_normalization_37[0][0] __________________________________________________________________________________________________ conv2d_33 (Conv2D) (None, 7, 7, 128) 114688 activation_32[0][0] __________________________________________________________________________________________________ conv2d_38 (Conv2D) (None, 7, 7, 128) 114688 activation_37[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 7, 7, 128) 384 conv2d_33[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 7, 7, 128) 384 conv2d_38[0][0] __________________________________________________________________________________________________ activation_33 (Activation) (None, 7, 7, 128) 0 batch_normalization_33[0][0] __________________________________________________________________________________________________ activation_38 (Activation) (None, 7, 7, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ average_pooling2d_4 (AveragePoo (None, 7, 7, 768) 0 mixed3[0][0] __________________________________________________________________________________________________ conv2d_31 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0] __________________________________________________________________________________________________ conv2d_34 (Conv2D) (None, 7, 7, 192) 172032 activation_33[0][0] __________________________________________________________________________________________________ conv2d_39 (Conv2D) (None, 7, 7, 192) 172032 activation_38[0][0] __________________________________________________________________________________________________ conv2d_40 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 7, 7, 192) 576 conv2d_31[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 7, 7, 192) 576 conv2d_34[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_39[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_40[0][0] __________________________________________________________________________________________________ activation_31 (Activation) (None, 7, 7, 192) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ activation_34 (Activation) (None, 7, 7, 192) 0 batch_normalization_34[0][0] __________________________________________________________________________________________________ activation_39 (Activation) (None, 7, 7, 192) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ activation_40 (Activation) (None, 7, 7, 192) 0 batch_normalization_40[0][0] __________________________________________________________________________________________________ mixed4 (Concatenate) (None, 7, 7, 768) 0 activation_31[0][0] activation_34[0][0] activation_39[0][0] activation_40[0][0] __________________________________________________________________________________________________ conv2d_45 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0] __________________________________________________________________________________________________ activation_45 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0] __________________________________________________________________________________________________ conv2d_46 (Conv2D) (None, 7, 7, 160) 179200 activation_45[0][0] __________________________________________________________________________________________________ batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0] __________________________________________________________________________________________________ activation_46 (Activation) (None, 7, 7, 160) 0 batch_normalization_46[0][0] __________________________________________________________________________________________________ conv2d_42 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ conv2d_47 (Conv2D) (None, 7, 7, 160) 179200 activation_46[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_42[0][0] __________________________________________________________________________________________________ batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_47[0][0] __________________________________________________________________________________________________ activation_42 (Activation) (None, 7, 7, 160) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ activation_47 (Activation) (None, 7, 7, 160) 0 batch_normalization_47[0][0] __________________________________________________________________________________________________ conv2d_43 (Conv2D) (None, 7, 7, 160) 179200 activation_42[0][0] __________________________________________________________________________________________________ conv2d_48 (Conv2D) (None, 7, 7, 160) 179200 activation_47[0][0] __________________________________________________________________________________________________ batch_normalization_43 (BatchNo (None, 7, 7, 160) 480 conv2d_43[0][0] __________________________________________________________________________________________________ batch_normalization_48 (BatchNo (None, 7, 7, 160) 480 conv2d_48[0][0] __________________________________________________________________________________________________ activation_43 (Activation) (None, 7, 7, 160) 0 batch_normalization_43[0][0] __________________________________________________________________________________________________ activation_48 (Activation) (None, 7, 7, 160) 0 batch_normalization_48[0][0] __________________________________________________________________________________________________ average_pooling2d_5 (AveragePoo (None, 7, 7, 768) 0 mixed4[0][0] __________________________________________________________________________________________________ conv2d_41 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0] __________________________________________________________________________________________________ conv2d_44 (Conv2D) (None, 7, 7, 192) 215040 activation_43[0][0] __________________________________________________________________________________________________ conv2d_49 (Conv2D) (None, 7, 7, 192) 215040 activation_48[0][0] __________________________________________________________________________________________________ conv2d_50 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 7, 7, 192) 576 conv2d_41[0][0] __________________________________________________________________________________________________ batch_normalization_44 (BatchNo (None, 7, 7, 192) 576 conv2d_44[0][0] __________________________________________________________________________________________________ batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_49[0][0] __________________________________________________________________________________________________ batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_50[0][0] __________________________________________________________________________________________________ activation_41 (Activation) (None, 7, 7, 192) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ activation_44 (Activation) (None, 7, 7, 192) 0 batch_normalization_44[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 7, 7, 192) 0 batch_normalization_49[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 7, 7, 192) 0 batch_normalization_50[0][0] __________________________________________________________________________________________________ mixed5 (Concatenate) (None, 7, 7, 768) 0 activation_41[0][0] activation_44[0][0] activation_49[0][0] activation_50[0][0] __________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 7, 7, 160) 179200 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 7, 7, 160) 0 batch_normalization_56[0][0] __________________________________________________________________________________________________ conv2d_52 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 7, 7, 160) 179200 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_52[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 7, 7, 160) 0 batch_normalization_52[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 7, 7, 160) 0 batch_normalization_57[0][0] __________________________________________________________________________________________________ conv2d_53 (Conv2D) (None, 7, 7, 160) 179200 activation_52[0][0] __________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 7, 7, 160) 179200 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 7, 7, 160) 480 conv2d_53[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 7, 7, 160) 480 conv2d_58[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 7, 7, 160) 0 batch_normalization_53[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 7, 7, 160) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ average_pooling2d_6 (AveragePoo (None, 7, 7, 768) 0 mixed5[0][0] __________________________________________________________________________________________________ conv2d_51 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0] __________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 7, 7, 192) 215040 activation_53[0][0] __________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 7, 7, 192) 215040 activation_58[0][0] __________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_51 (BatchNo (None, 7, 7, 192) 576 conv2d_51[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 7, 7, 192) 576 conv2d_54[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_59[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_60[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 7, 7, 192) 0 batch_normalization_51[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 7, 7, 192) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 7, 7, 192) 0 batch_normalization_59[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 7, 7, 192) 0 batch_normalization_60[0][0] __________________________________________________________________________________________________ mixed6 (Concatenate) (None, 7, 7, 768) 0 activation_51[0][0] activation_54[0][0] activation_59[0][0] activation_60[0][0] __________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 7, 7, 192) 258048 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 7, 7, 192) 0 batch_normalization_66[0][0] __________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_66[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 7, 7, 192) 0 batch_normalization_62[0][0] __________________________________________________________________________________________________ activation_67 (Activation) (None, 7, 7, 192) 0 batch_normalization_67[0][0] __________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 7, 7, 192) 258048 activation_62[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 7, 7, 192) 258048 activation_67[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_63[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 7, 7, 192) 0 batch_normalization_63[0][0] __________________________________________________________________________________________________ activation_68 (Activation) (None, 7, 7, 192) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ average_pooling2d_7 (AveragePoo (None, 7, 7, 768) 0 mixed6[0][0] __________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 7, 7, 192) 258048 activation_63[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 7, 7, 192) 258048 activation_68[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_61[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_70[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 7, 7, 192) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ activation_69 (Activation) (None, 7, 7, 192) 0 batch_normalization_69[0][0] __________________________________________________________________________________________________ activation_70 (Activation) (None, 7, 7, 192) 0 batch_normalization_70[0][0] __________________________________________________________________________________________________ mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_61[0][0] activation_64[0][0] activation_69[0][0] activation_70[0][0] __________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_73[0][0] __________________________________________________________________________________________________ activation_73 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0] __________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 7, 7, 192) 258048 activation_73[0][0] __________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0] __________________________________________________________________________________________________ activation_74 (Activation) (None, 7, 7, 192) 0 batch_normalization_74[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 7, 7, 192) 258048 activation_74[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 7, 7, 192) 576 conv2d_71[0][0] __________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 7, 7, 192) 576 conv2d_75[0][0] __________________________________________________________________________________________________ activation_71 (Activation) (None, 7, 7, 192) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ activation_75 (Activation) (None, 7, 7, 192) 0 batch_normalization_75[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 3, 3, 320) 552960 activation_71[0][0] __________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 3, 3, 192) 331776 activation_75[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 3, 3, 320) 960 conv2d_72[0][0] __________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 3, 3, 192) 576 conv2d_76[0][0] __________________________________________________________________________________________________ activation_72 (Activation) (None, 3, 3, 320) 0 batch_normalization_72[0][0] __________________________________________________________________________________________________ activation_76 (Activation) (None, 3, 3, 192) 0 batch_normalization_76[0][0] __________________________________________________________________________________________________ max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 768) 0 mixed7[0][0] __________________________________________________________________________________________________ mixed8 (Concatenate) (None, 3, 3, 1280) 0 activation_72[0][0] activation_76[0][0] max_pooling2d_4[0][0] __________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_81 (BatchNo (None, 3, 3, 448) 1344 conv2d_81[0][0] __________________________________________________________________________________________________ activation_81 (Activation) (None, 3, 3, 448) 0 batch_normalization_81[0][0] __________________________________________________________________________________________________ conv2d_78 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0] __________________________________________________________________________________________________ conv2d_82 (Conv2D) (None, 3, 3, 384) 1548288 activation_81[0][0] __________________________________________________________________________________________________ batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_78[0][0] __________________________________________________________________________________________________ batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0] __________________________________________________________________________________________________ activation_78 (Activation) (None, 3, 3, 384) 0 batch_normalization_78[0][0] __________________________________________________________________________________________________ activation_82 (Activation) (None, 3, 3, 384) 0 batch_normalization_82[0][0] __________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ conv2d_84 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ average_pooling2d_8 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0] __________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_79[0][0] __________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 3, 3, 384) 1152 conv2d_80[0][0] __________________________________________________________________________________________________ batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0] __________________________________________________________________________________________________ batch_normalization_84 (BatchNo (None, 3, 3, 384) 1152 conv2d_84[0][0] __________________________________________________________________________________________________ conv2d_85 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 3, 3, 320) 960 conv2d_77[0][0] __________________________________________________________________________________________________ activation_79 (Activation) (None, 3, 3, 384) 0 batch_normalization_79[0][0] __________________________________________________________________________________________________ activation_80 (Activation) (None, 3, 3, 384) 0 batch_normalization_80[0][0] __________________________________________________________________________________________________ activation_83 (Activation) (None, 3, 3, 384) 0 batch_normalization_83[0][0] __________________________________________________________________________________________________ activation_84 (Activation) (None, 3, 3, 384) 0 batch_normalization_84[0][0] __________________________________________________________________________________________________ batch_normalization_85 (BatchNo (None, 3, 3, 192) 576 conv2d_85[0][0] __________________________________________________________________________________________________ activation_77 (Activation) (None, 3, 3, 320) 0 batch_normalization_77[0][0] __________________________________________________________________________________________________ mixed9_0 (Concatenate) (None, 3, 3, 768) 0 activation_79[0][0] activation_80[0][0] __________________________________________________________________________________________________ concatenate_1 (Concatenate) (None, 3, 3, 768) 0 activation_83[0][0] activation_84[0][0] __________________________________________________________________________________________________ activation_85 (Activation) (None, 3, 3, 192) 0 batch_normalization_85[0][0] __________________________________________________________________________________________________ mixed9 (Concatenate) (None, 3, 3, 2048) 0 activation_77[0][0] mixed9_0[0][0] concatenate_1[0][0] activation_85[0][0] __________________________________________________________________________________________________ conv2d_90 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_90 (BatchNo (None, 3, 3, 448) 1344 conv2d_90[0][0] __________________________________________________________________________________________________ activation_90 (Activation) (None, 3, 3, 448) 0 batch_normalization_90[0][0] __________________________________________________________________________________________________ conv2d_87 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0] __________________________________________________________________________________________________ conv2d_91 (Conv2D) (None, 3, 3, 384) 1548288 activation_90[0][0] __________________________________________________________________________________________________ batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0] __________________________________________________________________________________________________ batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0] __________________________________________________________________________________________________ activation_87 (Activation) (None, 3, 3, 384) 0 batch_normalization_87[0][0] __________________________________________________________________________________________________ activation_91 (Activation) (None, 3, 3, 384) 0 batch_normalization_91[0][0] __________________________________________________________________________________________________ conv2d_88 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_89 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ conv2d_93 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ average_pooling2d_9 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0] __________________________________________________________________________________________________ conv2d_86 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_88[0][0] __________________________________________________________________________________________________ batch_normalization_89 (BatchNo (None, 3, 3, 384) 1152 conv2d_89[0][0] __________________________________________________________________________________________________ batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0] __________________________________________________________________________________________________ batch_normalization_93 (BatchNo (None, 3, 3, 384) 1152 conv2d_93[0][0] __________________________________________________________________________________________________ conv2d_94 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_9[0][0] __________________________________________________________________________________________________ batch_normalization_86 (BatchNo (None, 3, 3, 320) 960 conv2d_86[0][0] __________________________________________________________________________________________________ activation_88 (Activation) (None, 3, 3, 384) 0 batch_normalization_88[0][0] __________________________________________________________________________________________________ activation_89 (Activation) (None, 3, 3, 384) 0 batch_normalization_89[0][0] __________________________________________________________________________________________________ activation_92 (Activation) (None, 3, 3, 384) 0 batch_normalization_92[0][0] __________________________________________________________________________________________________ activation_93 (Activation) (None, 3, 3, 384) 0 batch_normalization_93[0][0] __________________________________________________________________________________________________ batch_normalization_94 (BatchNo (None, 3, 3, 192) 576 conv2d_94[0][0] __________________________________________________________________________________________________ activation_86 (Activation) (None, 3, 3, 320) 0 batch_normalization_86[0][0] __________________________________________________________________________________________________ mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_88[0][0] activation_89[0][0] __________________________________________________________________________________________________ concatenate_2 (Concatenate) (None, 3, 3, 768) 0 activation_92[0][0] activation_93[0][0] __________________________________________________________________________________________________ activation_94 (Activation) (None, 3, 3, 192) 0 batch_normalization_94[0][0] __________________________________________________________________________________________________ mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_86[0][0] mixed9_1[0][0] concatenate_2[0][0] activation_94[0][0] ================================================================================================== Total params: 21,802,784 Trainable params: 0 Non-trainable params: 21,802,784 __________________________________________________________________________________________________ None ###Markdown In a normal Inception network, you would see from the model summary that the last two layers were a global average pooling layer, and a fully-connected "Dense" layer. However, since we set `include_top` to `False`, both of these get dropped. If you otherwise wanted to drop additional layers, you would use:```inception.layers.pop()```Note that `pop()` works from the end of the model backwards. It's important to note two things here:1. How many layers you drop is up to you, typically. We dropped the final two already by setting `include_top` to False in the original loading of the model, but you could instead just run `pop()` twice to achieve similar results. (Note: Keras requires us to set `include_top` to False in order to change the `input_shape`.) Additional layers could be dropped by additional calls to `pop()`.2. If you make a mistake with `pop()`, you'll want to reload the model. If you use it multiple times, the model will continue to drop more and more layers, so you may need to check `model.summary()` again to check your work. Adding new layersNow, you can start to add your own layers. While we've used Keras's `Sequential` model before for simplicity, we'll actually use the [Model API](https://keras.io/models/model/) this time. This functions a little differently, in that instead of using `model.add()`, you explicitly tell the model which previous layer to attach to the current layer. This is useful if you want to use more advanced concepts like [skip layers](https://en.wikipedia.org/wiki/Residual_neural_network), for instance (which were used heavily in ResNet).For example, if you had a previous layer named `inp`:```x = Dropout(0.2)(inp)```is how you would attach a new dropout layer `x`, with it's input coming from a layer with the variable name `inp`.We are going to use the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which consists of 60,000 32x32 images of 10 classes. We need to use Keras's `Input` function to do so, and then we want to re-size the images up to the `input_size` we specified earlier (139x139). ###Code from keras.layers import Input, Lambda import tensorflow as tf # Makes the input placeholder layer 32x32x3 for CIFAR-10 cifar_input = Input(shape=(32,32,3)) # Re-sizes the input with Kera's Lambda layer & attach to cifar_input resized_input = Lambda(lambda image: tf.image.resize_images( image, (input_size, input_size)))(cifar_input) # Feeds the re-sized input into Inception model # You will need to update the model name if you changed it earlier! inp = inception(resized_input) # Imports fully-connected "Dense" layers & Global Average Pooling from keras.layers import Dense, GlobalAveragePooling2D ## TODO: Setting `include_top` to False earlier also removed the ## GlobalAveragePooling2D layer, but we still want it. ## Add it here, and make sure to connect it to the end of Inception out = GlobalAveragePooling2D()(inp) ## TODO: Create two new fully-connected layers using the Model API ## format discussed above. The first layer should use `out` ## as its input, along with ReLU activation. You can choose ## how many nodes it has, although 512 or less is a good idea. ## The second layer should take this first layer as input, and ## be named "predictions", with Softmax activation and ## 10 nodes, as we'll be using the CIFAR10 dataset. x = Dense(512, activation=tf.nn.relu)(out) predictions = Dense(10,activation=tf.nn.softmax)(x) ###Output _____no_output_____ ###Markdown We're almost done with our new model! Now we just need to use the actual Model API to create the full model. ###Code # Imports the Model API from keras.models import Model # Creates the model, assuming your final layer is named "predictions" model = Model(inputs=cifar_input, outputs=predictions) # Compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) # Check the summary of this new model to confirm the architecture model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) (None, 32, 32, 3) 0 _________________________________________________________________ lambda_1 (Lambda) (None, 139, 139, 3) 0 _________________________________________________________________ inception_v3 (Model) (None, 3, 3, 2048) 21802784 _________________________________________________________________ global_average_pooling2d_1 ( (None, 2048) 0 _________________________________________________________________ dense_1 (Dense) (None, 512) 1049088 _________________________________________________________________ dense_2 (Dense) (None, 10) 5130 ================================================================= Total params: 22,857,002 Trainable params: 1,054,218 Non-trainable params: 21,802,784 _________________________________________________________________ ###Markdown Great job creating a new model architecture from Inception! Notice how this method of adding layers before InceptionV3 and appending to the end of it made InceptionV3 condense down into one line in the summary; if you use the Inception model's normal input (which you could gather from `inception.layers.input`), it would instead show all the layers like before.Most of the rest of the code in the notebook just goes toward loading our data, pre-processing it, and starting our training in Keras, although there's one other good point to make here - Keras callbacks. Keras CallbacksKeras [callbacks](https://keras.io/callbacks/) allow you to gather and store additional information during training, such as the best model, or even stop training early if the validation accuracy has stopped improving. These methods can help to avoid overfitting, or avoid other issues.There's two key callbacks to mention here, `ModelCheckpoint` and `EarlyStopping`. As the names may suggest, model checkpoint saves down the best model so far based on a given metric, while early stopping will end training before the specified number of epochs if the chosen metric no longer improves after a given amount of time.To set these callbacks, you could do the following:```checkpoint = ModelCheckpoint(filepath=save_path, monitor='val_loss', save_best_only=True)```This would save a model to a specified `save_path`, based on validation loss, and only save down the best models. If you set `save_best_only` to `False`, every single epoch will save down another version of the model.