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LabExercise8Answer.ipynb
###Markdown 1D and 2D Discrete Wavelet Transform ###Code import numpy as np # we need to install PyWavelets labrary either by running "pip install PyWavelets" or "conda install pywavelets" import pywt from matplotlib.pyplot import imshow import matplotlib.pyplot as plt # The PyWavelets library contains 14 Mother wavelets, varying by shape, smoothness and compactness. # They satisfy two mathematical conditions: 1. They are localized in time and frequency, 2. They have 0 mean. # Explore wavelets further http://wavelets.pybytes.com/ print(pywt.families(short=False)) discrete_wavelets=['Haar','db7','sym3','coif3'] continuous_wavelets=['mexh','morl','gaus5','cgau7'] wavelets=[discrete_wavelets, continuous_wavelets] funcs=[pywt.Wavelet, pywt.ContinuousWavelet] fig, axarr = plt.subplots(nrows=2, ncols=4, figsize=(16,8)) for i, get_wavelets in enumerate(wavelets): func=funcs[i] row_no=i for col_no, wavel_name in enumerate(get_wavelets): wavelet=func(wavel_name) family_name=wavelet.family_name if i==0: f=wavelet.wavefun() wavelet_function=f[0] # get an array of y-values x_values=f[-1] # get an array of x-values else: wavelet_function, x_values=wavelet.wavefun() if col_no==0 and i==0: axarr[row_no, col_no].set_ylabel("Discrete Wavelets", fontsize=16) if col_no==0 and i==1: axarr[row_no, col_no].set_ylabel("Continuous Wavelets", fontsize=16) axarr[row_no, col_no].set_title("{}".format(family_name), fontsize=16) axarr[row_no, col_no].plot(x_values, wavelet_function) axarr[row_no, col_no].set_yticks([]) axarr[row_no, col_no].set_yticklabels([]) plt.show() ###Output _____no_output_____ ###Markdown How are these wavelets different? Discrete Wavelet transform: 1D study We have seen that the DWT is implemented as a filter bank or a cascade of high-pass and low-pass filters. To apply the DWT on a signal, we start with the smallest scale. Small scales correspond to high frequencies. We first analyze high frequency behavior. At the second stage, the scale increases by a factor of 2 (the frequency decreases by a factor of 2). At this stage, we analyze the signal sections of half of the maximum frequency. We keep iterating the decomposition process until we reach a maximum decomposition level.Understanding of the maximum decomposition level: Due to downsampling, at some stage in the process, the number of samples in the signal will become smaller than the length of the wavelet filter and we will have reached the maximum decomposition level. ###Code # create a signal to analyse from scipy.signal import chirp, spectrogram # e.g., linear chirp satisfies the following equation: f(t)=f0+(f1-f0)*t/t1 t=np.linspace(0, 10, 1500) # 1500 sampling points in 10 seconds signal=chirp(t, f0=6, f1=1,t1=10, method='linear') plt.plot(t,signal) plt.title("Linear Chirp, f(0)=6, f(10)=1") plt.xlabel('t (sec)') plt.show() ###Output _____no_output_____ ###Markdown Computing the frequency range of different levels of the coefficients.We have 1500 sampling points in 10 sec. This means that we have the frequency of 150 samples per second.So, the first approximation level will contain frequencies from 0 to 75, and the detail from 75 to 150.The second level approximation will contain frequencies from 0 to 37.5, and the detail will contain the subband from 37.5 until 75.The third level approximation will contain frequencies up to 18.75, and the detail will contain a subband between 18.75 and 37.5.Finally, the fourth level will contain frequencies up to 9.375, and the detail will contain the frequency range of [9.375, 18.75]. ###Code data = signal waveletname = 'db7' # let's setup a 4-step filter bank to find the approximation and detail wavelet coefficients of the signal wavelet transform fig, axarr = plt.subplots(nrows=4, ncols=2, figsize=(8,8)) #collect the wavelet coefficients into app_coeffs=[] det_coeffs=[] for i in range(4): (data, coeff_d) = pywt.dwt(data, waveletname) # perform single stage iteratively app_coeffs.append(data)# approximation coefs det_coeffs.append(coeff_d) axarr[i, 0].plot(data, 'b') axarr[i, 1].plot(coeff_d, 'g') axarr[i, 0].set_ylabel("Level {}".format(i + 1), fontsize=14, rotation=90) axarr[i, 0].set_yticklabels([]) if i == 0: axarr[i, 0].set_title("Approximation coefficients", fontsize=14) axarr[i, 1].set_title("Detail coefficients", fontsize=14) axarr[i, 1].set_yticklabels([]) plt.tight_layout() plt.show() ###Output _____no_output_____ ###Markdown Question 1: Given results obtained above, what represents the output of the high pass filter? What happens to signal resolution as you go from one level of the wavelet transform to the next? How were detail coefficients at each level generated? ###Code # leave your answer here ## Signal reconstruction/ synthesis #___________________________________ order=[3,2,1,0] app_coeffs=[app_coeffs[i] for i in order] det_coeffs=[det_coeffs[i] for i in order] coeffs = pywt.wavedec(signal, 'db7', level=4)# prepare all coefficients in the right format for .waverec function signal_r=pywt.waverec(coeffs, 'db7') fig=plt.figure(figsize=(16,8)) plt.subplot(121) plt.plot(t,signal) plt.title("Original") plt.xlabel('t (sec)') plt.subplot(122) plt.plot(t,signal_r, color='r') plt.title("Reconsructed from 4-step filter bank") plt.xlabel('t (sec)') plt.show() coeffs ###Output _____no_output_____ ###Markdown 2D DWT for image denoising Recall 2D coordinate conventions imshow convention ----------------------- axis y |----------> | | | axis x V Load an image as an array of 515x512 with pixel intensities in the range from 0 to 255. We corrupt the image with Gaussian noise ($\sigma=16$) and perform denoising using Haar wavelet coefficients. ###Code import scipy image1 = scipy.misc.ascent().astype(float) noiseSigma = 32.0 image = image1+ np.random.normal(0, noiseSigma, size=image1.shape) plt.subplot(121) imshow(image, cmap='gray') plt.title('Noisy image') plt.subplot(122) imshow(image1, cmap='gray') plt.title('Original image') wavelet = pywt.Wavelet('haar') levels=? ###Output _____no_output_____ ###Markdown Question 2. What is the maximum highest decomposition level we can reach if we apply the multi-step filter bank? Hint: Consider the size of the image and how many times you can downsample it before you run out of image samples. ###Code # Leave you answer here 512-->256-->128 levels=9 wavelet_coeffs=pywt.wavedec2(image, wavelet, level=levels) print("approximation at the highest level", wavelet_coeffs[0]) print("detail coefficients at the highest level (horizontal, vertical, diagonal)", wavelet_coeffs[1]) print("approximation at the second highest level", wavelet_coeffs[1]) print("detail coefficients at the second highest level (horizontal, vertical, diagonal)", wavelet_coeffs[2]) ###Output _____no_output_____ ###Markdown In order to denoise our image, we will be using a threshold model available in pywt library, specifically, pywt.thresholding.soft.We will be applying it to each single wavelet coefficient. ###Code threshold=noiseSigma*np.sqrt(2*np.log2(image.size)) # We use a soft thresholding on each of the wavelet coefficients. Data values with absolute value less than "threshold" # are replaced with a substitute new=[] k=0 for s in wavelet_coeffs: if k==0: new_ar=np.ndarray((1,1),buffer=np.zeros((1,1))) new_ar=s new.append(new_ar) else: new_ar=[] for i in range(len(s)): s_soft = pywt.threshold(s[i], value=threshold, mode='soft') new_ar.append(s_soft) new_ar=tuple(new_ar) new.append(new_ar) k=k+1 # We obtain the corresponding reconstruction newimage = pywt.waverec2(new, wavelet) imshow(newimage, cmap='gray') plt.title("Reconstructed image with Haar wavelet") ###Output _____no_output_____ ###Markdown Question 3: Why are you observing a block-like artifact in the reconstructed image? Does the choice of the wavelet matter? ###Code # Type your answer here #Can we find a better solution with a different choice of wavelet? In the function below, we keep the threshold the same, # but we can explore other choices of wavelet functions. def denoise(data, wavelet, noiseSigma): levels=9 wave_c=pywt.wavedec2(data,wavelet,level=levels) threshold=noiseSigma*np.sqrt(2*np.log2(data.size)) new=[] k=0 for s in wave_c: if k==0: new_ar=np.ndarray((1,1),buffer=np.zeros((1,1))) new_ar=s new.append(new_ar) else: new_ar=[] for i in range(len(s)): s_soft = pywt.threshold(s[i], value=threshold, mode='soft') new_ar.append(s_soft) new_ar=tuple(new_ar) new.append(new_ar) k=k+1 # We obtain the corresponding reconstruction newimage = pywt.waverec2(new, wavelet) return newimage # Let's see the result with coif3 image_coif=denoise(data=image, wavelet='coif3',noiseSigma=32.0) imshow(image_coif, cmap='gray') plt.title("Reconstructed image with coif3 wavelet") ###Output _____no_output_____ ###Markdown Question 4: Choose other two wavelets from discrete_wavelets=['Haar','db7','sym3','coif3'] , use the "denoise" function for noise reduction and comment on the quality of image denoising depending on the choice of the wavelet. What do you think other ways we should try in order to improve denoising result? ###Code # Leave your answer here ###Output _____no_output_____
bureau.ipynb
###Markdown Bureau and Bureau Balance data*bureau.csv* data concerns client's earlier credits from other financial institutions. Some of the credits may be active and some are closed. Each previous (or ongoing) credit has its own row (only one row per credit) in *bureau* dataset. As a single client might have taken other loans from other financial institutions, for each row in the *application_train* data (ie *application_train.csv*) we can have multiple rows in this table. Feature explanations for this dataset are as below. Feature explanations Bureau tableSK_ID_CURR: ID of loan in our sample - one loan in our sample can have 0,1,2 or more related previous credits in credit bureau SK_BUREAU_ID: Recoded ID of previous Credit Bureau credit related to our loan (unique coding for each loan application)CREDIT_ACTIVE: Status of the Credit Bureau (CB) reported creditsCREDIT_CURRENCY: Recoded currency of the Credit Bureau creditDAYS_CREDIT: How many days before current application did client apply for Credit Bureau creditCREDIT_DAY_OVERDUE: Number of days past due on CB credit at the time of application for related loan in our sampleDAYS_CREDIT_ENDDATE: Remaining duration of CB credit (in days) at the time of application in Home CreditDAYS_ENDDATE_FACT: Days since CB credit ended at the time of application in Home Credit (only for closed credit)AMT_CREDIT_MAX_OVERDUE: Maximal amount overdue on the Credit Bureau credit so far (at application date of loan in our sample)CNT_CREDIT_PROLONG: How many times was the Credit Bureau credit prolongedAMT_CREDIT_SUM: Current credit amount for the Credit Bureau creditAMT_CREDIT_SUM_DEBT: Current debt on Credit Bureau creditAMT_CREDIT_SUM_LIMIT: Current credit limit of credit card reported in Credit BureauAMT_CREDIT_SUM_OVERDUE: Current amount overdue on Credit Bureau creditCREDIT_TYPE: Type of Credit Bureau credit (Car, cash,...)DAYS_CREDIT_UPDATE: How many days before loan application did last information about the Credit Bureau credit comeAMT_ANNUITY: Annuity of the Credit Bureau credit Bureau Balance tableSK_BUREAU_ID: Recoded ID of Credit Bureau credit (unique coding for each application) - use this to join to CREDIT_BUREAU tableMONTHS_BALANCE: Month of balance relative to application date (-1 means the freshest balance date) time only relative to the applicationSTATUS: Status of Credit Bureau loan during the month ###Code # Last amended: 21st October, 2020 # Myfolder: C:\Users\Administrator\OneDrive\Documents\home_credit_default_risk # Objective: # Solving Kaggle problem: Home Credit Default Risk # Processing bureau and bureau_balance datasets. # # Data Source: https://www.kaggle.com/c/home-credit-default-risk/data # Ref: https://www.kaggle.com/jsaguiar/lightgbm-with-simple-features # 1.0 Libraries # (Some of these may not be needed here.) %reset -f import numpy as np import pandas as pd import gc # 1.1 Reduce read data size # There is a file reducing.py # in this folder. A class # in it is used to reduce # dataframe size # (Code modified to # exclude 'category' dtype) import reducing # 1.2 Misc import warnings import os warnings.simplefilter(action='ignore', category=FutureWarning) # 1.3 pd.set_option('display.max_colwidth', -1) # 1.4 Display multiple commands outputs from a cell from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" # 2.0 One-hot encoding function. Uses pd.get_dummies() # i) To transform 'object' columns to dummies. # ii) Treat NaN as one of the categories # iii) Returns transformed-data and new-columns created def one_hot_encoder(df, nan_as_category = True): original_columns = list(df.columns) categorical_columns = [col for col in df.columns if df[col].dtype == 'object'] df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category # Treat NaNs as category ) new_columns = [c for c in df.columns if c not in original_columns] return df, new_columns # 3.0 Prepare to read data pathToData = "C:\\Users\\Administrator\\OneDrive\\Documents\\home_credit_default_risk" os.chdir(pathToData) # 3.1 Some constants num_rows=None # Implies read all rows nan_as_category = True # While transforming # 'object' columns to dummies # 3.2 Read bureau data first bureau = pd.read_csv( 'bureau.csv.zip', nrows = None # Read all rows ) # 3.2.1 Reduce memory usage by appropriately # changing data-types per feature: bureau = reducing.Reducer().reduce(bureau) # 3.2.2 Explore data now bureau.head(5) bureau.shape # (rows:17,16,428, cols: 17) bureau.dtypes # 3.2.3 In all, how many are categoricals? bureau.dtypes.value_counts() # 3.3 bureau.shape # (1716428, 17) # 3.3.1 # What is the actual number of persons # who might have taken multiple loans? bureau['SK_ID_CURR'].nunique() # 305811 -- Many duplicate values exist # Consider SK_ID_CURR as Foreign Key # Primary key exists in application_train data # Primary key: SK_ID_BUREAU # 3.3.2 # As expected, there are no duplicate values here bureau['SK_ID_BUREAU'].nunique() # 1716428 -- Unique id for each row # 3.4 Summary of active/closed cases from bureau # We aggregate on these also bureau['CREDIT_ACTIVE'].value_counts() ###Output _____no_output_____ ###Markdown Aggregationbureau_balance will be aggregated and merged with bureau. bureau will then be aggregated and merged with 'application_train' data. bureau will be aggregated in three different ways. This aggregation will be by SK_ID_CURR. Finally, aggregated bureau, called bureau_agg, will be merged with 'application_train' over (SK_ID_CURR).Aggregation over time is one way to extract behaviour of client. All categorical data is first OneHotEncoded (OHE). What is unique about this OHE is that NaN values are treated as categories. ###Code # 4.0 OneHotEncode 'object' types in bureau bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category) # 4.1 bureau.head() bureau.shape # (1716428, 40); 17-->40 print(bureau_cat) # List of added columns ###Output _____no_output_____ ###Markdown bureau_balanceIt is monthly data about the remaining balance of each one of the previous credits of clients that exist in dataset bureau. Each previous credit is identified by a unique ID, SK_ID_BUREAU, in dataset bureau. Each row in bureau_balance is one month of credit-due (from previous credit), and a single previous credit can have multiple rows, one for each month of the credit length. In my personal view, it should be in decreasing order. That is, for every person identified by SK_ID_BUREAU, credits should be decreasing each passing month. ###Code # 5.0 Read over bureau_balance data # and reduce memory usage through # conversion of data-types: bb = pd.read_csv('bureau_balance.csv.zip', nrows = None) bb = reducing.Reducer().reduce(bb) # 5.0.1 Display few rows bb.head(10) # 5.0.2 & Compare bb.shape # (27299925, 3) bureau.shape # (1716428, 17) # 5.1 There is just one 'object' column bb.dtypes.value_counts() # 5.2 Is the data about all bureau cases? # No, it appears it is not for all cases bb['SK_ID_BUREAU'].nunique() # 817395 << 1716428 # 5.3 Just which cases are present in 'bureau' but absent # in 'bb' bb_id_set = set(bb['SK_ID_BUREAU']) # Set of IDs in bb bureau_id_set = set(bureau['SK_ID_BUREAU']) # Set of IDs in bureau # 5.4 And here is the difference list. # How many of them? list(bureau_id_set - bb_id_set)[:5] # sample [6292791,6292792,6292793,6292795,6292796,6292797,6292798,6292799] len(bureau_id_set - bb_id_set) # 942074 # 5.5 OK. So let us OneHotEncode bb bb, bb_cat = one_hot_encoder(bb, nan_as_category) # 5.6 Examine the results bb.head() bb.shape # (27299925, 11) ; 3-->11 # 1 (ID) + 1 (numeric) + 9 (dummy) bb_cat # New columns added ###Output _____no_output_____ ###Markdown Performing aggregations in bbThere is one numeric feature: 'MONTHS_BALANCE'. On this feature we will perform ['min', 'max', 'size']. And on the rest of the features,dummy features, we will perform [mean]. Aggregation is by unique bureau ID, SK_ID_BUREAU. Resulting dataset is called bureau_agg. ###Code # 6.0 Bureau balance: Perform aggregations and merge with bureau.csv # First prepare a dictionary listing operations to be performed # on various features: bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']} for col in bb_cat: bb_aggregations[col] = ['mean'] # 6.0.1 len(bb_aggregations) # 10 # 6.1 So what all aggregations to perform column-wise bb_aggregations # 6.2 Perform aggregations now in bb: grouped = bb.groupby('SK_ID_BUREAU') bb_agg = bb.groupby('SK_ID_BUREAU').agg(bb_aggregations) # 6.3 bb_agg.shape # (817395, 12) bb_agg.columns # 6.3.1 Note that 'SK_ID_BUREAU' # the grouping column is # now table-index bb_agg.head() # 6.4 Rename bb_agg columns bb_agg.columns = pd.Index([e[0] + "_" + e[1].upper() for e in bb_agg.columns.tolist()]) # 6.4.1 bb_agg.columns.tolist() bb_agg.head() # 6.5 Merge aggregated bb with bureau bureau = bureau.join( bb_agg, how='left', on='SK_ID_BUREAU' ) # 6.5.1 bureau.head() bureau.shape # (1716428, 52) bureau.dtypes # 6.5.2 Just for curiosity, what happened # to those rows in 'bureau' where there # was no matching record in bb_agg. The list # of such IDs is: # [6292791,6292792,6292793,6292795,6292796,6292797,6292798,6292799] bureau[bureau['SK_ID_BUREAU'] ==6292791] # 6.6 Drop SK_ID_BUREAU as bb has finally merged. bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True ) # We have three types of columns # Categorical columns generated from bureau # Categorical columns generated from bb # Numerical columns ###Output _____no_output_____ ###Markdown Performing aggregations in bureauAggregate 14 original numeric columns, as: ['min', 'max', 'mean', 'var']Aggregate rest of the columns that is dummy columns as: [mean]. This constitutes one of the three aggretaions. Aggregation is by SK_ID_CURR. Resulting dataset is called bureau_agg ###Code # 7.0 Have a look at bureau again. # SK_ID_CURR repeats for many cases. # So, there is a case for aggregation bureau.shape # (1716428, 51) bureau.head() ## Aggregation strategy # 7.1 Numeric features # Columns: Bureau + bureau_balance numeric features # Last three columns are from bureau_balance # Total: 11 + 3 = 14 num_aggregations = { 'DAYS_CREDIT': ['min', 'max', 'mean', 'var'], 'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'], 'DAYS_CREDIT_UPDATE': ['mean'], 'CREDIT_DAY_OVERDUE': ['max', 'mean'], 'AMT_CREDIT_MAX_OVERDUE': ['mean'], 'AMT_CREDIT_SUM': ['max', 'mean', 'sum'], 'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'], 'AMT_CREDIT_SUM_OVERDUE': ['mean'], 'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'], 'AMT_ANNUITY': ['max', 'mean'], 'CNT_CREDIT_PROLONG': ['sum'], 'MONTHS_BALANCE_MIN': ['min'], 'MONTHS_BALANCE_MAX': ['max'], 'MONTHS_BALANCE_SIZE': ['mean', 'sum'] } len(num_aggregations) # 14 # 7.2 Bureau categorical features. Derived from: # 'CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE', # Total: cat_aggregations = {} bureau_cat # bureau_cat are newly created dummy columns # but all are numerical columns # 7.2.1 len(bureau_cat) # 26 # 7.2.2 For all these new dummy columns in bureau, we will # take mean for cat in bureau_cat: cat_aggregations[cat] = ['mean'] cat_aggregations len(cat_aggregations) # 26 # 7.3.1 In addition, we have in bureau. columns that merged # from 'bb' ie bb_cat # So here is our full list bb_cat len(bb_cat) # 9 # 7.3.2 for cat in bb_cat: cat_aggregations[cat + "_MEAN"] = ['mean'] cat_aggregations len(cat_aggregations) # 26 + 9 = 35 # 7.4 Have a look at bureau columns again # Just to compare above results with what # already exists bureau.columns # 51 len(bureau.columns) # 35 (dummy) + 14 (num) + 1 (SK_ID_CURR) + 1 (DAYS_ENDDATE_FACT) = 51 # 7.5 Now that we have decided # our aggregation strategy for each column # (except 2), let us now aggregate: # Note that SK_ID_CURR now becomes an index to data grouped = bureau.groupby('SK_ID_CURR') bureau_agg = grouped.agg({**num_aggregations, **cat_aggregations}) # 7.6 bureau_agg.head() bureau_agg.shape # (305811, 62) (including newly created min, max etc columns) # 7.7 Remove hierarchical index from bureau_agg bureau_agg.columns # 62 bureau_agg.columns = pd.Index(['BURO_' + e[0] + "_" + e[1].upper() for e in bureau_agg.columns.tolist()]) # 7.8 bureau_agg.head() # 7.8.1 Note that SK_ID_CURR is now an index to table bureau_agg.columns # 62: Due to creation of min, max, var etc columns # 7.9 No duplicate index bureau_agg.index.nunique() # 305811 len(set(bureau_agg.index)) # 305811 ###Output _____no_output_____ ###Markdown More Aggregation and mergerWe now filter bureau on CREDIT_ACTIVE_Active feature. This will create two subsets of data. This feature has values of 1 and 0. Filter data where CREDIT_ACTIVE_Active value is 1. Then aggregate(only) numeric features of this filtered data-subset by grouping on SK_ID_CURR. Next, filter, bureau, on CREDIT_ACTIVE_Closed = 1 . And again aggregate the subset on numeric features. Merge all these with bureau_agg (NOT bureau.) It is as if we are trying to extract the behaviour of those whose credits are active and those whose credits are closed. ###Code # 8.0 In which cases credit is active? Filter data active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1] active.head() active.shape # (630607, 51) # 8.1 Aggregate numercial features of the filtered subset over SK_ID_CURR active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations) # 8.1.1 active_agg.head() active_agg.shape # (251815, 27) # 8.1.2 Rename multi-indexed columns active_agg.columns = pd.Index(['ACTIVE_' + e[0] + "_" + e[1].upper() for e in active_agg.columns.tolist()]) active_agg.columns # 9.0 Difference between length of two datasets active_agg_set = set(active_agg.index) bureau_agg_set = set(bureau_agg.index) len(bureau_agg_set) # 305811 len(active_agg_set) # 251815 list(bureau_agg_set - active_agg_set)[:4] # Few examples: {131074, 393220, 262149, 262153] # 9.1 Merge bureau_agg with active_agg over 'SK_ID_CURR' bureau_agg = bureau_agg.join( active_agg, how='left', on='SK_ID_CURR' ) # 9.2 bureau_agg.shape # (305811, 89) # 9.3 Obviouly some rows will hold NaN values for merged columns bureau_agg.loc[[131074,393220,262149, 262153]] # 9.4 Release memory del active, active_agg gc.collect() # 10.0 Same steps for the CREDIT_ACTIVE_Closed =1 cases # Bureau: Closed credits - using only numerical aggregations closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1] closed_agg = closed.groupby('SK_ID_CURR').agg(num_aggregations) closed_agg.columns = pd.Index(['CLOSED_' + e[0] + "_" + e[1].upper() for e in closed_agg.columns.tolist()]) bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR') # 10.1 bureau_agg.shape # (305811, 116) # 10.2 del closed, closed_agg, bureau gc.collect() # 10.3 SK_ID_CURR is index. Index is also saved by-default. bureau_agg.to_csv("processed_bureau_agg.csv.zip", compression = "zip") ################## ###Output _____no_output_____
DeepForge.ipynb
###Markdown Run the Two sections in different Notebooks Section-1 (Cross Validation Training) ###Code import tensorflow as tf import numpy as np import os import matplotlib.pyplot as plt sample = '../input/signature-forgery-detection/Signatures_2/Train_Set/001/Real/001_02.PNG' sample = tf.io.read_file(sample) sample = tf.image.decode_jpeg(sample) sample.shape shape = (400,800,3) def load_img(path): image_file = tf.io.read_file(path) image = tf.image.decode_jpeg(image_file) image = np.resize(image,shape) return image !find . -name "*.DS_Store" -type f -delete train_path = '../input/signature-forgery-detection/Signatures_2/Train_Set/' dataset = [] targets = [] real_count =0 forged_count = 0 persons = os.listdir(train_path) for person in persons: path = os.path.join(train_path,person) real = os.path.join(path,'Real/') real_files = os.listdir(real) fraud = os.path.join(path,'Forged/') fraud_files = os.listdir(fraud) for j in range(len(real_files)): for k in range(len(real_files)): if j==k: continue real_count +=1 img1 = load_img(os.path.join(real,real_files[j])) img2 = load_img(os.path.join(real,real_files[k])) dataset.append([img1,img2]) targets.append(0.) for j in range(len(real_files)): for k in range(len(fraud_files)): if j==k: continue forged_count+=1 img1 = load_img(os.path.join(real,real_files[j])) img2 = load_img(os.path.join(fraud,fraud_files[k])) dataset.append([img1,img2]) targets.append(1.) print(real_count) print(forged_count) def Siamese_Model(input_shape=(400,800,3)): input_one = tf.keras.layers.Input(shape=input_shape) input_two = tf.keras.layers.Input(shape=input_shape) cnn = tf.keras.models.Sequential() cnn.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Flatten()) cnn.add(tf.keras.layers.Dropout(0.3)) cnn.add(tf.keras.layers.Dense(128)) distance_layer = tf.keras.layers.Lambda(lambda tensor: abs(tensor[0]-tensor[1])) out1 = cnn(input_one) out2 = cnn(input_two) l1_distance = distance_layer([out1,out2]) final_out = tf.keras.layers.Dense(1,activation='sigmoid')(l1_distance) model = tf.keras.Model([input_one,input_two],final_out) return model dataset = np.array(dataset) targets = np.array(targets) from sklearn.model_selection import KFold kf = KFold(n_splits=5, shuffle=True,random_state=32) cvscores = [] for train,val in kf.split(dataset,targets): train = np.array(train) val = np.array(val) model = Siamese_Model() model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) model.fit([dataset[train,0],dataset[train,1]],targets[train],epochs =15,verbose=0) score = model.evaluate([dataset[val,0],dataset[val,1]],targets[val],verbose=0) print(score) cvscores.append(score) tf.keras.backend.clear_session() cvscores = np.array(cvscores) errors = cvscores[:,1] mean = np.mean(errors) median = np.median(errors) std = np.std(errors) print('Mean Median Std') print(mean,median,std) tf.keras.backend.clear_session() ###Output _____no_output_____ ###Markdown Section-2 (Normal Training) ###Code import tensorflow as tf import numpy as np import os import matplotlib.pyplot as plt sample = '../input/signature-forgery-detection/Signatures_2/Train_Set/001/Real/001_02.PNG' sample = tf.io.read_file(sample) sample = tf.image.decode_jpeg(sample) sample.shape shape = (400,800,3) def load_img(path): image_file = tf.io.read_file(path) image = tf.image.decode_jpeg(image_file) image = np.resize(image,shape) return image !find . -name "*.DS_Store" -type f -delete pairs = 20 # KEEP 10 FOR CROSS VALIDATION train_path = '../input/signature-forgery-detection/Signatures_2/Train_Set' dataset = [] targets = [] persons = os.listdir(train_path) for person in persons: path = os.path.join(train_path,person) real = os.path.join(path,'Real/') real_files = os.listdir(real) fraud = os.path.join(path,'Forged/') fraud_files = os.listdir(fraud) for j in range(pairs//2): ind1 = np.random.randint(0,len(real_files)-1) ind2 = np.random.randint(0,len(real_files)-1) ind3 = np.random.randint(0,len(fraud_files)-1) img1 = load_img(os.path.join(real,real_files[ind1])) img2 = load_img(os.path.join(real,real_files[ind2])) img3 = load_img(os.path.join(fraud,fraud_files[ind3])) dataset.append([img1,img2]) dataset.append([img1,img3]) dataset.append([img2,img3]) targets.append(0.) targets.append(1.) targets.append(1.) def Siamese_Model(input_shape=(400,800,3)): input_one = tf.keras.layers.Input(shape=input_shape) input_two = tf.keras.layers.Input(shape=input_shape) cnn = tf.keras.models.Sequential() cnn.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',padding='same')) cnn.add(tf.keras.layers.AveragePooling2D((2,2))) cnn.add(tf.keras.layers.Flatten()) cnn.add(tf.keras.layers.Dropout(0.3)) cnn.add(tf.keras.layers.Dense(128)) distance_layer = tf.keras.layers.Lambda(lambda tensor: abs(tensor[0]-tensor[1])) out1 = cnn(input_one) out2 = cnn(input_two) l1_distance = distance_layer([out1,out2]) final_out = tf.keras.layers.Dense(1,activation='sigmoid')(l1_distance) model = tf.keras.Model([input_one,input_two],final_out) return model dataset = np.array(dataset) targets = np.array(targets) reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) model = Siamese_Model() model.compile(optimizer=tf.keras.optimizers.Adam(0.001),loss='binary_crossentropy',metrics=['accuracy']) history = model.fit([dataset[:,0],dataset[:,1]],targets,epochs=30,callbacks=[reduce_lr],validation_split=0.1) history_dict = history.history acc = history_dict['accuracy'] val_acc = history_dict['val_accuracy'] loss = history_dict['loss'] val_loss = history_dict['val_loss'] plt.plot(acc) plt.plot(val_acc) plt.plot(loss) plt.plot(val_loss) ###Output _____no_output_____ ###Markdown Testing Manually ###Code sample1 = load_img('../input/signature-forgery-detection/Signatures_2/Test_Set/006/Real/11_054.png') # True Signature of a person sample2 = load_img('../input/signature-forgery-detection/Signatures_2/Test_Set/006/Forged/01_0102054.PNG') # Forged version of the signature sample3 = load_img('../input/signature-forgery-detection/Signatures_2/Test_Set/006/Forged/01_0207054.PNG') # Another Forged Version of the Signature sample1 = np.expand_dims(sample1,0) sample2 = np.expand_dims(sample2,0) sample3 = np.expand_dims(sample3,0) ans = model.predict([sample1,sample3]) ans = np.around(ans,decimals=2) if ans < 0.5: print("Genuine Signature") else: print("Fruad Signature") ###Output _____no_output_____ ###Markdown Test Accuracy ###Code train_path = '../input/signature-forgery-detection/Signatures_2/Test_Set' dataset = [] targets = [] persons = os.listdir(train_path) for person in persons: path = os.path.join(train_path,person) real = os.path.join(path,'Real/') real_files = os.listdir(real) fraud = os.path.join(path,'Forged/') fraud_files = os.listdir(fraud) for j in range(pairs//2): ind1 = np.random.randint(0,len(real_files)-1) ind2 = np.random.randint(0,len(real_files)-1) ind3 = np.random.randint(0,len(fraud_files)-1) img1 = load_img(os.path.join(real,real_files[ind1])) img2 = load_img(os.path.join(real,real_files[ind2])) img3 = load_img(os.path.join(fraud,fraud_files[ind3])) dataset.append([img1,img2]) dataset.append([img1,img3]) dataset.append([img2,img3]) targets.append(0.) targets.append(1.) targets.append(1.) dataset = np.array(dataset) targets = np.array(targets) score = model.evaluate([dataset[:,0],dataset[:,1]],targets) print("Test Accuracy ",end='') print(score[1]) ###Output _____no_output_____
frontend/assets/backup/dev/frontend.ipynb
###Markdown Recording Screen ###Code %run -i images2 ###Output ./IMG/gameplay_2021_10_23_16_31_09_977.jpg ./IMG/gameplay_2021_10_23_16_31_11_996.jpg ./IMG/gameplay_2021_10_23_16_31_14_000.jpg ./IMG/gameplay_2021_10_23_16_31_16_013.jpg ./IMG/gameplay_2021_10_23_16_31_18_038.jpg ./IMG/gameplay_2021_10_23_16_31_20_082.jpg ./IMG/gameplay_2021_10_23_16_31_22_138.jpg ./IMG/gameplay_2021_10_23_16_31_24_736.jpg ./IMG/gameplay_2021_10_23_16_31_26_781.jpg ./IMG/gameplay_2021_10_23_16_31_28_828.jpg ./IMG/gameplay_2021_10_23_16_31_30_838.jpg ./IMG/gameplay_2021_10_23_16_31_32_846.jpg ./IMG/gameplay_2021_10_23_16_31_34_855.jpg ./IMG/gameplay_2021_10_23_16_31_36_902.jpg ./IMG/gameplay_2021_10_23_16_31_38_907.jpg ./IMG/gameplay_2021_10_23_16_31_40_913.jpg ./IMG/gameplay_2021_10_23_16_31_42_953.jpg ./IMG/gameplay_2021_10_23_16_31_44_957.jpg ./IMG/gameplay_2021_10_23_16_31_46_958.jpg ./IMG/gameplay_2021_10_23_16_31_49_013.jpg ###Markdown Recording Keyboard The first step is learn how to record the keybord ###Code # Testing the record %run -i keyboard #!python keyboard.py #with open('log.txt') as f: # lines = f.readlines() #print(lines) #!python video.py ###Output _____no_output_____
nbs/00_connectors.gcp.ipynb
###Markdown Connectors for GCP> API details. ###Code #hide from nbdev.showdoc import * ###Output _____no_output_____ ###Markdown GCS ###Code # exports import json from io import BytesIO import pandas as pd from google.cloud import storage class GCSConnector: """ Object: GCSConnector(Object) Purpose: Connector to the GCS account """ def __init__(self, credentials, bucketname): """ Initialize Google Cloud Storage Connector to bucket :param credentials: (str) JSON credentials filename :param bucketname: (str) bucket name """ self._CREDENTIALS = credentials self._BUCKETNAME = bucketname self._gcsclient = storage.Client.from_service_account_json(self._CREDENTIALS) self._bucket = self._gcsclient.get_bucket(self._BUCKETNAME) def get_file(self, filename): """ Get file content from GCS :param filename: :return: (BytesIO) GCS File as byte """ blob = storage.Blob(filename, self._bucket) content = blob.download_as_string() return BytesIO(content) def send_json(self, json_file, filename): """ :param json_file: :param filename: :return: """ self._bucket.blob(filename).upload_from_string(json.dumps(json_file, ensure_ascii=False)) def send_dataframe(self, df, filename, **kwargs): """ :param filename: :param kwargs: :return: """ self._bucket.blob(filename).upload_from_string( df.to_csv(**kwargs), content_type="application/octet-stream") def open_csv_as_dataframe(self, filename, **kwargs): """ :param filename: :param kwargs: :return: """ return pd.read_csv(self.get_file(filename=filename), **kwargs) def open_json_as_dataframe(self, filename, **kwargs): """ :param filename: :param kwargs: :return: """ return pd.read_json(self.get_file(filename=filename), **kwargs) def open_excel_as_dataframe(self, filename, **kwargs): """ :param filename: :param kwargs: :return: """ return pd.read_excel(self.get_file(filename=filename), **kwargs) def file_exists(self, filename): """ Check if 'filename' file exists within bucket :param filename: :return: (Bool) """ return storage.Blob(filename, self._bucket).exists(self._gcsclient) def list_files(self, prefix, delimiter=None): return [blob.name for blob in self._bucket.list_blobs(prefix=prefix, delimiter=delimiter)] show_doc(GCSConnector) ###Output _____no_output_____ ###Markdown Big Query ###Code # exports import pandas_gbq from google.cloud import bigquery from google.oauth2 import service_account class BQConnector: """ Object: BQConnector(Object) Purpose: Connector to the Big Query account """ def __init__(self, credentials, project_id): self.project_id = project_id # Enable the Google Drive API self.credentials = service_account.Credentials.from_service_account_file( credentials ) self.credentials = self.credentials.with_scopes( [ 'https://www.googleapis.com/auth/drive', 'https://www.googleapis.com/auth/cloud-platform' ] ) self._client = bigquery.Client(credentials=self.credentials) self._credentials_gbq = service_account.Credentials.from_service_account_file(credentials) def read_df(self, bq_sql_query): return self._client.query(bq_sql_query).to_dataframe() def write_df(self, df_to_write, dataset, table, if_exists='replace'): pandas_gbq.to_gbq( df_to_write , '{}.{}'.format(dataset, table) , project_id=self.project_id , if_exists=if_exists , credentials=self._credentials_gbq ) def run_job(self, sql_query): self._client.query(sql_query).result() show_doc(BQConnector) #hide from nbdev.export import notebook2script notebook2script() ###Output Converted 00_connectors.gcp.ipynb. Converted 01_nlp.fasttext.ipynb. Converted 02_forecasting.dataprep.ipynb. Converted 03_models.catboost.ipynb. Converted index.ipynb.
nytac/corpora_stats/nytac_word_frequency.ipynb
###Markdown Get Word Frequency and Statistics on the New York Times Annotated Corpus ###Code %load_ext autoreload %autoreload 2 from collections import Counter import csv from pathlib import Path import pickle import string import pandas as pd import spacy from tqdm import tqdm import lxml.etree as ET from annorxiver_modules.document_helper import dump_article_text lemma_model = spacy.load("en_core_web_sm") lemma_model.max_length = 9000000 ###Output _____no_output_____ ###Markdown Get the Listing of NYTAC documents ###Code document_gen = list( Path("../nyt_corpus/extracted_data") .rglob("*.xml") ) print(len(document_gen)) ###Output 1855658 ###Markdown Parse the Corpus ###Code document_list = [ f"{doc.stem}.xml" for doc in document_gen ] sentence_length = get_word_stats( document_list=document_list, document_folder="../nyt_corpus/extracted_data", tag_path="//body/body.head/headline/hl1|//body/body.content/block/p", output_folder="output/", ) pickle.dump( sentence_length, open("nytac_sentence_length.pkl", "wb") ) ###Output _____no_output_____
Assignment_for_newcomer/2_Python_library_basic_quiz.ipynb
###Markdown 3๊ธฐ ๊ณผ์ œ - ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ๊ธฐ์ดˆ- ์ง€๊ธˆ๊นŒ์ง€ ๋ฐฐ์šด ๊ธฐ์ดˆ ๋ฌธ๋ฒ•์„ ํ† ๋Œ€๋กœ ์•„๋ž˜์˜ ๋ฌธ์ œ๋“ค์„ ํ’€์–ด๋ณด์„ธ์š”- **์ตœ๋Œ€ํ•œ ๊ฐ„๋‹จํ•˜๊ฒŒ** ์†Œ์Šค์ฝ”๋“œ๋ฅผ ์ž‘์„ฑํ•ด ์ฃผ์„ธ์š”- '์ด๋ฆ„_์ •๋‹ต.zip' ๋กœ ์ €์žฅ ํ›„ ๊ณผ์ œ ํด๋”์— ์ œ์ถœํ•ด์ฃผ์„ธ์š” ๊ฒ€์ƒ‰ ๋˜๋Š” ๋ฉ˜ํ† ์—๊ฒŒ ์งˆ๋ฌธ์„ ํ†ตํ•ด ์˜ค๋ฅ˜์—†์ด ์ „์ฒด์ฝ”๋“œ๋ฅผ ๊ผญ **์™„์„ฑ**ํ•ด ์ฃผ์„ธ์š”. > 0๋ฒˆ. Numpy, Pandas, Matplotlib ํŒจํ‚ค์ง€๋ฅผ import ํ•˜์„ธ์š”. > 1๋ฒˆ. 4x2 ํฌ๊ธฐ์˜ ์ •์ˆ˜๋กœ๋œ numpy array ๋ฅผ ๋งŒ๋“ค๊ณ  ์•„๋ž˜์˜ ๋‘๊ฐ€์ง€ ํŠน์„ฑ๊ณผ array๋ฅผ ์ถœ๋ ฅํ•˜์„ธ์š”. - array ์˜ ๋ชจ๋“  ์›์†Œ๋“ค์€ int16 ํƒ€์ž…์„ ๊ฐ€์ ธ์•ผ ํ•ฉ๋‹ˆ๋‹ค. - 1) array์˜ shape - 2) array์˜ dimension **์‹คํ–‰ ์˜ˆ** : [[12 32] [32 0] [52 462] [ 0 0]] (4, 2) 2 > 2๋ฒˆ. ๊ฐ ์›์†Œ ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ 10์ด ๋˜๋„๋ก 100 ~ 200 ๋ฒ”์œ„์˜ **5X2** ์ •์ˆ˜ numpy array๋ฅผ ์ƒ์„ฑํ•˜๊ณ  ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ** : [[100 110] [120 130] [140 150] [160 170] [180 190]] > 3๋ฒˆ. ๋‹ค์Œ ์ œ๊ณต๋œ numpy array ์˜ ๋ชจ๋“  ํ–‰์—์„œ ๋‘๋ฒˆ์งธ ์—ด์˜ ์›์†Œ๋“ค์„ ์ถœ๋ ฅํ•˜์„ธ์š”**์‹คํ–‰ ์˜ˆ** : [[11 22 33] [44 55 66] [77 88 99]] [22 55 88] ###Code arr = np.array([[11 ,22, 33], [44, 55, 66], [77, 88, 99]]) print(arr) print() # --- start code --- # --- end code --- ###Output _____no_output_____ ###Markdown > 4๋ฒˆ. ๋‹ค์Œ ์ œ๊ณต๋œ numpu array ์˜ ์›์†Œ ์ค‘ ํ™€์ˆ˜ ํ–‰์ด๋ฉด์„œ ์ง์ˆ˜ ์—ด์— ์œ„์น˜ํ•˜๋Š” ์›์†Œ๋“ค๋ฅผ ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[ 3 6 9 12] [15 18 21 24] [27 30 33 36] [39 42 45 48] [51 54 57 60]] [[ 6 12] [30 36] [54 60]] ###Code arr = np.array([[3 ,6, 9, 12], [15 ,18, 21, 24], [27 ,30, 33, 36], [39 ,42, 45, 48], [51 ,54, 57, 60]]) print(arr) print() # --- start code --- # --- end code --- ###Output _____no_output_____ ###Markdown > 5๋ฒˆ. ์ฃผ์–ด์ง„ ๋‘ numpy array์˜ ํ•ฉ์„ ๊ณ„์‚ฐํ•˜์—ฌ ์ถœ๋ ฅํ•˜๊ณ , ๊ณ„์‚ฐํ•œ ํ•ฉ numpy array๋ฅผ ์ œ๊ณฑํ•˜์—ฌ ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** ํ•ฉ : [[20 39 33] [25 25 28]] ์ œ๊ณฑ : [[ 400 1521 1089] [ 625 625 784]] ###Code arrone = np.array([[5, 6, 9], [21 ,18, 27]]) arrtwo = np.array([[15 ,33, 24], [4 ,7, 1]]) # --- start code --- # --- end code --- ###Output _____no_output_____ ###Markdown > 6๋ฒˆ. ๊ฐ ์›์†Œ ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ 1์ด ๋˜๋„๋ก 10์—์„œ 34 ์‚ฌ์ด์˜ ๋ฒ”์œ„์—์„œ 8X3 ์ •์ˆ˜ numpy array ๋ฅผ ๋งŒ๋“ค์–ด ์ถœ๋ ฅํ•œ ๋‹ค์Œ, numpy array ์„ 4๊ฐœ์˜ ๋™์ผํ•œ ํฌ๊ธฐ์˜ ํ•˜์œ„ numpy array ๋กœ ๋ถ„ํ• ํ•ด์„œ ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[10 11 12] [13 14 15] [16 17 18] [19 20 21] [22 23 24] [25 26 27] [28 29 30] [31 32 33]] [array([[10, 11, 12],[13, 14, 15]]), array([[16, 17, 18],[19, 20, 21]]), array([[22, 23, 24],[25, 26, 27]]), array([[28, 29, 30],[31, 32, 33]])] > 7๋ฒˆ. ์ฃผ์–ด์ง„ numpy array์— ๋Œ€ํ•˜์—ฌ, ํ–‰๊ณผ ์—ด ๊ธฐ์ค€์œผ๋กœ ์ตœ๋Œ“๊ฐ’๊ณผ ์ตœ์†Ÿ๊ฐ’์„ ๊ฐ๊ฐ ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[34 43 73] [82 22 12] [53 94 66]] [34 22 12] [34 12 53] [82 94 73] [73 82 94] ###Code arr = np.array([[34,43,73],[82,22,12],[53,94,66]]) print(arr) print() # --- start code --- # --- end code --- ###Output _____no_output_____ ###Markdown > 8๋ฒˆ. ์ฃผ์–ด์ง„ arr ์— ๋Œ€ํ•˜์—ฌ, ๋‘๋ฒˆ์งธ ํ–‰์„ ์‚ญ์ œํ•˜๊ณ  new_arr์„ ์‚ฝ์ž…ํ•˜์—ฌ ์ถœ๋ ฅํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[34 43 73] [82 22 12] [53 94 66]] [[34 10 73] [82 10 12] [53 10 66]] ###Code arr = np.array([[34,43,73],[82,22,12],[53,94,66]]) new_arr = np.array([[10,10,10]]) print (arr) print() # --- start code --- # --- end code --- ###Output _____no_output_____ ###Markdown > 9๋ฒˆ. 'company_sales_data.csv' ํŒŒ์ผ์„ ๋ถˆ๋Ÿฌ์™€ 'Month number' ์— ๋Œ€ํ•œ 'Total profit'์„ ์„  ํ”Œ๋กฏ์œผ๋กœ ์‹œ๊ฐํ™” ํ•˜์„ธ์š”. 'Total profit' ๋ฐ์ดํ„ฐ๋Š” ๋งค๋‹ฌ ์ œ๊ณต๋ฉ๋‹ˆ๋‹ค. ์„  ํ”Œ๋กฏ์„ ์•„๋ž˜์˜ ์š”์†Œ๋“ค์„ ์ ์šฉ์‹œ์ผœ ํ•จ๊ป˜ ์ถœ๋ ฅํ•˜์„ธ์š”. ํƒ€์ดํ‹€ = Company profit per month X ๋ผ๋ฒจ = Month Number Y ๋ผ๋ฒจ = Total profit **์‹คํ–‰ ์˜ˆ :** [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex1.jpg) > 10๋ฒˆ. 'company_sales_data.csv' ํŒŒ์ผ์„ ๋ถˆ๋Ÿฌ์™€ ๋ชจ๋“  ์ œํ’ˆ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ฝ์–ด์˜จ ๋’ค multiline plot ์œผ๋กœ ์‹œ๊ฐํ™” ํ•˜์„ธ์š”. ๊ฐ ์ œํ’ˆ์— ๋Œ€ํ•ด ๋งค์›” ํŒ๋งค๋˜๋Š” ๋‹จ์œ„ ์ˆ˜๋ฅผ ํ‘œ์‹œํ•˜์„ธ์š”. ์ฆ‰, ๊ฐ ์ œํ’ˆ๋งˆ๋‹ค ๋‹ค๋ฅธ plot line ์„ ๊ฐ€์ ธ์•ผํ•ฉ๋‹ˆ๋‹ค. **์‹คํ–‰ ์˜ˆ** : [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex2.jpg) > 11๋ฒˆ. ์œ„์™€ ๊ฐ™์€ csvํŒŒ์ผ์—์„œ ๋งค๋‹ฌ toothpaste ํŒ๋งค๋Ÿ‰ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜จ ๋’ค scatter plot ์œผ๋กœ ์‹œ๊ฐํ™” ํ•˜์„ธ์š”. plot์—๋Š” ๊ฒฉ์ž๊ฐ€ ํ‘œ์‹œ๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค - gridline style :โ€œ--" **์‹คํ–‰ ์˜ˆ :** [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex3.jpg) ###Code ###Output _____no_output_____ ###Markdown > 12๋ฒˆ. ์œ„์™€ ๊ฐ™์€ csvํŒŒ์ผ์—์„œ ๋งค๋‹ฌ bathing soap ํŒ๋งค๋Ÿ‰ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜จ ๋’ค bar chart ๋กœ ์‹œ๊ฐํ™” ํ•˜์„ธ์š”. plot์„ jpg ํŒŒ์ผ๋กœ ์ปดํ“จํ„ฐ์— ์ €์žฅํ•œ ๋’ค (hint : savefig), ๊ณผ์ œ๋ฅผ ์ œ์ถœํ• ๋•Œ ipynb ํŒŒ์ผ๊ณผ ํ•จ๊ป˜ ์••์ถ•ํ•ด์„œ ์ œ์ถœํ•ด ์ฃผ์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex4.jpg) > 13๋ฒˆ. "iris.csv" ๋ฅผ ๋ถˆ๋Ÿฌ์™€์„œ (150,5) ํฌ๊ธฐ์˜ numpy array๋กœ ๋งŒ๋“œ์„ธ์š”.๊ทธ๋ฆฌ๊ณ  4๊ฐœ์˜ ์†์„ฑ (sepal.length, sepal.width, petal.length, petal.width) ๋ฅผ **์ •๊ทœํ™”** ํ•ด์„œ ๋งจ์•ž๋ถ€ํ„ฐ ๋‹ค์„ฏ ํ–‰๋งŒ ์ถœ๋ ฅํ•˜์„ธ์š”. (์ฆ‰, ๊ฐ ์†์„ฑ์€ ํ‰๊ท ์€ 0, ํ‘œ์ค€ํŽธ์ฐจ๊ฐ€ 1์ด ๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.) **์‹คํ–‰ ์˜ˆ :** array([[-0.90068117, 1.01900435, -1.34022653, -1.3154443 ], [-1.14301691, -0.13197948, -1.34022653, -1.3154443 ], [-1.38535265, 0.32841405, -1.39706395, -1.3154443 ], [-1.50652052, 0.09821729, -1.2833891 , -1.3154443 ], [-1.02184904, 1.24920112, -1.34022653, -1.3154443 ]]) > 14๋ฒˆ. Iris ๋ฐ์ดํ„ฐ์˜ 4๊ฐ€์ง€ ์†์„ฑ์„ boxplot ์œผ๋กœ ์‹œ๊ฐํ™”ํ•˜์„ธ์š”. **์‹คํ–‰ ์˜ˆ :** [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex5.jpg) > 15๋ฒˆ. x์ถ•์€ ํ‰๊ท ์ด 5, ํ‘œ์ค€ํŽธ์ฐจ๊ฐ€ 3, y์ถ•์€ ํ‰๊ท ์ด 3, ํ‘œ์ค€ํŽธ์ฐจ๊ฐ€ 2 ์ธ ์ƒ˜ํ”Œ์„ 1000๊ฐœ ๋งŒ๋“ค์–ด์„œ ์ด๋ฅผ ์‚ฐ์ ๋„๋กœ ์‹œ๊ฐํ™”ํ•˜์„ธ์š”. (hint : np.random.normal() ์‚ฌ์šฉ) ์ถ•์˜ ๋น„์œจ (x์ถ•๊ณผ y์ถ•์˜ ๋ˆˆ๊ธˆ๊ธธ์ด) ์„ ์ผ์ •ํ•˜๊ฒŒ ๋งŒ๋“œ์„ธ์š” (hint : plt.axis()์‚ฌ์šฉ) **์‹คํ–‰ ์˜ˆ** : [[click]](https://github.com/OH-Seoyoung/Data_Analysis_Club_Assignment/blob/master/Assignment_for_newcomer/examples/ex6.jpg) > 16๋ฒˆ. ์‹œ๊ทธ๋ชจ์ด๋“œ ํ•จ์ˆ˜๋Š” ๋กœ์ง€์Šคํ‹ฑ ํ•จ์ˆ˜๋กœ ์•Œ๋ ค์ ธ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋จธ์‹ ๋Ÿฌ๋‹๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋”ฅ๋Ÿฌ๋‹์—์„œ๋„ ์‚ฌ์šฉ๋˜๋Š” ๋น„์„ ํ˜• ํ•จ์ˆ˜์ž…๋‹ˆ๋‹ค. ์‹ค์ˆ˜ x ์˜ ์‹œ๊ทธ๋ชจ์ด๋“œ ๊ฐ’์„ ๋ฐ˜ํ™˜ํ•˜๋Š” ํ•จ์ˆ˜๋ฅผ ์ •์˜ํ•ด์ฃผ์„ธ์š”. ์‹œ๊ทธ๋ชจ์ด๋“œ ํ•จ์ˆ˜ ์‹์€ ์•„๋ž˜์™€ ๊ฐ™์Šต๋‹ˆ๋‹ค. **์‹คํ–‰ ์˜ˆ** : 0.6456563062257954 ###Code # don't touch here from IPython.display import display, Math, Latex display(Math(r'sigmoid(x) = \frac{1}{1+e^{-x}}')) import math # math ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์‚ฌ์šฉํ•˜๊ธฐ def sigmoid(x): # --- start code ------ # --- end code -------- return s print(sigmoid(0.6)) ###Output _____no_output_____ ###Markdown > 17๋ฒˆ. ์•„๋ฌด jpg ์‚ฌ์ง„ํŒŒ์ผ์„ ๊ฐ€์ ธ์™€์„œ numpy array๋กœ ๋ถˆ๋Ÿฌ์˜จ๋’ค, ์ด๋ฏธ์ง€์˜ shape์™€ ๋ฐ์ดํ„ฐํƒ€์ž…์„ ์ถœ๋ ฅํ•˜์„ธ์š” ###Code from PIL import Image img = np.array(Image.open('์ด๋ฏธ์ง€ ์ด๋ฆ„์„ ์“ฐ์„ธ์š”.jpg')) # --- start code ------ # --- end code -------- plt.imshow(img) plt.show() ###Output _____no_output_____ ###Markdown > 18๋ฒˆ. ์œ„์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ ๋™์ผํ•œ ์ด๋ฏธ์ง€๋ฅผ ํ‘๋ฐฑ์œผ๋กœ ๋ถˆ๋Ÿฌ์™€๋ณด์„ธ์š”. ๊ทธ๋ฆฌ๊ณ  shape์™€ ๋ฐ์ดํ„ฐํƒ€์ž…์„ ์ถœ๋ ฅํ•˜์„ธ์š”. ์ถœ๋ ฅ๋œ shape์˜ ํ˜•ํƒœ์„ ํ†ตํ•ด ์•Œ ์ˆ˜ ์žˆ๋Š” ์ปฌ๋Ÿฌ์ด๋ฏธ์ง€์™€ ํ‘๋ฐฑ์ด๋ฏธ์ง€์˜ ์ฐจ์ด๋ฅผ ์ฃผ์„์„ ํ†ตํ•ด ์„ค๋ช…ํ•˜์„ธ์š”. ###Code # --- start code ------ # --- end code -------- plt.imshow(img_gray, cmap = 'gray') plt.show() ###Output _____no_output_____ ###Markdown > 19๋ฒˆ. ์œ„์˜ **ํ‘๋ฐฑ ์ด๋ฏธ์ง€**๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์•„๋ž˜ ์„ธ๊ฐ€์ง€ ์‹์„ ๊ณ„์‚ฐํ•˜๊ณ , ๊ฐ๊ฐ์˜ ์ตœ๋Œ“๊ฐ’๊ณผ ์ตœ์†Ÿ๊ฐ’์„ ์ถœ๋ ฅํ•˜์„ธ์š”. ###Code # don't touch here from IPython.display import display, Math, Latex print('X : ํ‘๋ฐฑ ์ด๋ฏธ์ง€ numpy array') display(Math('X1 = 255 - X')) display(Math('X2 = (100 / 255) * X + 100 ')) display(Math('X3 = 255 * (X / 255) ** 2 ')) # --- start code ------ ###Output _____no_output_____
stats/Java sorting.ipynb
###Markdown Initial code to strip the fields with Regex```import reimport pandas as pddata = pd.read_csv("java_sorting_24_7_17.txt", sep="|")def filter_data(data): data.columns= [re.sub(r'\s+(\S+)\s+', r'\1', x) for x in data.columns] for i in range(1, len(data.columns)): try: data.iloc[:,i] = data.iloc[:,i].apply(lambda x: re.sub(r'\s+(\S+)\s+', r'\1', x)) except Exception as e: print(e) data.loc[:, 'shuffle'] = data.loc[:, 'shuffle'].apply(lambda x: re.sub(r'\/(\d+)', r'\1',x)) return datadata = filter_data(data)``` ###Code # Using strip to filter the values in the txt import pandas as pd import numpy as np def read_stats(data_file): data = pd.read_csv(data_file, sep="|") data.columns = [ x.strip() for x in data.columns] # Filter integer indexes str_idxs = [idx for idx,dtype in zip(range(0,len(data.dtypes)), data.dtypes) if dtype != 'int64' ] # Strip fields for i in str_idxs: key = data.columns[i] if data[key].dtype == np.dtype('str'): data.loc[:,key] = [ x.strip() for x in data.loc[:, key]] return data data = read_stats("java_sorting_127.0.1.1_Di_1._Aug_07:39:03_UTC_2017.csv") # data.to_csv("java_sorting_127.0.1.1_Di_1._Aug_07:39:03_UTC_2017.csv") [x for x in zip(range(0, len(data.columns)),data.columns)] import plotly import plotly.plotly as py import plotly.figure_factory as ff from plotly.graph_objs import * #plotly.offline.init_notebook_mode() def filter_by(data, name, value): data_length = len(data) return [idx for idx in range(0, data_length) if data.loc[idx,name] == value] # using ~/.plotly/.credentials # plotly.tools.set_credentials_file(username="", api_key="") algorithms = set(data.loc[:, 'name']) alg = algorithms.pop() idxs = filter_by(data, 'name', alg) X = data.loc[idxs, 'elements'] Y = data.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data, 'name', alg) X = data.loc[idxs, 'elements'] Y = data.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data, 'name', alg) X = data.loc[idxs, 'elements'] Y = data.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data, 'name', alg) X = data.loc[idxs, 'elements'] Y = data.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data, 'name', alg) X = data.loc[idxs, 'elements'] Y = data.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) ###Output _____no_output_____ ###Markdown The same stats as before but with 9 Million dataThe **merge sort** algorithm we developed is a bit less than O(N). We couldn't find out in that run the worst case performance of O(n log(n)) [see](https://en.wikipedia.org/wiki/Merge_sort). The worst case of our **merge sort** (single threaded) is better than the worst case of the java platform Arrays.sort, however the stats are not independend the runs were not isolated.We loop through all sorting algorithms, the garbage collection of the previous algorithmmight affect the performance of the next one. The garbage collection of **merge sort** might change the performance of **Arrays.sort** ###Code data2 = read_stats("java_sorting_127.0.1.1_Fr_4._Aug_23:59:33_UTC_2017.txt") algorithms = set(data2.loc[:, 'name']) alg = algorithms.pop() idxs = filter_by(data2, 'name', alg) X = data2.loc[idxs, 'elements'] Y = data2.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data2, 'name', alg) X = data2.loc[idxs, 'elements'] Y = data2.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data2, 'name', alg) X = data2.loc[idxs, 'elements'] Y = data2.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data2, 'name', alg) X = data2.loc[idxs, 'elements'] Y = data2.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) alg = algorithms.pop() idxs = filter_by(data2, 'name', alg) X = data2.loc[idxs, 'elements'] Y = data2.loc[idxs, 'duration_ms'] plot_data = [Bar(x = X, y = Y, name=alg)] layout = Layout(title= alg + ' performance (java) ', xaxis=dict(title='Elements'), yaxis=dict(title='Time')) fig = Figure(data=plot_data, layout=layout) py.iplot(fig) ###Output _____no_output_____ ###Markdown Better visualization ###Code data2.loc[:,'name'] =[x.strip() for x in data2.loc[:,'name']] algorithms = set(data2.loc[:, 'name']) algorithms import plotly.graph_objs as go algorithms.remove('Linked Hashmap') def get_bar(data, algorithm_name): idxs = filter_by(data, 'name', algorithm_name) X1 = data2.loc[idxs, 'elements'] Y1 = data2.loc[idxs, 'duration_ms'] return go.Bar(x=X1, y=Y1, name=algorithm_name) plot_data = [get_bar(data2, name) for name in algorithms] layout = go.Layout(title= 'Performance comparison', xaxis=dict(title='Elements (32 bits / -2,147,483,648 to +2,147,483,647)'), yaxis=dict(title='Time (ms)'), barmode='stack') fig = go.Figure(data=plot_data, layout=layout) py.iplot(fig) ###Output _____no_output_____
src/notebook/(2_1)StrokeColor_Skatch_A_Net_ipynb_.ipynb