```stopper = EarlyStopping(monitor='val_acc', min_delta=0.0003, patience=5)```This will monitor validation accuracy, and if it has not decreased by more than 0.0003 from the previous best validation accuracy for 5 epochs, training will end early.You still need to actually feed these callbacks into `fit()` when you train the model (along with all other relevant data to feed into `fit`):```model.fit(callbacks=[checkpoint, stopper])``` GPU timeThe rest of the notebook will give you the code for training, so you can turn on the GPU at this point - but first, make sure to save your jupyter notebook. Once the GPU is turned on, it will load whatever your last notebook checkpoint is. While we suggest reading through the code below to make sure you understand it, you can otherwise go ahead and select Cell > Run All (or Kernel > Restart & Run All if already using GPU) to run through all cells in the notebook. ###Code from sklearn.utils import shuffle from sklearn.preprocessing import LabelBinarizer from keras.datasets import cifar10 (X_train, y_train), (X_val, y_val) = cifar10.load_data() # One-hot encode the labels label_binarizer = LabelBinarizer() y_one_hot_train = label_binarizer.fit_transform(y_train) y_one_hot_val = label_binarizer.fit_transform(y_val) # Shuffle the training & test data X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train) X_val, y_one_hot_val = shuffle(X_val, y_one_hot_val) # We are only going to use the first 10,000 images for speed reasons # And only the first 2,000 images from the test set X_train = X_train[:10000] y_one_hot_train = y_one_hot_train[:10000] X_val = X_val[:2000] y_one_hot_val = y_one_hot_val[:2000] ###Output _____no_output_____ ###Markdown You can check out Keras's [ImageDataGenerator documentation](https://faroit.github.io/keras-docs/2.0.9/preprocessing/image/) for more information on the below - you can also add additional image augmentation through this function, although we are skipping that step here so you can potentially explore it in the upcoming project. ###Code # Use a generator to pre-process our images for ImageNet from keras.preprocessing.image import ImageDataGenerator from keras.applications.inception_v3 import preprocess_input if preprocess_flag == True: datagen = ImageDataGenerator(preprocessing_function=preprocess_input) val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) else: datagen = ImageDataGenerator() val_datagen = ImageDataGenerator() # Train the model batch_size = 32 epochs = 5 # Note: we aren't using callbacks here since we only are using 5 epochs to conserve GPU time model.fit_generator(datagen.flow(X_train, y_one_hot_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, verbose=1, validation_data=val_datagen.flow(X_val, y_one_hot_val, batch_size=batch_size), validation_steps=len(X_val)/batch_size) ###Output Epoch 1/5 313/312 [==============================] - 52s 166ms/step - loss: 2.2473 - acc: 0.1694 - val_loss: 2.4345 - val_acc: 0.1305 Epoch 2/5 313/312 [==============================] - 48s 154ms/step - loss: 2.0575 - acc: 0.2298 - val_loss: 1.9968 - val_acc: 0.2485 Epoch 3/5 313/312 [==============================] - 48s 154ms/step - loss: 2.0088 - acc: 0.2480 - val_loss: 1.9359 - val_acc: 0.2715 Epoch 4/5 313/312 [==============================] - 48s 154ms/step - loss: 1.9933 - acc: 0.2555 - val_loss: 1.9452 - val_acc: 0.2790 Epoch 5/5 313/312 [==============================] - 48s 154ms/step - loss: 1.9740 - acc: 0.2688 - val_loss: 1.8939 - val_acc: 0.2880 ###Markdown Lab: Transfer LearningWelcome to the lab on Transfer Learning! Here, you'll get a chance to try out training a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers. GPU usageIn our previous examples in this lesson, we've avoided using GPU, but this time around you'll have the option to enable it. You do not need it on to begin with, but make sure anytime you switch from non-GPU to GPU, or vice versa, that you save your notebook! If not, you'll likely be reverted to the previous checkpoint. We also suggest only using the GPU when performing the (mostly minor) training below - you'll want to conserve GPU hours for your Behavioral Cloning project coming up next! ###Code # Set a couple flags for training - you can ignore these for now freeze_flag = True # `True` to freeze layers, `False` for full training weights_flag = 'imagenet' # 'imagenet' or None preprocess_flag = True # Should be true for ImageNet pre-trained typically # Loads in InceptionV3 from keras.applications.inception_v3 import InceptionV3 # We can use smaller than the default 299x299x3 input for InceptionV3 # which will speed up training. Keras v2.0.9 supports down to 139x139x3 input_size = 139 # Using Inception with ImageNet pre-trained weights inception = InceptionV3(weights=weights_flag, include_top=False, input_shape=(input_size,input_size,3)) ###Output Using TensorFlow backend. ###Markdown We'll use Inception V3 for this lab, although you can use the same techniques with any of the models in [Keras Applications](https://keras.io/applications/). Do note that certain models are only available in certain versions of Keras; this workspace uses Keras v2.0.9, for which you can see the available models [here](https://faroit.github.io/keras-docs/2.0.9/applications/).In the above, we've set Inception to use an `input_shape` of 139x139x3 instead of the default 299x299x3. This will help us to speed up our training a bit later (and we'll actually be upsampling from smaller images, so we aren't losing data here). In order to do so, we also must set `include_top` to `False`, which means the final fully-connected layer with 1,000 nodes for each ImageNet class is dropped, as well as a Global Average Pooling layer. Pre-trained with frozen weightsTo start, we'll see how an ImageNet pre-trained model with all weights frozen in the InceptionV3 model performs. We will also drop the end layer and append new layers onto it, although you could do this in different ways (not drop the end and add new layers, drop more layers than we will here, etc.).You can freeze layers by setting `layer.trainable` to False for a given `layer`. Within a `model`, you can get the list of layers with `model.layers`. ###Code if freeze_flag == True: ## TODO: Iterate through the layers of the Inception model ## loaded above and set all of them to have trainable = False layers = inception.layers for i in range(len(layers)): layers[i].trainable = False print('All {} layer weights frozen'.format(len(layers))) ###Output All 311 layer weights frozen ###Markdown Dropping layersYou can drop layers from a model with `model.layers.pop()`. Before you do this, you should check out what the actual layers of the model are with Keras's `.summary()` function. ###Code ## TODO: Use the model summary function to see all layers in the ## loaded Inception model print(inception.summary()) ###Output __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 139, 139, 3) 0 __________________________________________________________________________________________________ conv2d_1 (Conv2D) (None, 69, 69, 32) 864 input_1[0][0] __________________________________________________________________________________________________ batch_normalization_1 (BatchNor (None, 69, 69, 32) 96 conv2d_1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 69, 69, 32) 0 batch_normalization_1[0][0] __________________________________________________________________________________________________ conv2d_2 (Conv2D) (None, 67, 67, 32) 9216 activation_1[0][0] __________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 67, 67, 32) 96 conv2d_2[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 67, 67, 32) 0 batch_normalization_2[0][0] __________________________________________________________________________________________________ conv2d_3 (Conv2D) (None, 67, 67, 64) 18432 activation_2[0][0] __________________________________________________________________________________________________ batch_normalization_3 (BatchNor (None, 67, 67, 64) 192 conv2d_3[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 67, 67, 64) 0 batch_normalization_3[0][0] __________________________________________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 33, 33, 64) 0 activation_3[0][0] __________________________________________________________________________________________________ conv2d_4 (Conv2D) (None, 33, 33, 80) 5120 max_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_4 (BatchNor (None, 33, 33, 80) 240 conv2d_4[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 33, 33, 80) 0 batch_normalization_4[0][0] __________________________________________________________________________________________________ conv2d_5 (Conv2D) (None, 31, 31, 192) 138240 activation_4[0][0] __________________________________________________________________________________________________ batch_normalization_5 (BatchNor (None, 31, 31, 192) 576 conv2d_5[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 31, 31, 192) 0 batch_normalization_5[0][0] __________________________________________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 15, 15, 192) 0 activation_5[0][0] __________________________________________________________________________________________________ conv2d_9 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_9 (BatchNor (None, 15, 15, 64) 192 conv2d_9[0][0] __________________________________________________________________________________________________ activation_9 (Activation) (None, 15, 15, 64) 0 batch_normalization_9[0][0] __________________________________________________________________________________________________ conv2d_7 (Conv2D) (None, 15, 15, 48) 9216 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_10 (Conv2D) (None, 15, 15, 96) 55296 activation_9[0][0] __________________________________________________________________________________________________ batch_normalization_7 (BatchNor (None, 15, 15, 48) 144 conv2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_10 (BatchNo (None, 15, 15, 96) 288 conv2d_10[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 15, 15, 48) 0 batch_normalization_7[0][0] __________________________________________________________________________________________________ activation_10 (Activation) (None, 15, 15, 96) 0 batch_normalization_10[0][0] __________________________________________________________________________________________________ average_pooling2d_1 (AveragePoo (None, 15, 15, 192) 0 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_6 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_8 (Conv2D) (None, 15, 15, 64) 76800 activation_7[0][0] __________________________________________________________________________________________________ conv2d_11 (Conv2D) (None, 15, 15, 96) 82944 activation_10[0][0] __________________________________________________________________________________________________ conv2d_12 (Conv2D) (None, 15, 15, 32) 6144 average_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_6 (BatchNor (None, 15, 15, 64) 192 conv2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_8 (BatchNor (None, 15, 15, 64) 192 conv2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_11 (BatchNo (None, 15, 15, 96) 288 conv2d_11[0][0] __________________________________________________________________________________________________ batch_normalization_12 (BatchNo (None, 15, 15, 32) 96 conv2d_12[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 15, 15, 64) 0 batch_normalization_6[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 15, 15, 64) 0 batch_normalization_8[0][0] __________________________________________________________________________________________________ activation_11 (Activation) (None, 15, 15, 96) 0 batch_normalization_11[0][0] __________________________________________________________________________________________________ activation_12 (Activation) (None, 15, 15, 32) 0 batch_normalization_12[0][0] __________________________________________________________________________________________________ mixed0 (Concatenate) (None, 15, 15, 256) 0 activation_6[0][0] activation_8[0][0] activation_11[0][0] activation_12[0][0] __________________________________________________________________________________________________ conv2d_16 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ batch_normalization_16 (BatchNo (None, 15, 15, 64) 192 conv2d_16[0][0] __________________________________________________________________________________________________ activation_16 (Activation) (None, 15, 15, 64) 0 batch_normalization_16[0][0] __________________________________________________________________________________________________ conv2d_14 (Conv2D) (None, 15, 15, 48) 12288 mixed0[0][0] __________________________________________________________________________________________________ conv2d_17 (Conv2D) (None, 15, 15, 96) 55296 activation_16[0][0] __________________________________________________________________________________________________ batch_normalization_14 (BatchNo (None, 15, 15, 48) 144 conv2d_14[0][0] __________________________________________________________________________________________________ batch_normalization_17 (BatchNo (None, 15, 15, 96) 288 conv2d_17[0][0] __________________________________________________________________________________________________ activation_14 (Activation) (None, 15, 15, 48) 0 batch_normalization_14[0][0] __________________________________________________________________________________________________ activation_17 (Activation) (None, 15, 15, 96) 0 batch_normalization_17[0][0] __________________________________________________________________________________________________ average_pooling2d_2 (AveragePoo (None, 15, 15, 256) 0 mixed0[0][0] __________________________________________________________________________________________________ conv2d_13 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ conv2d_15 (Conv2D) (None, 15, 15, 64) 76800 activation_14[0][0] __________________________________________________________________________________________________ conv2d_18 (Conv2D) (None, 15, 15, 96) 82944 activation_17[0][0] __________________________________________________________________________________________________ conv2d_19 (Conv2D) (None, 15, 15, 64) 16384 average_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_13 (BatchNo (None, 15, 15, 64) 192 conv2d_13[0][0] __________________________________________________________________________________________________ batch_normalization_15 (BatchNo (None, 15, 15, 64) 192 conv2d_15[0][0] __________________________________________________________________________________________________ batch_normalization_18 (BatchNo (None, 15, 15, 96) 288 conv2d_18[0][0] __________________________________________________________________________________________________ batch_normalization_19 (BatchNo (None, 15, 15, 64) 192 conv2d_19[0][0] __________________________________________________________________________________________________ activation_13 (Activation) (None, 15, 15, 64) 0 batch_normalization_13[0][0] __________________________________________________________________________________________________ activation_15 (Activation) (None, 15, 15, 64) 0 batch_normalization_15[0][0] __________________________________________________________________________________________________ activation_18 (Activation) (None, 15, 15, 96) 0 batch_normalization_18[0][0] __________________________________________________________________________________________________ activation_19 (Activation) (None, 15, 15, 64) 0 batch_normalization_19[0][0] __________________________________________________________________________________________________ mixed1 (Concatenate) (None, 15, 15, 288) 0 activation_13[0][0] activation_15[0][0] activation_18[0][0] activation_19[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ batch_normalization_23 (BatchNo (None, 15, 15, 64) 192 conv2d_23[0][0] __________________________________________________________________________________________________ activation_23 (Activation) (None, 15, 15, 64) 0 batch_normalization_23[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 15, 15, 48) 13824 mixed1[0][0] __________________________________________________________________________________________________ conv2d_24 (Conv2D) (None, 15, 15, 96) 55296 activation_23[0][0] __________________________________________________________________________________________________ batch_normalization_21 (BatchNo (None, 15, 15, 48) 144 conv2d_21[0][0] __________________________________________________________________________________________________ batch_normalization_24 (BatchNo (None, 15, 15, 96) 288 conv2d_24[0][0] __________________________________________________________________________________________________ activation_21 (Activation) (None, 15, 15, 48) 0 batch_normalization_21[0][0] __________________________________________________________________________________________________ activation_24 (Activation) (None, 15, 15, 96) 0 batch_normalization_24[0][0] __________________________________________________________________________________________________ average_pooling2d_3 (AveragePoo (None, 15, 15, 288) 0 mixed1[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 15, 15, 64) 76800 activation_21[0][0] __________________________________________________________________________________________________ conv2d_25 (Conv2D) (None, 15, 15, 96) 82944 activation_24[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 15, 15, 64) 18432 average_pooling2d_3[0][0] __________________________________________________________________________________________________ batch_normalization_20 (BatchNo (None, 15, 15, 64) 192 conv2d_20[0][0] __________________________________________________________________________________________________ batch_normalization_22 (BatchNo (None, 15, 15, 64) 192 conv2d_22[0][0] __________________________________________________________________________________________________ batch_normalization_25 (BatchNo (None, 15, 15, 96) 288 conv2d_25[0][0] __________________________________________________________________________________________________ batch_normalization_26 (BatchNo (None, 15, 15, 64) 192 conv2d_26[0][0] __________________________________________________________________________________________________ activation_20 (Activation) (None, 15, 15, 64) 0 batch_normalization_20[0][0] __________________________________________________________________________________________________ activation_22 (Activation) (None, 15, 15, 64) 0 batch_normalization_22[0][0] __________________________________________________________________________________________________ activation_25 (Activation) (None, 15, 15, 96) 0 batch_normalization_25[0][0] __________________________________________________________________________________________________ activation_26 (Activation) (None, 15, 15, 64) 0 batch_normalization_26[0][0] __________________________________________________________________________________________________ mixed2 (Concatenate) (None, 15, 15, 288) 0 activation_20[0][0] activation_22[0][0] activation_25[0][0] activation_26[0][0] __________________________________________________________________________________________________ conv2d_28 (Conv2D) (None, 15, 15, 64) 18432 mixed2[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 15, 15, 64) 192 conv2d_28[0][0] __________________________________________________________________________________________________ activation_28 (Activation) (None, 15, 15, 64) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ conv2d_29 (Conv2D) (None, 15, 15, 96) 55296 activation_28[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 15, 15, 96) 288 conv2d_29[0][0] __________________________________________________________________________________________________ activation_29 (Activation) (None, 15, 15, 96) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ conv2d_27 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0] __________________________________________________________________________________________________ conv2d_30 (Conv2D) (None, 7, 7, 96) 82944 activation_29[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 7, 7, 384) 1152 conv2d_27[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 7, 7, 96) 288 conv2d_30[0][0] __________________________________________________________________________________________________ activation_27 (Activation) (None, 7, 7, 384) 0 batch_normalization_27[0][0] __________________________________________________________________________________________________ activation_30 (Activation) (None, 7, 7, 96) 0 batch_normalization_30[0][0] __________________________________________________________________________________________________ max_pooling2d_3 (MaxPooling2D) (None, 7, 7, 288) 0 mixed2[0][0] __________________________________________________________________________________________________ mixed3 (Concatenate) (None, 7, 7, 768) 0 activation_27[0][0] activation_30[0][0] max_pooling2d_3[0][0] __________________________________________________________________________________________________ conv2d_35 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0] __________________________________________________________________________________________________ activation_35 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ conv2d_36 (Conv2D) (None, 7, 7, 128) 114688 activation_35[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0] __________________________________________________________________________________________________ activation_36 (Activation) (None, 7, 7, 128) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ conv2d_32 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ conv2d_37 (Conv2D) (None, 7, 7, 128) 114688 activation_36[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_32[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_37[0][0] __________________________________________________________________________________________________ activation_32 (Activation) (None, 7, 7, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ activation_37 (Activation) (None, 7, 7, 128) 0 batch_normalization_37[0][0] __________________________________________________________________________________________________ conv2d_33 (Conv2D) (None, 7, 7, 128) 114688 activation_32[0][0] __________________________________________________________________________________________________ conv2d_38 (Conv2D) (None, 7, 7, 128) 114688 activation_37[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 7, 7, 128) 384 conv2d_33[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 7, 7, 128) 384 conv2d_38[0][0] __________________________________________________________________________________________________ activation_33 (Activation) (None, 7, 7, 128) 0 batch_normalization_33[0][0] __________________________________________________________________________________________________ activation_38 (Activation) (None, 7, 7, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ average_pooling2d_4 (AveragePoo (None, 7, 7, 768) 0 mixed3[0][0] __________________________________________________________________________________________________ conv2d_31 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0] __________________________________________________________________________________________________ conv2d_34 (Conv2D) (None, 7, 7, 192) 172032 activation_33[0][0] __________________________________________________________________________________________________ conv2d_39 (Conv2D) (None, 7, 7, 192) 172032 activation_38[0][0] __________________________________________________________________________________________________ conv2d_40 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 7, 7, 192) 576 conv2d_31[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 7, 7, 192) 576 conv2d_34[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_39[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_40[0][0] __________________________________________________________________________________________________ activation_31 (Activation) (None, 7, 7, 192) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ activation_34 (Activation) (None, 7, 7, 192) 0 batch_normalization_34[0][0] __________________________________________________________________________________________________ activation_39 (Activation) (None, 7, 7, 192) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ activation_40 (Activation) (None, 7, 7, 192) 0 batch_normalization_40[0][0] __________________________________________________________________________________________________ mixed4 (Concatenate) (None, 7, 7, 768) 0 activation_31[0][0] activation_34[0][0] activation_39[0][0] activation_40[0][0] __________________________________________________________________________________________________ conv2d_45 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0] __________________________________________________________________________________________________ activation_45 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0] __________________________________________________________________________________________________ conv2d_46 (Conv2D) (None, 7, 7, 160) 179200 activation_45[0][0] __________________________________________________________________________________________________ batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0] __________________________________________________________________________________________________ activation_46 (Activation) (None, 7, 7, 160) 0 batch_normalization_46[0][0] __________________________________________________________________________________________________ conv2d_42 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ conv2d_47 (Conv2D) (None, 7, 7, 160) 179200 activation_46[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_42[0][0] __________________________________________________________________________________________________ batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_47[0][0] __________________________________________________________________________________________________ activation_42 (Activation) (None, 7, 7, 160) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ activation_47 (Activation) (None, 7, 7, 160) 0 batch_normalization_47[0][0] __________________________________________________________________________________________________ conv2d_43 (Conv2D) (None, 7, 7, 160) 179200 activation_42[0][0] __________________________________________________________________________________________________ conv2d_48 (Conv2D) (None, 7, 7, 160) 179200 activation_47[0][0] __________________________________________________________________________________________________ batch_normalization_43 (BatchNo (None, 7, 7, 160) 480 conv2d_43[0][0] __________________________________________________________________________________________________ batch_normalization_48 (BatchNo (None, 7, 7, 160) 480 conv2d_48[0][0] __________________________________________________________________________________________________ activation_43 (Activation) (None, 7, 7, 160) 0 batch_normalization_43[0][0] __________________________________________________________________________________________________ activation_48 (Activation) (None, 7, 7, 160) 0 batch_normalization_48[0][0] __________________________________________________________________________________________________ average_pooling2d_5 (AveragePoo (None, 7, 7, 768) 0 mixed4[0][0] __________________________________________________________________________________________________ conv2d_41 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0] __________________________________________________________________________________________________ conv2d_44 (Conv2D) (None, 7, 7, 192) 215040 activation_43[0][0] __________________________________________________________________________________________________ conv2d_49 (Conv2D) (None, 7, 7, 192) 215040 activation_48[0][0] __________________________________________________________________________________________________ conv2d_50 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 7, 7, 192) 576 conv2d_41[0][0] __________________________________________________________________________________________________ batch_normalization_44 (BatchNo (None, 7, 7, 192) 576 conv2d_44[0][0] __________________________________________________________________________________________________ batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_49[0][0] __________________________________________________________________________________________________ batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_50[0][0] __________________________________________________________________________________________________ activation_41 (Activation) (None, 7, 7, 192) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ activation_44 (Activation) (None, 7, 7, 192) 0 batch_normalization_44[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 7, 7, 192) 0 batch_normalization_49[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 7, 7, 192) 0 batch_normalization_50[0][0] __________________________________________________________________________________________________ mixed5 (Concatenate) (None, 7, 7, 768) 0 activation_41[0][0] activation_44[0][0] activation_49[0][0] activation_50[0][0] __________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 7, 7, 160) 179200 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 7, 7, 160) 0 batch_normalization_56[0][0] __________________________________________________________________________________________________ conv2d_52 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 7, 7, 160) 179200 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_52[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 7, 7, 160) 0 batch_normalization_52[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 7, 7, 160) 0 batch_normalization_57[0][0] __________________________________________________________________________________________________ conv2d_53 (Conv2D) (None, 7, 7, 160) 179200 activation_52[0][0] __________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 7, 7, 160) 179200 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 7, 7, 160) 480 conv2d_53[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 7, 7, 160) 480 conv2d_58[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 7, 7, 160) 0 batch_normalization_53[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 7, 7, 160) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ average_pooling2d_6 (AveragePoo (None, 7, 7, 768) 0 mixed5[0][0] __________________________________________________________________________________________________ conv2d_51 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0] __________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 7, 7, 192) 215040 activation_53[0][0] __________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 7, 7, 192) 215040 activation_58[0][0] __________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_51 (BatchNo (None, 7, 7, 192) 576 conv2d_51[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 7, 7, 192) 576 conv2d_54[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_59[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_60[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 7, 7, 192) 0 batch_normalization_51[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 7, 7, 192) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 7, 7, 192) 0 batch_normalization_59[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 7, 7, 192) 0 batch_normalization_60[0][0] __________________________________________________________________________________________________ mixed6 (Concatenate) (None, 7, 7, 768) 0 activation_51[0][0] activation_54[0][0] activation_59[0][0] activation_60[0][0] __________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 7, 7, 192) 258048 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 7, 7, 192) 0 batch_normalization_66[0][0] __________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_66[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 7, 7, 192) 0 batch_normalization_62[0][0] __________________________________________________________________________________________________ activation_67 (Activation) (None, 7, 7, 192) 0 batch_normalization_67[0][0] __________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 7, 7, 192) 258048 activation_62[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 7, 7, 192) 258048 activation_67[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_63[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 7, 7, 192) 0 batch_normalization_63[0][0] __________________________________________________________________________________________________ activation_68 (Activation) (None, 7, 7, 192) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ average_pooling2d_7 (AveragePoo (None, 7, 7, 768) 0 mixed6[0][0] __________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 7, 7, 192) 258048 activation_63[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 7, 7, 192) 258048 activation_68[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_61[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_70[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 7, 7, 192) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ activation_69 (Activation) (None, 7, 7, 192) 0 batch_normalization_69[0][0] __________________________________________________________________________________________________ activation_70 (Activation) (None, 7, 7, 192) 0 batch_normalization_70[0][0] __________________________________________________________________________________________________ mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_61[0][0] activation_64[0][0] activation_69[0][0] activation_70[0][0] __________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_73[0][0] __________________________________________________________________________________________________ activation_73 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0] __________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 7, 7, 192) 258048 activation_73[0][0] __________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0] __________________________________________________________________________________________________ activation_74 (Activation) (None, 7, 7, 192) 0 batch_normalization_74[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 7, 7, 192) 258048 activation_74[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 7, 7, 192) 576 conv2d_71[0][0] __________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 7, 7, 192) 576 conv2d_75[0][0] __________________________________________________________________________________________________ activation_71 (Activation) (None, 7, 7, 192) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ activation_75 (Activation) (None, 7, 7, 192) 0 batch_normalization_75[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 3, 3, 320) 552960 activation_71[0][0] __________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 3, 3, 192) 331776 activation_75[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 3, 3, 320) 960 conv2d_72[0][0] __________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 3, 3, 192) 576 conv2d_76[0][0] __________________________________________________________________________________________________ activation_72 (Activation) (None, 3, 3, 320) 0 batch_normalization_72[0][0] __________________________________________________________________________________________________ activation_76 (Activation) (None, 3, 3, 192) 0 batch_normalization_76[0][0] __________________________________________________________________________________________________ max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 768) 0 mixed7[0][0] __________________________________________________________________________________________________ mixed8 (Concatenate) (None, 3, 3, 1280) 0 activation_72[0][0] activation_76[0][0] max_pooling2d_4[0][0] __________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_81 (BatchNo (None, 3, 3, 448) 1344 conv2d_81[0][0] __________________________________________________________________________________________________ activation_81 (Activation) (None, 3, 3, 448) 0 batch_normalization_81[0][0] __________________________________________________________________________________________________ conv2d_78 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0] __________________________________________________________________________________________________ conv2d_82 (Conv2D) (None, 3, 3, 384) 1548288 activation_81[0][0] __________________________________________________________________________________________________ batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_78[0][0] __________________________________________________________________________________________________ batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0] __________________________________________________________________________________________________ activation_78 (Activation) (None, 3, 3, 384) 0 batch_normalization_78[0][0] __________________________________________________________________________________________________ activation_82 (Activation) (None, 3, 3, 384) 0 batch_normalization_82[0][0] __________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ conv2d_84 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ average_pooling2d_8 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0] __________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_79[0][0] __________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 3, 3, 384) 1152 conv2d_80[0][0] __________________________________________________________________________________________________ batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0] __________________________________________________________________________________________________ batch_normalization_84 (BatchNo (None, 3, 3, 384) 1152 conv2d_84[0][0] __________________________________________________________________________________________________ conv2d_85 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 3, 3, 320) 960 conv2d_77[0][0] __________________________________________________________________________________________________ activation_79 (Activation) (None, 3, 3, 384) 0 batch_normalization_79[0][0] __________________________________________________________________________________________________ activation_80 (Activation) (None, 3, 3, 384) 0 batch_normalization_80[0][0] __________________________________________________________________________________________________ activation_83 (Activation) (None, 3, 3, 384) 0 batch_normalization_83[0][0] __________________________________________________________________________________________________ activation_84 (Activation) (None, 3, 3, 384) 0 batch_normalization_84[0][0] __________________________________________________________________________________________________ batch_normalization_85 (BatchNo (None, 3, 3, 192) 576 conv2d_85[0][0] __________________________________________________________________________________________________ activation_77 (Activation) (None, 3, 3, 320) 0 batch_normalization_77[0][0] __________________________________________________________________________________________________ mixed9_0 (Concatenate) (None, 3, 3, 768) 0 activation_79[0][0] activation_80[0][0] __________________________________________________________________________________________________ concatenate_1 (Concatenate) (None, 3, 3, 768) 0 activation_83[0][0] activation_84[0][0] __________________________________________________________________________________________________ activation_85 (Activation) (None, 3, 3, 192) 0 batch_normalization_85[0][0] __________________________________________________________________________________________________ mixed9 (Concatenate) (None, 3, 3, 2048) 0 activation_77[0][0] mixed9_0[0][0] concatenate_1[0][0] activation_85[0][0] __________________________________________________________________________________________________ conv2d_90 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_90 (BatchNo (None, 3, 3, 448) 1344 conv2d_90[0][0] __________________________________________________________________________________________________ activation_90 (Activation) (None, 3, 3, 448) 0 batch_normalization_90[0][0] __________________________________________________________________________________________________ conv2d_87 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0] __________________________________________________________________________________________________ conv2d_91 (Conv2D) (None, 3, 3, 384) 1548288 activation_90[0][0] __________________________________________________________________________________________________ batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0] __________________________________________________________________________________________________ batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0] __________________________________________________________________________________________________ activation_87 (Activation) (None, 3, 3, 384) 0 batch_normalization_87[0][0] __________________________________________________________________________________________________ activation_91 (Activation) (None, 3, 3, 384) 0 batch_normalization_91[0][0] __________________________________________________________________________________________________ conv2d_88 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_89 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ conv2d_93 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ average_pooling2d_9 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0] __________________________________________________________________________________________________ conv2d_86 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_88[0][0] __________________________________________________________________________________________________ batch_normalization_89 (BatchNo (None, 3, 3, 384) 1152 conv2d_89[0][0] __________________________________________________________________________________________________ batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0] __________________________________________________________________________________________________ batch_normalization_93 (BatchNo (None, 3, 3, 384) 1152 conv2d_93[0][0] __________________________________________________________________________________________________ conv2d_94 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_9[0][0] __________________________________________________________________________________________________ batch_normalization_86 (BatchNo (None, 3, 3, 320) 960 conv2d_86[0][0] __________________________________________________________________________________________________ activation_88 (Activation) (None, 3, 3, 384) 0 batch_normalization_88[0][0] __________________________________________________________________________________________________ activation_89 (Activation) (None, 3, 3, 384) 0 batch_normalization_89[0][0] __________________________________________________________________________________________________ activation_92 (Activation) (None, 3, 3, 384) 0 batch_normalization_92[0][0] __________________________________________________________________________________________________ activation_93 (Activation) (None, 3, 3, 384) 0 batch_normalization_93[0][0] __________________________________________________________________________________________________ batch_normalization_94 (BatchNo (None, 3, 3, 192) 576 conv2d_94[0][0] __________________________________________________________________________________________________ activation_86 (Activation) (None, 3, 3, 320) 0 batch_normalization_86[0][0] __________________________________________________________________________________________________ mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_88[0][0] activation_89[0][0] __________________________________________________________________________________________________ concatenate_2 (Concatenate) (None, 3, 3, 768) 0 activation_92[0][0] activation_93[0][0] __________________________________________________________________________________________________ activation_94 (Activation) (None, 3, 3, 192) 0 batch_normalization_94[0][0] __________________________________________________________________________________________________ mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_86[0][0] mixed9_1[0][0] concatenate_2[0][0] activation_94[0][0] ================================================================================================== Total params: 21,802,784 Trainable params: 0 Non-trainable params: 21,802,784 __________________________________________________________________________________________________ None ###Markdown In a normal Inception network, you would see from the model summary that the last two layers were a global average pooling layer, and a fully-connected "Dense" layer. However, since we set `include_top` to `False`, both of these get dropped. If you otherwise wanted to drop additional layers, you would use:```inception.layers.pop()```Note that `pop()` works from the end of the model backwards. It's important to note two things here:1. How many layers you drop is up to you, typically. We dropped the final two already by setting `include_top` to False in the original loading of the model, but you could instead just run `pop()` twice to achieve similar results. (Note: Keras requires us to set `include_top` to False in order to change the `input_shape`.) Additional layers could be dropped by additional calls to `pop()`.2. If you make a mistake with `pop()`, you'll want to reload the model. If you use it multiple times, the model will continue to drop more and more layers, so you may need to check `model.summary()` again to check your work. Adding new layersNow, you can start to add your own layers. While we've used Keras's `Sequential` model before for simplicity, we'll actually use the [Model API](https://keras.io/models/model/) this time. This functions a little differently, in that instead of using `model.add()`, you explicitly tell the model which previous layer to attach to the current layer. This is useful if you want to use more advanced concepts like [skip layers](https://en.wikipedia.org/wiki/Residual_neural_network), for instance (which were used heavily in ResNet).For example, if you had a previous layer named `inp`:```x = Dropout(0.2)(inp)```is how you would attach a new dropout layer `x`, with it's input coming from a layer with the variable name `inp`.We are going to use the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which consists of 60,000 32x32 images of 10 classes. We need to use Keras's `Input` function to do so, and then we want to re-size the images up to the `input_size` we specified earlier (139x139). ###Code from keras.layers import Input, Lambda import tensorflow as tf # Makes the input placeholder layer 32x32x3 for CIFAR-10 cifar_input = Input(shape=(32,32,3)) # Re-sizes the input with Kera's Lambda layer & attach to cifar_input resized_input = Lambda(lambda image: tf.image.resize_images( image, (input_size, input_size)))(cifar_input) # Feeds the re-sized input into Inception model # You will need to update the model name if you changed it earlier! inp = inception(resized_input) # Imports fully-connected "Dense" layers & Global Average Pooling from keras.layers import Dense, GlobalAveragePooling2D ## TODO: Setting `include_top` to False earlier also removed the ## GlobalAveragePooling2D layer, but we still want it. ## Add it here, and make sure to connect it to the end of Inception x = GlobalAveragePooling2D()(inp) ## TODO: Create two new fully-connected layers using the Model API ## format discussed above. The first layer should use `out` ## as its input, along with ReLU activation. You can choose ## how many nodes it has, although 512 or less is a good idea. ## The second layer should take this first layer as input, and ## be named "predictions", with Softmax activation and ## 10 nodes, as we'll be using the CIFAR10 dataset. x=Dense(512,activation='relu')(x) predictions=Dense(10,activation='softmax')(x) ###Output _____no_output_____ ###Markdown We're almost done with our new model! Now we just need to use the actual Model API to create the full model. ###Code # Imports the Model API from keras.models import Model # Creates the model, assuming your final layer is named "predictions" model = Model(inputs=cifar_input, outputs=predictions) # Compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) # Check the summary of this new model to confirm the architecture model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) (None, 32, 32, 3) 0 _________________________________________________________________ lambda_1 (Lambda) (None, 139, 139, 3) 0 _________________________________________________________________ inception_v3 (Model) (None, 3, 3, 2048) 21802784 _________________________________________________________________ global_average_pooling2d_1 ( (None, 2048) 0 _________________________________________________________________ dense_1 (Dense) (None, 512) 1049088 _________________________________________________________________ dense_2 (Dense) (None, 10) 5130 ================================================================= Total params: 22,857,002 Trainable params: 1,054,218 Non-trainable params: 21,802,784 _________________________________________________________________ ###Markdown Great job creating a new model architecture from Inception! Notice how this method of adding layers before InceptionV3 and appending to the end of it made InceptionV3 condense down into one line in the summary; if you use the Inception model's normal input (which you could gather from `inception.layers.input`), it would instead show all the layers like before.Most of the rest of the code in the notebook just goes toward loading our data, pre-processing it, and starting our training in Keras, although there's one other good point to make here - Keras callbacks. Keras CallbacksKeras [callbacks](https://keras.io/callbacks/) allow you to gather and store additional information during training, such as the best model, or even stop training early if the validation accuracy has stopped improving. These methods can help to avoid overfitting, or avoid other issues.There's two key callbacks to mention here, `ModelCheckpoint` and `EarlyStopping`. As the names may suggest, model checkpoint saves down the best model so far based on a given metric, while early stopping will end training before the specified number of epochs if the chosen metric no longer improves after a given amount of time.To set these callbacks, you could do the following:```checkpoint = ModelCheckpoint(filepath=save_path, monitor='val_loss', save_best_only=True)```This would save a model to a specified `save_path`, based on validation loss, and only save down the best models. If you set `save_best_only` to `False`, every single epoch will save down another version of the model.```stopper = EarlyStopping(monitor='val_acc', min_delta=0.0003, patience=5)```This will monitor validation accuracy, and if it has not decreased by more than 0.0003 from the previous best validation accuracy for 5 epochs, training will end early.You still need to actually feed these callbacks into `fit()` when you train the model (along with all other relevant data to feed into `fit`):```model.fit(callbacks=[checkpoint, stopper])``` GPU timeThe rest of the notebook will give you the code for training, so you can turn on the GPU at this point - but first, make sure to save your jupyter notebook. Once the GPU is turned on, it will load whatever your last notebook checkpoint is. While we suggest reading through the code below to make sure you understand it, you can otherwise go ahead and select Cell > Run All (or Kernel > Restart & Run All if already using GPU) to run through all cells in the notebook. ###Code from sklearn.utils import shuffle from sklearn.preprocessing import LabelBinarizer from keras.datasets import cifar10 (X_train, y_train), (X_val, y_val) = cifar10.load_data() # One-hot encode the labels label_binarizer = LabelBinarizer() y_one_hot_train = label_binarizer.fit_transform(y_train) y_one_hot_val = label_binarizer.fit_transform(y_val) # Shuffle the training & test data X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train) X_val, y_one_hot_val = shuffle(X_val, y_one_hot_val) # We are only going to use the first 10,000 images for speed reasons # And only the first 2,000 images from the test set X_train = X_train[:10000] y_one_hot_train = y_one_hot_train[:10000] X_val = X_val[:2000] y_one_hot_val = y_one_hot_val[:2000] ###Output Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170500096/170498071 [==============================] - 3s 0us/step ###Markdown You can check out Keras's [ImageDataGenerator documentation](https://faroit.github.io/keras-docs/2.0.9/preprocessing/image/) for more information on the below - you can also add additional image augmentation through this function, although we are skipping that step here so you can potentially explore it in the upcoming project. ###Code # Use a generator to pre-process our images for ImageNet from keras.preprocessing.image import ImageDataGenerator from keras.applications.inception_v3 import preprocess_input if preprocess_flag == True: datagen = ImageDataGenerator(preprocessing_function=preprocess_input) val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) else: datagen = ImageDataGenerator() val_datagen = ImageDataGenerator() # Train the model batch_size = 32 epochs = 5 # Note: we aren't using callbacks here since we only are using 5 epochs to conserve GPU time model.fit_generator(datagen.flow(X_train, y_one_hot_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, verbose=1, validation_data=val_datagen.flow(X_val, y_one_hot_val, batch_size=batch_size), validation_steps=len(X_val)/batch_size) ###Output Epoch 1/5 313/312 [==============================] - 51s 163ms/step - loss: 1.2886 - acc: 0.5687 - val_loss: 0.9030 - val_acc: 0.7180 Epoch 2/5 313/312 [==============================] - 44s 142ms/step - loss: 0.9635 - acc: 0.6691 - val_loss: 0.9498 - val_acc: 0.6900 Epoch 3/5 313/312 [==============================] - 44s 142ms/step - loss: 0.8921 - acc: 0.6943 - val_loss: 0.9054 - val_acc: 0.6975 Epoch 4/5 313/312 [==============================] - 44s 142ms/step - loss: 0.8179 - acc: 0.7214 - val_loss: 0.9029 - val_acc: 0.7145 Epoch 5/5 313/312 [==============================] - 44s 141ms/step - loss: 0.7912 - acc: 0.7230 - val_loss: 0.9993 - val_acc: 0.6965 ###Markdown As you may have noticed, CIFAR-10 is a fairly tough dataset. However, given that we are only training on a small subset of the data, only training for five epochs, and not using any image augmentation, the results are still fairly impressive!We achieved ~70% validation accuracy here, although your results may vary. [Optional] Test without frozen weights, or by training from scratch.Since the majority of the model was frozen above, training speed is pretty quick. You may also want to check out the training speed, as well as final accuracy, if you don't freeze the weights. Note that this can be fairly slow, so we're marking this as optional in order to conserve GPU time. If you do want to see the results from doing so, go back to the first code cell and set `freeze_flag` to `False`. If you want to completely train from scratch without ImageNet pre-trained weights, follow the previous step as well as setting `weights_flag` to `None`. Then, go to Kernel > Restart & Run All. ComparisonSo that you don't use up your GPU time, we've tried out these results ourselves as well.Training Mode \| Val Acc @ 1 epoch \| Val Acc @ 5 epoch \| Time per epoch---- \| :----: \| :----: \| ----:Frozen weights \| 65.5% \| 70.3% \| 50 secondsUnfrozen weights \| 50.6% \| 71.6% \| 142 secondsNo pre-trained weights \| 19.2% \| 39.2% \| 142 secondsFrom the above, we can see that the pre-trained model with frozen weights actually began converging the fastest (already at 65.5% after 1 epoch), while the model re-training from the pre-trained weights slightly edged it out after 5 epochs.However, this does not tell the whole story - the training accuracy was substantially higher, nearing 87% for the unfrozen weights model. It actually began overfit the data much more under this method. We would likely be able to counteract some of this issue by using data augmentation. On the flip side, the model using frozen weights could also have been improved by actually only freezing a portion of the weights; some of these are likely more specific to ImageNet classes as it gets later in the network, as opposed to the simpler features extracted early in the network. The Power of Transfer LearningComparing the last line to the other two really shows the power of transfer learning. After five epochs, a model without ImageNet pre-training had only achieved 39.2% accuracy, compared to over 70% for the other two. As such, pre-training the network has saved substantial time, especially given the additional training time needed when the weights are not frozen.There is also evidence found in various research that pre-training on ImageNet weights will result in a higher overall accuracy than completely training from scratch, even when using a substantially different dataset. ###Code # Read data import csv import cv2 import matplotlib.image as mpimg import matplotlib.pyplot as plt import tensorflow as tf import numpy as np from keras.models import Sequential from keras.layers import Input,Flatten,Dense,Lambda,Cropping2D,Convolution2D,Dropout,MaxPooling2D from keras.preprocessing.image import ImageDataGenerator from sklearn.utils import shuffle import sklearn # Setting up the model model = Sequential() model.add(Lambda(lambda x: x/255.0 - 0.5,input_shape=(160,320,3))) model.add(Cropping2D(cropping=((70,20), (0,0)))) model.add(Convolution2D(24,(5,5),subsample=(2,2),activation='relu')) model.add(Convolution2D(36,(5,5),subsample=(2,2),activation='relu')) model.add(Convolution2D(48,(5,5),subsample=(2,2),activation='relu')) model.add(Convolution2D(64,(3,3),activation='relu')) model.add(Dropout(0.75)) model.add(Convolution2D(64,(3,3),activation='relu')) model.add(Flatten()) model.add(Dense(100)) model.add(Dense(50)) model.add(Dense(10)) model.add(Dense(1)) print(model.summary()) img = plt.imread('data/IMG/left_2016_12_01_13_30_48_287.jpg') plt.imshow(img) flipped = np.fliplr(img) plt.imsave('examples/left_camera.jpg',img) plt.imsave('examples/left_camera_flipped.jpg',flipped) ###Output _____no_output_____ ###Markdown Lab: Transfer LearningWelcome to the lab on Transfer Learning! Here, you'll get a chance to try out training a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers. GPU usageIn our previous examples in this lesson, we've avoided using GPU, but this time around you'll have the option to enable it. You do not need it on to begin with, but make sure anytime you switch from non-GPU to GPU, or vice versa, that you save your notebook! If not, you'll likely be reverted to the previous checkpoint. We also suggest only using the GPU when performing the (mostly minor) training below - you'll want to conserve GPU hours for your Behavioral Cloning project coming up next! ###Code # Set a couple flags for training - you can ignore these for now freeze_flag = True # `True` to freeze layers, `False` for full training weights_flag = 'imagenet' # 'imagenet' or None preprocess_flag = True # Should be true for ImageNet pre-trained typically # Loads in InceptionV3 from keras.applications.inception_v3 import InceptionV3 # We can use smaller than the default 299x299x3 input for InceptionV3 # which will speed up training. Keras v2.0.9 supports down to 139x139x3 input_size = 139 # Using Inception with ImageNet pre-trained weights inception = InceptionV3(weights=weights_flag, include_top=False, input_shape=(input_size,input_size,3)) ###Output Using TensorFlow backend. ###Markdown We'll use Inception V3 for this lab, although you can use the same techniques with any of the models in [Keras Applications](https://keras.io/applications/). Do note that certain models are only available in certain versions of Keras; this workspace uses Keras v2.0.9, for which you can see the available models [here](https://faroit.github.io/keras-docs/2.0.9/applications/).In the above, we've set Inception to use an `input_shape` of 139x139x3 instead of the default 299x299x3. This will help us to speed up our training a bit later (and we'll actually be upsampling from smaller images, so we aren't losing data here). In order to do so, we also must set `include_top` to `False`, which means the final fully-connected layer with 1,000 nodes for each ImageNet class is dropped, as well as a Global Average Pooling layer. Pre-trained with frozen weightsTo start, we'll see how an ImageNet pre-trained model with all weights frozen in the InceptionV3 model performs. We will also drop the end layer and append new layers onto it, although you could do this in different ways (not drop the end and add new layers, drop more layers than we will here, etc.).You can freeze layers by setting `layer.trainable` to False for a given `layer`. Within a `model`, you can get the list of layers with `model.layers`. ###Code if freeze_flag == True: ## TODO: Iterate through the layers of the Inception model ## loaded above and set all of them to have trainable = False for layer in inception.layers: layer.trainable = False ###Output _____no_output_____ ###Markdown Dropping layersYou can drop layers from a model with `model.layers.pop()`. Before you do this, you should check out what the actual layers of the model are with Keras's `.summary()` function. ###Code ## TODO: Use the model summary function to see all layers in the ## loaded Inception model inception.summary() ###Output __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 139, 139, 3) 0 __________________________________________________________________________________________________ conv2d_1 (Conv2D) (None, 69, 69, 32) 864 input_1[0][0] __________________________________________________________________________________________________ batch_normalization_1 (BatchNor (None, 69, 69, 32) 96 conv2d_1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 69, 69, 32) 0 batch_normalization_1[0][0] __________________________________________________________________________________________________ conv2d_2 (Conv2D) (None, 67, 67, 32) 9216 activation_1[0][0] __________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 67, 67, 32) 96 conv2d_2[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 67, 67, 32) 0 batch_normalization_2[0][0] __________________________________________________________________________________________________ conv2d_3 (Conv2D) (None, 67, 67, 64) 18432 activation_2[0][0] __________________________________________________________________________________________________ batch_normalization_3 (BatchNor (None, 67, 67, 64) 192 conv2d_3[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 67, 67, 64) 0 batch_normalization_3[0][0] __________________________________________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 33, 33, 64) 0 activation_3[0][0] __________________________________________________________________________________________________ conv2d_4 (Conv2D) (None, 33, 33, 80) 5120 max_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_4 (BatchNor (None, 33, 33, 80) 240 conv2d_4[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 33, 33, 80) 0 batch_normalization_4[0][0] __________________________________________________________________________________________________ conv2d_5 (Conv2D) (None, 31, 31, 192) 138240 activation_4[0][0] __________________________________________________________________________________________________ batch_normalization_5 (BatchNor (None, 31, 31, 192) 576 conv2d_5[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 31, 31, 192) 0 batch_normalization_5[0][0] __________________________________________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 15, 15, 192) 0 activation_5[0][0] __________________________________________________________________________________________________ conv2d_9 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_9 (BatchNor (None, 15, 15, 64) 192 conv2d_9[0][0] __________________________________________________________________________________________________ activation_9 (Activation) (None, 15, 15, 64) 0 batch_normalization_9[0][0] __________________________________________________________________________________________________ conv2d_7 (Conv2D) (None, 15, 15, 48) 9216 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_10 (Conv2D) (None, 15, 15, 96) 55296 activation_9[0][0] __________________________________________________________________________________________________ batch_normalization_7 (BatchNor (None, 15, 15, 48) 144 conv2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_10 (BatchNo (None, 15, 15, 96) 288 conv2d_10[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 15, 15, 48) 0 batch_normalization_7[0][0] __________________________________________________________________________________________________ activation_10 (Activation) (None, 15, 15, 96) 0 batch_normalization_10[0][0] __________________________________________________________________________________________________ average_pooling2d_1 (AveragePoo (None, 15, 15, 192) 0 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_6 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_8 (Conv2D) (None, 15, 15, 64) 76800 activation_7[0][0] __________________________________________________________________________________________________ conv2d_11 (Conv2D) (None, 15, 15, 96) 82944 activation_10[0][0] __________________________________________________________________________________________________ conv2d_12 (Conv2D) (None, 15, 15, 32) 6144 average_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_6 (BatchNor (None, 15, 15, 64) 192 conv2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_8 (BatchNor (None, 15, 15, 64) 192 conv2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_11 (BatchNo (None, 15, 15, 96) 288 conv2d_11[0][0] __________________________________________________________________________________________________ batch_normalization_12 (BatchNo (None, 15, 15, 32) 96 conv2d_12[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 15, 15, 64) 0 batch_normalization_6[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 15, 15, 64) 0 batch_normalization_8[0][0] __________________________________________________________________________________________________ activation_11 (Activation) (None, 15, 15, 96) 0 batch_normalization_11[0][0] __________________________________________________________________________________________________ activation_12 (Activation) (None, 15, 15, 32) 0 batch_normalization_12[0][0] __________________________________________________________________________________________________ mixed0 (Concatenate) (None, 15, 15, 256) 0 activation_6[0][0] activation_8[0][0] activation_11[0][0] activation_12[0][0] __________________________________________________________________________________________________ conv2d_16 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ batch_normalization_16 (BatchNo (None, 15, 15, 64) 192 conv2d_16[0][0] __________________________________________________________________________________________________ activation_16 (Activation) (None, 15, 15, 64) 0 batch_normalization_16[0][0] __________________________________________________________________________________________________ conv2d_14 (Conv2D) (None, 15, 15, 48) 12288 mixed0[0][0] __________________________________________________________________________________________________ conv2d_17 (Conv2D) (None, 15, 15, 96) 55296 activation_16[0][0] __________________________________________________________________________________________________ batch_normalization_14 (BatchNo (None, 15, 15, 48) 144 conv2d_14[0][0] __________________________________________________________________________________________________ batch_normalization_17 (BatchNo (None, 15, 15, 96) 288 conv2d_17[0][0] __________________________________________________________________________________________________ activation_14 (Activation) (None, 15, 15, 48) 0 batch_normalization_14[0][0] __________________________________________________________________________________________________ activation_17 (Activation) (None, 15, 15, 96) 0 batch_normalization_17[0][0] __________________________________________________________________________________________________ average_pooling2d_2 (AveragePoo (None, 15, 15, 256) 0 mixed0[0][0] __________________________________________________________________________________________________ conv2d_13 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ conv2d_15 (Conv2D) (None, 15, 15, 64) 76800 