###Markdown Connect Google Drive ###Code from google.colab import drive drive.mount('/content/gdrive') ###Output Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True). ###Markdown Import ###Code import tensorflow as tf import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import os from tensorflow import keras from tensorflow.keras.layers import Input from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import MaxPool2D from tensorflow.keras.layers import ReLU from tensorflow.keras.layers import Softmax from tensorflow.keras.layers import Flatten from tensorflow.keras.layers import Dropout from tensorflow.keras.layers import BatchNormalization from tensorflow.keras.metrics import sparse_top_k_categorical_accuracy from tensorflow.keras.callbacks import CSVLogger from ast import literal_eval ###Output _____no_output_____ ###Markdown Parameters and Work-Space Paths ###Code # parameters BATCH_SIZE = 200 EPOCHS = 50 STEPS_PER_EPOCH = 850 VALIDATION_STEPS = 100 EVALUATE_STEPS = 850 IMAGE_SIZE = 225 LINE_SIZE = 3 # load path TRAIN_DATA_PATH = 'gdrive/My Drive/QW/Data/Data_10000/All_classes_10000.csv' VALID_DATA_PATH = 'gdrive/My Drive/QW/Data/My_test_data/My_test_data.csv' LABEL_DICT_PATH = 'gdrive/My Drive/QW/Data/labels_dict.npy' # save path CKPT_PATH = 'gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt' LOSS_PLOT_PATH = 'gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/loss_plot_2_1.png' ACC_PLOT_PATH = 'gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/acc_plot_2_1.png' LOG_PATH = 'gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/Log_2_1.log' print('finish!') ###Output finish! ###Markdown Generator ###Code def generate_data(data, batch_size, choose_recognized): data = data.sample(frac = 1) while 1: # get columns' values named 'drawing', 'word' and 'recognized' drawings = data["drawing"].values drawing_recognized = data["recognized"].values drawing_class = data["word"].values # initialization cnt = 0 data_X =[] data_Y =[] # generate batch for i in range(len(drawings)): if choose_recognized: if drawing_recognized[i] == 'False': #Choose according to recognized value continue draw = drawings[i] label = drawing_class[i] stroke_vec = literal_eval(draw) img = np.zeros([256, 256]) x = [] for j in range(len(stroke_vec)): line = np.array(stroke_vec[j]).T cv2.polylines(img, [line], False, 255-(13*min(j,10)), LINE_SIZE) img = cv2.resize(img, (IMAGE_SIZE,IMAGE_SIZE), interpolation = cv2.INTER_NEAREST) img = img[:,:, np.newaxis] x = img y = labels2nums_dict[label] data_X.append(x) data_Y.append(y) cnt += 1 if cnt==batch_size: #generate a batch when cnt reaches batch_size cnt = 0 yield (np.array(data_X), np.array(data_Y)) data_X = [] data_Y = [] print('finish!') ###Output finish! ###Markdown Callbacks ###Code # define a class named LossHitory class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.losses = {'batch':[], 'epoch':[]} self.accuracy = {'batch':[], 'epoch':[]} self.val_loss = {'batch':[], 'epoch':[]} self.val_acc = {'batch':[], 'epoch':[]} def on_batch_end(self, batch, logs={}): self.losses['batch'].append(logs.get('loss')) self.accuracy['batch'].append(logs.get('acc')) self.val_loss['batch'].append(logs.get('val_loss')) self.val_acc['batch'].append(logs.get('val_acc')) def on_epoch_end(self, batch, logs={}): self.losses['epoch'].append(logs.get('loss')) self.accuracy['epoch'].append(logs.get('acc')) self.val_loss['epoch'].append(logs.get('val_loss')) self.val_acc['epoch'].append(logs.get('val_acc')) def loss_plot(self, loss_type, loss_fig_save_path, acc_fig_save_path): iters = range(len(self.losses[loss_type])) plt.figure('acc') plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc') plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc') plt.grid(True) plt.xlabel(loss_type) plt.ylabel('acc') plt.legend(loc="upper right") plt.savefig(acc_fig_save_path) plt.show() plt.figure('loss') plt.plot(iters, self.losses[loss_type], 'g', label='train loss') plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss') plt.grid(True) plt.xlabel(loss_type) plt.ylabel('loss') plt.legend(loc="upper right") plt.savefig(loss_fig_save_path) plt.show() # create a object from LossHistory class History = LossHistory() print("finish!") cp_callback = tf.keras.callbacks.ModelCheckpoint( CKPT_PATH, verbose = 1, monitor='val_acc', mode = 'max', save_best_only=True) print("finish!") ReduceLR = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=3, min_delta=0.005, mode='max', cooldown=3, verbose=1) csv_logger = CSVLogger(LOG_PATH, separator=',', append=True) ###Output _____no_output_____ ###Markdown Load Data ###Code # load train data and valid data # labels_dict and data path # labels convert into nums labels_dict = np.load(LABEL_DICT_PATH) labels2nums_dict = {v: k for k, v in enumerate(labels_dict)} # read csv train_data = pd.read_csv(TRAIN_DATA_PATH) valid_data = pd.read_csv(VALID_DATA_PATH) print('finish!') ###Output finish! ###Markdown Model ###Code x_input = Input(shape=(IMAGE_SIZE,IMAGE_SIZE,1), name='Input') x = Conv2D(64, (15,15), strides=3, padding='valid',name='Conv2D_1')(x_input) x = BatchNormalization(name='BN_1')(x) x = ReLU(name='ReLU_1')(x) x = MaxPool2D(pool_size=(3,3),strides=2, name='Pooling_1')(x) x = Conv2D(128, (5,5), strides=1, padding='valid',name='Conv2D_2')(x) x = BatchNormalization(name='BN_2')(x) x = ReLU(name='ReLU_2')(x) x = MaxPool2D(pool_size=(3,3),strides=2, name='Pooling_2')(x) x = Conv2D(256, (3,3), strides=1, padding='same',name='Conv2D_3')(x) x = BatchNormalization(name='BN_3')(x) x = ReLU(name='ReLU_3')(x) x = Conv2D(256, (3,3), strides=1, padding='same',name='Conv2D_4')(x) x = BatchNormalization(name='BN_4')(x) x = ReLU(name='ReLU_4')(x) x = Conv2D(256, (3,3), strides=1, padding='same',name='Conv2D_5')(x) x = BatchNormalization(name='BN_5')(x) x = ReLU(name='ReLU_5')(x) x = MaxPool2D(pool_size=(3,3),strides=2, name='Pooling_5')(x) x_shape = x.shape[1] x = Conv2D(512, (int(x_shape),int(x_shape)), strides=1, padding='valid',name='Conv2D_FC_6')(x) x = BatchNormalization(name='BN_6')(x) x = ReLU(name='ReLU_6')(x) x = Dropout(0.5,name='Dropout_6')(x) x = Conv2D(512, (1,1), strides=1, padding='valid',name='Conv2D_FC_7')(x) x = BatchNormalization(name='BN_7')(x) x = ReLU(name='ReLU_7')(x) x = Dropout(0.5,name='Dropout_7')(x) x = Conv2D(340, (1,1), strides=1, padding='valid',name='Conv2D_FC_8')(x) x = Flatten(name='Flatten')(x) x_output = Softmax(name='Softmax')(x) MODEL = keras.models.Model(inputs=x_input, outputs=x_output) MODEL.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= Input (InputLayer) (None, 225, 225, 1) 0 _________________________________________________________________ Conv2D_1 (Conv2D) (None, 71, 71, 64) 14464 _________________________________________________________________ BN_1 (BatchNormalization) (None, 71, 71, 64) 256 _________________________________________________________________ ReLU_1 (ReLU) (None, 71, 71, 64) 0 _________________________________________________________________ Pooling_1 (MaxPooling2D) (None, 35, 35, 64) 0 _________________________________________________________________ Conv2D_2 (Conv2D) (None, 31, 31, 128) 204928 _________________________________________________________________ BN_2 (BatchNormalization) (None, 31, 31, 128) 512 _________________________________________________________________ ReLU_2 (ReLU) (None, 31, 31, 128) 0 _________________________________________________________________ Pooling_2 (MaxPooling2D) (None, 15, 15, 128) 0 _________________________________________________________________ Conv2D_3 (Conv2D) (None, 15, 15, 256) 295168 _________________________________________________________________ BN_3 (BatchNormalization) (None, 15, 15, 256) 1024 _________________________________________________________________ ReLU_3 (ReLU) (None, 15, 15, 256) 0 _________________________________________________________________ Conv2D_4 (Conv2D) (None, 15, 15, 256) 590080 _________________________________________________________________ BN_4 (BatchNormalization) (None, 15, 15, 256) 1024 _________________________________________________________________ ReLU_4 (ReLU) (None, 15, 15, 256) 0 _________________________________________________________________ Conv2D_5 (Conv2D) (None, 15, 15, 256) 590080 _________________________________________________________________ BN_5 (BatchNormalization) (None, 15, 15, 256) 1024 _________________________________________________________________ ReLU_5 (ReLU) (None, 15, 15, 256) 0 _________________________________________________________________ Pooling_5 (MaxPooling2D) (None, 7, 7, 256) 0 _________________________________________________________________ Conv2D_FC_6 (Conv2D) (None, 1, 1, 512) 6423040 _________________________________________________________________ BN_6 (BatchNormalization) (None, 1, 1, 512) 2048 _________________________________________________________________ ReLU_6 (ReLU) (None, 1, 1, 512) 0 _________________________________________________________________ Dropout_6 (Dropout) (None, 1, 1, 512) 0 _________________________________________________________________ Conv2D_FC_7 (Conv2D) (None, 1, 1, 512) 262656 _________________________________________________________________ BN_7 (BatchNormalization) (None, 1, 1, 512) 2048 _________________________________________________________________ ReLU_7 (ReLU) (None, 1, 1, 512) 0 _________________________________________________________________ Dropout_7 (Dropout) (None, 1, 1, 512) 0 _________________________________________________________________ Conv2D_FC_8 (Conv2D) (None, 1, 1, 340) 174420 _________________________________________________________________ Flatten (Flatten) (None, 340) 0 _________________________________________________________________ Softmax (Softmax) (None, 340) 0 ================================================================= Total params: 8,562,772 Trainable params: 8,558,804 Non-trainable params: 3,968 _________________________________________________________________ ###Markdown TPU Complie ###Code model = MODEL model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print('finish') ###Output finish ###Markdown Train ###Code print('start training') # callbacks = [History, cp_callback] history = model.fit_generator(generate_data(train_data, BATCH_SIZE, True), steps_per_epoch = STEPS_PER_EPOCH, epochs = EPOCHS, validation_data = generate_data(valid_data, BATCH_SIZE, False) , validation_steps = VALIDATION_STEPS, verbose = 1, initial_epoch = 0, callbacks = [History,cp_callback,ReduceLR,csv_logger] ) print("finish training") History.loss_plot('epoch', LOSS_PLOT_PATH, ACC_PLOT_PATH) print('finish!') ###Output start training Epoch 1/50 849/850 [============================>.] - ETA: 0s - loss: 3.8941 - acc: 0.1969 Epoch 00001: val_acc improved from -inf to 0.39465, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 387s 456ms/step - loss: 3.8926 - acc: 0.1971 - val_loss: 2.6247 - val_acc: 0.3946 Epoch 2/50 849/850 [============================>.] - ETA: 0s - loss: 2.6675 - acc: 0.3848 Epoch 00002: val_acc improved from 0.39465 to 0.47900, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 444ms/step - loss: 2.6675 - acc: 0.3848 - val_loss: 2.1809 - val_acc: 0.4790 Epoch 3/50 849/850 [============================>.] - ETA: 0s - loss: 2.3318 - acc: 0.4522 Epoch 00003: val_acc improved from 0.47900 to 0.54445, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 2.3316 - acc: 0.4522 - val_loss: 1.8988 - val_acc: 0.5444 Epoch 4/50 849/850 [============================>.] - ETA: 0s - loss: 2.1677 - acc: 0.4867 Epoch 00004: val_acc improved from 0.54445 to 0.56575, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 2.1676 - acc: 0.4868 - val_loss: 1.8030 - val_acc: 0.5657 Epoch 5/50 849/850 [============================>.] - ETA: 0s - loss: 2.0528 - acc: 0.5103 Epoch 00005: val_acc improved from 0.56575 to 0.58080, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 443ms/step - loss: 2.0527 - acc: 0.5104 - val_loss: 1.7436 - val_acc: 0.5808 Epoch 6/50 849/850 [============================>.] - ETA: 0s - loss: 1.9624 - acc: 0.5287 Epoch 00006: val_acc improved from 0.58080 to 0.60580, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 443ms/step - loss: 1.9621 - acc: 0.5287 - val_loss: 1.6372 - val_acc: 0.6058 Epoch 7/50 849/850 [============================>.] - ETA: 0s - loss: 1.8950 - acc: 0.5424 Epoch 00007: val_acc improved from 0.60580 to 0.62155, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 443ms/step - loss: 1.8948 - acc: 0.5425 - val_loss: 1.5491 - val_acc: 0.6216 Epoch 8/50 849/850 [============================>.] - ETA: 0s - loss: 1.8435 - acc: 0.5566 Epoch 00008: val_acc improved from 0.62155 to 0.62295, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 443ms/step - loss: 1.8434 - acc: 0.5567 - val_loss: 1.5613 - val_acc: 0.6229 Epoch 9/50 849/850 [============================>.] - ETA: 0s - loss: 1.8053 - acc: 0.5638 Epoch 00009: val_acc improved from 0.62295 to 0.63075, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 376s 443ms/step - loss: 1.8055 - acc: 0.5638 - val_loss: 1.5016 - val_acc: 0.6308 Epoch 10/50 849/850 [============================>.] - ETA: 0s - loss: 1.7765 - acc: 0.5722 Epoch 00010: val_acc improved from 0.63075 to 0.64190, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 444ms/step - loss: 1.7762 - acc: 0.5723 - val_loss: 1.4643 - val_acc: 0.6419 Epoch 11/50 849/850 [============================>.] - ETA: 0s - loss: 1.7327 - acc: 0.5814 Epoch 00011: val_acc improved from 0.64190 to 0.64475, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 379s 446ms/step - loss: 1.7328 - acc: 0.5813 - val_loss: 1.4487 - val_acc: 0.6448 Epoch 12/50 849/850 [============================>.] - ETA: 0s - loss: 1.7013 - acc: 0.5872 Epoch 00012: val_acc improved from 0.64475 to 0.66140, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 376s 443ms/step - loss: 1.7013 - acc: 0.5872 - val_loss: 1.3741 - val_acc: 0.6614 Epoch 13/50 849/850 [============================>.] - ETA: 0s - loss: 1.6855 - acc: 0.5926 Epoch 00013: val_acc did not improve from 0.66140 850/850 [==============================] - 377s 443ms/step - loss: 1.6855 - acc: 0.5926 - val_loss: 1.3968 - val_acc: 0.6534 Epoch 14/50 849/850 [============================>.] - ETA: 0s - loss: 1.6563 - acc: 0.5971 Epoch 00014: val_acc improved from 0.66140 to 0.66705, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.6563 - acc: 0.5971 - val_loss: 1.3629 - val_acc: 0.6671 Epoch 15/50 849/850 [============================>.] - ETA: 0s - loss: 1.6341 - acc: 0.6046 Epoch 00015: val_acc improved from 0.66705 to 0.66855, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.6343 - acc: 0.6046 - val_loss: 1.3530 - val_acc: 0.6686 Epoch 16/50 849/850 [============================>.] - ETA: 0s - loss: 1.6199 - acc: 0.6069 Epoch 00016: val_acc did not improve from 0.66855 850/850 [==============================] - 376s 442ms/step - loss: 1.6202 - acc: 0.6069 - val_loss: 1.3713 - val_acc: 0.6575 Epoch 17/50 849/850 [============================>.] - ETA: 0s - loss: 1.6173 - acc: 0.6075 Epoch 00017: val_acc improved from 0.66855 to 0.67000, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. 850/850 [==============================] - 379s 446ms/step - loss: 1.6176 - acc: 0.6074 - val_loss: 1.3229 - val_acc: 0.6700 Epoch 18/50 849/850 [============================>.] - ETA: 0s - loss: 1.5386 - acc: 0.6271 Epoch 00018: val_acc improved from 0.67000 to 0.69380, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 379s 445ms/step - loss: 1.5386 - acc: 0.6272 - val_loss: 1.2598 - val_acc: 0.6938 Epoch 19/50 849/850 [============================>.] - ETA: 0s - loss: 1.5188 - acc: 0.6306 Epoch 00019: val_acc improved from 0.69380 to 0.69660, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.5188 - acc: 0.6306 - val_loss: 1.2129 - val_acc: 0.6966 Epoch 20/50 849/850 [============================>.] - ETA: 0s - loss: 1.4950 - acc: 0.6357 Epoch 00020: val_acc did not improve from 0.69660 850/850 [==============================] - 375s 441ms/step - loss: 1.4949 - acc: 0.6358 - val_loss: 1.2308 - val_acc: 0.6928 Epoch 21/50 849/850 [============================>.] - ETA: 0s - loss: 1.4978 - acc: 0.6373 Epoch 00021: val_acc improved from 0.69660 to 0.69905, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 443ms/step - loss: 1.4976 - acc: 0.6373 - val_loss: 1.2007 - val_acc: 0.6990 Epoch 22/50 849/850 [============================>.] - ETA: 0s - loss: 1.4804 - acc: 0.6399 Epoch 00022: val_acc improved from 0.69905 to 0.70405, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 376s 443ms/step - loss: 1.4806 - acc: 0.6399 - val_loss: 1.1987 - val_acc: 0.7040 Epoch 23/50 849/850 [============================>.] - ETA: 0s - loss: 1.4638 - acc: 0.6436 Epoch 00023: val_acc did not improve from 0.70405 850/850 [==============================] - 374s 440ms/step - loss: 1.4637 - acc: 0.6436 - val_loss: 1.2002 - val_acc: 0.7013 Epoch 24/50 849/850 [============================>.] - ETA: 0s - loss: 1.4643 - acc: 0.6436 Epoch 00024: val_acc did not improve from 0.70405 Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. 850/850 [==============================] - 375s 441ms/step - loss: 1.4643 - acc: 0.6436 - val_loss: 1.2037 - val_acc: 0.7014 Epoch 25/50 849/850 [============================>.] - ETA: 0s - loss: 1.4363 - acc: 0.6510 Epoch 00025: val_acc improved from 0.70405 to 0.71090, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 444ms/step - loss: 1.4364 - acc: 0.6510 - val_loss: 1.1670 - val_acc: 0.7109 Epoch 26/50 849/850 [============================>.] - ETA: 0s - loss: 1.4184 - acc: 0.6538 Epoch 00026: val_acc did not improve from 0.71090 850/850 [==============================] - 375s 441ms/step - loss: 1.4181 - acc: 0.6539 - val_loss: 1.1788 - val_acc: 0.7091 Epoch 27/50 849/850 [============================>.] - ETA: 0s - loss: 1.4016 - acc: 0.6568 Epoch 00027: val_acc improved from 0.71090 to 0.71460, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 380s 447ms/step - loss: 1.4013 - acc: 0.6569 - val_loss: 1.1414 - val_acc: 0.7146 Epoch 28/50 849/850 [============================>.] - ETA: 0s - loss: 1.3937 - acc: 0.6610 Epoch 00028: val_acc did not improve from 0.71460 850/850 [==============================] - 376s 442ms/step - loss: 1.3936 - acc: 0.6611 - val_loss: 1.1373 - val_acc: 0.7125 Epoch 29/50 849/850 [============================>.] - ETA: 0s - loss: 1.3896 - acc: 0.6597 Epoch 00029: val_acc improved from 0.71460 to 0.71685, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 379s 445ms/step - loss: 1.3896 - acc: 0.6598 - val_loss: 1.1344 - val_acc: 0.7169 Epoch 30/50 849/850 [============================>.] - ETA: 0s - loss: 1.3840 - acc: 0.6609 Epoch 00030: val_acc improved from 0.71685 to 0.71710, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.3838 - acc: 0.6610 - val_loss: 1.1308 - val_acc: 0.7171 Epoch 31/50 849/850 [============================>.] - ETA: 0s - loss: 1.3710 - acc: 0.6644 Epoch 00031: val_acc improved from 0.71710 to 0.72155, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.3711 - acc: 0.6644 - val_loss: 1.1181 - val_acc: 0.7215 Epoch 32/50 849/850 [============================>.] - ETA: 0s - loss: 1.3580 - acc: 0.6681 Epoch 00032: val_acc did not improve from 0.72155 Epoch 00032: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. 850/850 [==============================] - 376s 442ms/step - loss: 1.3578 - acc: 0.6681 - val_loss: 1.1584 - val_acc: 0.7098 Epoch 33/50 849/850 [============================>.] - ETA: 0s - loss: 1.3556 - acc: 0.6703 Epoch 00033: val_acc did not improve from 0.72155 850/850 [==============================] - 376s 442ms/step - loss: 1.3554 - acc: 0.6704 - val_loss: 1.1211 - val_acc: 0.7193 Epoch 34/50 849/850 [============================>.] - ETA: 0s - loss: 1.3362 - acc: 0.6718 Epoch 00034: val_acc did not improve from 0.72155 850/850 [==============================] - 377s 443ms/step - loss: 1.3360 - acc: 0.6718 - val_loss: 1.1250 - val_acc: 0.7190 Epoch 35/50 849/850 [============================>.] - ETA: 0s - loss: 1.3302 - acc: 0.6732 Epoch 00035: val_acc improved from 0.72155 to 0.72275, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 378s 445ms/step - loss: 1.3304 - acc: 0.6732 - val_loss: 1.1230 - val_acc: 0.7228 Epoch 36/50 849/850 [============================>.] - ETA: 0s - loss: 1.3245 - acc: 0.6756 Epoch 00036: val_acc improved from 0.72275 to 0.72670, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 380s 447ms/step - loss: 1.3247 - acc: 0.6756 - val_loss: 1.0932 - val_acc: 0.7267 Epoch 37/50 849/850 [============================>.] - ETA: 0s - loss: 1.3298 - acc: 0.6737 Epoch 00037: val_acc did not improve from 0.72670 850/850 [==============================] - 376s 443ms/step - loss: 1.3301 - acc: 0.6736 - val_loss: 1.1155 - val_acc: 0.7211 Epoch 38/50 849/850 [============================>.] - ETA: 0s - loss: 1.3323 - acc: 0.6736 Epoch 00038: val_acc did not improve from 0.72670 Epoch 00038: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. 850/850 [==============================] - 377s 443ms/step - loss: 1.3322 - acc: 0.6736 - val_loss: 1.1010 - val_acc: 0.7235 Epoch 39/50 849/850 [============================>.] - ETA: 0s - loss: 1.3270 - acc: 0.6735 Epoch 00039: val_acc improved from 0.72670 to 0.73410, saving model to gdrive/My Drive/QW/Notebook/Quick Draw/Thesis_pre_research/(2-1)StrokeColor_Skatch-A-Net/best_model_2_1.ckpt 850/850 [==============================] - 377s 444ms/step - loss: 1.3272 - acc: 0.6734 - val_loss: 1.0813 - val_acc: 0.7341 Epoch 40/50 849/850 [============================>.] - ETA: 0s - loss: 1.3100 - acc: 0.6791 Epoch 00040: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3098 - acc: 0.6792 - val_loss: 1.1073 - val_acc: 0.7233 Epoch 41/50 849/850 [============================>.] - ETA: 0s - loss: 1.3206 - acc: 0.6757 Epoch 00041: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3205 - acc: 0.6758 - val_loss: 1.1091 - val_acc: 0.7260 Epoch 42/50 849/850 [============================>.] - ETA: 0s - loss: 1.3115 - acc: 0.6784 Epoch 00042: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3116 - acc: 0.6784 - val_loss: 1.1056 - val_acc: 0.7268 Epoch 43/50 849/850 [============================>.] - ETA: 0s - loss: 1.3003 - acc: 0.6803 Epoch 00043: val_acc did not improve from 0.73410 Epoch 00043: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. 850/850 [==============================] - 376s 442ms/step - loss: 1.3003 - acc: 0.6803 - val_loss: 1.1134 - val_acc: 0.7251 Epoch 44/50 849/850 [============================>.] - ETA: 0s - loss: 1.2990 - acc: 0.6813 Epoch 00044: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 442ms/step - loss: 1.2992 - acc: 0.6813 - val_loss: 1.0807 - val_acc: 0.7263 Epoch 45/50 849/850 [============================>.] - ETA: 0s - loss: 1.3236 - acc: 0.6775 Epoch 00045: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3237 - acc: 0.6775 - val_loss: 1.0877 - val_acc: 0.7267 Epoch 46/50 849/850 [============================>.] - ETA: 0s - loss: 1.3123 - acc: 0.6787 Epoch 00046: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3120 - acc: 0.6787 - val_loss: 1.0856 - val_acc: 0.7257 Epoch 47/50 849/850 [============================>.] - ETA: 0s - loss: 1.3018 - acc: 0.6800 Epoch 00047: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 441ms/step - loss: 1.3015 - acc: 0.6800 - val_loss: 1.0814 - val_acc: 0.7313 Epoch 48/50 849/850 [============================>.] - ETA: 0s - loss: 1.2945 - acc: 0.6830 Epoch 00048: val_acc did not improve from 0.73410 Epoch 00048: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. 850/850 [==============================] - 375s 441ms/step - loss: 1.2945 - acc: 0.6831 - val_loss: 1.0808 - val_acc: 0.7329 Epoch 49/50 849/850 [============================>.] - ETA: 0s - loss: 1.2904 - acc: 0.6821 Epoch 00049: val_acc did not improve from 0.73410 850/850 [==============================] - 374s 441ms/step - loss: 1.2903 - acc: 0.6822 - val_loss: 1.1028 - val_acc: 0.7255 Epoch 50/50 849/850 [============================>.] - ETA: 0s - loss: 1.2929 - acc: 0.6814 Epoch 00050: val_acc did not improve from 0.73410 850/850 [==============================] - 375s 442ms/step - loss: 1.2927 - acc: 0.6814 - val_loss: 1.0952 - val_acc: 0.7276 finish training ###Markdown Evaluate ###Code def top_3_accuracy(X, Y): return sparse_top_k_categorical_accuracy(X, Y, 3) def top_5_accuracy(X, Y): return sparse_top_k_categorical_accuracy(X, Y, 5) model_E = MODEL model_E.compile(loss=tf.keras.losses.sparse_categorical_crossentropy, optimizer=tf.train.AdamOptimizer(), metrics=['accuracy',top_3_accuracy, top_5_accuracy]) model_weights_path = CKPT_PATH model_E.load_weights(model_weights_path) print('finish') result = model_E.evaluate_generator( generate_data(valid_data, BATCH_SIZE, False), steps = EVALUATE_STEPS, verbose = 1 ) print('loss:', result[0]) print('top1 accuracy:', result[1]) print('top3 accuracy:', result[2]) print('top3 accuracy:', result[3]) ###Output 850/850 [==============================] - 170s 200ms/step loss: 1.1010285833302667 top1 accuracy: 0.7247588239697849 top3 accuracy: 0.8787941166232613 top3 accuracy: 0.9123647066424875
Notebooks/successful-models/submission.ipynb
###Markdown Data Mining Challange: *Reddit Gender Text-Classification*The full description of the challange and its solution can be found in this [Github page](https://inphyt.github.io/DataMiningChallange/), while all the relevant notebooks are publicly available in the associated [Github repository](https://github.com/InPhyT/DataMiningChallange). Modules ###Code # Numpy & matplotlib for notebooks %pylab inline # Pandas import pandas as pd # Data analysis and manipulation # Sklearn from sklearn.preprocessing import StandardScaler # to standardize features by removing the mean and scaling to unit variance (z=(x-u)/s) from sklearn.neural_network import MLPClassifier # Multi-layer Perceptron classifier which optimizes the log-loss function using LBFGS or sdg. from sklearn.model_selection import train_test_split # to split arrays or matrices into random train and test subsets from sklearn.model_selection import KFold # K-Folds cross-validator providing train/test indices to split data in train/test sets. from sklearn.decomposition import PCA, TruncatedSVD # Principal component analysis (PCA); dimensionality reduction using truncated SVD. from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import MultinomialNB # Naive Bayes classifier for multinomial models from sklearn.feature_extraction.text import CountVectorizer # Convert a collection of text documents to a matrix of token counts from sklearn.metrics import roc_auc_score as roc # Compute Area Under the Receiver Operating Characteristic Curve from prediction scores from sklearn.metrics import roc_curve, auc # Compute ROC; Compute Area Under the Curve (AUC) using the trapezoidal rule # Matplotlib import matplotlib # Data visualization import matplotlib.pyplot as plt import matplotlib.patches as mpatches # Seaborn import seaborn as sns # Statistical data visualization (based on matplotlib) ###Output Populating the interactive namespace from numpy and matplotlib ###Markdown Data Collection ###Code # Import the test dataset and create a list of authors test_data = pd.read_csv("../input/final-dataset/test_data.csv", encoding="utf8") a_test = [] for author, group in test_data.groupby("author"): a_test.append(author) # Load predictions on validation # MLP on doc2vec x1 = np.load("../input/final-dataset/y_scoremlpClf.npy") #y_D2V-mlpClf.npy # XGB on countvectorized texts x2 = np.load("../input/final-dataset/y_predict_XGB.npy") # MLP on binary countvectorized subreddits x3 = np.load("../input/final-dataset/y_score_MLPs.npy") # Load predictions of all models y = np.load("../input/final-dataset/y_valid.npy") # common validation y of previous steps # Load predicted test doc2vec t1 = np.load("../input/final-dataset/y_testD2V.npy") # Load predicted countvectorized test texts t2 = np.load("../input/final-dataset/y_predict_testXGBnS.npy") # #y_testXGBnS.npy # Load predicted countvectorized test subreddits t3 = np.load("../input/final-dataset/y_testMLPs.npy") ###Output _____no_output_____ ###Markdown Validation Data Manipulation ###Code a = np.vstack((x3,x2,x1)) t = np.vstack((t3,t2,t1)) X = a.T # transpose T = t.T # transpose ###Output _____no_output_____ ###Markdown Validation Data Visualization ###Code # Plot the test data along the 2 dimensions of largest variance def plot_LSA(test_data, test_labels, savepath="PCA_demo.csv", plot=True): lsa = TruncatedSVD(n_components=2) lsa.fit(test_data) lsa_scores = lsa.transform(test_data) colors = ['orange','blue'] if plot: plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors)) orange_patch = mpatches.Patch(color='orange', label='M') blue_patch = mpatches.Patch(color='blue', label='F') plt.legend(handles=[orange_patch, blue_patch], prop={'size': 20}) fig = plt.figure(figsize=(8, 8)) plot_LSA(X, y) plt.show() ###Output _____no_output_____ ###Markdown Model Definition & Training ###Code # Logistic regression lrClf = LogisticRegression(class_weight = "balanced",solver = "saga",C = 0.00005) #modello # Model fit lrClf.fit(X, y) ###Output _____no_output_____ ###Markdown Final Prediction & Submission ###Code # Final prediction y_scorel = lrClf.predict_proba(T)[:,1] # Create test dictionary test = {'author': a_test, 'gender': y_scorel } # Create DataFrame df = pd.DataFrame(test, columns = ['author', 'gender']) # Create submission csv file df.to_csv(r'../working/Submission.csv', index = False) ###Output _____no_output_____
base/auxils/plotting_tool/PriceElectricityHourly.ipynb
###Markdown Import Required Packages ###Code # Imports import os import datetime import glob import pandas as pd import numpy as np import matplotlib.pyplot as plt import time ###Output _____no_output_____ ###Markdown Input data from User ###Code #Market analysed: 'Investment','FullYear','DayAhead','Balancing' (choose one or several) markets=['DayAhead'] output='PriceElectricityHourly' first_timestep="2012-01-02" #Meaning of SSS and TTT in the data: 'DaysHours','Hours5min','WeeksHours' meaning_SSS_TTT='DaysHours' #Time size of each time step in TTT for creating timestamp size_timestep="3600s" ###Output _____no_output_____ ###Markdown Plot Settings ###Code # Set plotting specifications %matplotlib inline plt.rcParams.update({'font.size': 21}) plt.rcParams['xtick.major.pad']='12' plt.rc('legend', fontsize=16) y_limit = 1.1 lw = 3 ###Output _____no_output_____ ###Markdown Read Input Files ###Code data=pd.DataFrame() for market in markets: csvfiles = [] for file in glob.glob("./input/results/" + market + "/*.csv"): csvfiles.append(file) csvfiles=[file.replace('./input\\','') for file in csvfiles] csvfiles=[file.replace('.csv','') for file in csvfiles] csvfiles=[file.split('_') for file in csvfiles] csvfiles = np.asarray(csvfiles) csvfiles=pd.DataFrame.from_records(csvfiles) csvfiles.rename(columns={0: 'Output', 1: 'Scenario',2: 'Year',3:'Subset'}, inplace=True) scenarios=csvfiles.Scenario.unique().tolist() years=csvfiles.Year.unique().tolist() subsets=csvfiles.Subset.unique().tolist() for scenario in scenarios: for year in years: for subset in subsets: file = "./input/results/"+ market + "/"+ output + "_" + scenario + "_" + year + "_" + subset + ".csv" if os.path.isfile(file): df=pd.read_csv(file,encoding='utf8') df['Scenario'] = scenario df['Market'] = market #Renaming columns just in case timeconversion was required df.rename(columns = {'G':'GGG', 'C':'CCC', 'Y':'YYY','TTT_NEW':'TTT','SSS_NEW':'SSS'}, inplace = True) data=data.append(df) #Timestamp addition full_timesteps = pd.read_csv('./input/full_timesteps_'+meaning_SSS_TTT+'.csv') full_timesteps.Key=full_timesteps['SSS']+full_timesteps['TTT'] number_periods=len(full_timesteps.Key.unique()) full_timesteps['timestamp']= pd.date_range(first_timestep, periods = number_periods, freq =size_timestep) dict_timestamp=dict(zip(full_timesteps.Key, full_timesteps.timestamp)) data['timestamp']=data['SSS']+data['TTT'] data['timestamp']=data['timestamp'].map(dict_timestamp) ###Output C:\Users\s151529\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:3: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access This is separate from the ipykernel package so we can avoid doing imports until ###Markdown Additional set declaration ###Code ccc = list(data.CCC.unique()) rrr = list(data.RRR.unique()) #tech_type = list(data.TECH_TYPE.unique()) #commodity = list(data.COMMODITY.unique()) #fff = list(data.FFF.unique()) sss = list(full_timesteps.SSS.unique()) ttt = list(full_timesteps.TTT.unique()) ###Output _____no_output_____ ###Markdown Time step selection ###Code # Seasons to investigate # season_names = ['S01', 'S07', 'S20', 'S24', 'S28', 'S38', 'S42', 'S43'] # Make a list of every nth element of sss (1 <= nth <= number of elements in sss) nth = 1 s = sss[0::nth] # Or select seasons by names # s = season_names # Terms to investigate # term_names = ['T005', 'T019', 'T033', 'T047', 'T061', 'T075', 'T089', 'T103', 'T117', 'T131', 'T145', 'T159'] # Make a list of every nth element of ttt (1 <= nth <= number of elements in ttt) nth = 1 t = ttt[0::nth] # Or select terms by name # t = term_names ###Output _____no_output_____ ###Markdown Make Directories ###Code # Make output folder if not os.path.isdir('output'): os.makedirs('output') # Make CurtailmentHourly folder if not os.path.isdir('output/' + output): os.makedirs('output/' + output) # Make market folder for market in markets: if not os.path.isdir('output/' + output + '/'+ market +'/Country_wise'): os.makedirs('output/' + output + '/'+ market +'/Country_wise') # Make country folder if not os.path.isdir('output/' + output + '/'+ market +'/Country_wise'): os.makedirs('output/' + output + '/'+ market +'/Country_wise') # Make country wise folders for c in ccc: if not os.path.isdir('output/' + output + '/'+ market +'/Country_wise/' + c): os.makedirs('output/' + output + '/'+ market +'/Country_wise/' + c) ###Output _____no_output_____ ###Markdown Plotting ###Code # Make data frames to plot data_plot = data[(data.SSS.isin(s)) & (data.TTT.isin(t))] ###Output _____no_output_____ ###Markdown Data export ###Code table = pd.pivot_table(data, values='Val', index=['Market','Scenario','YYY','timestamp'], ... columns=['RRR'], aggfunc=np.sum, fill_value=0).reset_index() for market in markets: for scenario in scenarios: for year in years: table_export=table[table.columns.difference(['Market','Scenario','YYY'])].loc[(table['Market'] == market) & (table['Scenario'] == scenario) & (table['YYY'].astype(str) == year)] table_export=table_export.set_index(['timestamp']) table_export.to_csv('Output/'+output+'/'+output+'_'+market+'_'+scenario+'_'+year+'.csv') ###Output _____no_output_____
XML to Dataframe.ipynb
###Markdown First, we make a get request to obtain the Wikipedia page on Mars in XML format, using the Wikipedia API. ###Code url = 'https://en.wikipedia.org/w/api.php?action=query&prop=extracts&format=xml&exintro=&titles=Mars' xml_data = requests.get(url).content # Create a BeautifulSoup object from the xml soup = BeautifulSoup(xml_data, "lxml") # Prettify the BeautifulSoup object pretty_soup = BeautifulSoup.prettify(soup) # Print the response print(pretty_soup) # with open('Mars.xml', 'w') as file: # file.write(pretty_soup) ###Output _____no_output_____ ###Markdown We wish to extract the data above and put into a (pandas) dataframe. ###Code import xml.etree.ElementTree as ET import pandas as pd class XML2DataFrame: def __init__(self, xml_data): self.root = ET.XML(xml_data) def parse_element(self, element, parsed=None): if parsed is None: parsed = dict() for key in element.keys(): parsed[key] = element.attrib.get(key) if element.text: parsed[element.tag] = element.text for child in list(element): # RECURSION for nested tags self.parse_element(child, parsed) return parsed def parse_root(self, root): return [self.parse_element(child) for child in iter(root)] # list(element) vs iter(root) ? def process_data(self): structure_data = self.parse_root(self.root) return pd.DataFrame(structure_data) # Citation: http://www.austintaylor.io/lxml/python/pandas/xml/dataframe/2016/07/08/convert-xml-to-pandas-dataframe/ xml2df = XML2DataFrame(xml_data) xml_dataframe = xml2df.process_data() xml_dataframe.iloc[:,0:5] # Access intro of Wikipedia article on Mars xml_dataframe.iloc[0,1] ###Output _____no_output_____ ###Markdown Multiple XML files Now for the case of multiple XML files, we loop through each file. ###Code earth_pages = 'https://en.wikipedia.org/w/api.php?action=query&generator=allpages&gaplimit=100&gapfrom=Earth&format=xml&gapfilterredir=nonredirects' earth_data = requests.get(earth_pages).content # Create a BeautifulSoup object from the xml earth_soup = BeautifulSoup(earth_data, "lxml") earth_soup earth_tags = earth_soup.find_all('page') earth_tags id_list = [] for link in earth_tags: id_list.append(int(link.get('pageid'))) id_list base_url = 'https://en.wikipedia.org/w/api.php?action=query&prop=extracts&format=xml&exintro=&' for pageid in id_list: query = 'pageids=%i' % pageid # perform a GET request using the base_url and query xml_data = requests.get(base_url+query).content xml2df = XML2DataFrame(xml_data) xml_dataframe_2 = xml2df.process_data() xml_dataframe = pd.concat([xml_dataframe,xml_dataframe_2], ignore_index=True, join='inner') xml_dataframe.iloc[:,0:5] ###Output _____no_output_____
dl4j-examples/tutorials/08. RNNs- Sequence Classification of Synthetic Control Data.zepp.ipynb
###Markdown NoteView the README.md [here](https://github.com/deeplearning4j/dl4j-examples/tree/overhaul_tutorials/tutorials/README.md) to learn about installing, setting up dependencies and importing notebooks in Zeppelin BackgroundRecurrent neural networks (RNN's) are used when the input is sequential in nature. Typically RNN's are much more effective than regular feed forward neural networks for sequential data because they can keep track of dependencies in the data over multiple time steps. This is possible because the output of a RNN at a time step depends on the current input and the output of the previous time step. RNN's can also be applied to situations where the input is sequential but the output isn't. In these cases the output of the last time step of the RNN is typically taken as the output for the overall observation. For classification, the output of the last time step will be the predicted class label for the observation. In this notebook we will show how to build a RNN using the MultiLayerNetwork class of deeplearning4j (DL4J). This tutorial will focus on applying a RNN for a classification task. We will be using the MNIST data, which is a dataset that consists of images of handwritten digits, as the input for the RNN. Although the MNIST data isn't time series in nature, we can interpret it as such since there are 784 inputs. Thus, each observation or image will be interpreted to have 784 time steps consisting of one scalar value for a pixel. Note that we use a RNN for this task for purely pedagogical reasons. In practice, convolutional neural networks (CNN's) are better suited for image classification tasks. Imports ###Code import org.deeplearning4j.eval.Evaluation import org.deeplearning4j.nn.api.OptimizationAlgorithm import org.deeplearning4j.nn.conf.MultiLayerConfiguration import org.deeplearning4j.nn.conf.NeuralNetConfiguration import org.deeplearning4j.nn.conf.Updater import org.deeplearning4j.nn.multilayer.MultiLayerNetwork import org.deeplearning4j.nn.weights.WeightInit import org.deeplearning4j.nn.conf.layers.{DenseLayer, GravesLSTM, OutputLayer, RnnOutputLayer} import org.deeplearning4j.nn.conf.distribution.UniformDistribution import org.deeplearning4j.nn.conf.layers.GravesLSTM import org.deeplearning4j.nn.conf.layers.RnnOutputLayer import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator import org.deeplearning4j.optimize.listeners.ScoreIterationListener import org.datavec.api.split.NumberedFileInputSplit import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader import org.nd4j.linalg.dataset.DataSet import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction import org.nd4j.linalg.api.ndarray.INDArray import org.nd4j.linalg.activations.Activation import org.nd4j.linalg.dataset.api.iterator.DataSetIterator import org.slf4j.Logger import org.slf4j.LoggerFactory import org.apache.commons.io.IOUtils import java.nio.charset.Charset import java.util.Random import java.net.URL ###Output _____no_output_____ ###Markdown Download the datasetUCI has a number of datasets available for machine learning, make sure you have enough space on your local disk. The UCI synthetic control dataset can be found at [http://archive.ics.uci.edu/ml/datasets/synthetic+control+chart+time+series](http://archive.ics.uci.edu/ml/datasets/synthetic+control+chart+time+series). The code below will check if the data already exists and download the file. ###Code val dataPath = new File(cache, "/uci_synthetic_control/") if(!dataPath.exists()) { val url = "https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data" println("Downloading file...") val data = IOUtils.toString(new URL(url), Charset.defaultCharset()) val lines = data.split("\n") var lineCount = 0; var index = 0 val linesList = scala.collection.mutable.ListBuffer.empty[String] println("Extracting file...") for (line <- lines) { val count = new java.lang.Integer(lineCount / 100) var newLine: String = null newLine = line.replaceAll("\\s+", ", " + count.toString() + "\n") newLine = line + ", " + count.toString() linesList.add(newLine) lineCount += 1 } util.Random.shuffle(linesList) for (line <- linesList) { val outPath = new File(dataPath, index + ".csv") FileUtils.writeStringToFile(outPath, line, Charset.defaultCharset()) index += 1 } println("Done.") } else { println("File already exists.") } ###Output _____no_output_____ ###Markdown Iterating from diskNow that we've saved our dataset to a CSV sequence format, we need to set up a `CSVSequenceRecordReader` and iterator that will read our saved sequences and feed them to our network. If you have already saved your data to disk, you can run this code block (and remaining code blocks) as much as you want without preprocessing the dataset again. Convenient! ###Code val batchSize = 128 val numLabelClasses = 6 // training data val trainRR = new CSVSequenceRecordReader(0, ", ") trainRR.initialize(new NumberedFileInputSplit(dataPath.getAbsolutePath() + "/%d.csv", 0, 449)) val trainIter = new SequenceRecordReaderDataSetIterator(trainRR, batchSize, numLabelClasses, 1) // testing data val testRR = new CSVSequenceRecordReader(0, ", ") testRR.initialize(new NumberedFileInputSplit(dataPath.getAbsolutePath() + "/%d.csv", 450, 599)) val testIter = new SequenceRecordReaderDataSetIterator(testRR, batchSize, numLabelClasses, 1) ###Output _____no_output_____ ###Markdown Configuring a RNN for ClassificationOnce everything needed is imported we can jump into the code. To build the neural network, we can use a set up like what is shown below. Because there are 784 timesteps and 10 class labels, nIn is set to 784 and nOut is set to 10 in the MultiLayerNetwork configuration. ###Code val conf = new NeuralNetConfiguration.Builder() .seed(123) //Random number generator seed for improved repeatability. Optional. .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .iterations(1) .weightInit(WeightInit.XAVIER) .updater(Updater.NESTEROVS) .learningRate(0.005) .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) //Not always required, but helps with this data set .gradientNormalizationThreshold(0.5) .list() .layer(0, new GravesLSTM.Builder().activation(Activation.TANH).nIn(1).nOut(10).build()) .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .activation(Activation.SOFTMAX).nIn(10).nOut(numLabelClasses).build()) .pretrain(false).backprop(true).build(); val model: MultiLayerNetwork = new MultiLayerNetwork(conf) model.setListeners(new ScoreIterationListener(20)) ###Output _____no_output_____ ###Markdown Training the classifierTo train the model, pass the training iterator to the model's `fit()` method. We can use a loop to train the model using a prespecified number of epochs or passes through the training data. ###Code val numEpochs = 1 (1 to numEpochs).foreach(_ => model.fit(trainIter) ) ###Output _____no_output_____ ###Markdown Model EvaluationOnce training is complete we only a couple lines of code to evaluate the model on a test set. Using a test set to evaluate the model typically needs to be done in order to avoid overfitting on the training data. If we overfit on the training data, we have essentially fit to the noise in the data. An `Evaluation` class has more built-in methods if you need to extract a confusion matrix, and other tools are also available for calculating the Area Under Curve (AUC). ###Code val evaluation = model.evaluate(testIter) // print the basic statistics about the trained classifier println("Accuracy: "+evaluation.accuracy()) println("Precision: "+evaluation.precision()) println("Recall: "+evaluation.recall()) ###Output _____no_output_____
evolucao_pais.ipynb
###Markdown Evoluรงรฃo do paรญs ###Code #loc[df['tipo'].isin(['ubs', 'unidade_servico_apoio_diagnose_terapia', 'nucleos_apoio_saude_familia', 'hospital_geral', 'hospital_especializado', 'clinicas_ambulatorios_especializados'])]\ df.groupby('tipo').sum().reset_index() df.groupby('tipo').sum().reset_index().to_csv('evolucao_pais.csv', index=False) ###Output _____no_output_____ ###Markdown Anรกlise de recursos por regiรฃo do paรญs ###Code df1 = df.drop(columns=['uf', 'municipio', 'pop_municipio', 'pop_uf', '6cod_municipio']) df1.head() ###Output _____no_output_____
notebook/analysis_data_Chloe.ipynb
###Markdown Notebook use to analyse one specific file.You can click `shift` + `enter` to run one cell, you can also click run in top menu.To run all the cells, you can click `kernel` and `Restart and run all` in the top menu. ###Code import time tp1 = time.time() # Some magic so that the notebook will reload external python modules; # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython %load_ext autoreload %autoreload 2 %reload_ext autoreload # Ignore warnings in notebook import warnings warnings.filterwarnings('ignore') # Matplotlib to plot the data %matplotlib inline import matplotlib.pyplot as plt import matplotlib.cm as cm import matplotlib.patches as patches plt.rcParams['figure.figsize'] = 8,8 plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' # Some module needed in the notebook import numpy as np import javabridge import bioformats from itkwidgets import view from sklearn.externals import joblib ###Output _____no_output_____ ###Markdown The following path should direct to the folder "utils", on Window env it should have slash " / " and not backslash " \ " . ###Code # Create a temporary python PATH to the module that we are using for the analysis import sys sys.path.insert(0, "/Users/Espenel/Desktop/Mini-Grant-Image-analysis/2018/Chloe/ChromosomeDetectionChloe/utils") from chromosome_dsb import * # Need to create a javabridge to use bioformats to open proprietary format javabridge.start_vm(class_path=bioformats.JARS) ###Output _____no_output_____ ###Markdown In the path_data variable you should enter the path to your data: ###Code path_data = '/Users/Espenel/Desktop/Mini-Grant-Image-analysis/2018/Chloe/data_chloe/test_batch/exp1/' position, time_point = load_data.stage_position(path_data) ###Output _____no_output_____ ###Markdown Set Parameters ###Code # Size kernel for background substraction, should be a little larger than the object of interest back_sub_FOCI = 5 back_sub_Nucleus = 20 # LOCI detection: # Smallest object (in pixels) to be detected smaller = 1 # Largest object to be detected largest = 5 # Threshold above which to look for threshold = 12000 ###Output _____no_output_____ ###Markdown Find "Skeleton" of gonad ###Code skelete = load_data.skeleton_coord(position,time_point) ###Output _____no_output_____ ###Markdown Load Image In the path_img you can enter the name of your specific image "/....dv" ###Code path_img = path_data + '/2017-04-12_RAD51-HTP3_cku80-exo1_002_visit_13_D3D_ALX.dv' image, meta, directory = load_data.load_bioformats(path_img) ###Output _____no_output_____ ###Markdown Plot "Skeleton" of gonad ###Code data = np.concatenate((position,time_point[:, np.newaxis]), axis=1) sort_data = data[np.argsort(data[:,2])] fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.scatter(skelete[:,0], skelete[:,1], s=0.5) stage_pos = ax.scatter(sort_data[:,0], sort_data[:,1]) working_on = ax.scatter(meta["PositionX"], meta["PositionY"], s=300, color = "r") plt.legend([stage_pos, working_on], ["Stage Positions", "Image currently working on"], loc=0,fontsize='large') img = image[:,:,:,3] ###Output _____no_output_____ ###Markdown Optionally you can visualyze your data ###Code #view(visualization.convert_view(img)) ###Output _____no_output_____ ###Markdown Find the nucleus in the image First need to load the classifier (clf) and scaler. ###Code clf = joblib.load("/Users/Espenel/Desktop/Mini-Grant-Image-analysis/2018/Chloe/ChromosomeDetectionChloe/clf_scaler/clf") scaler = joblib.load("/Users/Espenel/Desktop/Mini-Grant-Image-analysis/2018/Chloe/ChromosomeDetectionChloe/clf_scaler/scaler") tp_1 = time.time() result = search.rolling_window(img, clf, scaler) tp_2 = time.time() print(tp_2-tp_1) bbox_ML = search.non_max_suppression(result, probaThresh=0.8, overlapThresh=0.3) fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.imshow(np.amax(img,axis=0), vmax=img.max()/2, alpha = 0.8) for coord in bbox_ML: circles1 = patches.Circle((coord[0]+35,coord[1]+35),30, linewidth=3,edgecolor='r',facecolor='none') ax.add_patch(circles1) ###Output _____no_output_____ ###Markdown Background Substraction ###Code FOCI_ch, _ = img_analysis.background_correct(image, ch=1, size=back_sub_FOCI) Nucleus_ch, _ = img_analysis.background_correct(image, ch=3, size=back_sub_Nucleus) visualization.plot_background(image, FOCI_ch, Nucleus_ch) ###Output _____no_output_____ ###Markdown Finding the Blobs/FOCI ###Code blobs = img_analysis.find_blob(FOCI_ch, meta, directory, smaller = smaller, largest = largest, thresh = threshold, plot=True) ###Output _____no_output_____ ###Markdown Binarization of the Channel with nucleus ###Code binary = img_analysis.binarization(Nucleus_ch) ###Output _____no_output_____ ###Markdown Optionaly, you can visualyze the result of the binarization ###Code #view(visualization.convert_view(binary)) ###Output _____no_output_____ ###Markdown Load the position of the different nucleus ###Code #bbox_ML = np.load("/Users/Espenel/Desktop/Mini-Grant-Image-analysis/2018/Chloe/13/bbox_3D.npy") ###Output _____no_output_____ ###Markdown Mask FOCI that are not on the nucleus ###Code masked = search.find_foci(blobs, FOCI_ch, Nucleus_ch, binary, bbox_ML) ###Output _____no_output_____ ###Markdown Mask FOCI that are not on a nucleus found by the Machine Learning ###Code res, bb_mask = search.binary_select_foci(bbox_ML, Nucleus_ch, masked) ###Output _____no_output_____ ###Markdown Find and remove FOCI that were counted twice ###Code num, cts, dup_idx, mask = search.find_duplicate(res, bb_mask) visualization.plot_result(img, res, bbox_ML,\ cts, num, meta, directory, save = False) dist_tip = img_analysis.distance_to_tip(bbox_ML, skelete, meta) chro_pos = np.squeeze(np.dstack((bbox_ML[:,0]+35, bbox_ML[:,1]+35, bbox_ML[:,4]))) df = img_analysis.final_table(meta, bbox_ML, \ dist_tip, cts, num, \ directory, save = False) df to_save = {'back_sub_ch1' : back_sub_ch1, 'back_sub_ch2' : back_sub_ch2, 'back_sub_ch3' : back_sub_ch3, 'small_object' : smaller, 'large_object' : largest, 'threshold' : thresh} log.log_file(directory, meta, **to_save) tp2 = time.time() print("It took {}sec".format(int(tp2-tp1))) ###Output _____no_output_____
02. Linear and Logistic Regression/Lab/.ipynb_checkpoints/Linear and Logistic Regression Lab-checkpoint.ipynb
###Markdown Linear and Logistic Regression Lab Getting acquainted with the tools. Performing some common tasks and creating our first models You will receive labs in this format. Edit the file to make everything work.You can add some cells as you wish. Some cells are read-only - you won't be able to edit them.**Notes:** 1. **DO NOT** copy everything in a new file. Edit this one (.ipynb), save it and submit it. **DO NOT** rename the file.2. Be careful what is asked of you - all problems have checks that you need to pass in order to get the points.3. There are tests that you can see, as well as hidden tests. You'll have to perform well on both the visible and the hidden tests. **In this assignment only**, there are no hidden tests. This is just for your convenience.4. If you have used other files, upload them too. You don't need to upload any files supplied with the lab assignment.5. Each lab is scored on a scale from 0 to 10. You can get partial credit (e. g. 5 / 10). Problem 1. Read the data (1 point)The dataset comes from [here](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/). It contains information about the marketing of a Portuguese bank.The data you need to read is the `bank.csv` file in the `data` folder (use ";" as the column separator). The `bank-names.txt` file contains information about the dataset. Read it and you'll get some information about what it contains.Read the dataset using `pandas` (you can use the library with the alias `pd`). Save it in the `bank_data` variable. ###Code bank_data = None # YOUR CODE HERE bank_data = pd.read_csv("data/bank.csv", sep=";") np.random.seed(42) assert_is_not_none(bank_data) assert_equal(bank_data.shape, (4521, 17)) ###Output _____no_output_____ ###Markdown Problem 2. Separate features and labels (2 points) Separate the explanatory variables and the output variable (it's called `y` in this case). Create two new variables. ###Code bank_features = None # explanatory features - 16 total bank_output = None # output feature # YOUR CODE HERE bank_features = bank_data.drop("y", axis = 1) bank_output = bank_data.y assert_equal(bank_features.shape, (4521, 16)) assert_equal(bank_output.shape, (4521,)) ###Output _____no_output_____ ###Markdown Problem 3. Convert categorical variables (1 + 1 points)Convert all categorical variables in `bank_features` into indicator variables (dummies). Save the result in the same variable. (1 point) ###Code # YOUR CODE HERE bank_features = pd.get_dummies(bank_features) assert_equal(bank_features.shape, (4521, 51)) ###Output _____no_output_____ ###Markdown Convert the `bank_output` variable to an indicator variable. This can be done in many ways. Look up how in StackOverflow if you get stuck.The goal is to **rewrite the column** (replace the values): it should be numeric, and be equal to 1 if the original value was "yes" and 0 otherwise. (1 point) ###Code # YOUR CODE HERE bank_output = bank_output.map(dict(yes=1, no=0)) assert_equal(bank_output.dtype, np.int64) ###Output _____no_output_____ ###Markdown Problem 4. Perform logistic regression on the original features (1 point)Perform logistic regression. Save the model in the variable `bank_model`. Use all the data. This is not generally recommended but we'll think of a workaround next time.Pass a large number for the parameter `C = 1e6` (which is equivalent to `C = 1000000`). ###Code bank_model = None # YOUR CODE HERE bank_model = LogisticRegression(C = 1e6) bank_model.fit(bank_features, bank_output) assert_is_not_none(bank_model) assert_equal(bank_model.C, 1e6) ###Output _____no_output_____ ###Markdown Problem 5. Get an estimate of the model performance (1 point)Use `bank_model.score()` to get an accuracy score. We'll talk about what it represents later in the course. Save the resulting score in the variable `accuracy_score`. To generate the score, use all data. Once again, this is not what we do usually but it's a good start anyway. ###Code accuracy_score = None # YOUR CODE HERE accuracy_score = bank_model.score(bank_features, bank_output) print(accuracy_score) assert_almost_equal(accuracy_score, 0.9042247290422473, delta = 0.05) ###Output _____no_output_____ ###Markdown We have to make a note here. If we explore how the output classes are distributed, we can see that "class 1" is about 11.5% of all samples, i.e. very few clients actually subscribed after the call, which is expected. This means the data is **highly imbalanced**. In this case, accuracy is not a good measure of the overall model performance. We have to look at other scoring measures to get a better estimate of what's going on.But once again, we're just getting started. ###Code # There's nothing to do here, just execute the cell and view the plot and print results. # Cells like these are here only for your convenience and to help you understand the task better plt.bar([0, 1], [len(bank_output[bank_output == 0]), len(bank_output[bank_output == 1])]) plt.xticks([0, 1]) plt.xlabel("Class") plt.ylabel("Count") plt.show() print("Positive cases: {:.3f}% of all".format(bank_output.sum() / len(bank_output) * 100)) ###Output _____no_output_____ ###Markdown Problem 6. More features (1 point)The score is pretty high. But can we improve it? One way to try and improve it is to use polynomial features. As we saw, this creates all possible multiples of input features. In the real world, this corresponds to **feature interaction**.Create a model for quadratic features (`degree = 2`). Save it in the variable `quad_feature_transformer`. Also, set `interaction_only` to True: let's suppose we don't want to square each feature. This means that we have all single features $x_1, x_2, \dots$ and all interactions $x_1x_2, x_1x_3, \dots$ but no $x_1^2, x_2^2, \dots$Using it, transform all `bank_features`. Save them in the variable `bank_features_quad`.Note how the number of features exploded: from 51 we get more than 1300. ###Code quad_feature_transformer = None bank_features_quad = None # YOUR CODE HERE quad_feature_transformer = PolynomialFeatures(degree = 2, interaction_only = True) bank_features_quad = quad_feature_transformer.fit_transform(bank_features) assert_equal(quad_feature_transformer.degree, 2) assert_equal(quad_feature_transformer.interaction_only, True) assert_equal(bank_features_quad.shape, (4521, 1327)) ###Output _____no_output_____ ###Markdown Problem 7. Train a model on the quadratic features (1 point)You know the drill. Fit a logistic regression model with all data in `bank_features_quad` and `bank_output`. Use `C = 1e6`. Save it in `bank_model_quad`. Score it and save the score in the variable `accuracy_score_quad`. ###Code bank_model_quad = None accuracy_score_quad = None # YOUR CODE HERE bank_model_quad = LogisticRegression(C = 1e6) bank_model_quad.fit(bank_features_quad, bank_output) accuracy_score_quad = bank_model_quad.score(bank_features_quad, bank_output) print("Accuracy: {:.3f}".format(accuracy_score_quad)) assert_is_not_none(bank_model_quad) assert_equal(bank_model_quad.C, 1e6) assert_equal(len(bank_model_quad.coef_[0]), bank_features_quad.shape[1]) # This is a simple check that the model has been trained assert_almost_equal(accuracy_score_quad, 0.9, delta = 0.1) ###Output _____no_output_____ ###Markdown Interesting... we have many more features but the accuracy actually dropped a little. We would observe the same behaviour if we took polynomials of degree 3: more than 20 000 features and accuracy less than 0.87.This is our first example of model selection. Why is the seemingly more complex model less accurate? There are two main reasons:* As we said, the default score (accuracy) is not good for this dataset, so its values aren't too relevant.* The number of features is alarmingly high. This leads to what we call "overfitting": our model is too complex. We can't quite catch it with this scoring scheme but we will be able to do that later.We can try a lot of things: test our model better, improve our scoring schemes, come up with better features, etc. In general, we need to take care of several things:* Are all parameters relevant? Can we discard some of them and how?* How do we deal with imbalanced data?* Is logistic regression the best type of model overall? Are there models that do better on this data?* What are the best hyperparameters for the model? We chose `C = 1e6` arbitrarily.We'll continue to do this next time. Let's try just one more thing. Problem 8. Perform normalization and compare results (1 point)We saw very strange results. A part of the problem might be that our data isn't normalized.Use the `MinMaxScaler` to scale all values in `bank_features_quad`. Save them in `bank_features_quad_scaled`. This will take several seconds.Perform a logistic regression on the new, scaled features: `bank_features_quad_scaled` and `bank_output`. Use the same parameters to score it.You should observe that the score improved the score significantly. ###Code bank_model_quad_scaled = None accuracy_score_quad_scaled = None # YOUR CODE HERE scaler = MinMaxScaler() bank_features_quad_scaled = scaler.fit_transform(bank_features_quad) bank_model_quad_scaled = LogisticRegression(C = 1e6) bank_model_quad_scaled.fit(bank_features_quad_scaled, bank_output) accuracy_score_quad_scaled = bank_model_quad_scaled.score(bank_features_quad_scaled, bank_output) assert_is_not_none(bank_model_quad) assert_equal(bank_model_quad.C, 1e6) assert_equal(len(bank_model_quad.coef_[0]), bank_features_quad.shape[1]) assert_almost_equal(accuracy_score_quad_scaled, 0.969033399690334, delta = 0.05) ###Output _____no_output_____