activation_14[0][0] __________________________________________________________________________________________________ conv2d_18 (Conv2D) (None, 15, 15, 96) 82944 activation_17[0][0] __________________________________________________________________________________________________ conv2d_19 (Conv2D) (None, 15, 15, 64) 16384 average_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_13 (BatchNo (None, 15, 15, 64) 192 conv2d_13[0][0] __________________________________________________________________________________________________ batch_normalization_15 (BatchNo (None, 15, 15, 64) 192 conv2d_15[0][0] __________________________________________________________________________________________________ batch_normalization_18 (BatchNo (None, 15, 15, 96) 288 conv2d_18[0][0] __________________________________________________________________________________________________ batch_normalization_19 (BatchNo (None, 15, 15, 64) 192 conv2d_19[0][0] __________________________________________________________________________________________________ activation_13 (Activation) (None, 15, 15, 64) 0 batch_normalization_13[0][0] __________________________________________________________________________________________________ activation_15 (Activation) (None, 15, 15, 64) 0 batch_normalization_15[0][0] __________________________________________________________________________________________________ activation_18 (Activation) (None, 15, 15, 96) 0 batch_normalization_18[0][0] __________________________________________________________________________________________________ activation_19 (Activation) (None, 15, 15, 64) 0 batch_normalization_19[0][0] __________________________________________________________________________________________________ mixed1 (Concatenate) (None, 15, 15, 288) 0 activation_13[0][0] activation_15[0][0] activation_18[0][0] activation_19[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ batch_normalization_23 (BatchNo (None, 15, 15, 64) 192 conv2d_23[0][0] __________________________________________________________________________________________________ activation_23 (Activation) (None, 15, 15, 64) 0 batch_normalization_23[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 15, 15, 48) 13824 mixed1[0][0] __________________________________________________________________________________________________ conv2d_24 (Conv2D) (None, 15, 15, 96) 55296 activation_23[0][0] __________________________________________________________________________________________________ batch_normalization_21 (BatchNo (None, 15, 15, 48) 144 conv2d_21[0][0] __________________________________________________________________________________________________ batch_normalization_24 (BatchNo (None, 15, 15, 96) 288 conv2d_24[0][0] __________________________________________________________________________________________________ activation_21 (Activation) (None, 15, 15, 48) 0 batch_normalization_21[0][0] __________________________________________________________________________________________________ activation_24 (Activation) (None, 15, 15, 96) 0 batch_normalization_24[0][0] __________________________________________________________________________________________________ average_pooling2d_3 (AveragePoo (None, 15, 15, 288) 0 mixed1[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 15, 15, 64) 76800 activation_21[0][0] __________________________________________________________________________________________________ conv2d_25 (Conv2D) (None, 15, 15, 96) 82944 activation_24[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 15, 15, 64) 18432 average_pooling2d_3[0][0] __________________________________________________________________________________________________ batch_normalization_20 (BatchNo (None, 15, 15, 64) 192 conv2d_20[0][0] __________________________________________________________________________________________________ batch_normalization_22 (BatchNo (None, 15, 15, 64) 192 conv2d_22[0][0] __________________________________________________________________________________________________ batch_normalization_25 (BatchNo (None, 15, 15, 96) 288 conv2d_25[0][0] __________________________________________________________________________________________________ batch_normalization_26 (BatchNo (None, 15, 15, 64) 192 conv2d_26[0][0] __________________________________________________________________________________________________ activation_20 (Activation) (None, 15, 15, 64) 0 batch_normalization_20[0][0] __________________________________________________________________________________________________ activation_22 (Activation) (None, 15, 15, 64) 0 batch_normalization_22[0][0] __________________________________________________________________________________________________ activation_25 (Activation) (None, 15, 15, 96) 0 batch_normalization_25[0][0] __________________________________________________________________________________________________ activation_26 (Activation) (None, 15, 15, 64) 0 batch_normalization_26[0][0] __________________________________________________________________________________________________ mixed2 (Concatenate) (None, 15, 15, 288) 0 activation_20[0][0] activation_22[0][0] activation_25[0][0] activation_26[0][0] __________________________________________________________________________________________________ conv2d_28 (Conv2D) (None, 15, 15, 64) 18432 mixed2[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 15, 15, 64) 192 conv2d_28[0][0] __________________________________________________________________________________________________ activation_28 (Activation) (None, 15, 15, 64) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ conv2d_29 (Conv2D) (None, 15, 15, 96) 55296 activation_28[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 15, 15, 96) 288 conv2d_29[0][0] __________________________________________________________________________________________________ activation_29 (Activation) (None, 15, 15, 96) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ conv2d_27 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0] __________________________________________________________________________________________________ conv2d_30 (Conv2D) (None, 7, 7, 96) 82944 activation_29[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 7, 7, 384) 1152 conv2d_27[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 7, 7, 96) 288 conv2d_30[0][0] __________________________________________________________________________________________________ activation_27 (Activation) (None, 7, 7, 384) 0 batch_normalization_27[0][0] __________________________________________________________________________________________________ activation_30 (Activation) (None, 7, 7, 96) 0 batch_normalization_30[0][0] __________________________________________________________________________________________________ max_pooling2d_3 (MaxPooling2D) (None, 7, 7, 288) 0 mixed2[0][0] __________________________________________________________________________________________________ mixed3 (Concatenate) (None, 7, 7, 768) 0 activation_27[0][0] activation_30[0][0] max_pooling2d_3[0][0] __________________________________________________________________________________________________ conv2d_35 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0] __________________________________________________________________________________________________ activation_35 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ conv2d_36 (Conv2D) (None, 7, 7, 128) 114688 activation_35[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0] __________________________________________________________________________________________________ activation_36 (Activation) (None, 7, 7, 128) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ conv2d_32 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ conv2d_37 (Conv2D) (None, 7, 7, 128) 114688 activation_36[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_32[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_37[0][0] __________________________________________________________________________________________________ activation_32 (Activation) (None, 7, 7, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ activation_37 (Activation) (None, 7, 7, 128) 0 batch_normalization_37[0][0] __________________________________________________________________________________________________ conv2d_33 (Conv2D) (None, 7, 7, 128) 114688 activation_32[0][0] __________________________________________________________________________________________________ conv2d_38 (Conv2D) (None, 7, 7, 128) 114688 activation_37[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 7, 7, 128) 384 conv2d_33[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 7, 7, 128) 384 conv2d_38[0][0] __________________________________________________________________________________________________ activation_33 (Activation) (None, 7, 7, 128) 0 batch_normalization_33[0][0] __________________________________________________________________________________________________ activation_38 (Activation) (None, 7, 7, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ average_pooling2d_4 (AveragePoo (None, 7, 7, 768) 0 mixed3[0][0] __________________________________________________________________________________________________ conv2d_31 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0] __________________________________________________________________________________________________ conv2d_34 (Conv2D) (None, 7, 7, 192) 172032 activation_33[0][0] __________________________________________________________________________________________________ conv2d_39 (Conv2D) (None, 7, 7, 192) 172032 activation_38[0][0] __________________________________________________________________________________________________ conv2d_40 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 7, 7, 192) 576 conv2d_31[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 7, 7, 192) 576 conv2d_34[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_39[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_40[0][0] __________________________________________________________________________________________________ activation_31 (Activation) (None, 7, 7, 192) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ activation_34 (Activation) (None, 7, 7, 192) 0 batch_normalization_34[0][0] __________________________________________________________________________________________________ activation_39 (Activation) (None, 7, 7, 192) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ activation_40 (Activation) (None, 7, 7, 192) 0 batch_normalization_40[0][0] __________________________________________________________________________________________________ mixed4 (Concatenate) (None, 7, 7, 768) 0 activation_31[0][0] activation_34[0][0] activation_39[0][0] activation_40[0][0] __________________________________________________________________________________________________ conv2d_45 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0] __________________________________________________________________________________________________ activation_45 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0] __________________________________________________________________________________________________ conv2d_46 (Conv2D) (None, 7, 7, 160) 179200 activation_45[0][0] __________________________________________________________________________________________________ batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0] __________________________________________________________________________________________________ activation_46 (Activation) (None, 7, 7, 160) 0 batch_normalization_46[0][0] __________________________________________________________________________________________________ conv2d_42 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ conv2d_47 (Conv2D) (None, 7, 7, 160) 179200 activation_46[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_42[0][0] __________________________________________________________________________________________________ batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_47[0][0] __________________________________________________________________________________________________ activation_42 (Activation) (None, 7, 7, 160) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ activation_47 (Activation) (None, 7, 7, 160) 0 batch_normalization_47[0][0] __________________________________________________________________________________________________ conv2d_43 (Conv2D) (None, 7, 7, 160) 179200 activation_42[0][0] __________________________________________________________________________________________________ conv2d_48 (Conv2D) (None, 7, 7, 160) 179200 activation_47[0][0] __________________________________________________________________________________________________ batch_normalization_43 (BatchNo (None, 7, 7, 160) 480 conv2d_43[0][0] __________________________________________________________________________________________________ batch_normalization_48 (BatchNo (None, 7, 7, 160) 480 conv2d_48[0][0] __________________________________________________________________________________________________ activation_43 (Activation) (None, 7, 7, 160) 0 batch_normalization_43[0][0] __________________________________________________________________________________________________ activation_48 (Activation) (None, 7, 7, 160) 0 batch_normalization_48[0][0] __________________________________________________________________________________________________ average_pooling2d_5 (AveragePoo (None, 7, 7, 768) 0 mixed4[0][0] __________________________________________________________________________________________________ conv2d_41 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0] __________________________________________________________________________________________________ conv2d_44 (Conv2D) (None, 7, 7, 192) 215040 activation_43[0][0] __________________________________________________________________________________________________ conv2d_49 (Conv2D) (None, 7, 7, 192) 215040 activation_48[0][0] __________________________________________________________________________________________________ conv2d_50 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 7, 7, 192) 576 conv2d_41[0][0] __________________________________________________________________________________________________ batch_normalization_44 (BatchNo (None, 7, 7, 192) 576 conv2d_44[0][0] __________________________________________________________________________________________________ batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_49[0][0] __________________________________________________________________________________________________ batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_50[0][0] __________________________________________________________________________________________________ activation_41 (Activation) (None, 7, 7, 192) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ activation_44 (Activation) (None, 7, 7, 192) 0 batch_normalization_44[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 7, 7, 192) 0 batch_normalization_49[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 7, 7, 192) 0 batch_normalization_50[0][0] __________________________________________________________________________________________________ mixed5 (Concatenate) (None, 7, 7, 768) 0 activation_41[0][0] activation_44[0][0] activation_49[0][0] activation_50[0][0] __________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 7, 7, 160) 179200 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 7, 7, 160) 0 batch_normalization_56[0][0] __________________________________________________________________________________________________ conv2d_52 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 7, 7, 160) 179200 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_52[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 7, 7, 160) 0 batch_normalization_52[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 7, 7, 160) 0 batch_normalization_57[0][0] __________________________________________________________________________________________________ conv2d_53 (Conv2D) (None, 7, 7, 160) 179200 activation_52[0][0] __________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 7, 7, 160) 179200 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 7, 7, 160) 480 conv2d_53[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 7, 7, 160) 480 conv2d_58[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 7, 7, 160) 0 batch_normalization_53[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 7, 7, 160) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ average_pooling2d_6 (AveragePoo (None, 7, 7, 768) 0 mixed5[0][0] __________________________________________________________________________________________________ conv2d_51 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0] __________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 7, 7, 192) 215040 activation_53[0][0] __________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 7, 7, 192) 215040 activation_58[0][0] __________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_51 (BatchNo (None, 7, 7, 192) 576 conv2d_51[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 7, 7, 192) 576 conv2d_54[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_59[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_60[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 7, 7, 192) 0 batch_normalization_51[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 7, 7, 192) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 7, 7, 192) 0 batch_normalization_59[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 7, 7, 192) 0 batch_normalization_60[0][0] __________________________________________________________________________________________________ mixed6 (Concatenate) (None, 7, 7, 768) 0 activation_51[0][0] activation_54[0][0] activation_59[0][0] activation_60[0][0] __________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 7, 7, 192) 258048 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 7, 7, 192) 0 batch_normalization_66[0][0] __________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_66[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 7, 7, 192) 0 batch_normalization_62[0][0] __________________________________________________________________________________________________ activation_67 (Activation) (None, 7, 7, 192) 0 batch_normalization_67[0][0] __________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 7, 7, 192) 258048 activation_62[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 7, 7, 192) 258048 activation_67[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_63[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 7, 7, 192) 0 batch_normalization_63[0][0] __________________________________________________________________________________________________ activation_68 (Activation) (None, 7, 7, 192) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ average_pooling2d_7 (AveragePoo (None, 7, 7, 768) 0 mixed6[0][0] __________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 7, 7, 192) 258048 activation_63[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 7, 7, 192) 258048 activation_68[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_61[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_70[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 7, 7, 192) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ activation_69 (Activation) (None, 7, 7, 192) 0 batch_normalization_69[0][0] __________________________________________________________________________________________________ activation_70 (Activation) (None, 7, 7, 192) 0 batch_normalization_70[0][0] __________________________________________________________________________________________________ mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_61[0][0] activation_64[0][0] activation_69[0][0] activation_70[0][0] __________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_73[0][0] __________________________________________________________________________________________________ activation_73 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0] __________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 7, 7, 192) 258048 activation_73[0][0] __________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0] __________________________________________________________________________________________________ activation_74 (Activation) (None, 7, 7, 192) 0 batch_normalization_74[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 7, 7, 192) 258048 activation_74[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 7, 7, 192) 576 conv2d_71[0][0] __________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 7, 7, 192) 576 conv2d_75[0][0] __________________________________________________________________________________________________ activation_71 (Activation) (None, 7, 7, 192) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ activation_75 (Activation) (None, 7, 7, 192) 0 batch_normalization_75[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 3, 3, 320) 552960 activation_71[0][0] __________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 3, 3, 192) 331776 activation_75[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 3, 3, 320) 960 conv2d_72[0][0] __________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 3, 3, 192) 576 conv2d_76[0][0] __________________________________________________________________________________________________ activation_72 (Activation) (None, 3, 3, 320) 0 batch_normalization_72[0][0] __________________________________________________________________________________________________ activation_76 (Activation) (None, 3, 3, 192) 0 batch_normalization_76[0][0] __________________________________________________________________________________________________ max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 768) 0 mixed7[0][0] __________________________________________________________________________________________________ mixed8 (Concatenate) (None, 3, 3, 1280) 0 activation_72[0][0] activation_76[0][0] max_pooling2d_4[0][0] __________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_81 (BatchNo (None, 3, 3, 448) 1344 conv2d_81[0][0] __________________________________________________________________________________________________ activation_81 (Activation) (None, 3, 3, 448) 0 batch_normalization_81[0][0] __________________________________________________________________________________________________ conv2d_78 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0] __________________________________________________________________________________________________ conv2d_82 (Conv2D) (None, 3, 3, 384) 1548288 activation_81[0][0] __________________________________________________________________________________________________ batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_78[0][0] __________________________________________________________________________________________________ batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0] __________________________________________________________________________________________________ activation_78 (Activation) (None, 3, 3, 384) 0 batch_normalization_78[0][0] __________________________________________________________________________________________________ activation_82 (Activation) (None, 3, 3, 384) 0 batch_normalization_82[0][0] __________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ conv2d_84 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ average_pooling2d_8 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0] __________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_79[0][0] __________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 3, 3, 384) 1152 conv2d_80[0][0] __________________________________________________________________________________________________ batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0] __________________________________________________________________________________________________ batch_normalization_84 (BatchNo (None, 3, 3, 384) 1152 conv2d_84[0][0] __________________________________________________________________________________________________ conv2d_85 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 3, 3, 320) 960 conv2d_77[0][0] __________________________________________________________________________________________________ activation_79 (Activation) (None, 3, 3, 384) 0 batch_normalization_79[0][0] __________________________________________________________________________________________________ activation_80 (Activation) (None, 3, 3, 384) 0 batch_normalization_80[0][0] __________________________________________________________________________________________________ activation_83 (Activation) (None, 3, 3, 384) 0 batch_normalization_83[0][0] __________________________________________________________________________________________________ activation_84 (Activation) (None, 3, 3, 384) 0 batch_normalization_84[0][0] __________________________________________________________________________________________________ batch_normalization_85 (BatchNo (None, 3, 3, 192) 576 conv2d_85[0][0] __________________________________________________________________________________________________ activation_77 (Activation) (None, 3, 3, 320) 0 batch_normalization_77[0][0] __________________________________________________________________________________________________ mixed9_0 (Concatenate) (None, 3, 3, 768) 0 activation_79[0][0] activation_80[0][0] __________________________________________________________________________________________________ concatenate_1 (Concatenate) (None, 3, 3, 768) 0 activation_83[0][0] activation_84[0][0] __________________________________________________________________________________________________ activation_85 (Activation) (None, 3, 3, 192) 0 batch_normalization_85[0][0] __________________________________________________________________________________________________ mixed9 (Concatenate) (None, 3, 3, 2048) 0 activation_77[0][0] mixed9_0[0][0] concatenate_1[0][0] activation_85[0][0] __________________________________________________________________________________________________ conv2d_90 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_90 (BatchNo (None, 3, 3, 448) 1344 conv2d_90[0][0] __________________________________________________________________________________________________ activation_90 (Activation) (None, 3, 3, 448) 0 batch_normalization_90[0][0] __________________________________________________________________________________________________ conv2d_87 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0] __________________________________________________________________________________________________ conv2d_91 (Conv2D) (None, 3, 3, 384) 1548288 activation_90[0][0] __________________________________________________________________________________________________ batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0] __________________________________________________________________________________________________ batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0] __________________________________________________________________________________________________ activation_87 (Activation) (None, 3, 3, 384) 0 batch_normalization_87[0][0] __________________________________________________________________________________________________ activation_91 (Activation) (None, 3, 3, 384) 0 batch_normalization_91[0][0] __________________________________________________________________________________________________ conv2d_88 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_89 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ conv2d_93 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ average_pooling2d_9 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0] __________________________________________________________________________________________________ conv2d_86 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_88[0][0] __________________________________________________________________________________________________ batch_normalization_89 (BatchNo (None, 3, 3, 384) 1152 conv2d_89[0][0] __________________________________________________________________________________________________ batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0] __________________________________________________________________________________________________ batch_normalization_93 (BatchNo (None, 3, 3, 384) 1152 conv2d_93[0][0] __________________________________________________________________________________________________ conv2d_94 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_9[0][0] __________________________________________________________________________________________________ batch_normalization_86 (BatchNo (None, 3, 3, 320) 960 conv2d_86[0][0] __________________________________________________________________________________________________ activation_88 (Activation) (None, 3, 3, 384) 0 batch_normalization_88[0][0] __________________________________________________________________________________________________ activation_89 (Activation) (None, 3, 3, 384) 0 batch_normalization_89[0][0] __________________________________________________________________________________________________ activation_92 (Activation) (None, 3, 3, 384) 0 batch_normalization_92[0][0] __________________________________________________________________________________________________ activation_93 (Activation) (None, 3, 3, 384) 0 batch_normalization_93[0][0] __________________________________________________________________________________________________ batch_normalization_94 (BatchNo (None, 3, 3, 192) 576 conv2d_94[0][0] __________________________________________________________________________________________________ activation_86 (Activation) (None, 3, 3, 320) 0 batch_normalization_86[0][0] __________________________________________________________________________________________________ mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_88[0][0] activation_89[0][0] __________________________________________________________________________________________________ concatenate_2 (Concatenate) (None, 3, 3, 768) 0 activation_92[0][0] activation_93[0][0] __________________________________________________________________________________________________ activation_94 (Activation) (None, 3, 3, 192) 0 batch_normalization_94[0][0] __________________________________________________________________________________________________ mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_86[0][0] mixed9_1[0][0] concatenate_2[0][0] activation_94[0][0] ================================================================================================== Total params: 21,802,784 Trainable params: 0 Non-trainable params: 21,802,784 __________________________________________________________________________________________________ ###Markdown In a normal Inception network, you would see from the model summary that the last two layers were a global average pooling layer, and a fully-connected "Dense" layer. However, since we set `include_top` to `False`, both of these get dropped. If you otherwise wanted to drop additional layers, you would use:```inception.layers.pop()```Note that `pop()` works from the end of the model backwards. It's important to note two things here:1. How many layers you drop is up to you, typically. We dropped the final two already by setting `include_top` to False in the original loading of the model, but you could instead just run `pop()` twice to achieve similar results. (Note: Keras requires us to set `include_top` to False in order to change the `input_shape`.) Additional layers could be dropped by additional calls to `pop()`.2. If you make a mistake with `pop()`, you'll want to reload the model. If you use it multiple times, the model will continue to drop more and more layers, so you may need to check `model.summary()` again to check your work. Adding new layersNow, you can start to add your own layers. While we've used Keras's `Sequential` model before for simplicity, we'll actually use the [Model API](https://keras.io/models/model/) this time. This functions a little differently, in that instead of using `model.add()`, you explicitly tell the model which previous layer to attach to the current layer. This is useful if you want to use more advanced concepts like [skip layers](https://en.wikipedia.org/wiki/Residual_neural_network), for instance (which were used heavily in ResNet).For example, if you had a previous layer named `inp`:```x = Dropout(0.2)(inp)```is how you would attach a new dropout layer `x`, with it's input coming from a layer with the variable name `inp`.We are going to use the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which consists of 60,000 32x32 images of 10 classes. We need to use Keras's `Input` function to do so, and then we want to re-size the images up to the `input_size` we specified earlier (139x139). ###Code from keras.layers import Input, Lambda import tensorflow as tf # Makes the input placeholder layer 32x32x3 for CIFAR-10 cifar_input = Input(shape=(32,32,3)) # Re-sizes the input with Kera's Lambda layer & attach to cifar_input resized_input = Lambda(lambda image: tf.image.resize_images( image, (input_size, input_size)))(cifar_input) # Feeds the re-sized input into Inception model # You will need to update the model name if you changed it earlier! inp = inception(resized_input) # Imports fully-connected "Dense" layers & Global Average Pooling from keras.layers import Dense, GlobalAveragePooling2D ## TODO: Setting `include_top` to False earlier also removed the ## GlobalAveragePooling2D layer, but we still want it. ## Add it here, and make sure to connect it to the end of Inception x = GlobalAveragePooling2D()(inp) ## TODO: Create two new fully-connected layers using the Model API ## format discussed above. The first layer should use `out` ## as its input, along with ReLU activation. You can choose ## how many nodes it has, although 512 or less is a good idea. ## The second layer should take this first layer as input, and ## be named "predictions", with Softmax activation and ## 10 nodes, as we'll be using the CIFAR10 dataset. x = Dense(512, activation = 'relu')(x) predictions = Dense(10, activation = 'softmax')(x) ###Output _____no_output_____ ###Markdown We're almost done with our new model! Now we just need to use the actual Model API to create the full model. ###Code # Imports the Model API from keras.models import Model # Creates the model, assuming your final layer is named "predictions" model = Model(inputs=cifar_input, outputs=predictions) # Compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) # Check the summary of this new model to confirm the architecture model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) (None, 32, 32, 3) 0 _________________________________________________________________ lambda_1 (Lambda) (None, 139, 139, 3) 0 _________________________________________________________________ inception_v3 (Model) (None, 3, 3, 2048) 21802784 _________________________________________________________________ global_average_pooling2d_1 ( (None, 2048) 0 _________________________________________________________________ dense_1 (Dense) (None, 512) 1049088 _________________________________________________________________ dense_2 (Dense) (None, 10) 5130 ================================================================= Total params: 22,857,002 Trainable params: 1,054,218 Non-trainable params: 21,802,784 _________________________________________________________________ ###Markdown Great job creating a new model architecture from Inception! Notice how this method of adding layers before InceptionV3 and appending to the end of it made InceptionV3 condense down into one line in the summary; if you use the Inception model's normal input (which you could gather from `inception.layers.input`), it would instead show all the layers like before.Most of the rest of the code in the notebook just goes toward loading our data, pre-processing it, and starting our training in Keras, although there's one other good point to make here - Keras callbacks. Keras CallbacksKeras [callbacks](https://keras.io/callbacks/) allow you to gather and store additional information during training, such as the best model, or even stop training early if the validation accuracy has stopped improving. These methods can help to avoid overfitting, or avoid other issues.There's two key callbacks to mention here, `ModelCheckpoint` and `EarlyStopping`. As the names may suggest, model checkpoint saves down the best model so far based on a given metric, while early stopping will end training before the specified number of epochs if the chosen metric no longer improves after a given amount of time.To set these callbacks, you could do the following:```checkpoint = ModelCheckpoint(filepath=save_path, monitor='val_loss', save_best_only=True)```This would save a model to a specified `save_path`, based on validation loss, and only save down the best models. If you set `save_best_only` to `False`, every single epoch will save down another version of the model.```stopper = EarlyStopping(monitor='val_acc', min_delta=0.0003, patience=5)```This will monitor validation accuracy, and if it has not decreased by more than 0.0003 from the previous best validation accuracy for 5 epochs, training will end early.You still need to actually feed these callbacks into `fit()` when you train the model (along with all other relevant data to feed into `fit`):```model.fit(callbacks=[checkpoint, stopper])``` GPU timeThe rest of the notebook will give you the code for training, so you can turn on the GPU at this point - but first, make sure to save your jupyter notebook. Once the GPU is turned on, it will load whatever your last notebook checkpoint is. While we suggest reading through the code below to make sure you understand it, you can otherwise go ahead and select Cell > Run All (or Kernel > Restart & Run All if already using GPU) to run through all cells in the notebook. ###Code from sklearn.utils import shuffle from sklearn.preprocessing import LabelBinarizer from keras.datasets import cifar10 (X_train, y_train), (X_val, y_val) = cifar10.load_data() # One-hot encode the labels label_binarizer = LabelBinarizer() y_one_hot_train = label_binarizer.fit_transform(y_train) y_one_hot_val = label_binarizer.fit_transform(y_val) # Shuffle the training & test data X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train) X_val, y_one_hot_val = shuffle(X_val, y_one_hot_val) # We are only going to use the first 10,000 images for speed reasons # And only the first 2,000 images from the test set X_train = X_train[:10000] y_one_hot_train = y_one_hot_train[:10000] X_val = X_val[:2000] y_one_hot_val = y_one_hot_val[:2000] ###Output Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170500096/170498071 [==============================] - 2s 0us/step ###Markdown You can check out Keras's [ImageDataGenerator documentation](https://faroit.github.io/keras-docs/2.0.9/preprocessing/image/) for more information on the below - you can also add additional image augmentation through this function, although we are skipping that step here so you can potentially explore it in the upcoming project. ###Code # Use a generator to pre-process our images for ImageNet from keras.preprocessing.image import ImageDataGenerator from keras.applications.inception_v3 import preprocess_input if preprocess_flag == True: datagen = ImageDataGenerator(preprocessing_function=preprocess_input) val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) else: datagen = ImageDataGenerator() val_datagen = ImageDataGenerator() # Train the model batch_size = 32 epochs = 5 # Note: we aren't using callbacks here since we only are using 5 epochs to conserve GPU time model.fit_generator(datagen.flow(X_train, y_one_hot_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, verbose=1, validation_data=val_datagen.flow(X_val, y_one_hot_val, batch_size=batch_size), validation_steps=len(X_val)/batch_size) ###Output Epoch 1/5 312/312 [============================>.] - ETA: 0s - loss: 0.9493 - acc: 0.6760 ###Markdown Lab: Transfer LearningWelcome to the lab on Transfer Learning! Here, you'll get a chance to try out training a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers. GPU usageIn our previous examples in this lesson, we've avoided using GPU, but this time around you'll have the option to enable it. You do not need it on to begin with, but make sure anytime you switch from non-GPU to GPU, or vice versa, that you save your notebook! If not, you'll likely be reverted to the previous checkpoint. We also suggest only using the GPU when performing the (mostly minor) training below - you'll want to conserve GPU hours for your Behavioral Cloning project coming up next! ###Code # Set a couple flags for training - you can ignore these for now freeze_flag = True # `True` to freeze layers, `False` for full training weights_flag = 'imagenet' # 'imagenet' or None preprocess_flag = True # Should be true for ImageNet pre-trained typically # Loads in InceptionV3 from keras.applications.inception_v3 import InceptionV3 # We can use smaller than the default 299x299x3 input for InceptionV3 # which will speed up training. Keras v2.0.9 supports down to 139x139x3 input_size = 139 # Using Inception with ImageNet pre-trained weights. We are setting up our model here. inception = InceptionV3(weights=weights_flag, include_top=False, input_shape=(input_size,input_size,3)) """incepton is equivalent to our model variable that we had been using till now""" ###Output Using TensorFlow backend. ###Markdown We'll use Inception V3 for this lab, although you can use the same techniques with any of the models in [Keras Applications](https://keras.io/applications/). Do note that certain models are only available in certain versions of Keras; this workspace uses Keras v2.0.9, for which you can see the available models [here](https://faroit.github.io/keras-docs/2.0.9/applications/).In the above, we've set Inception to use an `input_shape` of 139x139x3 instead of the default 299x299x3. This will help us to speed up our training a bit later (and we'll actually be upsampling from smaller images, so we aren't losing data here). In order to do so, we also must set `include_top` to `False`, which means the final fully-connected layer with 1,000 nodes for each ImageNet class is dropped, as well as a Global Average Pooling layer. Pre-trained with frozen weightsTo start, we'll see how an ImageNet pre-trained model with all weights frozen in the InceptionV3 model performs. We will also drop the end layer and append new layers onto it, although you could do this in different ways (not drop the end and add new layers, drop more layers than we will here, etc.).You can freeze layers by setting `layer.trainable` to False for a given `layer`. Within a `model`, you can get the list of layers with `model.layers`. ###Code if freeze_flag == True: ## TODO: Iterate through the layers of the Inception model ## loaded above and set all of them to have trainable = False for layer in inception.layers: #our model is inception here layer.trainable = False ###Output _____no_output_____ ###Markdown Dropping layersYou can drop layers from a model with `model.layers.pop()`. Before you do this, you should check out what the actual layers of the model are with Keras's `.summary()` function. ###Code ## TODO: Use the model summary function to see all layers in the ## loaded Inception model inception.summary() ###Output __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 139, 139, 3) 0 __________________________________________________________________________________________________ conv2d_1 (Conv2D) (None, 69, 69, 32) 864 input_1[0][0] __________________________________________________________________________________________________ batch_normalization_1 (BatchNor (None, 69, 69, 32) 96 conv2d_1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 69, 69, 32) 0 batch_normalization_1[0][0] __________________________________________________________________________________________________ conv2d_2 (Conv2D) (None, 67, 67, 32) 9216 activation_1[0][0] __________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 67, 67, 32) 96 conv2d_2[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 67, 67, 32) 0 batch_normalization_2[0][0] __________________________________________________________________________________________________ conv2d_3 (Conv2D) (None, 67, 67, 64) 18432 activation_2[0][0] __________________________________________________________________________________________________ batch_normalization_3 (BatchNor (None, 67, 67, 64) 192 conv2d_3[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 67, 67, 64) 0 batch_normalization_3[0][0] __________________________________________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 33, 33, 64) 0 activation_3[0][0] __________________________________________________________________________________________________ conv2d_4 (Conv2D) (None, 33, 33, 80) 5120 max_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_4 (BatchNor (None, 33, 33, 80) 240 conv2d_4[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 33, 33, 80) 0 batch_normalization_4[0][0] __________________________________________________________________________________________________ conv2d_5 (Conv2D) (None, 31, 31, 192) 138240 activation_4[0][0] __________________________________________________________________________________________________ batch_normalization_5 (BatchNor (None, 31, 31, 192) 576 conv2d_5[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 31, 31, 192) 0 batch_normalization_5[0][0] __________________________________________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 15, 15, 192) 0 activation_5[0][0] __________________________________________________________________________________________________ conv2d_9 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_9 (BatchNor (None, 15, 15, 64) 192 conv2d_9[0][0] __________________________________________________________________________________________________ activation_9 (Activation) (None, 15, 15, 64) 0 batch_normalization_9[0][0] __________________________________________________________________________________________________ conv2d_7 (Conv2D) (None, 15, 15, 48) 9216 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_10 (Conv2D) (None, 15, 15, 96) 55296 activation_9[0][0] __________________________________________________________________________________________________ batch_normalization_7 (BatchNor (None, 15, 15, 48) 144 conv2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_10 (BatchNo (None, 15, 15, 96) 288 conv2d_10[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 15, 15, 48) 0 batch_normalization_7[0][0] __________________________________________________________________________________________________ activation_10 (Activation) (None, 15, 15, 96) 0 batch_normalization_10[0][0] __________________________________________________________________________________________________ average_pooling2d_1 (AveragePoo (None, 15, 15, 192) 0 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_6 (Conv2D) (None, 15, 15, 64) 12288 max_pooling2d_2[0][0] __________________________________________________________________________________________________ conv2d_8 (Conv2D) (None, 15, 15, 64) 76800 activation_7[0][0] __________________________________________________________________________________________________ conv2d_11 (Conv2D) (None, 15, 15, 96) 82944 activation_10[0][0] __________________________________________________________________________________________________ conv2d_12 (Conv2D) (None, 15, 15, 32) 6144 average_pooling2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_6 (BatchNor (None, 15, 15, 64) 192 conv2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_8 (BatchNor (None, 15, 15, 64) 192 conv2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_11 (BatchNo (None, 15, 15, 96) 288 conv2d_11[0][0] __________________________________________________________________________________________________ batch_normalization_12 (BatchNo (None, 15, 15, 32) 96 conv2d_12[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 15, 15, 64) 0 batch_normalization_6[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 15, 15, 64) 0 batch_normalization_8[0][0] __________________________________________________________________________________________________ activation_11 (Activation) (None, 15, 15, 96) 0 batch_normalization_11[0][0] __________________________________________________________________________________________________ activation_12 (Activation) (None, 15, 15, 32) 0 batch_normalization_12[0][0] __________________________________________________________________________________________________ mixed0 (Concatenate) (None, 15, 15, 256) 0 activation_6[0][0] activation_8[0][0] activation_11[0][0] activation_12[0][0] __________________________________________________________________________________________________ conv2d_16 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ batch_normalization_16 (BatchNo (None, 15, 15, 64) 192 conv2d_16[0][0] __________________________________________________________________________________________________ activation_16 (Activation) (None, 15, 15, 64) 0 batch_normalization_16[0][0] __________________________________________________________________________________________________ conv2d_14 (Conv2D) (None, 15, 15, 48) 12288 mixed0[0][0] __________________________________________________________________________________________________ conv2d_17 (Conv2D) (None, 15, 15, 96) 55296 activation_16[0][0] __________________________________________________________________________________________________ batch_normalization_14 (BatchNo (None, 15, 15, 48) 144 conv2d_14[0][0] __________________________________________________________________________________________________ batch_normalization_17 (BatchNo (None, 15, 15, 96) 288 conv2d_17[0][0] __________________________________________________________________________________________________ activation_14 (Activation) (None, 15, 15, 48) 0 batch_normalization_14[0][0] __________________________________________________________________________________________________ activation_17 (Activation) (None, 15, 15, 96) 0 batch_normalization_17[0][0] __________________________________________________________________________________________________ average_pooling2d_2 (AveragePoo (None, 15, 15, 256) 0 mixed0[0][0] __________________________________________________________________________________________________ conv2d_13 (Conv2D) (None, 15, 15, 64) 16384 mixed0[0][0] __________________________________________________________________________________________________ conv2d_15 (Conv2D) (None, 15, 15, 64) 76800 activation_14[0][0] __________________________________________________________________________________________________ conv2d_18 (Conv2D) (None, 15, 15, 96) 82944 activation_17[0][0] __________________________________________________________________________________________________ conv2d_19 (Conv2D) (None, 15, 15, 64) 16384 average_pooling2d_2[0][0] __________________________________________________________________________________________________ batch_normalization_13 (BatchNo (None, 15, 15, 64) 192 conv2d_13[0][0] __________________________________________________________________________________________________ batch_normalization_15 (BatchNo (None, 15, 15, 64) 192 conv2d_15[0][0] __________________________________________________________________________________________________ batch_normalization_18 (BatchNo (None, 15, 15, 96) 288 conv2d_18[0][0] __________________________________________________________________________________________________ batch_normalization_19 (BatchNo (None, 15, 15, 64) 192 conv2d_19[0][0] __________________________________________________________________________________________________ activation_13 (Activation) (None, 15, 15, 64) 0 batch_normalization_13[0][0] __________________________________________________________________________________________________ activation_15 (Activation) (None, 15, 15, 64) 0 batch_normalization_15[0][0] __________________________________________________________________________________________________ activation_18 (Activation) (None, 15, 15, 96) 0 batch_normalization_18[0][0] __________________________________________________________________________________________________ activation_19 (Activation) (None, 15, 15, 64) 0 batch_normalization_19[0][0] __________________________________________________________________________________________________ mixed1 (Concatenate) (None, 15, 15, 288) 0 activation_13[0][0] activation_15[0][0] activation_18[0][0] activation_19[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ batch_normalization_23 (BatchNo (None, 15, 15, 64) 192 conv2d_23[0][0] __________________________________________________________________________________________________ activation_23 (Activation) (None, 15, 15, 64) 0 batch_normalization_23[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 15, 15, 48) 13824 mixed1[0][0] __________________________________________________________________________________________________ conv2d_24 (Conv2D) (None, 15, 15, 96) 55296 activation_23[0][0] __________________________________________________________________________________________________ batch_normalization_21 (BatchNo (None, 15, 15, 48) 144 conv2d_21[0][0] __________________________________________________________________________________________________ batch_normalization_24 (BatchNo (None, 15, 15, 96) 288 conv2d_24[0][0] __________________________________________________________________________________________________ activation_21 (Activation) (None, 15, 15, 48) 0 batch_normalization_21[0][0] __________________________________________________________________________________________________ activation_24 (Activation) (None, 15, 15, 96) 0 batch_normalization_24[0][0] __________________________________________________________________________________________________ average_pooling2d_3 (AveragePoo (None, 15, 15, 288) 0 mixed1[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 15, 15, 64) 18432 mixed1[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 15, 15, 64) 76800 activation_21[0][0] __________________________________________________________________________________________________ conv2d_25 (Conv2D) (None, 15, 15, 96) 82944 activation_24[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 15, 15, 64) 18432 average_pooling2d_3[0][0] __________________________________________________________________________________________________ batch_normalization_20 (BatchNo (None, 15, 15, 64) 192 conv2d_20[0][0] __________________________________________________________________________________________________ batch_normalization_22 (BatchNo (None, 15, 15, 64) 192 conv2d_22[0][0] __________________________________________________________________________________________________ batch_normalization_25 (BatchNo (None, 15, 15, 96) 288 conv2d_25[0][0] __________________________________________________________________________________________________ batch_normalization_26 (BatchNo (None, 15, 15, 64) 192 conv2d_26[0][0] __________________________________________________________________________________________________ activation_20 (Activation) (None, 15, 15, 64) 0 batch_normalization_20[0][0] __________________________________________________________________________________________________ activation_22 (Activation) (None, 15, 15, 64) 0 batch_normalization_22[0][0] __________________________________________________________________________________________________ activation_25 (Activation) (None, 15, 15, 96) 0 batch_normalization_25[0][0] __________________________________________________________________________________________________ activation_26 (Activation) (None, 15, 15, 64) 0 batch_normalization_26[0][0] __________________________________________________________________________________________________ mixed2 (Concatenate) (None, 15, 15, 288) 0 activation_20[0][0] activation_22[0][0] activation_25[0][0] activation_26[0][0] __________________________________________________________________________________________________ conv2d_28 (Conv2D) (None, 15, 15, 64) 18432 mixed2[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 15, 15, 64) 192 conv2d_28[0][0] __________________________________________________________________________________________________ activation_28 (Activation) (None, 15, 15, 64) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ conv2d_29 (Conv2D) (None, 15, 15, 96) 55296 activation_28[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 15, 15, 96) 288 conv2d_29[0][0] __________________________________________________________________________________________________ activation_29 (Activation) (None, 15, 15, 96) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ conv2d_27 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0] __________________________________________________________________________________________________ conv2d_30 (Conv2D) (None, 7, 7, 96) 82944 activation_29[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 7, 7, 384) 1152 conv2d_27[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 7, 7, 96) 288 conv2d_30[0][0] __________________________________________________________________________________________________ activation_27 (Activation) (None, 7, 7, 384) 0 batch_normalization_27[0][0] __________________________________________________________________________________________________ activation_30 (Activation) (None, 7, 7, 96) 0 batch_normalization_30[0][0] __________________________________________________________________________________________________ max_pooling2d_3 (MaxPooling2D) (None, 7, 7, 288) 0 mixed2[0][0] __________________________________________________________________________________________________ mixed3 (Concatenate) (None, 7, 7, 768) 0 activation_27[0][0] activation_30[0][0] max_pooling2d_3[0][0] __________________________________________________________________________________________________ conv2d_35 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0] __________________________________________________________________________________________________ activation_35 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ conv2d_36 (Conv2D) (None, 7, 7, 128) 114688 activation_35[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0] __________________________________________________________________________________________________ activation_36 (Activation) (None, 7, 7, 128) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ conv2d_32 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0] __________________________________________________________________________________________________ conv2d_37 (Conv2D) (None, 7, 7, 128) 114688 activation_36[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_32[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_37[0][0] __________________________________________________________________________________________________ activation_32 (Activation) (None, 7, 7, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ activation_37 (Activation) (None, 7, 7, 128) 0 batch_normalization_37[0][0] __________________________________________________________________________________________________ conv2d_33 (Conv2D) (None, 7, 7, 128) 114688 activation_32[0][0] __________________________________________________________________________________________________ conv2d_38 (Conv2D) (None, 7, 7, 128) 114688 activation_37[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 7, 7, 128) 384 conv2d_33[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 7, 7, 128) 384 conv2d_38[0][0] __________________________________________________________________________________________________ activation_33 (Activation) (None, 7, 7, 128) 0 batch_normalization_33[0][0] __________________________________________________________________________________________________ activation_38 (Activation) (None, 7, 7, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ average_pooling2d_4 (AveragePoo (None, 7, 7, 768) 0 mixed3[0][0] __________________________________________________________________________________________________ conv2d_31 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0] __________________________________________________________________________________________________ conv2d_34 (Conv2D) (None, 7, 7, 192) 172032 activation_33[0][0] __________________________________________________________________________________________________ conv2d_39 (Conv2D) (None, 7, 7, 192) 172032 activation_38[0][0] __________________________________________________________________________________________________ conv2d_40 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 7, 7, 192) 576 conv2d_31[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 7, 7, 192) 576 conv2d_34[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_39[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_40[0][0] __________________________________________________________________________________________________ activation_31 (Activation) (None, 7, 7, 192) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ activation_34 (Activation) (None, 7, 7, 192) 0 batch_normalization_34[0][0] __________________________________________________________________________________________________ activation_39 (Activation) (None, 7, 7, 192) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ activation_40 (Activation) (None, 7, 7, 192) 0 batch_normalization_40[0][0] __________________________________________________________________________________________________ mixed4 (Concatenate) (None, 7, 7, 768) 0 activation_31[0][0] activation_34[0][0] activation_39[0][0] activation_40[0][0] __________________________________________________________________________________________________ conv2d_45 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0] __________________________________________________________________________________________________ activation_45 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0] __________________________________________________________________________________________________ conv2d_46 (Conv2D) (None, 7, 7, 160) 179200 activation_45[0][0] __________________________________________________________________________________________________ batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0] __________________________________________________________________________________________________ activation_46 (Activation) (None, 7, 7, 160) 0 batch_normalization_46[0][0] __________________________________________________________________________________________________ conv2d_42 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0] __________________________________________________________________________________________________ conv2d_47 (Conv2D) (None, 7, 7, 160) 179200 activation_46[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_42[0][0] __________________________________________________________________________________________________ batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_47[0][0] __________________________________________________________________________________________________ activation_42 (Activation) (None, 7, 7, 160) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ activation_47 (Activation) (None, 7, 7, 160) 0 batch_normalization_47[0][0] __________________________________________________________________________________________________ conv2d_43 (Conv2D) (None, 7, 7, 160) 179200 activation_42[0][0] __________________________________________________________________________________________________ conv2d_48 (Conv2D) (None, 7, 7, 160) 179200 activation_47[0][0] __________________________________________________________________________________________________ batch_normalization_43 (BatchNo (None, 7, 7, 160) 480 conv2d_43[0][0] __________________________________________________________________________________________________ batch_normalization_48 (BatchNo (None, 7, 7, 160) 480 conv2d_48[0][0] __________________________________________________________________________________________________ activation_43 (Activation) (None, 7, 7, 160) 0 batch_normalization_43[0][0] __________________________________________________________________________________________________ activation_48 (Activation) (None, 7, 7, 160) 0 batch_normalization_48[0][0] __________________________________________________________________________________________________ average_pooling2d_5 (AveragePoo (None, 7, 7, 768) 0 mixed4[0][0] __________________________________________________________________________________________________ conv2d_41 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0] __________________________________________________________________________________________________ conv2d_44 (Conv2D) (None, 7, 7, 192) 215040 activation_43[0][0] __________________________________________________________________________________________________ conv2d_49 (Conv2D) (None, 7, 7, 192) 215040 activation_48[0][0] __________________________________________________________________________________________________ conv2d_50 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 