2020_08_03/ๅ…จ่ฟžๆŽฅ็ฝ‘็ปœ็š„ๆ‰‹ๅ†™ๆ•ฐๅญ—่ฏ†ๅˆซ(MNIST).ipynb
###Markdown ๆ•ฐๆฎๅŠ ่ฝฝ ๅˆ›ๅปบdataset- ๅŠ ่ฝฝMNISTๆ•ฐๆฎ- ่ฟ›่กŒๆ•ฐๆฎ้ข„ๅค„็†, ่ฝฌๆขไธบtensor ๅˆ›ๅปบdataloader- ๅฐ†datasetไผ ๅ…ฅdataloader, ่ฎพ็ฝฎbatchsize ###Code # ๅฐ†ๆ•ฐๆฎ้›†ๅˆไธ‹่ฝฝๅˆฐๆŒ‡ๅฎš็›ฎๅฝ•ไธ‹,่ฟ™้‡Œ็š„transform่กจ็คบ๏ผŒๆ•ฐๆฎๅŠ ่ฝฝๆ—ถๆ‰€้œ€่ฆๅš็š„้ข„ๅค„็†ๆ“ไฝœ # ๅŠ ่ฝฝ่ฎญ็ปƒ้›†ๅˆ(Train) train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=torchvision.transforms.ToTensor(), download=True) # ๅŠ ่ฝฝๆต‹่ฏ•้›†ๅˆ(Test) test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) print(train_dataset) # ่ฎญ็ปƒ้›† print(test_dataset) # ๆต‹่ฏ•้›† batch_size = 100 # ๆ นๆฎๆ•ฐๆฎ้›†ๅฎšไน‰ๆ•ฐๆฎๅŠ ่ฝฝๅ™จ train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) # ๆŸฅ็œ‹ๆ•ฐๆฎ examples = iter(test_loader) example_data, example_target = examples.next() # 100*1*28*28 for i in range(9): plt.subplot(3,3,i+1).set_title(example_target[i]) plt.imshow(example_data[i][0], 'gray') plt.tight_layout() plt.show() ###Output ###Markdown ็ฝ‘็ปœ็š„ๆž„ๅปบ ###Code # ่พ“ๅ…ฅ่Š‚็‚นๆ•ฐๅฐฑไธบๅ›พ็‰‡็š„ๅคงๅฐ๏ผš28ร—28ร—1 input_size = 784 # ็”ฑไบŽๆ•ฐๅญ—ไธบ 0-9๏ผŒๅ› ๆญคๆ˜ฏ10ๅˆ†็ฑป้—ฎ้ข˜๏ผŒๅ› ๆญค่พ“ๅ‡บ่Š‚็‚นๆ•ฐไธบ 10 num_classes = 10 # ็ฝ‘็ปœ็š„ๅปบ็ซ‹ class NeuralNet(nn.Module): # ่พ“ๅ…ฅๆ•ฐๆฎ็š„็ปดๅบฆ๏ผŒไธญ้—ดๅฑ‚็š„่Š‚็‚นๆ•ฐ๏ผŒ่พ“ๅ‡บๆ•ฐๆฎ็š„็ปดๅบฆ def __init__(self, input_size, hidden_size, num_classes): super(NeuralNet, self).__init__() self.input_size = input_size self.l1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.l2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.relu(self.l1(x)) out = self.l2(out) return out model = NeuralNet(input_size, 500, num_classes).to(device) model # ็ฎ€ๅ•ๆต‹่ฏ•ๆจกๅž‹็š„่พ“ๅ‡บ examples = iter(test_loader) example_data, _ = examples.next() # 100*1*28*28 model(example_data.reshape(example_data.size(0),-1)).shape ###Output _____no_output_____ ###Markdown ๅฎšไน‰ๆŸๅคฑๅ‡ฝๆ•ฐๅ’Œไผ˜ๅŒ–ๅ™จ ###Code # ๅฎšไน‰ๅญฆไน ็Ž‡ learning_rate = 0.001 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) ###Output _____no_output_____ ###Markdown ๆจกๅž‹็š„่ฎญ็ปƒไธŽๆต‹่ฏ• ###Code num_epochs = 10 n_total_steps = len(train_loader) LossList = [] # ่ฎฐๅฝ•ๆฏไธ€ไธชepoch็š„loss AccuryList = [] # ๆฏไธ€ไธชepoch็š„accury for epoch in range(num_epochs): # ------- # ๅผ€ๅง‹่ฎญ็ปƒ # ------- model.train() # ๅˆ‡ๆขไธบ่ฎญ็ปƒๆจกๅž‹ totalLoss = 0 for i, (images, labels) in enumerate(train_loader): images = images.reshape(-1, 28*28).to(device) # ๅ›พ็‰‡ๅคงๅฐ่ฝฌๆข labels = labels.to(device) # ๆญฃๅ‘ไผ ๆ’ญไปฅๅŠๆŸๅคฑ็š„ๆฑ‚ๅ– outputs = model(images) loss = criterion(outputs, labels) totalLoss = totalLoss + loss.item() # ๅๅ‘ไผ ๆ’ญ optimizer.zero_grad() # ๆขฏๅบฆๆธ…็ฉบ loss.backward() # ๅๅ‘ไผ ๆ’ญ optimizer.step() # ๆƒ้‡ๆ›ดๆ–ฐ if (i+1) % 300 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, n_total_steps, totalLoss/(i+1))) LossList.append(totalLoss/(i+1)) # --------- # ๅผ€ๅง‹ๆต‹่ฏ• # --------- model.eval() with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.reshape(-1, 28*28).to(device) labels = labels.to(device) outputs = model(images) _, predicted = torch.max(outputs.data, 1) # ้ข„ๆต‹็š„็ป“ๆžœ total += labels.size(0) correct += (predicted == labels).sum().item() acc = 100.0 * correct / total # ๅœจๆต‹่ฏ•้›†ไธŠๆ€ป็š„ๅ‡†็กฎ็Ž‡ AccuryList.append(acc) print('Accuracy of the network on the {} test images: {} %'.format(total, acc)) print("ๆจกๅž‹่ฎญ็ปƒๅฎŒๆˆ") # ็ป˜ๅˆถloss็š„ๅ˜ๅŒ– fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(13,7)) axes.plot(LossList, 'k--') # ็ป˜ๅˆถloss็š„ๅ˜ๅŒ– fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(13,7)) axes.plot(AccuryList, 'k--') ###Output _____no_output_____ ###Markdown ไฝฟ็”จๅฎž้™…ไพ‹ๅญ่ฟ›่กŒ้ชŒ่ฏ ###Code # ๆต‹่ฏ•ๆ ทไพ‹ examples = iter(test_loader) example_data, example_targets = examples.next() # ๅฎž้™…ๅ›พ็‰‡ for i in range(9): plt.subplot(3, 3, i+1) plt.imshow(example_data[i][0], cmap='gray') plt.show() # ็ป“ๆžœ็š„้ข„ๆต‹ images = example_data.reshape(-1, 28*28).to(device) labels = example_targets.to(device) # ๆญฃๅ‘ไผ ๆ’ญไปฅๅŠๆŸๅคฑ็š„ๆฑ‚ๅ– outputs = model(images) # ๅฐ† Tensor ็ฑปๅž‹็š„ๅ˜้‡ example_targets ่ฝฌไธบ numpy ็ฑปๅž‹็š„๏ผŒๆ–นไพฟๅฑ•็คบ print("ไธŠ้ขไธ‰ๅผ ๅ›พ็‰‡็š„็œŸๅฎž็ป“ๆžœ๏ผš", example_targets[0:9].detach().numpy()) # ๅฐ†ๅพ—ๅˆฐ้ข„ๆต‹็ป“ๆžœ # ็”ฑไบŽ้ข„ๆต‹็ป“ๆžœๆ˜ฏ Nร—10 ็š„็Ÿฉ้˜ต๏ผŒๅ› ๆญคๅˆฉ็”จ np.argmax ๅ‡ฝๆ•ฐๅ–ๆฏ่กŒๆœ€ๅคง็š„้‚ฃไธชๅ€ผ๏ผŒๆœ€ไธบ้ข„ๆต‹ๅ€ผ print("ไธŠ้ขไธ‰ๅผ ๅ›พ็‰‡็š„้ข„ๆต‹็ป“ๆžœ๏ผš", np.argmax(outputs[0:9].detach().numpy(), axis=1)) ###Output _____no_output_____
19-Timedelta.ipynb
###Markdown PYTHON Pandas - TimedeltaTimedelta, gรผnler, saatler, dakikalar, saniyeler gibi fark birimlerinde ifade edilen zamanlardaki farklฤฑlฤฑklardฤฑr. Hem olumlu hem de olumsuz olabilirler.AลŸaฤŸฤฑda gรถsterildiฤŸi gibi รงeลŸitli argรผmanlar kullanarak Timedelta nesneleri oluลŸturabiliriz StringBir dize cรผmle geรงirerek, bir timedelta nesnesi oluลŸturabiliriz. ###Code import pandas as pd pd.Timedelta('2 days 2 hours 15 minutes 30 seconds') ###Output _____no_output_____ ###Markdown IntegerTam sayฤฑ kullanarak ta bir Timedalta nesnesi oluลŸturabiliriz. ###Code import pandas as pd pd.Timedelta(6,unit='h') ###Output _____no_output_____ ###Markdown Data OffsetsVeri uzaklฤฑklar gibi - hafta, gรผn, saat, dakika, saniye, milisaniye, mikrosaniye, nanosaniye yapฤฑmฤฑnda da kullanฤฑlabilir. ###Code import pandas as pd pd.Timedelta(days=2) ###Output _____no_output_____ ###Markdown to_timedelta()Girdi bir seri ise, girdi skaler, bir skaler ise seri oluลŸturacaktฤฑr, aksi takdirde bir Timedeltaindex รงฤฑkacaktฤฑr ###Code import pandas as pd pd.Timedelta(days=2) ###Output _____no_output_____ ###Markdown OperationsSeri/ Veri รงerรงeveleri รผzerinde รงalฤฑลŸabilir ve datetime64[ns] serisindeki รงฤฑkarma iลŸlemleri aracฤฑlฤฑฤŸฤฑyla timedelta64[ns] serisini veya zaman damgalarฤฑnฤฑ oluลŸturabilirsiniz.ลžimdi Timedelta ve datetime nesneleriyle bir dataframe oluลŸturalฤฑm ve รผzerinde bazฤฑ aritmetik iลŸlemler gerรงekleลŸtirelim โˆ’ ###Code import pandas as pd s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D')) td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ]) df = pd.DataFrame(dict(A = s, B = td)) df ###Output _____no_output_____ ###Markdown Ekleme ฤฐลŸlemi ###Code import pandas as pd s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D')) td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ]) df = pd.DataFrame(dict(A = s, B = td)) df['C']=df['A']+df['B'] df ###Output _____no_output_____ ###Markdown ร‡ฤฑkarma ฤฐลŸlemi ###Code import pandas as pd s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D')) td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ]) df = pd.DataFrame(dict(A = s, B = td)) df['C']=df['A']-df['B'] df['D']=df['C']-df['B'] df ###Output _____no_output_____
spacy_multiprocess.ipynb
###Markdown Turbo-charge your spaCy NLP pipeline> Tips and tricks to significantly speed up text processing using multi-core spaCy custom pipelines.Consider you have a large tabular dataset on which you want to apply some non-trivial NLP transformations, such as stopword removal followed by lemmatizing (i.e. reducing to root form) the words in a text. [spaCy](https://spacy.io/usage) is an industrial strength NLP library designed for just such a task.In the example shown below, the [New York Times Kaggle dataset](https://www.kaggle.com/nzalake52/new-york-times-articles) is used to showcase how to significantly speed up a spaCy NLP pipeline. the goal is to take in an article's text, and speedily return a list of lemmas with unnecessary words, called "stopwords", removed.Pandas DataFrames provide a convenient interface to work with tabular data of this nature. First, import the necessary modules shown. ###Code import re import pandas as pd import spacy from tqdm.notebook import tqdm tqdm.pandas() ###Output _____no_output_____ ###Markdown Input variablesThe tabular data is stored in a tab-separated file obtained by running the preprocessing notebook `preprocessing.ipynb` on the raw text data from Kaggle and stored in the `data/` directory. A curated stopword file is also provided in this location.Additionally, during initial testing, we can limit the size of the DataFrame being worked on (to around $2000$ samples) for faster execution. For the final run, disable the limit by setting it to zero. ###Code inputfile = "data/nytimes.tsv" stopwordfile = "data/stopwords/stopwords.txt" limit = 0 ###Output _____no_output_____ ###Markdown Load spaCy modelFor lemmatization, a simple spaCy model can be initialized. Since we will not be doing any specialized tasks such as dependency parsing and named entity recognition in this exercise, these components are disabled.spaCy has a `sentencizer` component that can instead be enabled - this simply performs tokenization and sentence boundary detection, following which lemmas can be extracted as token properties. ###Code nlp = spacy.load('en_core_web_sm', disable=['tagger', 'parser', 'ner']) nlp.add_pipe(nlp.create_pipe('sentencizer')) ###Output _____no_output_____ ###Markdown A method is defined to read in stopwords from a text file and convert it to a set in Python (for efficient lookup). ###Code def get_stopwords(): "Return a set of stopwords read in from a file." with open(stopwordfile) as f: stopwords = [] for line in f: stopwords.append(line.strip("\n")) # Convert to set for performance stopwords_set = set(stopwords) return stopwords_set stopwords = get_stopwords() ###Output _____no_output_____ ###Markdown Read in New York Times DatasetThe pre-processed version of the NYT news dataset is read in as a Pandas DataFrame. The columns are named `date`, `headline` and `content` - the text present in the content column is what will be preprocessed to remove stopwords and generate token lemmas. ###Code def read_data(inputfile): "Read in a tab-separated file with date, headline and news content" df = pd.read_csv(inputfile, sep='\t', header=None, names=['date', 'headline', 'content']) df['date'] = pd.to_datetime(df['date'], format="%Y-%m-%d") return df df = read_data(inputfile) df.head() ###Output _____no_output_____ ###Markdown Define text cleanerSince the news article data comes from a raw HTML dump, it is very messy and contains a host of unnecessary symbols, social media handles, URLs and other artifacts. An easy way to clean it up is to use a regex that parses only alphanumeric strings and hyphens (so as to include hyphenated words) that are between a given length (3 and 50). This filters each document down to only meaningful text for the lemmatizer. ###Code def cleaner(df): "Extract relevant text from DataFrame using a regex" # Regex pattern for only alphanumeric, hyphenated text with 3 or more chars pattern = re.compile(r"[A-Za-z0-9\-]{3,50}") df['clean'] = df['content'].str.findall(pattern).str.join(' ') if limit > 0: return df.iloc[:limit, :].copy() else: return df df_preproc = cleaner(df) df_preproc.head(3) ###Output _____no_output_____ ###Markdown Now that we have just the clean, alphanumeric tokens left over, these can be further cleaned up by removing stopwords before proceeding to lemmatization. Option 1. Work directly on the data using `pandas.Series.apply`The straightforward way to process this text is to use an existing method, in this case the `lemmatize` method shown below, and apply it to the `clean` column of the DataFrame. Lemmatization is done using the spaCy's underlying [`Doc` representation](https://spacy.io/usage/spacy-101annotations) of each token, which contains a `lemma_` property. Stopwords are removed simultaneously with the lemmatization process, as each of these steps involves iterating through the same list of tokens. ###Code def lemmatize(text): """Perform lemmatization and stopword removal in the clean text Returns a list of lemmas """ doc = nlp(text) lemma_list = [str(tok.lemma_).lower() for tok in doc if tok.is_alpha and tok.text.lower() not in stopwords] return lemma_list ###Output _____no_output_____ ###Markdown The resulting lemmas as stored as a list in a separate column `preproc` as shown below. ###Code %%time df_preproc['preproc'] = df_preproc['clean'].progress_apply(lemmatize) df_preproc[['date', 'content', 'preproc']].head(3) ###Output _____no_output_____ ###Markdown Applying this method to the `clean` column of the DataFrame and timing it shows that it takes almost a minute to run on $8800$ news articles. Option 2. Use `nlp.pipe`Can we do better? in the [spaCy documentation](https://spacy.io/api/languagepipe), it is stated that "processing texts as a stream is usually more efficient than processing them one-by-one". This is done by calling a language pipe, which internally divides the data into batches to reduce the number of pure-Python function calls. This means that the larger the data, the better the performance gain that can be obtained by `nlp.pipe`.To use the language pipe to stream texts, a separate lemmatizer method is defined that directly works on a spaCy `Doc` object. This method is then called in batches to work on a *sequence* of `Doc` objects that are streamed through the pipe as shown below. ###Code def lemmatize_pipe(doc): lemma_list = [str(tok.lemma_).lower() for tok in doc if tok.is_alpha and tok.text.lower() not in stopwords] return lemma_list def preprocess_pipe(texts): preproc_pipe = [] for doc in tqdm(nlp.pipe(texts, batch_size=20), total=len(df_preproc)): preproc_pipe.append(lemmatize_pipe(doc)) return preproc_pipe ###Output _____no_output_____ ###Markdown Just as before, a new column is created by passing data from the `clean` column of the existing DataFrame. Note that unlike in workflow $1$, we do not use the `apply` method - instead, the column of data (an iterable) is directly passed as an argument to the preprocessor pipe method. ###Code %%time df_preproc['preproc_pipe'] = preprocess_pipe(df_preproc['clean']) df_preproc[['date', 'content', 'preproc_pipe']].head(3) ###Output _____no_output_____ ###Markdown Timing this workflow shows barely any improvement, but it still takes almost a minute on the entire set of $8800$ news articles. One would expect that as we work on bigger and bigger datasets, the timing gain using `nlp.pipe` would become more noticeable (on average). Option 3. Parallelize the work using joblibWe can do still better! The previous workflows sequentially worked through each news document to produce the lemma lists, which were then appended to the DataFrame as a new column. Because each row's output is completely independent of the other, this is an *embarassingly parallel* problem, making it ideal for using multiple cores.The `joblib` library is recommended by spaCy for processing blocks of an NLP pipeline in parallel. Make sure that you `pip install joblib` before running the below section.To parallelize the workflow, a few more helper methods must be defined. * **Chunking:** The news article content is a list of (long) strings where each document represents a single article's text. This data must be fed in "chunks" to each worker process started by `joblib`. Each call of the `chunker` method returns a generator that only contains that particular chunk's text as a list of strings. During lemmatization, each new chunk is retrieved based on the iterator index (with the previous chunks being "forgotten").* **Flattening:** Once joblib creates a set of worker processes that work on each chunk, each worker returns a "list of list" containing lemmas for each document. These lists are then combined by the executor to provide a deeply nested final "list of list of lists". To ensure that the length of the output from the executor is the same as the actual number of articles, a "flatten" method is defined to combine the result into a list of lists containing lemmas. For example, if the executor returns a final result `[[[a, b, c], [d, e, f]], [[g, h, i], [j, k, l]]]`, a flattened version of this result would be `[[a, b, c], [d, e, f], [g, h, i], [j, k, l]]`.In addition to the above methods, a similar `nlp.pipe` method is used as in workflow $2$, on each chunk of texts. Each of these methods is wrapped into a `preprocess_parallel` method that defines the number of worker processes to be used ($7$ in this case), breaks the input data into chunks and returns a flattened result that can then be appended to the DataFrame. ###Code from joblib import Parallel, delayed from functools import partial def chunker(iterable, total_length, chunksize): return (iterable[pos: pos + chunksize] for pos in range(0, total_length, chunksize)) def flatten(list_of_lists): "Flatten a list of lists to a combined list" return [item for sublist in list_of_lists for item in sublist] def process_chunk(texts): preproc_pipe = [] for doc in nlp.pipe(texts, batch_size=20): preproc_pipe.append(lemmatize_pipe(doc)) return preproc_pipe def preprocess_parallel(texts, chunksize=100): executor = Parallel(n_jobs=7, backend='multiprocessing', prefer="processes") do = delayed(process_chunk) tasks = (do(chunk) for chunk in chunker(texts, len(df_preproc), chunksize=chunksize)) result = executor(tasks) return flatten(result) %%time df_preproc['preproc_parallel'] = preprocess_parallel(df_preproc['clean'], chunksize=1000) df_preproc[['date', 'content', 'preproc_parallel']].head(3) ###Output _____no_output_____ ###Markdown Timing this parallelized workflow shows significant performance gains (almost **3x** reduction in run time)! As the number of documents becomes larger, the additional overhead of starting multiple worker threads with `joblib` is quickly paid for, and this method can significantly outperform the sequential methods. Effect of chunk size and batch sizeNote that in the parallelized workflow, two parameters need to be specified - the optimum number can vary depending on the dataset. The `chunksize` controls the number of chunks being worked on by each process. In this example, for $8800$ documents, a chunksize of $1000$ is used. Too small a chunksize would mean that a large number of worker threads would spawn (each one waiting for other threads to complete), which can slow down execution. Generally, a chunksize of around $1/10^{th}$ of the total number of documents can be used as a starting point (assuming that all chunks fit into memory at any given time).The batch size is parameter specific to `nlp.pipe`, and again, a good value depends on the data being worked on. For reasonably long-sized text such as news articles, it makes sense to keep the batch size reasonably small (so that each batch doesn't contain *really* long texts), so in this case $20$ was chosen for the batch size. For other cases (e.g. Tweets) where each document is much shorter in length, a larger batch size can be used.**It is recommended to experiment with either parameter to see which combination produces the best performance**. Bonus: Use sets over lists for lookups wherever possibleNote that in the `get_stopwords()` method defined earlier on, the list of stopwords read in from the stopword file was converted to a set before using it in the lemmatizer method for stopword removal via lookups. This is a very useful trick in general, but specifically for stopword removal, the use of sets becomes **all the more important**. Why? In any realistic stopword list, such as this one for a news dataset, it's reasonable to expect *several hundred* stopwords. This is because for downstream tasks such as topic modelling or sentiment analysis, there are a number of domain-specific words that need to be removed (very common verbs, useless abbreviations such as timezones, days of the week, etc.). Each word in each and every document needs to be compared against every word in the stopword list, which is an expensive operation over tens of thousands of documents.It's well known that sets have $O(1)$ (i.e. consant) lookup time as opposed to lists, which have $O(n)$ lookup time. In the `lemmatize()` method, since we're checking each word for membership in the set of stopwords, we would expect sets to be much better than lists. To test this, we can rerun workflow $1$ but this time, use a stopword *list* instead. ###Code stopwords = list(stopwords) %%time df_preproc['preproc_stopword_list'] = df_preproc['clean'].progress_apply(lemmatize) df_preproc[['date', 'content', 'preproc_stopword_list']].head(3) ###Output _____no_output_____
jupyter_demo/wikidata phenomizer demo.ipynb
###Markdown Wikidata PhenomizerThe phenomizer tool takes an ontology file and an association file. We'll use the HPO and the HPO's disease->phenotype association file, but supplemented with Wikidata-derived disease->phenotype associations setup **The Wikidata-phenomizer repo contains a python script for generating this association file** ###Code !git clone [email protected]:SuLab/Wikidata-phenomizer.git import os os.chdir("Wikidata-phenomizer/") !wget -N http://compbio.charite.de/jenkins/job/hpo.annotations/lastStableBuild/artifact/misc/phenotype_annotation.tab !python phenomizer.py ###Output /home/gstupp/projects/phenomizer/jupyter_demo/venv/lib/python3.5/site-packages/pandas/core/frame.py:6211: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'. sort=sort) number of hpo annotations: 159162 number of wikidata annotations: 472 number overlap annotations: 307 top unique disease-phenotypes in wd: NGLY1-deficiency 71 Colchicine poisoning 20 Mercury poisoning 16 lead poisoning 14 toxocariasis 8 Q fever 8 hymenolepiasis 7 tick-borne encephalitis 7 acute lymphocytic leukemia 6 Japanese encephalitis 6 Name: DB_Name, dtype: int64 ###Markdown **The Boqa repo contains the Java tool for running Phenomizer** ###Code os.chdir("..") !git clone [email protected]:SuLab/boqa.git os.chdir("boqa/") !wget -N http://purl.obolibrary.org/obo/hp.obo hpo_ids = "HP:0001263,HP:0001252,HP:0000522,HP:0012804,HP:0000559,HP:0011968,HP:0009830,HP:0001265,HP:0002167,HP:0000970,HP:0040129" ass_file = "../Wikidata-phenomizer/phenotype_annotation_wd.tab" !java -jar target/boqa-0.0.3-SNAPSHOT.jar -hpo {hpo_ids} -obo hp.obo -af {ass_file} ###Output init new BoqaService Dec 27, 2018 12:44:48 PM sonumina.boqa.server.BOQACore <init> INFO: Starting sonumina.boqa.server.BOQACore Dec 27, 2018 12:44:48 PM ontologizer.go.OBOParser doParse INFO: Got 14090 terms and 17755 relations in 127 ms Dec 27, 2018 12:44:48 PM sonumina.boqa.server.BOQACore <init> INFO: OBO file "hp.obo" parsed Dec 27, 2018 12:44:49 PM ontologizer.go.Ontology assignLevel1TermsAndFixRoot INFO: Ontology contains a single level-one term (All (HP:0000001) Dec 27, 2018 12:44:49 PM sonumina.boqa.server.BOQACore <init> INFO: Ontology graph with 14090 terms created Dec 27, 2018 12:44:49 PM ontologizer.association.PafLineScanner processParsedAssociation WARNING: PafLineScanner: Line 159532: Expected that dbObject "ORPHA:244305" maps to symbol "hypophosphatemic nephrolithiasis/osteoporosis 1 (ORPHA:244305)" but it maps to "hypophosphatemic nephrolithiasis/osteoporosis 2 (ORPHA:244305)" Skipping association of item "NGLY1-deficiency (ORPHA:404454)" to HP:0008051 because term is obsolete! (Are the obo file and the association file in sync?) Dec 27, 2018 12:44:49 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: 159978 associations parsed, 159977 of which were kept while 0 malformed lines had to be ignored. Dec 27, 2018 12:44:49 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: A further 1 associations were skipped due to various reasons whereas 0 of those where explicitly qualified with NOT, 1 referred to obsolete terms and 0 didn't match the requested evidence codes Dec 27, 2018 12:44:49 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: PAF-parse: A total of 8278 terms are directly associated to 10859 items. Dec 27, 2018 12:44:49 PM sonumina.boqa.server.BOQACore <init> INFO: Got ontology and associations in 0.83 seconds Dec 27, 2018 12:44:49 PM sonumina.boqa.server.BOQACore init INFO: Setting up BOQA Dec 27, 2018 12:44:49 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: 10860 items shall be considered Dec 27, 2018 12:44:49 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: Available evidences: PCS->7312,IEA->46892,TAS->105325,ICE->10, Dec 27, 2018 12:44:50 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: 10860 items passed criterias (supplied evidence codes) Dec 27, 2018 12:44:50 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Determining differences 1200054 differences detected (110.50220994475139 per item) Dec 27, 2018 12:44:50 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Determining differences with frequencies for maximal 5 terms Dec 27, 2018 12:44:52 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Done with differences! Dec 27, 2018 12:44:52 PM sonumina.boqa.server.BOQACore init INFO: Sort terms Dec 27, 2018 12:44:53 PM sonumina.boqa.server.BOQACore <init> INFO: Starting sonumina.boqa.server.BOQACore Dec 27, 2018 12:44:53 PM ontologizer.go.OBOParser doParse INFO: Got 14090 terms and 17755 relations in 85 ms Dec 27, 2018 12:44:53 PM sonumina.boqa.server.BOQACore <init> INFO: OBO file "hp.obo" parsed Dec 27, 2018 12:44:53 PM ontologizer.go.Ontology assignLevel1TermsAndFixRoot INFO: Ontology contains a single level-one term (All (HP:0000001) Dec 27, 2018 12:44:53 PM sonumina.boqa.server.BOQACore <init> INFO: Ontology graph with 14090 terms created Dec 27, 2018 12:44:54 PM ontologizer.association.PafLineScanner processParsedAssociation WARNING: PafLineScanner: Line 159532: Expected that dbObject "ORPHA:244305" maps to symbol "hypophosphatemic nephrolithiasis/osteoporosis 1 (ORPHA:244305)" but it maps to "hypophosphatemic nephrolithiasis/osteoporosis 2 (ORPHA:244305)" Skipping association of item "NGLY1-deficiency (ORPHA:404454)" to HP:0008051 because term is obsolete! (Are the obo file and the association file in sync?) Dec 27, 2018 12:44:54 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: 159978 associations parsed, 159977 of which were kept while 0 malformed lines had to be ignored. Dec 27, 2018 12:44:54 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: A further 1 associations were skipped due to various reasons whereas 0 of those where explicitly qualified with NOT, 1 referred to obsolete terms and 0 didn't match the requested evidence codes Dec 27, 2018 12:44:54 PM ontologizer.association.AssociationParser importAssociationFileFromPaf INFO: PAF-parse: A total of 8278 terms are directly associated to 10859 items. Dec 27, 2018 12:44:54 PM sonumina.boqa.server.BOQACore <init> INFO: Got ontology and associations in 0.619 seconds Dec 27, 2018 12:44:54 PM sonumina.boqa.server.BOQACore init INFO: Setting up BOQA Dec 27, 2018 12:44:54 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: 10860 items shall be considered Dec 27, 2018 12:44:54 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: Available evidences: PCS->7312,IEA->46892,TAS->105325,ICE->10, Dec 27, 2018 12:44:54 PM sonumina.boqa.calculation.BOQA provideGlobals INFO: 10860 items passed criterias (supplied evidence codes) Dec 27, 2018 12:44:55 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Determining differences 1200054 differences detected (110.50220994475139 per item) Dec 27, 2018 12:44:55 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Determining differences with frequencies for maximal 5 terms Dec 27, 2018 12:44:57 PM sonumina.boqa.calculation.DiffVectors initDiffVectors INFO: Done with differences! Dec 27, 2018 12:44:57 PM sonumina.boqa.server.BOQACore init INFO: Sort terms itemName|score CONE-ROD DYSTROPHY, X-LINKED, 1 (OMIM:304020)|0.5618521679105415 CONGENITAL DISORDER OF DEGLYCOSYLATION; CDDG (OMIM:615273)|0.32472793279466233 NGLY1-deficiency (ORPHA:404454)|0.07001706350516379 Cyclic neutropenia (ORPHA:2686)|0.020875879762404497 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE II (OMIM:201300)|0.009914522181086032 METACHROMATIC LEUKODYSTROPHY DUE TO SAPOSIN B DEFICIENCY (OMIM:249900)|0.0046590651687770665 YUAN-HAREL-LUPSKI SYNDROME; YUHAL (OMIM:616652)|0.0025038227609720147 MITOCHONDRIAL DNA DEPLETION SYNDROME 14 (OMIM:616896)|0.0017445706391903551 severe acute respiratory syndrome (ORPHA:140896)|8.395633227508864E-4 CHROMOSOME 3pter-p25 DELETION SYNDROME (OMIM:613792)|6.124569153543484E-4
presentations/SciPy 2017.ipynb
###Markdown IPython-Unittest Test support for Jupyter/IPython through cell magics Joรฃo Felipe Nicolaci Pimentel ([email protected]) `pip install ipython_unittest` https://github.com/JoaoFelipe/ipython-unittest Before I start 1- Survey about computational experiments http://scipy.npimentel.net 2- Poster about Provenance in Python scripts noWorkflow: collecting; managing; and provenance from Python scripts** - last row, behind the screen ###Code %load_ext ipython_unittest.dojo def add(x, y): return x + y %%unittest -p 1 assert add(1, 1) == 2 assert add(1, 2) == 3 assert add(2, 2) == 4 import unittest import sys class JupyterTest(unittest.TestCase): def test_add_1_1_returns_2(self): self.assertEqual(add(1, 1), 2) def test_add_1_2_returns_3(self): self.assertEqual(add(1, 2), 3) def test_add_2_2_returns_4(self): self.assertEqual(add(2, 2), 4) suite = unittest.TestLoader().loadTestsFromTestCase(JupyterTest) unittest.TextTestRunner(verbosity=1, stream=sys.stdout).run(suite) %%unittest -u "add 1 + 1 returns 2" assert add(1, 1) == 2 "add 1 + 2 returns 3" assert add(1, 2) == 3 "add 2 + 2 returns 4" assert add(2, 2) == 4 ###Output _____no_output_____ ###Markdown Other magics: ###Code %%write javascript test.js var assert = require('assert'); describe('Array', function() { describe('#indexOf()', function() { it('should return -1 when the value is not present', function() { assert.equal(-1, [1,2,3].indexOf(4)); }); }); }); %%external --previous 1 mocha test.js %%unittest_main class JupyterTest(unittest.TestCase): def test_add_1_1_returns_2(self): self.assertEqual(add(1, 1), 2) def test_add_1_2_returns_3(self): self.assertEqual(add(1, 2), 3) def test_add_2_2_returns_4(self): self.assertEqual(add(2, 2), 4) %%unittest_testcase def test_add_1_1_returns_2(self): self.assertEqual(add(1, 1), 2) def test_add_1_2_returns_3(self): self.assertEqual(add(1, 2), 3) def test_add_2_2_returns_4(self): self.assertEqual(add(2, 2), 4) ###Output _____no_output_____
src/w3techs/top-n.ipynb
###Markdown Notebook for adding top_sitesThese come in batches from Matthias. Last time, all the data were from the same date.Assuming that continues, just set the DATE below to that date PLUS one day. ###Code import pandas as pd MEASUREMENTS_TIME = pd.Timestamp('2021-01-02') from os.path import join from os import listdir from src.shared.utils import get_country from datetime import datetime ###Output _____no_output_____ ###Markdown Parsing the dataframes ###Code def parse_df (df: pd.DataFrame) -> pd.DataFrame: ''' Returns a df with columns name, marketshare ''' def percentify (x): try: n = x.split('%')[0] return float(n)/100 except: return 0 # name of columns where percentages are perc_col_name = [c for c in df.columns if c.startswith('Percentage')][0] df['marketshare'] = df[perc_col_name].apply(percentify) # if this is a heirarchical csv, # get top-level entries only if 'Rank' in df.columns: df['top-level'] = df['Rank'].apply(lambda x: str(x).endswith('.0')) df = df[df['top-level']==True] # get names from 1st column n = df.columns[1] else: # get names from 0th column n = df.columns[0] # get jurisdictions df['name'] = df[n] # remove 'and territories' for server locations df['name'] = df['name'].apply(lambda x: x.split(' and territories')[0]) df['jurisdiction_alpha2'] = df['name'].apply(get_country) return df[['name', 'marketshare', 'jurisdiction_alpha2']] ex_fn = listdir('top-sites')[1] ex_df = pd.read_csv(join('top-sites', ex_fn)) parse_df(ex_df) ###Output _____no_output_____ ###Markdown Extracting market/top-n from filenames ###Code dfs = [] for my_dir in listdir('top-sites'): fn = my_dir.split('.csv')[0] if fn.split('-')[1]=='hierarchy': market, h, top_n, date_str = fn.split('-') date = datetime.strptime(date_str, '%Y%M') print(market, top_n, date) df = pd.read_csv(join('top-sites', my_dir)) df = parse_df(df) df['measurement_scope'] = top_n df['market'] = market df['date'] = date dfs.append(df) df = pd.concat(dfs) df.market.unique() dfs = [] for my_dir in listdir('top-sites'): fn = my_dir.split('.csv')[0] market = fn.split('-')[0] # if we don't already have this data from the heirarchical files if (market not in df.market.unique()): market, top_n, date_str = fn.split('-') date = datetime.strptime(date_str, '%Y%M') print(market, top_n, date) t_df = pd.read_csv(join('top-sites', my_dir)) t_df = parse_df(t_df) t_df['measurement_scope'] = top_n t_df['market'] = market t_df['date'] = date dfs.append(t_df) concat = pd.concat(dfs) concat = pd.concat([concat, df]) concat.market.unique() ###Output _____no_output_____ ###Markdown Simple analyseshh,, hh,, ###Code concat.to_csv('out/top-sites-combined.csv') df = pd.read_csv('out/top-sites-combined.csv').drop('Unnamed: 0', axis=1) df df.market = df.market.replace({ 'dns_servers': 'dns-server', 'server_locations': 'server-location', 'data_center': 'data-centers', 'ssl_certificate': 'ssl-certificate', 'web_hosting': 'web-hosting', 'reverse_proxy': 'proxy', 'top_level_domains': 'top-level-domain', }) ###Output _____no_output_____ ###Markdown TODO: Write to databaseNOTE FOR README: In 'heirarchical' files, i took the top-level only (e.g., 'DigiCert Group' vs. 'DigiCert' + its other subsidiaries). ###Code from config import config postgres_config = config['postgres'] import psycopg2 conn = psycopg2.connect(**postgres_config) cur = conn.cursor() from imp import reload # conn.commit() # provider marketshare for each import src.w3techs.types reload(src.w3techs.types) from src.w3techs.types import ProviderMarketshare from src.shared.types import Alpha2 for i, row in df.iterrows(): try: alpha2 = Alpha2(row.jurisdiction_alpha2) except: alpha2 = None marketshare = ProviderMarketshare( row['name'], None, alpha2, row.measurement_scope, row.market, float(row['marketshare']), pd.Timestamp(row.date)) marketshare.write_to_db(cur, conn, commit=False) conn.commit() ###Output _____no_output_____ ###Markdown find pop weighted gini for top 1k and top 10k ###Code from src.w3techs.collect import included_markets import src.w3techs.utils as utils # time = pd.Timestamp(df.date.unique()[0], tz='America/Los_Angeles') for measurement_scope in ['top_1k', 'top_10k']: print(measurement_scope) for market in included_markets: pop_weighted_gini = utils.population_weighted_gini( cur, measurement_scope, market, MEASUREMENTS_TIME) if pop_weighted_gini: print(f'[X] {market}') pop_weighted_gini.write_to_db(cur, conn) else: print(f'[ ] {market}') ###Output top_1k [X] web-hosting [X] ssl-certificate [X] proxy [X] data-centers [X] dns-server [X] server-location [X] top-level-domain top_10k [X] web-hosting [X] ssl-certificate [X] proxy [X] data-centers [X] dns-server [X] server-location [X] top-level-domain
Forecasting-workshop/2a_Amazon_Forecast_Model.ipynb
###Markdown Bike-Share Demand Forecasting 2a: Modelling with [Amazon Forecast](https://aws.amazon.com/forecast/)์ด์ „ [1_Data_Preparation](1_Data_Preparation.ipynb) ๋…ธํŠธ๋ถ์—์„œ ์ˆ˜ํ–‰ํ•œ bike-share ์ˆ˜์š” ์˜ˆ์ธก ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด 3๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์‚ดํŽด๋ด…๋‹ˆ๋‹ค.1. AWS "Managed AI"์„œ๋น„์Šค ([Amazon Forecast] (https://aws.amazon.com/forecast/))์œผ๋กœ ์ผ๋ฐ˜์ /๊ทœ๊ฒฉํ™”๋œ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค.2. SageMaker์˜ built-in๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ([DeepAR] (https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html))์„ ์‚ฌ์šฉํ•˜์—ฌ 1๋ฒˆ๊ณผ ๋™์ผํ•œ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค.3. custom SageMaker ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ€๊ฐ€์ ์ธ ์ฐจ๋ณ„์  SageMaker์˜ ๊ธฐ๋Šฅ์„ ํ™œ์šฉํ•˜๋ฉด์„œ ํ•ต์‹ฌ ๋ชจ๋ธ๋ง์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค.**์ด ๋…ธํŠธ๋ถ์€ AWS ์ฝ˜์†”์„ ํ†ตํ•ด Amazon Forecast ์„œ๋น„์Šค๋ฅผ ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋ณด์—ฌ ์ฃผ์ง€๋งŒ ๋Œ€์‹  ๋™์ผํ•œ ์ž‘์—…์„ ๋ชจ๋‘ API๋ฅผ ํ†ตํ•ด ์ˆ˜ํ–‰ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.** Dependencies and configuration๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ๋กœ๋”ฉํ•œ ๋‹ค์Œ, ์„ค์ •๊ฐ’์„ ์ •์˜ํ•˜๊ณ , AWS SDKs์— ์—ฐ๊ฒฐํ•ฉ๋‹ˆ๋‹ค. ###Code # Basic data configuration is initialised and stored in the Data Preparation notebook # ...We just retrieve it here: %store -r assert bucket, "Variable `bucket` missing from IPython store" assert data_prefix, "Variable `data_prefix` missing from IPython store" assert target_train_filename, "Variable `target_train_filename` missing from IPython store" assert target_test_filename, "Variable `target_test_filename` missing from IPython store" assert related_filename, "Variable `related_filename` missing from IPython store" %load_ext autoreload %autoreload 1 # Built-Ins: from datetime import datetime, timedelta # External Dependencies: import boto3 from IPython.core.display import display, HTML import pandas as pd # Local Dependencies: %aimport util session = boto3.Session() region = session.region_name forecast = session.client(service_name="forecast") forecast_query = session.client(service_name="forecastquery") s3 = session.client(service_name="s3") ###Output _____no_output_____ ###Markdown Overview์•„๋ž˜ ์š”์•ฝ ๋‚ด์šฉ์„ ๋ณด๋ฉด, Amazon Forecast์˜ ์ „์ฒด ์›Œํฌํ”Œ๋กœ์šฐ๋Š” ์ „ํ˜•์ ์ธ Batch ML ๋ชจ๋ธ ํ•™์Šต ์ ‘๊ทผ ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค.์œ„์—์„œ ์ดˆ๊ธฐํ™”ํ•œ forecast์˜ SDK๋Š” AWS Console์—์„œ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ชจ๋“  ๋‹จ๊ณ„์˜ Amazon Forecast ์ˆ˜ํ–‰์„ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ฐฉ์‹์œผ๋กœ ์ง€์›ํ•ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์—์„œ๋Š” **AWS Console** ๋ฐฉ์‹์˜ ์‚ฌ์šฉ๋ฐฉ๋ฒ•์„ ์•Œ๋ ค๋“œ๋ฆฝ๋‹ˆ๋‹ค, Step 1: Selecting the Amazon Forecast domainAmazon Forecast๋Š” ๋ช‡๊ฐ€์ง€ **domains** (documented [here](https://docs.aws.amazon.com/forecast/latest/dg/howitworks-domains-ds-types.html))๋“ค์„ ์ •์˜ํ•ฉ๋‹ˆ๋‹ค.domain์€ ํŠน์ • use case์— ๋งž๋„๋ก ์กฐ์ •๋œ **๊ธฐ๋ณธ ๋ฐ์ดํ„ฐ ์Šคํ‚ค๋งˆ**์™€ featureํ™” ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜๋“ค์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ custom ๋ฐ์ดํ„ฐ ํ•„๋“œ๋ฅผ ์ถ”๊ฐ€ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ผ๋ฐ˜์ ์œผ๋กœ ๊ธฐ๋ณธ domain model์—์„œ ๊ตฌ์กฐ๋ฅผ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ๋งŽ์„์ˆ˜๋ก, ๋” ๋‚˜์€ ๋ชจ๋ธ ์„ฑ๋Šฅ์„ ์–ป์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.์ด๋ฒˆ ์˜ˆ์ œ์—์„œ๋Š” [`RETAIL`](https://docs.aws.amazon.com/forecast/latest/dg/retail-domain.html) domain์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ์ถ”๊ฐ€๋กœ, [`METRICS`](https://docs.aws.amazon.com/forecast/latest/dg/metrics-domain.html) ๋˜๋Š” ๋‹ค๋ฅธ domain์ด ๋” ์ข‹์€ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์˜ฌ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์‹œ๊ฐ„์ด ๋˜์‹œ๋ฉด, ๋‹ค๋ฅธ domain์— ๋Œ€ํ•œ ์‹คํ—˜๋„ ํ•ด๋ณด๋ฉด์„œ, ์„ฑ๋Šฅ ๊ฐœ์„ ์ด ๋˜์—ˆ๋Š”์ง€ ํ™•์ธํ•ด๋ณด์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค. Step 2: Preparing the data[domain documentation](https://docs.aws.amazon.com/forecast/latest/dg/retail-domain.html)์—์„œ ์ œ๊ณตํ•  ํ•„์š”๊ฐ€ ์žˆ๋Š” ํ•„์ˆ˜ ํ•„๋“œ๊ฐ€ ๋ฌด์—‡์ธ์ง€ ์•Œ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ๋ฅผ ์•ฝ๊ฐ„ ์กฐ์ •ํ•œ ๋‹ค์Œ ๋‹ค์‹œ S3๋กœ ์—…๋กœ๋“œํ•˜์‹œ๋ฉด ๋ฉ๋‹ˆ๋‹ค. ###Code target_train_df = pd.read_csv(f"./data/{target_train_filename}") target_test_df = pd.read_csv(f"./data/{target_test_filename}") related_df = pd.read_csv(f"./data/{related_filename}") ###Output _____no_output_____ ###Markdown Retail domain ์—์„œ๋Š” Target timeseries ๋ฐ์ดํ„ฐ์…‹์—๋Š” ์ปฌ๋Ÿผ ์ด๋ฆ„์€ `timestamp`, `item_id`, `demand`๋กœ ์ •ํ•ด์ ธ ์žˆ์œผ๋ฉฐ, ๋‹ค๋ฅธํ•„๋“œ๋Š” ์—†์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.์šฐ๋ฆฌ๊ฐ€ ์ด์ „ ๋…ธํŠธ๋ถ์—์„œ ์ž‘์—…ํ•œ ๋ฐ์ดํ„ฐ์…‹์€ customer_typeํ•„๋“œ๋ฅผ item_id๋กœ ๋ณ€๊ฒฝํ•˜๋Š” ๊ฒƒ ์™ธ์—๋Š” ์ด๋ฏธ ์œ„ ๊ธฐ์ค€์— ์ ํ•ฉํ•˜๊ฒŒ ์ž‘์—…์„ ํ–ˆ์Šต๋‹ˆ๋‹ค. ###Code target_train_df.rename(columns={ "customer_type": "item_id" }, inplace=True) target_test_df.rename(columns={ "customer_type": "item_id" }, inplace=True) target_train_df.head() ###Output _____no_output_____ ###Markdown Retail ๋„๋ฉ”์ธ์—์„œ Related timeseries๋Š”:1. (์šฐ๋ฆฌ๊ฐ€ ์ด๋ฏธ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”) `timestamp`์ด ํฌํ•จ๋˜์–ด ์žˆ์œผ๋ฉฐ,2. (๋‚ ์”จ ์ •๋ณด๊ฐ€ customer_type์— ๋”ฐ๋ผ ๋‹ค๋ฅด์ง€ ์•Š์ง€๋งŒ) `item_id` ๊ฐ€ ์ถ”๊ฐ€๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.3. ์—ฌ๋Ÿฌ optional ํ•œ domain ํ•„๋“œ๋ฅผ ๊ธฐ๋ณธ์ ์œผ๋กœ ์ œ์•ˆํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์ด ๊ฐ’๋“ค์€ ํ˜„์žฌ ๋ฐ์ดํ„ฐ์…‹๊ณผ ํฌ๊ฒŒ ๊ด€๋ จ์ด ์—†๊ธฐ์— ๋ณ€๊ฒฝํ•ฉ๋‹ˆ๋‹ค.์ถ”๊ฐ€์ ์œผ๋กœ ์ผ๋ฐ˜์ ์ธ ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•ด์„œ๋Š” ์•„๋ž˜ ์‚ฌํ•ญ์„ ๊ณ ๋ คํ•˜์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค:4. Forecast์—์„œ ์‚ฌ์šฉํ•˜๋Š” [reserved field names](https://docs.aws.amazon.com/forecast/latest/dg/reserved-field-names.html) (including `temp`)๋Š” ์ปฌ๋Ÿผ์— ์‚ฌ์šฉํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.5. ์‚ฌ์šฉ์ž๊ฐ€ ์ถ”๊ฐ€ํ•˜๋Š” fields์˜ [schema](https://docs.aws.amazon.com/forecast/latest/dg/API_SchemaAttribute.html)๋Š” `string`, `integer`, `float`, `timestamp` ํƒ€์ž…์œผ๋กœ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.boolean ํ•„๋“œ์— ๋Œ€ํ•ด์„œ๋Š” string ํ˜•ํƒœ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ๋กœ๋“œํ•˜๋ฉด ๋™์ผํ•œ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.๋”ฐ๋ผ์„œ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋ฐ์ดํ„ฐ๋ฅผ ์ค€๋น„ํ•ฉ๋‹ˆ๋‹ค.* Related timeseries ๋ฐ์ดํ„ฐ์— `item_id` ์ถ”๊ฐ€ํ•ฉ๋‹ˆ๋‹ค. (2๋ฒˆ ํ•ญ๋ชฉ)* ์ปฌ๋Ÿผ `temp`๋ฅผ `temperature`๋กœ ์ด๋ฆ„์„ ๋ณ€๊ฒฝํ•ฉ๋‹ˆ๋‹ค.(4๋ฒˆ ํ•ญ๋ชฉ) ###Code # Duplicate data for each item_id in the target dataframe: related_peritem_dfs = [] item_ids = target_train_df["item_id"].unique() for item_id in item_ids: df = related_df.copy() df["item_id"] = item_id related_peritem_dfs.append(df) related_df = pd.concat(related_peritem_dfs).sort_values(["timestamp", "item_id"]).reset_index(drop=True) # Rename any reserved columns to keep Forecast happy: related_df.rename(columns={ "temp": "temperature" }, inplace=True) related_df.head() ###Output _____no_output_____ ###Markdown ...Amazon Forecast๋กœ ๊ฐ€์ ธ์˜ฌ ์ค€๋น„๊ฐ€ ๋œ S3์— ๋ฐ์ดํ„ฐ๋ฅผ ์ €์žฅํ•ฉ๋‹ˆ๋‹ค. ###Code print("Writing dataframes to file...") !mkdir -p ./data/amzforecast target_train_df.to_csv( f"./data/amzforecast/{target_train_filename}", index=False ) target_test_df.to_csv( f"./data/amzforecast/{target_test_filename}", index=False ) related_df.to_csv( f"./data/amzforecast/{related_filename}", index=False ) print("Uploading dataframes to S3...") s3.upload_file( Filename=f"./data/amzforecast/{target_train_filename}", Bucket=bucket, Key=f"{data_prefix}amzforecast/{target_train_filename}" ) print(f"s3://{bucket}/{data_prefix}amzforecast/{target_train_filename}") s3.upload_file( Filename=f"./data/amzforecast/{target_test_filename}", Bucket=bucket, Key=f"{data_prefix}amzforecast/{target_test_filename}" ) print(f"s3://{bucket}/{data_prefix}amzforecast/{target_test_filename}") s3.upload_file( Filename=f"./data/amzforecast/{related_filename}", Bucket=bucket, Key=f"{data_prefix}amzforecast/{related_filename}" ) print(f"s3://{bucket}/{data_prefix}amzforecast/{related_filename}") print("Done") ###Output _____no_output_____ ###Markdown Step 3: Create a Dataset Group์ด๋ฏธ ์ด์ „์— ์„ ํƒํ•œ `region` ์—์„œ Amazon Forecast console์„ ์—ฝ๋‹ˆ๋‹ค. ์•„๋ž˜์— ํŽ˜์ด์ง€๊ฐ€ ํ‘œ์‹œ๋˜๊ฑฐ๋‚˜, ์ด์ „์— ์„œ๋น„์Šค๋ฅผ ์‚ฌ์šฉํ•œ ์ ์ด ์žˆ์œผ๋ฉด ๋‹ค๋ฅธ ๋Œ€์‹œ๋ณด๋“œ๊ฐ€ ํ‘œ์‹œ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์•„๋ž˜์™€ ๊ฐ™์€ landing ํŽ˜์ด์ง€๋‚˜ ๋˜๋Š” ์™ผ์ชฝ ๋ฉ”๋‰ด์— ์žˆ๋Š” "Dataset Groups"์—์„œ "Create Dataset Group"์„ ํด๋ฆญํ•ฉ๋‹ˆ๋‹ค.> **Create dataset group** - Dataset group name : **`bikeshare_dataset_group`** - Forecasting domain : **`Retail`****Next** ๋ฒ„ํŠผ์„ ํด๋ฆญํ•ฉ๋‹ˆ๋‹ค. Step 4: Create a Target Dataset์•„๋ž˜์™€ ๊ฐ™์€ ํ˜•์‹์œผ๋กœ target ๋ฐ์ดํ„ฐ์…‹์„ ์ƒ์„ฑํ•˜๋Š” ๋ฉ”์‹œ์ง€๊ฐ€ ํ‘œ์‹œ๋ฉ๋‹ˆ๋‹ค. (๊ทธ๋ ‡์ง€ ์•Š์„ ๊ฒฝ์šฐ ๋Œ€์‹œ๋ณด๋“œ์—์„œ target ๋ฐ์ดํ„ฐ์„ธ์„ ์ž‘์„ฑํ•˜๋„๋ก ์„ ํƒํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. )> **Create target time series dataset** - Dataset name : **`bikeshare_target_dataset`** - Frequency of your data : **`hourly`** - Data schema : **`Re-order the columns in the data schema`**์œ„ ๋ณ€๊ฒฝ ํ›„์— "Next"๋ฅผ ํด๋ฆญํ•ฉ๋‹ˆ๋‹ค.์šฐ์„ , ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์˜ ๊ตฌ์กฐ๋ฅผ ๊ฒ€ํ† ํ•ฉ๋‹ˆ๋‹ค: ###Code target_train_df.head() ###Output _____no_output_____ ###Markdown Step 5: Import target timeseries data๋‹ค์Œ์œผ๋กœ *dataset import job*์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค.(๋งŒ์ผ ๋ฐ”๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์—†๋‹ค๋ฉด, ๋Œ€์‹œ๋ณด๋“œ์—์„œ ์„ ํƒํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.)> **Import target times series data** - Dataset import name : **`bikeshare_target_import`** - Timestamp format : **`yyyy-MM-dd HH:mm:ss`** (๋ณ€๊ฒฝ์ด ํ•„์š”์—†์ด default ๊ฐ’ ์‚ฌ์šฉ) - Custom IAM role ARN : **`์•„๋ž˜ ์ถœ๋ ฅ๋œ ๊ฐ’์„ copyํ•ด์„œ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.`** - Data location : **`์•„๋ž˜ ์ถœ๋ ฅ๋œ ๊ฐ’์„ copyํ•ด์„œ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.`** **"Start Import"** ํด๋ฆญํ•˜๊ฒŒ ๋˜๋ฉด, ๋‹ค์‹œ Forecast ๋Œ€์‹œ๋ณด๋“œ๋กœ ๋Œ์•„๊ฐ€๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. ###Code iam = boto3.client('iam') role = iam.list_roles(PathPrefix='/service-role/') iam_arn= role['Roles'][0]['Arn'] print("Custom IAM role ARN ๊ฐ’์€ ์•„๋ž˜ ํ•œ ์ค„์„ copyํ•ด์„œ ์‚ฌ์šฉํ•˜์„ธ์š”. \n{}".format(iam_arn) ) print("Data location ๊ฐ’์€ ์•„๋ž˜ ํ•œ ์ค„์„ copyํ•ด์„œ ์‚ฌ์šฉํ•˜์„ธ์š”") print(f"s3://{bucket}/{data_prefix}amzforecast/{target_train_filename}") ###Output _____no_output_____ ###Markdown * Amazon Forecast๋Š” ํ™•์žฅ ๊ฐ€๋Šฅํ•œ ๋ฐฉ์‹์œผ๋กœ ์ž‘์—…์„ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋ฆฌ์†Œ์Šค๋ฅผ spin up ํ•œ ๋‹ค์Œ ๋ฐ์ดํ„ฐ ์…‹์˜ ์œ ํšจ์„ฑ์„ ๊ฒ€์‚ฌํ•˜๋Š” ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•˜๋ฏ€๋กœ Dataset import ์ž‘์—…์„ ์™„๋ฃŒํ•˜๋Š”๋ฐ ๋ช‡ ๋ถ„์˜ ์‹œ๊ฐ„์ด ๊ฑธ๋ฆฝ๋‹ˆ๋‹ค. (ํ•ด๋‹น ๋ฐ์ดํ„ฐ์…‹์—์„œ 10~15๋ถ„ ์†Œ์š”)* Target data import๊ฐ€ ์ž‘์—…๋˜๋Š” ๋™์•ˆ ๋ณ„๋„ ๋Œ€๊ธฐ ์—†์ด ๋‹ค์Œ ๋‹จ๊ณ„์˜ Related data import ์ˆ˜ํ–‰์„ ๋ฐ”๋กœ ํ•˜์‹œ๋ฉด ๋ฉ๋‹ˆ๋‹ค.* Target data๋ฅผ importํ•œ ๋‹ค์Œ "predictor" (forecast ๋ชจ๋ธ)์˜ ํ•™์Šต์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค.ํ•˜์ง€๋งŒ, related data๋ฅผ ํ™œ์šฉํ•˜๋‹ค๋ฉด ๋”์šฑ ์„ฑ๋Šฅ์„ ๋†’์ผ ์ˆ˜ ์žˆ๊ธฐ์— related data๊ฐ€ import ๋œ ๋‹ค์Œ์— 'predictor'๋ฅผ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค. Step 6: Create and import Related Timeseries Dataset๋‹ค์Œ ๋‹จ๊ณ„๋Š” related dataset์„ createํ•˜๊ณ  import ํ•˜๋Š” ๊ณผ์ •์ž…๋‹ˆ๋‹ค.์•„๋ž˜ related data์˜ ๊ตฌ์กฐ๋ฅผ ๊ฒ€ํ† ํ•ฉ๋‹ˆ๋‹ค. ###Code related_df.head() ###Output _____no_output_____ ###Markdown > **Create related time series dataset** - Dataset name : **`bikeshare_related_dataset`** - Frequency of your data : **`hourly`** - Data schema : **`์•„๋ž˜ schema๋ฅผ copyํ•˜์—ฌ ๊ธฐ์กด ๋‚ด์šฉ์„ ์ง€์šด ํ›„ ๋ถ™์—ฌ๋„ฃ๊ธฐ ํ•ฉ๋‹ˆ๋‹ค.`**```json{ "Attributes": [ { "AttributeName": "timestamp", "AttributeType": "timestamp" }, { "AttributeName": "season", "AttributeType": "float" }, { "AttributeName": "holiday", "AttributeType": "string" }, { "AttributeName": "weekday", "AttributeType": "float" }, { "AttributeName": "workingday", "AttributeType": "string" }, { "AttributeName": "weathersit", "AttributeType": "float" }, { "AttributeName": "temperature", "AttributeType": "float" }, { "AttributeName": "atemp", "AttributeType": "float" }, { "AttributeName": "hum", "AttributeType": "float" }, { "AttributeName": "windspeed", "AttributeType": "float" }, { "AttributeName": "item_id", "AttributeType": "string" } ]}```API docs๋Š” ๊ฐ [SchemaAttribute](https://docs.aws.amazon.com/forecast/latest/dg/API_SchemaAttribute.html) ๊ฐ€ ํฌํ•จํ•  ์ˆ˜ ์žˆ๋Š” low-level์˜ ์ƒ์„ธ ๋‚ด์šฉ๊ณผ ์ „์ฒด [Schema](https://docs.aws.amazon.com/forecast/latest/dg/API_Schema.html) ๊ฐ์ฒด๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ์…‹ ์ž‘์—…์ด ์™„๋ฃŒ๋˜๋ฉด, ๋‹ค์Œ ๋ฐ์ดํ„ฐ์…‹ import job์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค.> **Import related times series data** - Dataset import name : **`bikeshare_related_import`** - Timestamp format : **`yyyy-MM-dd HH:mm:ss`** (๋ณ€๊ฒฝ์ด ํ•„์š”์—†์ด default ๊ฐ’ ์‚ฌ์šฉ) - Custom IAM role ARN : **`arn:aws:iam::XXXXXXXX:role/service-role/ForecastDemoLab-XXX`** (๋ณ€๊ฒฝ์ด ํ•„์š”์—†์ด default ๊ฐ’ ์‚ฌ์šฉ) - Data location : **`์•„๋ž˜ ์ถœ๋ ฅ๋œ ๊ฐ’์„ copyํ•ด์„œ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.`**"Start import" ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ฒŒ ๋˜๋ฉด, ๋ฐ์ดํ„ฐ๊ฐ€ ๋กœ๋“œ๋˜๋Š” ๋™์•ˆ ๋Œ€์‹œ๋ณด๋“œ ํ™”๋ฉด์œผ๋กœ ๋Œ์•„๊ฐ€๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. ###Code print("Data location ๊ฐ’์€ ์•„๋ž˜ ํ•œ ์ค„์„ copyํ•ด์„œ ์‚ฌ์šฉํ•˜์„ธ์š”") print(f"s3://{bucket}/{data_prefix}amzforecast/{related_filename}") ###Output _____no_output_____ ###Markdown Step 7: While the datasets import...๋ฐ์ดํ„ฐ์˜ ๋ณผ๋ฅจ์— ๋”ฐ๋ผ import๋Š” ์ˆ˜๋ถ„ ์ด์ƒ ๊ฑธ๋ฆด ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๋Š” ๊ฒฝ์šฐ์—๋Š” [SageMaker์—์„œ ๋ชจ๋ธ์„ ํ•™์Šตํ•˜๋Š” ๋…ธํŠธ๋ถ](2b_SageMaker_Built-In_DeepAR.ipynb)์„ ์ˆ˜ํ–‰ํ•ด ๋ณด๋Š” ๊ฒƒ๋„ ํ•˜๋‚˜์˜ ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค.์ฐธ๊ณ : ์ผ๋ฐ˜์ ์œผ๋กœ๋Š” ๋Œ€์‹œ๋ณด๋“œ์—์„œ ์‹ค์‹œ๊ฐ„ ์—…๋ฐ์ดํŠธ๋ฅผ ํ•˜์ง€๋งŒ, ์ตœ์‹  ์ƒํƒœ๋ฅผ ๋ณด๊ธฐ ์œ„ํ•ด Amazon Forecast์˜ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ์ƒˆ๋กœ๊ณ ์นจํ•ด์•ผ ํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. Step 8: Train a "Prophet" predictor์•„๋ž˜ ๋Œ€์‹œ๋ณด๋“œ์—์„œ Target๊ณผ Related ๋ฐ์ดํ„ฐ import๊ฐ€ ์™„๋ฃŒ๋˜๋ฉด, predictor ํ•™์Šต์„ ์‹œ์ž‘ํ•  ์ค€๋น„๊ฐ€ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.> ** Train predictor** - Predictor name: **`bikeshare_prophet_predictor`** - Forecast horizon: **`336 (2 weeks at 24hrs/day)`** - Forecast frequency: **`hour`**(์›๋ณธ ๋ฐ์ดํ„ฐ์˜ frequency์— ๋งž๊ฒŒ ์ค€๋น„ํ•ฉ๋‹ˆ๋‹ค) - Algorithm selection: **`Manual`** - Algorithm: **`Prophet`** - Country for holidays: **`United States`**(ํ˜„์žฌ ๋ฐ์ดํ„ฐ์…‹์€ ๋ฏธ๊ตญ ๊ธฐ์ค€์ž…๋‹ˆ๋‹ค) - Number of backtest windows: **`4`** - Backtest window offset: **`1080`**(์•„๋ž˜ ์ฐธ์กฐ)์ฒซ ๋ฒˆ์งธ predictor๋Š” Facebook์˜ [Prophet](https://facebook.github.io/prophet/) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ additive-component regression ๊ธฐ๋ฐ˜์œผ๋กœ ํฌ๊ฒŒ ์ธ๊ธฐ๋ฅผ ์–ป์€ ์˜คํ”ˆ์†Œ์Šค ํ”„๋ ˆ์ž„์›Œํฌ์ž…๋‹ˆ๋‹ค. ([paper](https://peerj.com/preprints/3190/)).๋จผ์ € ํ…Œ์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ์ดํ„ฐ์…‹ ์ข…๋‹จ์—์„œ ์งœ๋ฅธ target series ๋ฐ์ดํ„ฐ์˜ ํฌ๊ธฐ๋ฅผ ๊ฒ€ํ† ํ•ฉ๋‹ˆ๋‹ค. ###Code n_train_samples = len(target_train_df["timestamp"].unique()) n_test_samples = len(target_test_df["timestamp"].unique()) n_related_samples = len(related_df["timestamp"].unique()) print(f" {n_train_samples} training samples") print(f"+ {n_test_samples} testing samples") print(f"= {n_related_samples} total samples (related dataset)") assert ( n_train_samples + n_test_samples == n_related_samples ), "Mismatch between target train+test timeseries and related timeseries coverage" ###Output _____no_output_____ ###Markdown [`BackTestWindowOffset`](https://docs.aws.amazon.com/forecast/latest/dg/API_EvaluationParameters.htmlforecast-Type-EvaluationParameters-BackTestWindowOffset) ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” ๋งˆ์ง€๋ง‰ forecast validation window๊ฐ€ ์‹œ์ž‘ํ•˜๋Š” ์œ„์น˜๋กœ ์„ค์ •ํ•˜๋ฉฐ, ์™ธ๋ถ€ ํ…Œ์ŠคํŠธ๋ฅผ ์œ„ํ•ด ๋ณ„๋„ ๋ฐ์ดํ„ฐ๊ฐ€ ์—†๋‹ค๋Š” ๊ฐ€์ • ํ•˜์—, ๊ธฐ๋ณธ์ ์œผ๋กœ๋Š” `ForecastHorizon`๊ณผ ๋™์ผํ•œ ๊ฐ’์ด ์ž๋™์œผ๋กœ ๋ณด์—ฌ์ง‘๋‹ˆ๋‹ค.์ด ์˜ˆ์ œ์—์„œ๋Š” ๋ณ„๋„ ํ…Œ์ŠคํŠธ ์…‹์„ ์ค€๋น„ํ–ˆ๊ธฐ ๋•Œ๋ฌธ์—, ํ…Œ์ŠคํŠธ ์…‹์œผ๋กœ ์ค€๋น„๋œ ์ƒ˜ํ”Œ ์ˆ˜ ๋งŒํผ์˜ ๊ฐ’์„ ๋Š˜๋ ค์•ผ ํ•ฉ๋‹ˆ๋‹ค. (์œ„ ์ฝ”๋“œ ์…€ ์ฐธ๊ณ )์ง€๊ธˆ๊นŒ์ง€์˜ ์„ค์ • ๊ฐ’์ด ๋™์ผํ•˜๋‹ค๋Š” ๊ฐ€์ •ํ•˜์—์„œ , ๊ฐ’์€ 336 + 744 = **1,080** ๋กœ ๋ณ€๊ฒฝํ•ฉ๋‹ˆ๋‹ค.*NumberOfBacktestWindows* ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” Amazon Forecast์—์„œ [๋ชจ๋ธ์˜ ์ •ํ™•๋„](https://docs.aws.amazon.com/forecast/latest/dg/metrics.html)๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉํ•˜๋Š” ๋ถ„๋ฆฌ๋œ window์˜ ๊ฐœ์ˆ˜์ž…๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ฐ์ดํ„ฐ์…‹์˜ ๋งˆ์ง€๋ง‰ ๋ถ€๋ถ„์˜ window๋งŒ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ๋ณด๋‹ค ๋‹ค์–‘ํ•œ validation ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ์„ฑ๋Šฅ์„ ์ธก์ •ํ•˜๊ธฐ์— ๋”์šฑ ๊ฐ•๋ ฅํ•œ ๋ชจ๋ธ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. Step 9: Train a "DeepAR+" predictorDeepAR+๋Š” Amazon Forecast์˜ "์‹œ๊ทธ๋‹ˆ์ฒ˜" ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ž…๋‹ˆ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ [SageMaker์˜ built-in ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html)๊ณผ ๋™์ผํ•œ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์‹œ๊ณ„์—ด ๋ชจ๋ธ๋ง [approach](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_how-it-works.html)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์ง€๋งŒ Amazon Forecast์—์„œ๋Š” ๋ช‡๊ฐ€์ง€ ์ „์šฉ ํ™•์žฅ ๊ธฐ๋Šฅ๊ณผ ๊ฐœ์„  ์‚ฌํ•ญ์„ ๊ตฌํ˜„ํ•˜์—ฌ DeepAR+๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์œ„์—์„œ ์ˆ˜ํ–‰ํ•œ Prophet predictor๊ฐ€ ์™„๋ฃŒ๋˜๋Š” ๊ฒƒ์„ ๊ธฐ๋‹ค๋ฆด ํ•„์š”๊ฐ€ ์—†์ด, ๋‹ค๋ฅธ predictor์˜ ํ•™์Šต ์ž‘์—…์„ ์‹œ์ž‘ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์™ผ์ชฝ ๋ฉ”๋‰ด์—์„œ Predictors๋ฅผ ํด๋ฆญํ•œ ๋‹ค์Œ, "Train new predictor" ๋ฒ„ํŠผ์„ ํด๋ฆญํ•˜๋ฉด ๋ฉ๋‹ˆ๋‹ค.> ** Train predictor** - Predictor name: **`bikeshare_deeparplus_predictor`** - Forecast horizon: **`336 (2 weeks at 24hrs/day)`** - Forecast frequency: **`hour`**(์›๋ณธ ๋ฐ์ดํ„ฐ์˜ frequency์— ๋งž๊ฒŒ ์ค€๋น„ํ•ฉ๋‹ˆ๋‹ค) - Algorithm selection: **`Manual`** - Algorithm: **`Deep_AR_Plus`** - Country for holidays: **`United States`**(ํ˜„์žฌ ๋ฐ์ดํ„ฐ์…‹์€ ๋ฏธ๊ตญ ๊ธฐ์ค€์ž…๋‹ˆ๋‹ค) - Number of backtest windows: **`4`** - Backtest window offset: **`1080`**(์•„๋ž˜ ์ฐธ์กฐ)ํ•™์Šต์„ ์‹œ์ž‘ํ•œ ๋‹ค์Œ, ๋‹ค์‹œ "Predcitors" ํ™”๋ฉด์œผ๋กœ ๋Œ์•„์™€์„œ 2๊ฐœ ํ•™์Šต ์ค‘์ธ predictors์˜ ์ƒํƒœ๋ฅผ ํ™•์ธํ•ฉ๋‹ˆ๋‹ค. Step 10: Create forecasts (and maybe custom predictors?)๋‹ค๋ฅธ ๋ชจ๋ธ์„ fitํ•˜๊ธฐ๋ฅผ ์›ํ•œ๋‹ค๋ฉด (์˜ˆ, AutoML ๋ชจ๋ธ ์„ ํƒ ๋˜๋Š” ARIMA์™€ ๊ฐ™์€ baseline ์•„ํ‚คํ…์ฒ˜ ์ค‘ ํ•˜๋‚˜๋ฅผ ์‚ฌ์šฉ), ์œ„์™€ ์œ ์‚ฌํ•œ ๋ฐฉ์‹์œผ๋กœ ๋” ๋งŽ์€ ํ•™์Šต ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜์…”๋„ ๋ฉ๋‹ˆ๋‹ค.๋‹ค์Œ ๋‹จ๊ณ„๋Š” ๊ฐ predictor์— ๋Œ€ํ•ด "forecast"๋ฅผ ์ƒ์„ฑํ•ฉ๋‹ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋ชจ๋ธ์„ ์‹คํ–‰ํ•˜๊ณ  ์˜ˆ์ธก ์‹ ๋ขฐ ๊ตฌ๊ฐ„์„ ์ถ”์ถœํ•ฉ๋‹ˆ๋‹ค.ํ›ˆ๋ จ์ด ์™„๋ฃŒ๋  ๋•Œ๋งˆ๋‹ค forecast ์ƒ์„ฑ์„ ์‹œ์ž‘ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, Prophet์˜ ํ•™์Šต์ด ๋น„๊ต์  ๋น ๋ฅด๊ฒŒ ๊ธ‘๋‚˜๊ธฐ ๋•Œ๋ฌธ์— ์ง€๊ธˆ ์ฏค ์ด์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. predictor๋ฅผ ํ•™์Šตํ•˜๊ณ  forecast๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด ์˜ค๋ž˜ ๊ฑธ๋ฆด ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๋‹ค๋ฅธ SageMaker ๋ชจ๋ธ๋กœ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐฉ๋ฒ•๋„ ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. forecast๋ฅผ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด, ์™ผ์ชฝ ๋ฉ”๋‰ด์—์„œ "Forecasts"๋ฅผ ํด๋ฆญํ•ฉ๋‹ˆ๋‹ค.๊ทธ๋ฆฌ๊ณ , "Create a Forecast" ๋ฒ„ํŠผ์„ ํด๋ฆญํ•ฉ๋‹ˆ๋‹ค. ๊ฐ forecast ์„ค์ •์€ ์•„๋ž˜์™€ ๊ฐ™์ด ํ•ฉ๋‹ˆ๋‹ค.> ** Create a forecast** - Forecast name: **`bikeshare_prophet_forecast`**, **`bikeshare_deeparplus_forecast`**, etc - Predictor: **`Dropdown์—์„œ ์„ ํƒ`** (predictor ํ•™์Šต์ด ๋๋‚˜์ง€ ์•Š์œผ๋ฉด dropdown์—์„œ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.) - Forecast types : **`.10, .50, .90, mean`** AWS Console์—์„œ๋Š” forecast ์ƒ์„ฑ์ด ๋˜์ž๋งˆ์ž ๋ชฉ๋ก์—์„œ ํ•ด๋‹น ํ•ญ๋ชฉ์„ ์„ ํƒํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์•„๋ž˜ **Forecast ARN** ๋ฅผ ์ž…๋ ฅํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.์ด ๋…ธํŠธ๋ถ์—์„œ๋Š” **Forecast ARN** ๊ฐ’์„ AWS SDKs๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ€์ ธ์˜ฌ ์ˆ˜ ์žˆ๋„๋ก ๊ตฌํ˜„ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— forecast ์ž‘์—…์ด ์™„๋ฃŒ๋œ ํ›„ ์•„๋ž˜ cell ๋ถ€ํ„ฐ ๋˜๋Š” ์ „์ฒด cell์„ ์žฌ์‹คํ–‰(Run All)ํ•˜๋ฉด ๊ฒฐ๊ณผ๊ฐ’์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.**์ฃผ์˜ ์‚ฌํ•ญ์€ forecast ARN๊ณผ predictor ARN์€ ์„œ๋กœ ๋‹ค๋ฆ…๋‹ˆ๋‹ค.!** ์™ผ์ชฝ ๋ฉ”๋‰ด์— "Forecasts"์—์„œ forecasts๋ฅผ ์ƒ์„ฑํ•œ ๋ชฉ๋ก์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ###Code forecast= boto3.client('forecast') forecast_result = forecast.list_forecasts( Filters=[ { 'Key': 'Status', 'Value': 'ACTIVE', 'Condition': 'IS' }, ] ) bikeshare_prophet_forecast = "" bikeshare_deeparplus_forecast = "" try: for f_result in forecast_result['Forecasts']: if f_result['ForecastName'] =='bikeshare_prophet_forecast': bikeshare_prophet_forecast = f_result['ForecastArn'] print('bikeshare_prophet_forecast Status : ACTIVE') except: print('bikeshare_prophet_forecast Status : CREATE_IN_PROGRESS or Nothing') try: for f_result in forecast_result['Forecasts']: if f_result['ForecastName'] =='bikeshare_deeparplus_forecast': bikeshare_deeparplus_forecast = f_result['ForecastArn'] print('bikeshare_deeparplus_forecast Status : ACTIVE') except: print('bikeshare_deeparplus_forecast Status : CREATE_IN_PROGRESS or Nothing') forecast_arns = { # Each example should look something like this: # "a_nice_name": "arn:aws:forecast:[REGION?]:[ACCOUNT?]:forecast/[FORECASTNAME?]" "bikeshare_prophet_forecast": bikeshare_prophet_forecast, # TODO , "bikeshare_deeparplus_forecast": bikeshare_deeparplus_forecast# TODO # More entries if you created other forecasts with different settings too? } ###Output _____no_output_____ ###Markdown Step 11: Review model accuracy metrics*์‹ ๋ขฐ๊ตฌ๊ฐ„* ๋‚ด ํ™•๋ฅ ์ ์ธ forecasts ๊ฐ’์„ ์ƒ์„ฑํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐฉ์‹์€ RMSE ์ ์ˆ˜๋ฅผ ๋น„๊ตํ•˜๋Š” ๊ฒƒ๊ณผ ๊ฐ™์ด ๋‹จ์ˆœํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์„œ, ์•„๋ž˜ ๋‘ ๊ฐœ์˜ metrics ๊ฐ„์˜ **trade-off**๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค.* accuracy : ์‹ค์ œ ๊ฐ’๋“ค์ด ์ œ์•ˆ๋œ ์‹ ๋ขฐ๊ตฌ๊ฐ„/ํ™•๋ฅ  ๋ถ„ํฌ ๋‚ด ์กด์žฌํ•˜๋Š”์ง€ ์—ฌ๋ถ€ ํ‰๊ฐ€* precision : ์ œ์•ˆ๋œ ์‹ ๋ขฐ ๊ตฌ๊ฐ„์ด ์–ผ๋งˆ๋‚˜ ์ข์•„์ง€๋Š”์ง€ ํ‰๊ฐ€Predictor์˜ metrics๋Š” ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹ ๋‚ด backtesting windows๋ฅผ ์ด์šฉํ•œ ๊ฒฐ๊ณผ๊ฐ’์ด๋ฉฐ, AWS Console์—์„œ๋„ ์ง์ ‘ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.* ์™ผ์ชฝ ๋ฉ”๋‰ด์—์„œ "Predictors"๋กœ ์ด๋™* ํ•™์Šต์ด ์™„๋ฃŒ๋œ predictor ์ค‘ ๊ฒ€ํ† ๋ฅผ ์œ„ํ•˜๋Š” predictor ์„ ํƒ* scroll์„ ์กฐ๊ธˆ ๋‚ด๋ฆฌ๋ฉด "Predictor metrics" ์—์„œ ๋‚ด์šฉ ํ™•์ธ์•„๋ž˜ screenshot ์˜ˆ์ œ์—์„œ ๋ณผ ์ˆ˜ ์žˆ๋“ฏ์ด ๊ฐ๊ฐ์˜ ์˜ˆ์ธก window์™€ ํ‰๊ท ์œผ๋กœ ์š”์•ฝํ•œ RMSE๊ณผ 10%, 50%, 90% 3๊ฐ€์ง€ ํ‰๊ฐ€ ์ง€์ ์—์„œ์˜ ํ‰๊ท  ๋ฐ ๊ฐ€์ค‘ quantile ์†์‹ค๊ฐ’์„ ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.** ์ด๋Ÿฌํ•œ metrics ๊ธฐ๋ฐ˜์œผ๋กœ ์–ด๋–ค predictor๊ฐ€ ๊ฐ€์žฅ ์„ฑ๋Šฅ์ด ์ข‹์„๊นŒ์š”? ๋‹ค๋ฅธ prediction windows์— ๋”ฐ๋ผ accuracy์—์„œ ์–ด๋–ค ํŒจํ„ด์ด ์žˆ์Šต๋‹ˆ๊นŒ?** Step 12: Visualise and evaluate forecast quality์™ผ์ชฝ ๋ฉ”๋‰ด์— ์žˆ๋Š” "Forecast Lookup"์—์„œ forecast์˜ ๊ฒฐ๊ณผ๋ฅผ ์ง์ ‘ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.์ด ๋…ธํŠธ๋ถ์—์„œ๋Š” Forecast Query API๋ฅผ ์ด์šฉํ•˜์—ฌ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ฐฉ์‹์œผ๋กœ ๊ฒฐ๊ณผ๋ฅผ ๋‹ค์šด๋กœ๋“œ๋ฐ›์€ ํ›„ ๊ทธ๋ž˜ํ”„๋กœ ์‹œ๊ฐํ™”ํ•ด์„œ ๋ณด์—ฌ ์ค๋‹ˆ๋‹ค. ๋‹ค์–‘ํ•œ ์‹œ๊ฐํ™” ๋ฐฉ์‹์ด๋‚˜ custom ํ‰๊ฐ€ metrics๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.๋ชจ๋ธ ํ•™์Šต์„ ์œ„ํ•ด ์›๋ณธ ์†Œ์Šค ๋ฐ์ดํ„ฐ์˜ timestamps๋ฅผ ์ดํ•ดํ–ˆ์ง€๋งŒ, inference ์—์„œ ๋”์šฑ ์—„๊ฒฉํ•œ ์š”๊ตฌ์‚ฌํ•ญ์„ ๊ฐ–๋Š”๋‹ค๋Š” ์ ์—์„œ ์ ํ•ฉํ•œ ISO ํ˜•์‹์œผ๋กœ ์‹œ์ž‘๊ณผ ๋์˜ timestamps๋ฅผ ์ƒ์„ฑํ•  ํ•„์š”๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ###Code first_test_ts = target_test_df["timestamp"].iloc[0] # Remember we predict to 2 weeks horizon # [Python 3.6 doesn't have fromisoformat()] test_end_dt = datetime( int(first_test_ts[0:4]), int(first_test_ts[5:7]), int(first_test_ts[8:10]), int(first_test_ts[11:13]), int(first_test_ts[14:16]), int(first_test_ts[17:]) ) + timedelta(days=14, hours=-1) # Forecast wants a slightly different timestamp format to the dataset: fcst_start_date = first_test_ts.replace(" ", "T") fcst_end_date = test_end_dt.isoformat() print(f"Forecasting\nFrom: {fcst_start_date}\nTo: {fcst_end_date}") forecasts = { predictor_name: { "forecast_arn": forecast_arn, "forecasts": { item_id: forecast_query.query_forecast( ForecastArn=forecast_arn, StartDate=fcst_start_date, EndDate=fcst_end_date, Filters={ "item_id": item_id } ) for item_id in item_ids } } for (predictor_name, forecast_arn) in forecast_arns.items() if forecast_arn is not ''} ###Output _____no_output_____ ###Markdown Amazon Forecast์™€ ๋‹ค์–‘ํ•œ SageMakers ๋ชจ๋ธ๋“ค์ด ๋‹ค์–‘ํ•œ ํ˜•์‹์œผ๋กœ ๊ฒฐ๊ณผ๋ฅผ ์ถœ๋ ฅํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ด๋ฅผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ **๊ฒฐ๊ณผ๋ฅผ ํ‘œ์ค€ํ™”**ํ•˜์—ฌ local CSV ํŒŒ์ผ๋กœ ์ €์žฅํ•ฉ๋‹ˆ๋‹ค. ###Code clean_results_df = pd.DataFrame() for predictor_name, predictor_data in forecasts.items(): for item_id, forecast_data in predictor_data["forecasts"].items(): predictions = forecast_data["Forecast"]["Predictions"] pred_mean_df = pd.DataFrame(predictions["mean"]) pred_timestamps = pd.to_datetime(pred_mean_df["Timestamp"].apply(lambda s: s.replace("T", " "))) df = pd.DataFrame() df["timestamp"] = pred_timestamps df["model"] = f"amzforecast-{predictor_name}" df["customer_type"] = item_id df["mean"] = pred_mean_df["Value"] df["p10"] = pd.DataFrame(predictions["p10"])["Value"] df["p50"] = pd.DataFrame(predictions["p50"])["Value"] df["p90"] = pd.DataFrame(predictions["p90"])["Value"] clean_results_df = clean_results_df.append(df) !mkdir -p results/amzforecast clean_results_df.to_csv( f"./results/amzforecast/results_clean.csv", index=False ) print("Clean results saved to ./results/amzforecast/results_clean.csv") clean_results_df.head() ###Output _____no_output_____ ###Markdown ์ตœ์ข…์ ์œผ๋กœ ํ‘œ์ค€ํ™”๋œ ํ˜•์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ ์‹œ๊ฐํ™”ํ•ฉ๋‹ˆ๋‹ค.(๋…ธํŠธ๋ถ์—์„œ ๋‹จ์ˆœํ™”๋œ ์‹œ๊ฐํ™”๋ฅผ ์œ„ํ•ด์„œ util ํด๋” ์•„๋ž˜ ํ”Œ๋กœํŒ… ๊ธฐ๋Šฅ์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค.) ###Code # First, prepare the actual data (training + test) for easy plotting: first_plot_dt = test_end_dt - timedelta(days=21) actuals_df = target_train_df.append(target_test_df) actuals_df["timestamp"] = pd.to_datetime(actuals_df["timestamp"]) actuals_plot_df = actuals_df[ (actuals_df["timestamp"] >= first_plot_dt) & (actuals_df["timestamp"] <= test_end_dt) ] actuals_plot_df.rename(columns={ "item_id": "customer_type"}, inplace=True) util.plot_fcst_results(actuals_plot_df, clean_results_df) ###Output _____no_output_____