7, 7, 192) 576 conv2d_41[0][0] __________________________________________________________________________________________________ batch_normalization_44 (BatchNo (None, 7, 7, 192) 576 conv2d_44[0][0] __________________________________________________________________________________________________ batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_49[0][0] __________________________________________________________________________________________________ batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_50[0][0] __________________________________________________________________________________________________ activation_41 (Activation) (None, 7, 7, 192) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ activation_44 (Activation) (None, 7, 7, 192) 0 batch_normalization_44[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 7, 7, 192) 0 batch_normalization_49[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 7, 7, 192) 0 batch_normalization_50[0][0] __________________________________________________________________________________________________ mixed5 (Concatenate) (None, 7, 7, 768) 0 activation_41[0][0] activation_44[0][0] activation_49[0][0] activation_50[0][0] __________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 7, 7, 160) 179200 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 7, 7, 160) 0 batch_normalization_56[0][0] __________________________________________________________________________________________________ conv2d_52 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0] __________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 7, 7, 160) 179200 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_52[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 7, 7, 160) 0 batch_normalization_52[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 7, 7, 160) 0 batch_normalization_57[0][0] __________________________________________________________________________________________________ conv2d_53 (Conv2D) (None, 7, 7, 160) 179200 activation_52[0][0] __________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 7, 7, 160) 179200 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 7, 7, 160) 480 conv2d_53[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 7, 7, 160) 480 conv2d_58[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 7, 7, 160) 0 batch_normalization_53[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 7, 7, 160) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ average_pooling2d_6 (AveragePoo (None, 7, 7, 768) 0 mixed5[0][0] __________________________________________________________________________________________________ conv2d_51 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0] __________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 7, 7, 192) 215040 activation_53[0][0] __________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 7, 7, 192) 215040 activation_58[0][0] __________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_51 (BatchNo (None, 7, 7, 192) 576 conv2d_51[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 7, 7, 192) 576 conv2d_54[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_59[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_60[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 7, 7, 192) 0 batch_normalization_51[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 7, 7, 192) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 7, 7, 192) 0 batch_normalization_59[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 7, 7, 192) 0 batch_normalization_60[0][0] __________________________________________________________________________________________________ mixed6 (Concatenate) (None, 7, 7, 768) 0 activation_51[0][0] activation_54[0][0] activation_59[0][0] activation_60[0][0] __________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 7, 7, 192) 258048 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 7, 7, 192) 0 batch_normalization_66[0][0] __________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_66[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 7, 7, 192) 0 batch_normalization_62[0][0] __________________________________________________________________________________________________ activation_67 (Activation) (None, 7, 7, 192) 0 batch_normalization_67[0][0] __________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 7, 7, 192) 258048 activation_62[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 7, 7, 192) 258048 activation_67[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_63[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 7, 7, 192) 0 batch_normalization_63[0][0] __________________________________________________________________________________________________ activation_68 (Activation) (None, 7, 7, 192) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ average_pooling2d_7 (AveragePoo (None, 7, 7, 768) 0 mixed6[0][0] __________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0] __________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 7, 7, 192) 258048 activation_63[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 7, 7, 192) 258048 activation_68[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_7[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_61[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_70[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 7, 7, 192) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ activation_69 (Activation) (None, 7, 7, 192) 0 batch_normalization_69[0][0] __________________________________________________________________________________________________ activation_70 (Activation) (None, 7, 7, 192) 0 batch_normalization_70[0][0] __________________________________________________________________________________________________ mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_61[0][0] activation_64[0][0] activation_69[0][0] activation_70[0][0] __________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_73[0][0] __________________________________________________________________________________________________ activation_73 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0] __________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 7, 7, 192) 258048 activation_73[0][0] __________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0] __________________________________________________________________________________________________ activation_74 (Activation) (None, 7, 7, 192) 0 batch_normalization_74[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0] __________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 7, 7, 192) 258048 activation_74[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 7, 7, 192) 576 conv2d_71[0][0] __________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 7, 7, 192) 576 conv2d_75[0][0] __________________________________________________________________________________________________ activation_71 (Activation) (None, 7, 7, 192) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ activation_75 (Activation) (None, 7, 7, 192) 0 batch_normalization_75[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 3, 3, 320) 552960 activation_71[0][0] __________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 3, 3, 192) 331776 activation_75[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 3, 3, 320) 960 conv2d_72[0][0] __________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 3, 3, 192) 576 conv2d_76[0][0] __________________________________________________________________________________________________ activation_72 (Activation) (None, 3, 3, 320) 0 batch_normalization_72[0][0] __________________________________________________________________________________________________ activation_76 (Activation) (None, 3, 3, 192) 0 batch_normalization_76[0][0] __________________________________________________________________________________________________ max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 768) 0 mixed7[0][0] __________________________________________________________________________________________________ mixed8 (Concatenate) (None, 3, 3, 1280) 0 activation_72[0][0] activation_76[0][0] max_pooling2d_4[0][0] __________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_81 (BatchNo (None, 3, 3, 448) 1344 conv2d_81[0][0] __________________________________________________________________________________________________ activation_81 (Activation) (None, 3, 3, 448) 0 batch_normalization_81[0][0] __________________________________________________________________________________________________ conv2d_78 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0] __________________________________________________________________________________________________ conv2d_82 (Conv2D) (None, 3, 3, 384) 1548288 activation_81[0][0] __________________________________________________________________________________________________ batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_78[0][0] __________________________________________________________________________________________________ batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0] __________________________________________________________________________________________________ activation_78 (Activation) (None, 3, 3, 384) 0 batch_normalization_78[0][0] __________________________________________________________________________________________________ activation_82 (Activation) (None, 3, 3, 384) 0 batch_normalization_82[0][0] __________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 3, 3, 384) 442368 activation_78[0][0] __________________________________________________________________________________________________ conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ conv2d_84 (Conv2D) (None, 3, 3, 384) 442368 activation_82[0][0] __________________________________________________________________________________________________ average_pooling2d_8 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0] __________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0] __________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_79[0][0] __________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 3, 3, 384) 1152 conv2d_80[0][0] __________________________________________________________________________________________________ batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0] __________________________________________________________________________________________________ batch_normalization_84 (BatchNo (None, 3, 3, 384) 1152 conv2d_84[0][0] __________________________________________________________________________________________________ conv2d_85 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_8[0][0] __________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 3, 3, 320) 960 conv2d_77[0][0] __________________________________________________________________________________________________ activation_79 (Activation) (None, 3, 3, 384) 0 batch_normalization_79[0][0] __________________________________________________________________________________________________ activation_80 (Activation) (None, 3, 3, 384) 0 batch_normalization_80[0][0] __________________________________________________________________________________________________ activation_83 (Activation) (None, 3, 3, 384) 0 batch_normalization_83[0][0] __________________________________________________________________________________________________ activation_84 (Activation) (None, 3, 3, 384) 0 batch_normalization_84[0][0] __________________________________________________________________________________________________ batch_normalization_85 (BatchNo (None, 3, 3, 192) 576 conv2d_85[0][0] __________________________________________________________________________________________________ activation_77 (Activation) (None, 3, 3, 320) 0 batch_normalization_77[0][0] __________________________________________________________________________________________________ mixed9_0 (Concatenate) (None, 3, 3, 768) 0 activation_79[0][0] activation_80[0][0] __________________________________________________________________________________________________ concatenate_1 (Concatenate) (None, 3, 3, 768) 0 activation_83[0][0] activation_84[0][0] __________________________________________________________________________________________________ activation_85 (Activation) (None, 3, 3, 192) 0 batch_normalization_85[0][0] __________________________________________________________________________________________________ mixed9 (Concatenate) (None, 3, 3, 2048) 0 activation_77[0][0] mixed9_0[0][0] concatenate_1[0][0] activation_85[0][0] __________________________________________________________________________________________________ conv2d_90 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_90 (BatchNo (None, 3, 3, 448) 1344 conv2d_90[0][0] __________________________________________________________________________________________________ activation_90 (Activation) (None, 3, 3, 448) 0 batch_normalization_90[0][0] __________________________________________________________________________________________________ conv2d_87 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0] __________________________________________________________________________________________________ conv2d_91 (Conv2D) (None, 3, 3, 384) 1548288 activation_90[0][0] __________________________________________________________________________________________________ batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0] __________________________________________________________________________________________________ batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0] __________________________________________________________________________________________________ activation_87 (Activation) (None, 3, 3, 384) 0 batch_normalization_87[0][0] __________________________________________________________________________________________________ activation_91 (Activation) (None, 3, 3, 384) 0 batch_normalization_91[0][0] __________________________________________________________________________________________________ conv2d_88 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_89 (Conv2D) (None, 3, 3, 384) 442368 activation_87[0][0] __________________________________________________________________________________________________ conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ conv2d_93 (Conv2D) (None, 3, 3, 384) 442368 activation_91[0][0] __________________________________________________________________________________________________ average_pooling2d_9 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0] __________________________________________________________________________________________________ conv2d_86 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0] __________________________________________________________________________________________________ batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_88[0][0] __________________________________________________________________________________________________ batch_normalization_89 (BatchNo (None, 3, 3, 384) 1152 conv2d_89[0][0] __________________________________________________________________________________________________ batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0] __________________________________________________________________________________________________ batch_normalization_93 (BatchNo (None, 3, 3, 384) 1152 conv2d_93[0][0] __________________________________________________________________________________________________ conv2d_94 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_9[0][0] __________________________________________________________________________________________________ batch_normalization_86 (BatchNo (None, 3, 3, 320) 960 conv2d_86[0][0] __________________________________________________________________________________________________ activation_88 (Activation) (None, 3, 3, 384) 0 batch_normalization_88[0][0] __________________________________________________________________________________________________ activation_89 (Activation) (None, 3, 3, 384) 0 batch_normalization_89[0][0] __________________________________________________________________________________________________ activation_92 (Activation) (None, 3, 3, 384) 0 batch_normalization_92[0][0] __________________________________________________________________________________________________ activation_93 (Activation) (None, 3, 3, 384) 0 batch_normalization_93[0][0] __________________________________________________________________________________________________ batch_normalization_94 (BatchNo (None, 3, 3, 192) 576 conv2d_94[0][0] __________________________________________________________________________________________________ activation_86 (Activation) (None, 3, 3, 320) 0 batch_normalization_86[0][0] __________________________________________________________________________________________________ mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_88[0][0] activation_89[0][0] __________________________________________________________________________________________________ concatenate_2 (Concatenate) (None, 3, 3, 768) 0 activation_92[0][0] activation_93[0][0] __________________________________________________________________________________________________ activation_94 (Activation) (None, 3, 3, 192) 0 batch_normalization_94[0][0] __________________________________________________________________________________________________ mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_86[0][0] mixed9_1[0][0] concatenate_2[0][0] activation_94[0][0] ================================================================================================== Total params: 21,802,784 Trainable params: 0 Non-trainable params: 21,802,784 __________________________________________________________________________________________________ ###Markdown In a normal Inception network, you would see from the model summary that the last two layers were a global average pooling layer, and a fully-connected "Dense" layer. However, since we set `include_top` to `False`, both of these get dropped. If you otherwise wanted to drop additional layers, you would use:```inception.layers.pop()```Note that `pop()` works from the end of the model backwards. It's important to note two things here:1. How many layers you drop is up to you, typically. We dropped the final two already by setting `include_top` to False in the original loading of the model, but you could instead just run `pop()` twice to achieve similar results. (Note: Keras requires us to set `include_top` to False in order to change the `input_shape`.) Additional layers could be dropped by additional calls to `pop()`.2. If you make a mistake with `pop()`, you'll want to reload the model. If you use it multiple times, the model will continue to drop more and more layers, so you may need to check `model.summary()` again to check your work. Adding new layersNow, you can start to add your own layers. While we've used Keras's `Sequential` model before for simplicity, we'll actually use the [Model API](https://keras.io/models/model/) this time. This functions a little differently, in that instead of using `model.add()`, you explicitly tell the model which previous layer to attach to the current layer. This is useful if you want to use more advanced concepts like [skip layers](https://en.wikipedia.org/wiki/Residual_neural_network), for instance (which were used heavily in ResNet).For example, if you had a previous layer named `inp`:```x = Dropout(0.2)(inp)```is how you would attach a new dropout layer `x`, with it's input coming from a layer with the variable name `inp`.We are going to use the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which consists of 60,000 32x32 images of 10 classes. We need to use Keras's `Input` function to do so, and then we want to re-size the images up to the `input_size` we specified earlier (139x139). ###Code from keras.layers import Input, Lambda import tensorflow as tf # Makes the input placeholder layer 32x32x3 for CIFAR-10 cifar_input = Input(shape=(32,32,3)) # Re-sizes the input with Kera's Lambda layer & attach to cifar_input resized_input = Lambda(lambda image: tf.image.resize_images( image, (input_size, input_size)))(cifar_input) # Feeds the re-sized input into Inception model # You will need to update the model name if you changed it earlier! inp = inception(resized_input) """Last step mein ham mostly input layer ke aage model ko append kar rahe hain""" # Imports fully-connected "Dense" layers & Global Average Pooling from keras.layers import Dense, GlobalAveragePooling2D ## TODO: Setting `include_top` to False earlier also removed the ## GlobalAveragePooling2D layer, but we still want it. ## Add it here, and make sure to connect it to the end of Inception out1 = GlobalAveragePooling2D()(inp) ## TODO: Create two new fully-connected layers using the Model API ## format discussed above. The first layer should use `out` ## as its input, along with ReLU activation. You can choose ## how many nodes it has, although 512 or less is a good idea. ## The second layer should take this first layer as input, and ## be named "predictions", with Softmax activation and ## 10 nodes, as we'll be using the CIFAR10 dataset. out2 = Dense(512, activation='relu')(out1) predictions = Dense(10, activation='softmax')(out2) ###Output _____no_output_____ ###Markdown We're almost done with our new model! Now we just need to use the actual Model API to create the full model. ###Code # Imports the Model API from keras.models import Model # Creates the model, assuming your final layer is named "predictions" model = Model(inputs=cifar_input, outputs=predictions) # Compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy']) # Check the summary of this new model to confirm the architecture model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) (None, 32, 32, 3) 0 _________________________________________________________________ lambda_1 (Lambda) (None, 139, 139, 3) 0 _________________________________________________________________ inception_v3 (Model) (None, 3, 3, 2048) 21802784 _________________________________________________________________ global_average_pooling2d_1 ( (None, 2048) 0 _________________________________________________________________ dense_1 (Dense) (None, 512) 1049088 _________________________________________________________________ dense_2 (Dense) (None, 10) 5130 ================================================================= Total params: 22,857,002 Trainable params: 1,054,218 Non-trainable params: 21,802,784 _________________________________________________________________ ###Markdown Great job creating a new model architecture from Inception! Notice how this method of adding layers before InceptionV3 and appending to the end of it made InceptionV3 condense down into one line in the summary; if you use the Inception model's normal input (which you could gather from `inception.layers.input`), it would instead show all the layers like before.Most of the rest of the code in the notebook just goes toward loading our data, pre-processing it, and starting our training in Keras, although there's one other good point to make here - Keras callbacks. Keras CallbacksKeras [callbacks](https://keras.io/callbacks/) allow you to gather and store additional information during training, such as the best model, or even stop training early if the validation accuracy has stopped improving. These methods can help to avoid overfitting, or avoid other issues.There's two key callbacks to mention here, `ModelCheckpoint` and `EarlyStopping`. As the names may suggest, model checkpoint saves down the best model so far based on a given metric, while early stopping will end training before the specified number of epochs if the chosen metric no longer improves after a given amount of time.To set these callbacks, you could do the following:```checkpoint = ModelCheckpoint(filepath=save_path, monitor='val_loss', save_best_only=True)```This would save a model to a specified `save_path`, based on validation loss, and only save down the best models. If you set `save_best_only` to `False`, every single epoch will save down another version of the model.```stopper = EarlyStopping(monitor='val_acc', min_delta=0.0003, patience=5)```This will monitor validation accuracy, and if it has not decreased by more than 0.0003 from the previous best validation accuracy for 5 epochs, training will end early.You still need to actually feed these callbacks into `fit()` when you train the model (along with all other relevant data to feed into `fit`):```model.fit(callbacks=[checkpoint, stopper])``` GPU timeThe rest of the notebook will give you the code for training, so you can turn on the GPU at this point - but first, make sure to save your jupyter notebook. Once the GPU is turned on, it will load whatever your last notebook checkpoint is. While we suggest reading through the code below to make sure you understand it, you can otherwise go ahead and select Cell > Run All (or Kernel > Restart & Run All if already using GPU) to run through all cells in the notebook. ###Code from sklearn.utils import shuffle from sklearn.preprocessing import LabelBinarizer from keras.datasets import cifar10 (X_train, y_train), (X_val, y_val) = cifar10.load_data() # One-hot encode the labels label_binarizer = LabelBinarizer() y_one_hot_train = label_binarizer.fit_transform(y_train) y_one_hot_val = label_binarizer.fit_transform(y_val) # Shuffle the training & test data X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train) X_val, y_one_hot_val = shuffle(X_val, y_one_hot_val) # We are only going to use the first 10,000 images for speed reasons # And only the first 2,000 images from the test set X_train = X_train[:10000] y_one_hot_train = y_one_hot_train[:10000] X_val = X_val[:2000] y_one_hot_val = y_one_hot_val[:2000] ###Output _____no_output_____ ###Markdown You can check out Keras's [ImageDataGenerator documentation](https://faroit.github.io/keras-docs/2.0.9/preprocessing/image/) for more information on the below - you can also add additional image augmentation through this function, although we are skipping that step here so you can potentially explore it in the upcoming project. ###Code # Use a generator to pre-process our images for ImageNet from keras.preprocessing.image import ImageDataGenerator from keras.applications.inception_v3 import preprocess_input if preprocess_flag == True: datagen = ImageDataGenerator(preprocessing_function=preprocess_input) val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input) else: datagen = ImageDataGenerator() val_datagen = ImageDataGenerator() # Train the model batch_size = 32 epochs = 5 # Note: we aren't using callbacks here since we only are using 5 epochs to conserve GPU time model.fit_generator(datagen.flow(X_train, y_one_hot_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size, epochs=epochs, verbose=1, validation_data=val_datagen.flow(X_val, y_one_hot_val, batch_size=batch_size), validation_steps=len(X_val)/batch_size) ###Output Epoch 1/5 312/312 [============================>.] - ETA: 5s - loss: 1.3562 - acc: 0.5491
lab/Lab 03 - Polynomial Fitting.ipynb	###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=1, size=len(y_)) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=10, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * from currency_converter import CurrencyConverter response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() # go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), # layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", # xaxis_title=r"$k$ - Fitted Polynomial Degree", # yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. y = y_ + np.random.normal(size=len(y_))ks = range(2, 8)fig = make_subplots(2, 3, subplot_titles=list(ks))for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2))fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code from sklearn.model_selection import ParameterGrid # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(testX, testY).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] # give all samples from 0 with 2 skip x is 20 points y_ = response(x) # put in polynom df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) #skip first 2 rows, the first col serves as row? x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) #5 points , poly degree 4 pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) #add noise ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() #make avrage to mse by k sigma go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=np.linspace(0, 5, 10), size=len(y_)) testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() #make avrage to mse by k sigma go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * import numpy as np import pandas as pd response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], ) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code from sklearn.model_selection import ParameterGrid # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting # testY = response(testX) + np.random.normal(scale=___, size=len(y_)) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1), repetition=range(10))): y = response(testX) + np.random.normal(scale=setting["k"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() 1 ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output [-1.2 -1.03589744 -0.87179487 -0.70769231 -0.54358974 -0.37948718 -0.21538462 -0.05128205 0.11282051 0.27692308 0.44102564 0.60512821 0.76923077 0.93333333 1.0974359 1.26153846 1.42564103 1.58974359 1.75384615 1.91794872] ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output [[ 1. -1.2 1.44 -1.728 2.0736 ] [ 1. -1.03589744 1.0730835 -1.11160444 1.15150819] [ 1. -0.87179487 0.7600263 -0.66258703 0.57763997] [ 1. -0.70769231 0.5008284 -0.35443241 0.25082909] [ 1. -0.54358974 0.29548981 -0.16062523 0.08731423]] ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) df = [] for setting in ParameterGrid(dict(k=range(len(testX)), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(testX, testY).predict(X) df.append([setting["k"], setting["s"], np.mean((y_ - y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting # testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting['s'], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) testY = response(testX) + np.random.normal(scale=s, size=len(y_)) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting #testY = response(testX) + np.random.normal(scale=___, size=len(y_)) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) normal_y = response(X) + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, normal_y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary print(df) make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output Level Salary Position Business Analyst 1 45000 Junior Consultant 2 50000 Senior Consultant 3 60000 Manager 4 80000 Country Manager 5 110000 Region Manager 6 150000 Partner 7 200000 Senior Partner 8 300000 C-level 9 500000 CEO 10 1000000 ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() print(df) go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output k sigma mse 0 0 0.000000 3.598577 1 0 0.555556 3.974319 2 0 1.111111 4.887394 3 0 1.666667 5.692945 4 0 2.222222 6.687744 .. .. ... ... 95 9 2.777778 4.100094 96 9 3.333333 6.002319 97 9 3.888889 8.120578 98 9 4.444444 14.079196 99 9 5.000000 12.801462 [100 rows x 3 columns] ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df2 = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df2.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df2 = pd.DataFrame.from_records(df2, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() # print(df2) go.Figure(go.Heatmap(x=df2.k, y=df2.sigma, z=df2.