wrangling/spotify_api.ipynb
###Markdown raw data ###Code df = pd.read_pickle('../data/songs_counts_200.pkl') # df = df[:550] df len(df) ###Output _____no_output_____ ###Markdown track features ###Code # define batches batch_size = 100 num_batches = math.ceil(len(df)/batch_size) # initialize list to save API calls track_features = [] start_time = time.time() # looping through the batches for i in range(num_batches): # define start and end of the batch start_point = i*batch_size end_point = min(start_point + batch_size, len(df)) # API call track_list = list(df['track_uri'][start_point:end_point]) track_features.extend(sp.audio_features(track_list)) if i%100 == 0: print('{}/{}, {}s'.format(i, num_batches, time.time()-start_time)) start_time = time.time() # track_features = [i for i in track_features if i is not None] track_features_df = pd.DataFrame(track_features) track_features_df # counter = 0 track_features_df.to_csv('../data/track_features'+str(counter)+'.csv') track_features_df.to_pickle('../data/track_features'+str(counter)+'.pkl') counter += 1 ###Output _____no_output_____ ###Markdown artist info ###Code unique_artists = list(df['artist_uri'].unique()) len(unique_artists) # define batches batch_size = 50 num_batches = math.ceil(len(unique_artists)/batch_size) # initialize list to save API calls artist_info = [] start_time = time.time() # looping through the batches for i in range(num_batches): # define start and end of the batch start_point = i*batch_size end_point = min(start_point + batch_size, len(df)) # API call artist_list = unique_artists[start_point:end_point] artist_info.extend(sp.artists(artist_list)['artists']) if i%100 == 0: print('{}/{}, {}s'.format(i, num_batches, time.time()-start_time)) start_time = time.time() # artist_info = [i for i in artist_info if i is not None] artist_info_df = pd.DataFrame(artist_info) artist_info_df len(set(list(chain.from_iterable(artist_info_df['genres'])))) counter = 0 # path = '../data/artist_info'+str(counter2)+'.pkl' # with open(path, 'wb') as file: # pickle.dump(artist_info, file) # counter2 += 1 # with open(path, 'rb') as file: # artist_data = pickle.load(file) artist_info_df.to_csv('../data/artist_info'+str(counter)+'.csv') artist_info_df.to_pickle('../data/artist_info'+str(counter)+'.pkl') counter += 1 # counter ###Output _____no_output_____ ###Markdown album info ###Code unique_albums = list(df['album_uri'].unique()) len(unique_albums) # albums_exist = pd.read_pickle('../data/album_info1.pkl') albums_exist = list(albums_exist['uri']) print(len(albums_exist)) # albums_exist len(set(unique_albums_new)) # unique_albums_new = [i for i in unique_albums if i not in albums_exist] unique_albums_new = list(set(unique_albums) - set(albums_exist)) print(len(unique_albums_new)) # unique_albums_new unique_albums = unique_albums_new[140000:] len(unique_albums) # define batches batch_size = 20 num_batches = math.ceil(len(unique_albums)/batch_size) # initialize list to save API calls album_info = [] start_time = time.time() # looping through the batches for i in range(num_batches): # define start and end of the batch start_point = i*batch_size end_point = min(start_point + batch_size, len(df)) # API call album_list = unique_albums[start_point:end_point] album_info.extend(sp.albums(album_list)['albums']) if i%100 == 0: print('{}/{}, {}s'.format(i, num_batches, time.time()-start_time)) start_time = time.time() album_info = [i for i in album_info if i is not None] album_info_df = pd.DataFrame(album_info) album_info_df # counter = 4 album_info_df.to_csv('../data/album_info'+str(counter)+'.csv') album_info_df.to_pickle('../data/album_info'+str(counter)+'.pkl') counter += 1 counter ###Output _____no_output_____ ###Markdown data joining ###Code album_columns = ['genres','popularity','release_date','uri'] albums1 = pd.read_csv('../data/album_info1.csv', usecols=album_columns) albums2 = pd.read_csv('../data/album_info2.csv', usecols=album_columns) albums3 = pd.read_csv('../data/album_info3.csv', usecols=album_columns) albums4 = pd.read_csv('../data/album_info4.csv', usecols=album_columns) albums5 = pd.read_csv('../data/album_info5.csv', usecols=album_columns) albums6 = pd.read_csv('../data/album_info6.csv', usecols=album_columns) albums = pd.concat([albums1, albums2, albums3, albums4, albums5, albums6], axis=0, ignore_index=True) albums = albums.rename(columns={'genres': 'album_genres', 'popularity': 'album_popularity', 'release_date': 'album_release_date', 'uri': 'album_uri'}) albums = albums.drop_duplicates() albums artist_columns = ['genres','popularity','uri'] artists = pd.read_csv('../data/artist_info1.csv', usecols=artist_columns) artists = artists.rename(columns={'genres': 'artist_genres', 'popularity': 'artist_popularity', 'uri': 'artist_uri'}) artists = artists.drop_duplicates() artists track_columns = ['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','time_signature','uri'] tracks = pd.read_csv('../data/track_features3.csv', usecols=track_columns) tracks = tracks.rename(columns={'uri': 'track_uri'}) tracks = tracks.drop_duplicates() tracks master = pd.read_pickle('../data/songs_counts_200.pkl') master['song_id'] = master.index master master = master.merge(track_features, on='track_uri', suffixes=(None, '_tracks')) master = master.merge(artists, on='artist_uri', suffixes=(None, '_artists')) master = master.merge(albums, on='album_uri', suffixes=(None, '_albums')) master = master.set_index('song_id') master master.shape master.to_csv('../data/master200.csv') master.to_pickle('../data/master200.pkl') ###Output _____no_output_____
notebooks/02_RNAAS.ipynb
###Markdown RNAAS**Author(s):** Weixiang Yu & Gordon Richards**Last updated:** 12-09-20**Short description:**This notebook contains the code to make the figure included in the RNAAS paper. 0. Software Setup ###Code import matplotlib.pyplot as plt import matplotlib as mpl import pandas as pd import numpy as np import glob import os mpl.rc_file('../src/lsst_dcr.rc') %matplotlib inline # automatically extract username your_username = os.getcwd().split('/')[5] print(f'Your automatically extracted username is: {your_username}.' '\nIf it is incorrect, please mannually reset it.') ###Output Your automatically extracted username is: ywx649999311. If it is incorrect, please mannually reset it. ###Markdown Import the sims_maf modules needed. ###Code # import lsst.sim.maf moduels modules import lsst.sims.maf.db as db import lsst.sims.maf.metrics as metrics import lsst.sims.maf.slicers as slicers import lsst.sims.maf.stackers as stackers from lsst.sims.maf.stackers import BaseStacker import lsst.sims.maf.plots as plots import lsst.sims.maf.metricBundles as metricBundles # import convenience functions from opsimUtils import * ###Output _____no_output_____ ###Markdown 1. Compare DCR metric to desired slopes 1.1 Load in simulated DCR data (used to define metric)Files created by Tina Peters as part of the efforts discussed at [DCR_AGN_metric_analysis.ipynb](https://github.com/RichardsGroup/LSSTprep/blob/master/DCR/DCR_AGN_metric_analysis.ipynb), which records the means colors and DCR slopes as a function of redshift for SDSS quasars. There seems to be some discrepancy between the g-band slopes and the plots of Kaczmarczik et al. 2009, so they should be double-checked before finalizing any quasar-specific DCR metric. ###Code # load in data and merge into one df dcr_data_dir = '../data/DCR_data/' dfZ = pd.read_csv(os.path.join(dcr_data_dir, 'fittingS82_zshifts.dat')) dfQSO = pd.read_csv(os.path.join(dcr_data_dir, 'fittingS82_zshiftfit.dat'), \ index_col=0, header=None, sep=' ').T.dropna().reset_index(drop=True) dfDCR = pd.concat([dfZ, dfQSO], axis=1) dfDCR.head() ###Output _____no_output_____ ###Markdown 1.2 Load in metric results ###Code if your_username == '': # do NOT put your username here, put it in the cell at the top of the notebook. raise Exception('Please provide your username! See the top of the notebook.') resultDbPath = f'/home/idies/workspace/Temporary/{your_username}/scratch/MAFOutput/DCR/RNAAS/ResultDBs/' metricDataPath = f'/home/idies/workspace/Temporary/{your_username}/scratch/MAFOutput/DCR/RNAAS/MetricData/' # import metric evaluations bundleDicts = {} resultDbsView = getResultsDbs(resultDbPath) for runName in resultDbsView: bundleDicts[runName] = bundleDictFromDisk(resultDbsView[runName], runName, metricDataPath) # check keys dbRuns = list(resultDbsView.keys()) bd_keys = list(bundleDicts[dbRuns[1]].keys()) print(bd_keys) ###Output [(1, 'DCR_20_g'), (2, 'DCR_22_g'), (3, 'DCR_24_g'), (4, 'DCR_20.15_u'), (5, 'DCR_22.15_u'), (6, 'DCR_24.15_u')] ###Markdown __Note:__ The keys you see (in your own notebbook) could be different than what are shown above, which as a result you will need to modify the plotting code below to make the cell run properly. 2. Make plots ###Code # return median metric data def get_dcr_median(mb): mask = mb.metricValues.mask data = mb.metricValues.data[~mask] data = data[~(np.isnan(data) | np.isinf(data))] return np.median(data) # get the median values from all opsims # for normaliation in plotting def get_metric_medians(key, bd, func): mds = [] for run in bd: keys = [*bd[run].keys()] run_key = [elem for elem in keys if elem[1] == key[1]][0] mds.append(func(bd[run][run_key])) return mds # get the metrics for plotting Key1, Key2 = (1, 'DCR_22_g'), (4, 'DCR_22.15_u') fig = plt.figure(figsize=(10,4.5), dpi=200) ax1 = fig.add_axes([0.065, 0.15, 0.34, 0.8]) ax2 = fig.add_axes([0.645, 0.15, 0.34, 0.8]) # plot right panel gKey = Key1 uKey = Key2 mds_g = np.sort(get_metric_medians(gKey, bundleDicts, get_dcr_median)) mds_u = np.sort(get_metric_medians(uKey, bundleDicts, get_dcr_median)) # create normalization object gNorm = mpl.colors.LogNorm(vmin=mds_g[1], vmax=mds_g[-1]) uNorm = mpl.colors.LogNorm(vmin=mds_u[1], vmax=mds_u[-1]) # gNorm = mpl.colors.Normalize(vmin=mds_g[1], vmax=mds_g[-1]*1.04) # uNorm = mpl.colors.Normalize(vmin=mds_u[1], vmax=mds_u[-1]) # seperate loops for u and g to keep the legend clean for i, run in enumerate(resultDbsView): # look for the correct combination of metricID and metricName keys = [*bundleDicts[run].keys()] metricKeyG = [elem for elem in keys if elem[1] == gKey[1]][0] md_g = get_dcr_median(bundleDicts[run][metricKeyG]) if run == 'dcr_nham1_ugri_v1.5_10yrs': ax2.plot(dfDCR['zshifts'].values, np.abs(dfDCR['g-slope']/md_g), \ color='k', linewidth=1, alpha=0.5) else: ax2.plot(dfDCR['zshifts'].values, np.abs(dfDCR['g-slope']/md_g), \ color=mpl.cm.summer(gNorm(md_g)), linewidth=0.7) # seperate loops for u and g to keep the legend clean for i, run in enumerate(resultDbsView): # look for the correct combination of metricID and metricName keys = [*bundleDicts[run].keys()] metricKeyU = [elem for elem in keys if elem[1] == uKey[1]][0] md_u = get_dcr_median(bundleDicts[run][metricKeyU]) if run == 'dcr_nham1_ugri_v1.5_10yrs': ax2.plot(dfDCR['zshifts'].values, np.abs(dfDCR['u-slope']/md_u), linestyle='--', \ color='k', linewidth=1, alpha=0.5) else: ax2.plot(dfDCR['zshifts'].values, np.abs(dfDCR['u-slope']/md_u), linestyle='--', \ color=mpl.cm.winter(uNorm(md_u)), linewidth=0.7) g_line = ax2.plot([], [], label='g band', linewidth=1, color='k') u_line = ax2.plot([], [], label='u band', linestyle='--', linewidth=1, color='k') # option to set uniform ylim ylim = 22 if ylim is not None: ax2.set_ylim(top=ylim, bottom=-.5) ax2.set_xlim(0.2, 4.2) ax2.tick_params(top=True, right=True, which='both') ax2.yaxis.set_major_locator(plt.FixedLocator([5, 10, 15, 20])) ax2.set_xlabel("Redshift") ax2.set_ylabel("Abs(S/N)", fontsize=14) ax2.legend(handles=(g_line[0], u_line[0]), loc=2) # get normalization shift run = 'baseline_2snaps_v1.5_10yrs' keys = [*bundleDicts[run].keys()] metricKey = [elem for elem in keys if elem[1] == Key1[1]][0] norm_precision = get_dcr_median(bundleDicts[run][metricKey]) # get plotting order unsort_mds_g = get_metric_medians(gKey, bundleDicts, get_dcr_median) runs = list(bundleDicts.keys()) sort_order = np.argsort(unsort_mds_g) # other plot setting density = False bins = 60 # plot left panel for order in sort_order: run = runs[order] # look for the correct combination of metricID and metricName keys = [*bundleDicts[run].keys()] metricKey = [elem for elem in keys if elem[1] == Key1[1]][0] # need to mask the pixels that have no available data mask = bundleDicts[run][metricKey].metricValues.mask data = bundleDicts[run][metricKey].metricValues.data[~mask] data = data[~(np.isnan(data) | np.isinf(data))] # weights = np.ones_like(data)*54.967783/3600 # match color to panel2 md_g = get_dcr_median(bundleDicts[run][metricKey]) # plot if run == 'dcr_nham1_ugri_v1.5_10yrs': c = 'k' _ = ax1.hist(norm_precision/data, bins=bins, histtype='step', color=c, \ density=density, alpha=0.5, label=f"{run.rsplit('_', 2)[0]}", zorder = 10) else: # deal with non standard db names if run in ['third_obs_pt120v1.5_10yrs', 'footprint_gp_smoothv1.5_10yrs']: run = run.replace('v1.5', '_v1.5') c = mpl.cm.summer(gNorm(md_g)) _ = ax1.hist(norm_precision/data, bins=bins, histtype='step', color=c, \ density=density, label=f"{run.rsplit('_', 2)[0]}") ax1.set_xscale('log', basex=10) # tick & format ax1.set_xbound(lower=0.47, upper=2.3) ax1.tick_params(top=True, right=True, which='both') ax1.xaxis.set_major_locator(plt.FixedLocator([0.5, 1, 1.5, 2])) ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%.1f')) ax1.xaxis.set_minor_locator(plt.NullLocator()) # label & legend ax1.set_xlabel('DCR Precision Metric', fontsize=12) ax1.legend(fontsize=7.5, bbox_to_anchor=(1.0, 1.02), edgecolor='k', loc=2, labelspacing=0.45) ax1.yaxis.set_major_locator(plt.FixedLocator(np.array([500, 1000, 1500, 2000])/(54.967783/60)**2)) y_vals = ax1.get_yticks() ax1.set_yticklabels(['{:.0f}'.format(x * (54.967783/60)**2) for x in y_vals], rotation=90) ax1.set_ylabel('Area ($\mathrm{degree^{2}}$)', labelpad=7) plt.savefig('summer_winters.pdf') ###Output _____no_output_____
_notebooks/2020-03-07-Python5 - Les ensembles.ipynb
###Markdown Les ensembles (set)> Dรฉcouverte de la structure d'ensembles en Python- toc: true- badges: true- comments: false- categories: [python, ISN]Un ensemble en python (***set***) est une structure pouvant contenir plusieurs donnรฉes, mais contrairement aux listes, ces donnรฉes sont uniques et non ordonnรฉes. Il n'y a pas de moyen d'accรฉder ร  une donnรฉe en particulier en utilisant son numรฉro d'index.Les ensembles sont par contre extrรจmement efficaces pour la recherche d'un รฉlรฉment : Contrairement aux listes dans lesquelles une recherche impose de parcourir tous les รฉlรฉments, les ensembles utilisent des techniques d'optimisation (table de hachage) rendant la recherche trรจs performante.Voici quelques illustrations de l'utilisation des ***set*** ###Code # crรฉer un un ensemble ensemble = {1,5,9,5,1,2,4} ensemble ###Output _____no_output_____ ###Markdown Comme on peut le voir, les รฉlรฉments en doubles dans *ensemble* ont รฉtรฉ รฉliminรฉs et l'ordre affichรฉ n'est pas celui dans lequel les รฉlรฉments ont รฉtรฉ saisis. ###Code # essayons quelque chose... ensemble[3] ###Output _____no_output_____ ###Markdown l'accรจs aux รฉlรฉments par indice comme pour les listes n'est pas possible, cela n'a tout simpliement pas de sens. Conversion list set ###Code liste = [1,5,9,5,1,2,4] ensemble = set(liste) ensemble ensemble = {1, 9, 5, 4, 2} liste = list(ensemble) liste ###Output _____no_output_____ ###Markdown Mรฉthodes sur les ensembles ajout et retrait : add et remove ###Code ensemble = {1, 9, 5, 4, 2} ensemble.add(18) ensemble.remove(9) ensemble ###Output _____no_output_____ ###Markdown Attention de bien tester si un รฉlรฉment est dans l'ensemble avant la suppression car sinon... ###Code ensemble.remove(3) ###Output _____no_output_____ ###Markdown et du coup ... tester si un รฉlรฉment est prรฉsent dans un ensemble : in ###Code 3 in ensemble 18 in ensemble ###Output _____no_output_____ ###Markdown Longueur et ensemble vide ###Code # l'ensemble vide est notรฉ {} ou set() vide = set() vide.add(3) vide.remove(3) # Calculer le nb d'รฉlรฉments d'un ensemble len(vide) ###Output _____no_output_____ ###Markdown ApplicationCrรฉer une fonction **ensembleCarres** prenant en paramรจtre un entier $n$ e renvoyant un ensemble contenant les carrรฉs des entiers de 1 ร  $n$ ###Code def ensembleCarres(n): # YOUR CODE HERE raise NotImplementedError() ec = ensembleCarres(10) assert len(ec)==10 assert 64 in ec ###Output _____no_output_____ ###Markdown - Crรฉez une liste *l* de carrรฉs jusqu'ร  un million. - Crรฉez un ensemble *s* de carrรฉs jusqu'ร  un million. - Recherchez si $874466246641$ est un carrรฉ ###Code # YOUR CODE HERE raise NotImplementedError() ###Output _____no_output_____ ###Markdown Comparez les **temps de recherche d'un mรชme nombre** dans l'ensemble et dans la liste. ###Code %%time assert 874466246641 in s %%time assert 874466246641 in l ###Output _____no_output_____ ###Markdown quelques autres mรฉthodes sur les set s.isdisjoint(s2) s.issubset(s2) s.issuperset(s2) s = s2). s = s2). set.union(s1, s2, s3) : renvoie la rรฉunion de plusieurs sets. set.intersection(s1, s2, s3) : renvoie l'intersection de plusieurs sets ExerciceCrรฉer une fonction **ensembleCubes** prenant en paramรจtre un entier $n$ e renvoyant un ensemble contenant les cubes des entiers de 1 ร  $n$ ###Code def ensembleCubes(n): # YOUR CODE HERE raise NotImplementedError() assert 27 in ensembleCubes(10) ###Output _____no_output_____ ###Markdown En dรฉduire en une ligne de python combien de nombres entre 1 et 100 sont ร  la fois des carrรฉs et des cubes ###Code # Tapez votre ligne dans la cellule ci-dessous # Attention, pas plus d'une ligne de Python !! ###Output _____no_output_____
notebooks/Plot_Bfield.ipynb
###Markdown Mean of the square of the derivative of the k=2 Legendre polynomial ###Code from scipy.special import legendre costh = linspace(-1,1,10000) dB = legendre(2)(costh) dB = gradient(dB,arccos(costh)) print(trapz(dB**2.0,costh)/2) ###Output 1.1999998699530594
CNN/.ipynb_checkpoints/hp-covidcxr-checkpoint.ipynb
###Markdown CNN Hyperparameters COVIDcxr Dataset ###Code from fastai.vision.all import * path = Path('/home/jupyter/covidcxr') torch.cuda.empty_cache() # fix result def seed_everything(seed): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True SEED = 42 seed_everything(SEED) df = pd.read_csv(path/'covidcxr.csv') df get_x=lambda x:path/f"{x[0]}" get_y=lambda x:x[1] splitter=RandomSplitter(seed=SEED) metrics=[accuracy, RocAuc(average='macro', multi_class='ovr'), MatthewsCorrCoef(sample_weight=None), Precision(average='macro'), Recall(average='macro'), F1Score(average='macro')] item_tfms=Resize(480, method='squish', pad_mode='zeros', resamples=(2, 0)) batch_tfms=[*aug_transforms(mult=1.0, do_flip=False, flip_vert=False, max_rotate=20.0, max_zoom=1.2, max_lighting=0.3, max_warp=0.2, p_affine=0.75, p_lighting=0.75, xtra_tfms=None, size=None, mode='bilinear', pad_mode='reflection', align_corners=True, batch=False, min_scale=1.0), Normalize.from_stats(*imagenet_stats)] db = DataBlock(blocks=(ImageBlock(cls=PILImageBW), CategoryBlock), get_x=get_x, get_y=get_y, splitter=splitter, item_tfms = item_tfms, batch_tfms=batch_tfms) ###Output _____no_output_____ ###Markdown VGG-16 Epoch 10 ###Code from torchvision.models import vgg16 arch = vgg16 epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code from torchvision.models import vgg16 arch = vgg16 epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code from torchvision.models import vgg16 arch = vgg16 epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code from torchvision.models import vgg16 arch = vgg16 bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code from torchvision.models import vgg16 arch = vgg16 bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code from torchvision.models import vgg16 arch = vgg16 loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown VGG-19 Epoch 10 ###Code from torchvision.models import vgg19 arch = vgg19 epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code from torchvision.models import vgg19 arch = vgg19 epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code from torchvision.models import vgg19 arch = vgg19 epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code from torchvision.models import vgg19 arch = vgg19 bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code from torchvision.models import vgg19 arch = vgg19 bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code from torchvision.models import vgg19 arch = vgg19 loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown ResNet-18 Epoch 10 ###Code arch = resnet18 epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code arch = resnet18 epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) dl = db.dataloaders(df, bs=bs) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code arch = resnet18 epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code arch = resnet18 bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code arch = resnet18 bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code arch = resnet18 loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown ResNet-34 Epoch 10 ###Code arch = resnet34 epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code arch = resnet34 epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code arch = resnet34 epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code arch = resnet34 bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code arch = resnet34 bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code arch = resnet34 loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown ResNet-50 Epoch 10 ###Code arch = resnet50 epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code arch = resnet50 epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code arch = resnet50 epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code arch = resnet50 bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code arch = resnet50 bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code arch = resnet50 loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = cnn_learner(dl, arch=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Efficientnet-B0 Epoch 10 ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") epoch = 10 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 20 ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") epoch = 20 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 40 ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") epoch = 40 bs = 32 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Batch Size 8 ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") bs = 8 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown 16 ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") bs = 16 epoch = 30 loss_func=CrossEntropyLossFlat() dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____ ###Markdown Loss Function LabelSmoothingCrossEntropyFlat() ###Code from efficientnet_pytorch import EfficientNet arch = EfficientNet.from_pretrained("efficientnet-b0") loss_func=LabelSmoothingCrossEntropyFlat(axis=-1, eps=0.06, reduction='mean', flatten=True, floatify=False, is_2d=True) bs = 32 epoch = 30 dl = db.dataloaders(df, bs=bs) learn = Learner(dl, model=arch, loss_func=loss_func, metrics=metrics) learn.fine_tune(epoch) ###Output _____no_output_____
tutorials/PyCSEP_tutorial_catalog.ipynb
###Markdown Catalog-based forecast tutorial - UCERF3 Landers In this tutorial we will look at an example of a catalog-based forecast. Our goal is to test whether the forecast number of earthquakes from a UCERF3-ETAS aftershock model is consistent with observations for the 1992 Landers sequence.The PyCSEP package has been designed so that the order of the steps that we take to do this is very similar to that for the gridded forecasts with a few differences. This tutorial aims to familiarise the user with some of the differences involved and further understanding of how these new CSEP tests are carried out.Full documentation of the package can be found [here](https://docs.cseptesting.org/) and any issues can be reported on the [PyCSEP Github page](https://github.com/SCECcode/pycsep). ###Code import numpy import cartopy # Most of the core functionality can be imported from the top-level csep package. import csep # Or you could import directly from submodules, like csep.core or csep.utils submodules. from csep.core import regions, catalog_evaluations from csep.core import poisson_evaluations as poisson from csep.utils import datasets, time_utils, comcat, plots ###Output _____no_output_____ ###Markdown 1. Load forecastForecasts should define a time horizon in which they are valid. The choice is flexible for catalog-based forecasts, because the catalogs can be filtered to accommodate multiple end-times. Conceptually, these should be separate forecasts. For catalog-based forecasts, we need to explicitly compute bin-wise rates. Before we can compute the bin-wise rates we need to define a spatial region and a set of magnitude bin edges. The magnitude bin edges are the lower bound (inclusive) except for the last bin, which is treated as extending to infinity. We can bind these to the forecast object. The spatial region should also be explicitly defined, in contrast to the gridded forecast where this is extracted from the data. In this example, we use the RELM polygon included in the package. This can also be done by passing the region as keyword arguments into `csep.load_catalog_forecast()`. ###Code ### Set up model parameters # Start and end time start_time = time_utils.strptime_to_utc_datetime("1992-06-28 11:57:34.14") end_time = time_utils.strptime_to_utc_datetime("1992-07-28 11:57:34.14") # Magnitude bins properties min_mw = 4.95 max_mw = 8.95 dmw = 0.1 # Create space and magnitude regions. The forecast is already filtered in space and magnitude magnitudes = regions.magnitude_bins(min_mw, max_mw, dmw) region = regions.california_relm_region() # Bind region information to the forecast (this will be used for binning of the catalogs) space_magnitude_region = regions.create_space_magnitude_region(region, magnitudes) ###Output _____no_output_____ ###Markdown To reduce the file size of this example, weโ€™ve already pre-filtered the catalogs to the appropriate magnitudes and spatial locations. The original forecast was computed for 1 year following the start date, so we still need to filter the catalog in time. We can do this by passing a list of filtering arguments to the forecast or updating the class.By default, the forecast loads catalogs on-demand, so the filters are applied as the catalog loads. On-demand means that until we loop over the forecast in some capacity, none of the catalogs are actually loaded! More fine-grain control and optimizations can be achieved by creating a `csep.core.forecasts.CatalogForecast` directly. ###Code forecast = csep.load_catalog_forecast(datasets.ucerf3_ascii_format_landers_fname, start_time = start_time, end_time = end_time, region = space_magnitude_region) ###Output _____no_output_____ ###Markdown The `csep.core.forecasts.CatalogForecast` provides a method to compute the expected number of events in spatial cells. This requires a region with magnitude information. ###Code # Assign filters to forecast (in this case time) forecast.filters = [f'origin_time >= {forecast.start_epoch}', f'origin_time < {forecast.end_epoch}'] expected_rates = forecast.get_expected_rates(verbose=True) ###Output _____no_output_____ ###Markdown The expected rates can now be plotted in a similar manner to the gridded forecast plots. Again, we can specify plot arguments as we did for the gridded forecasts. ###Code args_forecast = {'title': 'Landers aftershock forecast', 'grid_labels': True, 'borders': True, 'feature_lw': 0.5, 'basemap': 'ESRI_imagery', 'cmap': 'rainbow', 'alpha_exp': 0.9, 'projection': cartopy.crs.Mercator(), 'clim':[-3.5, 0]} ax = expected_rates.plot(plot_args = args_forecast) ###Output _____no_output_____ ###Markdown 2. Filter evaluation catalogIn this example we use the `csep.query_comcat` function to obtain a catalog directly from [ComCat](https://earthquake.usgs.gov/data/comcat/). We need to filter the ComCat catalog to be consistent with the forecast. This can be done either through the ComCat API or using catalog filtering strings (see the gridded forecast example). Here weโ€™ll use the Comcat API to make the data access quicker for this example. We still need to filter the observed catalog in space though. ###Code # Obtain Comcat catalog and filter to region. comcat_catalog = csep.query_comcat(start_time, end_time, min_magnitude=forecast.min_magnitude) # Filter observed catalog using the same region as the forecast comcat_catalog = comcat_catalog.filter_spatial(forecast.region) ###Output _____no_output_____ ###Markdown 3. Plot the catalogThe catalog can be plotted easily using the plot function. ###Code comcat_catalog.plot() ###Output _____no_output_____ ###Markdown * Let's try changing some plot arguments by looking at the docs 4. Composite plot Let's do a multiple plot, that includes the forecast expected rates and the observed catalog.* We must first create a forecast plot, which returns a matplotlib.pyplot.ax object.* This ax object should be passed to catalog.plot() as argument. * The plot order could be reversed, depending which layer is wanted above ###Code args_catalog = {'basemap': 'ESRI_terrain', 'markercolor': 'black', 'markersize': 4} ax_1 = expected_rates.plot(plot_args=args_forecast) ax_2 = comcat_catalog.plot(ax=ax_1, plot_args=args_catalog) ###Output _____no_output_____ ###Markdown 4. Perform a test Now that we have a forecast and evaluation catalog, tests can be easily applied in a similar way as with gridded forecasts. For example, we can perform the Number test on the catalog based forecast using the observed catalog we obtained from Comcat. ###Code number_test_result = catalog_evaluations.number_test(forecast, comcat_catalog) ax = number_test_result.plot() ###Output _____no_output_____ ###Markdown We can also quickly perform a spatial test ###Code spatial_test_result = catalog_evaluations.spatial_test(forecast, comcat_catalog) ax = spatial_test_result.plot() ###Output _____no_output_____
sql-scavenger-hunt-day-1.ipynb
###Markdown If you haven't used BigQuery datasets on Kaggle previously, check out the Scavenger Hunt Handbook kernel to get started. SELECT, FROM & WHEREToday, we're going to learn how to use SELECT, FROM and WHERE to get data from a specific column based on the value of another column. For the purposes of this explanation, we'll be using this imaginary database, `pet_records` which has just one table in it, called `pets`, which looks like this:![](https://i.imgur.com/Ef4Puo3.png) SELECT ... FROM___The most basic SQL query is to select a single column from a specific table. To do this, you need to tell SELECT which column to select and then specify what table that column is from using from. > **Do you need to capitalize SELECT and FROM?** No, SQL doesn't care about capitalization. However, it's customary to capitalize your SQL commands and it makes your queries a bit easier to read.So, if we wanted to select the "Name" column from the pets table of the pet_records database (if that database were accessible as a BigQuery dataset on Kaggle , which it is not, because I made it up), we would do this: SELECT Name FROM `bigquery-public-data.pet_records.pets`Which would return the highlighted data from this figure.![](https://i.imgur.com/8FdVyFP.png) WHERE ...___When you're working with BigQuery datasets, you're almost always going to want to return only certain rows, usually based on the value of a different column. You can do this using the WHERE clause, which will only return the rows where the WHERE clause evaluates to true.Let's look at an example: SELECT Name FROM `bigquery-public-data.pet_records.pets` WHERE Animal = "Cat"This query will only return the entries from the "Name" column that are in rows where the "Animal" column has the text "Cat" in it. Those are the cells highlighted in blue in this figure:![](https://i.imgur.com/Va52Qdl.png) Example: What are all the U.S. cities in the OpenAQ dataset?___Now that you've got the basics down, let's work through an example with a real dataset. Today we're going to be working with the OpenAQ dataset, which has information on air quality around the world. (The data in it should be current: it's updated weekly.)To help get you situated, I'm going to run through a complete query first. Then it will be your turn to get started running your queries!First, I'm going to set up everything we need to run queries and take a quick peek at what tables are in our database. ###Code # import package with helper functions import bq_helper # create a helper object for this dataset open_aq = bq_helper.BigQueryHelper(active_project="bigquery-public-data", dataset_name="openaq") # print all the tables in this dataset (there's only one!) open_aq.list_tables() ###Output _____no_output_____ ###Markdown I'm going to take a peek at the first couple of rows to help me see what sort of data is in this dataset. ###Code # print the first couple rows of the "global_air_quality" dataset open_aq.head("global_air_quality") ###Output _____no_output_____ ###Markdown Great, everything looks good! Now that I'm set up, I'm going to put together a query. I want to select all the values from the "city" column for the rows there the "country" column is "us" (for "United States"). > **What's up with the triple quotation marks (""")?** These tell Python that everything inside them is a single string, even though we have line breaks in it. The line breaks aren't necessary, but they do make it much easier to read your query. ###Code # query to select all the items from the "city" column where the # "country" column is "us" query = """SELECT city FROM `bigquery-public-data.openaq.global_air_quality` WHERE country = 'US' """ ###Output _____no_output_____ ###Markdown > **Important:** Note that the argument we pass to FROM is *not* in single or double quotation marks (' or "). It is in backticks (\`). If you use quotation marks instead of backticks, you'll get this error when you try to run the query: `Syntax error: Unexpected string literal` Now I can use this query to get information from our open_aq dataset. I'm using the `BigQueryHelper.query_to_pandas_safe()` method here because it won't run a query if it's larger than 1 gigabyte, which helps me avoid accidentally running a very large query. See the [Scavenger Hunt Handbook ](https://www.kaggle.com/rtatman/sql-scavenger-hunt-handbook/)for more details. ###Code # the query_to_pandas_safe will only return a result if it's less # than one gigabyte (by default) us_cities = open_aq.query_to_pandas_safe(query) ###Output _____no_output_____ ###Markdown Now I've got a dataframe called us_cities, which I can use like I would any other dataframe: ###Code # What five cities have the most measurements taken there? us_cities.city.value_counts().head() ###Output _____no_output_____ ###Markdown Scavenger hunt___Now it's your turn! Here's the questions I would like you to get the data to answer:* Which countries use a unit other than ppm to measure any type of pollution? (Hint: to get rows where the value *isn't* something, use "!=")* Which pollutants have a value of exactly 0?In order to answer these questions, you can fork this notebook by hitting the blue "Fork Notebook" at the very top of this page (you may have to scroll up). "Forking" something is making a copy of it that you can edit on your own without changing the original. ###Code # Your code goes here :) query1 = """SELECT country FROM `bigquery-public-data.openaq.global_air_quality` WHERE unit != 'ppm' """ query2 = """SELECT pollutant FROM `bigquery-public-data.openaq.global_air_quality` WHERE value = 0.0 """ country_unit = open_aq.query_to_pandas_safe(query1) pollutant_v = open_aq.query_to_pandas_safe(query2) country_unit.country.unique() pollutant_v.pollutant.unique() ###Output _____no_output_____
examples/Test_NXDS.ipynb
###Markdown Create ้ป˜่ฎคๅ‘ `_default` ๅˆ›ๅปบๅ›พ ###Code nxds.create_graph(val=sample_graph1) ###Output _____no_output_____ ###Markdown ไธ€ๆฌกๆ€งๅˆ›ๅปบๅคšไธชๅŒๆ ท็š„ๅ›พ ###Code nxds.create_graph(key=['graph2', 'graph3', 'graph4'], val = sample_graph2) nxds.create_graph(key='foo') nxds.create_node(sample_node, val = {'blah': True}) nxds.create_edge(sample_edge, {'baz': False}) ###Output _____no_output_____ ###Markdown Read ้ป˜่ฎค่ฏปๅ–ๆ‰€ๆœ‰ๅ›พ ###Code nxds.read_graph() ###Output _____no_output_____ ###Markdown ๅ›พๅฑžๆ€งๅœจๅฏน่ฑก็š„.graph้‡Œ๏ผˆNetworkX็‰นๆ€ง๏ผ‰ ###Code nxds.read_graph('_default')['_default'].graph ###Output _____no_output_____ ###Markdown ้ป˜่ฎค่ฏปๅ–ๆ‰€ๆœ‰่Š‚็‚น ###Code nxds.read_node() ###Output _____no_output_____ ###Markdown ้€š้…็ฌฆ่ฟ‡ๆปค๏ผˆๆ‰€ๆœ‰ๅ›พไธญๅไธบ0็š„่Š‚็‚น๏ผ‰ ###Code nxds.read_node(('@*', 0)) ###Output _____no_output_____ ###Markdown ้ป˜่ฎค่ฏปๅ–ๆ‰€ๆœ‰่พน ###Code nxds.read_edge() ###Output _____no_output_____ ###Markdown ้€š้…็ฌฆ่ฟ‡ๆปค๏ผˆๆ‰€ๆœ‰ๅ›พไธญๆŒ‡ๅ‘0่Š‚็‚น็š„่พน๏ผ‰PS๏ผšๆณจๆ„Graphๆฒกๆœ‰ๅ‡บๅ…ฅ่พนๅŒบๅˆซ๏ผŒ่ฟ™ไนˆๅ†™้€š้…็ฌฆไผš่‡ชๅŠจ่ฝฌๆขไธบๅŒ0็›ธ่ฟž็š„่พน๏ผˆๆœชๆฅๆœ‰ๅฏ่ƒฝไผšๅœจlogger้‡Œ้ขๅŠ ไธชwarning๏ผ‰ ###Code nxds.read_edge(('@*', ('@*', 0))) ###Output _____no_output_____ ###Markdown Update ###Code nxds.update_graph(val = GraphValType( attr = { 'graph_title': 'A New Graph' }, nodes = [ 7, 8, 9 ], edges = [ (7, 8), (9, 7) ], node_attr = { 'role': 'follower' }, edge_attr = { 'create_date': '2018-01-20' } )) nxds.read_graph('_default')['_default'].graph nxds.update_node(('foo', 0), {'build': 'yes'}) nxds.read_node(('foo', 0)) nxds.update_edge(('foo', (0, 2)), {'hello': 'world'}) nxds.read_edge(('foo', (0, 2))) ###Output _____no_output_____ ###Markdown Delete ###Code nxds.delete_graph('foo') nxds.read_graph() nxds.delete_graph() nxds.read_graph() nxds.delete_node(('@*', 0)) nxds.read_node() nxds.delete_edge(('@*', (2, '@*'))) nxds.read_edge() ###Output _____no_output_____ ###Markdown flush, clear, and reload ###Code nxds.flush() nxds.clear() nxds.reload() ###Output _____no_output_____
[kaggle] Ingredients_For_Chicken_Dinner.ipynb
###Markdown **Univariate Analysis** ###Code ## Id #search for duplicates any(data['Id'].duplicated()) ## Id #total no of players len(data['Id']) ## groupId #Check NaN data[data['groupId'].isnull()] #No nan present ## groupId #No. of people per group groupIdData=pd.DataFrame(data['groupId'].value_counts()) groupIdData.reset_index(level=0, inplace=True) groupIdData.columns = ['groupId', 'Members'] groupIdData.head() ## groupId #Basic Stats on the members in each group groupIdData['Members'].describe() ## groupId # removing invalid groups where members more than 4 / could be just "useless" bots groupIdDataValid=groupIdData[groupIdData['Members']<=4] groupIdDataValid.head() ## groupId #Basic Stats on the members in each VALID group groupIdDataValid['Members'].describe() ## matchId # Total no. people in a match matchIdData=pd.DataFrame(data['matchId'].value_counts()) matchIdData.reset_index(level=0, inplace=True) matchIdData.columns = ['matchId', 'Players'] matchIdData.head() ## matchId # Total no. of matches len(matchIdData) ## matchId #Basic Stats on the players in each match matchIdData['Players'].describe() ## matchId # removing invalid matches where players are equal to 10 or less # we need good comepition to identify most import fratures for a win matchIdDataValid=matchIdData[matchIdData['Players']>10] matchIdDataValid.tail() ## matchId #Basic Stats on the members in each VALID group matchIdDataValid['Players'].describe() ## Main DataSet # remove invalid groups from further analysis groupIdDataValidList=list(groupIdDataValid['groupId']) data=data[data['groupId'].isin(groupIdDataValidList)] matchIdDataValidList=list(matchIdDataValid['matchId']) data=data[data['matchId'].isin(matchIdDataValidList)] len(data['Id']) ## assists #Basic Stats on the player assists in each match data['assists'].describe() ## boosts #Basic Stats on the player boosts in each match data['boosts'].describe() ## damageDealt #Basic Stats on the player damage dealt in each match data['damageDealt'].describe() ## Killing Stats # Basic Stats on player headshotKills, kills, roadKills and friendlyKills killing=data[['kills','headshotKills','roadKills','teamKills']] killing.describe(include='all') ## heals #Basic Stats on the player healing items used in each match data['heals'].describe() ## revives # Basic Stats on the player reviving another player in a match data['revives'].describe() ## weaponsAcquired # Basic Stats on the no. of weapon picked up a player data['weaponsAcquired'].describe() ## numGroups # Basic Stats on the no. of groups joining a game data['numGroups'].describe() ## killPlace #Basic Stats on the player rank based on her/his kills in the match # Just checking for a min max limits else it is not useful data['killPlace'].describe() ## Travel # Basic descriptive analysis of player travel distance on foot, vehicle and swim # All values are in 'm' data['totalDistance']=data.walkDistance+data.rideDistance+data.swimDistance travel=data[['walkDistance','rideDistance','swimDistance','totalDistance']] travel.describe(include='all') ## Elo Rating # basic description of Kill and win Elo rating of each players Elo=data[['winPoints','killPoints']] Elo.describe(include='all') ### Does this makes sense as Elo rating evolves with time and same player can increase/decrease so mean and all may not be meaningful # Some rating for group participation groupIdDataList=list(set(data['groupId'])) for group in groupIdDataList: #if (i+1)%100 ==0: # print(i+1,'/',len(groupIdDataList)) data.loc[data['groupId']==group,'totalTeamsKills']=data[data['groupId']==group]['kills'].mean() data.loc[data['groupId']==group,'totalTeamWinPoints']=data[data['groupId']==group]['winPoints'].mean() data.loc[data['groupId']==group,'totalTeamKillPoints']=data[data['groupId']==group]['killPoints'].mean() # Some elo based expectation caluation matchIdDataList=list(set(data['matchId'])) for match in matchIdDataList: matchData=data[data['matchId']== match] groupsMatchList=list(set(matchData['groupId'])) for group in groupsMatchList: data.loc[data['groupId']==group,'ExpectedWinPoints']=1/(1+10**(-abs(matchData[matchData['groupId']==group]['totalTeamWinPoints'].mean()-matchData['totalTeamWinPoints'].mean())/400)) data.loc[data['groupId']==group,'ExpectedKillPoints']=1/(1+10**(-abs(matchData[matchData['groupId']==group]['totalTeamKillPoints'].mean()-matchData['totalTeamKillPoints'].mean())/400)) ###Output _____no_output_____ ###Markdown **Bivariate Analysis** ###Code dropCols = ['Id', 'groupId', 'matchId'] # These have no outcome on the game; #'maxPlace'=='numGroups' #data=data.drop(['maxPlace'], axis=1) keepCols = [col for col in data.columns if col not in dropCols] corr = data[keepCols].corr() plt.figure(figsize=(15,10)) plt.title("Correlation Heat Map of Data") sns.heatmap( corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, annot=True, cmap="RdYlGn", ) plt.show() data.to_csv('../working/cleanedTrain.csv') print(os.listdir("../working")) ###Output _____no_output_____
GrowthModels/SIR_Model.ipynb
###Markdown ###Code import numpy as np import matplotlib.pylab as plt import pandas as pd from scipy.integrate import ode from ipywidgets import interactive,IntSlider,FloatSlider ###Output _____no_output_____ ###Markdown Let's S be the susceptable population, I the infected populatioin, R the fraction of the population removed from the desease (recovered or death) The SRI model describe $\begin{cases}\frac{dS}{dt}=-\frac{\beta I S}{N},\\\frac{dI}{dt}=\frac{\beta I S}{N}-\gamma I,\\\frac{dR}{dt}=\gamma I\end{cases}$ Because $N$ is costantant $S(0)+I(0)+R(0)=N$ and in general $S(t)+I(t)+R(t)=N$. We can consider $N=1$ and $S(t)+I(t)+R(t)=1$, so that value of S,I,R represent the fraction of Succetable, Infected and Removed in the population.Without loss of generality we can rewrite the system as:$\begin{cases}\frac{dS}{dt}=-\beta I S,\\\frac{dI}{dt}=\beta I S-\gamma I,\\\frac{dR}{dt}=\gamma I\end{cases}$We are interested when $\frac{dI}{dt}<0$ that occur when $\beta I S-\gamma I<0$ that occur when $I\gamma(\frac{\beta S}{\gamma}-1)$. We define $R_{o}=\frac{\beta}{\gamma}$, then $SR_{o}<1$ ###Code def ode_SIR(t, Y,beta,gamma): A=beta*Y[1]*Y[0] B=gamma*Y[1] return [-A,A-B,B] r=ode(ode_SIR) S0=0.99 I0=1-S0 R0=1-I0-S0 SIR0=[S0,I0,R0] beta=0.05 gamma=0.01 r.set_initial_value(SIR0, 0).set_f_params(beta,gamma) t1=365*2 dt=1 sol=[] while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) plt.plot(sol[:,0],sol[:,1],label='S') plt.plot(sol[:,0],sol[:,2],label='I') plt.plot(sol[:,0],sol[:,3],label='R') plt.xlabel(r"$Time$") plt.legend(); plt.title(r"$R_o=\frac{\beta}{\gamma}=%1.2f \quad S_o R_o=%1.2f$"%(beta/gamma,S0*beta/gamma)+'\nis '+"$S_o R_o<1 \quad %s$"%(S0*beta/gamma<1)) plt.grid() def update(i0,beta,gamma,t1): S0=1-i0 SIR0=[S0,i0,0] r.set_initial_value(SIR0, 0).set_f_params(beta,gamma) dt=1 sol=[] while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) plt.figure() [plt.plot(sol[:,0],sol[:,i]) for i in (1,2,3)] plt.title(r"$R_o=\frac{\beta}{\gamma}=%1.4f \quad S_o R_o=%1.2f$" %(beta/gamma,S0*beta/gamma)+'\nis '+"$S_o R_o<1 \quad %s$" %(S0*beta/gamma<1)) plt.grid() plt.show() r=ode(ode_SIR) #interactive_plot = interactive(update, i0=(0, 0.2,0.01), beta=(0.01, 0.2, 0.002) # ,gamma=(0.001,0.1,0.002),t1=(700,1000,5)) timeSlider=IntSlider(value=360,min=300,max=1080,step=30,description="days") iniInfectedSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description="i0") betaSlider=FloatSlider(value=0.05, min=0.01,max=0.2,step=0.01,readout_format='.2f',description=r'<MATH>&beta;</MATH>') gammaSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description=r'<MATH>&gamma;</MATH>') interactive_plot = interactive(update, t1=timeSlider,i0=iniInfectedSlider,gamma=gammaSlider, beta=betaSlider) output = interactive_plot.children[-1] output.layout.height = '450px' interactive_plot ###Output _____no_output_____ ###Markdown If we consider that during the dynamics over long period of time we need to account for the fact newborn and natural death. We can consider this new system of ODEs:$\begin{cases}\frac{dS}{dt}=\Lambda -\beta I S -\mu S,\\\frac{dI}{dt}=\beta I S-\gamma I -\mu I,\\\frac{dR}{dt}=\gamma I -\mu R\end{cases}$Moreover, if we impose that the population is constant and equal 1 i.e. $S(t)+I(t)+R(t)=1$ it can be easilly show that $\Lambda=\mu$We are in general interested to stationary state i.e. when $\frac{dS}{dt}=\frac{dI}{dt}=\frac{dR}{dt}=0$a trivial solution can be easilly found for $(S_{\infty}=1;I_{\infty}=0;R_{\infty}=0)$.If $I_{\infty}>0$, we can show that stationary solution are:$S_{\infty}=R_o^{-1},\\I_{\infty}=\frac{\mu}{\beta}(R_o-1),\\R_{\infty}=\frac{\gamma}{\beta}(R_o-1),\\$with $R_o=\frac{\beta}{\gamma+\mu}$We also point that, for virus to remain endemic in the population, we must have $(R_o-1)>0$ i.e. $\frac{\beta}{\gamma+\mu}>1$ ###Code def ode_SIR_vd(t, Y,beta,gamma,mu): Lambda=mu A=beta*Y[1]*Y[0] B=gamma*Y[1] return [Lambda -A-mu*Y[0],A-B-mu*Y[1],B-mu*Y[2]] r=ode(ode_SIR_vd) i0=0.01 S0=1-i0 SIR0=[S0,i0,0] mu=0.01 r.set_initial_value(SIR0, 0).set_f_params(beta,gamma,mu) dt=1 sol=[] while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) plt.figure() [plt.plot(sol[:,0],sol[:,i]) for i in (1,2,3)] def updateSIR_vd(i0,beta,gamma,mu,t1): def fooPlot(ax,sol,i,j,mytitle): ''' simple function to format phase space plot ''' ax.plot(sol[:,i],sol[:,j]) ax.set_title(mytitle) ax.grid() S0=1-i0 SIR0=[S0,i0,0] r.set_initial_value(SIR0, 0).set_f_params(beta,gamma,mu) dt=1 sol=[] Ro=beta/(gamma+mu) while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) ax=plt.subplot(211) #plt.figure() mycolors=['b','r','g'] ax.hlines(1/Ro,0,t1,color='b',ls=':') ax.hlines(mu*(Ro-1)/beta,0,t1,color='r',ls=':') ax.hlines(gamma*(Ro-1)/beta,0,t1,color='g',ls=':') ax.set_title(r"$R_o=\frac{\beta}{\gamma+\mu}=%.2f$" %(Ro)+'\nis '+r"$R_o<1 \quad %s$" %(Ro<1)) [ax.plot(sol[:,0],sol[:,i],color=mycolors[i-1]) for i in (1,2,3)] plt.grid() fooPlot(plt.subplot(234),sol,1,2,r"$S vs I$") fooPlot(plt.subplot(235),sol,1,3,r"$S vs R$") fooPlot(plt.subplot(236),sol,2,3,r"$I vs R$") plt.tight_layout() plt.show() r=ode(ode_SIR_vd) #interactive_plot = interactive(update, i0=(0, 0.2,0.01), beta=(0.01, 0.2, 0.002) # ,gamma=(0.001,0.1,0.002),t1=(700,1000,5)) timeSlider=IntSlider(value=360,min=300,max=4000,step=30,description="days") iniInfectedSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description="i0") betaSlider=FloatSlider(value=0.05, min=0.01,max=0.2,step=0.01,readout_format='.2f',description=r'<MATH>&beta;</MATH>') gammaSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description=r'<MATH>&gamma;</MATH>') #LambdaSlider=FloatSlider(value=0.1, min=0.,max=0.3,step=0.01,description=r'<MATH>&Lambda;</MATH>') muSlider=FloatSlider(value=0.001, min=0.,max=0.02,step=0.002,readout_format='.3f',description=r'<MATH>&Lambda;=&mu;</MATH>') interactive_plot = interactive(updateSIR_vd,i0=iniInfectedSlider, gamma=gammaSlider, beta=betaSlider,mu=muSlider, t1=timeSlider) output = interactive_plot.children[-1] output.layout.height = '450px' interactive_plot ###Output _____no_output_____ ###Markdown $\begin{cases}\frac{dS}{dt}=(1-p)\Lambda -\beta I S -\mu S,\\\frac{dI}{dt}=\beta I S-\gamma I -\mu I,\\\frac{dR}{dt}=\gamma I -\mu R,\\\frac{dV}{dt}=p\Lambda-\mu V,\end{cases}$We have introduced vaccination at birth (V), with p beeing the fraction of childs vaccinated at birth.Once again under constant (=1) population assumption it can be shown that $\lambda=\mu$a trivial statinary solution can be easilly found for $(S_{\infty}=1;I_{\infty}=0;R_{\infty}=0)$.If $I_{\infty}>0$, we can show that stationary solution are:$S_{\infty}=R_o^{-1},\\I_{\infty}=\frac{\mu}{\beta}((1-p)*R_o-1),\\R_{\infty}=\frac{\gamma}{\beta}((1-p)*R_o-1),\\$with $R_o=\frac{\beta}{\gamma+\mu}$We also point that, for virus to remain endemic in the population, we must have $((1-p)R_o-1)>0$ i.e. $\frac{\beta}{\gamma+\mu}>1$ ###Code def ode_SIRV(t, Y,beta,gamma,mu,p): A=beta*Y[1]*Y[0] B=gamma*Y[1] return [(1-p)*mu -A-mu*Y[0],A-B-mu*Y[1],B-mu*Y[2],mu*(p-Y[3])] S0=1-i0 SIR0=[S0,i0,0,0] r=ode(ode_SIRV) p=0.1 r.set_initial_value(SIR0, 0).set_f_params(beta,gamma,mu,p) dt=1 sol=[] while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) plt.figure() [plt.plot(sol[:,0],sol[:,i]) for i in (1,2,3,4)] def updateSIRV(i0,beta,gamma,mu,t1,p): def fooPlot(ax,sol,i,j,mytitle): ''' simple function to format phase space plot ''' ax.plot(sol[:,i],sol[:,j]) ax.set_title(mytitle) ax.grid() S0=1-i0 SIR0=[S0,i0,0,0] r.set_initial_value(SIR0, 0).set_f_params(beta,gamma,mu,p) dt=1 sol=[] while r.successful() and r.t < t1: sol.append(np.concatenate(([r.t+dt],r.integrate(r.t+dt)))) sol=np.array(sol) Ro=beta/(gamma+mu) ax=plt.subplot(211) #plt.figure() mycolors=['b','r','g','gold'] ax.hlines(1/Ro,0,t1,color='b',ls=':') ax.hlines(mu*((1-p)*Ro-1)/beta,0,t1,color='r',ls=':') ax.hlines(gamma*((1-p)*Ro-1)/beta,0,t1,color='g',ls=':') ax.hlines(p,0,t1,color='gold',ls=':') ax.set_title(r"$(1-p)R_o=\frac{\beta}{\gamma+\mu}=%.2f$" %((1-p)*Ro) +'\nis '+r"$(1-p)R_o<1 \quad %s$" %((1-p)*Ro<1)) [ax.plot(sol[:,0],sol[:,i],color=mycolors[i-1]) for i in (1,2,3,4)] plt.grid() fooPlot(plt.subplot(234),sol,1,2,r"$S\quad vs \quad I$") fooPlot(plt.subplot(235),sol,1,3,r"$S\quad vs \quad R$") fooPlot(plt.subplot(236),sol,2,3,r"$I \quad vs \quad R$") plt.tight_layout() plt.show() r=ode(ode_SIRV) #interactive_plot = interactive(update, i0=(0, 0.2,0.01), beta=(0.01, 0.2, 0.002) # ,gamma=(0.001,0.1,0.002),t1=(700,1000,5)) timeSlider=IntSlider(value=360,min=300,max=10000,step=30,description="days") iniInfectedSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description="i0") betaSlider=FloatSlider(value=0.05, min=0.01,max=0.2,step=0.01,readout_format='.2f',description=r'<MATH>&beta;</MATH>') gammaSlider=FloatSlider(value=0.01, min=0.,max=0.3,step=0.01,description=r'<MATH>&gamma;</MATH>') pSlider=FloatSlider(value=0.1, min=0.,max=1,step=0.01,description='p') muSlider=FloatSlider(value=0.001, min=0.,max=0.02,step=0.002,readout_format='.3f',description=r'<MATH>&mu;</MATH>') interactive_plot = interactive(updateSIRV,i0=iniInfectedSlider, gamma=gammaSlider, beta=betaSlider,mu=muSlider, t1=timeSlider,p=pSlider ) output = interactive_plot.children[-1] output.layout.height = '550px' interactive_plot ###Output _____no_output_____
yolo_train_Speed_bumpYasmi_inline_trainning_3.ipynb
###Markdown ###Code !git clone https://github.com/Yasmic/SpeedBump3AnchorBox %cd SpeedBump3AnchorBox/ !pip install -r requirements.txt %cd /content/SpeedBump3AnchorBox/ !rm bump.pkl pwd %pycat config.json %%writefile config.json { "model" : { "min_input_size": 288, "max_input_size": 448, "anchors": [195,7, 219,15, 276,40, 291,25, 351,11, 382,58, 386,17, 401,33, 409,96], "labels": ["bump"] }, "train": { "train_image_folder": "dataset/hump/hump/data/", "train_annot_folder": "dataset/hump/hump/dataAnot/", "cache_name": "bump.pkl", "train_times": 8, "batch_size": 4, "learning_rate": 1e-4, "nb_epochs": 100, "warmup_epochs": 3, "ignore_thresh": 0.5, "gpus": "0", "grid_scales": [1,1,1], "obj_scale": 5, "noobj_scale": 1, "xywh_scale": 1, "class_scale": 1, "tensorboard_dir": "logs", "saved_weights_name": "bump.h5", "debug": true }, "valid": { "valid_image_folder": "", "valid_annot_folder": "", "cache_name": "", "valid_times": 1 } } !python gen_anchors.py -c config.json import requests def download_file_from_google_drive(id, destination): URL = "https://docs.google.com/uc?export=download" session = requests.Session() response = session.get(URL, params = { 'id' : id }, stream = True) token = get_confirm_token(response) if token: params = { 'id' : id, 'confirm' : token } response = session.get(URL, params = params, stream = True) save_response_content(response, destination) def get_confirm_token(response): for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None def save_response_content(response, destination): CHUNK_SIZE = 32768 with open(destination, "wb") as f: for chunk in response.iter_content(CHUNK_SIZE): if chunk: # filter out keep-alive new chunks f.write(chunk) file_id = '1ED7de6kr0u3TZTJkxN8YafWRgsme9WHP' destination = 'backend.h5' download_file_from_google_drive(file_id, destination) import argparse import os import numpy as np import json from voc import parse_voc_annotation from yolo import create_yolov3_model, dummy_loss from generator import BatchGenerator from utils.utils import normalize, evaluate, makedirs from keras.callbacks import EarlyStopping, ReduceLROnPlateau from keras.optimizers import Adam from callbacks import CustomModelCheckpoint, CustomTensorBoard from utils.multi_gpu_model import multi_gpu_model import tensorflow as tf import keras from keras.models import load_model from keras.utils import plot_model config = tf.compat.v1.ConfigProto( gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.9) # device_count = {'GPU': 1} ) config.gpu_options.allow_growth = True session = tf.compat.v1.Session(config=config) tf.compat.v1.keras.backend.set_session(session) def create_training_instances( train_annot_folder, train_image_folder, train_cache, valid_annot_folder, valid_image_folder, valid_cache, labels, ): # parse annotations of the training set train_ints, train_labels = parse_voc_annotation(train_annot_folder, train_image_folder, train_cache, labels) # parse annotations of the validation set, if any, otherwise split the training set if os.path.exists(valid_annot_folder): valid_ints, valid_labels = parse_voc_annotation(valid_annot_folder, valid_image_folder, valid_cache, labels) else: print("valid_annot_folder not exists. Spliting the trainining set.") train_valid_split = int(0.8*len(train_ints)) np.random.seed(0) np.random.shuffle(train_ints) np.random.seed() valid_ints = train_ints[train_valid_split:] train_ints = train_ints[:train_valid_split] # compare the seen labels with the given labels in config.json if len(labels) > 0: overlap_labels = set(labels).intersection(set(train_labels.keys())) print('Seen labels: \t' + str(train_labels) + '\n') print('Given labels: \t' + str(labels)) # return None, None, None if some given label is not in the dataset if len(overlap_labels) < len(labels): print('Some labels have no annotations! Please revise the list of labels in the config.json.') return None, None, None else: print('No labels are provided. Train on all seen labels.') print(train_labels) labels = train_labels.keys() max_box_per_image = max([len(inst['object']) for inst in (train_ints + valid_ints)]) return train_ints, valid_ints, sorted(labels), max_box_per_image def create_callbacks(saved_weights_name, tensorboard_logs, model_to_save): makedirs(tensorboard_logs) early_stop = EarlyStopping( monitor = 'loss', min_delta = 0.01, patience = 7, mode = 'min', verbose = 1 ) checkpoint = CustomModelCheckpoint( model_to_save = model_to_save, filepath = saved_weights_name,# + '{epoch:02d}.h5', monitor = 'loss', verbose = 1, save_best_only = True, mode = 'min', period = 1 ) reduce_on_plateau = ReduceLROnPlateau( monitor = 'loss', factor = 0.1, patience = 2, verbose = 1, mode = 'min', epsilon = 0.01, cooldown = 0, min_lr = 0 ) tensorboard = CustomTensorBoard( log_dir = tensorboard_logs, write_graph = True, write_images = True, ) return [early_stop, checkpoint, reduce_on_plateau, tensorboard] def create_model( nb_class, anchors, max_box_per_image, max_grid, batch_size, warmup_batches, ignore_thresh, multi_gpu, saved_weights_name, lr, grid_scales, obj_scale, noobj_scale, xywh_scale, class_scale ): if multi_gpu > 1: with tf.device('/cpu:0'): template_model, infer_model = create_yolov3_model( nb_class = nb_class, anchors = anchors, max_box_per_image = max_box_per_image, max_grid = max_grid, batch_size = batch_size//multi_gpu, warmup_batches = warmup_batches, ignore_thresh = ignore_thresh, grid_scales = grid_scales, obj_scale = obj_scale, noobj_scale = noobj_scale, xywh_scale = xywh_scale, class_scale = class_scale ) else: template_model, infer_model = create_yolov3_model( nb_class = nb_class, anchors = anchors, max_box_per_image = max_box_per_image, max_grid = max_grid, batch_size = batch_size, warmup_batches = warmup_batches, ignore_thresh = ignore_thresh, grid_scales = grid_scales, obj_scale = obj_scale, noobj_scale = noobj_scale, xywh_scale = xywh_scale, class_scale = class_scale ) # load the pretrained weight if exists, otherwise load the backend weight only if os.path.exists(saved_weights_name): print("\nLoading pretrained weights.\n") template_model.load_weights(saved_weights_name) else: template_model.load_weights("backend.h5", by_name=True) if multi_gpu > 1: train_model = multi_gpu_model(template_model, gpus=multi_gpu) else: train_model = template_model optimizer = Adam(lr=lr, clipnorm=0.001) train_model.compile(loss=dummy_loss, optimizer=optimizer) return train_model, infer_model !python train.py -c config.json config_path = "config.json" with open(config_path) as config_buffer: config = json.loads(config_buffer.read()) ############################### # Parse the annotations ############################### train_ints, valid_ints, labels, max_box_per_image = create_training_instances( config['train']['train_annot_folder'], config['train']['train_image_folder'], config['train']['cache_name'], config['valid']['valid_annot_folder'], config['valid']['valid_image_folder'], config['valid']['cache_name'], config['model']['labels'] ) print('\nTraining on: \t' + str(labels) + '\n') ############################### # Create the generators ############################### train_generator = BatchGenerator( instances = train_ints, anchors = config['model']['anchors'], labels = labels, downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3 max_box_per_image = max_box_per_image, batch_size = config['train']['batch_size'], min_net_size = config['model']['min_input_size'], max_net_size = config['model']['max_input_size'], shuffle = True, jitter = 0.3, norm = normalize ) valid_generator = BatchGenerator( instances = valid_ints, anchors = config['model']['anchors'], labels = labels, downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3 max_box_per_image = max_box_per_image, batch_size = config['train']['batch_size'], min_net_size = config['model']['min_input_size'], max_net_size = config['model']['max_input_size'], shuffle = True, jitter = 0.0, norm = normalize ) if os.path.exists(config['train']['saved_weights_name']): config['train']['warmup_epochs'] = 0 warmup_batches = config['train']['warmup_epochs'] * (config['train']['train_times']*len(train_generator)) os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus'] multi_gpu = len(config['train']['gpus'].split(',')) train_model, infer_model = create_model( nb_class = len(labels), anchors = config['model']['anchors'], max_box_per_image = max_box_per_image, max_grid = [config['model']['max_input_size'], config['model']['max_input_size']], batch_size = config['train']['batch_size'], warmup_batches = warmup_batches, ignore_thresh = config['train']['ignore_thresh'], multi_gpu = multi_gpu, saved_weights_name = config['train']['saved_weights_name'], lr = config['train']['learning_rate'], grid_scales = config['train']['grid_scales'], obj_scale = config['train']['obj_scale'], noobj_scale = config['train']['noobj_scale'], xywh_scale = config['train']['xywh_scale'], class_scale = config['train']['class_scale'], ) plot_model( train_model, to_file="model.png", show_shapes=True, show_layer_names=True) callbacks = create_callbacks(config['train']['saved_weights_name'], config['train']['tensorboard_dir'], infer_model) history = train_model.fit_generator( generator = train_generator, steps_per_epoch = len(train_generator) * config['train']['train_times'], epochs = config['train']['nb_epochs'] + config['train']['warmup_epochs'], verbose = 2 if config['train']['debug'] else 1, validation_data = valid_generator, validation_steps = len(valid_generator) * config['train']['train_times'], #np.floor(valid_generator / batch_size) callbacks = callbacks, workers = 4, max_queue_size = 8 ) import matplotlib.pyplot as plt plt.plot(history.history["loss"],label='loss') plt.plot(history.history["val_loss"],label='val_loss') plt.legend() import requests def download_file_from_google_drive(id, destination): URL = "https://docs.google.com/uc?export=download" session = requests.Session() response = session.get(URL, params = { 'id' : id }, stream = True) token = get_confirm_token(response) if token: params = { 'id' : id, 'confirm' : token } response = session.get(URL, params = params, stream = True) save_response_content(response, destination) def get_confirm_token(response): for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None def save_response_content(response, destination): CHUNK_SIZE = 32768 with open(destination, "wb") as f: for chunk in response.iter_content(CHUNK_SIZE): if chunk: # filter out keep-alive new chunks f.write(chunk) #https://drive.google.com/open?id=1WyfUiAKwidyUkc5COZ8QD2ymzH5_Gb-e file_id = '1WyfUiAKwidyUkc5COZ8QD2ymzH5_Gb-e' destination = 'bump.h5' download_file_from_google_drive(file_id, destination) !python predict.py -c config.json -i dataset/hump/hump_test/data/imgge15.jpg -x dataset/hump/hump_test/dataAnot/imgge15.xml !python predict.py -c config.json -i dataset/hump/hump_test/data/bg1.jpg !python predict.py -c config.json -i dataset/hump/hump_test/data/Image00017.jpg -x dataset/hump/hump_test/dataAnot/Image00017.xml !python predict.py -c config.json -i dataset/hump/hump/data/Image00008.jpg -x dataset/hump/hump/dataAnot/Image00008.xml !python predict.py -c config.json -i dataset/hump/hump/data/imgge62.jpg -x dataset/hump/hump/dataAnot/imgge62.xml ###Output _____no_output_____ ###Markdown Results on unseen Data ###Code import matplotlib.pyplot as plt my_img = plt.imread('output/Image00017.jpg') plt.imshow(my_img) import matplotlib.pyplot as plt my_img = plt.imread('output/imgge15.jpg') plt.imshow(my_img) import matplotlib.pyplot as plt my_img = plt.imread('output/bg1.jpg') plt.imshow(my_img) import matplotlib.pyplot as plt my_img = plt.imread('output/bg.jpg') plt.imshow(my_img) ###Output _____no_output_____ ###Markdown Results on Seen Data ###Code import matplotlib.pyplot as plt my_img = plt.imread('output/Image00008.jpg') plt.imshow(my_img) my_img = plt.imread('output/imgge62.jpg') plt.imshow(my_img) ###Output _____no_output_____ ###Markdown Copy to drive ###Code from google.colab import drive drive.mount('/content/drive') cp -r /content/SpeedBump3AnchorBox/ /content/drive/'My Drive'/SpeedBump3AnchorBox ls /content/drive/'My Drive'/SpeedBump3AnchorBox drive.flush_and_unmount() ###Output _____no_output_____
trafic_sign.ipynb
###Markdown CNN์„ ์ด์šฉํ•œ ํ‘œ์ง€ํŒ ๋ถ„๋ฅ˜ - Step1 : ๋„๋กœ๊ตํ†ต๊ณต๋‹จ ๊ตํ†ต์•ˆ์ „ํ‘œ์ง€ ์ผ๋žŒํ‘œ์—์„œ ํ‘œ์ง€ํŒ ์ด๋ฏธ์ง€ ์ถ”์ถœ - ์ถœ์ฒ˜ : https://www.koroad.or.kr/kp_web/safeDataView.do?board_code=DTBBS_030&board_num=100162- Step 2 : Image Augementation ์ง„ํ–‰ - ์‹ค์ œ ํ‘œ์ง€ํŒ ๋ถ„๋ฅ˜์— ํ™œ์šฉ๋  ์˜์ƒ์„ ๊ณ ๋ คํ–์—ฌ, ํšŒ์ „, ํœ˜์–ด์ง ์ •๋„, ๋ช…๋„, ๊ฐ๋„ ๋“ฑ์„ ๊ณ ๋ ค- Step 3 : ๋ชจ๋ธ๋ง - keras๋กœ CNN ๊ตฌํ˜„- Step 4 : ๊ฒ€์ฆ - ๊ฒฐ๊ณผ ๋ˆˆ์œผ๋กœ ์ง์ ‘ ํ™•์ธ image augementation- ๊ฐ ํ‘œ์ง€ํŒ ์ด๋ฏธ์ง€๋ฅผ 1000์žฅ์”ฉ augementation ์ง„ํ–‰- ์˜์ƒ์—์„œ ํ‘œ์ง€ํŒ์ด ์ธ์‹๋˜๋Š” ์ด๋ฏธ์ง€์˜ ๋ชจ์–‘์„ ๊ณ ๋ คํ•˜์—ฌ, ์ด๋ฏธ์ง€ ์™œ๊ณก, ํšŒ์ „๋“ฑ์˜ ๊ฐ’์„ ์„ค์ • ###Code import os import glob import numpy as np path = './traffin_sign_png/' full_names = os.listdir(path) labels = sorted([each.split('.')[0] for each in full_names]) # example of brighting image augmentation from tqdm.notebook import tqdm from numpy import expand_dims from keras.preprocessing.image import load_img from keras.preprocessing.image import img_to_array from keras.preprocessing.image import ImageDataGenerator import cv2 os.mkdir('./traffic_image') range_ = tqdm(labels) for dir_num in range_: # ์ด๋ฏธ์ง€ ๋กœ๋“œ img = load_img("./traffin_sign_png/{}.png".format(dir_num)) # Numpy array ๋กœ ๋ณ€ํ™˜ data = img_to_array(img) # expand dimension to one sample samples = expand_dims(data, 0) # image data augmentation generator ์ƒ์„ฑ datagen = ImageDataGenerator( brightness_range=[0.2, 2.0], zoom_range=[0.3, 1], rotation_range=20, height_shift_range=0.2, width_shift_range=0.2) # prepare iterator it = datagen.flow(samples, batch_size=1) os.mkdir('./traffic_image/{}'.format(dir_num)) for i in range(1000): batch = it.next() image = batch[0].astype("uint8") # rgb ๋ณ€ํ™˜ b, g, r = cv2.split(image) img_astro3_rgb = cv2.merge([r, g, b]) cv2.imwrite("./traffic_image/{}/{}_{}.png".format(dir_num, dir_num, i), img_astro3_rgb) ###Output _____no_output_____ ###Markdown X, Y ์„ค์ •, Train, Test split ###Code from PIL import Image import os import glob import numpy as np from sklearn.model_selection import train_test_split caltech_dir = "./traffic_image/" categories = labels nb_classes = len(labels) image_w = 64 image_h = 64 X = [] y = [] for idx, cat in enumerate(categories): # one-hot ๋Œ๋ฆฌ๊ธฐ. label = [0 for i in range(nb_classes)] label[idx] = 1 image_dir = caltech_dir + "/" + str(cat) files = glob.glob(image_dir+"/*.png") print(cat, " ํŒŒ์ผ ๊ธธ์ด : ", len(files)) # ์ด๋ฏธ์ง€ ํŒŒ์ผ์„ 64 x 64 ๋กœ ์ค„์ด๊ณ , ๋ฒกํ„ฐํ™” ์‹œ์ผœ X์— ์ €์žฅ, one-hot-encoding๋œ ๋ผ๋ฒจ๋„ ์ €์žฅ for i, f in enumerate(files): img = Image.open(f) img = img.convert("RGB") img = img.resize((image_w, image_h)) data = np.asarray(img) X.append(data) y.append(label) X = np.array(X) y = np.array(y) X_train, X_test, y_train, y_test = train_test_split(X, y) xy = (X_train, X_test, y_train, y_test) X_train.shape y_train_test = y_train.reshape(-1,1) y_train_test.shape y_train.shape ###Output _____no_output_____ ###Markdown npyํŒŒ์ผ๋กœ ์ €์žฅ- ํ–‰๋ ฌ๊ฐ’ ์ €์žฅ ###Code import pickle pickle.dump(xy, open("./model/multi_image_data.npy", 'wb'), protocol=4) X_train, X_test, y_train, y_test = np.load('./model/multi_image_data.npy',allow_pickle=True) X_train.shape y_train.shape ###Output _____no_output_____ ###Markdown ํ”ฝ์…€๊ฐ’ ์ •๊ทœํ™”- ํ”ฝ์…€ ์ •๋ณด๋Š” 0~255 ๊ฐ’๋งŒ ๊ฐ€์ง€๋ฏ€๋กœ ์ผ๋ฐ˜์ ์œผ๋กœ ๋ฐ”๋กœ 255๋กœ ๋‚˜๋ˆ„๋Š” ๋ฐฉ์‹์œผ๋กœ ์ •๊ทœํ™” ์ง„ํ–‰ ###Code # ์ผ๋ฐ˜ํ™” X_train = X_train.astype(float) / 255 X_test = X_test.astype(float) / 255 ###Output _____no_output_____ ###Markdown ๋ชจ๋ธ๋ง ###Code import os import glob import numpy as np from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout from keras.callbacks import EarlyStopping, ModelCheckpoint from keras.layers import BatchNormalization import matplotlib.pyplot as plt import keras.backend.tensorflow_backend as K nb_classes = len(labels) with K.tf_ops.device('/device:GPU:0'): model = Sequential() model.add(Conv2D(32, (3, 3), padding="same", input_shape=X_train.shape[1:], activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding="same", activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(128, (3, 3), padding="same", activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes, activation='softmax')) # ํ•™์Šต์„ ๋Œ๋ฆฌ๋Š” ๋ฐฉ๋ฒ•์„ ์ •์˜ : cost function์„ ์„ค์ •ํ•˜๊ณ , ์–ด๋–ป๊ฒŒ ์ตœ์ ํ™” ํ• ๊ฑด์ง€ ๋ฐฉ๋ฒ•์„ ์ •ํ•˜๊ณ  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model_dir = './model' if not os.path.exists(model_dir): os.mkdir(model_dir) model_path = model_dir + '/multi_img_classification.model' checkpoint = ModelCheckpoint( filepath=model_path, monitor='val_loss', verbose=1, save_best_only=True) early_stopping = EarlyStopping(monitor='val_loss', patience=6) model.summary() history = model.fit(X_train, y_train, batch_size=32, epochs=50, validation_split=0.2, callbacks=[checkpoint, early_stopping]) ###Output Train on 40800 samples, validate on 10200 samples Epoch 1/50 40800/40800 [==============================] - 229s 6ms/step - loss: 2.7341 - accuracy: 0.2342 - val_loss: 1.3379 - val_accuracy: 0.6121 Epoch 00001: val_loss improved from inf to 1.33788, saving model to ./model/multi_img_classification.model Epoch 2/50 40800/40800 [==============================] - 201s 5ms/step - loss: 1.3139 - accuracy: 0.5773 - val_loss: 0.6551 - val_accuracy: 0.8278 Epoch 00002: val_loss improved from 1.33788 to 0.65508, saving model to ./model/multi_img_classification.model Epoch 3/50 40800/40800 [==============================] - 183s 4ms/step - loss: 0.8574 - accuracy: 0.7219 - val_loss: 0.4560 - val_accuracy: 0.8602 Epoch 00003: val_loss improved from 0.65508 to 0.45595, saving model to ./model/multi_img_classification.model Epoch 4/50 40800/40800 [==============================] - 176s 4ms/step - loss: 0.6784 - accuracy: 0.7828 - val_loss: 0.3402 - val_accuracy: 0.9067 Epoch 00004: val_loss improved from 0.45595 to 0.34021, saving model to ./model/multi_img_classification.model Epoch 5/50 40800/40800 [==============================] - 175s 4ms/step - loss: 0.5557 - accuracy: 0.8187 - val_loss: 0.2888 - val_accuracy: 0.9120 Epoch 00005: val_loss improved from 0.34021 to 0.28880, saving model to ./model/multi_img_classification.model Epoch 6/50 40800/40800 [==============================] - 174s 4ms/step - loss: 0.4797 - accuracy: 0.8439 - val_loss: 0.2505 - val_accuracy: 0.9230 Epoch 00006: val_loss improved from 0.28880 to 0.25052, saving model to ./model/multi_img_classification.model Epoch 7/50 40800/40800 [==============================] - 174s 4ms/step - loss: 0.4351 - accuracy: 0.8588 - val_loss: 0.2197 - val_accuracy: 0.9316 Epoch 00007: val_loss improved from 0.25052 to 0.21968, saving model to ./model/multi_img_classification.model Epoch 8/50 40800/40800 [==============================] - 173s 4ms/step - loss: 0.3964 - accuracy: 0.8727 - val_loss: 0.2093 - val_accuracy: 0.9372 Epoch 00008: val_loss improved from 0.21968 to 0.20934, saving model to ./model/multi_img_classification.model Epoch 9/50 40800/40800 [==============================] - 173s 4ms/step - loss: 0.3563 - accuracy: 0.8862 - val_loss: 0.2033 - val_accuracy: 0.9375 Epoch 00009: val_loss improved from 0.20934 to 0.20327, saving model to ./model/multi_img_classification.model Epoch 10/50 40800/40800 [==============================] - 173s 4ms/step - loss: 0.3330 - accuracy: 0.8924 - val_loss: 0.1958 - val_accuracy: 0.9431 Epoch 00010: val_loss improved from 0.20327 to 0.19580, saving model to ./model/multi_img_classification.model Epoch 11/50 40800/40800 [==============================] - 174s 4ms/step - loss: 0.3109 - accuracy: 0.8997 - val_loss: 0.1620 - val_accuracy: 0.9505 Epoch 00011: val_loss improved from 0.19580 to 0.16204, saving model to ./model/multi_img_classification.model Epoch 12/50 40800/40800 [==============================] - 174s 4ms/step - loss: 0.2979 - accuracy: 0.9035 - val_loss: 0.1520 - val_accuracy: 0.9542 Epoch 00012: val_loss improved from 0.16204 to 0.15200, saving model to ./model/multi_img_classification.model Epoch 13/50 40800/40800 [==============================] - 175s 4ms/step - loss: 0.2798 - accuracy: 0.9094 - val_loss: 0.1489 - val_accuracy: 0.9548 Epoch 00013: val_loss improved from 0.15200 to 0.14888, saving model to ./model/multi_img_classification.model Epoch 14/50 40800/40800 [==============================] - 175s 4ms/step - loss: 0.2687 - accuracy: 0.9132 - val_loss: 0.1521 - val_accuracy: 0.9522 Epoch 00014: val_loss did not improve from 0.14888 Epoch 15/50 40800/40800 [==============================] - 176s 4ms/step - loss: 0.2523 - accuracy: 0.9200 - val_loss: 0.1471 - val_accuracy: 0.9543 Epoch 00015: val_loss improved from 0.14888 to 0.14709, saving model to ./model/multi_img_classification.model Epoch 16/50 40800/40800 [==============================] - 180s 4ms/step - loss: 0.2478 - accuracy: 0.9202 - val_loss: 0.1481 - val_accuracy: 0.9539 Epoch 00016: val_loss did not improve from 0.14709 Epoch 17/50 40800/40800 [==============================] - 204s 5ms/step - loss: 0.2333 - accuracy: 0.9257 - val_loss: 0.1351 - val_accuracy: 0.9578 Epoch 00017: val_loss improved from 0.14709 to 0.13514, saving model to ./model/multi_img_classification.model Epoch 18/50 40800/40800 [==============================] - 199s 5ms/step - loss: 0.2230 - accuracy: 0.9289 - val_loss: 0.1304 - val_accuracy: 0.9596 Epoch 00018: val_loss improved from 0.13514 to 0.13043, saving model to ./model/multi_img_classification.model Epoch 19/50 40800/40800 [==============================] - 199s 5ms/step - loss: 0.2247 - accuracy: 0.9276 - val_loss: 0.1339 - val_accuracy: 0.9580 Epoch 00019: val_loss did not improve from 0.13043 Epoch 20/50 40800/40800 [==============================] - 202s 5ms/step - loss: 0.2174 - accuracy: 0.9308 - val_loss: 0.1184 - val_accuracy: 0.9608 Epoch 00020: val_loss improved from 0.13043 to 0.11840, saving model to ./model/multi_img_classification.model Epoch 21/50 40800/40800 [==============================] - 202s 5ms/step - loss: 0.2077 - accuracy: 0.9335 - val_loss: 0.1295 - val_accuracy: 0.9584 Epoch 00021: val_loss did not improve from 0.11840 Epoch 22/50 40800/40800 [==============================] - 201s 5ms/step - loss: 0.1983 - accuracy: 0.9371 - val_loss: 0.1241 - val_accuracy: 0.9591 Epoch 00022: val_loss did not improve from 0.11840 Epoch 23/50 40800/40800 [==============================] - 204s 5ms/step - loss: 0.1959 - accuracy: 0.9374 - val_loss: 0.1174 - val_accuracy: 0.9623 Epoch 00023: val_loss improved from 0.11840 to 0.11738, saving model to ./model/multi_img_classification.model Epoch 24/50 40800/40800 [==============================] - 198s 5ms/step - loss: 0.1983 - accuracy: 0.9382 - val_loss: 0.1301 - val_accuracy: 0.9608 Epoch 00024: val_loss did not improve from 0.11738 Epoch 25/50 40800/40800 [==============================] - 196s 5ms/step - loss: 0.1912 - accuracy: 0.9391 - val_loss: 0.1230 - val_accuracy: 0.9633 Epoch 00025: val_loss did not improve from 0.11738 Epoch 26/50 40800/40800 [==============================] - 185s 5ms/step - loss: 0.1855 - accuracy: 0.9406 - val_loss: 0.1116 - val_accuracy: 0.9643 Epoch 00026: val_loss improved from 0.11738 to 0.11155, saving model to ./model/multi_img_classification.model Epoch 27/50 40800/40800 [==============================] - 192s 5ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1100 - val_accuracy: 0.9639 Epoch 00027: val_loss improved from 0.11155 to 0.11002, saving model to ./model/multi_img_classification.model Epoch 28/50 40800/40800 [==============================] - 186s 5ms/step - loss: 0.1761 - accuracy: 0.9443 - val_loss: 0.1148 - val_accuracy: 0.9653 Epoch 00028: val_loss did not improve from 0.11002 Epoch 29/50 40800/40800 [==============================] - 185s 5ms/step - loss: 0.1785 - accuracy: 0.9429 - val_loss: 0.1227 - val_accuracy: 0.9626 Epoch 00029: val_loss did not improve from 0.11002 Epoch 30/50 40800/40800 [==============================] - 184s 5ms/step - loss: 0.1676 - accuracy: 0.9467 - val_loss: 0.1162 - val_accuracy: 0.9644 Epoch 00030: val_loss did not improve from 0.11002 Epoch 31/50 40800/40800 [==============================] - 190s 5ms/step - loss: 0.1697 - accuracy: 0.9473 - val_loss: 0.1118 - val_accuracy: 0.9632 Epoch 00031: val_loss did not improve from 0.11002 Epoch 32/50 40800/40800 [==============================] - 188s 5ms/step - loss: 0.1673 - accuracy: 0.9470 - val_loss: 0.1127 - val_accuracy: 0.9650 Epoch 00032: val_loss did not improve from 0.11002 Epoch 33/50 40800/40800 [==============================] - 186s 5ms/step - loss: 0.1715 - accuracy: 0.9471 - val_loss: 0.1194 - val_accuracy: 0.9621 Epoch 00033: val_loss did not improve from 0.11002 ###Markdown accuracy, loss ๊ฐ’ ํ™•์ธ ###Code plot_target = ['loss', 'val_loss', 'accuracy', 'val_accuracy'] for each in plot_target: plt.plot(history.history[each], label=each) plt.legend() plt.show() model.evaluate(X_test, y_test) ###Output 17000/17000 [==============================] - 19s 1ms/step ###Markdown ํ‹€๋ฆฐ์• ๋“ค ๋ˆˆ์œผ๋กœ ํ™•์ธ ###Code from keras.models import load_model model = load_model('model/multi_img_classification.model') y_test[0] import numpy as np predicted_result = model.predict(X_test) predicted_labels = np.argmax(predicted_result, axis=1) predicted_labels[:10] ###Output _____no_output_____ ###Markdown - test ๋ฐ์ดํ„ฐ์—์„œ ๋ผ๋ฒจ ์ถ”์ถœ ###Code y_labels = [] for vector in y_test: for idx, i in enumerate(vector): if i != 0: y_labels.append(idx) y_labels = np.array(y_labels) y_labels ###Output _____no_output_____ ###Markdown - ์˜ˆ์ธกํ•œ ๋ผ๋ฒจ๊ณผ ์‹ค์ œ ๋ผ๋ฒจ์„ ๋น„๊ตํ•ด ์ž˜๋ชป ์˜ˆ์ธก๋œ ๊ฒฐ๊ณผ ์ถ”์ถœ ###Code wrong_result = [] for n in range(0, len(y_test)): if predicted_labels[n] != y_labels[n]: wrong_result.append(n) len(wrong_result) ###Output _____no_output_____ ###Markdown - ์ž˜๋ชป ์˜ˆ์ธก๋œ ๊ฒฐ๊ณผ๋ฅผ ๋žœ๋คํ•˜๊ฒŒ ์„ ํƒ ###Code import random samples = random.choices(population=wrong_result, k=4) ###Output _____no_output_____ ###Markdown ์ง์ ‘ ๋ˆˆ์œผ๋กœ ํ™•์ธ ###Code label_to_str = ["+์žํ˜•๊ต์ฐจ๋กœ","T์žํ˜•๊ต์ฐจ๋กœ","Y์žํ˜•๊ต์ฐจ๋กœ","ใ…์žํ˜•๊ต์ฐจ๋กœ","ใ…“์žํ˜•๊ต์ฐจ๋กœ","์šฐ์„ ๋„๋กœ","์šฐํ•ฉ๋ฅ˜๋„๋กœ","์ขŒํ•ฉ๋ฅ˜๋„๋กœ","ํšŒ์ „ํ˜•๊ต์ฐจ๋กœ","์ฒ ๊ธธ๊ฑด๋„๋ชฉ","์šฐ๋กœ๊ตฝ์€๋„๋กœ","์ขŒ๋กœ๊ตฝ์€๋„๋กœ","์šฐ์ขŒ๋กœ์ด์ค‘๊ตฝ์€๋„๋กœ","์ขŒ์šฐ๋กœ์ด์ค‘๊ตฝ์€๋„๋กœ","2๋ฐฉํ–ฅํ†ตํ–‰","์˜ค๋ฅด๋ง‰๊ฒฝ์‚ฌ","๋‚ด๋ฆฌ๋ง‰๊ฒฝ์‚ฌ","๋„๋กœํญ์ด์ข์•„์ง","์šฐ์ธก์ฐจ๋กœ์—†์–ด์ง","์ขŒ์ธก์ฐจ๋กœ์—†์–ด์ง","์šฐ์ธก๋ฐฉํ†ตํ–‰","์–‘์ธก๋ฐฉํ†ตํ–‰","์ค‘์•™๋ถ„๋ฆฌ๋Œ€์‹œ์ž‘","์ค‘์•™๋ถ„๋ฆฌ๋Œ€๋๋‚จ","์‹ ํ˜ธ๊ธฐ","๋ฏธ๋„๋Ÿฌ์šด๋„๋กœ","๊ฐ•๋ณ€๋„๋กœ","๋…ธ๋ฉด๊ณ ๋ฅด์ง€๋ชปํ•จ","๊ณผ์†๋ฐฉ์ง€ํ„ฑ","๋‚™์„๋„๋กœ","ํšก๋‹จ๋ณด๋„","์–ด๋ฆฐ์ด๋ณดํ˜ธ","์ž์ „๊ฑฐ","๋„๋กœ๊ณต์‚ฌ์ค‘","๋น„ํ–‰๊ธฐ","ํšกํ’","ํ„ฐ๋„","๊ต๋Ÿ‰","์•ผ์ƒ๋™๋ฌผ๋ณดํ˜ธ","์œ„ํ—˜","์ƒ์Šต์ •์ฒด๊ตฌ๊ฐ„","ํ†ตํ–‰๊ธˆ์ง€","์ž๋™์ฐจํ†ตํ–‰๊ธˆ์ง€","ํ™”๋ฌผ์ž๋™์ฐจํ†ตํ–‰๊ธˆ์ง€","์Šนํ•ฉ์ž๋™์ฐจํ†ตํ–‰๊ธˆ์ง€","์ด๋ฅœ์ž๋™์ฐจ๋ฐ์›๋™๊ธฐ์žฅ์น˜์ž์ „๊ฑฐํ†ตํ–‰๊ธˆ์ง€","์ž๋™์ฐจ, ์ด๋ฅœ์ž๋™์ฐจ๋น›์›๋™๊ธฐ์žฅ์น˜์ž์ „๊ฑฐํ†ตํ–‰๊ธˆ์ง€","๊ฒฝ์šด๊ธฐ, ํŠธ๋ ‰ํ„ฐ๋ฐ ์†์ˆ˜๋ ˆํ†ตํ–‰๊ธˆ์ง€","์ž์ „๊ฑฐํ†ตํ–‰๊ธˆ์ง€","์ง„์ž…๊ธˆ์ง€","์ง์ง„๊ธˆ์ง€","์šฐํšŒ์ „๊ธˆ์ง€","์ขŒํšŒ์ „๊ธˆ์ง€","์œ ํ„ด๊ธˆ์ง€","์•ž์ง€๋ฅด๊ธฐ๊ธˆ์ง€","์ •์ฐจ,์ฃผ์ฐจ๊ธˆ์ง€","์ฃผ์ฐจ๊ธˆ์ง€","์ฐจ์ค‘๋Ÿ‰์ œํ•œ","์ฐจ๋†’์ด์ œํ•œ","์ฐจํญ์ œํ•œ","์ฐจ๊ฐ„๊ฑฐ๋ฆฌํ™•๋ณด","์ตœ๊ณ ์†๋„์ œํ•œ","์ตœ์ €์†๋„์ œํ•œ","์„œํ–‰","์ผ์‹œ์ •์ง€","์–‘๋ณด","๋ณดํ–‰์ž๋ณดํ–‰๊ธˆ์ง€","์œ„ํ—˜๋ฌผ์ ์žฌ์ฐจ๋Ÿ‰ ํ†ตํ–‰๊ธˆ์ง€"] plt.figure(figsize=(14,12)) for idx, n in enumerate(samples): plt.subplot(4, 2, idx+1) plt.imshow(X_test[n].reshape(64,64,3), cmap='Greys', interpolation='nearest') plt.title('Label : ' + label_to_str[y_labels[n]] + ', Predict : ' + label_to_str[predicted_labels[n]]) plt.axis('off') plt.show() ###Output _____no_output_____ ###Markdown ์‹ค์ œ ์ดฌ์˜์ด๋ฏธ์ง€๋กœ ํ…Œ์ŠคํŠธ ###Code from keras.models import load_model model = load_model('model/multi_img_classification.model') from PIL import Image # ์ด๋ฏธ์ง€ ํŒŒ์ผ์„ 64 x 64 ๋กœ ์ค„์ด๊ณ , ๋ฒกํ„ฐํ™” ์‹œ์ผœ X์— ์ €์žฅ image_w = 64 image_h = 64 X = [] img = Image.open('test2.jpeg') img = img.convert("RGB") img_resized = img.resize((image_w, image_h)) data = np.asarray(img_resized) X.append(data) X = np.array(X) X = X.astype(float) / 255 img result = model.predict(X) label_to_str[np.argmax(result, axis=1)[0]] ###Output _____no_output_____
data/attention-index-august.ipynb
###Markdown Exploring data for the attention indexThe idea of the attention index is to provide a score that indicates the impact of an article, and can easily be aggregated by subject, publisher or other axis.The index comprises of two parts:- **promotion** how important the article was to the publisher, based on the extent to which they chose to editorially promote it- **response** how readers reacted to the article, based on social engagementsThe index will be a number between 0 and 100. 50% is driven by the promotion, and 50% by response:![Attention Index](../images/kaleida-attention-index-data-factors-chart.png) Promotion ScoreThe promotion score should take into account:- whether the publisher chose make the article a lead article on their primary front (30%)- how long the publisher chose to retain the article on their front (40%)- whether they chose to push the article on their facebook brand page (30%)It should be scaled based on the value of that promotion, so a popular, well-visited site should score higher than one on the fringes. And similarly a powerful, well-followed brand page should score higher than one less followed. Response ScoreThe response score takes into account the number of engagements on Facebook. The rest of this notebook explores how those numbers could work, starting with the response score because that is easier, I think. Setup ###Code %matplotlib inline import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np data = pd.read_csv("articles_2017-08-01_2017-08-31.csv", index_col="id", \ parse_dates=["published", "discovered"]) data.head() ###Output _____no_output_____ ###Markdown Response Score The response score is a number between 0 and 50 that indicates the level of response to an article.Perhaps in the future we may choose to include other factors, but for now we just include engagements on Facebook. The maximum score of 50 should be achieved by an article that does really well compared with others. ###Code pd.options.display.float_format = '{:.2f}'.format data.fb_engagements.describe([0.5, 0.75, 0.9, 0.95, 0.99, 0.995, 0.999]) ###Output _____no_output_____ ###Markdown There's a few articles there with 1 million plus engagements, let's just double check that. ###Code data[data.fb_engagements > 1000000] data.fb_engagements.mode() ###Output _____no_output_____ ###Markdown Going back to the enagement counts, we see the mean is 1,542, mode is zero, median is 29, 90th percentile is 2,085, 99th percentile is 27,998, 99.5th percentile is 46,698. The standard deviation is 12,427, significantly higher than the mean, so this is not a normal distribution. We want to provide a sensible way of allocating this to the 50 buckets we have available. Let's just bucket geometrically first: ###Code mean = data.fb_engagements.mean() median = data.fb_engagements.median() plt.figure(figsize=(12,4.5)) plt.hist(data.fb_engagements, bins=50) plt.axvline(mean, linestyle=':', label=f'Mean ({mean:,.0f})', color='green') plt.axvline(median, label=f'Median ({median:,.0f})', color='red') leg = plt.legend() ###Output _____no_output_____ ###Markdown Well that's not very useful. Almost everything will score less than 0 if we just do that, which isn't a useful metric.Let's start by excluding zeros. ###Code non_zero_fb_enagagements = data.fb_engagements[data.fb_engagements > 0] plt.figure(figsize=(12,4.5)) plt.hist(non_zero_fb_enagagements, bins=50) plt.axvline(mean, linestyle=':', label=f'Mean ({mean:,.0f})', color='green') plt.axvline(median, label=f'Median ({median:,.0f})', color='red') leg = plt.legend() ###Output _____no_output_____ ###Markdown That's still a big number at the bottom, and so not a useful score.Next, we exclude the outliers: cap at the 99.9th percentile (i.e. 119211), so that 0.1% of articles should receive the maximum score. ###Code non_zero_fb_enagagements_without_outliers = non_zero_fb_enagagements.clip_upper(119211) plt.figure(figsize=(12,4.5)) plt.hist(non_zero_fb_enagagements_without_outliers, bins=50) plt.axvline(mean, linestyle=':', label=f'Mean ({mean:,.0f})', color='green') plt.axvline(median, label=f'Median ({median:,.0f})', color='red') leg = plt.legend() ###Output _____no_output_____ ###Markdown That's a bit better, but still way too clustered at the low end. Let's look at a log normal distribution. ###Code mean = data.fb_engagements.mean() median = data.fb_engagements.median() ninety = data.fb_engagements.quantile(.90) ninetyfive = data.fb_engagements.quantile(.95) ninetynine = data.fb_engagements.quantile(.99) plt.figure(figsize=(12,4.5)) plt.hist(np.log(non_zero_fb_enagagements + median), bins=50) plt.axvline(np.log(mean), linestyle=':', label=f'Mean ({mean:,.0f})', color='green') plt.axvline(np.log(median), label=f'Median ({median:,.0f})', color='green') plt.axvline(np.log(ninety), linestyle='--', label=f'90% percentile ({ninety:,.0f})', color='red') plt.axvline(np.log(ninetyfive), linestyle='-.', label=f'95% percentile ({ninetyfive:,.0f})', color='red') plt.axvline(np.log(ninetynine), linestyle=':', label=f'99% percentile ({ninetynine:,.0f})', color='red') leg = plt.legend() ###Output _____no_output_____ ###Markdown That's looking a bit more interesting. After some exploration, to avoid too much emphasis on the lower end of the scale, we move the numbers to the right a bit by adding on the median. ###Code log_engagements = (non_zero_fb_enagagements .clip_upper(data.fb_engagements.quantile(.999)) .apply(lambda x: np.log(x + median)) ) log_engagements.describe() ###Output _____no_output_____ ###Markdown Use standard feature scaling to bring that to a 1 to 50 range ###Code def scale_log_engagements(engagements_logged): return np.ceil( 50 * (engagements_logged - log_engagements.min()) / (log_engagements.max() - log_engagements.min()) ) def scale_engagements(engagements): return scale_log_engagements(np.log(engagements + median)) scaled_non_zero_engagements = scale_log_engagements(log_engagements) scaled_non_zero_engagements.describe() # add in the zeros, as zero scaled_engagements = pd.concat([scaled_non_zero_engagements, data.fb_engagements[data.fb_engagements == 0]]) proposed = pd.DataFrame({"fb_engagements": data.fb_engagements, "response_score": scaled_engagements}) proposed.response_score.plot.hist(bins=50) ###Output _____no_output_____ ###Markdown Now look at how the shares distribute to score: ###Code plt.figure(figsize=(15,8)) shares = np.arange(1, 60000) plt.plot(shares, scale_engagements(shares)) plt.xlabel("shares") plt.ylabel("score") plt.axhline(scale_engagements(mean), linestyle=':', label=f'Mean ({mean:,.0f})', color='green') plt.axhline(scale_engagements(median), label=f'Median ({median:,.0f})', color='green') plt.axhline(scale_engagements(ninety), linestyle='--', label=f'90% percentile ({ninety:,.0f})', color='red') plt.axhline(scale_engagements(ninetyfive), linestyle='-.', label=f'95% percentile ({ninetyfive:,.0f})', color='red') plt.axhline(scale_engagements(ninetynine), linestyle=':', label=f'99% percentile ({ninetynine:,.0f})', color='red') plt.legend(frameon=True, shadow=True) proposed.groupby("response_score").fb_engagements.agg([np.size, np.min, np.max]) ###Output _____no_output_____ ###Markdown Looks good to me, lets save that. ###Code data["response_score"] = proposed.response_score ###Output _____no_output_____ ###Markdown ProposalThe maximum of 50 points is awarded when the engagements are greater than the 99.9th percentile, rolling over the last month. i.e. where $limit$ is the 99.5th percentile of engagements calculated over the previous month, the response score for article $a$ is:\begin{align}basicScore_a & = \begin{cases} 0 & \text{if } engagements_a = 0 \\ \log(\min(engagements_a,limit) + median(engagements)) & \text{if } engagements_a > 0\end{cases} \\responseScore_a & = \begin{cases} 0 & \text{if } engagements_a = 0 \\ 50 \cdot \frac{basicScore_a - \min(basicScore)}{\max(basicScore) - \min(basicScore)} & \text{if } engagements_a > 0\end{cases} \\\\\text{The latter equation can be expanded to:} \\responseScore_a & = \begin{cases} 0 & \text{if } engagements_a = 0 \\ 50 \cdot \frac{\log(\min(engagements_a,limit) + median(engagements)) - \log(1 + median(engagements))} {\log(limit + median(engagements)) - \log(1 + median(engagements))} & \text{if } engagements_a > 0\end{cases} \\\end{align} Promotion ScoreThe aim of the promotion score is to indicate how important the article was to the publisher, by tracking where they chose to promote it. This is a number between 0 and 50 comprised of:- 20 points based on whether the article was promoted as the "lead" story on the publisher's home page- 15 points based on how long the article was promoted anywhere on the publisher's home page- 15 points based on whether the article was promoted on the publisher's main facebook brand pageThe first two should be scaled by the popularity/reach of the home page, for which we use the alexa page rank as a proxy.The last should be scaled by the popularity/reach of the brand page, for which we use the number of likes the brand page has. Lead story (20 points) ###Code data.mins_as_lead.describe([0.5, 0.75, 0.9, 0.95, 0.99, 0.995, 0.999]) ###Output _____no_output_____ ###Markdown As expected, the vast majority of articles don't make it as lead. Let's explore how long typically publishers put something as lead for. ###Code lead_articles = data[data.mins_as_lead > 0] lead_articles.mins_as_lead.describe([0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.995, 0.999]) lead_articles.mins_as_lead.plot.hist(bins=50) ###Output _____no_output_____ ###Markdown For lead, it's a significant thing for an article to be lead at all, so although we want to penalise articles that were lead for a very short time, mostly we want to score the maximum even if it wasn't lead for ages. So we'll give maximum points when something has been lead for an hour. ###Code lead_articles.mins_as_lead.clip_upper(60).plot.hist(bins=50) ###Output _____no_output_____ ###Markdown We also want to scale this by the alexa page rank, such that the maximum score of 20 points is for an article that was on the front for 4 hours for the most popular site.So lets explore the alexa nunbers. ###Code alexa_ranks = data.groupby(by="publisher_id").alexa_rank.mean().sort_values() alexa_ranks alexa_ranks.plot.bar(figsize=[10,5]) ###Output _____no_output_____ ###Markdown Let's try the simple option first: just divide the number of minutes as lead by the alexa rank. What's the scale of numbers we get then. ###Code lead_proposal_1 = lead_articles.mins_as_lead.clip_upper(60) / lead_articles.alexa_rank lead_proposal_1.plot.hist() ###Output _____no_output_____ ###Markdown Looks like there's too much of a cluster around 0. Have we massively over penalised the publishers with a high alexa rank? ###Code lead_proposal_1.groupby(data.publisher_id).mean().plot.bar(figsize=[10,5]) ###Output _____no_output_____ ###Markdown Yes. Let's try taking the log of the alexa rank and see if that looks better. ###Code lead_proposal_2 = (lead_articles.mins_as_lead.clip_upper(60) / np.log(lead_articles.alexa_rank)) lead_proposal_2.plot.hist() lead_proposal_2.groupby(data.publisher_id).describe() lead_proposal_2.groupby(data.publisher_id).min().plot.bar(figsize=[10,5]) ###Output _____no_output_____ ###Markdown That looks about right, as long as the smaller publishers were closer to zero. So let's apply feature scaling to this, to give a number between 1 and 20. (Anything not as lead will pass though as zero.) ###Code def rescale(series): return (series - series.min()) / (series.max() - series.min()) lead_proposal_3 = np.ceil(20 * rescale(lead_proposal_2)) lead_proposal_2.min(), lead_proposal_2.max() lead_proposal_3.plot.hist() lead_proposal_3.groupby(data.publisher_id).median().plot.bar(figsize=[10,5]) data["lead_score"] = pd.concat([lead_proposal_3, data.mins_as_lead[data.mins_as_lead==0]]) data.lead_score.value_counts().sort_index() data.lead_score.groupby(data.publisher_id).max() ###Output _____no_output_____ ###Markdown In summary then, score for article $a$ is:$$unscaledLeadScore_a = \frac{\min(minsAsLead_a, 60)}{\log(alexaRank_a)}\\leadScore_a = 19 \cdot \frac{unscaledLeadScore_a - \min(unscaledLeadScore)}{\max(unscaledLeadScore) - \min(unscaledLeadScore)} + 1$$Since the minium value of $minsAsLead$ is 1, $\min(unscaledLeadScore)$ is pretty insignificant. So we can simplify this to:$$leadScore_a = 20 \cdot \frac{unscaledLeadScore_a } {\max(unscaledLeadScore)} $$or: $$leadScore_a = 20 \cdot \frac{\frac{\min(minsAsLead_a, 60)}{\log(alexaRank_a)} } {\frac{60}{\log(\max(alexaRank))}} $$$$leadScore_a = \left( 20 \cdot \frac{\min(minsAsLead_a, 60)}{\log(alexaRank_a)} \cdot {\frac{\log(\max(alexaRank))}{60}} \right)$$ Time on front score (15 points)This is similar to time as lead, so lets try doing the same calculation, except we also want to factor in the number of slots on the front:$$frontScore_a = 15 \left(\frac{\min(minsOnFront_a, 1440)}{alexaRank_a \cdot numArticlesOnFront_a}\right) \left( \frac{\min(alexaRank \cdot numArticlesOnFront)}{1440} \right)$$ ###Code (data.alexa_rank * data.num_articles_on_front).min() / 1440 time_on_front_proposal_1 = np.ceil(data.mins_on_front.clip_upper(1440) / (data.alexa_rank * data.num_articles_on_front) * (2.45) * 15) time_on_front_proposal_1.plot.hist(figsize=(15, 7), bins=15) time_on_front_proposal_1.value_counts().sort_index() time_on_front_proposal_1.groupby(data.publisher_id).sum() ###Output _____no_output_____ ###Markdown That looks good to me. ###Code data["front_score"] = np.ceil(data.mins_on_front.clip_upper(1440) / (data.alexa_rank * data.num_articles_on_front) * (2.45) * 15).fillna(0) data.front_score ###Output _____no_output_____ ###Markdown Facebook brand page promotion (15 points)One way a publisher has of promoting content is to post to their brand page. The significance of doing so is stronger when the brand page has more followers (likes).$$ facebookPromotionProposed1_a = 15 \left( \frac {brandPageLikes_a} {\max(brandPageLikes)} \right) $$Now lets explore the data to see if that makes sense. **tr;dr the formula above is incorrect** ###Code data.fb_brand_page_likes.max() facebook_promotion_proposed_1 = np.ceil((15 * (data.fb_brand_page_likes / data.fb_brand_page_likes.max())).fillna(0)) facebook_promotion_proposed_1.value_counts().sort_index().plot.bar() facebook_promotion_proposed_1.groupby(data.publisher_id).describe() ###Output _____no_output_____ ###Markdown That's too much variation: sites like the Guardian, which have a respectable 7.5m likes, should not be scoring a 3. Lets try applying a log to it, and then standard feature scaling again. ###Code data.fb_brand_page_likes.groupby(data.publisher_id).max() np.log(2149) np.log(data.fb_brand_page_likes.groupby(data.publisher_id).max()) ###Output _____no_output_____ ###Markdown That's more like it, but the lower numbers should be smaller. ###Code np.log(data.fb_brand_page_likes.groupby(data.publisher_id).max() / 1000) scaled_fb_brand_page_likes = (data.fb_brand_page_likes / 1000) facebook_promotion_proposed_2 = np.ceil(\ (15 * \ (np.log(scaled_fb_brand_page_likes) / np.log(scaled_fb_brand_page_likes.max()))\ )\ ).fillna(0) facebook_promotion_proposed_2.groupby(data.publisher_id).max() ###Output _____no_output_____ ###Markdown LGTM. So the equation is$$ facebookPromotion_a = 15 \left( \frac {\log(\frac {brandPageLikes_a}{1000})} {\log(\frac {\max(brandPageLikes)}{1000}))} \right) $$ Now, let's try applying standard feature scaling approch to this, rather than using a magic number of 1,000. That equation would be:\begin{align}unscaledFacebookPromotion_a &= \log(brandPageLikes_a) \\facebookPromotion_a &= 15 \cdot \frac{unscaledFacebookPromotion_a - \min(unscaledFacebookPromotion)}{\max(unscaledFacebookPromotion) - \min(unscaledFacebookPromotion)} \\\\\text{The scaling can be simplified to:} \\facebookPromotion_a &= 15 \cdot \frac{unscaledFacebookPromotion_a - \log(\min(brandPageLikes))}{\log(\max(brandPageLikes)) - \log(\min(brandPageLikes))} \\\\\text{Meaning the overall equation becomes:} \\facebookPromotion_a &= 15 \cdot \frac{\log(brandPageLikes_a) - \log(\min(brandPageLikes))}{\log(\max(brandPageLikes)) - \log(\min(brandPageLikes))} \end{align} ###Code facebook_promotion_proposed_3 = np.ceil( (14 * ( (np.log(data.fb_brand_page_likes) - np.log(data.fb_brand_page_likes.min()) ) / (np.log(data.fb_brand_page_likes.max()) - np.log(data.fb_brand_page_likes.min())) ) ) + 1 ) facebook_promotion_proposed_3.groupby(data.publisher_id).max() data["facebook_promotion_score"] = facebook_promotion_proposed_3.fillna(0.0) ###Output _____no_output_____ ###Markdown Review ###Code data["promotion_score"] = (data.lead_score + data.front_score + data.facebook_promotion_score) data["attention_index"] = (data.promotion_score + data.response_score) data.promotion_score.plot.hist(bins=np.arange(50), figsize=(15,6)) data.attention_index.plot.hist(bins=np.arange(100), figsize=(15,6)) data.attention_index.value_counts().sort_index() # and lets see the articles with the biggest attention index data.sort_values("attention_index", ascending=False) data["score_diff"] = data.promotion_score - data.response_score # promoted but low response data.sort_values("score_diff", ascending=False).head(25) # high response but not promoted data.sort_values("score_diff", ascending=True).head(25) ###Output _____no_output_____ ###Markdown Write that data to a file. Note that the scores here are provisional for two reasons:1. they should be using a rolling-month based on the article publication date to calculate medians/min/max etc, whereas in this workbook we as just using values for the month of May2. for analysis, we've rounded the numbers; we don't expect to do that for the actual scores ###Code data.to_csv("articles_with_provisional_scores_2017-08-01_2017-08-31.csv") ###Output _____no_output_____
05_hot_bats/bin/qpcr_pro_tbdt_counts.ipynb
###Markdown Read data ###Code mg_abund = pd.read_csv('../data/timeseries2plot.tsv', delimiter='\t', parse_dates=['date']) mg_abund.head() qpcr_abund = pd.read_csv('../data/hot_bats_prochlorococcus_qpcr_data.tsv', delimiter='\t', parse_dates=['date']) qpcr_abund.head() ###Output _____no_output_____ ###Markdown Wrangling Set a min and max date for the time window. Time stamps in each individual dataset will be relative to those absolute times ###Code dmin = pd.to_datetime('2002-10-01 00:00:00+00:00') dmax = pd.to_datetime('2005-04-01 00:00:00+00:00') ###Output _____no_output_____ ###Markdown First wrangle the qPCR data ###Code qpcr_abund['dmin'] = dmin qpcr_abund['dmax'] = dmax qpcr_abund_red = qpcr_abund.dropna(subset=['abundance']) qpcr_abund_red['tdiff'] = (qpcr_abund_red['date'] - qpcr_abund_red['dmin']).dt.days qpcr_abund_red['abundance_trans'] = (qpcr_abund_red['abundance']+1)**(1/3) ###Output <ipython-input-17-af1f83480e54>:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy qpcr_abund_red['tdiff'] = (qpcr_abund_red['date'] - qpcr_abund_red['dmin']).dt.days <ipython-input-17-af1f83480e54>:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy qpcr_abund_red['abundance_trans'] = (qpcr_abund_red['abundance']+1)**(1/3) ###Markdown Now wrangle the metagenome data ###Code mypad = 0.000 mg_abund['dmin'] = dmin mg_abund['dmax'] = dmax mg_abund['tdiff'] = (mg_abund['date'] - mg_abund['dmin']).dt.days mg_abund.head() ###Output _____no_output_____ ###Markdown Make dictionaries of data to plot ###Code stcld = ['HOT_HLII', 'HOT_HLI', 'HOT_LLI', 'HOT_LLII', 'HOT_LLIV', 'BATS_HLII', 'BATS_HLI', 'BATS_LLI', 'BATS_LLII', 'BATS_LLIV'] qpcr_dict = {} for station in ['HOT', "BATS"]: for clade in ['HLII', 'HLI', 'LLI', 'LLII', 'LLIV']: qpcr_dict[station+'_'+clade] = qpcr_abund_red.query("clade == @clade & location == @station & date >= dmin & date<= dmax") mg_dict = {} for station in ['HOT', "BATS"]: for clade in ['HLII', 'HLI', 'LLI', 'LLII', 'LLIV']: mg_dict[station+'_'+clade] = mg_abund.query("location == @station & date >= dmin & date<= dmax") ###Output _____no_output_____ ###Markdown Preparing to plot Make some manual axis ticks that acutally correspond to 4 month cutoffs ###Code pd.to_datetime('2005-01-01')-pd.to_datetime('2002-10-01') dlabs = ['2003-01-01', '2003-04-01', '2003-07-01', '2003-10-01', '2004-01-01', '2004-04-01', '2004-07-01', '2004-10-01', '2005-01-01'] dvals = [92, 182, 273, 365, 457, 548, 639, 731, 823] ###Output _____no_output_____ ###Markdown Do the plot ###Code scale = 450 fig, axs = plt.subplots(2,5, figsize=(25, 6), facecolor='w', edgecolor='k', sharey=True) fig.subplots_adjust(hspace = .25, wspace=0.03) axs = axs.ravel() axs_bin = [] for i in range(10): x0=qpcr_dict[stcld[i]]['tdiff'] y0=qpcr_dict[stcld[i]]['depth'] z0=qpcr_dict[stcld[i]]['abundance_trans'] x1=mg_dict[stcld[i]]['tdiff'] y1=mg_dict[stcld[i]]['depth'] z1=mg_dict[stcld[i]]['RA'] axs[i].tricontour(x0, y0, z0, levels=4, linewidths=0.5, colors='k') cntr = axs[i].tricontourf(x0, y0, z0, levels=15, cmap="viridis") #cmo.algae #cntr = axs[i].tripcolor(x0, y0, z0, cmap="cmo.algae") #viridis #axs[i].plot(x0, y0, 'ko', ms=0.5) axs_bin.append(axs[i].scatter(x1, y1, edgecolor = '#000000', c='white', s=scale*z1, alpha=1, marker='o')) axs[i].set_title(stcld[i]) axs[i].axis([min(x0), max(x0), max(y0), 0]) axs[i].set_xticks(dvals) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.81, 0.15, 0.01, 0.7]) mycbar = fig.colorbar(cntr, cax=cbar_ax) mycbar.set_label("cubic root abun", rotation=270) kw = dict(prop="sizes", num=6, func=lambda s: (s/scale)) fig.legend(*axs_bin[0].legend_elements(**kw), loc=[0.9, 0.3], title="TBDT\nrel abund") plt.savefig("../figs/all_ecotypes.png", dpi=300, format="png") plt.savefig("../figs/all_ecotypes.svg", format="svg") ###Output _____no_output_____ ###Markdown Plot with only HLI HLII and LLI ###Code stcld_r = stcld[0:3]+stcld[5:8] stcld_r scale = 450 fig, axs = plt.subplots(2,3, figsize=(15, 6), facecolor='w', edgecolor='k', sharey=True) fig.subplots_adjust(hspace = .25, wspace=0.03) axs = axs.ravel() axs_bin = [] for i in range(6): x0=qpcr_dict[stcld_r[i]]['tdiff'] y0=qpcr_dict[stcld_r[i]]['depth'] z0=qpcr_dict[stcld_r[i]]['abundance_trans'] x1=mg_dict[stcld_r[i]]['tdiff'] y1=mg_dict[stcld_r[i]]['depth'] z1=mg_dict[stcld_r[i]]['RA'] axs[i].tricontour(x0, y0, z0, levels=4, linewidths=0.5, colors='k') cntr = axs[i].tricontourf(x0, y0, z0, levels=15, cmap="viridis") #cmo.algae #cntr = axs[i].tripcolor(x0, y0, z0, cmap="cmo.algae") #viridis #axs[i].plot(x0, y0, 'ko', ms=0.5) axs_bin.append(axs[i].scatter(x1, y1, edgecolor = '#000000', c='white', s=scale*z1, alpha=1, marker='o')) axs[i].set_title(stcld[i]) axs[i].axis([min(x0), max(x0), max(y0), 0]) axs[i].set_xticks(dvals) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.81, 0.15, 0.01, 0.7]) mycbar = fig.colorbar(cntr, cax=cbar_ax) mycbar.set_label("cubic root abun", rotation=270) kw = dict(prop="sizes", num=6, func=lambda s: (s/scale)) fig.legend(*axs_bin[0].legend_elements(**kw), loc=[0.9, 0.3], title="TBDT\nrel abund") plt.savefig("../figs/main_ecotypes.png", dpi=300, format="png") plt.savefig("../figs/main_ecotypes.svg", format="svg") ###Output _____no_output_____