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without* an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=1, size=len(y_)) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code from sklearn.model_selection import ParameterGrid # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Test } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() # # Generate the x values of the test set # testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting # testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code from sklearn.model_selection import ParameterGrid df = [] testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): # Generate the x values of the test set # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) # y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] #choose only 20 samples y_ = response(x) #apply the response function df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures #make from each point the polynomial dgrees m, k, X = 5, 4, x.reshape(-1, 1) #make it vector amyda pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) #add noise ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) #more noise affect k = 4 fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) testY = response(testX) + np.random.normal(scale=k, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous** response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=___, size=len(y_)) ###Output _____no_output_____ ###Markdown Lab 03 - Polynomial FittingIn the previous lab we discussed linear regression and the OLS estimator for solving the minimization of the RSS. As wementioned, regression problems are a very wide family of settings and algorithms which we use to try to estimate the relation between a set of explanatory variables and a continuous response (i.e. $\mathcal{Y}\in\mathbb{R}^p$). In the following lab we will discuss one such setting called "Polynomial Fitting". Sometimes, the data (and the relation between the explanatory variables and response) can be described by some polynomialof some degree. Here, we only focus on the case where it is a polynomial of a single variable. That is: $$ p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R} $$So our hypothesis class is of the form:$$ \mathcal{H}^k_{poly}=\left\{p_k\,\Big\|\, p_k\left(x\right)=\sum_{i=0}^{k}\alpha_i x^i\quad\alpha_0,\ldots,\alpha_k\in\mathbb{R}\right\} $$Notice that similar to linear regression, each hypothesis in the class is defined by a coefficients vector. Below are twoexamples (simulated and real) for datasets where the relation between the explanatory variable and response is polynomial. ###Code import sys sys.path.append("../") from utils import * response = lambda x: x*4 - 2x*3 - .5x*2 + 1 x = np.linspace(-1.2, 2, 40)[0::2] y_ = response(x) df = pd.read_csv("../datasets/Position_Salaries.csv", skiprows=2, index_col=0) x2, y2 = df.index, df.Salary make_subplots(1, 2, subplot_titles=(r"$\text{Simulated Data: }y=x^4-2x^3-0.5x^2+1$", r"$\text{Positions Salary}$"))\ .add_traces([go.Scatter(x=x, y=y_, mode="markers", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x2, y=y2, mode="markers",marker=dict(color="black", opacity=.7), showlegend=False)], rows=[1,1], cols=[1,2])\ .update_layout(title=r"$\text{(1) Datasets For Polynomial Fitting}$", margin=dict(t=100)).show() ###Output _____no_output_____ ###Markdown As we have discussed in class, solving a polynomial fitting problem can be done by first manipulating the input data,such that we represent each sample $x_i\in\mathbb{R}$ as a vector $\mathbf{x}_i=\left(x^0,x^1,\ldots,x^k\right)$. Then,we treat the data as a design matrix $\mathbf{X}\in\mathbb{R}^{m\times k}$ of a linear regression problem.For the simulated dataset above, which is of a polynomial of degree 4, the design matrix looks as follows: ###Code from sklearn.preprocessing import PolynomialFeatures m, k, X = 5, 4, x.reshape(-1, 1) pd.DataFrame(PolynomialFeatures(k).fit_transform(X[:m]), columns=[rf"$x^{{0}}$".format(i) for i in range(0, k+1)], index=[rf"$x_{{0}}$".format(i) for i in range(1, m+1)]) ###Output _____no_output_____ ###Markdown Fitting A Polynomial Of Different DegreesNext, let us fit polynomials of different degrees and different noise properties to study how it influences the learned model.We begin with the noise-less case where we fit for different values of $k$. As we increase $k$ we manage to fit a modelthat describes the data in a better way, reflected by the decrease in the MSE.Notice that in both the `PolynomialFeatures` and `LinearRegression` functions we can add the bias/intercept parameter. As in this case it makes no difference, we will include the bias in the polynomial features transformation and fit a linear regression without an intercept. The bias parameter of the polynomial features (i.e. $x^0$) will in reality be the intercept of the linear regression.* ###Code from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline ks = [2, 3, 4, 5] fig = make_subplots(1, 4, subplot_titles=list(ks)) for i, k in enumerate(ks): y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=1, cols=i+1) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y_-y_hat)2), 2)) fig.update_layout(title=r"$\text{(2) Simulated Data - Fitting Polynomials of Different Degrees}$", margin=dict(t=60), yaxis_title=r"$\widehat{y}$", height=300).show() ###Output _____no_output_____ ###Markdown Once we find the right $k$ (which in our case is 4) we managed to fit a perfect model, after which, as we increase $k$, the additional coefficients will be zero. ###Code coefs = {} for k in ks: fit = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y_) coefs[rf"$k={{{k}}}$"] = [round(c,3) for c in fit.steps[1][1].coef_] pd.DataFrame.from_dict(coefs, orient='index', columns=[rf"$w_{{{i}}}$" for i in range(max(ks)+1)]) ###Output _____no_output_____ ###Markdown Fitting Polynomial Of Different Degrees - With Sample NoiseStill fitting for different values of $k$, let us add some standard Gaussian noise (i.e. $\mathcal{N}\left(0,1\right)$).This time we observe two things:- Even for the correct $k=4$ model we are not able to achieve zero MSE.- As we increase $4<k\rightarrow 7$ we manage to decrease the error more and more. ###Code y = y_ + np.random.normal(size=len(y_)) ks = range(2, 8) fig = make_subplots(2, 3, subplot_titles=list(ks)) for i, k in enumerate(ks): r,c = i//3+1, i%3+1 y_hat = make_pipeline(PolynomialFeatures(k), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$k={{0}}, MSE={{1}}$".format(k, round(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(4) Simulated Data With Noise - Fitting Polynomials of Different Degrees}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown How is it that we are able to fit "better" models for $k$s larger than the true one? As we increase $k$ we enable the modelmore "degrees of freedom" to try and adapt itself to the observed data. The higher $k$ the more the learner will "go afterthe noise" and miss the real signal of the data. In other words, what we have just observed is what is known as overfitting.Later in the course we will learn methods for detection and avoidance of overfitting. Fitting Polynomial Over Different Sample Noise LevelsNext, let us set $k=4$ (the true values) and study the outputted models when training over different noise levels. Thoughwe will only be changing the scale of the noise (i.e. the variance, $\sigma^2$), changing other properties such as itsdistribution is interesting too. As we would expect, as we increase the scale of the noise our error increases. We canobserve this also in a visual manner, where the fitted polynomial (in blue) less and less resembles the actual model (in black). ###Code scales = range(6) fig = make_subplots(2, 3, subplot_titles=list(map(str, scales))) for i, s in enumerate(scales): r,c = i//3+1, i%3+1 y = y_ + np.random.normal(scale=s, size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(4), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) fig.add_traces([go.Scatter(x=x, y=y_, mode="markers", name="Real Points", marker=dict(color="black", opacity=.7), showlegend=False), go.Scatter(x=x, y=y, mode="markers", name="Observed Points", marker=dict(color="red", opacity=.7), showlegend=False), go.Scatter(x=x, y=y_hat, mode="markers", name="Predicted Points", marker=dict(color="blue", opacity=.7), showlegend=False)], rows=r, cols=c) fig["layout"]["annotations"][i]["text"] = rf"$\sigma^2={{0}}, MSE={{1}}$".format(s, rou=nd(np.mean((y-y_hat)2), 2)) fig.update_layout(title=r"$\text{(5) Simulated Data - Different Noise Scales}$", margin=dict(t=80)).show() ###Output _____no_output_____ ###Markdown The Influence Of $k$ And $\sigma^2$ On ErrorLastly, let us check how the error is influenced by both $k$ and $\sigma^2$. For each value of $k$ and $\sigma^2$ we willadd noise drawn from $\mathcal{N}\left(0,\sigma^2\right)$ and then, based on the noisy data, let the learner select anhypothesis from $\mathcal{H}_{poly}^k$. We repeat the process for each set of $\left(k,\sigma^2\right)$ 10 times and reportthe mean MSE value. Results are seen in heatmap below: ###Code from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(X) df.append([setting["k"], setting["s"], np.mean((y-y_hat)2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____ ###Markdown Time To Think...In the above figure, we observe the following trends:- As already seen before, for the noise-free data, once we reach the correct $k$ we achieve zero MSE.- Across all values of $k$, as we increase $\sigma^2$ we get higher MSE values.- For all noise levels, we manage to reduce MSE values by increasing $k$.So, by choosing a richer hypothesis class (i.e. larger and that can express more functions - polynomials of higherdegree) we are able to choose an hypothesis that fits the observed data better, regardless to how noisy the data is.Try and think how the above heatmap would look if instead of calculating the MSE over the training samples (i.e train error)we would have calculated it over a new set of test samples drawn from the same distribution.Use the below code to create a test set. Change the code generating figure 6 such that the reported error is a test error. Do not forget to add the noise (that depends on $\sigma^2$) to the test data. What has changed between what we observe for the train error to the test error? What happens for high/low values of $\sigma^2$? What happens for high/low values of $k$? ###Code # Generate the x values of the test set testX = np.linspace(-1.2, 2, 40)[1::2].reshape(-1,1) # Generate the noisy y values of the test set. Set the noise level (the scale parameter) according to the specific setting testY = response(testX) + np.random.normal(scale=setting["s"], size=len(y_)) from sklearn.model_selection import ParameterGrid df = [] for setting in ParameterGrid(dict(k=range(10), s=np.linspace(0, 5, 10), repetition=range(10))): y = y_ + np.random.normal(scale=setting["s"], size=len(y_)) y_hat = make_pipeline(PolynomialFeatures(setting["k"]), LinearRegression(fit_intercept=False)).fit(X, y).predict(testX) df.append([setting["k"], setting["s"], np.mean((testY-y_hat)**2)]) df = pd.DataFrame.from_records(df, columns=["k", "sigma","mse"]).groupby(["k","sigma"]).mean().reset_index() go.Figure(go.Heatmap(x=df.k, y=df.sigma, z=df.mse, colorscale="amp"), layout=go.Layout(title=r"$\text{(6) Average Train } MSE \text{ As Function of } \left(k,\sigma^2\right)$", xaxis_title=r"$k$ - Fitted Polynomial Degree", yaxis_title=r"$\sigma^2$ - Noise Levels")).show() ###Output _____no_output_____
impl/jupyter_notebooks/cicids2017_attacks/3_friday/ddos/data_exploration.ipynb	###Markdown 0. Load the data ###Code # imports: import pandas as pd import matplotlib.pyplot as plt import os PREFIX_PATH = '/home/sramkova/diploma_thesis_data/cicids2017/attacks' PREFIX_PATH = PREFIX_PATH + '/' + '/'.join(os.getcwd().split('/')[-2:]) + '/' INPUT_CSV = PREFIX_PATH + 'query_output_processing.csv' print(INPUT_CSV) raw_data = pd.read_csv(INPUT_CSV) # check that all columns are correctly loaded raw_data.head() pd.set_option('display.max_rows', None) raw_data.dtypes pd.set_option('display.max_rows', 10) # select only original connection columns (no neighbourhood): raw_data = raw_data[['originated_ip', 'responded_ip', 'connection.uid', 'connection.ts', 'connection.resp_pkts', 'connection.resp_ip_bytes', 'connection.orig_p', 'connection.duration', 'connection.resp_bytes', 'connection.orig_pkts', 'connection.proto', 'connection.service', 'connection.orig_bytes', 'connection.conn_state', 'connection.resp_p', 'connection.orig_ip_bytes', 'dns_count', 'ssh_count', 'http_count', 'ssl_count', 'files_count', ]] raw_data.head() ###Output _____no_output_____ ###Markdown 1. Get to know the data 1.0 Basic statistics ###Code # types and number of non-null values in columns: raw_data.info() # simple statistics of numerical attributes: raw_data.describe() # source: https://www.geeksforgeeks.org/python-pandas-dataframe-describe-method/ # remove null values to avoid errors nonnull_data = raw_data.dropna() # percentile list perc =[.20, .40, .60, .80] # list of dtypes to include include =['object', 'float', 'int'] # calling describe method nonnull_data.describe(percentiles = perc, include = include) ###Output _____no_output_____ ###Markdown 1.1 Show distinct counts ###Code distinct_counter = raw_data.apply(lambda x: len(x.unique())) distinct_counter ###Output _____no_output_____ ###Markdown 1.2 Visualize frequencies Get Frequency Counts of Categorical Columns: - connection.proto- connection.service- connection.conn_state(https://cmdlinetips.com/2018/02/how-to-get-frequency-counts-of-a-column-in-pandas-dataframe/) ###Code pd.reset_option('display.max_rows') raw_data['connection.proto'].value_counts() raw_data['connection.service'].value_counts(dropna=False) # display null values as well raw_data['connection.conn_state'].value_counts() ###Output _____no_output_____ ###Markdown Get Frequency Counts of Non-categorical Columns: - originated_ip- responded_ip- connection.duration- connection.orig_pkts- connection.orig_ip_bytes- connection.resp_p- connection.orig_bytes- connection.resp_bytes- connection.resp_ip_bytes- connection.orig_p- connection.resp_pkts ###Code raw_data['originated_ip'].value_counts() # or print relative frequencies of unique values: # (sometimes percentage is a better criterion then the count) raw_data['originated_ip'].value_counts(normalize=True) raw_data['responded_ip'].value_counts() raw_data['connection.duration'].value_counts() pd.set_option('display.max_rows', None) raw_data['connection.duration'].value_counts().nlargest(100) # raw_data['connection.orig_pkts'].value_counts() raw_data['connection.orig_pkts'].value_counts().nlargest(40) # raw_data['connection.orig_ip_bytes'].value_counts() # raw_data['connection.orig_ip_bytes'].value_counts().nlargest(40) # raw_data['connection.resp_p'].value_counts() pd.set_option('display.max_rows', 30) raw_data['connection.resp_p'].value_counts() pd.set_option('display.max_rows', None) raw_data['connection.resp_p'].value_counts().nlargest(40) # raw_data['connection.orig_bytes'].value_counts() raw_data['connection.orig_bytes'].value_counts().nlargest(40) # raw_data['connection.resp_bytes'].value_counts() raw_data['connection.resp_bytes'].value_counts().nlargest(40) # raw_data['connection.resp_ip_bytes'].value_counts() raw_data['connection.resp_ip_bytes'].value_counts().nlargest(40) pd.set_option('display.max_rows', 30) raw_data['connection.orig_p'].value_counts() # raw_data['connection.orig_p'].value_counts().nlargest(40) # raw_data['connection.resp_pkts'].value_counts() pd.set_option('display.max_rows', 30) raw_data['connection.resp_pkts'].value_counts().nlargest(40) ###Output _____no_output_____ ###Markdown 1.3 Application data counts ###Code raw_data['dns_count'].value_counts() raw_data['ssh_count'].value_counts() raw_data['http_count'].value_counts() raw_data['ssl_count'].value_counts() raw_data['files_count'].value_counts() ###Output _____no_output_____
python-baseball-simulator/Simulating baseball in Python.ipynb	###Markdown Simulating baseball in PythonThis notebook provides the methodology and code used in the blog post, [How much does batting order matter in Major League Baseball? A simulation approach](http://www.randalolson.com/2018/07/04/does-batting-order-matter-in-major-league-baseball-a-simulation-approach). Notebook by [Randal S. Olson](http://www.randalolson.com)Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projectslicense) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible. Required Python librariesIf you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.This code uses base Python libraries except for `seaborn`, `tqdm`, and `joblib` packages. You can install these packages using `pip` by typing the following commands into your command line:> pip install seaborn tqdm joblib Using the baseball simulatorBelow is the Python code used to simulate baseball in my blog post, run the simulations, and generate the data visualizations shown in my blog post. When I get more time, I plan to clean up and comment this code better than it currently is.For the data visualizations, you will need to place [this tableau10.mplstyle](https://gist.github.com/rhiever/d0a7332fe0beebfdc3d5) in your `~/.matplotlib/stylelib/` directory for the visualizations to show up as they do in my blog post. Otherwise, you will have to use [other matplotlib styles](https://matplotlib.org/users/style_sheets.html).If you have any comments or questions about this project, I prefer that you [file an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) on this GitHub repository. If you don't feel comfortable with GitHub, feel free to [contact me by email](http://www.randalolson.com/contact/). ###Code %matplotlib inline import matplotlib.pyplot as plt import seaborn as sb import numpy as np from tqdm import tqdm_notebook as tqdm from joblib import Parallel, delayed import time # ParallelExecutor code taken and modified from https://gist.github.com/MInner/12f9cf961059aed1a60e72c5531a697f def text_progessbar(seq, total=None): step = 1 tick = time.time() while True: time_diff = time.time() - tick avg_speed = time_diff / step total_str = 'of {}'.format(total if total else '') print('step', step, '{}'.format(round(time_diff, 2)), 'avg: {} iter/sec'.format(round(avg_speed)), total_str) step += 1 yield next(seq) all_bar_funcs = { 'tqdm': lambda args: lambda x: tqdm(x, args), 'txt': lambda args: lambda x: text_progessbar(x, args), 'False': lambda args: iter, 'None': lambda args: iter, } def ParallelExecutor(use_bar='tqdm', joblib_args): def aprun(bar=use_bar, tq_args): def tmp(op_iter): if str(bar) in all_bar_funcs.keys(): bar_func = all_bar_funcs[str(bar)](tq_args) else: raise ValueError('Value {} not supported as bar type'.format(bar)) return Parallel(*joblib_args)(bar_func(op_iter)) return tmp return aprun def simulate_game(batters, return_stats=False): '''Simulates the batting side of a Major League Baseball game This is a simplified simulation of a baseball game, where each batter performs randomly according to their corresponding batting average. This simulation incorporates different types of hits, such as singles, doubles, triples, and home runs, and uses 2017-2018 Major League averages for the probabilities of those hit types occurring. This simulation leaves out other aspects of the game, such as individual-level hit type tendencies, double plays, stolen bases, errors, and so forth. Parameters ---------- batters: list A list of batting averages for 9 batters in the desired batting order Example input: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25] Returns ---------- runs_scored: int The number of runs scored by the batters in one simulated game batting_stats: dict Dictionary containing batting statistics for each batter ''' runs_scored = 0 batter_num = 0 # NOTE: Earned Bases is the total number of bases that the batter advanced themselves # AND their teammates through batting batting_stats = {} for batter in range(len(batters)): batting_stats[batter] = { 'At Bat': 0, 'Single': 0, 'Double': 0, 'Triple': 0, 'Home Run': 0, 'Out': 0, 'RBI': 0, 'Earned Bases': 0, 'Players On Base': 0, 'Bases Loaded': 0, 'Grand Slam': 0 } # Assume the game lasts for only 9 innings (no extra innings) for inning in range(9): bases = [ False, # First base, index 0 False, # Second base, index 1 False # Third base, index 2 ] batters_out = 0 while batters_out < 3: batting_stats[batter_num]['At Bat'] += 1 if bases[2] and bases[1] and bases[0]: batting_stats[batter_num]['Bases Loaded'] += 1 if bases[2]: batting_stats[batter_num]['Players On Base'] += 1 if bases[1]: batting_stats[batter_num]['Players On Base'] += 1 if bases[0]: batting_stats[batter_num]['Players On Base'] += 1 if np.random.random() < batters[batter_num]: # Batting estimates from MLB.com statistics in 2017/2018 seasons: # Single base hit: 64% of hits # Double base hit: 20% of hits # Triple base hit: 2% of hits # Home run: 14% of hits hit_type = np.random.choice(['Single', 'Double', 'Triple', 'Home Run'], p=[.64, .2, .02, .14]) if hit_type == 'Single': batting_stats[batter_num]['Single'] += 1 # All base runners advance 1 base if bases[2]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 1 bases[2] = False if bases[1]: bases[2] = True bases[1] = False batting_stats[batter_num]['Earned Bases'] += 1 if bases[0]: bases[1] = True batting_stats[batter_num]['Earned Bases'] += 1 bases[0] = True batting_stats[batter_num]['Earned Bases'] += 1 elif hit_type == 'Double': batting_stats[batter_num]['Double'] += 1 # All base runners advance 2 bases if bases[2]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 1 bases[2] = False if bases[1]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 2 bases[1] = False if bases[0]: bases[2] = True batting_stats[batter_num]['Earned Bases'] += 2 bases[0] = False bases[1] = True batting_stats[batter_num]['Earned Bases'] += 2 elif hit_type == 'Triple': batting_stats[batter_num]['Triple'] += 1 # All base runners advance 3 bases if bases[2]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 1 bases[2] = False if bases[1]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 2 bases[1] = False if bases[0]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 3 bases[0] = False bases[2] = True batting_stats[batter_num]['Earned Bases'] += 3 elif hit_type == 'Home Run': batting_stats[batter_num]['Home Run'] += 1 # Check if a Grand Slam was scored if bases[0] and bases[1] and bases[2]: batting_stats[batter_num]['Grand Slam'] += 1 # All base runners and the hitter score a run if bases[2]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 1 bases[2] = False if bases[1]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 2 bases[1] = False if bases[0]: runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 3 bases[0] = False runs_scored += 1 batting_stats[batter_num]['RBI'] += 1 batting_stats[batter_num]['Earned Bases'] += 4 else: # Batter struck out, flew out, or grounded out batters_out += 1 batting_stats[batter_num]['Out'] += 1 batter_num = (batter_num + 1) % len(batters) return runs_scored, batting_stats designated_hitter_spot_scores = {} num_simulated_games = 1000000 for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): for designated_hitter_spot in range(9): batters = [team_avg] 9 batters[designated_hitter_spot] = 0.35 aprun = ParallelExecutor(n_jobs=-1, use_bar=False) designated_hitter_spot_scores[(team_avg, designated_hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))] dh_batting_avgs = [] for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): dh_spot_avgs = [] for designated_hitter_spot in range(9): dh_spot_avgs.append(np.mean(designated_hitter_spot_scores[(team_avg, designated_hitter_spot)])) dh_spot_avgs = np.array(dh_spot_avgs) / np.mean(dh_spot_avgs) dh_batting_avgs.append(dh_spot_avgs) plt.figure(figsize=(9, 9)) sb.heatmap(dh_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False) plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12) plt.xlabel('DH Batting Position (BA=0.35)', fontsize=14) plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center') plt.ylabel('Team Batting Average (BA)', fontsize=14) plt.title('Batting order matters when one player is much\nbetter than their teammates\n\n', fontsize=20) plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the DH batting position\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center') plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-dh.png', bbox_inches='tight') ; from scipy.stats import ranksums from itertools import product for team_avg1, designated_hitter_spot1, team_avg2, designated_hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)): if team_avg1 != team_avg2: continue if designated_hitter_spot1 > designated_hitter_spot2: continue if team_avg1 == team_avg2 and designated_hitter_spot1 == designated_hitter_spot2: continue statistic, pval = ranksums(designated_hitter_spot_scores[(team_avg1, designated_hitter_spot1)], designated_hitter_spot_scores[(team_avg2, designated_hitter_spot2)]) if pval < 1e-5: print('sig diff: team avg={}, dh pos={} vs. dh pos={} [p={}]'.format(team_avg1, designated_hitter_spot1 + 1, designated_hitter_spot2 + 1, pval)) pitcher_spot_scores = {} num_simulated_games = 1000000 for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): for pitcher_spot in range(9): batters = [team_avg] * 9 batters[pitcher_spot] = 0.1 aprun = ParallelExecutor(n_jobs=-1, use_bar=False) pitcher_spot_scores[(team_avg, pitcher_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))] p_batting_avgs = [] for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): p_spot_avgs = [] for pitcher_spot in range(9): p_spot_avgs.append(np.mean(pitcher_spot_scores[(team_avg, pitcher_spot)])) p_spot_avgs = np.array(p_spot_avgs) / np.mean(p_spot_avgs) p_batting_avgs.append(p_spot_avgs) plt.figure(figsize=(9, 9)) sb.heatmap(p_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False) plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12) plt.xlabel('Pitcher Batting Position (BA=0.1)', fontsize=14) plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center') plt.ylabel('Team Batting Average (BA)', fontsize=14) plt.title('Batting order matters when one player is much\nworse than their teammates\n\n', fontsize=20) plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Pitcher batting position\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center') plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-pitcher.png', bbox_inches='tight') ; from scipy.stats import ranksums from itertools import product for team_avg1, pitcher_spot1, team_avg2, pitcher_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)): if team_avg1 != team_avg2: continue if pitcher_spot1 > pitcher_spot2: continue if team_avg1 == team_avg2 and pitcher_spot1 == pitcher_spot2: continue statistic, pval = ranksums(pitcher_spot_scores[(team_avg1, pitcher_spot1)], pitcher_spot_scores[(team_avg2, pitcher_spot2)]) if pval < 1e-5: print('sig diff: team avg={}, pitcher pos={} vs. pitcher pos={} [p={}]'.format(team_avg1, pitcher_spot1 + 1, pitcher_spot2 + 1, pval)) hitter_spot_scores = {} num_simulated_games = 1000000 for hitter_ba in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): for hitter_spot in range(9): batters = [0.25] * 9 batters[hitter_spot] = hitter_ba aprun = ParallelExecutor(n_jobs=-1, use_bar=False) hitter_spot_scores[(hitter_ba, hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))] hitter_batting_avgs = [] for hitter_ba in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]): hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean(hitter_spot_scores[(hitter_ba, hitter_spot)])) hitter_spot_avgs = np.array(hitter_spot_avgs) / np.mean(hitter_spot_avgs) hitter_batting_avgs.append(hitter_spot_avgs) plt.figure(figsize=(9, 9)) sb.heatmap(hitter_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False) plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12) plt.xlabel('Hitter Batting Position (Team BA=0.25)', fontsize=14) plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center') plt.ylabel('Hitter Batting Average (BA)', fontsize=14) plt.title('Exceptional batters should lead the batting line-up,\npoor batters should conclude the line-up\n\n', fontsize=20) plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Hitter batting position & BA\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center') plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-varying-hitter.png', bbox_inches='tight') ; from scipy.stats import ranksums from itertools import product for hitter_avg1, hitter_spot1, hitter_avg2, hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)): if hitter_avg1 != hitter_avg2: continue if hitter_spot1 > hitter_spot2: continue if hitter_avg1 == hitter_avg2 and hitter_spot1 == hitter_spot2: continue statistic, pval = ranksums(hitter_spot_scores[(hitter_avg1, hitter_spot1)], hitter_spot_scores[(hitter_avg2, hitter_spot2)]) if pval < 1e-5: print('sig diff: batter avg={}, batter pos={} vs. batter pos={} [p={}]'.format(hitter_avg1, hitter_spot1 + 1, hitter_spot2 + 1, pval)) batters = [0.25] * 9 num_simulated_games = 1000000 aprun = ParallelExecutor(n_jobs=-1, use_bar=False) average_team_stats = [game_stats for _, game_stats in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))] batting_stat = 'At Bat' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) with plt.style.context('tableau10'): plt.figure() plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD') batting_stat += 's per Game' plt.ylabel(batting_stat) plt.xticks(range(9), [str(x) for x in range(1, 10)]) plt.xlabel('Batting Position') plt.title('Earlier batters have more At Bats on average') plt.text(-1.3, -0.75, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight') ; batting_stat = 'At Bat' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) hitter_spot_avgs batting_stat = 'Players On Base' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) with plt.style.context('tableau10'): plt.figure() plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD') batting_stat += ' per Game' plt.ylabel(batting_stat) plt.xticks(range(9), [str(x) for x in range(1, 10)]) plt.xlabel('Batting Position') plt.title('Middle batters tend to have more players on base when batting') plt.text(-1.5, -0.4, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight') ; batting_stat = 'RBI' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) with plt.style.context('tableau10'): plt.figure() plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD') batting_stat += ' per Game' plt.ylabel(batting_stat) plt.xticks(range(9), [str(x) for x in range(1, 10)]) plt.xlabel('Batting Position') plt.title('Middle batters tend to contribute more RBI') plt.text(-1.6, -0.06, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight') ; batting_stat = 'RBI' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) hitter_spot_avgs batting_stat = 'Bases Loaded' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) with plt.style.context('tableau10'): plt.figure() plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD') batting_stat += ' per Game' plt.ylabel(batting_stat) plt.xticks(range(9), [str(x) for x in range(1, 10)]) plt.xlabel('Batting Position') plt.title('The 6th batter is most likely to face a Bases Loaded situation') plt.text(-1.6, -0.013, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight') ; batting_stat = 'Grand Slam' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) with plt.style.context('tableau10'): plt.figure() plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD') batting_stat += 's per Game' plt.ylabel(batting_stat) plt.xticks(range(9), [str(x) for x in range(1, 10)]) plt.xlabel('Batting Position') plt.title('The 6th batter is most likely to hit a Grand Slam') plt.text(-1.85, -0.0005, 'Data source: League averages & custom baseball simulations\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left') plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight') ; batting_stat = 'Grand Slam' hitter_spot_avgs = [] for hitter_spot in range(9): hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats])) hitter_spot_avgs ###Output _____no_output_____
MTGNN+model5 (1).ipynb	###Markdown vacc.append(val_acc) vrae.append(val_rae) vcorr.append(val_corr) acc.append(test_acc) rae.append(test_rae) corr.append(test_corr)print('\n\n')print('10 runs average')print('\n\n')print("valid\trse\trae\tcorr")print("mean\t{:5.4f}\t{:5.4f}\t{:5.4f}".format(np.mean(vacc), np.mean(vrae), np.mean(vcorr)))print("std\t{:5.4f}\t{:5.4f}\t{:5.4f}".format(np.std(vacc), np.std(vrae), np.std(vcorr)))print('\n\n')print("test\trse\trae\tcorr")print("mean\t{:5.4f}\t{:5.4f}\t{:5.4f}".format(np.mean(acc), np.mean(rae), np.mean(corr)))print("std\t{:5.4f}\t{:5.4f}\t{:5.4f}".format(np.std(acc), np.std(rae), np.std(corr))) ###Code device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(device) print(device) #parser = argparse.ArgumentParser(description='PyTorch Time series forecasting' data='solar_AL.txt' data='C:\0.ml\MTGNN\data\solar_AL.txt' log_interval=2000 metavar='N'#,help='report interval' save='model/model.pt'#,help='path to save the final model' optim2='adam' L1Loss=True normalize=2 #device='cuda:1' device='cuda:0' gcn_true=True buildA_true=True gcn_depth=2 num_nodes=137 dropout=0.3#,help='dropout rate' subgraph_size=20#,help='k' node_dim=40#,help='dim of nodes' dilation_exponential=2#,help='dilation exponential' conv_channels=16#,help='convolution channels' residual_channels=16#,help='residual channels' skip_channels=32#,help='skip channels' end_channels=64#,help='end channels' in_dim=1#,help='inputs dimension' seq_in_len=247#,help='input sequence length' seq_out_len=1#,help='output sequence length' horizon=3 layers=5#,help='number of layers' batch_size=32#,help='batch size' lr=0.0001#,help='learning rate' weight_decay=0.00001#,help='weight decay rate' clip=5#,help='clip' propalpha=0.05#,help='prop alpha' tanhalpha=3#,help='tanh alpha' epochs=1 num_split=1#,help='number of splits for graphs' step_size=100#,help='step_size' fin = open('C:\\0.ml\\MTGNN\\data\\solar_AL.txt') rawdat = np.loadtxt(fin, delimiter=',') #self.rawdat=load(file_name) dat = np.zeros(rawdat.shape) n, m = dat.shape print('iter') print(rawdat.shape) df = pd.DataFrame(dat) df ttt=Sampled_inputs[0] for i in range(1,Sampled_inputs.shape[0]): ttt=np.concatenate( (ttt,Sampled_inputs[1] ), axis=0) #print(ttt.shape) print(type(rawdat)) print(type(dat)) print(type(Sampled_inputs)) print(type(df)) df rawdat2="" print(rawdat2.shape) temp=rawdat2[0] print(temp.shape) df = pd.DataFrame(temp) print(df.shape) df #util.py import pickle import numpy as np import os import scipy.sparse as sp import torch from scipy.sparse import linalg from torch.autograd import Variable def load(file_name): with open(file_name, 'rb') as fp: obj = pickle.load(fp) return obj def normal_std(x): return x.std() np.sqrt((len(x) - 1.)/(len(x))) class DataLoaderS(object): # train and valid is the ratio of training set and validation set. test = 1 - train - valid def __init__(self, file_name, train, valid, device, horizon, window, normalize=2): print('__init__111') self.P = window self.h = horizon self.rawdat=rawdat2 print('self.rawdat: ',self.rawdat.shape) #fin = open(file_name) #self.rawdat = np.loadtxt(fin, delimiter=',') #self.rawdat=load(file_name) self.dat = np.zeros(self.rawdat.shape) print('self.dat.shape: ',self.dat.shape) self.n, self.m = self.dat.shape self.normalize = 2 self.scale = np.ones(self.m) self._normalized(normalize) self._split(int(train * self.n), int((train + valid) * self.n), self.n) self.scale = torch.from_numpy(self.scale).float() tmp = self.test[1] * self.scale.expand(self.test[1].size(0), self.m) self.scale = self.scale.to(device) self.scale = Variable(self.scale) self.rse = normal_std(tmp) self.rae = torch.mean(torch.abs(tmp - torch.mean(tmp))) self.device = device def _normalized(self, normalize): print('_normalized') # normalized by the maximum value of entire matrix. if (normalize == 0): self.dat = self.rawdat if (normalize == 1): self.dat = self.rawdat / np.max(self.rawdat) # normlized by the maximum value of each row(sensor). if (normalize == 2): for i in range(self.m): self.scale[i] = np.max(np.abs(self.rawdat[:, i])) self.dat[:, i] = self.rawdat[:, i] / np.max(np.abs(self.rawdat[:, i])) def _split(self, train, valid, test): print('_split') #print("type(train): ",type(train)) tarin_start=self.P + self.h - 1 valid_stop=self.n print("tarin_start: ",tarin_start) print("train: ",train) print("valid: ",valid) print("valid_stop: ",valid_stop) train_set = range(tarin_start, train) valid_set = range(train, valid) test_set = range(valid, valid_stop) print("train_set: ",train_set) print("valid_set: ",valid_set) print("test_set: ",test_set) self.train = self._batchify(train_set, self.h) self.valid = self._batchify(valid_set, self.h) self.test = self._batchify(test_set, self.h) def _batchify(self, idx_set, horizon): print('_batchify') print("idx_set: ", idx_set) #print("type(idx_set): ", type(idx_set)) n = len(idx_set) X = torch.zeros((n, self.P, self.m)) Y = torch.zeros((n, self.m)) print("len(idx_set):",len(idx_set)) print("X.shape:",X.shape) print("Y.shape:",Y.shape) for i in range(n): end = idx_set[i] - self.h + 1 start = end - self.P #print("**********") X[i, :, :] = torch.from_numpy(self.dat[start:end, :]) Y[i, :] = torch.from_numpy(self.dat[idx_set[i], :]) return [X, Y] def get_batches(self, inputs, targets, batch_size, shuffle=True): print('get_batches') length = len(inputs) if shuffle: index = torch.randperm(length) else: index = torch.LongTensor(range(length)) start_idx = 0 while (start_idx < length): end_idx = min(length, start_idx + batch_size) excerpt = index[start_idx:end_idx] X = inputs[excerpt] Y = targets[excerpt] X = X.to(self.device) Y = Y.to(self.device) yield Variable(X), Variable(Y) start_idx += batch_size class DataLoaderM(object): def __init__(self, xs, ys, batch_size, pad_with_last_sample=True): """ :param xs: :param ys: :param batch_size: :param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size. """ self.batch_size = batch_size print( 'DataLoaderM __init__') self.current_ind = 0 if pad_with_last_sample: num_padding = (batch_size - (len(xs) % batch_size)) % batch_size x_padding = np.repeat(xs[-1:], num_padding, axis=0) y_padding = np.repeat(ys[-1:], num_padding, axis=0) xs = np.concatenate([xs, x_padding], axis=0) ys = np.concatenate([ys, y_padding], axis=0) self.size = len(xs) self.num_batch = int(self.size // self.batch_size) self.xs = xs self.ys = ys def shuffle(self): print('shuffle') permutation = np.random.permutation(self.size) xs, ys = self.xs[permutation], self.ys[permutation] self.xs = xs self.ys = ys def get_iterator(self): print('get_iterator') self.current_ind = 0 def _wrapper(): while self.current_ind < self.num_batch: start_ind = self.batch_size self.current_ind end_ind = min(self.size, self.batch_size * (self.current_ind + 1)) x_i = self.xs[start_ind: end_ind, ...] y_i = self.ys[start_ind: end_ind, ...] yield (x_i, y_i) self.current_ind += 1 return _wrapper() class StandardScaler(): """ Standard the input """ def __init__(self, mean, std): self.mean = mean self.std = std def transform(self, data): return (data - self.mean) / self.std def inverse_transform(self, data): return (data * self.std) + self.mean def sym_adj(adj): """Symmetrically normalize adjacency matrix.""" adj = sp.coo_matrix(adj) rowsum = np.array(adj.sum(1)) d_inv_sqrt = np.power(rowsum, -0.5).flatten() d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. d_mat_inv_sqrt = sp.diags(d_inv_sqrt) return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense() def asym_adj(adj): """Asymmetrically normalize adjacency matrix.""" adj = sp.coo_matrix(adj) rowsum = np.array(adj.sum(1)).flatten() d_inv = np.power(rowsum, -1).flatten() d_inv[np.isinf(d_inv)] = 0. d_mat= sp.diags(d_inv) return d_mat.dot(adj).astype(np.float32).todense() def calculate_normalized_laplacian(adj): """ # L = D^-1/2 (D-A) D^-1/2 = I - D^-1/2 A D^-1/2 # D = diag(A 1) :param adj: :return: """ adj = sp.coo_matrix(adj) d = np.array(adj.sum(1)) d_inv_sqrt = np.power(d, -0.5).flatten() d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. d_mat_inv_sqrt = sp.diags(d_inv_sqrt) normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() return normalized_laplacian def calculate_scaled_laplacian(adj_mx, lambda_max=2, undirected=True): if undirected: adj_mx = np.maximum.reduce([adj_mx, adj_mx.T]) L = calculate_normalized_laplacian(adj_mx) if lambda_max is None: lambda_max, _ = linalg.eigsh(L, 1, which='LM') lambda_max = lambda_max[0] L = sp.csr_matrix(L) M, _ = L.shape I = sp.identity(M, format='csr', dtype=L.dtype) L = (2 / lambda_max * L) - I return L.astype(np.float32).todense() def load_pickle(pickle_file): try: with open(pickle_file, 'rb') as f: pickle_data = pickle.load(f) except UnicodeDecodeError as e: with open(pickle_file, 'rb') as f: pickle_data = pickle.load(f, encoding='latin1') except Exception as e: print('Unable to load data ', pickle_file, ':', e) raise return pickle_data def load_adj(pkl_filename): sensor_ids, sensor_id_to_ind, adj = load_pickle(pkl_filename) return adj def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_size=None): data = {} for category in ['train', 'val', 'test']: cat_data = np.load(os.path.join(dataset_dir, category + '.npz')) data['x_' + category] = cat_data['x'] data['y_' + category] = cat_data['y'] scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std()) # Data format for category in ['train', 'val', 'test']: data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0]) data['train_loader'] = DataLoaderM(data['x_train'], data['y_train'], batch_size) data['val_loader'] = DataLoaderM(data['x_val'], data['y_val'], valid_batch_size) data['test_loader'] = DataLoaderM(data['x_test'], data['y_test'], test_batch_size) data['scaler'] = scaler return data def masked_mse(preds, labels, null_val=np.nan): if np.isnan(null_val): mask = ~torch.isnan(labels) else: mask = (labels!=null_val) mask = mask.float() mask /= torch.mean((mask)) mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask) loss = (preds-labels)*2 loss = loss mask loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss) return torch.mean(loss) def masked_rmse(preds, labels, null_val=np.nan): return torch.sqrt(masked_mse(preds=preds, labels=labels, null_val=null_val)) def masked_mae(preds, labels, null_val=np.nan): if np.isnan(null_val): mask = ~torch.isnan(labels) else: mask = (labels!=null_val) mask = mask.float() mask /= torch.mean((mask)) mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask) loss = torch.abs(preds-labels) loss = loss * mask loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss) return torch.mean(loss) def masked_mape(preds, labels, null_val=np.nan): if np.isnan(null_val): mask = ~torch.isnan(labels) else: mask = (labels!=null_val) mask = mask.float() mask /= torch.mean((mask)) mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask) loss = torch.abs(preds-labels)/labels loss = loss * mask loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss) return torch.mean(loss) def metric(pred, real): mae = masked_mae(pred,real,0.0).item() mape = masked_mape(pred,real,0.0).item() rmse = masked_rmse(pred,real,0.0).item() return mae,mape,rmse def load_node_feature(path): fi = open(path) x = [] for li in fi: li = li.strip() li = li.split(",") e = [float(t) for t in li[1:]] x.append(e) x = np.array(x) mean = np.mean(x,axis=0) std = np.std(x,axis=0) z = torch.tensor((x-mean)/std,dtype=torch.float) return z def normal_std(x): return x.std() * np.sqrt((len(x) - 1.) / (len(x))) data="C:\\0.ml\\Sampled_inputs.pck" Sampled_inputs=load(data) trainData=Sampled_inputs temptrainData=np.empty([1540,60, 33]) n=len(trainData) for l in range(0, n): temp=trainData[l] #print(temp) #temp=np.transpose(temp) temp=temp.T #print(temp.shape) #print(temp) temptrainData[l,:,:]=temp n=n+1 #np.append(temptrainData, temp) #print(temptrainData) #print(temptrainData.shape) #print(trainData.shape) trainData=temptrainData print("Sampled_inputs.shape: ",Sampled_inputs.shape) print("trainData.shape: ",trainData.shape) #print(trainData[0]) temp=trainData[0] rawdat2=trainData[0] print("rawdat2:",rawdat2.shape) num_nodes= 33 print("num_nodes: ",num_nodes) ttt=trainData[0] rawdat2=ttt #Sampled_inputs.shape[0] for i in range(1,3): temp=np.concatenate( (temp,trainData[1] ), axis=0) #print(temp.shape) #rawdat2=temp print("rawdat2:",rawdat2.shape) data='C:\\0.ml\\MTGNN\\data\\solar_AL.txt' #data='C:\\0.ml\\MTGNN\\data\\electricity.txt' fin = open(data) rawdat2 = np.loadtxt(fin, delimiter=',') print("rawdat2.shape: ",rawdat2.shape) num_nodes= 137 print("num_nodes: ",num_nodes) save= "C:\0.ml\MTGNN\\model-save-3.pt" seq_in_len=1 #24*7#window size horizon=3 batch_size=32#,help='batch size' num_nodes= 33 device='cpu' #device='cuda:0' main() ###Output __init__111 self.rawdat: (52560, 137) self.dat.shape: (52560, 137) _normalized _split tarin_start: 3 train: 31536 valid: 42048 valid_stop: 52560 train_set: range(3, 31536) valid_set: range(31536, 42048) test_set: range(42048, 52560) _batchify idx_set: range(3, 31536) len(idx_set): 31533 X.shape: torch.Size([31533, 1, 137]) Y.shape: torch.Size([31533, 137]) _batchify idx_set: range(31536, 42048) len(idx_set): 10512 X.shape: torch.Size([10512, 1, 137]) Y.shape: torch.Size([10512, 137]) _batchify idx_set: range(42048, 52560) len(idx_set): 10512 X.shape: torch.Size([10512, 1, 137]) Y.shape: torch.Size([10512, 137]) The recpetive field size is 187 Number of model parameters is 339345 optim = Optim() done begin training train() X.shape: torch.Size([31533, 1, 137]) Y.shape: torch.Size([31533, 137]) batch_size: 32 get_batches iter: 0 \| loss: 1.328 ----------------------------------------------------------------------------------------- Exiting from training early
SVM/Untitled.ipynb	###Markdown AS PURCHASED COLUMN IS ALREADY ENCODED WE DONT HAVE TO USE ENCODER AND IT IS OUR DEPENEDENT VARIABLE . ###Code X=df[['Age','EstimatedSalary']] y=df['Purchased'] print(X.head()) print(y.head()) ###Output Age EstimatedSalary 0 19 19000 1 35 20000 2 26 43000 3 27 57000 4 19 76000 0 0 1 0 2 0 3 0 4 0 Name: Purchased, dtype: int64 ###Markdown NOW SPLIT THE DATASET INTO TRAINING SET AND TEST SET ###Code from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=23) print(X_train.head()) print(X_test.head()) print(y_train.head()) print(y_test.head()) ###Output Age EstimatedSalary 64 59 83000 16 47 25000 77 22 27000 272 60 42000 10 26 80000 Age EstimatedSalary 133 21 68000 331 48 119000 167 35 71000 335 36 54000 239 53 143000 64 0 16 1 77 0 272 1 10 0 Name: Purchased, dtype: int64 133 0 331 1 167 0 335 0 239 1 Name: Purchased, dtype: int64 ###Markdown NOW AS OUR INDEPENDENT VARIABLE MATRIX X IS NOT IN A SCALE .SO WE HAVE TO USE DATA SCALING ###Code from sklearn.preprocessing import StandardScaler sc=StandardScaler() X_train_sc=sc.fit_transform(X_train) X_test_sc=sc.fit_transform(X_test) X_train_sc,X_test_sc ###Output C:\Users\HP\Documents\New folder\lib\site-packages\sklearn\preprocessing\data.py:625: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler. return self.partial_fit(X, y) C:\Users\HP\Documents\New folder\lib\site-packages\sklearn\base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler. return self.fit(X, fit_params).transform(X) C:\Users\HP\Documents\New folder\lib\site-packages\sklearn\preprocessing\data.py:625: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler. return self.partial_fit(X, y) C:\Users\HP\Documents\New folder\lib\site-packages\sklearn\base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler. return self.fit(X, fit_params).transform(X) ###Markdown IMPORT THE LIBRARIES ###Code from sklearn.svm import SVC svc=SVC(kernel='rbf',random_state=23) svc.fit(X_train_sc,y_train) ###Output _____no_output_____ ###Markdown SCORE OF THE ALGORITHM ###Code score=100.0* svc.score(X_test_sc,y_test) print(score) #you can check by using differrent Kernels(linear and rbf(radial basis function)) #using linear kernel will give accuracy 84.1666 and using rbf kernel will give 90.8333. #By default it is rbf ###Output 90.83333333333333 ###Markdown CLASSIFICATION REPORT ###Code from sklearn.metrics import classification_report labels=['Low','High'] result=svc.predict(X_test_sc) print(classification_report(y_test,result)) ###Output precision recall f1-score support 0 0.96 0.90 0.93 79 1 0.83 0.93 0.87 41 micro avg 0.91 0.91 0.91 120 macro avg 0.89 0.91 0.90 120 weighted avg 0.91 0.91 0.91 120 ###Markdown PERFORMANCE METRICS ###Code from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score mae=mean_absolute_error(y_test,result) mse=mean_squared_error(y_test,result) r2=r2_score(y_test,result) print(mae) print(mse) print(r2) ###Output 0.09166666666666666 0.09166666666666666 0.5924668107440567
Course_1-PreLaunch_Preparatory_Content/Module_2-Python_Libraries/2-Pandas/Session4_Operations_on_Dataframes.ipynb	###Markdown Operations on PandasThis notebook will cover the following topics: * Filtering dataframes * Single and multiple conditions* Creating new columns* Lambda functions * Group by and aggregate functions* Pivot data* Merging data frames * Joins and concatenations Preparatory steps BackgroundAn FMCG company P&J found that the sales of their best selling items are affected by the weather and rainfall trend. For example, the sale of tea increases when it rains, sunscreen is sold on the days when it is least likely to rain, and the sky is clear. They would like to check whether the weather patterns play a vital role in the sale of certain items. Hence as initial experimentation, they would like you to forecast the weather trend in the upcoming days. The target region for this activity is Australia; accordingly, this exercise will be based on analysing and cleaning the weather data from the Australian region available on public platforms. Read the data into a dataframe ###Code import pandas as pd data = pd.read_csv("weatherdata.csv", header =0) ###Output _____no_output_____ ###Markdown Display the data ###Code data.head(5) ###Output _____no_output_____ ###Markdown Data Dictionary 1. Date: The date on which the recording was taken2. Location: The location of the recording3. MinTemp: Minimum temperature on the day of the recording (in C)4. MaxTemp: Maximum temperature in the day of the recording (in C)5. Rainfall: Rainfall in mm6. Evaporation: The so-called Class A pan evaporation (mm) in the 24 hours to 9am7. Sunshine: The number of hours of bright sunshine in the day.8. WindGustDir: The direction of the strongest wind gust in the 24 hours to midnight9. WindGustSpeed: The speed (km/h) of the strongest wind gust in the 24 hours to midnight Example 1.1: Filtering dataframesFind the days which had sunshine for more that 4 hours. These days will have increased sales of sunscreen. ###Code data["Sunshine"]>4 data[data["Sunshine"]>4] ###Output _____no_output_____ ###Markdown Note: High sunshine corresponds to low rainfall. Example 1.2: Filtering dataframesThe cold drink sales will most likely increase on the days which have high sunshine(>5) and high max temperature(>35). Use the filter operation to filter out these days ###Code data[(data["MaxTemp"]>35) & (data["Sunshine"]>4)] ###Output _____no_output_____ ###Markdown Note: The construction of the filter condition, it has individual filter conditions separated in parenthesis Example 2.1: Creating new columns If you noticed the filtering done in the earlier examples did not give precise information about the days, the data column simply has the dates. The date column can be split into the year, month and day of the month. Special module of pandas The "DatetimeIndex" is a particular module which has the capabilities to extract a day, month and year form the date. ###Code pd.DatetimeIndex(data["Date"]).year ###Output _____no_output_____ ###Markdown Adding New columns To add a new column in the dataframe just name the column and pass the instructions about the creation of the new column ###Code data["Year"] = pd.DatetimeIndex(data["Date"]).year data.head() data["Month"] = pd.DatetimeIndex(data["Date"]).month data["Dayofmonth"] = pd.DatetimeIndex(data["Date"]).day data.head(20) ###Output _____no_output_____ ###Markdown Example 2.2: Creating new columnsThe temperature given is in Celcius, convert it in Fahrenheit and store it in a new column for it. ###Code data["Maxtemp_F"] = data["MaxTemp"] * 9/5 +32 data.head() ###Output _____no_output_____ ###Markdown Example 3.1: Lambda FunctionsLet's create a new column which highlights the days which have rainfall more than 50 mm as rainy days and the rest are not. ###Code data.Rainfall.apply(lambda x: "Rainy" if x > 50 else "Not rainy") ###Output _____no_output_____ ###Markdown Note 1. New way of accessing a column in a dataframe by using the dot operator.2. "apply" function takes in a lambda operator as argument. ###Code type(data.Rainfall) type(data["Rainfall"]) data["is_raining"] = data.Rainfall.apply(lambda x: "Rainy" if x > 50 else "Not rainy") data[data["is_raining"] == "Rainy"] ###Output _____no_output_____ ###Markdown In Session questionYou are provided with the dataset of a company which has offices across three cities - Mumbai, Bangalore and New Delhi. The dataset contains the rating (out of 5) of all the employees from different departments (Finance, HR, Marketing and Sales). The company has come up with a new policy that any individual with a rating equal to or below 3.5 needs to attend a training. Using dataframes, load the dataset and then derive the column ‘Training’ which shows ‘Yes’ for people who require training and ‘No’ for those who do not.Find the department that has the most efficient team (the team with minimum percentage of employees who need training). ###Code rating = pd.read_csv('rating.csv') # Provide your answer below rating["Training"] = rating.Rating.apply(lambda x: "Yes" if x <= 3.5 else "No") print(rating.head()) for i in ['Finance', 'HR', 'Sales', 'Marketing']: print(i, len(rating[(rating['Training'] == 'No') & (rating['Department'] == i)]) / len(rating[rating['Department'] == i]) * 100) ###Output Finance 50.0 HR 57.25190839694656 Sales 49.23076923076923 Marketing 46.3768115942029 ###Markdown Example 4.1: Grouping and Aggregate functionsFind the location which received the most amount of rain in the given data. In this place, certain promotional offers can be put in place to boost sales of tea, umbrella etc. ###Code data_by_location = data.groupby(by=["Location"]).mean() data_by_location.head() data_by_location.sort_values("Rainfall", ascending=False).head() ###Output _____no_output_____ ###Markdown Example 4.2: Grouping and Aggregate functionsHot chocolate is the most sold product in the cold months. Find month which is the coldest so that the inventory team can keep the stock of hot chocolate ready well in advance. ###Code data_by_month = data.groupby(by=["Month"]).mean() data_by_month.head() data_by_month.sort_values("MinTemp") ###Output _____no_output_____ ###Markdown Example 4.3: Grouping and Aggregate functionsSometimes feeling cold is more than about low temperatures; a windy day can also make you cold. A factor called the cill factor can be used to quantify the cold based on the wind speed and the temperature. The formula for the chill factor is given by $ WCI = (10 * \sqrt{v} - v + 10.5) .(33 - T_{m}) $v is the speed of the wind and $ T_{m} $ is the minimum temperatureAdd a column for WCI and find the month with th lowest WCI. ###Code from math import sqrt def wci(x): velocity = x["WindGustSpeed"] minTemp = x["MinTemp"] return (10* sqrt(velocity) - velocity + 10.5)(33 - minTemp) data["WCI"] = data.apply(wci, axis=1) data.head() data_by_month = data.groupby(by=["Month"]).mean() data_by_month.sort_values("WCI", ascending=False) ###Output _____no_output_____ ###Markdown Dataframe groupingGroup the dataframe 'df' by 'month' and 'day' and find the mean value for column 'rain' and 'wind'. ###Code df = pd.read_csv("forestfires.csv") df_1 = df[["month", "day", "rain", "wind"]] df_1 = df_1.groupby(by=["month", "day"]).mean() df_1.head(20) ###Output _____no_output_____ ###Markdown Example 5.1: Merging DataframesThe join command is used to combine dataframes. Unlike hstack and vstack, the join command works by using a key to combine to dataframes. For example the total tea for the Newcastle store for the month of June 2011 is given in the file names ```junesales.csv``` Read in the data from the file and join it to the weather data exracted from the original dataframe. ###Code sales = pd.read_csv("junesales.csv", header = 0) sales["Dayofmonth"] = pd.DatetimeIndex(sales["Date"]).day sales.head() # Filter the sales data for the relevant month and the appropriate location to a new dataframe. Newcastle_data = data[(data['Location']=='Newcastle') & (data['Year']==2011) & (data['Month']==6)] Newcastle_data.head() merge_data = Newcastle_data.merge(sales, on = "Dayofmonth") merge_data.head(30) ###Output _____no_output_____ ###Markdown Example 5.2: Merging Dataframes Types of joins. INNER JOIN![](1.png)* LEFT JOIN![](2.png)* RIGHT JOIN![](5.png)* FULL JOIN![](4.png)Each state may have different tax laws, so we might want to add the states information to the data as well.The file ```locationsandstates.csv``` information about the states and location, the data in this file is not same as the weather data. It is possible that few locations in "data" (original dataframe) are not in this file, and all the locations in the file might not be in the original dataframe. In the original dataframe add the state data. ###Code state = pd.read_csv("locationsandstates.csv", header = 0) state state_data = data.merge(state, on = "Location", how = "left") state_data ###Output _____no_output_____ ###Markdown Dataframes MergePerform an inner merge on two data frames df_1 and df_2 on 'unique_id' and print the combined dataframe. ###Code df_1 = pd.read_csv('restaurant-1.csv') df_2 = pd.read_csv('restaurant-2.csv') df_3 = df_1.merge(df_2, how="inner", on="unique_id") print(df_3.head(20)) print(df_1.columns) print(df_2.columns) df_3 = pd.concat([df_1, df_2]) df_3.head() ###Output Index(['name', 'address', 'city', 'cuisine', 'unique_id'], dtype='object') Index(['name_2', 'address_2', 'city_2', 'cuisine_2', 'unique_id'], dtype='object') ###Markdown Given three data frames containing the number of gold, silver, and bronze Olympic medals won by some countries, determine the total number of medals won by each country. Note: All the three data frames don’t have all the same countries. So, ensure you use the ‘fill_value’ argument (set it to zero), to avoid getting NaN values. Also, ensure you sort the final dataframe, according to the total medal count in descending order. ###Code gold = pd.DataFrame({'Country': ['USA', 'France', 'Russia'], 'Medals': [15, 13, 9]} ) silver = pd.DataFrame({'Country': ['USA', 'Germany', 'Russia'], 'Medals': [29, 20, 16]} ) bronze = pd.DataFrame({'Country': ['France', 'USA', 'UK'], 'Medals': [40, 28, 27]} ) final_set = pd.concat([gold, silver, bronze]) result = final_set.groupby(['Country']).agg('sum').sort_values(['Medals'], ascending=False) result ###Output _____no_output_____ ###Markdown Example 6.1: pivot tablesUsing pivot tables find the average monthly rainfall in the year 2016 of all the locations. The information can then be used to predict the sales of tea in the year 2017. ###Code data_2016 = data[data["Year"] ==2016] data_2016 data_2016.pivot_table(index = "Location", columns = "Month", values = "Rainfall", aggfunc='mean') ###Output _____no_output_____ ###Markdown Find the Pandas pivot table documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html)This information can be used to decide the stocks of tea in each of the stores. You can modify the pivot_table command to get a lot of work done quickly. ###Code data_2016.pivot_table(index = "Location", columns = "Month", values = "Sunshine", aggfunc='mean') ###Output _____no_output_____ ###Markdown Note[Here](https://pandas.pydata.org/pandas-docs/stable/index.html) is the link to the official documentation of Pandas. Be sure to visit it inorder to explore to availability of functions in the library. Dataframe Pivot TableGroup the data 'df' by 'month' and 'day' and find the mean value for column 'rain' and 'wind' using the pivot table command. ###Code df = pd.read_csv("forestfires.csv") df_1 = df.pivot_table(index = ['month','day'], values = ['rain','wind'], aggfunc = 'mean') df_1.head(20) ###Output _____no_output_____
Exercises/RNN, LSTM, GRU/RNN, LSTM, GRU.ipynb	###Markdown Recurrent Neural NetworkThis notebook was created by Camille-Amaury JUGE, in order to better understand RNN, LSTM, GRU principles and how they work.(it follows the exercices proposed by Hadelin de Ponteves on Udemy : https://www.udemy.com/course/le-deep-learning-de-a-a-z/) Imports ###Code import numpy as np import pandas as pd import matplotlib.pyplot as plt from pylab import rcParams rcParams['figure.figsize'] = 16, 10 # keras import keras from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import LSTM, SimpleRNN, GRU # scikit learn from sklearn.preprocessing import MinMaxScaler ###Output _____no_output_____ ###Markdown preprocessingThe dataset represents the google's stock price over the past 5 years. It shows the basic features of the stocks.The train set is only compunded of 1 month.Our aim is to make prediction over the trends that the google's stock could have in the futur. ###Code df_train = pd.read_csv("Google_Stock_Price_Train.csv") df_train.head() df_test = pd.read_csv("Google_Stock_Price_Test.csv") df_test.head() df_train.isna().sum() df_test.isna().sum() df_train.dtypes df_test.dtypes df_train["Close"] = np.array([i.replace(",","") for i in df_train["Close"]]).astype(float) df_train["Volume"] = np.array([i.replace(",","") for i in df_train["Volume"]]).astype(float) df_test["Volume"] = np.array([i.replace(",","") for i in df_test["Volume"]]).astype(float) df_train.dtypes df_test.dtypes df_train.describe().transpose().round(2) df_test.describe().transpose().round(2) ###Output _____no_output_____ ###Markdown Data are quite centered around the mean (regarding median ~ mean), it seems that there are no outliers then we can pre-suppose that the evolution is more or less linear. ###Code plt.title("Opening/Closing Stock Price") plt.xlabel("Date") plt.ylabel("$ stock price") plt.xticks(np.arange(0, len(df_train["Date"]), 200)) plt.plot() plt.plot(df_train["Date"], df_train["Open"], color='blue', linewidth=4, label="Open") plt.plot(df_train["Date"], df_train["Close"], color='red', linewidth=2, label="Close") plt.legend() plt.title("High/Low Stock Price") plt.xlabel("Date") plt.ylabel("$ stock price") plt.xticks(np.arange(0, len(df_train["Date"]), 200)) plt.plot(df_train["Date"], df_train["Low"], color='blue', linewidth=4, label="Low") plt.plot(df_train["Date"], df_train["High"], color='red', linewidth=2, label="High") plt.legend() ###Output _____no_output_____ ###Markdown We cn observe a strange behavior on the data regarding to the close price. It seems that the data are not accurate until 23-5-2014we are going to scale the feature. ###Code X_train = df_train[["Open"]].values scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) X_train_scaled ###Output _____no_output_____ ###Markdown We are going to create timesteps in order to give a row the last days values : here we will choose two month (~60 days) ###Code _days_range = 60 X_train = [] y_train = [] for i in range(_days_range, X_train_scaled.shape[0]): X_train.append(X_train_scaled[i-_days_range:i, 0]) y_train.append(X_train_scaled[i, 0]) X_train = np.array(X_train) y_train = np.array(y_train) X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_train[0] df = pd.concat([df_train["Open"], df_test["Open"]], axis = 0) len(df) X_test = df[df.shape[0]-df_test.shape[0]-_days_range:].values X_test = X_test.reshape(-1,1) X_test_scaled = scaler.transform(X_test) X_test = [] for i in range(_days_range, X_test_scaled.shape[0]): X_test.append(X_test_scaled[i-_days_range:i, 0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) X_test.shape X_test[19] ###Output _____no_output_____ ###Markdown Model LSTM ###Code def lstm_model(input_shape): model = Sequential() model.add(LSTM(units=256, return_sequences=True, input_shape=input_shape)) model.add(Dropout(0.3)) model.add(LSTM(units=256, return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(units=128, return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(units=128, return_sequences=False)) model.add(Dropout(0.1)) model.add(Dense(units=128)) model.add(Dense(units=64)) model.add(Dense(units=1)) model.compile(optimizer="adam", loss="mean_squared_error") return model model = lstm_model((X_train.shape[1],1)) model.fit(X_train, y_train, epochs=50, batch_size = 32) y_pred = model.predict(X_test) y_pred_real = scaler.inverse_transform(y_pred) plt.title("Predicted/True Stock Price") plt.xlabel("Date") plt.ylabel("$ stock price") plt.plot() plt.plot(df_test["Open"], color='blue', linewidth=4, label="True") plt.plot(y_pred_real, color='red', linewidth=2, label="Predicted") plt.legend() ###Output _____no_output_____ ###Markdown GRU ###Code def gru_model(input_shape): model = Sequential() model.add(GRU(units=256, return_sequences=True, input_shape=input_shape)) model.add(Dropout(0.3)) model.add(GRU(units=256, return_sequences=True)) model.add(Dropout(0.2)) model.add(GRU(units=128, return_sequences=True)) model.add(Dropout(0.2)) model.add(GRU(units=128, return_sequences=False)) model.add(Dropout(0.1)) model.add(Dense(units=128)) model.add(Dense(units=64)) model.add(Dense(units=1)) model.compile(optimizer="adam", loss="mean_squared_error", metrics=["mean_squared_error"]) return model model = gru_model((X_train.shape[1],1)) model.fit(X_train, y_train, epochs=50, batch_size = 32) y_pred = model.predict(X_test) y_pred_real = scaler.inverse_transform(y_pred) plt.title("Predicted/True Stock Price GRU") plt.xlabel("Date") plt.ylabel("$ stock price") plt.plot() plt.plot(df_test["Open"], color='blue', linewidth=4, label="True") plt.plot(y_pred_real, color='red', linewidth=2, label="Predicted") plt.legend() ###Output _____no_output_____

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