topic_model_vis.ipynb
###Markdown **Topic Modeling for Biomedical Literature**> Author : Anoushkrit Goel--- Topic Model In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.[6] Libraries Gensim NLTK NumPy pandas pyLDAvis stop_words Importing Libraries ###Code import nltk nltk.download('stopwords') import re import numpy as np import pandas as pd from pprint import pprint # Gensim import gensim import gensim.corpora as corpora from gensim.utils import simple_preprocess from gensim.models import CoherenceModel # spacy for lemmatization import spacy # Plotting tools import pyLDAvis import pyLDAvis.gensim # don't skip this import matplotlib.pyplot as plt # Enable logging for gensim - optional import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR) import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) # NLTK Stop words from nltk.corpus import stopwords stop_words = stopwords.words('english') stop_words.extend(['from', 'subject', 're', 'edu', 'use']) pip install pyldavis # Import Dataset df = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json') print(df.target_names.unique()) df.head() # Convert to list data = df.content.values.tolist() # Remove Emails data = [re.sub('\S*@\S*\s?', '', sent) for sent in data] # Remove new line characters data = [re.sub('\s+', ' ', sent) for sent in data] # Remove distracting single quotes data = [re.sub("\'", "", sent) for sent in data] pprint(data[:1]) def sent_to_words(sentences): for sentence in sentences: yield(gensim.utils.simple_preprocess(str(sentence), deacc=True)) # deacc=True removes punctuations data_words = list(sent_to_words(data)) print(data_words[:1]) # Build the bigram and trigram models bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases. trigram = gensim.models.Phrases(bigram[data_words], threshold=100) # Faster way to get a sentence clubbed as a trigram/bigram bigram_mod = gensim.models.phrases.Phraser(bigram) trigram_mod = gensim.models.phrases.Phraser(trigram) # See trigram example print(trigram_mod[bigram_mod[data_words[0]]]) # Define functions for stopwords, bigrams, trigrams and lemmatization def remove_stopwords(texts): return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts] def make_bigrams(texts): return [bigram_mod[doc] for doc in texts] def make_trigrams(texts): return [trigram_mod[bigram_mod[doc]] for doc in texts] def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']): """https://spacy.io/api/annotation""" texts_out = [] for sent in texts: doc = nlp(" ".join(sent)) texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) return texts_out # Remove Stop Words data_words_nostops = remove_stopwords(data_words) # Form Bigrams data_words_bigrams = make_bigrams(data_words_nostops) # Initialize spacy 'en' model, keeping only tagger component (for efficiency) # python3 -m spacy download en nlp = spacy.load('en', disable=['parser', 'ner']) # Do lemmatization keeping only noun, adj, vb, adv data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']) print(data_lemmatized[:1]) # Create Dictionary id2word = corpora.Dictionary(data_lemmatized) # Create Corpus texts = data_lemmatized # Term Document Frequency corpus = [id2word.doc2bow(text) for text in texts] # View print(corpus[:1]) id2word[0] # Human readable format of corpus (term-frequency) [[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]] # Build LDA model lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=20, random_state=100, update_every=1, chunksize=100, passes=10, alpha='auto', per_word_topics=True) # Print the Keyword in the 10 topics pprint(lda_model.print_topics()) doc_lda = lda_model[corpus] # Compute Perplexity print('\nPerplexity: ', lda_model.log_perplexity(corpus)) # a measure of how good the model is. lower the better. # Compute Coherence Score coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v') coherence_lda = coherence_model_lda.get_coherence() print('\nCoherence Score: ', coherence_lda) # Visualize the topics pyLDAvis.enable_notebook() vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word) vis # Download File: http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip mallet_path = 'path/to/mallet-2.0.8/bin/mallet' # update this path ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=20, id2word=id2word) # Show Topics pprint(ldamallet.show_topics(formatted=False)) # Compute Coherence Score coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=data_lemmatized, dictionary=id2word, coherence='c_v') coherence_ldamallet = coherence_model_ldamallet.get_coherence() print('\nCoherence Score: ', coherence_ldamallet) def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3): """ Compute c_v coherence for various number of topics Parameters: ---------- dictionary : Gensim dictionary corpus : Gensim corpus texts : List of input texts limit : Max num of topics Returns: ------- model_list : List of LDA topic models coherence_values : Coherence values corresponding to the LDA model with respective number of topics """ coherence_values = [] model_list = [] for num_topics in range(start, limit, step): model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word) model_list.append(model) coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v') coherence_values.append(coherencemodel.get_coherence()) return model_list, coherence_values # Can take a long time to run. model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=6) # Show graph limit=40; start=2; step=6; x = range(start, limit, step) plt.plot(x, coherence_values) plt.xlabel("Num Topics") plt.ylabel("Coherence score") plt.legend(("coherence_values"), loc='best') plt.show() # Print the coherence scores for m, cv in zip(x, coherence_values): print("Num Topics =", m, " has Coherence Value of", round(cv, 4)) # Select the model and print the topics optimal_model = model_list[3] model_topics = optimal_model.show_topics(formatted=False) pprint(optimal_model.print_topics(num_words=10)) def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data): # Init output sent_topics_df = pd.DataFrame() # Get main topic in each document for i, row in enumerate(ldamodel[corpus]): row = sorted(row, key=lambda x: (x[1]), reverse=True) # Get the Dominant topic, Perc Contribution and Keywords for each document for j, (topic_num, prop_topic) in enumerate(row): if j == 0: # => dominant topic wp = ldamodel.show_topic(topic_num) topic_keywords = ", ".join([word for word, prop in wp]) sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True) else: break sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords'] # Add original text to the end of the output contents = pd.Series(texts) sent_topics_df = pd.concat([sent_topics_df, contents], axis=1) return(sent_topics_df) df_topic_sents_keywords = format_topics_sentences(ldamodel=optimal_model, corpus=corpus, texts=data) # Format df_dominant_topic = df_topic_sents_keywords.reset_index() df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text'] # Show df_dominant_topic.head(10) # Group top 5 sentences under each topic sent_topics_sorteddf_mallet = pd.DataFrame() sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic') for i, grp in sent_topics_outdf_grpd: sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], axis=0) # Reset Index sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True) # Format sent_topics_sorteddf_mallet.columns = ['Topic_Num', "Topic_Perc_Contrib", "Keywords", "Text"] # Show sent_topics_sorteddf_mallet.head() # Number of Documents for Each Topic topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts() # Percentage of Documents for Each Topic topic_contribution = round(topic_counts/topic_counts.sum(), 4) # Topic Number and Keywords topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']] # Concatenate Column wise df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1) # Change Column names df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents'] # Show df_dominant_topics ###Output _____no_output_____
_tutorials/tutorial_03/code/mle_hands_on.ipynb
###Markdown MLE - Taxi Ride Durations Initialization ###Code # Importing packages import numpy as np # Numerical package (mainly multi-dimensional arrays and linear algebra) import pandas as pd # A package for working with data frames import matplotlib.pyplot as plt # A plotting package ## Setup matplotlib to output figures into the notebook ## - To make the figures interactive (zoomable, tooltip, etc.) use ""%matplotlib notebook" instead %matplotlib inline ## Setting some nice matplotlib defaults plt.rcParams['figure.figsize'] = (5.0, 5.0) # Set default plot's sizes plt.rcParams['figure.dpi'] = 120 # Set default plot's dpi (increase fonts' size) plt.rcParams['axes.grid'] = True # Show grid by default in figures ## Auxiliary function for prining equations and pandas tables in cells output from IPython.core.display import display, HTML, Latex ## Setting style (not relevant in Colab) display(HTML('<link rel="stylesheet" href="../../../css/style.css">')) ## Use the same style as the rest of the site (mostly for titiles) display(HTML("<style>.output_png { display: table-cell; text-align: center; vertical-align: middle; }</style>")) ## Center output figures ###Output _____no_output_____ ###Markdown Preparing the Dataset Preparing the NYC taxi rides dataset. Loading the data - The data can be found at [https://technion046195.github.io/semester_2019_spring/datasets/nyc_taxi_rides.csv](https://technion046195.github.io/semester_2019_spring/datasets/nyc_taxi_rides.csv) ###Code data_file = 'https://technion046195.github.io/semester_2019_spring/datasets/nyc_taxi_rides.csv' ## Loading the data dataset = pd.read_csv(data_file) ###Output _____no_output_____ ###Markdown Previewing the dataprinting out the 10 first rows. ###Code ## Print the number of rows in the data set number_of_rows = len(dataset) display(Latex('Number of rows in the dataset: $N={}$'.format(number_of_rows))) ## Show the first 10 rows display(HTML(dataset.head(10).to_html())) ###Output _____no_output_____ ###Markdown Plotting the dataLet us plot again the histogram of the durations ###Code ## Prepare the figure fig, ax = plt.subplots() ax.hist(dataset['duration'].values, bins=300 ,density=True) ax.set_title('Historgram of Durations') ax.set_ylabel('PDF') ax.set_xlabel('Duration [min]'); ###Output _____no_output_____ ###Markdown Splitting the datasetWe will split the data into 80% train set and 20% test set for later evaluations ###Code n_samples = len(dataset) ## Generate a random generator with a fixed seed (this is important to make our result reproducible) rand_gen = np.random.RandomState(0) ## Generating a shuffled vector of indices indices = rand_gen.permutation(n_samples) ## Split the indices into 80% train / 20% test n_samples_train = int(n_samples * 0.8) train_indices = indices[:n_samples_train] test_indices = indices[n_samples_train:] train_set = dataset.iloc[train_indices] test_set = dataset.iloc[test_indices] ###Output _____no_output_____ ###Markdown Attempt 1 : Normal Distribution + MLE Calculating models parameters:$$\mu=\displaystyle{\frac{1}{N}\sum_i x_i} \\\sigma=\sqrt{\displaystyle{\frac{1}{N}\sum_i\left(x_i-\mu\right)^2}} \\$$ ###Code ## extarcting the samples x = train_set['duration'].values ## Normal distribution parameters mu = np.sum(x) / len(x) sigma = np.sqrt(np.sum((x - mu) ** 2) / len(x)) display(Latex('$\\mu = {:.01f}\\ \\text{{min}}$'.format(mu))) display(Latex('$\\sigma = {:.01f}\\ \\text{{min}}$'.format(sigma))) ###Output _____no_output_____ ###Markdown From here on we will use [np.mean](http://lagrange.univ-lyon1.fr/docs/numpy/1.11.0/reference/generated/numpy.mean.html) and [np.std](http://lagrange.univ-lyon1.fr/docs/numpy/1.11.0/reference/generated/numpy.std.html) functions to calculate the mean and standard deviation.In addition [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) has a wide range of distribution models. Each model comes with a set of methods for calculating the CDF, PDF, performing MLE fit, generate samples and more. ###Code ## Define the grid grid = np.arange(-10, 60 + 0.1, 0.1) ## Import the normal distribution model from SciPy from scipy.stats import norm ## Define the normal distribution object norm_dist = norm(mu, sigma) ## Calculate the normal distribution PDF over the grid norm_pdf = norm_dist.pdf(grid) ## Prepare the figure fig, ax = plt.subplots() ax.hist(dataset['duration'].values, bins=300 ,density=True, label='Histogram') ax.plot(grid, norm_pdf, label='Normal') ax.set_title('Distribution of Durations') ax.set_ylabel('PDF') ax.set_xlabel('Duration [min]') ax.legend(); fig.savefig('../media/normal.png') ###Output _____no_output_____ ###Markdown Attempt 2 : Rayleigh Distribution + MLE Calculating models parameters:$$\Leftrightarrow \sigma = \sqrt{\frac{1}{2N}\sum_i x^2}$$ ###Code ## Import the normal distribution model from SciPy from scipy.stats import rayleigh ## Find the model's parameters using SciPy _, sigma = rayleigh.fit(x, floc=0) ## equivalent to running: sigma = np.sqrt(np.sum(x ** 2) / len(x) / 2) display(Latex('$\\sigma = {:.01f}$'.format(sigma))) ## Define the Rayleigh distribution object rayleigh_dist = rayleigh(0, sigma) ## Calculate the Rayleigh distribution PDF over the grid rayleigh_pdf = rayleigh_dist.pdf(grid) ## Prepare the figure fig, ax = plt.subplots() ax.hist(dataset['duration'].values, bins=300 ,density=True, label='Histogram') ax.plot(grid, norm_pdf, label='Normal') ax.plot(grid, rayleigh_pdf, label='Rayleigh') ax.set_title('Distribution of Durations') ax.set_ylabel('PDF') ax.set_xlabel('Duration [min]') ax.legend(); fig.savefig('../media/rayleigh.png') ###Output _____no_output_____ ###Markdown Attempt 2 : Generalized Gamma Distribution + MLE Numerical solution ###Code ## Import the normal distribution model from SciPy from scipy.stats import gengamma ## Find the model's parameters using SciPy a, c, _, sigma = gengamma.fit(x, floc=0) display(Latex('$a = {:.01f}$'.format(a))) display(Latex('$c = {:.01f}$'.format(c))) display(Latex('$\\sigma = {:.01f}$'.format(sigma))) ## Define the generalized gamma distribution object gengamma_dist = gengamma(a, c, 0, sigma) ## Calculate the generalized gamma distribution PDF over the grid gengamma_pdf = gengamma_dist.pdf(grid) ## Prepare the figure fig, ax = plt.subplots() ax.hist(dataset['duration'].values, bins=300 ,density=True, label='Histogram') ax.plot(grid, norm_pdf, label='Normal') ax.plot(grid, rayleigh_pdf, label='Rayleigh') ax.plot(grid, gengamma_pdf, label='Generalized Gamma') ax.set_title('Distribution of Durations') ax.set_ylabel('PDF') ax.set_xlabel('Duration [min]') ax.legend(); fig.savefig('../media/generalized_gamma.png') ###Output _____no_output_____
ch03/08_Training_Dov2Vec_using_Gensim.ipynb
###Markdown Doc2Vecใ“ใฎใƒŽใƒผใƒˆใƒ–ใƒƒใ‚ฏใงใฏใ€gensimใ‚’็”จใ„ใฆใ€Doc2vecใฎใƒขใƒ‡ใƒซใ‚’ๅญฆ็ฟ’ใ™ใ‚‹ๆ–นๆณ•ใ‚’็ดนไป‹ใ—ใพใ™ใ€‚ ใƒ‘ใƒƒใ‚ฑใƒผใ‚ธใฎใ‚คใƒณใƒใƒผใƒˆgensimใจNLTKใฏใ‚คใƒณใ‚นใƒˆใƒผใƒซๆธˆใฟใจใ—ใพใ™ใ€‚ ###Code import warnings from pprint import pprint import nltk from gensim.models.doc2vec import Doc2Vec, TaggedDocument from nltk.tokenize import word_tokenize nltk.download('punkt') warnings.filterwarnings('ignore') ###Output [nltk_data] Downloading package punkt to /root/nltk_data... [nltk_data] Unzipping tokenizers/punkt.zip. ###Markdown ๅญฆ็ฟ’ใƒ‡ใƒผใ‚ฟใฎๆบ–ๅ‚™ใพใšใฏใ€gensimใงDoc2Vecใ‚’ๅญฆ็ฟ’ใ™ใ‚‹ใŸใ‚ใฎใƒ‡ใƒผใ‚ฟใจใ—ใฆใ€[TaggedDocument](https://radimrehurek.com/gensim/models/doc2vec.htmlgensim.models.doc2vec.TaggedDocument)ใ‚’็”จๆ„ใ—ใพใ™ใ€‚TaggedDocumentใฏใ€ๅ˜่ชžใฎ็ณปๅˆ—ใจใ‚ฟใ‚ฐใฎ็ณปๅˆ—ใ‹ใ‚‰ๆง‹ๆˆใ•ใ‚Œใพใ™ใ€‚ใ‚ฟใ‚ฐใฏ1ใคไปฅไธŠใฎๆ–‡ๅญ—ๅˆ—ใ‚’ไฝฟใˆใพใ™ใŒใ€ใƒกใƒขใƒชๅŠน็Ž‡ใฎ่ฆณ็‚นใ‹ใ‚‰ไธ€ๆ„ใชๆ•ดๆ•ฐใฎIDใ‚’ไฝฟใ†ใ“ใจใŒใปใจใ‚“ใฉใงใ™ใ€‚ ###Code data = [ "dog bites man", "man bites dog", "dog eats meat", "man eats food" ] tagged_data = [ TaggedDocument( words=word_tokenize(word), tags=[str(i)] ) for i, word in enumerate(data) ] tagged_data ###Output _____no_output_____ ###Markdown ใƒขใƒ‡ใƒซใฎๅญฆ็ฟ’ๅญฆ็ฟ’ใƒ‡ใƒผใ‚ฟใ‚’็”จๆ„ใ—ใŸใ‚‰ใ€[Doc2Vec](https://radimrehurek.com/gensim/models/doc2vec.htmlgensim.models.doc2vec.Doc2Vec)ใ‚ฏใƒฉใ‚นใซไธŽใˆใฆใ€ใƒขใƒ‡ใƒซใ‚’ๅญฆ็ฟ’ใ—ใพใ™ใ€‚ๆŒ‡ๅฎšใงใใ‚‹ใƒ‘ใƒฉใƒกใƒผใ‚ฟใฏๆง˜ใ€…ใ‚ใ‚Šใพใ™ใŒใ€`dm`ใงๅญฆ็ฟ’ใ‚ขใƒซใ‚ดใƒชใ‚บใƒ ใ‚’ๆŒ‡ๅฎšใงใใพใ™ใ€‚1ใ‚’ๆŒ‡ๅฎšใ™ใ‚‹ใจใ€ๅˆ†ๆ•ฃใƒกใƒขใƒชใ€0ใ‚’ๆŒ‡ๅฎšใ™ใ‚‹ใจๅˆ†ๆ•ฃBoWใซใชใ‚Šใพใ™ใ€‚ ###Code # ๅˆ†ๆ•ฃBoWใฎๅญฆ็ฟ’ model_dbow = Doc2Vec( tagged_data, vector_size=20, min_count=1, epochs=2, dm=0 ) ###Output _____no_output_____ ###Markdown ๅญฆ็ฟ’ใ‚’็ต‚ใˆใŸใ‚‰ใ€ใƒขใƒ‡ใƒซใฎ[inifer_vector](https://radimrehurek.com/gensim/models/doc2vec.htmlgensim.models.doc2vec.Doc2Vec.infer_vector)ใƒกใ‚ฝใƒƒใƒ‰ใซๆ–‡ๆ›ธใ‚’ไธŽใˆใฆใ€ใƒ™ใ‚ฏใƒˆใƒซใ‚’ๅ–ๅพ—ใ—ใพใ™ใ€‚ ###Code print(model_dbow.infer_vector(['man', 'eats', 'food'])) model_dbow.wv.most_similar("man", topn=5) # top 5 most simlar words. ###Output _____no_output_____ ###Markdown `n_similarity`ใ‚’ไฝฟใฃใฆใ€2ใคใฎๅ˜่ชž้›†ๅˆ้–“ใฎ้กžไผผๅบฆใ‚’่จˆ็ฎ—ใ—ใพใ™ใ€‚ ###Code model_dbow.wv.n_similarity(["dog"],["man"]) # ๅˆ†ๆ•ฃใƒกใƒขใƒชใฎๅญฆ็ฟ’ model_dm = Doc2Vec( tagged_data, min_count=1, vector_size=20, epochs=2, dm=1 ) print("Inference Vector of man eats food") print(model_dm.infer_vector(['man', 'eats', 'food'])) print("Most similar words to man in our corpus") print(model_dm.wv.most_similar("man",topn=5)) print("Similarity between man and dog: ", model_dm.wv.n_similarity(["dog"],["man"])) ###Output Inference Vector of man eats food [-0.01564045 0.0173833 -0.00516716 0.0037643 -0.01813941 -0.00460716 -0.01941588 -0.01952404 -0.00677244 -0.00411688 0.00786548 0.01587102 -0.00982586 -0.02477862 0.00217828 0.02137304 -0.00618664 0.00858937 0.01089258 -0.01651028] Most similar words to man in our corpus [('dog', 0.3310743570327759), ('eats', 0.2360897958278656), ('meat', 0.052991606295108795), ('food', -0.0032464265823364258), ('bites', -0.41033852100372314)] Similarity between man and dog: 0.33107436 ###Markdown ใƒœใ‚ญใƒฃใƒ–ใƒฉใƒชใซๅญ˜ๅœจใ—ใชใ„ๅ˜่ชžใ‚’้กžไผผๅบฆใฎๆฏ”่ผƒใซไฝฟใ†ใจไฝ•ใŒ่ตทใใ‚‹ใงใ—ใ‚‡ใ†ใ‹๏ผŸ ###Code model_dm.wv.n_similarity(['covid'],['man']) ###Output _____no_output_____
Training_Models.ipynb
###Markdown SetupFirst, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn โ‰ฅ0.20. ###Code # Python โ‰ฅ3.5 is required import sys assert sys.version_info >= (3, 5) # Scikit-Learn โ‰ฅ0.20 is required import sklearn assert sklearn.__version__ >= "0.20" # Common imports import numpy as np import os # to make this notebook's output stable across runs np.random.seed(42) # To plot pretty figures %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "." CHAPTER_ID = "training_linear_models" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) os.makedirs(IMAGES_PATH, exist_ok=True) def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) # Ignore useless warnings (see SciPy issue #5998) import warnings warnings.filterwarnings(action="ignore", message="^internal gelsd") ###Output _____no_output_____ ###Markdown The Normal Equation : Linear Regression ###Code import numpy as np X = 2 * np.random.rand(100, 1) y = 4 + 3 * X + np.random.randn(100, 1) import matplotlib.pyplot as plt plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([0, 2, 0, 15]) save_fig("generated_data_plot") plt.show(); X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) theta_best X_new = np.array([[0], [2]]) X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance y_predict = X_new_b.dot(theta_best) y_predict plt.plot(X_new, y_predict, "r-", linewidth=2, label="Predictions") plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc="upper left", fontsize=14) plt.axis([0, 2, 0, 15]) save_fig("linear_model_predictions_plot") plt.show() from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X,y) lin_reg.intercept_, lin_reg.coef_ lin_reg.predict(X_new) ###Output _____no_output_____ ###Markdown Singular Value Decomposition (SVD)The LinearRegression class is based on the scipy.linalg.lstsq() function (the name stands for "least squares"), which you could call directly: ###Code theta_best_svd, residuals, rank, s = np.linalg.lstsq(X_b, y, rcond=1e-6) theta_best_svd np.linalg.pinv(X_b).dot(y) ###Output _____no_output_____ ###Markdown Linear Regression using Batch Gradient Descent ###Code eta = 0.1 # learning rate n_iterations = 1000 m = 100 theta = np.random.randn(2,1) # random initialization for iteration in range(n_iterations): gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients theta X_new_b.dot(theta) theta_path_bgd = [] def plot_gradient_descent(theta, eta, theta_path=None): m = len(X_b) plt.plot(X, y, "b.") n_iterations = 1000 for iteration in range(n_iterations): if iteration < 10: y_predict = X_new_b.dot(theta) style = "b-" if iteration > 0 else "r--" plt.plot(X_new, y_predict, style) gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients if theta_path is not None: theta_path.append(theta) plt.xlabel("$x_1$", fontsize=18) plt.axis([0, 2, 0, 15]) plt.title(r"$\eta = {}$".format(eta), fontsize=16) np.random.seed(42) theta = np.random.randn(2,1) # random initialization plt.figure(figsize=(10,4)) plt.subplot(131); plot_gradient_descent(theta, eta=0.02) plt.ylabel("$y$", rotation=0, fontsize=18) plt.subplot(132); plot_gradient_descent(theta, eta=0.01, theta_path=theta_path_bgd) plt.subplot(133); plot_gradient_descent(theta, eta=0.5) save_fig("gradient_descent_plot") plt.show(); ###Output Saving figure gradient_descent_plot ###Markdown Stochastic Gradient Descent using a simple learning scheduleStochastic == Randomlearning schedule determines the learning rate at each iteration ###Code theta_path_sgd = [] m = len(X_b) np.random.seed(42) n_epochs = 50 t0, t1 = 5, 50 # learning schedule hyperparameters def learning_schedule(t): return t0 / (t + t1) theta = np.random.randn(2,1) # random initialization for epoch in range(n_epochs): for i in range(m): # not in book if epoch == 0 and i < 20: y_predict = X_new_b.dot(theta) style = "b-" if i > 0 else "r--" plt.plot(X_new, y_predict, style) # end of not in book random_index = np.random.randint(m) xi = X_b[random_index:random_index+1] yi = y[random_index:random_index+1] gradients = 2 * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(epoch * m + i) theta = theta - eta * gradients theta_path_sgd.append(theta) plt.plot(X,y, "b.") plt.xlabel("$x_1", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([0,2, 0,15]) save_fig("sgd_plot") plt.show(); theta from sklearn.linear_model import SGDRegressor sgd_reg = SGDRegressor(max_iter=1000, tol=1e-3, penalty=None, eta0=0.1, random_state=42) sgd_reg.fit(X, y.ravel()) sgd_reg.intercept_, sgd_reg.coef_ ###Output _____no_output_____ ###Markdown Mini-batch Gradient Descent ###Code theta_path_mgd = [] n_iterations = 50 minibatch_size = 20 np.random.seed(42) theta = np.random.randn(2,1) # random initialization t0, t1 = 200, 1000 def learning_schedule(t): return t0 / (t + t1) t = 0 for epoch in range(n_iterations): shuffled_indicies = np. random.permutation(m) X_b_shuffled = X_b[shuffled_indicies] y_shuffled = y[shuffled_indicies] for i in range(0, m, minibatch_size): t += 1 xi = X_b_shuffled[i:i+minibatch_size] yi = y_shuffled[i:i+minibatch_size] gradients = 2/minibatch_size * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(t) theta = theta - eta * gradients theta_path_mgd.append(theta) theta theta_path_bgd = np.array(theta_path_bgd) theta_path_sgd = np.array(theta_path_sgd) theta_path_mgd = np.array(theta_path_mgd) plt.figure(figsize=(7,4)) plt.plot(theta_path_sgd[:,0], theta_path_sgd[:, 1], "r-s", linewidth=1, label="Stochastic") plt.plot(theta_path_mgd[:,0], theta_path_mgd[:, 1], "g-+", linewidth=1, label="Mini-batch") plt.plot(theta_path_bgd[:,0], theta_path_bgd[:, 1], "b-o", linewidth=1, label="Batch") plt.legend(loc="upper left", fontsize=16) plt.xlabel(r"$\theta_0$", fontsize=20) plt.ylabel(r"$\theta_1$", fontsize=20, rotation=0) plt.axis([2.5, 4.5, 2.3, 3.9]) save_fig("gradient_descent_paths_plot") plt.show(); ###Output Saving figure gradient_descent_paths_plot ###Markdown Polynomial Regression ###Code import numpy as np import numpy.random as rnd np.random.seed(42) m = 100 X = 6 * np.random.rand(m, 1) - 3 y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1) plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([-3,3, 0,10]) save_fig("quadratic_data_plot") plt.show(); from sklearn.preprocessing import PolynomialFeatures poly_features = PolynomialFeatures(degree=2, include_bias=False) X_poly = poly_features.fit_transform(X) X[0] X_poly[0] ###Output _____no_output_____ ###Markdown X_poly now contains the original feature of X plust the square of the feature.Now you can fit a Linear Regression model to this extended training data. ###Code lin_reg = LinearRegression() lin_reg.fit(X_poly, y) lin_reg.intercept_, lin_reg.coef_ X_new=np.linspace(-3, 3, 100).reshape(100, 1) X_new_poly = poly_features.transform(X_new) y_new = lin_reg.predict(X_new_poly) plt.plot(X, y, "b.") plt.plot(X_new, y_new, "r-", linewidth=2, label="Predictions") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc='upper left', fontsize=14) plt.axis([-3,3, 0,10]) save_fig('quadratic_predictions_plot') plt.show(); from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline for style, width, degree in (("g-", 1, 300), ("b--",2 ,2),("r-+",2,1)): polybig_features = PolynomialFeatures(degree=degree, include_bias=False) std_scaler = StandardScaler() lin_reg = LinearRegression() polynomial_regression = Pipeline([ ('poly_features', polybig_features), ('std_scaler', std_scaler), ('lin_reg', lin_reg), ]) polynomial_regression.fit(X,y) y_newbig = polynomial_regression.predict(X_new) plt.plot(X_new, y_newbig, style, label=str(degree), linewidth=width) plt.plot(X, y, "b.", linewidth = width) plt.legend(loc='upper left') plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([-3,3, 0,10]) save_fig("high_degree_polynomials_plot") plt.show(); from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split def plot_learning_curves(model, X, y): X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10) train_errors, val_errors = [], [] for m in range(1, len(X_train)): model.fit(X_train[:m], y_train[:m]) y_train_predict = model.predict(X_train[:m]) y_val_predict = model.predict(X_val) train_errors.append(mean_squared_error(y_train[:m], y_train_predict)) val_errors.append(mean_squared_error(y_val, y_val_predict)) plt.plot(np.sqrt(train_errors), "r-+", linewidth=2, label="train") plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="val") plt.legend(loc='upper right', fontsize=14) plt.xlabel("Training set size", fontsize=14) plt.ylabel("RMSE", fontsize=14) lin_reg = LinearRegression() plot_learning_curves(lin_reg, X, y) plt.axis([0,80, 0,3]) save_fig("underfitting_learning_curves") plt.show() #132 ###Output _____no_output_____
1-ETL/10min_to_cuDF.ipynb
###Markdown 10 Minutes to cuDF=======================Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF, geared mainly for new users. ###Code import os import numpy as np import pandas as pd import cudf np.random.seed(12) #### Portions of this were borrowed from the #### cuDF cheatsheet, existing cuDF documentation, #### and 10 Minutes to Pandas. #### Created November, 2018. ###Output _____no_output_____ ###Markdown Object Creation--------------- Creating a `Series`. ###Code s = cudf.Series([1,2,3,None,4]) print(s) ###Output _____no_output_____ ###Markdown Creating a `DataFrame` by specifying values for each column. ###Code df = cudf.DataFrame([('a', list(range(20))), ('b', list(reversed(range(20)))), ('c', list(range(20)))]) print(df) ###Output _____no_output_____ ###Markdown Creating a `Dataframe` from a pandas `Dataframe`. ###Code pdf = pd.DataFrame({'a': [0, 1, 2, 3],'b': [0.1, 0.2, None, 0.3]}) gdf = cudf.DataFrame.from_pandas(pdf) print(gdf) ###Output _____no_output_____ ###Markdown Viewing Data------------- Viewing the top rows of the GPU dataframe. ###Code print(df.head(2)) ###Output _____no_output_____ ###Markdown Sorting by values. ###Code print(df.sort_values(by='a', ascending=False)) ###Output _____no_output_____ ###Markdown Selection------------ Getting Selecting a single column, which yields a `cudf.Series`, equivalent to `df.a`. ###Code print(df['a']) ###Output _____no_output_____ ###Markdown Selection by Label Selecting rows from index 2 to index 5 from columns 'a' and 'b'. ###Code print(df.loc[2:5, ['a', 'b']]) ###Output _____no_output_____ ###Markdown Selection by Position Selecting by integer slicing, like numpy/pandas. ###Code print(df[3:5]) ###Output _____no_output_____ ###Markdown Selecting elements of a `Series` with direct index access. ###Code print(s[2]) ###Output _____no_output_____ ###Markdown Boolean Indexing Selecting rows in a `Series` by direct Boolean indexing. ###Code print(df.b[df.b > 15]) ###Output _____no_output_____ ###Markdown Selecting values from a `DataFrame` where a Boolean condition is met, via the `query` API. ###Code print(df.query("b == 3")) ###Output _____no_output_____ ###Markdown Supported logical operators include `>`, `=`, `<=`, `==`, and `!=`. Setting Missing Data------------ Missing data can be replaced by using the `fillna` method. ###Code print(s.fillna(999)) ###Output _____no_output_____ ###Markdown Operations------------ Stats Calculating descriptive statistics for a `Series`. ###Code print(s.mean(), s.var()) ###Output _____no_output_____ ###Markdown Applymap Applying functions to a `Series`. ###Code def add_ten(num): return num + 10 print(df['a'].applymap(add_ten)) ###Output _____no_output_____ ###Markdown Histogramming Counting the number of occurrences of each unique value of variable. ###Code print(df.a.value_counts()) ###Output _____no_output_____ ###Markdown String Methods Merge------------ Concat Concatenating `Series` and `DataFrames` row-wise. ###Code print(cudf.concat([s, s])) print(cudf.concat([df.head(), df.head()], ignore_index=True)) ###Output _____no_output_____ ###Markdown Join Performing SQL style merges. ###Code df_a = cudf.DataFrame() df_a['key'] = [0, 1, 2, 3, 4] df_a['vals_a'] = [float(i + 10) for i in range(5)] df_b = cudf.DataFrame() df_b['key'] = [1, 2, 4] df_b['vals_b'] = [float(i+10) for i in range(3)] df_merged = df_a.merge(df_b, on=['key'], how='left') print(df_merged.sort_values('key')) ###Output _____no_output_____ ###Markdown Append Appending values from another `Series` or array-like object. `Append` does not support `Series` with nulls. For handling null values, use the `concat` method. ###Code print(df.a.head().append(df.b.head())) ###Output _____no_output_____ ###Markdown Grouping Like pandas, cuDF supports the Split-Apply-Combine groupby paradigm. ###Code df['agg_col1'] = [1 if x % 2 == 0 else 0 for x in range(len(df))] df['agg_col2'] = [1 if x % 3 == 0 else 0 for x in range(len(df))] ###Output _____no_output_____ ###Markdown Grouping and then applying the `sum` function to the grouped data. ###Code print(df.groupby('agg_col1').sum()) ###Output _____no_output_____ ###Markdown Grouping hierarchically then applying the `sum` function to grouped data. ###Code print(df.groupby(['agg_col1', 'agg_col2']).sum()) ###Output _____no_output_____ ###Markdown Grouping and applying statistical functions to specific columns, using `agg`. ###Code print(df.groupby('agg_col1').agg({'a':'max', 'b':'mean', 'c':'sum'})) ###Output _____no_output_____ ###Markdown Reshaping------------ Time Series------------ cuDF supports `datetime` typed columns, which allow users to interact with and filter data based on specific timestamps. ###Code import datetime as dt date_df = cudf.DataFrame() date_df['date'] = pd.date_range('11/20/2018', periods=72, freq='D') date_df['value'] = np.random.sample(len(date_df)) search_date = dt.datetime.strptime('2018-11-23', '%Y-%m-%d') print(date_df.query('date <= @search_date')) ###Output _____no_output_____ ###Markdown Categoricals------------ cuDF supports categorical columns. ###Code pdf = pd.DataFrame({"id":[1,2,3,4,5,6], "grade":['a', 'b', 'b', 'a', 'a', 'e']}) pdf["grade"] = pdf["grade"].astype("category") gdf = cudf.DataFrame.from_pandas(pdf) print(gdf) ###Output _____no_output_____ ###Markdown Accessing the categories of a column. ###Code print(gdf.grade.cat.categories) ###Output _____no_output_____ ###Markdown Accessing the underlying code values of each categorical observation. ###Code print(gdf.grade.cat.codes) ###Output _____no_output_____ ###Markdown Plotting------------ Converting Data Representation-------------------------------- Pandas Converting a cuDF `DataFrame` to a pandas `DataFrame`. ###Code print(df.head().to_pandas()) ###Output _____no_output_____ ###Markdown Numpy Converting a cuDF `DataFrame` to a numpy `rec.array`. ###Code print(df.to_records()) ###Output _____no_output_____ ###Markdown Converting a cuDF `Series` to a numpy `ndarray`. ###Code print(df['a'].to_array()) ###Output _____no_output_____ ###Markdown Arrow Converting a cuDF `DataFrame` to a PyArrow `Table`. ###Code print(df.to_arrow()) ###Output _____no_output_____ ###Markdown Getting Data In/Out------------------------ CSV Writing to a CSV file, by first sending data to a pandas `Dataframe` on the host. ###Code df.to_pandas().to_csv('foo.txt', index=False) ###Output _____no_output_____ ###Markdown Reading from a csv file. ###Code df = cudf.read_csv('foo.txt', delimiter=',', names=['a', 'b', 'c', 'a1', 'a2'], dtype=['int64', 'int64', 'int64', 'int64', 'int64'], skiprows=1) print(df) ###Output _____no_output_____
02/lab2.ipynb
###Markdown ะ›ะฐะฑะพั€ะฐั‚ะพั€ะฝะฐั ั€ะฐะฑะพั‚ะฐ 2 ะžะฟะธัะฐะฝะธะต ะดะฐั‚ะฐัะตั‚ะฐะญั‚ะธ ะดะฐะฝะฝั‹ะต ัะฒะปััŽั‚ัั ั…ะฐั€ะฐะบั‚ะตั€ะธัั‚ะธะบะฐะผะธ ะบั€ะฐัะฝะพะณะพ ะฒะธะฝะฐ. ะะฐะฑะพั€ ะดะฐะฝะฝั‹ั… ะทะฐะณั€ัƒะถะฐะตั‚ัั ะธะท ั€ะตะฟะพะทะธั‚ะพั€ะธั ะผะฐัˆะธะฝะฝะพะณะพ ะพะฑัƒั‡ะตะฝะธั UCI. ะ’ ะฝั‘ะผ 1599 ะทะฐะฟะธัะตะน ะธ 12 ะฐั‚ั€ะธะฑัƒั‚ะพะฒ. Attribute Information: - fixed acidity - volatile acidity - citric acid - residual sugar - chlorides - free sulfur dioxide - total sulfur dioxide - density - pH - sulphates - alcohol Output variable (based on sensory data): - quality (score between 0 and 10) ###Code import pandas as pd import numpy as np import matplotlib.pyplot as plt DATASET = pd.read_csv('../02/winequality-red.csv') DATASET.head() quality = DATASET['quality'].sort_values().values plt.hist(quality) plt.show() DS_PROCESSED = DATASET.copy() min_ = DS_PROCESSED.min() max_ = DS_PROCESSED.max() mean_ = DS_PROCESSED.mean() std_ = DS_PROCESSED.std() nulls = DS_PROCESSED.isnull().sum() stats = pd.DataFrame({'ะŸั€ะพะฟัƒัะบะธ': nulls, 'ะœะธะฝ.': min_, 'ะœะฐะบั.': max_, 'ะกั€ะตะดะฝ.': mean_, 'ะกั‚. ะพั‚ะบะป.': std_}) stats DS_PROCESSED = DATASET.copy() DS_PROCESSED.corr()['quality'].sort_values().to_frame() from sklearn.model_selection import train_test_split FEATURE_LABELS = ['alcohol', 'sulphates', 'citric acid','fixed acidity', 'residual sugar'] FEATURES = DS_PROCESSED[FEATURE_LABELS] TARGET = DS_PROCESSED['quality'] X_train, X_test, Y_train, Y_test = train_test_split(FEATURES, TARGET, test_size = 0.4) import seaborn as sns sns.set(style= 'whitegrid', context = 'notebook') sns.pairplot(FEATURES , height=2.5) plt.show() cm = np.corrcoef(FEATURES.values.T) sns.set(font_scale=1.5) _, ax = plt.subplots(figsize=(30, 30)) hm = sns.heatmap(cm, annot=True, cbar=True, square=True, fmt='.2f', ax=ax, xticklabels=FEATURE_LABELS, yticklabels=FEATURE_LABELS) plt.show() from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score def evaluate_model(Y_pred, Y): mse = mean_squared_error(Y, Y_pred) r2 = r2_score(Y, Y_pred) print(f'MSE = {mse}') print(f'R2 = {r2}') ###Output _____no_output_____ ###Markdown ะ›ะธะฝะตะนะฝะฐั ั€ะตะณั€ะตััะธั ###Code from sklearn.linear_model import LinearRegression linear = LinearRegression().fit(X_train, Y_train) evaluate_model(linear.predict(X_test), Y_test) ###Output MSE = 0.44006167307081745 R2 = 0.28472231520836666 ###Markdown ะŸะพะปะธะฝะพะผะธะฐะปัŒะฝั‹ะต ั€ะตะณั€ะตััะธั ###Code from sklearn.preprocessing import PolynomialFeatures def train_polynomial(degree): polynomial_features= PolynomialFeatures(degree=degree) X_train_poly = polynomial_features.fit_transform(X_train) polynomial = LinearRegression().fit(X_train_poly, Y_train) return polynomial_features, polynomial polynomial_features, polynomial = train_polynomial(degree=2) X_test_poly = polynomial_features.fit_transform(X_test) evaluate_model(polynomial.predict(X_test_poly), Y_test) polynomial_features, polynomial = train_polynomial(degree=3) X_test_poly = polynomial_features.fit_transform(X_test) evaluate_model(polynomial.predict(X_test_poly), Y_test) polynomial_features, polynomial = train_polynomial(degree=4) X_test_poly = polynomial_features.fit_transform(X_test) evaluate_model(polynomial.predict(X_test_poly), Y_test) ###Output MSE = 1.9240836606379819 R2 = -2.127411884163498 ###Markdown ะกะปัƒั‡ะฐะนะฝั‹ะน ะปะตั ###Code from sklearn.ensemble import RandomForestRegressor FEATURES = DS_PROCESSED.loc[:, DS_PROCESSED.columns != 'quality'] TARGET = DS_PROCESSED['quality'] forest = RandomForestRegressor(n_estimators=10).fit(FEATURES, TARGET) def print_importance(forest, columns): df = pd.DataFrame( zip( forest.feature_importances_.round(decimals=4), columns ), columns = ['importance', 'feature'] ).sort_values('importance', ascending=False) return df print_importance(forest, FEATURES.columns).head() FEATURES = DS_PROCESSED[['alcohol', 'volatile acidity', 'sulphates', 'total sulfur dioxide', 'citric acid']] TARGET = DS_PROCESSED['quality'] X_train, X_test, Y_train, Y_test = train_test_split(FEATURES, TARGET, test_size = 0.4) n_estimators = [2, 5, 10, 20, 50, 100, 200, 500] for n in n_estimators: print(f'\n{n} estimators') forest = RandomForestRegressor(n_estimators=n).fit(X_train, Y_train) evaluate_model(forest.predict(X_test), Y_test) ###Output 2 estimators MSE = 0.57734375 R2 = 0.1190287261903431 5 estimators MSE = 0.44212500000000005 R2 = 0.3253595896152083 10 estimators MSE = 0.423046875 R2 = 0.35447098136951394 20 estimators MSE = 0.41531250000000003 R2 = 0.36627289694558374 50 estimators MSE = 0.397635 R2 = 0.3932470690792047 100 estimators MSE = 0.39181609375000004 R2 = 0.40212616277553626 200 estimators MSE = 0.3858859375 R2 = 0.4111750133181339 500 estimators MSE = 0.39185013750000003 R2 = 0.40207421527319864
devkit/notebooks/final_notebooks/python_notes.ipynb
###Markdown numpy็ดขๅผ•็›ธๅ…ณ ๅคš่กŒๆททๅˆ็ดขๅผ•(ๅฝ“ๆฏ่กŒๅ–ๅพ—ๅ…ƒ็ด ไธชๆ•ฐ็›ธๅŒๆ—ถ)ๆฏ”ๅฆ‚ไฝ ๆœ‰ไธ€ไธชไบŒ็ปดๆ•ฐ็ป„a๏ผŒไฝ ๆœ‰ไธ€ไธช็ดขๅผ•ๅบๅˆ—ๆ•ฐ็ป„a_idx,len(a)==len(a_idx), a_idx.shape[1] = k,่กจ็คบๆฏ่กŒๅ–kไธชๅ…ƒ็ด ๏ผŒa_idx็š„ๆฏไธ€่กŒ็š„ๅ€ผ่กจ็คบ่ฆๅ–็š„ๅฏนๅบ”็š„ๅˆ—็ดขๅผ•๏ผŒๆฏ่กŒๅˆ—็ดขๅผ•้ƒฝไธไธ€ๆ ท๏ผ›่ฟ™ๆ ท็š„็ดขๅผ•ๆˆ‘็งฐไธบๅคš่กŒๆททๅˆ็ดขๅผ•๏ผŒๆ— ๆณ•็›ดๆŽฅไฝฟ็”จa[a_idx] ###Code a = np.array([[1.2, 1.4, 1.12, 2.3], [2.1, 2.12, 1.56, 1.74], [3.23, 2.12, 4.23, 2.34]]) a k = 3 # ๆฏ่กŒๅ–ๅพ—ๅ…ƒ็ด ไธชๆ•ฐๅฟ…้กป็›ธๅŒ๏ผŒๅฆๅˆ™ๆ— ๆณ•็›ดๆŽฅๆž„้€ ๆˆๆ•ฐ็ป„ a_idx = np.array([[0,3,2], [1,2,3], [0,1,2]]) # ๆƒณๅ–ๆ•ฐ็ป„a็ฌฌไธ€่กŒ็š„0,3,2ๅ…ƒ็ด ๏ผŒ็ฌฌไบŒ่กŒ็š„1๏ผŒ2๏ผŒ3ๅ…ƒ็ด ๏ผŒ็ฌฌไธ‰่กŒ็š„0,1,2ๅ…ƒ็ด  a[ np.repeat(np.arange(len(a_idx)), k), a_idx.ravel()].reshape(len(a_idx), k) ###Output _____no_output_____ ###Markdown Parallel Processing in Python ###Code import multiprocessing as mp np.random.RandomState(100) arr = np.random.randint(0, 10, size=[2000000, 5]) data = arr.tolist() def howmany_within_range(row, minimum, maximum): """Returns how many numbers lie within `maximum` and `minimum` in a given `row`""" count = 0 for n in row: if minimum <= n <= maximum: count = count + 1 return count results = [] for row in data: results.append(howmany_within_range(row, minimum=4, maximum=8)) mp.cpu_count() // 2 # Step 1: Init multiprocessing.Pool() pool = mp.Pool(mp.cpu_count()// 2) # Step 2: `pool.apply` the `howmany_within_range()` results = [pool.apply(howmany_within_range, args=(row, 4, 8)) for row in data] # Step 3: Don't forget to close pool.close() ###Output Process ForkPoolWorker-2: Process ForkPoolWorker-4: Process ForkPoolWorker-1: Process ForkPoolWorker-3: Traceback (most recent call last): ###Markdown ่ฎก็ฎ—ๆ ทๆœฌ้—ด่ท็ฆปๅนถๅช้€‰ๅ‡บๆœ€ๅฐ็š„kไธช่ท็ฆป็‚น - distance.pdist๏ผš่ฎก็ฎ—n็ปด็ฉบ้—ดXไธญๆ ทๆœฌ้—ด็š„ไธคไธค(ๆˆๅฏน)่ท็ฆปใ€‚ ๅ‚ๆ•ฐ๏ผšX, metric- distance.cdist๏ผš่ฎก็ฎ—X_Aๅ’ŒX_Bไน‹้—ด็š„ไธคไธค(ๆˆๅฏน)่ท็ฆปใ€‚ ๅ‚ๆ•ฐ๏ผšXA, XB, metric- np.partition: ๅฏนๆ‰€็ป™ๆ•ฐ็ป„ๆŒ‰ๆ‰พ็ป™ๅฎšไฝ็ฝฎ่ฟ›่กŒๅˆ†ๅ‰ฒ๏ผŒ่ฟ”ๅ›žๅˆ†ๅ‰ฒๅŽ็š„ๆ•ฐ็ป„ใ€‚ๅ‚ๆ•ฐ๏ผš ็ป™ๅฎšๆ•ฐ็ป„a๏ผŒๅŠไฝ็ฝฎ็ดขๅผ•kthๆฏ”ๅฆ‚ๆŒ‡ๅฎškth=10๏ผŒๅˆ™่กจ็คบๅ…ˆ็กฎๅฎšๆ‰€็ป™ๆ•ฐ็ป„็ฌฌ10ๅฐ็š„ๆ•ฐๅญ—ไธบn๏ผŒๅˆ™่ฆๆฑ‚่ฟ”ๅ›ž็š„ๆ•ฐ็ป„ๆปก่ถณ่ฟ™ไบ›ๆกไปถ๏ผšnไฝไบŽ็ฌฌ10ไธชไฝ็ฝฎ๏ผŒๅ‰10ไธชๅ…ƒ็ด ็š„ๅ€ผๅฟ…้กปๅฐไบŽn๏ผŒnไน‹ๅŽ็š„ๅ…ƒ็ด ๅฟ…้กปๅคงไบŽn๏ผŒไธค้ƒจๅˆ†ๅ†…้ƒจ็š„้กบๅบไธไฝœ่ฆๆฑ‚;kthๅฏไปฅไธบ่ดŸๆ•ฐ๏ผŒๅฆ‚-3๏ผŒๅˆ™่กจ็คบๆŒ‰็…งๆ•ฐ็ป„aไธญ็ฌฌ3็š„ๅ…ƒ็ด ๅฏนa่ฟ›่กŒๅˆ†ๅ‰ฒใ€‚ๅ…ถๅบ”็”จๅœบๆ™ฏไธบ๏ผšๆฏ”ๅฆ‚ๆˆ‘ไปฌไป…ๆƒณไปŽไธ€ไธชๅพˆๅคง็š„ๆ•ฐ็ป„้‡Œๆ‰พๅˆฐๆœ€ๅคง็š„10ไธชๅ€ผ๏ผŒๅฆ‚ๆžœๅ…ˆๅฏนๅ…ƒ็ด ่ฟ›่กŒๆŽ’ๅบ๏ผŒๅ†ๅ–ๅ‰10ไธชๅ…ƒ็ด ๏ผŒ่ฟ™ๆ ท็š„ไปฃไปทไผšๆฏ”่พƒๅคง๏ผ›่€ƒ่™‘ๅˆฐๅช้œ€ๅ‰10ไธช๏ผŒๅˆ™ๅฏไปฅ็”จnp.partition ###Code from scipy.spatial import distance nsamples = 10005 nfeatures = 20 X = np.random.randn(nsamples, nfeatures) njobs = 20 step = int(np.ceil(nsamples / njobs)) step X.shape i = 0 st = i*step end = (i+1)*step w = distance.cdist(XA=X[st:end], XB=X, metric="euclidean") w.shape w k = 10 kths = tuple(np.arange(1, k+1)) z = np.zeros((nsamples, k)) pairs = np.zeros_like(z) pairs.shape z.shape w.shape w_parted_ix = np.argpartition(w, kths, axis=1) w_parted_ix w_parted_ix[:, 1:k+1].shape z[st:end, :] = w_parted_ix[:, 1:k+1] z[0] ixs_rows = np.repeat(np.arange(len(w)), k) ixs_cols = tuple(w_parted_ix[:, 1:k+1].ravel()) pairs[st:end, :] = w[ixs_rows, ixs_cols].reshape(len(w), k) ###Output _____no_output_____
Model backlog/Train/135-jigsaw-fold1-xlm-roberta-ratio-1-2-sample-drop.ipynb
###Markdown Dependencies ###Code import json, warnings, shutil, glob from jigsaw_utility_scripts import * from scripts_step_lr_schedulers import * from transformers import TFXLMRobertaModel, XLMRobertaConfig from tensorflow.keras.models import Model from tensorflow.keras import optimizers, metrics, losses, layers SEED = 0 seed_everything(SEED) warnings.filterwarnings("ignore") pd.set_option('max_colwidth', 120) pd.set_option('display.float_format', lambda x: '%.4f' % x) ###Output wandb: WARNING W&B installed but not logged in. Run `wandb login` or set the WANDB_API_KEY env variable. ###Markdown TPU configuration ###Code strategy, tpu = set_up_strategy() print("REPLICAS: ", strategy.num_replicas_in_sync) AUTO = tf.data.experimental.AUTOTUNE ###Output Running on TPU grpc://10.0.0.2:8470 REPLICAS: 8 ###Markdown Load data ###Code database_base_path = '/kaggle/input/jigsaw-data-split-roberta-192-ratio-1-clean-polish/' k_fold = pd.read_csv(database_base_path + '5-fold.csv') valid_df = pd.read_csv("/kaggle/input/jigsaw-multilingual-toxic-comment-classification/validation.csv", usecols=['comment_text', 'toxic', 'lang']) print('Train samples: %d' % len(k_fold)) display(k_fold.head()) print('Validation samples: %d' % len(valid_df)) display(valid_df.head()) base_data_path = 'fold_1/' fold_n = 1 # Unzip files !tar -xf /kaggle/input/jigsaw-data-split-roberta-192-ratio-1-clean-polish/fold_1.tar.gz ###Output Train samples: 267220 ###Markdown Model parameters ###Code base_path = '/kaggle/input/jigsaw-transformers/XLM-RoBERTa/' config = { "MAX_LEN": 192, "BATCH_SIZE": 128, "EPOCHS": 3, "LEARNING_RATE": 1e-5, "ES_PATIENCE": None, "base_model_path": base_path + 'tf-xlm-roberta-large-tf_model.h5', "config_path": base_path + 'xlm-roberta-large-config.json' } with open('config.json', 'w') as json_file: json.dump(json.loads(json.dumps(config)), json_file) config ###Output _____no_output_____ ###Markdown Learning rate schedule ###Code lr_min = 1e-7 lr_start = 0 lr_max = config['LEARNING_RATE'] step_size = (len(k_fold[k_fold[f'fold_{fold_n}'] == 'train']) * 2) // config['BATCH_SIZE'] total_steps = config['EPOCHS'] * step_size hold_max_steps = 0 warmup_steps = total_steps * 0.1 decay = .9998 rng = [i for i in range(0, total_steps, config['BATCH_SIZE'])] y = [exponential_schedule_with_warmup(tf.cast(x, tf.float32), warmup_steps=warmup_steps, hold_max_steps=hold_max_steps, lr_start=lr_start, lr_max=lr_max, lr_min=lr_min, decay=decay) for x in rng] sns.set(style="whitegrid") fig, ax = plt.subplots(figsize=(20, 6)) plt.plot(rng, y) print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1])) ###Output Learning rate schedule: 0 to 9.96e-06 to 1.66e-06 ###Markdown Model ###Code module_config = XLMRobertaConfig.from_pretrained(config['config_path'], output_hidden_states=False) N_SAMPLES = 2 def model_fn(MAX_LEN): input_ids = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_ids') attention_mask = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='attention_mask') base_model = TFXLMRobertaModel.from_pretrained(config['base_model_path'], config=module_config) last_hidden_state, _ = base_model({'input_ids': input_ids, 'attention_mask': attention_mask}) x_avg = layers.GlobalAveragePooling1D()(last_hidden_state) x_max = layers.GlobalMaxPooling1D()(last_hidden_state) x = layers.Concatenate()([x_avg, x_max]) samples = [] sample_mask = layers.Dense(64, activation='relu') for n in range(N_SAMPLES): sample = layers.Dropout(.5)(x) sample = sample_mask(sample) sample = layers.Dense(1, activation='sigmoid', name=f'sample_{n}')(sample) samples.append(sample) output = layers.Average(name='output')(samples) model = Model(inputs=[input_ids, attention_mask], outputs=output) return model ###Output _____no_output_____ ###Markdown Train ###Code # Load data x_train = np.load(base_data_path + 'x_train.npy') y_train = np.load(base_data_path + 'y_train_int.npy').reshape(x_train.shape[1], 1).astype(np.float32) x_valid = np.load(base_data_path + 'x_valid.npy') y_valid = np.load(base_data_path + 'y_valid_int.npy').reshape(x_valid.shape[1], 1).astype(np.float32) x_valid_ml = np.load(database_base_path + 'x_valid.npy') y_valid_ml = np.load(database_base_path + 'y_valid.npy').reshape(x_valid_ml.shape[1], 1).astype(np.float32) #################### ADD TAIL #################### x_train_tail = np.load(base_data_path + 'x_train_tail.npy') y_train_tail = np.load(base_data_path + 'y_train_int_tail.npy').reshape(x_train_tail.shape[1], 1).astype(np.float32) x_train = np.hstack([x_train, x_train_tail]) y_train = np.vstack([y_train, y_train_tail]) step_size = x_train.shape[1] // config['BATCH_SIZE'] valid_step_size = x_valid_ml.shape[1] // config['BATCH_SIZE'] valid_2_step_size = x_valid.shape[1] // config['BATCH_SIZE'] # Build TF datasets train_dist_ds = strategy.experimental_distribute_dataset(get_training_dataset(x_train, y_train, config['BATCH_SIZE'], AUTO, seed=SEED)) valid_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid_ml, y_valid_ml, config['BATCH_SIZE'], AUTO, repeated=True, seed=SEED)) valid_2_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid, y_valid, config['BATCH_SIZE'], AUTO, repeated=True, seed=SEED)) train_data_iter = iter(train_dist_ds) valid_data_iter = iter(valid_dist_ds) valid_2_data_iter = iter(valid_2_dist_ds) # Step functions @tf.function def train_step(data_iter): def train_step_fn(x, y): with tf.GradientTape() as tape: probabilities = model(x, training=True) loss = loss_fn(y, probabilities) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) train_auc.update_state(y, probabilities) train_loss.update_state(loss) for _ in tf.range(step_size): strategy.experimental_run_v2(train_step_fn, next(data_iter)) @tf.function def valid_step(data_iter): def valid_step_fn(x, y): probabilities = model(x, training=False) loss = loss_fn(y, probabilities) valid_auc.update_state(y, probabilities) valid_loss.update_state(loss) for _ in tf.range(valid_step_size): strategy.experimental_run_v2(valid_step_fn, next(data_iter)) @tf.function def valid_2_step(data_iter): def valid_step_fn(x, y): probabilities = model(x, training=False) loss = loss_fn(y, probabilities) valid_2_auc.update_state(y, probabilities) valid_2_loss.update_state(loss) for _ in tf.range(valid_2_step_size): strategy.experimental_run_v2(valid_step_fn, next(data_iter)) # Train model with strategy.scope(): model = model_fn(config['MAX_LEN']) lr = lambda: exponential_schedule_with_warmup(tf.cast(optimizer.iterations, tf.float32), warmup_steps=warmup_steps, hold_max_steps=hold_max_steps, lr_start=lr_start, lr_max=lr_max, lr_min=lr_min, decay=decay) optimizer = optimizers.Adam(learning_rate=lr) loss_fn = losses.binary_crossentropy train_auc = metrics.AUC() valid_auc = metrics.AUC() valid_2_auc = metrics.AUC() train_loss = metrics.Sum() valid_loss = metrics.Sum() valid_2_loss = metrics.Sum() metrics_dict = {'loss': train_loss, 'auc': train_auc, 'val_loss': valid_loss, 'val_auc': valid_auc, 'val_2_loss': valid_2_loss, 'val_2_auc': valid_2_auc} history = custom_fit_2(model, metrics_dict, train_step, valid_step, valid_2_step, train_data_iter, valid_data_iter, valid_2_data_iter, step_size, valid_step_size, valid_2_step_size, config['BATCH_SIZE'], config['EPOCHS'], config['ES_PATIENCE'], save_last=False) # model.save_weights('model.h5') # Make predictions # x_train = np.load(base_data_path + 'x_train.npy') # x_valid = np.load(base_data_path + 'x_valid.npy') x_valid_ml_eval = np.load(database_base_path + 'x_valid.npy') # train_preds = model.predict(get_test_dataset(x_train, config['BATCH_SIZE'], AUTO)) # valid_preds = model.predict(get_test_dataset(x_valid, config['BATCH_SIZE'], AUTO)) valid_ml_preds = model.predict(get_test_dataset(x_valid_ml_eval, config['BATCH_SIZE'], AUTO)) # k_fold.loc[k_fold[f'fold_{fold_n}'] == 'train', f'pred_{fold_n}'] = np.round(train_preds) # k_fold.loc[k_fold[f'fold_{fold_n}'] == 'validation', f'pred_{fold_n}'] = np.round(valid_preds) valid_df[f'pred_{fold_n}'] = valid_ml_preds # Fine-tune on validation set #################### ADD TAIL #################### x_valid_ml_tail = np.hstack([x_valid_ml, np.load(database_base_path + 'x_valid_tail.npy')]) y_valid_ml_tail = np.vstack([y_valid_ml, y_valid_ml]) valid_step_size_tail = x_valid_ml_tail.shape[1] // config['BATCH_SIZE'] # Build TF datasets train_ml_dist_ds = strategy.experimental_distribute_dataset(get_training_dataset(x_valid_ml_tail, y_valid_ml_tail, config['BATCH_SIZE'], AUTO, seed=SEED)) train_ml_data_iter = iter(train_ml_dist_ds) # Step functions @tf.function def train_ml_step(data_iter): def train_step_fn(x, y): with tf.GradientTape() as tape: probabilities = model(x, training=True) loss = loss_fn(y, probabilities) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) train_auc.update_state(y, probabilities) train_loss.update_state(loss) for _ in tf.range(valid_step_size_tail): strategy.experimental_run_v2(train_step_fn, next(data_iter)) # Fine-tune on validation set optimizer = optimizers.Adam(learning_rate=3e-6) history_ml = custom_fit_2(model, metrics_dict, train_ml_step, valid_step, valid_2_step, train_ml_data_iter, valid_data_iter, valid_2_data_iter, valid_step_size_tail, valid_step_size, valid_2_step_size, config['BATCH_SIZE'], 2, config['ES_PATIENCE'], save_last=False) # Join history for key in history_ml.keys(): history[key] += history_ml[key] model.save_weights('model.h5') # Make predictions valid_ml_preds = model.predict(get_test_dataset(x_valid_ml_eval, config['BATCH_SIZE'], AUTO)) valid_df[f'pred_ml_{fold_n}'] = valid_ml_preds ### Delete data dir shutil.rmtree(base_data_path) ###Output Train for 125 steps, validate for 62 steps, validate_2 for 417 steps EPOCH 1/2 time: 207.4s loss: 0.2164 auc: 0.9387 val_loss: 0.1929 val_auc: 0.9677 val_2_loss: 0.2712 val_2_auc: 0.9709 EPOCH 2/2 time: 69.5s loss: 0.1602 auc: 0.9670 val_loss: 0.1444 val_auc: 0.9873 val_2_loss: 0.2397 val_2_auc: 0.9709 Training finished ###Markdown Model loss graph ###Code plot_metrics_2(history) ###Output _____no_output_____ ###Markdown Model evaluation ###Code # display(evaluate_model_single_fold(k_fold, fold_n, label_col='toxic_int').style.applymap(color_map)) ###Output _____no_output_____ ###Markdown Confusion matrix ###Code # train_set = k_fold[k_fold[f'fold_{fold_n}'] == 'train'] # validation_set = k_fold[k_fold[f'fold_{fold_n}'] == 'validation'] # plot_confusion_matrix(train_set['toxic_int'], train_set[f'pred_{fold_n}'], # validation_set['toxic_int'], validation_set[f'pred_{fold_n}']) ###Output _____no_output_____ ###Markdown Model evaluation by language ###Code display(evaluate_model_single_fold_lang(valid_df, fold_n).style.applymap(color_map)) # ML fine-tunned preds display(evaluate_model_single_fold_lang(valid_df, fold_n, pred_col='pred_ml').style.applymap(color_map)) ###Output _____no_output_____ ###Markdown Visualize predictions ###Code print('English validation set') display(k_fold[['comment_text', 'toxic'] + [c for c in k_fold.columns if c.startswith('pred')]].head(10)) print('Multilingual validation set') display(valid_df[['comment_text', 'toxic'] + [c for c in valid_df.columns if c.startswith('pred')]].head(10)) ###Output English validation set ###Markdown Test set predictions ###Code x_test = np.load(database_base_path + 'x_test.npy') test_preds = model.predict(get_test_dataset(x_test, config['BATCH_SIZE'], AUTO)) submission = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/sample_submission.csv') submission['toxic'] = test_preds submission.to_csv('submission.csv', index=False) display(submission.describe()) display(submission.head(10)) ###Output _____no_output_____
doc/ipython_notebooks_src/tutorial-coupled-relaxation-of-two-nanodisks.ipynb
###Markdown Relaxation of two coupled nanodisks **Author**: Maximilian Albert**Date**: Jan 2013**Purpose**: This notebook illustrates a typical relaxation. It uses a mesh consisting of two separated nanodisks and also shows how such a mesh can be produced by writing a `.geo` file directly from the code. --- In this simulation we explore the relaxation behaviour of two nanodisks which are separated by a small distance. The initial magnetisation in each disk is uniform, but with a different (random) direction for each of them. We hope to see some kind of interaction so that ideally in the final relaxed state the magnetisation in the two disks is aligned along the common axis (due to the magnetostatic interaction). First we import all the relevant modules and functions. We also change the logging level from the default 'DEBUG' to 'INFO' to avoid cluttering the notebook with lots of debugging message. ###Code from paraview import servermanager from finmag.util.meshes import from_geofile, plot_mesh, plot_mesh_with_paraview from finmag import sim_with from finmag.util.helpers import vector_valued_function from finmag.util.visualization import render_paraview_scene import finmag import numpy as np finmag.set_logging_level("INFO") ###Output [2014-06-09 18:43:58] INFO: Finmag logging output will be appended to file: '/home/albert/.finmag/global.log' [2014-06-09 18:43:58] DEBUG: Building modules in 'native'... [2014-06-09 18:43:59] DEBUG: FinMag 5047:e8225c1b7a79ea431efa470d26532258c63bb6ef [2014-06-09 18:43:59] DEBUG: Dolfin 1.4.0 Matplotlib 1.3.1 [2014-06-09 18:43:59] DEBUG: Numpy 1.8.1 Scipy 0.12.0 [2014-06-09 18:43:59] DEBUG: IPython 2.1.0 Python 2.7.5+ [2014-06-09 18:43:59] DEBUG: Paraview 4.0.1-1 Sundials 2.5.0 [2014-06-09 18:43:59] DEBUG: Boost-Python <unknown> Linux Linux Mint 16 Petra [2014-06-09 18:43:59] DEBUG: Registering debug signal handler. Press Ctrl-Z at any time to stop execution and jump into the debugger. ###Markdown First we set the parameters which define the geometry, such as the disk radius/height and their separation. The two disks are put on the positive and negative x-axis with equal distances to the origin. ###Code # Geometry parameters: r = 25 # disk radius h = 10 # disk height s = 30 # distance from the origin to the center of each disk maxh = 3.0 # mesh discretisation unit_length = 1e-9 # the mesh units are given in nm assert(s > r) # Disk separation should be greater than the radius ###Output _____no_output_____ ###Markdown Next we set the material parameters for Permalloy and define the initial magnetisations for each disk. ###Code # Material parameters Ms = 8.6e5 A = 13.0e-12 alpha = 1.0 m_init_1 = np.array([-0.7, 0.1, 0.1]) # m_init for first disk m_init_2 = np.array([0.6, 0.2, -0.2]) # m_init for second disk demag_solver = 'FK' ###Output _____no_output_____ ###Markdown We also define some helper strings used in filename, directory names etc., which make it easier to distinguish between simulations with different parameters. ###Code geom_descr = "r_{:05.1f}__h_{:05.1f}__s_{:05.1f}__maxh_{:04.1f}".format(r, h, s, maxh) sim_descr = "sim_{}__m_init_1_{:03.1f}_{:03.1f}_{:03.1f}__m_init_2_{:03.1f}_{:03.1f}_{:03.1f}".format( geom_descr, m_init_1[0], m_init_1[1], m_init_1[2], m_init_2[0], m_init_2[1], m_init_2[2]) print "geom_descr: {}".format(geom_descr) print "sim_descr: {}".format(sim_descr) ###Output geom_descr: r_025.0__h_010.0__s_030.0__maxh_03.0 sim_descr: sim_r_025.0__h_010.0__s_030.0__maxh_03.0__m_init_1_-0.7_0.1_0.1__m_init_2_0.6_0.2_-0.2 ###Markdown Now we create the mesh and load it. Since we would like to be able to change parameters, we write a CSG description of the mesh geometry here interactively and write it to a .geo file, which is then loaded. ###Code import textwrap import os meshfilename = os.path.join("meshes", "mesh__{}.geo".format(geom_descr)) if not os.path.exists(meshfilename): # Interactively write the mesh file from here so that we # can adapt the geometry parameters from this notebook. if not os.path.exists("meshes"): os.mkdir('meshes') csg = textwrap.dedent("""\ algebraic3d solid disk1 = cylinder (-{s}, 0, 1; -{s}, 0, -1; {r} ) and plane (0, 0, 0; 0, 0, -1) and plane (0, 0, {h}; 0, 0, 1) -maxh = {maxh}; solid disk2 = cylinder ({s}, 0, 1; {s}, 0, -1; {r} ) and plane (0, 0, 0; 0, 0, -1) and plane (0, 0, {h}; 0, 0, 1) -maxh = {maxh}; tlo disk1; tlo disk2; """.format(s=s, r=r, h=h, maxh=maxh)) with open(meshfilename, "w") as f: f.write(csg) mesh = from_geofile(meshfilename) ###Output [2014-06-09 18:44:11] WARNING: Warning: Ignoring netgen's output status of 34304. ###Markdown Plot the mesh to be sure that it looks ok. Unfortunately, matplotlib doesn't support equal aspect ratios for 3D plots (yet), but the axes labels indicate that the disks have the correct proportions. ###Code plot_mesh(mesh, figsize=(10, 5)) ###Output _____no_output_____ ###Markdown Alternatively, we can plot the mesh using Paraview. ###Code plot_mesh_with_paraview(mesh) ###Output _____no_output_____ ###Markdown Now we actually set the initial magnetisation in the two disks. Since m_init is different in each of the disks, this is done via a function that distinguishes the mesh points in each disk according to their x-coordinate. ###Code def fun_m_init(pt): if pt[0] < 0: return m_init_1 else: return m_init_2 m_init = vector_valued_function(fun_m_init, mesh) ###Output _____no_output_____ ###Markdown Now create the Simulation object (with the mesh and material parameters defined above). ###Code sim = sim_with(mesh, Ms=Ms, m_init=m_init, alpha=alpha, unit_length=unit_length, A=A, demag_solver=demag_solver, name="sim_01__relaxation_of_two_nanodisks") ###Output [2014-06-09 18:44:54] INFO: Finmag logging output will be written to file: '/home/albert/work/code/finmag/doc/ipython_notebooks_src/sim_01__relaxation_of_two_nanodisks.log' (any old content will be overwritten). [2014-06-09 18:44:54] INFO: Creating Sim object 'sim_01__relaxation_of_two_nanodisks' (rank=0/1). [2014-06-09 18:44:54] INFO: <Mesh of topological dimension 3 (tetrahedra) with 1973 vertices and 7295 cells, ordered> ###Markdown ... and relax the configuration. While doing so, we save vtk snapshots of the magnetisation configuration every 50 ps for later analysis. Since the simulation takes a few mintes to finish, we also print a message every 0.5 ns (of simulation time) to keep us informed about the progress (note that this would not be necessary if we had used the 'DEBUG' logging level above, but it is also a nice illustration of how to use the scheduler for these purposes). ###Code def print_simulation_time(sim): finmag.logger.info("Reached simulation time: {} ns".format(sim.t * 1e9)) sim.schedule(print_simulation_time, every=0.5e-9) sim.schedule('save_vtk', filename='snapshots/{}/relaxation.pvd'.format(sim_descr), every=5e-11, overwrite=True) sim.relax() ###Output [2014-06-09 18:45:00] INFO: Create integrator sundials with kwargs={} [2014-06-09 18:45:00] INFO: Simulation will run until relaxation of the magnetisation. [2014-06-09 18:45:00] INFO: Reached simulation time: 0.0 ns [2014-06-09 18:45:13] INFO: Reached simulation time: 0.5 ns [2014-06-09 18:45:20] INFO: Reached simulation time: 1.0 ns [2014-06-09 18:45:26] INFO: Reached simulation time: 1.5 ns [2014-06-09 18:45:32] INFO: Reached simulation time: 2.0 ns [2014-06-09 18:45:38] INFO: Reached simulation time: 2.5 ns [2014-06-09 18:45:41] INFO: Relaxation finished at time t = 2.8e-09. ###Markdown Here is an image of the initial state. It shows that each disk has uniform magnetisation but they point in different directions, as specified by `m_init_1` and `m_init_2`. ###Code render_paraview_scene('snapshots/{}/relaxation.pvd'.format(sim_descr), timesteps=0, color_by_axis='X', view_size=(1000, 800)) ###Output _____no_output_____ ###Markdown And this is an image of the relaxed state. It clearly shows how the magnetisation in the two disks is now aligned due the magnetostatic interaction. ###Code render_paraview_scene('snapshots/{}/relaxation.pvd'.format(sim_descr), timesteps=42, color_by_axis='Y', view_size=(1000, 800)) ###Output _____no_output_____
Deep Learning for Computer Vision/DL_CV_Assessment_Solution.ipynb
###Markdown Deep Learning for Image Classification Assessment SOLUTIONWelcome to your assessment! Follow the instructions in bold below to complete the assessment.If you get stuck, check out the solutions video and notebook. (Make sure to run the solutions notebook before posting a question to the QA forum please, thanks!)------------ The Challenge**Your task is to build an image classifier with Keras and Convolutional Neural Networks for the Fashion MNIST dataset. This data set includes 10 labels of different clothing types with 28 by 28 *grayscale* images. There is a training set of 60,000 images and 10,000 test images.** Label Description 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot The Data**TASK 1: Run the code below to download the dataset using Keras.** ###Code from keras.datasets import fashion_mnist (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() ###Output Using TensorFlow backend. ###Markdown Visualizing the Data**TASK 2: Use matplotlib to view an image from the data set. It can be any image from the data set.** ###Code import matplotlib.pyplot as plt %matplotlib inline x_train[0] plt.imshow(x_train[0]) y_train[0] ###Output _____no_output_____ ###Markdown Preprocessing the Data**TASK 3: Normalize the X train and X test data by dividing by the max value of the image arrays.** ###Code x_train.max() x_train = x_train/255 x_test = x_test/255 ###Output _____no_output_____ ###Markdown **Task 4: Reshape the X arrays to include a 4 dimension of the single channel. Similar to what we did for the numbers MNIST data set.** ###Code x_train.shape x_train = x_train.reshape(60000,28,28,1) x_test = x_test.reshape(10000,28,28,1) ###Output _____no_output_____ ###Markdown **TASK 5: Convert the y_train and y_test values to be one-hot encoded for categorical analysis by Keras.** ###Code from keras.utils import to_categorical y_train y_cat_train = to_categorical(y_train) y_cat_test = to_categorical(y_test) ###Output _____no_output_____ ###Markdown Building the Model**TASK 5: Use Keras to create a model consisting of at least the following layers (but feel free to experiment):*** 2D Convolutional Layer, filters=32 and kernel_size=(4,4)* Pooling Layer where pool_size = (2,2)* Flatten Layer* Dense Layer (128 Neurons, but feel free to play around with this value), RELU activation* Final Dense Layer of 10 Neurons with a softmax activation**Then compile the model with these parameters: loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']** ###Code from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPool2D, Flatten model = Sequential() # CONVOLUTIONAL LAYER model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(28, 28, 1), activation='relu',)) # POOLING LAYER model.add(MaxPool2D(pool_size=(2, 2))) # FLATTEN IMAGES FROM 28 by 28 to 764 BEFORE FINAL LAYER model.add(Flatten()) # 128 NEURONS IN DENSE HIDDEN LAYER (YOU CAN CHANGE THIS NUMBER OF NEURONS) model.add(Dense(128, activation='relu')) # LAST LAYER IS THE CLASSIFIER, THUS 10 POSSIBLE CLASSES model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.summary() ###Output _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 25, 25, 32) 544 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 4608) 0 _________________________________________________________________ dense_1 (Dense) (None, 128) 589952 _________________________________________________________________ dense_2 (Dense) (None, 10) 1290 ================================================================= Total params: 591,786 Trainable params: 591,786 Non-trainable params: 0 _________________________________________________________________ ###Markdown Training the Model**TASK 6: Train/Fit the model to the x_train set. Amount of epochs is up to you.** ###Code model.fit(x_train,y_cat_train,epochs=10) ###Output Epoch 1/10 60000/60000 [==============================] - 5s 86us/step - loss: 0.1802 - acc: 0.9365 Epoch 2/10 60000/60000 [==============================] - 5s 87us/step - loss: 0.1679 - acc: 0.9395 Epoch 3/10 60000/60000 [==============================] - 5s 88us/step - loss: 0.1579 - acc: 0.9439 Epoch 4/10 60000/60000 [==============================] - 5s 87us/step - loss: 0.1502 - acc: 0.9469 Epoch 5/10 60000/60000 [==============================] - 5s 86us/step - loss: 0.1427 - acc: 0.9496 Epoch 6/10 60000/60000 [==============================] - 5s 87us/step - loss: 0.1397 - acc: 0.9523 Epoch 7/10 60000/60000 [==============================] - 5s 87us/step - loss: 0.1312 - acc: 0.9551 Epoch 8/10 60000/60000 [==============================] - 5s 86us/step - loss: 0.1274 - acc: 0.9559 Epoch 9/10 60000/60000 [==============================] - 5s 84us/step - loss: 0.1238 - acc: 0.9582 Epoch 10/10 60000/60000 [==============================] - 5s 84us/step - loss: 0.1201 - acc: 0.9588 ###Markdown Evaluating the Model**TASK 7: Show the accuracy,precision,recall,f1-score the model achieved on the x_test data set. Keep in mind, there are quite a few ways to do this, but we recommend following the same procedure we showed in the MNIST lecture.** ###Code model.metrics_names model.evaluate(x_test,y_cat_test) from sklearn.metrics import classification_report predictions = model.predict_classes(x_test) y_cat_test.shape y_cat_test[0] predictions[0] y_test print(classification_report(y_test,predictions)) ###Output precision recall f1-score support 0 0.86 0.85 0.85 1000 1 0.99 0.97 0.98 1000 2 0.88 0.83 0.85 1000 3 0.91 0.91 0.91 1000 4 0.83 0.88 0.85 1000 5 0.97 0.98 0.98 1000 6 0.73 0.76 0.74 1000 7 0.95 0.97 0.96 1000 8 0.99 0.97 0.98 1000 9 0.98 0.94 0.96 1000 avg / total 0.91 0.91 0.91 10000
Daily/20150902_phoenix_cifist_bcs.ipynb
###Markdown Phoenix BT-Settl Bolometric CorrectionsFiguring out the best method of handling Phoenix bolometric correction files. ###Code %matplotlib inline import matplotlib.pyplot as plt import numpy as np import scipy.interpolate as scint ###Output _____no_output_____ ###Markdown Change to directory containing bolometric correction files. ###Code cd /Users/grefe950/Projects/starspot/starspot/color/tab/phx/CIFIST15/ ###Output /Users/grefe950/Projects/starspot/starspot/color/tab/phx/CIFIST15 ###Markdown Load a bolometric correction table, say for the Cousins AB photometric system. ###Code bc_table = np.genfromtxt('colmag.BT-Settl.server.JOHNSON.Vega', comments='!') ###Output _____no_output_____ ###Markdown Now, the structure of the file is quite irregular. The grid is not rectangular, which is not an immediate problem. The table is strucutred such that column 0 contains Teff in increasing order, followed by logg in column 1 in increasing order. However, metallicities in column 2 appear to be in decreasing order, which may be a problem for simple interpolation routines. Alpha abundances follow and are in increasing order, but since this is a "standard" grid, whereby alpha enrichment is a function of metallicity, we can ignore it for the moment.Let's take a first swing at the problem by using the LinearND Interpolator from SciPy. ###Code test_surface = scint.LinearNDInterpolator(bc_table[:, :2], bc_table[:, 4:]) ###Output _____no_output_____ ###Markdown The surface compiled, but that is not a guarantee that the interpolation will work successfully. Some tests are required to confirm this is the case. Let's try a few Teffs at logg = 5 with solar metallicity. ###Code test_surface(np.array([1500., 5.0])) ###Output _____no_output_____ ###Markdown This agrees with data in the bolometric correciton table.``` Teff logg [Fe/H] [a/Fe] B V R I1500.00 5.00 0.00 0.00 -15.557 -16.084 -11.560 -9.291```Now, let's raise the temperature. ###Code test_surface(np.array([3000., 5.0])) ###Output _____no_output_____ ###Markdown Again, we have a good match to tabulated values,``` Teff logg [Fe/H] [a/Fe] B V R I3000.00 5.00 0.00 0.00 -6.603 -5.641 -4.566 -3.273```However, since we are using a tabulated metallicity, the interpolation may proceed without too much trouble. If we select a metallicity between grid points, how do we fare? ###Code test_surface(np.array([3000., 5.0])) ###Output _____no_output_____ ###Markdown This appears consistent. What about progressing to lower metallicity values? ###Code test_surface(np.array([3000., 5.0])) ###Output _____no_output_____ ###Markdown For reference, at [Fe/H] = $-0.5$ dex, we have``` Teff logg [Fe/H] [a/Fe] B V R I3000.00 5.00 -0.50 0.20 -6.533 -5.496 -4.424 -3.154```The interpolation routine has seemingly handled the non-monotonic nature of the metallicity column, as all interpolate values lie between values at the two respective nodes.---Now let's import an isochrone and calcuate colors for stellar models for comparison against MARCS bolometric corrections. ###Code iso = np.genfromtxt('/Users/grefe950/evolve/dmestar/iso/dmestar_00120.0myr_z+0.00_a+0.00_marcs.iso') ###Output _____no_output_____ ###Markdown Make sure there are magnitudes and colors associated with this isochrone. ###Code iso.shape ###Output _____no_output_____ ###Markdown A standard isochrone would only have 6 columns, so 11 indicates this isochrone does have photometric magnitudes computed, likely BV(Ic) (JK)2MASS. ###Code test_bcs = test_surface(10**iso[:,1], iso[:, 2]) test_bcs.shape ###Output _____no_output_____ ###Markdown For each Teff and logg combination we now have BCs for BV(RI)c from BT-Settl models. Now we need to convert the bolometric corrections to absolute magnitudes. ###Code bol_mags = 4.74 - 2.5*iso[:, 3] for i in range(test_bcs.shape[1]): bcs = -1.0*np.log10(10**iso[:, 1]/5777.) + test_bcs[:, i] - 5.0*iso[:, 4] if i == 0: test_mags = bol_mags - bcs else: test_mags = np.column_stack((test_mags, bol_mags - bcs)) iso[50, 0:4], iso[50, 6:], test_mags[50] ###Output _____no_output_____ ###Markdown Let's try something different: using the color tables provided by the Phoenix group, from which the bolometric corrections are calculated. ###Code col_table = np.genfromtxt('colmag.BT-Settl.server.COUSINS.Vega', comments='!') ###Output _____no_output_____ ###Markdown Create an interpolation surface from the magnitude table. ###Code col_surface = scint.LinearNDInterpolator(col_table[:, :2], col_table[:, 4:8]) ###Output _____no_output_____ ###Markdown Compute magnitudes for a Dartmouth isochrone. ###Code phx_mags = col_surface(10.0**iso[:, 1], iso[:, 2]) ###Output _____no_output_____ ###Markdown Convert surface magnitudes to absolute magnitudes using the distance modulus and the radius of the star. ###Code for i in range(phx_mags.shape[1]): phx_mags[:, i] = phx_mags[:, i] - 5.0*np.log10(10**iso[:, 4]*6.956e10/3.086e18) + 5.0 ###Output _____no_output_____ ###Markdown Now compare against MARCS values. ###Code iso[40, :5], iso[40, 6:], phx_mags[40] ###Output _____no_output_____ ###Markdown Load an isochrone from the Lyon-Phoenix series. ###Code phx_iso = np.genfromtxt('/Users/grefe950/Notebook/Projects/ngc2516_spots/data/phx_isochrone_120myr.txt') fig, ax = plt.subplots(1, 2, figsize=(12., 8.), sharey=True) ax[0].set_xlim(0.0, 2.0) ax[1].set_xlim(0.0, 4.0) ax[0].set_ylim(16, 2) ax[0].plot(iso[:, 6] - iso[:, 7], iso[:, 7], lw=3, c="#b22222") ax[0].plot(phx_mags[:, 0] - phx_mags[:, 1], phx_mags[:, 1], lw=3, c="#1e90ff") ax[0].plot(phx_iso[:, 7] - phx_iso[:, 8], phx_iso[:, 8], dashes=(20., 5.), lw=3, c="#555555") ax[1].plot(iso[:, 7] - iso[:, 8], iso[:, 7], lw=3, c="#b22222") ax[1].plot(phx_mags[:, 1] - phx_mags[:, 3], phx_mags[:, 1], lw=3, c="#1e90ff") ax[1].plot(phx_iso[:, 8] - phx_iso[:, 10], phx_iso[:, 8], dashes=(20., 5.), lw=3, c="#555555") ###Output _____no_output_____ ###Markdown Export a new isochrone with colors from AGSS09 (PHX) ###Code new_isochrone = np.column_stack((iso[:, :6], phx_mags)) np.savetxt('/Users/grefe950/Notebook/Projects/pleiades_colors/data/dmestar_00120.0myr_z+0.00_a+0.00_mixed.iso', new_isochrone, fmt='%16.8f') ###Output _____no_output_____ ###Markdown --- Separate Test CaseThese are clearly not correct and are between 1 and 2 magnitudes off from expected values. Need to reproduce the Phoenix group's results, first. ###Code tmp = -10.*np.log10(3681./5777.) + test_surface(3681., 4.78, 0.0) #+ 5.0*np.log10(0.477) tmp 4.74 - 2.5*(-1.44) - tmp ###Output _____no_output_____
FuzzyMatching.ipynb
###Markdown This notebook demonstrates how to match metadata records in HTRC with metadata records from other sources.In this example, we use strings of "author" and "title" for matching.Here we use two datasets:(1) part of Hathifiles and (2) CMU Book Summary Dataset. ###Code #Yuerong Hu import pandas as pd from fuzzywuzzy import fuzz ###Output _____no_output_____ ###Markdown Import and preprocess the datasets 1. Import the open access dataset downloaded from https://www.kaggle.com/applecrazy/cmu-book-summary-dataset ###Code df=pd.read_csv('booksummaries.txt',sep='\t') # take a look at the dataset df.head() # We need to add column names to it and select rows # Add column names according to the readme file # 1. Wikipedia article ID # 2. Freebase ID # 3. Book title # 4. Author # 5. Publication date # 6. Book genres (Freebase ID:name tuples) # 7. Plot summary # Taking into consideration the matching later ,we use "title" and "author" instead of "Book title" and "Author" df.columns = ['Wikipedia article ID','Freebase ID','title','author','ublication date','Book genres','Plot summary'] df.head() # We only need two columns df = df[['title','author']] df.head() # import htrc metadata df2=pd.read_csv('hathi_upd_20190801.txt',sep='\t') df2.head() df2.shape[1] # Similarly, we rename the columns and select columns # a "readme" file for Hathifiles can be found at https://www.hathitrust.org/hathifiles_description df2.columns=['htid','access','rights', 'ht_bib_key', 'description','source','source_bib_num','oclc_num', 'isbn','issn','iccn','title','imprint','rights_reason_code','rights_timestamp','us_gov_doc_flag', 'rights_date_used','pub_place','lang','bib_fmt','collection_code', 'content_provider_code', 'responsible_entity_code','digitization_agent_code','access_profile_code','author'] df2.head() df2 = df2[['title','author']] df2.head() ###Output _____no_output_____ ###Markdown Accurate Matchingfind the pairs where the two author strings and the two title strings are exactly the same ###Code df_output=pd.merge(df, df2, on=['title','author']).drop_duplicates() df_output.to_csv(r'accurate_match.csv') print(df_output) # No match found ###Output _____no_output_____ ###Markdown Fuzzy MatchingWe only use the first 30 characters in a title because there are many redundant"volumn" information ###Code def fuzzyMatching(path): df=pd.read_csv('booksummaries.txt',sep='\t',nrows=1000) df.columns = ['id','Freebase ID','title','author','ublication date','Book genres','Plot summary'] df = df[['id','title','author']] df.name = str(path) #print(df.head()) authorratio = [] titleratio = [] sourceauthor = [] htrcauthor = [] sourcetitle = [] htrctitle = [] sourceIDlist= [] htrcDocid = [] #count df items in the loop counter = 0 #count df2 items in the loop counter2 = 0 # count number of output files df2=pd.read_csv('hathi_upd_20190801.txt',sep='\t',nrows=100) df2.columns=['htid','access','rights', 'ht_bib_key', 'description','source','source_bib_num','oclc_num', 'isbn','issn','iccn','title','imprint','rights_reason_code','rights_timestamp','us_gov_doc_flag', 'rights_date_used','pub_place','lang','bib_fmt','collection_code', 'content_provider_code', 'responsible_entity_code','digitization_agent_code','access_profile_code','author'] df2 = df2[['htid','title','author']] titlelist = df['title'].values authorlist = df["author"].values sourceidlist=df['id'].values for i in range(len(df)): counter = counter + 1 author = authorlist[i] sourceid = sourceidlist[i] title = str(titlelist[i])[0:30] authorlist2 = df2["author"].values titlelist2 = df2['title'].values htrcgidlist = df2['htid'].values for j in range(len(df2)): counter2 = counter2 + 1 author2 = authorlist2[j] title2 = str(titlelist2[j])[0:30] htrcid2 = htrcgidlist[j] aRatio = fuzz.ratio(str(author).lower(), str(author2).lower()) tRatio = fuzz.ratio(str(title).lower(), str(title2).lower()) if aRatio > 60 and tRatio > 70: sourceIDlist.append(sourceid) htrcDocid.append(htrcid2) authorratio.append(aRatio) sourceauthor.append(author) htrcauthor.append(author2) titleratio.append(tRatio) sourcetitle.append(title) htrctitle.append(title2) else: pass print(len(sourceauthor), len(htrctitle)) #print(counter,counter2) df_output = pd.DataFrame(list(zip(authorratio, titleratio, sourceauthor, htrcauthor, sourcetitle, htrctitle, sourceIDlist, htrcDocid)), columns=['authorratio', 'titleratio', 'sourceauthor', 'htrcauthor', 'sourcetitle', 'htrctitle', 'sourceId', 'htrcDocid']) fileName=df.name df_output.to_csv('fuzzyMatchingResult.csv', index=False) print('done') #this is a simple test, we did not find any pairs. path='booksummaries.txt' fuzzyMatching(path) # this is an exmple of some successful fuzzy matching cases using metadata datasets of British Periodicals and HTRC # first two rows represents the reliablity(100%) of the matching demo=pd.read_csv('example_bpo_htrc.csv') demo ###Output _____no_output_____
Session11/Day3/GalaxyPhotometryAndShapes.ipynb
###Markdown Practice with galaxy photometry and shape measurementTo accompany galaxy-measurement lecture from the LSSTC Data Science Fellowship Program, July 2020.All questions and corrections can be directed to me at [email protected]!_Gary Bernstein, 16 July 2020_ ###Code # Load the packages we will use import numpy as np import astropy.io.fits as fits import astropy.coordinates as co from matplotlib import pyplot as plt import scipy.fft as fft %matplotlib inline ###Output /Users/rmorgan/anaconda3/envs/DSFP/lib/python3.6/site-packages/matplotlib/style/core.py:167: UserWarning: In /Users/rmorgan/.matplotlib/stylelib/alex.mplstyle: The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1. styles = read_style_directory(stylelib_path) ###Markdown Useful toolsFor our galaxy measurement practice, we'll be testing out some of our techniques on *exponential profile* galaxies, which are define by$$ I(x,y) \propto e^{-r/r_0},$$where $r_0$ is the "scale length," and we'll allow our galaxy to potentially be elliptical shaped by setting$$ r^2 = (1-e^2) \left[ \frac{(x-x_0)^2}{1-e} + \frac{(y-y_0)^2}{1+e}\right].$$To reduce the complexity of our problem, I'm only letting the galaxy have the $e_+$ form of ellipticity, where $e>0$ ($e<0$) means the galaxy is stretched along the $x$ ($y$) axis.We're also going to assume that our galaxy is viewed through a circular Gaussian PSF:$$ T(x,y) \propto e^{-(x^2+y^2)/2\sigma_{\rm PSF}^2}.$$The function `drawDisk` below is provided to draw an image of an elliptical exponential galaxy as convolved with a Gaussian PSF. You don't have to understand how it works to do these exercises. But you might be interested (since this is how the `GalSim` galaxy simulation package works): the galaxy and the PSF are first "drawn" in Fourier space, and then multiplied, since a convolution in real space is multiplication in Fourier space (which is *much* faster). Then we use a Fast Fourier Transform (FFT) to get our image back in real space.I also include in this notebook two helpful things from the astrometry notebook:* The function `addBackground` which will add background noise of a chosen level (denoted as $n$ in the lecture notes) to any image.* The `x` and `y` arrays that give the location values of each pixel. In this set of exercises, we'll work exclusively with 64x64 images. Also I am going to redefine the coordinate system so that $(x,y)=(0,0)$ is actually at element `[32,32]` of the array. ###Code def addBackground(image, variance): # Add Gaussian noise with given variance to each pixel of the image noise = np.random.normal(scale=np.sqrt(variance),size=image.shape) return image + noise n_pix = 64 xy=np.indices( (n_pix,n_pix),dtype=float) x = xy[1].copy()- n_pix/2 y = xy[0].copy()- n_pix/2 plt.imshow(x,origin='lower',interpolation='nearest') plt.title("This is a plot of x coordinate") plt.colorbar() # Here is our elliptical exponential galaxy drawing function # It is always centered on the pixel just above right of the image center. def drawDisk(r0=4.,flux=1.,e=0.,sigma_psf=3.,n_pix=n_pix): # n_pix must be even. # Build arrays holding the (ky,kx) values # irfft2 wants array of this shape: tmp = np.ones((n_pix,n_pix//2+1),dtype=float) freqs = np.arange(-n_pix//2,n_pix//2) freqs = (2 * np.pi / n_pix)*np.roll(freqs,n_pix//2) kx = tmp * freqs[:n_pix//2+1] ky = tmp * freqs[:,np.newaxis] # Calculate the FT of the PSF ft = np.exp( (kx*kx+ky*ky)*(-sigma_psf*sigma_psf/2.)) # Produce the FT of the exponential - for the circular version, # it's (1+k^2 r_0^2)**(-3/2) # factors to "ellipticize" and scale the k's: a = np.power((1+e)/(1-e),0.25) ksqp1 = np.square(r0*kx*a) + np.square(r0*ky/a) + 1 ft *= flux / (ksqp1*np.sqrt(ksqp1)) # Now FFT back to real space img = fft.irfft2(ft) # And roll the origin to the center return np.roll(img, (n_pix//2,n_pix//2),axis=(0,1)) # As a test, let's draw an image with a small PSF size and # see if it really is exponential. # With e>0, it should be extended along x axis r0=4. img = drawDisk(e=0.2,flux=1e5,sigma_psf=3.,r0=r0) plt.imshow(img,origin='lower',interpolation='nearest') plt.title("Is it stretched along x?") # And also a plot of log(flux) vs x or y should look linear plt.figure() plt.plot(np.arange(-32,32)/r0,np.log(img[:,32]),label='Y') plt.plot(np.arange(-32,32)/r0,np.log(img[32,:]),label='X') plt.legend() plt.title("Are the lines straight and near unity slope?") plt.xlabel("(x or y)/r0") plt.ylabel("log(I)") plt.grid() ###Output _____no_output_____ ###Markdown Exercise 1: Aperture photometryHere we'll try out a few forms of aperture photometry and see how they compare in terms of the S/N ratios they provide on the galaxy flux.**(a)** Write a function `tophat_flux(img,R)` which implements a simple tophat aperture sum of flux in all pixels within radius `R` of the center of the galaxy. We will keep the center of our galaxy fixed at pixel \[32,32\] so you don't have to worry about iterating to find the centroid.Draw a noiseless version of a circular galaxy with the characteristics in the cell below. Then use your `tophat_flux` function to plot the "curve of growth" for this image, with `R` on the x axis going from 5 to 30 pixels, and the y axis showing the fraction of the total flux that falls in your aperture.How many scale radii do we need the aperture to be to miss <1% of the flux? ###Code r0 = 4. e = 0. flux = 1e4 sigma_psf = 2. def tophat_flux(img, R, center_x=32, center_y=32): yy, xx = np.meshgrid(range(img.shape[0]), range(img.shape[1])) distances = np.sqrt((center_x - xx)**2 + (center_y - yy)**2) return np.sum(img[np.where(distances < R)]) circ_gal_ = drawDisk(r0=r0,flux=flux,e=e,sigma_psf=sigma_psf) R_arr = np.arange(5., 31.) cog = np.array([tophat_flux(circ_gal_, R) / flux for R in R_arr]) plt.figure() plt.plot(R_arr, cog) plt.xlabel("Aperture Radius [pixels]") plt.ylabel("Fraction of Total Flux in Aperature") plt.show() print("You need {:.2f} scale radii".format(R_arr[np.argmin(np.abs(cog - 0.99))] / r0)) ###Output _____no_output_____ ###Markdown **(b)** Next let's add some background noise to our image, say `n_bg=100`. * First, make one such noisy version of your galaxy and `imshow` it. * Then, using **analytic** methods, estimate what the variance of your aperture flux measurements will be when `R=10`. * Finally, make 1000 different realizations of your noisy galaxy and measure their `tophat_flux` to see whether the real variance of the flux measurements matches your prediction. ###Code circ_gal_noise_ = addBackground(circ_gal_, 100) plt.imshow(circ_gal_noise_, origin='lower', interpolation='nearest') ###Output _____no_output_____ ###Markdown **(c)** Now create a plot of the S/N level of the flux measurement vs the radius `R` of the aperture. Here the signal is the mean, and the noise the std deviation, of the `tophat_flux` of many noisy measurements of this galaxy. You can use either an analytic or numeric estimate of these quantities. Report what the optimal tophat S/N is, and what `R` achieves it. ###Code # your work here... ###Output _____no_output_____ ###Markdown **(d)** Repeat part (c), but this time use a *Gaussian* aperture whose width $\sigma_w$ you vary to optimize the S/N ratio of the aperture flux, i.e. a function `gaussian_flux(img,sigma_w)` is needed. Which performs better, the optimized tophat or the optimized Gaussian? ###Code # your work here... ###Output _____no_output_____ ###Markdown Exercise 2: Spurious colorThis time let's consider that we want to measure an accurate $g-r$ color for our galaxy, but the seeing is $\sigma_{\rm PSF}=2$ pixels in the $r$ image but $\sigma_{\rm PSF}=2.5$ pixels in the $g$ image. Let's see how the size of our aperture biases our color measurement.**(a)** Draw a noiseless $g$-band and a noiseless $r$-band image of our galaxy. Let's assume that the true color $g-r \equiv 2.5\log_10(f_r/f_g) = 0,$ i.e. that the $g$ and $r$ fluxes of the galaxy are both equal to our nominal `flux`. Plot the difference between the two images: are they the same? ###Code # your work here... ###Output _____no_output_____ ###Markdown **(b)** Using either your Gaussian or your tophat aperture code, plot the *measured* $g-r$ color of the galaxy as a function of the size of the aperture. Since the true color is zero, this measurement is the size of the systematic error that is being made in color because of mismatched *pre-seeing* apertures. ###Code # your work here... ###Output _____no_output_____ ###Markdown We can see here that a naive use of "matched" apertures can cause significant spurious color, even when the aperture has a sigma that is many times that of the galaxy and PSF. But the tophat does better. So without any kind of PSF matching, we have to use algorithms with non-optimal S/N in order to approach true colors. Exercise 3: Degradation of ellipticity measurements by seeingIt's hard to measure the shape of a galaxy that is not resolved by the PSF. That means that poorly-resolved galaxies are less useful for detecting weak-lensing (WL) shear. Let's see if we can quantify this by using the Fisher matrix to determine the best possible measurement accuracy on the parameter $e$ of our model (we'll make things easy by holding all other parameters of the galaxy model as fixed).Remember how the Fisher matrix works: for an image signal $I_{xy}$ and noise $\sigma_{xy}$ in each pixel, the Fisher information for a parameter $\theta$ is$$ F_{\theta\theta} = \sum_{xy} \frac{1}{\sigma^2_{xy}} \left(\frac{\partial I_{xy}}{\partial\theta}\right)^2.$$Here we're interested in $\theta=e$.**(a)** Draw two versions of our standard galaxy, with $e = \pm0.01.$ Use these to calculate and plot the quantity we need, $\frac{\partial I_{xy}}{\partial e}.$ Comment on how this picture relates to the fact that we like to measure WL shear using the moment of $x^2-y^2$. ###Code # your work here... ###Output _____no_output_____ ###Markdown **(b)** Use this to calculate the best achievable measurement accuracy on $e$ for our standard image. ###Code # your work here... ###Output _____no_output_____ ###Markdown **(c)** Make a graph showing how the optimal $\sigma_e$ varies as the size $\sigma_{\rm PSF}$ of the Gaussian PSF varies from being $0.2\times r_0$ to being $3\times r_0.$. What's the lesson here? ###Code # your work here... ###Output _____no_output_____
Gaia/M34/M34.ipynb
###Markdown Import data from csv's ###Code datadir = os.getcwd() suffix = ['1-20', '21-40', '41-60', '61-80', '81-100', '101-120', '121-135'] #What we gave the ESA archive datafile_input = [] for i in range(0 , len(suffix)): temp = '/ids_{0}.csv'.format(suffix[i]) with open(datadir+temp, 'r') as f: reader = csv.reader(f) input_1_20 = list(reader) datafile_input.append(input_1_20) #What we got from the ESA archive datafile_output = [] for i in range(0 , len(suffix)): temp = '/{0}.csv'.format(suffix[i]) with open(datadir+temp, 'r') as f: reader = csv.reader(f) output_1_20 = list(reader) datafile_output.append(output_1_20) #extract gaia source IDs from the input files input_ids = [] for j in range(0, len(datafile_input)): input_idss = [] for i in range(0, len(datafile_input[j])): input_idss.append(int(datafile_input[j][i][0].split(" ")[2])) input_ids.append(input_idss) #extract gaia source IDs from the output files output_ids = [] for j in range(0, len(datafile_output)): temp = [int(datafile_output[j][i][0]) for i in range(1,len(datafile_output[j]))] output_ids.append(temp) #check if every pair of files (resp. first input and first output file) contain same IDs for i in range(0, len(output_ids)): print(set(output_ids[i]) == set(input_ids[i])) #we have to use set, because the output is not in the same order as the input #now extract all data into lists output_info = datafile_output[0][0] output_info rv = np.asarray([output_all[i][12] for i in range(0, len(output_all))]) rv rv_0 = [] for i in range(0, len(rv)): if rv[i] == "": rv_0.append(0) else: rv_0.append(float(rv[i])) plt.plot(np.arange(0,len(rv_0_new)), rv_0_new) #list that contains all data output_all = [] for j in range(0, len(datafile_output)): #print(j) for i in range(0, len(datafile_output[j])-1): #print(i) temp = datafile_output[j][1:][i] output_all.append(temp) len(output_all) ###Output _____no_output_____ ###Markdown Store data in arrays and exclude stars w/ no 5 parameter solutions ###Code #every star normally has an id, ra&dec and a magnitude. sid = np.array([int(output_all[i][0]) for i in range(0, len(output_all))]) ra = np.array([float(output_all[i][1]) for i in range(0, len(output_all))]) dec = np.array([float(output_all[i][3]) for i in range(0, len(output_all))]) #we can convert the magnitudes to fluxes magg = np.array([float(output_all[i][11]) for i in range(0, len(output_all))]) fluxg = 10**(-0.4*np.array(magg)) max(magg) #using ra&dec and the flux we can recreate our observation plt.subplots(1,1,figsize=(16,14)) plt.scatter(ra, dec, s=fluxg*5e5) plt.gca().invert_xaxis() plt.xlabel('RA (ยฐ)') plt.ylabel('DEC (ยฐ)') plt.show() #a histogram of the magnitudes fig, ax1 = plt.subplots(1, 1, figsize=(8,8)) ax1.hist(magg, bins=np.arange(7,18,0.5), edgecolor='black', linewidth=0.5) ax1.set_xticks(np.arange(7,18,1)) ax1.set_xlabel('Gaia magnitude') ax1.set_ylabel('frequency') plt.show() #because an (or some) element in the following lists is not a number we cant convert it yet into floats... pax = np.asarray([output_all[i][5] for i in range(0, len(output_all))]) pmra = np.asarray([output_all[i][7] for i in range(0, len(output_all))]) pmdec = np.asarray([output_all[i][9] for i in range(0, len(output_all))]) #Look for missing values for j in range(0, len(output_all[0])): for i in range(0, len(output_all)): if output_all[i][j] == '': print(output_info[j],i) #Where is/are the star/s with only a 2 parameter solution? two_para_star = [] for i in range(0, len(pax)): if pax[i] == '': print(i) two_para_star.append(i) if pmra[i] == '': print(i) two_para_star.append(i) if pmdec[i] == '': print(i) two_para_star.append(i) list(set(two_para_star)) # star 133 resp. element 132 has no pax, pmra & pmdec! # so the star will be removed from all lists sid[132] #remove element 132: sid_new = np.delete(sid, two_para_star) ra_new = np.delete(ra, two_para_star) dec_new = np.delete(dec, two_para_star) magg_new = np.delete(magg, two_para_star) fluxg_new = np.delete(fluxg, two_para_star) rv_0_new = np.delete(rv_0, two_para_star) pax_new = np.delete(pax, two_para_star).astype(float) pmra_new = np.delete(pmra, two_para_star).astype(float) pmdec_new = np.delete(pmdec, two_para_star).astype(float) #plot rv values #positive --> receding (visa versa) plt.scatter(np.arange(0,len(rv_0_new)), rv_0_new) plt.show() #so most stars - with rv values - are moving towards us #using ra&dec and the flux we can recreate our observation plt.subplots(1,1,figsize=(8,8)) plt.scatter(ra_new, dec_new, s=fluxg*5e5) plt.scatter(ra[132], dec[132], s=fluxg[132]*5e5, c='r') plt.gca().invert_xaxis() plt.xlabel('RA (ยฐ)') plt.ylabel('DEC (ยฐ)') plt.show() ###Output _____no_output_____ ###Markdown Reconstruct our Observation ###Code def arrows(x, y, pm_x, pm_y, scale): temp = [] for i in range(0, len(x)): temp2 = [x[i], y[i], scale * pm_x[i], scale * pm_y[i]] temp.append(temp2) return np.array(temp) soa = arrows(ra_new, dec_new, pmra_new*np.cos(dec_new), pmdec_new, 0.005) X, Y, U, V = zip(*soa) plt.subplots(1,1,figsize=(10,10)) ax = plt.gca() ax.quiver(X, Y, U, V, angles='xy', scale_units='xy', scale=1, width=0.0017, alpha=1, color='r') ax.scatter(ra[132], dec[132], s=np.array(fluxg[132])*3e5, c='k') ax.scatter(ra_new, dec_new, s=np.array(fluxg_new)*3e5) ax.invert_xaxis() ax.margins(0.15) ax.set_xlabel('RA (ยฐ)') ax.set_ylabel('DEC (ยฐ)') #plt.savefig('M34_pm.png', dpi=1000) plt.draw() plt.show() #0-->min and 1-->max def get_index_max(array, min_or_max): if min_or_max == 0: tmp = min(array) tmpi = list(array).index(tmp) name = "Gaia DR2 %i" % sid_new[tmpi] return tmp, name elif min_or_max == 1: tmp = max(array) tmpi = list(array).index(tmp) name = "Gaia DR2 %i" % sid_new[tmpi] return tmp, name else: print('Read the instructions.... dummy') get_index_max(pax_new, 1) # convert parallaxes into parsecs parcs = 1000./np.array(pax_new) pmra_new_c = pmra_new * np.cos(dec_new) fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(10,14)) ax1.hist(parcs, bins='auto') ax2.hist(parcs, bins=np.arange(0,1000,20)) ax3.hist(parcs, bins=np.arange(300,700,16.5)) ax4.hist(pmra_new_c, bins='auto') ax5.hist(pmdec_new, bins='auto') #ax1.set_title('distance') #ax2.set_title('distance zoom') #ax3.set_title('pm ra') #ax4.set_title('pm dec') ax1.set_xlabel('distance (parsec)') ax2.set_xlabel('distance (parsec)') ax3.set_xlabel('distance (parsec)') ax4.set_xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') ax5.set_xlabel('$\mu_\\delta$ (mas/yr)') ax1.set_ylabel('frequency') ax2.set_ylabel('frequency') ax3.set_ylabel('frequency') ax4.set_ylabel('frequency') ax5.set_ylabel('frequency') posx = 0.97 posy = 0.83 ax1.text(posx, posy, 'a', transform=ax1.transAxes, fontsize=16, fontweight='bold') ax2.text(posx, posy, 'b', transform=ax2.transAxes, fontsize=16, fontweight='bold') ax3.text(posx, posy, 'c', transform=ax3.transAxes, fontsize=16, fontweight='bold') ax4.text(posx, posy, 'd', transform=ax4.transAxes, fontsize=16, fontweight='bold') ax5.text(posx, posy, 'e', transform=ax5.transAxes, fontsize=16, fontweight='bold') plt.subplots_adjust(hspace=0.5) fig.savefig('M34_histogram.png', dpi=1000) plt.show() ###Output _____no_output_____ ###Markdown Extract Cluster Members ###Code mask_dist = [] mask_pmra = [] mask_pmdec = [] for i in range(len(parcs)): mask_dist.append(300 <= parcs[i] <= 700) for j in range(len(pmra_new_c)): mask_pmra.append(-1 <= pmra_new_c[j] <= 1.3) for k in range(len(pmdec_new)): mask_pmdec.append(-9 <= pmdec_new[k] <= -4) mask_dist = np.array(mask_dist) mask_pmra = np.array(mask_pmra) mask_pmdec = np.array(mask_pmdec) mask_cluster = [] for ind in range(max(len(mask_dist),len(mask_pmra),len(mask_pmdec))): if mask_dist[ind] and mask_pmra[ind] and mask_pmdec[ind]: mask_cluster.append(True) else: mask_cluster.append(False) mask_cluster = np.array(mask_cluster) mask_cluster ra_cl = ra_new[mask_cluster] dec_cl = dec_new[mask_cluster] pmra_new_c_cl = pmra_new_c[mask_cluster] pmdec_new_cl = pmdec_new[mask_cluster] parcs_cl = parcs[mask_cluster] fluxg_cl = fluxg_new[mask_cluster] mask_cluster_not = ~(mask_cluster) soa = arrows(ra_cl, dec_cl, pmra_new_c_cl, pmdec_new_cl, 0.005) X, Y, U, V = zip(*soa) plt.subplots(1,1,figsize=(8,8)) ax = plt.gca() ax.quiver(X, Y, U, V, angles='xy', scale_units='xy', scale=1, width=0.002, alpha=1, color='r') ax.scatter(ra_new, dec_new, s=np.array(fluxg_new)*5e5) ax.scatter(ra_cl, dec_cl, s=np.array(fluxg_cl)*5e5,c='k') ax.invert_xaxis() ax.margins(0.1) ax.set_xlabel('RA (ยฐ)') ax.set_ylabel('DEC (ยฐ)') #plt.savefig('M34_pm_mask.png', dpi=1000) plt.draw() plt.show() arrow_members = arrows(ra_new[mask_cluster], dec_new[mask_cluster], pmra_new_c[mask_cluster], pmdec_new[mask_cluster], 0.005) arrow_nomembers = arrows(ra_new[mask_cluster_not], dec_new[mask_cluster_not], pmra_new_c[mask_cluster_not], pmdec_new[mask_cluster_not], 0.005) X, Y, U, V = zip(*arrow_members) Xno, Yno, Uno, Vno = zip(*arrow_nomembers) d10 = list(map(math.log10, parcs[mask_cluster])) d10no = list(map(math.log10, parcs[mask_cluster_not])) from mpl_toolkits.mplot3d import Axes3D import random fig = plt.figure(figsize=(16,16)) ax = fig.add_subplot(111, projection='3d') ax.scatter(ra_new[mask_cluster_not], d10no , dec_new[mask_cluster_not], s = np.array(fluxg_new[mask_cluster_not])*5e5) ax.scatter(ra_new[mask_cluster], d10, dec_new[mask_cluster], s = np.array(fluxg_new[mask_cluster])*5e5, c='k') ax.set_xlabel('RA (ยฐ)', labelpad=15, fontsize=14) ax.set_ylabel('log$_{10}$(distance (parsec))', labelpad=15, fontsize=14) ax.set_zlabel('DEC (ยฐ)', labelpad=17, fontsize=14) ax.xaxis.set_tick_params(labelsize=13) ax.yaxis.set_tick_params(labelsize=13) ax.zaxis.set_tick_params(labelsize=13) ax.quiver(Xno, d10no, Yno, Uno, 0, Vno, alpha=0.6, color='skyblue', arrow_length_ratio = 0.01) ax.quiver(X, d10, Y, U, 0, V, alpha=0.8, color='darkblue', arrow_length_ratio = 0.01) ax.quiver(Xno, d10no, Yno, 0, rv_0_new[mask_cluster_not]*0.01, 0, alpha=0.6, color='y', arrow_length_ratio = 0.01) ax.quiver(X, d10, Y, 0, rv_0_new[mask_cluster]*0.01, 0, alpha=0.8, color='red', arrow_length_ratio = 0.01) #ax.tick_params(axis='x', which='major', pad=10) #ax.tick_params(axis='y', which='major', pad=10) ax.tick_params(axis='z', which='major', pad=11) ax.view_init(30, -60) ax.invert_xaxis() plt.show() fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8,10)) hist,bins, __ = ax1.hist(parcs_cl, bins=np.arange(300, 700, 16.6)) ax2.hist(pmra_new_c_cl, bins=np.arange(-1, 1.3, 0.173)) ax3.hist(pmdec_new_cl, bins=np.arange(-9, -4, 0.36)) ax1.set_xlabel('distance (parsec)') ax2.set_xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') ax3.set_xlabel('$\mu_\\delta$ (mas/yr)') plt.subplots_adjust(hspace=0.3) plt.show() values, bins, _ = plt.hist(parcs_cl, bins='auto')#np.arange(400, 600, 16.6) mu1, std1 = norm.fit(parcs_cl) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 100) area = sum(np.diff(bins)*values) p = norm.pdf(x, mu1, std1)*area plt.plot(x, p, 'k', linewidth=2) title = "Fit results: $\mu$ = %.1f, $\sigma$ = %.1f" % (mu1, std1) plt.title(title) plt.xlabel('distance (parsec)') plt.ylabel('frequency') #plt.savefig('M34_Gaussian_pc.png', dpi=1000) plt.show() values, bins, _ = plt.hist(pmra_new_c_cl, bins=np.arange(-0.8,1,0.173)) mu2, std2 = norm.fit(pmra_new_c_cl) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 100) area = sum(np.diff(bins)*values) p = norm.pdf(x, mu2, std2)*area plt.plot(x, p, 'k', linewidth=2) title = "Fit results: $\mu$ = %.2f, $\sigma$ = %.2f" % (mu2, std2) plt.title(title) plt.xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') plt.ylabel('frequency') #plt.savefig('M34_Gaussian_pmra.png', dpi=1000) plt.show() values, bins, _ = plt.hist(pmdec_new_cl, bins=np.arange(-8,-3,0.36)) mu3, std3 = norm.fit(pmdec_new_cl) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 100) area = sum(np.diff(bins)*values) p = norm.pdf(x, mu3, std3)*area plt.plot(x, p, 'k', linewidth=2) title = "Fit results: $\mu$ = %.1f, $\sigma$ = %.1f" % (mu3, std3) plt.title(title) plt.xlabel('$\mu_\\delta$ (mas/yr)') plt.ylabel('frequency') #plt.savefig('M34_Gaussian_pmdec.png', dpi=1000) plt.show() ###Output _____no_output_____ ###Markdown Error Analysis ###Code err_ra = np.asarray([output_all[i][2] for i in range(0, len(output_all))]) err_dec = np.asarray([output_all[i][4] for i in range(0, len(output_all))]) err_pax = np.asarray([output_all[i][6] for i in range(0, len(output_all))]) err_pmra = np.asarray([output_all[i][8] for i in range(0, len(output_all))]) err_pmdec = np.asarray([output_all[i][10] for i in range(0, len(output_all))]) err_ra_new = np.delete(err_ra, 132).astype(float) err_dec_new = np.delete(err_dec, 132).astype(float) err_pax_new = np.delete(err_pax, 132).astype(float) err_pmra_new = np.delete(err_pmra, 132).astype(float) err_pmdec_new = np.delete(err_pmdec, 132).astype(float) fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(10,14)) _,bins,__ = ax1.hist(err_ra_new, bins='auto') ax1.hist(err_ra_new[mask_cluster], bins) _,bins,__ = ax2.hist(err_dec_new, bins='auto') ax2.hist(err_dec_new[mask_cluster], bins) _,bins,__ = ax3.hist(err_pax_new, bins='auto') ax3.hist(err_pax_new[mask_cluster], bins) _,bins,__ = ax4.hist(err_pmra_new, bins='auto') ax4.hist(err_pmra_new[mask_cluster], bins) _,bins,__ = ax5.hist(err_pmdec_new, bins='auto') ax5.hist(err_pmdec_new[mask_cluster], bins) ax1.set_xlabel('distance (parsec)') ax2.set_xlabel('distance (parsec)') ax3.set_xlabel('distance (parsec)') ax4.set_xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') ax5.set_xlabel('$\mu_\\delta$ (mas/yr)') ax1.set_ylabel('frequency') ax2.set_ylabel('frequency') ax3.set_ylabel('frequency') ax4.set_ylabel('frequency') ax5.set_ylabel('frequency') posx = 0.97 posy = 0.83 ax1.text(posx, posy, 'a', transform=ax1.transAxes, fontsize=16, fontweight='bold') ax2.text(posx, posy, 'b', transform=ax2.transAxes, fontsize=16, fontweight='bold') ax3.text(posx, posy, 'c', transform=ax3.transAxes, fontsize=16, fontweight='bold') ax4.text(posx, posy, 'd', transform=ax4.transAxes, fontsize=16, fontweight='bold') ax5.text(posx, posy, 'e', transform=ax5.transAxes, fontsize=16, fontweight='bold') plt.subplots_adjust(hspace=0.5) #fig.savefig('M34_histogram.png', dpi=1000) plt.show() ###Output _____no_output_____ ###Markdown Gaia Magnitude ###Code fig, ax1 = plt.subplots(1, 1, figsize=(8,8)) ax1.hist(magg_new, bins=np.arange(7,17.5,0.5), edgecolor='black', linewidth=0.5) ax1.hist(magg_new[mask_cluster], bins=np.arange(7,17.5,0.5), edgecolor='black', linewidth=0.5, alpha=1) ax1.set_xticks(np.arange(7,18,1)) ax1.set_yticks(np.arange(0,22,3)) ax1.set_xlabel('Gaia magnitude') ax1.set_ylabel('frequency') plt.show() print( "#INPUT: %.1i" % (len(sid))) print( "WITH ALL 5 PARAMETERS: %.1i" % (len(sid_new))) print() print( "--> NO 5 parameter sols for: %.1i" % (len(sid)-len(sid_new))) print() print( "RV exist for: %.1i" % (rv_0.count(0))) print( "NO RV exist for: %.1i" % (len(sid)-rv_0.count(0))) print() print( "--> Fraction: %.3f" % (rv_0.count(0)/len(sid))) print() print() print( "Distance: %.1f +/- %.1f" % (mu1, std1)) print( "PM RA: %.1f +/- %.1f" % (mu2, std2)) print( "PM DEC: %.1f +/- %.1f" % (mu3, std3)) print() print() plt.scatter(pmra_new_c, pmdec_new,s=5) plt.xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') plt.ylabel('$\mu_\\delta$ (mas/yr)') plt.show() def x_both(lst): tmp = lst + [-x for x in lst] return tmp #1 SIGMA def x_ellipse1(a, b): xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(xel_both) def y_ellipse1(a, b): xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(yel_both) #2 SIGMA def x_ellipse2(a, b): a = 2*a b = 2*b xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(xel_both) def y_ellipse2(a, b): a = 2*a b = 2*b xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(yel_both) #3 SIGMA def x_ellipse3(a, b): a = 3*a b = 3*b xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(xel_both) def y_ellipse3(a, b): a = 3*a b = 3*b xel = np.arange(-a, a, 0.0001) xel_pow = xel**2 dis = a**2-xel_pow yel = b/a * np.sqrt(dis.tolist()) yel_both = [] for i in yel: yel_both.append(i) for i in yel: yel_both.append(-i) xel_both = x_both(xel.tolist()) return np.array(yel_both) plt.subplots(1,1,figsize=(12,12)) plt.scatter(pmra_new_c, pmdec_new,s=5) x_el1 = x_ellipse1(std2,std3)+mu2 y_el1 = y_ellipse1(std2,std3)+mu3 x_el2 = x_ellipse2(std2,std3)+mu2 y_el2 = y_ellipse2(std2,std3)+mu3 x_el3 = x_ellipse3(std2,std3)+mu2 y_el3 = y_ellipse3(std2,std3)+mu3 plt.plot(x_el1, y_el1, c='r', linewidth=1) plt.plot(x_el2, y_el2, c='r', linewidth=1) plt.plot(x_el3, y_el3, c='r', linewidth=1) plt.xlabel('$\mu_\\alpha$ cos $\delta$ (mas/yr)') plt.ylabel('$\mu_\\delta$ (mas/yr)') plt.xlim(-10,10) plt.ylim(-20,10) #plt.xscale("symlog") #plt.yscale("symlog") plt.show() ###Output _____no_output_____
analyses/seasonality_paper_nn/all/model_analysis_loco.ipynb
###Markdown Setup ###Code from specific import * ###Output _____no_output_____ ###Markdown Retrieve previous results from the 'model' notebook ###Code X_train, X_test, y_train, y_test = data_split_cache.load() rf = get_model() ###Output _____no_output_____ ###Markdown Get Dask Client ###Code def print_path(): import sys return sys.path client.submit(print_path).result() # client.scheduler_info()['workers']['tcp://10.149.10.103:40991'] def p2(): import wildfires return str(wildfires) for worker in client.scheduler_info()["workers"]: # print(worker) # print(client.submit(print_path, pure=False, workers={worker}).result()) try: print(client.submit(p2, pure=False, workers={worker}).result()) except: print("Failed:", worker) client.restart() # client = Client(n_workers=1, threads_per_worker=8, resources={'threads': 8}) client = get_client() client ###Output _____no_output_____ ###Markdown Dask LOCO ###Code loco_cache = SimpleCache("loco_results", cache_dir=CACHE_DIR) leave_out = [""] leave_out.extend(X_train.columns) # XXX: # loco_cache.clear() @loco_cache def get_loco_scores(): return dict( dask_fit_loco( rf, X_train, y_train, client, leave_out, local_n_jobs=31, verbose=True ) ) scores = get_loco_scores() scores ###Output _____no_output_____
12_CNN_in_TF_Part2/12_CNN_in_TF.ipynb
###Markdown **Convolutional Neural Network (CNN) in TensorFlow** Import TensorFlow ###Code import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets, layers, models, activations, Model, Input, regularizers import matplotlib.pyplot as plt ###Output _____no_output_____ ###Markdown Download and prepare the CIFAR10 datasetThe CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes are mutually exclusive and there is no overlap between them. ###Code (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() # Normalize pixel values to be between 0 and 1 train_images, test_images = train_images / 255.0, test_images / 255.0 ###Output _____no_output_____ ###Markdown Verify the dataTo verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image. ###Code class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] plt.figure(figsize=(10,10)) for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) # The CIFAR labels happen to be arrays, # which is why you need the extra index plt.xlabel(class_names[train_labels[i][0]]) plt.show() ###Output _____no_output_____ ###Markdown **Create the convolutional base** The 6 lines of code below define the convolutional base using a common pattern: a stack of [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) and [MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D) layers.As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument `input_shape` to our first layer. **CNN Layer** **Max Pooling Layer** **Batch Normalization**1. Speeds up training2. Decreases importance of initial weights3. Regularizes the model (a little bit) **Regularization**Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model's performance on the unseen data as well. ###Code def my_model(): inputs = Input(shape=(32, 32, 3)) x = layers.Conv2D(32, 3, padding="same", kernel_regularizer=regularizers.l2(0.01),)( inputs ) x = layers.BatchNormalization()(x) x = activations.relu(x) x = layers.MaxPooling2D()(x) x = layers.Conv2D(64, 3, padding="same", kernel_regularizer=regularizers.l2(0.01),)( x ) x = layers.BatchNormalization()(x) x = activations.relu(x) x = layers.MaxPooling2D()(x) x = layers.Conv2D( 128, 3, padding="same", kernel_regularizer=regularizers.l2(0.01), )(x) x = layers.BatchNormalization()(x) x = activations.relu(x) x = layers.Flatten()(x) x = layers.Dense(64, activation="relu", kernel_regularizer=regularizers.l2(0.01),)( x ) x = layers.Dropout(0.5)(x) outputs = layers.Dense(10)(x) model = Model(inputs=inputs, outputs=outputs) return model model = my_model() ###Output _____no_output_____ ###Markdown Here's the complete architecture of our model. ###Code model.summary() ###Output Model: "model_5" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_7 (InputLayer) [(None, 32, 32, 3)] 0 _________________________________________________________________ conv2d_33 (Conv2D) (None, 32, 32, 32) 896 _________________________________________________________________ batch_normalization_18 (Batc (None, 32, 32, 32) 128 _________________________________________________________________ tf.nn.relu_18 (TFOpLambda) (None, 32, 32, 32) 0 _________________________________________________________________ max_pooling2d_22 (MaxPooling (None, 16, 16, 32) 0 _________________________________________________________________ conv2d_34 (Conv2D) (None, 16, 16, 64) 18496 _________________________________________________________________ batch_normalization_19 (Batc (None, 16, 16, 64) 256 _________________________________________________________________ tf.nn.relu_19 (TFOpLambda) (None, 16, 16, 64) 0 _________________________________________________________________ max_pooling2d_23 (MaxPooling (None, 8, 8, 64) 0 _________________________________________________________________ conv2d_35 (Conv2D) (None, 8, 8, 128) 73856 _________________________________________________________________ batch_normalization_20 (Batc (None, 8, 8, 128) 512 _________________________________________________________________ tf.nn.relu_20 (TFOpLambda) (None, 8, 8, 128) 0 _________________________________________________________________ flatten_6 (Flatten) (None, 8192) 0 _________________________________________________________________ dense_12 (Dense) (None, 64) 524352 _________________________________________________________________ dropout_3 (Dropout) (None, 64) 0 _________________________________________________________________ dense_13 (Dense) (None, 10) 650 ================================================================= Total params: 619,146 Trainable params: 618,698 Non-trainable params: 448 _________________________________________________________________ ###Markdown As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers. Compile and train the model ###Code model.compile(keras.optimizers.Adam(lr=3e-4), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) history = model.fit(train_images, train_labels, batch_size=64,verbose=2,epochs=150, validation_data=(test_images, test_labels)) ###Output Epoch 1/150 782/782 - 4s - loss: 3.0447 - accuracy: 0.3548 - val_loss: 2.0338 - val_accuracy: 0.5449 Epoch 2/150 782/782 - 3s - loss: 1.9154 - accuracy: 0.4687 - val_loss: 1.5710 - val_accuracy: 0.6087 Epoch 3/150 782/782 - 3s - loss: 1.6150 - accuracy: 0.5213 - val_loss: 1.3750 - val_accuracy: 0.6316 Epoch 4/150 782/782 - 3s - loss: 1.4983 - accuracy: 0.5463 - val_loss: 1.3236 - val_accuracy: 0.6238 Epoch 5/150 782/782 - 3s - loss: 1.4293 - accuracy: 0.5669 - val_loss: 1.3829 - val_accuracy: 0.5921 Epoch 6/150 782/782 - 3s - loss: 1.3960 - accuracy: 0.5742 - val_loss: 1.4841 - val_accuracy: 0.5424 Epoch 7/150 782/782 - 3s - loss: 1.3539 - accuracy: 0.5915 - val_loss: 1.1862 - val_accuracy: 0.6781 Epoch 8/150 782/782 - 3s - loss: 1.3309 - accuracy: 0.5983 - val_loss: 1.1942 - val_accuracy: 0.6576 Epoch 9/150 782/782 - 3s - loss: 1.3104 - accuracy: 0.6078 - val_loss: 1.1453 - val_accuracy: 0.6806 Epoch 10/150 782/782 - 3s - loss: 1.2908 - accuracy: 0.6148 - val_loss: 1.2737 - val_accuracy: 0.6428 Epoch 11/150 782/782 - 3s - loss: 1.2702 - accuracy: 0.6237 - val_loss: 1.6426 - val_accuracy: 0.5309 Epoch 12/150 782/782 - 3s - loss: 1.2581 - accuracy: 0.6298 - val_loss: 1.0651 - val_accuracy: 0.7221 Epoch 13/150 782/782 - 3s - loss: 1.2463 - accuracy: 0.6383 - val_loss: 1.2789 - val_accuracy: 0.6369 Epoch 14/150 782/782 - 3s - loss: 1.2333 - accuracy: 0.6416 - val_loss: 1.3313 - val_accuracy: 0.6235 Epoch 15/150 782/782 - 3s - loss: 1.2173 - accuracy: 0.6466 - val_loss: 1.2409 - val_accuracy: 0.6445 Epoch 16/150 782/782 - 3s - loss: 1.2098 - accuracy: 0.6516 - val_loss: 1.1779 - val_accuracy: 0.6757 Epoch 17/150 782/782 - 3s - loss: 1.1913 - accuracy: 0.6594 - val_loss: 1.1438 - val_accuracy: 0.6924 Epoch 18/150 782/782 - 3s - loss: 1.1869 - accuracy: 0.6620 - val_loss: 0.9685 - val_accuracy: 0.7549 Epoch 19/150 782/782 - 3s - loss: 1.1776 - accuracy: 0.6674 - val_loss: 1.1231 - val_accuracy: 0.6889 Epoch 20/150 782/782 - 3s - loss: 1.1681 - accuracy: 0.6702 - val_loss: 1.1981 - val_accuracy: 0.6753 Epoch 21/150 782/782 - 3s - loss: 1.1561 - accuracy: 0.6753 - val_loss: 1.0402 - val_accuracy: 0.7273 Epoch 22/150 782/782 - 3s - loss: 1.1519 - accuracy: 0.6800 - val_loss: 1.1846 - val_accuracy: 0.6670 Epoch 23/150 782/782 - 3s - loss: 1.1410 - accuracy: 0.6827 - val_loss: 1.1373 - val_accuracy: 0.6978 Epoch 24/150 782/782 - 3s - loss: 1.1319 - accuracy: 0.6875 - val_loss: 1.0876 - val_accuracy: 0.7146 Epoch 25/150 782/782 - 3s - loss: 1.1234 - accuracy: 0.6905 - val_loss: 1.1434 - val_accuracy: 0.7050 Epoch 26/150 782/782 - 3s - loss: 1.1195 - accuracy: 0.6914 - val_loss: 1.4046 - val_accuracy: 0.6139 Epoch 27/150 782/782 - 3s - loss: 1.1136 - accuracy: 0.6965 - val_loss: 1.0912 - val_accuracy: 0.7197 Epoch 28/150 782/782 - 3s - loss: 1.1048 - accuracy: 0.7000 - val_loss: 1.1225 - val_accuracy: 0.7022 Epoch 29/150 782/782 - 3s - loss: 1.0957 - accuracy: 0.7045 - val_loss: 1.1090 - val_accuracy: 0.7200 Epoch 30/150 782/782 - 3s - loss: 1.0878 - accuracy: 0.7070 - val_loss: 1.3491 - val_accuracy: 0.6164 Epoch 31/150 782/782 - 3s - loss: 1.0907 - accuracy: 0.7085 - val_loss: 1.0553 - val_accuracy: 0.7261 Epoch 32/150 782/782 - 3s - loss: 1.0869 - accuracy: 0.7115 - val_loss: 1.1252 - val_accuracy: 0.7097 Epoch 33/150 782/782 - 3s - loss: 1.0718 - accuracy: 0.7127 - val_loss: 1.3919 - val_accuracy: 0.6360 Epoch 34/150 782/782 - 3s - loss: 1.0645 - accuracy: 0.7168 - val_loss: 1.1687 - val_accuracy: 0.6940 Epoch 35/150 782/782 - 3s - loss: 1.0648 - accuracy: 0.7184 - val_loss: 1.1681 - val_accuracy: 0.6907 Epoch 36/150 782/782 - 3s - loss: 1.0697 - accuracy: 0.7180 - val_loss: 0.9257 - val_accuracy: 0.7810 Epoch 37/150 782/782 - 3s - loss: 1.0549 - accuracy: 0.7237 - val_loss: 1.1641 - val_accuracy: 0.6907 Epoch 38/150 782/782 - 3s - loss: 1.0519 - accuracy: 0.7237 - val_loss: 1.0386 - val_accuracy: 0.7423 Epoch 39/150 782/782 - 3s - loss: 1.0506 - accuracy: 0.7240 - val_loss: 1.3564 - val_accuracy: 0.6433 Epoch 40/150 782/782 - 3s - loss: 1.0460 - accuracy: 0.7284 - val_loss: 0.9866 - val_accuracy: 0.7589 Epoch 41/150 782/782 - 3s - loss: 1.0428 - accuracy: 0.7280 - val_loss: 1.0603 - val_accuracy: 0.7409 Epoch 42/150 782/782 - 3s - loss: 1.0286 - accuracy: 0.7324 - val_loss: 1.1154 - val_accuracy: 0.7310 Epoch 43/150 782/782 - 3s - loss: 1.0323 - accuracy: 0.7350 - val_loss: 1.0924 - val_accuracy: 0.7233 Epoch 44/150 782/782 - 3s - loss: 1.0329 - accuracy: 0.7335 - val_loss: 1.2308 - val_accuracy: 0.6813 Epoch 45/150 782/782 - 3s - loss: 1.0269 - accuracy: 0.7359 - val_loss: 1.0198 - val_accuracy: 0.7540 Epoch 46/150 782/782 - 3s - loss: 1.0212 - accuracy: 0.7387 - val_loss: 1.2861 - val_accuracy: 0.6742 Epoch 47/150 782/782 - 3s - loss: 1.0239 - accuracy: 0.7382 - val_loss: 1.0078 - val_accuracy: 0.7600 Epoch 48/150 782/782 - 3s - loss: 1.0073 - accuracy: 0.7441 - val_loss: 1.0322 - val_accuracy: 0.7537 Epoch 49/150 782/782 - 3s - loss: 1.0111 - accuracy: 0.7448 - val_loss: 1.0264 - val_accuracy: 0.7496 Epoch 50/150 782/782 - 3s - loss: 1.0103 - accuracy: 0.7407 - val_loss: 1.1848 - val_accuracy: 0.6947 Epoch 51/150 782/782 - 3s - loss: 0.9929 - accuracy: 0.7492 - val_loss: 0.9890 - val_accuracy: 0.7655 Epoch 52/150 782/782 - 3s - loss: 1.0078 - accuracy: 0.7453 - val_loss: 0.9409 - val_accuracy: 0.7818 Epoch 53/150 782/782 - 3s - loss: 1.0000 - accuracy: 0.7461 - val_loss: 1.2011 - val_accuracy: 0.7082 Epoch 54/150 782/782 - 3s - loss: 0.9954 - accuracy: 0.7479 - val_loss: 1.0508 - val_accuracy: 0.7505 Epoch 55/150 782/782 - 3s - loss: 0.9992 - accuracy: 0.7487 - val_loss: 1.0921 - val_accuracy: 0.7343 Epoch 56/150 782/782 - 3s - loss: 0.9908 - accuracy: 0.7524 - val_loss: 1.0021 - val_accuracy: 0.7583 Epoch 57/150 782/782 - 3s - loss: 0.9896 - accuracy: 0.7515 - val_loss: 1.1293 - val_accuracy: 0.7240 Epoch 58/150 782/782 - 3s - loss: 0.9893 - accuracy: 0.7513 - val_loss: 1.1180 - val_accuracy: 0.7241 Epoch 59/150 782/782 - 3s - loss: 0.9835 - accuracy: 0.7564 - val_loss: 0.9288 - val_accuracy: 0.7810 Epoch 60/150 782/782 - 3s - loss: 0.9836 - accuracy: 0.7546 - val_loss: 1.0944 - val_accuracy: 0.7411 Epoch 61/150 782/782 - 3s - loss: 0.9777 - accuracy: 0.7565 - val_loss: 0.9803 - val_accuracy: 0.7639 Epoch 62/150 782/782 - 3s - loss: 0.9828 - accuracy: 0.7559 - val_loss: 1.0648 - val_accuracy: 0.7337 Epoch 63/150 782/782 - 3s - loss: 0.9778 - accuracy: 0.7584 - val_loss: 1.2527 - val_accuracy: 0.6731 Epoch 64/150 782/782 - 3s - loss: 0.9653 - accuracy: 0.7619 - val_loss: 1.0337 - val_accuracy: 0.7578 Epoch 65/150 782/782 - 3s - loss: 0.9696 - accuracy: 0.7599 - val_loss: 1.2728 - val_accuracy: 0.6917 Epoch 66/150 782/782 - 3s - loss: 0.9678 - accuracy: 0.7633 - val_loss: 1.1066 - val_accuracy: 0.7244 Epoch 67/150 782/782 - 3s - loss: 0.9702 - accuracy: 0.7604 - val_loss: 1.0591 - val_accuracy: 0.7412 Epoch 68/150 782/782 - 3s - loss: 0.9598 - accuracy: 0.7650 - val_loss: 1.0395 - val_accuracy: 0.7612 Epoch 69/150 782/782 - 3s - loss: 0.9705 - accuracy: 0.7598 - val_loss: 0.9432 - val_accuracy: 0.7842 Epoch 70/150 782/782 - 3s - loss: 0.9634 - accuracy: 0.7624 - val_loss: 1.0362 - val_accuracy: 0.7591 Epoch 71/150 782/782 - 3s - loss: 0.9592 - accuracy: 0.7645 - val_loss: 1.0383 - val_accuracy: 0.7479 Epoch 72/150 782/782 - 3s - loss: 0.9598 - accuracy: 0.7653 - val_loss: 1.2102 - val_accuracy: 0.7004 Epoch 73/150 782/782 - 3s - loss: 0.9599 - accuracy: 0.7654 - val_loss: 1.4327 - val_accuracy: 0.6493 Epoch 74/150 782/782 - 3s - loss: 0.9588 - accuracy: 0.7666 - val_loss: 1.0675 - val_accuracy: 0.7589 Epoch 75/150 782/782 - 3s - loss: 0.9556 - accuracy: 0.7665 - val_loss: 1.0195 - val_accuracy: 0.7522 Epoch 76/150 782/782 - 3s - loss: 0.9539 - accuracy: 0.7688 - val_loss: 1.1200 - val_accuracy: 0.7247 Epoch 77/150 782/782 - 3s - loss: 0.9457 - accuracy: 0.7720 - val_loss: 0.9975 - val_accuracy: 0.7633 Epoch 78/150 782/782 - 3s - loss: 0.9508 - accuracy: 0.7703 - val_loss: 1.1751 - val_accuracy: 0.7189 Epoch 79/150 782/782 - 3s - loss: 0.9435 - accuracy: 0.7722 - val_loss: 0.9880 - val_accuracy: 0.7714 Epoch 80/150 782/782 - 3s - loss: 0.9433 - accuracy: 0.7716 - val_loss: 1.1713 - val_accuracy: 0.7068 Epoch 81/150 782/782 - 3s - loss: 0.9462 - accuracy: 0.7722 - val_loss: 1.1444 - val_accuracy: 0.7179 Epoch 82/150 782/782 - 3s - loss: 0.9441 - accuracy: 0.7729 - val_loss: 0.9559 - val_accuracy: 0.7797 Epoch 83/150 782/782 - 3s - loss: 0.9415 - accuracy: 0.7742 - val_loss: 1.4211 - val_accuracy: 0.6499 Epoch 84/150 782/782 - 3s - loss: 0.9478 - accuracy: 0.7717 - val_loss: 1.1729 - val_accuracy: 0.7179 Epoch 85/150 782/782 - 3s - loss: 0.9449 - accuracy: 0.7712 - val_loss: 1.0739 - val_accuracy: 0.7492 Epoch 86/150 782/782 - 3s - loss: 0.9390 - accuracy: 0.7756 - val_loss: 1.1650 - val_accuracy: 0.7309 Epoch 87/150 782/782 - 3s - loss: 0.9400 - accuracy: 0.7751 - val_loss: 1.3710 - val_accuracy: 0.6640 Epoch 88/150 782/782 - 3s - loss: 0.9366 - accuracy: 0.7746 - val_loss: 1.0425 - val_accuracy: 0.7594 Epoch 89/150 782/782 - 3s - loss: 0.9332 - accuracy: 0.7729 - val_loss: 1.0161 - val_accuracy: 0.7581 Epoch 90/150 782/782 - 3s - loss: 0.9338 - accuracy: 0.7761 - val_loss: 1.0541 - val_accuracy: 0.7617 Epoch 91/150 782/782 - 3s - loss: 0.9397 - accuracy: 0.7750 - val_loss: 1.0404 - val_accuracy: 0.7545 Epoch 92/150 782/782 - 3s - loss: 0.9329 - accuracy: 0.7768 - val_loss: 1.1247 - val_accuracy: 0.7345 Epoch 93/150 782/782 - 3s - loss: 0.9305 - accuracy: 0.7755 - val_loss: 1.0979 - val_accuracy: 0.7397 Epoch 94/150 782/782 - 3s - loss: 0.9375 - accuracy: 0.7760 - val_loss: 1.0610 - val_accuracy: 0.7633 Epoch 95/150 782/782 - 3s - loss: 0.9323 - accuracy: 0.7780 - val_loss: 1.0498 - val_accuracy: 0.7711 Epoch 96/150 782/782 - 3s - loss: 0.9232 - accuracy: 0.7800 - val_loss: 1.2032 - val_accuracy: 0.7193 Epoch 97/150 782/782 - 3s - loss: 0.9306 - accuracy: 0.7761 - val_loss: 1.0579 - val_accuracy: 0.7494 Epoch 98/150 782/782 - 3s - loss: 0.9268 - accuracy: 0.7778 - val_loss: 0.9634 - val_accuracy: 0.7769 Epoch 99/150 782/782 - 3s - loss: 0.9304 - accuracy: 0.7777 - val_loss: 1.0616 - val_accuracy: 0.7504 Epoch 100/150 782/782 - 3s - loss: 0.9198 - accuracy: 0.7836 - val_loss: 1.1787 - val_accuracy: 0.7461 Epoch 101/150 782/782 - 3s - loss: 0.9326 - accuracy: 0.7795 - val_loss: 1.1036 - val_accuracy: 0.7408 Epoch 102/150 782/782 - 3s - loss: 0.9204 - accuracy: 0.7842 - val_loss: 1.0056 - val_accuracy: 0.7717 Epoch 103/150 782/782 - 3s - loss: 0.9256 - accuracy: 0.7782 - val_loss: 1.1710 - val_accuracy: 0.7064 Epoch 104/150 782/782 - 3s - loss: 0.9316 - accuracy: 0.7808 - val_loss: 1.1014 - val_accuracy: 0.7368 Epoch 105/150 782/782 - 3s - loss: 0.9224 - accuracy: 0.7837 - val_loss: 1.1095 - val_accuracy: 0.7375 Epoch 106/150 782/782 - 3s - loss: 0.9215 - accuracy: 0.7813 - val_loss: 0.9953 - val_accuracy: 0.7722 Epoch 107/150 782/782 - 3s - loss: 0.9224 - accuracy: 0.7824 - val_loss: 1.0859 - val_accuracy: 0.7489 Epoch 108/150 782/782 - 3s - loss: 0.9232 - accuracy: 0.7842 - val_loss: 1.0918 - val_accuracy: 0.7407 Epoch 109/150 782/782 - 3s - loss: 0.9172 - accuracy: 0.7846 - val_loss: 1.1252 - val_accuracy: 0.7343 Epoch 110/150 782/782 - 3s - loss: 0.9244 - accuracy: 0.7826 - val_loss: 0.9713 - val_accuracy: 0.7855 Epoch 111/150 782/782 - 3s - loss: 0.9231 - accuracy: 0.7828 - val_loss: 1.3571 - val_accuracy: 0.6969 Epoch 112/150 782/782 - 3s - loss: 0.9208 - accuracy: 0.7841 - val_loss: 1.1541 - val_accuracy: 0.7345 Epoch 113/150 782/782 - 3s - loss: 0.9213 - accuracy: 0.7843 - val_loss: 1.0826 - val_accuracy: 0.7580 Epoch 114/150 782/782 - 3s - loss: 0.9205 - accuracy: 0.7837 - val_loss: 1.1317 - val_accuracy: 0.7263 Epoch 115/150 782/782 - 3s - loss: 0.9171 - accuracy: 0.7844 - val_loss: 1.0250 - val_accuracy: 0.7526 Epoch 116/150 782/782 - 3s - loss: 0.9151 - accuracy: 0.7874 - val_loss: 1.0694 - val_accuracy: 0.7516 Epoch 117/150 782/782 - 3s - loss: 0.9146 - accuracy: 0.7865 - val_loss: 1.0348 - val_accuracy: 0.7632 Epoch 118/150 782/782 - 3s - loss: 0.9159 - accuracy: 0.7877 - val_loss: 0.9962 - val_accuracy: 0.7846 Epoch 119/150 782/782 - 3s - loss: 0.9106 - accuracy: 0.7886 - val_loss: 1.2255 - val_accuracy: 0.7032 Epoch 120/150 782/782 - 3s - loss: 0.9184 - accuracy: 0.7862 - val_loss: 1.2291 - val_accuracy: 0.7136 Epoch 121/150 782/782 - 3s - loss: 0.9160 - accuracy: 0.7859 - val_loss: 1.0188 - val_accuracy: 0.7795 Epoch 122/150 782/782 - 3s - loss: 0.9181 - accuracy: 0.7880 - val_loss: 1.0867 - val_accuracy: 0.7418 Epoch 123/150 782/782 - 3s - loss: 0.9061 - accuracy: 0.7907 - val_loss: 1.2490 - val_accuracy: 0.7197 Epoch 124/150 782/782 - 3s - loss: 0.9131 - accuracy: 0.7886 - val_loss: 1.3769 - val_accuracy: 0.6645 Epoch 125/150 782/782 - 3s - loss: 0.9155 - accuracy: 0.7870 - val_loss: 1.2258 - val_accuracy: 0.7181 Epoch 126/150 782/782 - 3s - loss: 0.9108 - accuracy: 0.7890 - val_loss: 1.0425 - val_accuracy: 0.7655 Epoch 127/150 782/782 - 3s - loss: 0.9110 - accuracy: 0.7894 - val_loss: 0.9999 - val_accuracy: 0.7820 Epoch 128/150 782/782 - 3s - loss: 0.9114 - accuracy: 0.7905 - val_loss: 1.1094 - val_accuracy: 0.7428 Epoch 129/150 782/782 - 3s - loss: 0.9117 - accuracy: 0.7901 - val_loss: 1.0939 - val_accuracy: 0.7437 Epoch 130/150 782/782 - 3s - loss: 0.8981 - accuracy: 0.7949 - val_loss: 1.0239 - val_accuracy: 0.7735 Epoch 131/150 782/782 - 3s - loss: 0.9088 - accuracy: 0.7922 - val_loss: 1.1108 - val_accuracy: 0.7532 Epoch 132/150 782/782 - 3s - loss: 0.9079 - accuracy: 0.7925 - val_loss: 0.9933 - val_accuracy: 0.7877 Epoch 133/150 782/782 - 3s - loss: 0.9067 - accuracy: 0.7924 - val_loss: 0.9864 - val_accuracy: 0.7822 Epoch 134/150 782/782 - 3s - loss: 0.9130 - accuracy: 0.7889 - val_loss: 1.2213 - val_accuracy: 0.7353 Epoch 135/150 782/782 - 3s - loss: 0.9125 - accuracy: 0.7901 - val_loss: 1.0716 - val_accuracy: 0.7587 Epoch 136/150 782/782 - 3s - loss: 0.9052 - accuracy: 0.7933 - val_loss: 1.2643 - val_accuracy: 0.7087 Epoch 137/150 782/782 - 3s - loss: 0.9073 - accuracy: 0.7921 - val_loss: 1.1047 - val_accuracy: 0.7563 Epoch 138/150 782/782 - 3s - loss: 0.9103 - accuracy: 0.7931 - val_loss: 1.2009 - val_accuracy: 0.7345 Epoch 139/150 782/782 - 3s - loss: 0.9099 - accuracy: 0.7903 - val_loss: 1.0580 - val_accuracy: 0.7655 Epoch 140/150 782/782 - 3s - loss: 0.9003 - accuracy: 0.7948 - val_loss: 1.0344 - val_accuracy: 0.7681 Epoch 141/150 782/782 - 3s - loss: 0.9090 - accuracy: 0.7908 - val_loss: 1.0771 - val_accuracy: 0.7551 Epoch 142/150 782/782 - 3s - loss: 0.9070 - accuracy: 0.7923 - val_loss: 1.4452 - val_accuracy: 0.6912 Epoch 143/150 782/782 - 3s - loss: 0.9098 - accuracy: 0.7930 - val_loss: 1.0144 - val_accuracy: 0.7781 Epoch 144/150 782/782 - 3s - loss: 0.9060 - accuracy: 0.7927 - val_loss: 1.2071 - val_accuracy: 0.7395 Epoch 145/150 782/782 - 3s - loss: 0.8996 - accuracy: 0.7944 - val_loss: 1.2634 - val_accuracy: 0.7182 Epoch 146/150 782/782 - 3s - loss: 0.8980 - accuracy: 0.7968 - val_loss: 1.0398 - val_accuracy: 0.7612 Epoch 147/150 782/782 - 3s - loss: 0.9031 - accuracy: 0.7934 - val_loss: 1.0147 - val_accuracy: 0.7721 Epoch 148/150 782/782 - 3s - loss: 0.9021 - accuracy: 0.7952 - val_loss: 0.9947 - val_accuracy: 0.7842 Epoch 149/150 782/782 - 3s - loss: 0.9003 - accuracy: 0.7963 - val_loss: 1.6127 - val_accuracy: 0.6525 Epoch 150/150 782/782 - 3s - loss: 0.9002 - accuracy: 0.7955 - val_loss: 0.9970 - val_accuracy: 0.7861 ###Markdown Evaluate the model ###Code plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label = 'val_accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.ylim([0.5, 1]) plt.legend(loc='lower right') test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2,batch_size=64) print(test_acc) ###Output 0.7860999703407288
python/210906-Python-basic2.ipynb
###Markdown ์ž๋ฃŒ๊ตฌ์กฐ> - ๋ฐ์ดํ„ฐ์˜ ์ง‘ํ•ฉ - ๋ฐ์ดํ„ฐ๊ฐ€ ๋“ค์–ด๊ฐ€ ์žˆ๋Š” ๋ฐ”๊ตฌ๋‹ˆ - ๋ณ€์ˆ˜์— ์ €์žฅํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ์ดํ„ฐ์˜ ์ง‘ํ•ฉ >> - **`list`**(๋ฆฌ์ŠคํŠธ) >> - **`tuple`**(ํŠœํ”Œ) >> - **`dict`**(๋”•์…”๋„ˆ๋ฆฌ) >> - **`set`**(์…‹) ๋ฆฌ์ŠคํŠธ(list)> - ํŒŒ์ด์ฌ์—์„œ ๊ฐ€์žฅ ํ”ํžˆ ์‚ฌ์šฉํ•˜๋Š” ๋ฐ์ดํ„ฐ์˜ ์ž๋ฃŒ๊ตฌ์กฐ - ์ˆœ์„œ๊ฐ€ ์žˆ๋‹ค. - **`[ ]`** ๋Œ€๊ด„ํ˜ธ๋กœ ๋ฌถ์–ด ์‚ฌ์šฉํ•œ๋‹ค. - ๋ฆฌ์ŠคํŠธ ์•ˆ์— ๋“ค์–ด๊ฐ€๋Š” ์š”์†Œ๋“ค์€ ,(์‰ผํ‘œ)๋กœ ๊ตฌ๋ถ„ํ•œ๋‹ค. ๋ฆฌ์ŠคํŠธ์˜ ์ƒ์„ฑ ###Code # ๋นˆ ๋ฆฌ์ŠคํŠธ ๋งŒ๋“ค๊ธฐ # ์šฉ์–ด ์ •๋ฆฌ : ๋ณ€์ˆ˜์— ๋ฐ์ดํ„ฐ๋ฅผ ์ €์žฅํ•˜๋Š” ์ž‘์—…์„ ์ €์žฅ, ํ• ๋‹น, ๋””ํŒŒ์ธ, ๋ฐ”์ธ๋”ฉ empty_list = list() # ๋Œ€๊ด„ํ˜ธ ๋ฟ ์•„๋‹ˆ๋ผ ๋ช…๋ น์–ด๋กœ๋„ ์ž‘๋™ํ•œ๋‹ค. empty_list2 = [] print(empty_list) print(empty_list2) # ๊ฐ’๊ณผ ํ•จ๊ป˜ ๋ฆฌ์ŠคํŠธ ๋งŒ๋“ค๊ธฐ # wallet ์ด๋ผ๋Š” ๋ณ€์ˆ˜์— 'coin', 'card', 'cash', 'id', 'licence' 5๊ฐœ์˜ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๊ฐ€ ๋“ค์–ด๊ฐ€ ์žˆ๋Š” ๋ฆฌ์ŠคํŠธ๋ฅผ ์ €์žฅ wallet = ['coin', 'card', 'cash', 'id', 'licence'] wallet # ๋ฆฌ์ŠคํŠธ in ๋ฆฌ์ŠคํŠธ # ๋ฆฌ์ŠคํŠธ ๋‚ด๋ถ€์—๋Š” ์ •์ˆ˜, ์‹ค์ˆ˜, ๋ฌธ์ž์—ด ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ž๋ฃŒ๊ตฌ์กฐ์ธ list์™€ ์•ž์œผ๋กœ ํ•™์Šตํ•  ๊ธฐํƒ€ ์ž๋ฃŒ๊ตฌ์กฐ๋„ ํฌํ•จ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. wallet2=[1,3.14,wallet] wallet2 ###Output _____no_output_____ ###Markdown ๋ฆฌ์ŠคํŠธ ๊ฐฏ์ˆ˜์„ธ๊ธฐ ###Code # ๋ฆฌ์ŠคํŠธ ์ „์ฒด ํ•ญ๋ชฉ์˜ ๊ฐฏ์ˆ˜๋ฅผ ๋ณด๊ณ ์‹ถ๋‹ค๋ฉด # len() ๋ช…๋ น์–ด๋Š” ์ž๋ฃŒ๊ตฌ์กฐ ๋‚ด ์›์†Œ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์„ธ๋Š” ๋ช…๋ น์–ด์ด๋‹ค. # python ๊ธฐ๋ณธ ๋ช…๋ น์–ด๋กœ ๋‹ค์–‘ํ•œ ์ž๋ฃŒ๊ตฌ์กฐ์—๋„ ์‚ฌ์šฉ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. len(wallet) ###Output _____no_output_____ ###Markdown ๋ฆฌ์ŠคํŠธ์˜ ์ธ๋ฑ์‹ฑ(indexing), ์Šฌ๋ผ์ด์‹ฑ(slicing)> - ๋ฆฌ์ŠคํŠธ์˜ ํŠน์ • ํ•ญ๋ชฉ์— ์ ‘๊ทผ(์ƒ‰์ธ)- ์ธ๋ฑ์‹ฑ์€ ํ•˜๋‚˜์˜ ๊ฐ’์— ์ ‘๊ทผ, ์Šฌ๋ผ์ด์‹ฑ์€ ์—ฌ๋Ÿฌ๊ฐœ์˜ ๊ฐ’์˜ ๋ฌถ์Œ์— ์ ‘๊ทผ - ๋ฆฌ์ŠคํŠธ์˜ ์ˆœ์„œ๋ฅผ ์ธ๋ฑ์Šค๋ผ๊ณ  ํ•˜๋ฉฐ **0**๋ถ€ํ„ฐ ์‹œ์ž‘ ###Code # ์ธ๋ฑ์‹ฑ # ํŒŒ์ด์ฌ์˜ ์ธ๋ฑ์Šค๋Š” 0 ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•œ๋‹ค. # ๋ฆฌ์ŠคํŠธ์˜ ์ฒซ๋ฒˆ์งธ ํ•ญ๋ชฉ ๊ฐ€์ ธ์˜ค๊ธฐ wallet[0] # ์Šฌ๋ผ์ด์‹ฑ # [start index : end index+1 : steps] # ๋ฆฌ์ŠคํŠธ์˜ ์ฒซ๋ฒˆ์งธ ํ•ญ๋ชฉ๋ถ€ํ„ฐ 3๋ฒˆ์งธ ํ•ญ๋ชฉ๊นŒ์ง€ ๊ฐ€์ ธ์˜ค๊ธฐ wallet[0:3] wallet[:3] wallet[-5:-2] # ๋ฆฌ์ŠคํŠธ์˜ 4๋ฒˆ์งธ ํ•ญ๋ชฉ๋ถ€ํ„ฐ ๋งˆ์ง€๋ง‰ ํ•ญ๋ชฉ๊นŒ์ง€ ๊ฐ€์ ธ์˜ค๊ธฐ wallet[3:] wallet[-2:] # ๋ฆฌ์ŠคํŠธ์˜ 4๋ฒˆ์งธ ํ•ญ๋ชฉ๊นŒ์ง€ ๊ฐ€์ ธ์˜ค๊ธฐ wallet[:4] wallet[0:4] # ๋ฆฌ์ŠคํŠธ์˜ ๋งจ ๋งˆ์ง€๋ง‰ ํ•ญ๋ชฉ ๊ฐ€์ ธ์˜ค๊ธฐ # ์ธ๋ฑ์Šค๋กœ ์Œ์ˆ˜๋ฅผ ์ „๋‹ฌํ•˜๋ฉด ๋งˆ์ง€๋ง‰ ์ธ๋ฑ์Šค๋ถ€ํ„ฐ ์—ญ์ˆœ์œผ๋กœ ์ธ๋ฑ์‹ฑ์„ ํ•œ๋‹ค. wallet[4] wallet[-1] # ๋ฆฌ์ŠคํŠธ์˜ ์ „์ฒด ํ•ญ๋ชฉ ์ค‘ 2์˜ ๋ฐฐ์ˆ˜ ์ธ๋ฑ์Šค ํ•ญ๋ชฉ๊ฐ€์ ธ์˜ค๊ธฐ test_list = [1,2,3,4,5,6,7,8,9,10] test_list[1::2] # ์œ„์˜ wallet2์˜ cash๋ฅผ ์ธ๋ฑ์‹ฑ ํ•ด๋ณด์ž wallet2 wallet2[2][2] ###Output _____no_output_____ ###Markdown ๋ฆฌ์ŠคํŠธ ํŽธ์ง‘ ###Code # ๋ฆฌ์ŠคํŠธ ๋‚ด ํŠน์ •ํ•ญ๋ชฉ ์—…๋ฐ์ดํŠธ # ์ธ๋ฑ์Šค ํ˜น์€ ์Šฌ๋ผ์ด์‹ฑ์œผ๋กœ ์ ‘๊ทผ ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ง์ ‘ ๋ณ€๊ฒฝํ•˜๋Š” ๋ฐฉ๋ฒ• ###Output _____no_output_____ ###Markdown ๋‚ด๋ถ€๋ช…๋ น์–ด> ๋ฆฌ์ŠคํŠธ๋Š” ์ž๋ฃŒ๊ตฌ์กฐ๋ฅผ ๊ฐ–๊ณ  ์žˆ์œผ๋ฉด์„œ ๋ฆฌ์ŠคํŠธ ์ž‘์—…์„ ์œ„ํ•ด ํ•„์š”ํ•œ ๋‚ด๋ถ€๋ช…๋ น์–ด๋„ ํ•จ๊ป˜ ์ œ๊ณตํ•œ๋‹ค. ์ดํ›„ ์ž์„ธํžˆ ๋‹ค๋ฃจ์ง€๋งŒ ์ง€๊ธˆ์€ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ• ์ •๋„๋งŒ์„ ๊ธฐ์–ตํ•ด๋‘์‹œ๋ฉด ๋ฉ๋‹ˆ๋‹ค ๋ฆฌ์ŠคํŠธ์ด๋ฆ„ ๋’ค .(์ฝค๋งˆ)๋กœ ๋ช…๋ น์–ด์— ์ ‘๊ทผ์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. - **`list_name`**.**`func()`** ###Code # ๋ฆฌ์ŠคํŠธ์— ํ•ญ๋ชฉ์ถ”๊ฐ€ # append() ๋ช…๋ น์–ด์— ์ถ”๊ฐ€ํ•  ๊ฐ’์„ ์ „๋‹ฌํ•˜์—ฌ ๋ฆฌ์ŠคํŠธ์— ์ถ”๊ฐ€ํ•ฉ๋‹ˆ๋‹ค. wallet2.append(500) #extend # wallet2.extend(wallet) # wallet2 # ๋ฆฌ์ŠคํŠธ์— ํŠน์ • ํ•ญ๋ชฉ ์‚ญ์ œ wallet2.remove(500) # ๋ฆฌ์ŠคํŠธ์˜ ๋งจ ๋งˆ์ง€๋ง‰ ํ•ญ๋ชฉ ๊บผ๋‚ด์˜ค๊ธฐ (๊ฐ’๋„ ๊บผ๋‚ด๋ฉด์„œ ๋ฆฌ์ŠคํŠธ์˜ ๋งˆ์ง€๋ง‰ ํ•ญ๋ชฉ ์‚ญ์ œ) wallet2.append(100) pop_val = wallet2.pop() wallet2 pop_val # ์œ„์—์„œ ๋นผ๋‚ธ ๋งจ ๋งˆ์ง€๋ง‰ ํ•ญ๋ชฉ์„ ๋ณ€์ˆ˜๋กœ ์ €์žฅํ•˜์—ฌ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. # ๊ฒฝ์šฐ์— ๋”ฐ๋ผ์„œ๋Š” ๋ณ€์ˆ˜๋กœ ์ €์žฅํ•˜์ง€ ์•Š๊ณ  ๊ฐ’์„ ์ œ๊ฑฐ ํ•  ๋•Œ๋„ ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. # ํ…Œ์ŠคํŠธ์— ์‚ฌ์šฉํ•  num_list ์ƒ์„ฑ num_list = [3, 4, 6, 1, 2, 8, 7] # ๋ฆฌ์ŠคํŠธ์˜ ๊ฐ’ ์ •๋ ฌ (์ˆซ์ž๋Š” ์˜ค๋ฆ„์ฐจ์ˆœ, ๋ฌธ์ž๋Š” ์•ŒํŒŒ๋ฒณ์ˆœ, ํ•œ๊ธ€์€ ๊ฐ€๋‚˜๋‹ค์ˆœ) num_list.sort() # ์—ญ์ •๋ ฌ๋„ ๊ฐ€๋Šฅ num_list.sort(reverse=True) num_list # ๋ฆฌ์ŠคํŠธ ์ˆœ์„œ ์—ญ์ˆœ(๋‹จ์ˆœ ์—ญ์ˆœ) num_list = [3, 4, 6, 1, 2, 8, 7] num_list.reverse() num_list num_list = [3, 4, 6, 1, 2, 8, 7] # ๋ฆฌ์ŠคํŠธ ๋‚ด 6์˜ ์ธ๋ฑ์Šค ์œ„์น˜๋ฒˆํ˜ธ๋ฅผ ๋ณด์—ฌ์คŒ num_list.index(6) # ๋ฆฌ์ŠคํŠธ์— 6์ด๋ผ๋Š” ๊ฐ’์ด ๋“ค์–ด์žˆ๋Š” ์ธ๋ฑ์Šค ๋ฒˆํ˜ธ๋ฅผ ๋„ฃ์–ด์„œ ๊ทธ ๊ฐ’์„ ์ฐพ๊ณ  100์œผ๋กœ ๋ณ€ํ™˜ num_list[num_list.index(6)] =100 num_list num_list # ๋ฆฌ์ŠคํŠธ ๋‚ด ํŠน์ •ํ•ญ๋ชฉ ๊ฐฏ์ˆ˜ ์„ธ๊ธฐ # append๋กœ coin ํ•˜๋‚˜ ์ถ”๊ฐ€ํ•œ ๊ฐ’ wallet.count('card') ###Output _____no_output_____ ###Markdown ๋ฆฌ์ŠคํŠธ ์—ฐ์‚ฐ ###Code # intํ˜• ๋ฐ์ดํ„ฐ๊ฐ€ ๋“ค์–ด๊ฐ€ ์žˆ๋Š” num1, num2 ๋ฆฌ์ŠคํŠธ ์ƒ์„ฑ num1 = [1, 2, 3, 4, 5] num2 = [4, 5, 6, 7, 8] # ๋ฆฌ์ŠคํŠธ์˜ ๋ง์…ˆ ์—ฐ์‚ฐ num1+num2 # ๋ฆฌ์ŠคํŠธ ๋‚ด๋ถ€ ํ•ญ๋ชฉ ํ•ฉ, ์ตœ์†Œ๊ฐ’, ์ตœ๋Œ€๊ฐ’ sum(num2), min(num2), max(num2) # ์ด๋ฏธ ํ•™์Šตํ•œ print, len ๋ช…๋ น์–ด์™€ ๊ฐ™์ด ๋‹ค๋ฅธ ์ž๋ฃŒ๊ตฌ์กฐ์—๋„ ์ ์šฉ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. # ์กฐ๊ฑด์—ฐ์‚ฐ์ž ํ…Œ์ŠคํŠธ 1 not in num1 # ๋ฆฌ์ŠคํŠธ์˜ ๊ณฑ์…ˆ ์—ฐ์‚ฐ num1[0]*num2[1] ###Output _____no_output_____ ###Markdown ๋ฆฌ์ŠคํŠธ ์‚ญ์ œ ###Code # ๋ฆฌ์ŠคํŠธ ์‚ญ์ œ (๋ณ€์ˆ˜ ์‚ญ์ œ, ๊ฐ’ ์‚ญ์ œ) # ์ƒ์„ฑํ•œ ๋ฆฌ์ŠคํŠธ๋Š” ๋‚จ์•„์žˆ์ง€๋งŒ ๋‚ด๋ถ€ ํ•ญ๋ชฉ์„ ๋ชจ๋‘ ์‚ญ์ œํ•ฉ๋‹ˆ๋‹ค. wallet.clear() wallet # ๋ฉ”๋ชจ๋ฆฌ์—์„œ ์™„์ „ํžˆ ์‚ญ์ œ del wallet wallet ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ(dict)> `key`์™€ `value`๊ฐ’์„ ์Œ์œผ๋กœ ์ €์žฅ๊ฐ€๋Šฅ ํ•œ ์ž๋ฃŒ๊ตฌ์กฐ - `key` : `value` - **`{ }`** ์ค‘๊ด„ํ˜ธ๋กœ ๋ฌถ์–ด ์‚ฌ์šฉํ•œ๋‹ค. - ๋”•์…”๋„ˆ๋ฆฌ ์š”์†Œ ๊ตฌ๋ถ„์€ ๋ฆฌ์ŠคํŠธ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์‰ผํ‘œ - indexing์ด ๋˜์ง€ ์•Š์Œ ๋”•์…”๋„ˆ๋ฆฌ ์ƒ์„ฑ ###Code # ๋นˆ ๋”•์…”๋„ˆ๋ฆฌ ์ƒ์„ฑ test_dict = {} # ๋”•์…”๋„ˆ๋ฆฌ๋Š” ์ค‘๊ด„ํ˜ธ ์‚ฌ์šฉ test_dict = dict() # ๊ฐ’์„ ์ถ”๊ฐ€ํ•˜๋ฉด์„œ ๋”•์…”๋„ˆ๋ฆฌ ์ƒ์„ฑ wallet = { 'card':'SK์นด๋“œ', 'cash':3000, 'coin':{'500์›': 2, '100์›' : 3}, #๋”•์…”๋„ˆ๋ฆฌ ๋‚ด ๋”•์…”๋„ˆ๋ฆฌ 'id':['์ฃผ๋ฏผ๋“ฑ๋ก์ฆ','์—ฌ๊ถŒ'], #๋”•์…”๋„ˆ๋ฆฌ ๋‚ด list 'licence':'์šด์ „๋ฉดํ—ˆ์ฆ' } # ์ค‘๊ด„ํ˜ธ ๋‚ด key : value ๊ฐ’์„ ์ „๋‹ฌํ•˜๊ณ  ๊ฐ ํ•ญ๋ชฉ์˜ ๊ตฌ๋ถ„์€ ์‰ผํ‘œ # ๋ฆฌ์ŠคํŠธ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋”•์…”๋„ˆ๋ฆฌ ๋‚ด๋ถ€์— ๋ฆฌ์ŠคํŠธ๋“ฑ ํƒ€ ์ž๋ฃŒ๊ตฌ์กฐ ์ €์žฅ ๊ฐ€๋Šฅ # ๋”•์…”๋„ˆ๋ฆฌ ํ˜ธ์ถœ wallet ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ ๊ฐฏ์ˆ˜์„ธ๊ธฐ ###Code len(wallet) ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ ๊ฐ’์— ์ ‘๊ทผํ•˜๊ธฐ ###Code # key๊ฐ’์œผ๋กœ ๋”•์…”๋„ˆ๋ฆฌ ๊ฐ’์— ์ ‘๊ทผ wallet['card'] # ๋ฆฌ์ŠคํŠธ์—์„œ ์ธ๋ฑ์Šค๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค๋ฉด ๋”•์…”๋„ˆ๋ฆฌ๋Š” key๊ฐ’์„ ์ „๋‹ฌํ•˜์—ฌ ๊ฐ’์— ์ ‘๊ทผํ•ฉ๋‹ˆ๋‹ค. # wallet 100์›์งœ๋ฆฌ ๊ฐ’์— ์ ‘๊ทผ wallet['coin']['100์›'] # '์—ฌ๊ถŒ' wallet['id'][1] ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ ํŽธ์ง‘ ###Code # ๋”•์…”๋„ˆ๋ฆฌ์— point card key๋ฅผ ๊ฐ–๋Š” ํ•ดํ”ผํฌ์ธํŠธ ๋ฌธ์ž์—ด์„ ๊ฐ’์œผ๋กœ ์ €์žฅ wallet['point card'] = 'ํ•ดํ”ผํฌ์ธํŠธ' wallet # key๊ฐ’์ด ์ˆซ์ž์—ฌ๋„ ๊ด€๊ณ„์—†์Œ(์ธ๋ฑ์‹ฑ์ด๋ž‘ ํ—ท๊ฐˆ๋ฆด ์ˆ˜ ์žˆ์Œ) # wallet[100] = 100 # wallet # ๋”•์…”๋„ˆ๋ฆฌ ํŠน์ • ํ•ญ๋ชฉ ์—…๋ฐ์ดํŠธ wallet.update({'point card': 'CJ one'}) # ์œ„์—์ฒ˜๋Ÿผ ์ƒˆ๋กœ ์ง€์ •ํ•ด์ฃผ๋Š”๊ฒŒ ๋” ํŽธํ•˜๊ธด ํ•จ wallet # ๋”•์…”๋„ˆ๋ฆฌ ํŠน์ • value๊ฐ’ ๋นผ์˜ค๊ธฐ, value๋ฅผ ์ง€์ •ํ•ด์ค˜์•ผํ•จ # pop ๋ช…๋ น์–ด๋กœ ๋นผ์˜จ ๊ฐ’, wallet์—๋Š” ํ•ด๋‹นํ•ญ๋ชฉ ์‚ฌ๋ผ์ ธ ์žˆ์Œ wallet.pop(100) wallet # ๋”•์…”๋„ˆ๋ฆฌ ๋‚ด๋ถ€ ์†์„ฑ ๊ฐ’์— ์ ‘๊ทผํ•ด์„œ ๊ฐ’ ์—…๋ฐ์ดํŠธ๋„ ๊ฐ€๋Šฅํ•˜๋‹ค. # wallet์˜ coin key๊ฐ’์„ ๊ฐ–๋Š” ๊ฐ’์— 50์›:1 ๊ฐ’์„ ์ถ”๊ฐ€ํ•œ๋‹ค. wallet['coin']['50์›'] = 1 # ๋˜๋Š” wallet['coin'].update({'50์›': 1}) wallet # wallet id key๊ฐ’์— '์šด์ „๋ฉดํ—ˆ์ฆ' ์ถ”๊ฐ€ wallet['id'].append('์šด์ „๋ฉดํ—ˆ์ฆ') wallet ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ ์‚ญ์ œ ###Code # ๋”•์…”๋„ˆ๋ฆฌ ํ•ญ๋ชฉ ์ œ๊ฑฐ # ๋”•์…”๋„ˆ๋ฆฌ์˜ key๊ฐ’๊นŒ์ง€ ์ „๋‹ฌํ•˜์—ฌ ํ•ด๋‹น key๊ฐ’๊ณผ ๊ฐ’์„ ๋™์‹œ์— ์ œ๊ฑฐ del wallet['id'] # ๋”•์…”๋„ˆ๋ฆฌ ์›์†Œ ์ „์ฒด ์‚ญ์ œ wallet.clear() wallet # ๋”•์…”๋„ˆ๋ฆฌ ๋ณ€์ˆ˜ ์™„์ „ ์‚ญ์ œ del wallet ###Output _____no_output_____ ###Markdown ๋”•์…”๋„ˆ๋ฆฌ ์ถ”๊ฐ€ ๋ช…๋ น์–ด ###Code # ๋”•์…”๋„ˆ๋ฆฌ ๋‚ด ํ‚ค ๊ฐ’์„ ํ™•์ธ wallet.keys() # ๋”•์…”๋„ˆ๋ฆฌ ๋‚ด ๊ฐ’์„ ํ™•์ธ wallet.values() wallet['coin'].keys() # ๋”•์…”๋„ˆ๋ฆฌ์˜ key, value ์Œ์„ ํ™•์ธ wallet.items() ###Output _____no_output_____ ###Markdown ํŠœํ”Œ(tuple)> - ๋ฐ์ดํ„ฐ๊ฐ€ ๊ณ ์ •์ด ๋˜์–ด ๋ณ€๊ฒฝ์ด ๋ถˆ๊ฐ€๋Šฅ ํ•œ ๋ฐ์ดํ„ฐ ์ง‘ํ•ฉ - ์—ฐ์‚ฐ์ด๋‚˜ ์ž…๋ ฅ๊ฐ’์„ ์ „๋‹ฌํ•˜๋Š” ์ž๋ฃŒ๊ตฌ์กฐ๋กœ๋Š” ์ž˜ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ๊ฒฐ๊ณผ๊ฐ’์„ ์ถœ๋ ฅํ•˜๋Š” ๊ฒฝ์šฐ ๋งŽ์ด ์‚ฌ์šฉํ•œ๋‹ค. - **`( )`** ์†Œ๊ด„ํ˜ธ๋กœ ๋ฌถ์–ด ์‚ฌ์šฉํ•œ๋‹ค. - ๊ตฌ๋ถ„์€ , ์ฝค๋งˆ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ํŠœํ”Œ ์ƒ์„ฑ ###Code test_tuple = () # ์˜๋ฏธ๊ฐ€ ๋ชจํ˜ธํ•ด์„œ ์ž˜ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š”๋‹ค. test_tuple1 = tuple() test_tuple2 = (1, 2, 3, 4) test_tuple3 = 5, 6 ###Output _____no_output_____ ###Markdown ํŠœํ”Œ ์ธ๋ฑ์‹ฑ ###Code # ํŠœํ”Œ ์ธ๋ฑ์‹ฑ ํ…Œ์ŠคํŠธ test_tuple3 test_tuple2[1] ###Output _____no_output_____ ###Markdown ํŠœํ”Œ ํŽธ์ง‘ํ•œ๋ฒˆ ํŠœํ”Œ์— ๋“ค์–ด๊ฐ„ ๊ฐ’์€ ๋ณ€๊ฒฝ์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋‹ค๋งŒ ๊ฐ’ ์ถ”๊ฐ€๋Š” ๊ฐ€๋Šฅํ•˜๋‹ค ###Code # ํŠœํ”Œ ์—…๋ฐ์ดํŠธ ํ…Œ์ŠคํŠธ test_tuple2[1] = 2 # ๊ฐ’์ด ๋ณ€๊ฒฝ๋˜์ง€ ์•Š์Œ(ํŠœํ”Œ ํŠน์ง•) ###Output _____no_output_____ ###Markdown ํŠœํ”Œ ์‚ญ์ œํŠœํ”Œ ์ „์ฒด์‚ญ์ œ๋Š” ๊ฐ€๋Šฅํ•˜์ง€๋งŒ ๊ฐ’ ์‚ญ์ œ๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ###Code # ํŠœํ”Œ ์‚ญ์ œ ํ…Œ์ŠคํŠธ del test_tuple3 ###Output _____no_output_____ ###Markdown ์–ธํŒฉ (๋ฆฌ์ŠคํŠธ, ๋ฌธ์ž์—ด๋„ ๊ฐ€๋Šฅ) ###Code # ๊ฐ’์„ ๋ณ€๊ฒฝํ•˜์ง€ ๋ชปํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ’์„ ๋ถ„๋ฆฌ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ a,b,c,d, = test_tuple2 print(a,b,c,d) ###Output 1 2 3 4 ###Markdown ์…‹(set), ์ง‘ํ•ฉ> - ์ค‘๋ณต๋œ ๊ฐ’์„ ํ—ˆ์šฉํ•˜์ง€ ์•Š๋Š” ์ž๋ฃŒ๊ตฌ์กฐ- ๋ฐ์ดํ„ฐ์…‹์˜ ๊ณ ์œ ๊ฐ’์„ ํ™•์ธํ•˜๋Š” ์šฉ๋„๋กœ๋„ ์‚ฌ์šฉ(์ค‘๋ณต์ œ๊ฑฐ) - **`{ }`** ๋Œ€๊ด„ํ˜ธ๋กœ ๋ฌถ์–ด ์‚ฌ์šฉํ•œ๋‹ค.- **์ง‘ํ•ฉ ์—ฐ์‚ฐ**์„ ์ง€์›ํ•œ๋‹ค. ์…‹ ์ƒ์„ฑ ###Code # ์…‹ ์ƒ์„ฑ ํ…Œ์ŠคํŠธ empty_set = {} empty_set = set() test_set1 = {1, 2, 3, 4} test_set2 = {3, 4, 5, 6} ###Output _____no_output_____ ###Markdown ์…‹ ํŽธ์ง‘ ###Code # ์…‹์— ๊ฐ’ ํ•˜๋‚˜ ์ถ”๊ฐ€ # ์ค‘๋ณต์„ ํ—ˆ์šฉํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ์…‹์— ๊ฐ™์€ ๊ฐ’์ด ์žˆ์„ ๊ฒฝ์šฐ ์ถ”๊ฐ€ํ•˜์ง€ ์•Š์Œ test_set1.add(5) test_set1 # ์…‹์— ๊ฐ’ ์—ฌ๋Ÿฌ๊ฐœ ์ถ”๊ฐ€ test_set1.update({5,6,7}) test_set1 # ์…‹ ๊ฐ’ ํ•˜๋‚˜์‚ญ์ œ test_set1.discard(5) test_set1 ###Output _____no_output_____ ###Markdown ์…‹ ์ง‘ํ•ฉ ์—ฐ์‚ฐ ###Code # ๊ต์ง‘ํ•ฉ # test_set1 and test_set2 test_set1.intersection(test_set2) # ํ•ฉ์ง‘ํ•ฉ # test_set1 or test_set2 test_set1.union(test_set2) # ์ฐจ์ง‘ํ•ฉa # test_set1 - test_set2 test_set1.difference(test_set2) ###Output _____no_output_____ ###Markdown ์…‹ ์‚ญ์ œ ###Code # ์…‹ ์‚ญ์ œ ํ…Œ์ŠคํŠธ del test_set1 test_set1 ###Output _____no_output_____
dmu26/dmu26_XID+SPIRE_CDFS-SWIRE/XID+SPIRE_prior.ipynb
###Markdown This notebook uses all the raw data from the XID+MIPS catalogue, maps, PSF and relevant MOCs to create XID+ prior object and relevant tiling scheme Read in MOCsThe selection functions required are the main MOC associated with the masterlist. ###Code Sel_func=pymoc.MOC() Sel_func.read('../data/CDFS-SWIRE/holes_CDFS-SWIRE_irac1_O16_MOC.fits') ###Output _____no_output_____ ###Markdown Read in XID+MIPS catalogue ###Code XID_MIPS=Table.read('../data/CDFS-SWIRE/MIPS/dmu26_XID+MIPS_CDFS-SWIRE_cat_20170901.fits') XID_MIPS[0:10] skew=(XID_MIPS['FErr_MIPS_24_u']-XID_MIPS['F_MIPS_24'])/(XID_MIPS['F_MIPS_24']-XID_MIPS['FErr_MIPS_24_l']) skew.name='(84th-50th)/(50th-16th) percentile' g=sns.jointplot(x=np.log10(XID_MIPS['F_MIPS_24']),y=skew, kind='hex') ###Output _____no_output_____ ###Markdown The uncertianties become Gaussian by $\sim 20 \mathrm{\mu Jy}$ ###Code good=XID_MIPS['F_MIPS_24']>20 good.sum() ###Output _____no_output_____ ###Markdown Read in Maps ###Code pswfits='../data/CDFS-SWIRE/SPIRE/CDFS-SWIRE-NEST_image_250_SMAP_v6.0.fits'#SPIRE 250 map pmwfits='../data/CDFS-SWIRE/SPIRE/CDFS-SWIRE-NEST_image_350_SMAP_v6.0.fits'#SPIRE 350 map plwfits='../data/CDFS-SWIRE/SPIRE/CDFS-SWIRE-NEST_image_500_SMAP_v6.0.fits'#SPIRE 500 map #output folder output_folder='./' from astropy.io import fits from astropy import wcs #-----250------------- hdulist = fits.open(pswfits) im250phdu=hdulist[0].header im250hdu=hdulist[1].header im250=hdulist[1].data*1.0E3 #convert to mJy nim250=hdulist[2].data*1.0E3 #convert to mJy w_250 = wcs.WCS(hdulist[1].header) pixsize250=3600.0*w_250.wcs.cd[1,1] #pixel size (in arcseconds) hdulist.close() #-----350------------- hdulist = fits.open(pmwfits) im350phdu=hdulist[0].header im350hdu=hdulist[1].header im350=hdulist[1].data*1.0E3 #convert to mJy nim350=hdulist[2].data*1.0E3 #convert to mJy w_350 = wcs.WCS(hdulist[1].header) pixsize350=3600.0*w_350.wcs.cd[1,1] #pixel size (in arcseconds) hdulist.close() #-----500------------- hdulist = fits.open(plwfits) im500phdu=hdulist[0].header im500hdu=hdulist[1].header im500=hdulist[1].data*1.0E3 #convert to mJy nim500=hdulist[2].data*1.0E3 #convert to mJy w_500 = wcs.WCS(hdulist[1].header) pixsize500=3600.0*w_500.wcs.cd[1,1] #pixel size (in arcseconds) hdulist.close() ## Set XID+ prior class #---prior250-------- prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu, moc=Sel_func)#Initialise with map, uncertianty map, wcs info and primary header prior250.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_CDFS-SWIRE_cat_20170901.fits',ID=XID_MIPS['help_id'][good])#Set input catalogue prior250.prior_bkg(-5.0,5)#Set prior on background (assumes Gaussian pdf with mu and sigma) #---prior350-------- prior350=xidplus.prior(im350,nim350,im350phdu,im350hdu, moc=Sel_func) prior350.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_CDFS-SWIRE_cat_20170901.fits',ID=XID_MIPS['help_id'][good]) prior350.prior_bkg(-5.0,5) #---prior500-------- prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu, moc=Sel_func) prior500.prior_cat(XID_MIPS['RA'][good],XID_MIPS['Dec'][good],'dmu26_XID+MIPS_CDFS-SWIRE_cat_20170901.fits',ID=XID_MIPS['help_id'][good]) prior500.prior_bkg(-5.0,5) #pixsize array (size of pixels in arcseconds) pixsize=np.array([pixsize250,pixsize350,pixsize500]) #point response function for the three bands prfsize=np.array([18.15,25.15,36.3]) #use Gaussian2DKernel to create prf (requires stddev rather than fwhm hence pfwhm/2.355) from astropy.convolution import Gaussian2DKernel ##---------fit using Gaussian beam----------------------- prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101) prf250.normalize(mode='peak') prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101) prf350.normalize(mode='peak') prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101) prf500.normalize(mode='peak') pind250=np.arange(0,101,1)*1.0/pixsize[0] #get 250 scale in terms of pixel scale of map pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map pind500=np.arange(0,101,1)*1.0/pixsize[2] #get 500 scale in terms of pixel scale of map prior250.set_prf(prf250.array,pind250,pind250)#requires psf as 2d grid, and x and y bins for grid (in pixel scale) prior350.set_prf(prf350.array,pind350,pind350) prior500.set_prf(prf500.array,pind500,pind500) import pickle #from moc, get healpix pixels at a given order from xidplus import moc_routines order=9 tiles=moc_routines.get_HEALPix_pixels(order,prior250.sra,prior250.sdec,unique=True) order_large=6 tiles_large=moc_routines.get_HEALPix_pixels(order_large,prior250.sra,prior250.sdec,unique=True) print('----- There are '+str(len(tiles))+' tiles required for input catalogue and '+str(len(tiles_large))+' large tiles') output_folder='./' outfile=output_folder+'Master_prior.pkl' with open(outfile, 'wb') as f: pickle.dump({'priors':[prior250,prior350,prior500],'tiles':tiles,'order':order,'version':xidplus.io.git_version()},f) outfile=output_folder+'Tiles.pkl' with open(outfile, 'wb') as f: pickle.dump({'tiles':tiles,'order':order,'tiles_large':tiles_large,'order_large':order_large,'version':xidplus.io.git_version()},f) raise SystemExit() prior250.nsrc ###Output _____no_output_____
05Natural Language Processing/01Lexical Processing/02Basic Lexical Processing/02Word Frequencies and Stop Words/stopwords.ipynb
###Markdown Plotting word frequencies ###Code import requests from nltk import FreqDist from nltk.corpus import stopwords import seaborn as sns %matplotlib inline ###Output _____no_output_____ ###Markdown Download text of 'Alice in Wonderland' ebook from https://www.gutenberg.org/ ###Code url = "https://www.gutenberg.org/files/11/11-0.txt" alice = requests.get(url) print(alice.text) ###Output รฏยปยฟThe Project Gutenberg EBook of Aliceรขย€ย™s Adventures in Wonderland, by Lewis Carroll This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you'll have to check the laws of the country where you are located before using this ebook. Title: Aliceรขย€ย™s Adventures in Wonderland Author: Lewis Carroll Release Date: June 25, 2008 [EBook #11] [Most recently updated: October 12, 2020] Language: English Character set encoding: UTF-8 *** START OF THIS PROJECT GUTENBERG EBOOK ALICEรขย€ย™S ADVENTURES IN WONDERLAND *** Produced by Arthur DiBianca and David Widger [Illustration] Aliceรขย€ย™s Adventures in Wonderland by Lewis Carroll THE MILLENNIUM FULCRUM EDITION 3.0 Contents CHAPTER I. Down the Rabbit-Hole CHAPTER II. The Pool of Tears CHAPTER III. A Caucus-Race and a Long Tale CHAPTER IV. The Rabbit Sends in a Little Bill CHAPTER V. Advice from a Caterpillar CHAPTER VI. Pig and Pepper CHAPTER VII. A Mad Tea-Party CHAPTER VIII. The Queenรขย€ย™s Croquet-Ground CHAPTER IX. The Mock Turtleรขย€ย™s Story CHAPTER X. The Lobster Quadrille CHAPTER XI. Who Stole the Tarts? CHAPTER XII. Aliceรขย€ย™s Evidence CHAPTER I. Down the Rabbit-Hole Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, รขย€ยœand what is the use of a book,รขย€ย thought Alice รขย€ยœwithout pictures or conversations?รขย€ย So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her. There was nothing so _very_ remarkable in that; nor did Alice think it so _very_ much out of the way to hear the Rabbit say to itself, รขย€ยœOh dear! Oh dear! I shall be late!รขย€ย (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when the Rabbit actually _took a watch out of its waistcoat-pocket_, and looked at it, and then hurried on, Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge. In another moment down went Alice after it, never once considering how in the world she was to get out again. The rabbit-hole went straight on like a tunnel for some way, and then dipped suddenly down, so suddenly that Alice had not a moment to think about stopping herself before she found herself falling down a very deep well. Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled รขย€ยœORANGE MARMALADEรขย€ย, but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody underneath, so managed to put it into one of the cupboards as she fell past it. รขย€ยœWell!รขย€ย thought Alice to herself, รขย€ยœafter such a fall as this, I shall think nothing of tumbling down stairs! How brave theyรขย€ย™ll all think me at home! Why, I wouldnรขย€ย™t say anything about it, even if I fell off the top of the house!รขย€ย (Which was very likely true.) Down, down, down. Would the fall _never_ come to an end? รขย€ยœI wonder how many miles Iรขย€ย™ve fallen by this time?รขย€ย she said aloud. รขย€ยœI must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I thinkรขย€ย”รขย€ย (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a _very_ good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) รขย€ยœรขย€ย”yes, thatรขย€ย™s about the right distanceรขย€ย”but then I wonder what Latitude or Longitude Iรขย€ย™ve got to?รขย€ย (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.) Presently she began again. รขย€ยœI wonder if I shall fall right _through_ the earth! How funny itรขย€ย™ll seem to come out among the people that walk with their heads downward! The Antipathies, I thinkรขย€ย”รขย€ย (she was rather glad there _was_ no one listening, this time, as it didnรขย€ย™t sound at all the right word) รขย€ยœรขย€ย”but I shall have to ask them what the name of the country is, you know. Please, Maรขย€ย™am, is this New Zealand or Australia?รขย€ย (and she tried to curtsey as she spokeรขย€ย”fancy _curtseying_ as youรขย€ย™re falling through the air! Do you think you could manage it?) รขย€ยœAnd what an ignorant little girl sheรขย€ย™ll think me for asking! No, itรขย€ย™ll never do to ask: perhaps I shall see it written up somewhere.รขย€ย Down, down, down. There was nothing else to do, so Alice soon began talking again. รขย€ยœDinahรขย€ย™ll miss me very much to-night, I should think!รขย€ย (Dinah was the cat.) รขย€ยœI hope theyรขย€ย™ll remember her saucer of milk at tea-time. Dinah my dear! I wish you were down here with me! There are no mice in the air, Iรขย€ย™m afraid, but you might catch a bat, and thatรขย€ย™s very like a mouse, you know. But do cats eat bats, I wonder?รขย€ย And here Alice began to get rather sleepy, and went on saying to herself, in a dreamy sort of way, รขย€ยœDo cats eat bats? Do cats eat bats?รขย€ย and sometimes, รขย€ยœDo bats eat cats?รขย€ย for, you see, as she couldnรขย€ย™t answer either question, it didnรขย€ย™t much matter which way she put it. She felt that she was dozing off, and had just begun to dream that she was walking hand in hand with Dinah, and saying to her very earnestly, รขย€ยœNow, Dinah, tell me the truth: did you ever eat a bat?รขย€ย when suddenly, thump! thump! down she came upon a heap of sticks and dry leaves, and the fall was over. Alice was not a bit hurt, and she jumped up on to her feet in a moment: she looked up, but it was all dark overhead; before her was another long passage, and the White Rabbit was still in sight, hurrying down it. There was not a moment to be lost: away went Alice like the wind, and was just in time to hear it say, as it turned a corner, รขย€ยœOh my ears and whiskers, how late itรขย€ย™s getting!รขย€ย She was close behind it when she turned the corner, but the Rabbit was no longer to be seen: she found herself in a long, low hall, which was lit up by a row of lamps hanging from the roof. There were doors all round the hall, but they were all locked; and when Alice had been all the way down one side and up the other, trying every door, she walked sadly down the middle, wondering how she was ever to get out again. Suddenly she came upon a little three-legged table, all made of solid glass; there was nothing on it except a tiny golden key, and Aliceรขย€ย™s first thought was that it might belong to one of the doors of the hall; but, alas! either the locks were too large, or the key was too small, but at any rate it would not open any of them. However, on the second time round, she came upon a low curtain she had not noticed before, and behind it was a little door about fifteen inches high: she tried the little golden key in the lock, and to her great delight it fitted! Alice opened the door and found that it led into a small passage, not much larger than a rat-hole: she knelt down and looked along the passage into the loveliest garden you ever saw. How she longed to get out of that dark hall, and wander about among those beds of bright flowers and those cool fountains, but she could not even get her head through the doorway; รขย€ยœand even if my head would go through,รขย€ย thought poor Alice, รขย€ยœit would be of very little use without my shoulders. Oh, how I wish I could shut up like a telescope! I think I could, if I only knew how to begin.รขย€ย For, you see, so many out-of-the-way things had happened lately, that Alice had begun to think that very few things indeed were really impossible. There seemed to be no use in waiting by the little door, so she went back to the table, half hoping she might find another key on it, or at any rate a book of rules for shutting people up like telescopes: this time she found a little bottle on it, (รขย€ยœwhich certainly was not here before,รขย€ย said Alice,) and round the neck of the bottle was a paper label, with the words รขย€ยœDRINK ME,รขย€ย beautifully printed on it in large letters. It was all very well to say รขย€ยœDrink me,รขย€ย but the wise little Alice was not going to do _that_ in a hurry. รขย€ยœNo, Iรขย€ย™ll look first,รขย€ย she said, รขย€ยœand see whether itรขย€ย™s marked รขย€ย˜_poison_รขย€ย™ or notรขย€ย; for she had read several nice little histories about children who had got burnt, and eaten up by wild beasts and other unpleasant things, all because they _would_ not remember the simple rules their friends had taught them: such as, that a red-hot poker will burn you if you hold it too long; and that if you cut your finger _very_ deeply with a knife, it usually bleeds; and she had never forgotten that, if you drink much from a bottle marked รขย€ยœpoison,รขย€ย it is almost certain to disagree with you, sooner or later. However, this bottle was _not_ marked รขย€ยœpoison,รขย€ย so Alice ventured to taste it, and finding it very nice, (it had, in fact, a sort of mixed flavour of cherry-tart, custard, pine-apple, roast turkey, toffee, and hot buttered toast,) she very soon finished it off. * * * * * * * * * * * * * * * * * * * * รขย€ยœWhat a curious feeling!รขย€ย said Alice; รขย€ยœI must be shutting up like a telescope.รขย€ย And so it was indeed: she was now only ten inches high, and her face brightened up at the thought that she was now the right size for going through the little door into that lovely garden. First, however, she waited for a few minutes to see if she was going to shrink any further: she felt a little nervous about this; รขย€ยœfor it might end, you know,รขย€ย said Alice to herself, รขย€ยœin my going out altogether, like a candle. I wonder what I should be like then?รขย€ย And she tried to fancy what the flame of a candle is like after the candle is blown out, for she could not remember ever having seen such a thing. After a while, finding that nothing more happened, she decided on going into the garden at once; but, alas for poor Alice! when she got to the door, she found she had forgotten the little golden key, and when she went back to the table for it, she found she could not possibly reach it: she could see it quite plainly through the glass, and she tried her best to climb up one of the legs of the table, but it was too slippery; and when she had tired herself out with trying, the poor little thing sat down and cried. รขย€ยœCome, thereรขย€ย™s no use in crying like that!รขย€ย said Alice to herself, rather sharply; รขย€ยœI advise you to leave off this minute!รขย€ย She generally gave herself very good advice, (though she very seldom followed it), and sometimes she scolded herself so severely as to bring tears into her eyes; and once she remembered trying to box her own ears for having cheated herself in a game of croquet she was playing against herself, for this curious child was very fond of pretending to be two people. รขย€ยœBut itรขย€ย™s no use now,รขย€ย thought poor Alice, รขย€ยœto pretend to be two people! Why, thereรขย€ย™s hardly enough of me left to make _one_ respectable person!รขย€ย Soon her eye fell on a little glass box that was lying under the table: she opened it, and found in it a very small cake, on which the words รขย€ยœEAT MEรขย€ย were beautifully marked in currants. รขย€ยœWell, Iรขย€ย™ll eat it,รขย€ย said Alice, รขย€ยœand if it makes me grow larger, I can reach the key; and if it makes me grow smaller, I can creep under the door; so either way Iรขย€ย™ll get into the garden, and I donรขย€ย™t care which happens!รขย€ย She ate a little bit, and said anxiously to herself, รขย€ยœWhich way? Which way?รขย€ย, holding her hand on the top of her head to feel which way it was growing, and she was quite surprised to find that she remained the same size: to be sure, this generally happens when one eats cake, but Alice had got so much into the way of expecting nothing but out-of-the-way things to happen, that it seemed quite dull and stupid for life to go on in the common way. So she set to work, and very soon finished off the cake. * * * * * * * * * * * * * * * * * * * * CHAPTER II. The Pool of Tears รขย€ยœCuriouser and curiouser!รขย€ย cried Alice (she was so much surprised, that for the moment she quite forgot how to speak good English); รขย€ยœnow Iรขย€ย™m opening out like the largest telescope that ever was! Good-bye, feet!รขย€ย (for when she looked down at her feet, they seemed to be almost out of sight, they were getting so far off). รขย€ยœOh, my poor little feet, I wonder who will put on your shoes and stockings for you now, dears? Iรขย€ย™m sure _I_ shanรขย€ย™t be able! I shall be a great deal too far off to trouble myself about you: you must manage the best way you can;รขย€ย”but I must be kind to them,รขย€ย thought Alice, รขย€ยœor perhaps they wonรขย€ย™t walk the way I want to go! Let me see: Iรขย€ย™ll give them a new pair of boots every Christmas.รขย€ย And she went on planning to herself how she would manage it. รขย€ยœThey must go by the carrier,รขย€ย she thought; รขย€ยœand how funny itรขย€ย™ll seem, sending presents to oneรขย€ย™s own feet! And how odd the directions will look! _Aliceรขย€ย™s Right Foot, Esq., Hearthrug, near the Fender,_ (_with Aliceรขย€ย™s love_). Oh dear, what nonsense Iรขย€ย™m talking!รขย€ย Just then her head struck against the roof of the hall: in fact she was now more than nine feet high, and she at once took up the little golden key and hurried off to the garden door. Poor Alice! It was as much as she could do, lying down on one side, to look through into the garden with one eye; but to get through was more hopeless than ever: she sat down and began to cry again. รขย€ยœYou ought to be ashamed of yourself,รขย€ย said Alice, รขย€ยœa great girl like you,รขย€ย (she might well say this), รขย€ยœto go on crying in this way! Stop this moment, I tell you!รขย€ย But she went on all the same, shedding gallons of tears, until there was a large pool all round her, about four inches deep and reaching half down the hall. After a time she heard a little pattering of feet in the distance, and she hastily dried her eyes to see what was coming. It was the White Rabbit returning, splendidly dressed, with a pair of white kid gloves in one hand and a large fan in the other: he came trotting along in a great hurry, muttering to himself as he came, รขย€ยœOh! the Duchess, the Duchess! Oh! wonรขย€ย™t she be savage if Iรขย€ย™ve kept her waiting!รขย€ย Alice felt so desperate that she was ready to ask help of any one; so, when the Rabbit came near her, she began, in a low, timid voice, รขย€ยœIf you please, sirรขย€ย”รขย€ย The Rabbit started violently, dropped the white kid gloves and the fan, and skurried away into the darkness as hard as he could go. Alice took up the fan and gloves, and, as the hall was very hot, she kept fanning herself all the time she went on talking: รขย€ยœDear, dear! How queer everything is to-day! And yesterday things went on just as usual. I wonder if Iรขย€ย™ve been changed in the night? Let me think: was I the same when I got up this morning? I almost think I can remember feeling a little different. But if Iรขย€ย™m not the same, the next question is, Who in the world am I? Ah, _thatรขย€ย™s_ the great puzzle!รขย€ย And she began thinking over all the children she knew that were of the same age as herself, to see if she could have been changed for any of them. รขย€ยœIรขย€ย™m sure Iรขย€ย™m not Ada,รขย€ย she said, รขย€ยœfor her hair goes in such long ringlets, and mine doesnรขย€ย™t go in ringlets at all; and Iรขย€ย™m sure I canรขย€ย™t be Mabel, for I know all sorts of things, and she, oh! she knows such a very little! Besides, _sheรขย€ย™s_ she, and _Iรขย€ย™m_ I, andรขย€ย”oh dear, how puzzling it all is! Iรขย€ย™ll try if I know all the things I used to know. Let me see: four times five is twelve, and four times six is thirteen, and four times seven isรขย€ย”oh dear! I shall never get to twenty at that rate! However, the Multiplication Table doesnรขย€ย™t signify: letรขย€ย™s try Geography. London is the capital of Paris, and Paris is the capital of Rome, and Romeรขย€ย”no, _thatรขย€ย™s_ all wrong, Iรขย€ย™m certain! I must have been changed for Mabel! Iรขย€ย™ll try and say รขย€ย˜_How doth the little_รขย€ย”รขย€ย™รขย€ย and she crossed her hands on her lap as if she were saying lessons, and began to repeat it, but her voice sounded hoarse and strange, and the words did not come the same as they used to do:รขย€ย” รขย€ยœHow doth the little crocodile Improve his shining tail, And pour the waters of the Nile On every golden scale! รขย€ยœHow cheerfully he seems to grin, How neatly spread his claws, And welcome little fishes in With gently smiling jaws!รขย€ย รขย€ยœIรขย€ย™m sure those are not the right words,รขย€ย said poor Alice, and her eyes filled with tears again as she went on, รขย€ยœI must be Mabel after all, and I shall have to go and live in that poky little house, and have next to no toys to play with, and oh! ever so many lessons to learn! No, Iรขย€ย™ve made up my mind about it; if Iรขย€ย™m Mabel, Iรขย€ย™ll stay down here! Itรขย€ย™ll be no use their putting their heads down and saying รขย€ย˜Come up again, dear!รขย€ย™ I shall only look up and say รขย€ย˜Who am I then? Tell me that first, and then, if I like being that person, Iรขย€ย™ll come up: if not, Iรขย€ย™ll stay down here till Iรขย€ย™m somebody elseรขย€ย™รขย€ย”but, oh dear!รขย€ย cried Alice, with a sudden burst of tears, รขย€ยœI do wish they _would_ put their heads down! I am so _very_ tired of being all alone here!รขย€ย As she said this she looked down at her hands, and was surprised to see that she had put on one of the Rabbitรขย€ย™s little white kid gloves while she was talking. รขย€ยœHow _can_ I have done that?รขย€ย she thought. รขย€ยœI must be growing small again.รขย€ย She got up and went to the table to measure herself by it, and found that, as nearly as she could guess, she was now about two feet high, and was going on shrinking rapidly: she soon found out that the cause of this was the fan she was holding, and she dropped it hastily, just in time to avoid shrinking away altogether. รขย€ยœThat _was_ a narrow escape!รขย€ย said Alice, a good deal frightened at the sudden change, but very glad to find herself still in existence; รขย€ยœand now for the garden!รขย€ย and she ran with all speed back to the little door: but, alas! the little door was shut again, and the little golden key was lying on the glass table as before, รขย€ยœand things are worse than ever,รขย€ย thought the poor child, รขย€ยœfor I never was so small as this before, never! And I declare itรขย€ย™s too bad, that it is!รขย€ย As she said these words her foot slipped, and in another moment, splash! she was up to her chin in salt water. Her first idea was that she had somehow fallen into the sea, รขย€ยœand in that case I can go back by railway,รขย€ย she said to herself. (Alice had been to the seaside once in her life, and had come to the general conclusion, that wherever you go to on the English coast you find a number of bathing machines in the sea, some children digging in the sand with wooden spades, then a row of lodging houses, and behind them a railway station.) However, she soon made out that she was in the pool of tears which she had wept when she was nine feet high. รขย€ยœI wish I hadnรขย€ย™t cried so much!รขย€ย said Alice, as she swam about, trying to find her way out. รขย€ยœI shall be punished for it now, I suppose, by being drowned in my own tears! That _will_ be a queer thing, to be sure! However, everything is queer to-day.รขย€ย Just then she heard something splashing about in the pool a little way off, and she swam nearer to make out what it was: at first she thought it must be a walrus or hippopotamus, but then she remembered how small she was now, and she soon made out that it was only a mouse that had slipped in like herself. รขย€ยœWould it be of any use, now,รขย€ย thought Alice, รขย€ยœto speak to this mouse? Everything is so out-of-the-way down here, that I should think very likely it can talk: at any rate, thereรขย€ย™s no harm in trying.รขย€ย So she began: รขย€ยœO Mouse, do you know the way out of this pool? I am very tired of swimming about here, O Mouse!รขย€ย (Alice thought this must be the right way of speaking to a mouse: she had never done such a thing before, but she remembered having seen in her brotherรขย€ย™s Latin Grammar, รขย€ยœA mouseรขย€ย”of a mouseรขย€ย”to a mouseรขย€ย”a mouseรขย€ย”O mouse!รขย€ย) The Mouse looked at her rather inquisitively, and seemed to her to wink with one of its little eyes, but it said nothing. รขย€ยœPerhaps it doesnรขย€ย™t understand English,รขย€ย thought Alice; รขย€ยœI daresay itรขย€ย™s a French mouse, come over with William the Conqueror.รขย€ย (For, with all her knowledge of history, Alice had no very clear notion how long ago anything had happened.) So she began again: รขย€ยœOรƒยน est ma chatte?รขย€ย which was the first sentence in her French lesson-book. The Mouse gave a sudden leap out of the water, and seemed to quiver all over with fright. รขย€ยœOh, I beg your pardon!รขย€ย cried Alice hastily, afraid that she had hurt the poor animalรขย€ย™s feelings. รขย€ยœI quite forgot you didnรขย€ย™t like cats.รขย€ย รขย€ยœNot like cats!รขย€ย cried the Mouse, in a shrill, passionate voice. รขย€ยœWould _you_ like cats if you were me?รขย€ย รขย€ยœWell, perhaps not,รขย€ย said Alice in a soothing tone: รขย€ยœdonรขย€ย™t be angry about it. And yet I wish I could show you our cat Dinah: I think youรขย€ย™d take a fancy to cats if you could only see her. She is such a dear quiet thing,รขย€ย Alice went on, half to herself, as she swam lazily about in the pool, รขย€ยœand she sits purring so nicely by the fire, licking her paws and washing her faceรขย€ย”and she is such a nice soft thing to nurseรขย€ย”and sheรขย€ย™s such a capital one for catching miceรขย€ย”oh, I beg your pardon!รขย€ย cried Alice again, for this time the Mouse was bristling all over, and she felt certain it must be really offended. รขย€ยœWe wonรขย€ย™t talk about her any more if youรขย€ย™d rather not.รขย€ย รขย€ยœWe indeed!รขย€ย cried the Mouse, who was trembling down to the end of his tail. รขย€ยœAs if _I_ would talk on such a subject! Our family always _hated_ cats: nasty, low, vulgar things! Donรขย€ย™t let me hear the name again!รขย€ย รขย€ยœI wonรขย€ย™t indeed!รขย€ย said Alice, in a great hurry to change the subject of conversation. รขย€ยœAre youรขย€ย”are you fondรขย€ย”ofรขย€ย”of dogs?รขย€ย The Mouse did not answer, so Alice went on eagerly: รขย€ยœThere is such a nice little dog near our house I should like to show you! A little bright-eyed terrier, you know, with oh, such long curly brown hair! And itรขย€ย™ll fetch things when you throw them, and itรขย€ย™ll sit up and beg for its dinner, and all sorts of thingsรขย€ย”I canรขย€ย™t remember half of themรขย€ย”and it belongs to a farmer, you know, and he says itรขย€ย™s so useful, itรขย€ย™s worth a hundred pounds! He says it kills all the rats andรขย€ย”oh dear!รขย€ย cried Alice in a sorrowful tone, รขย€ยœIรขย€ย™m afraid Iรขย€ย™ve offended it again!รขย€ย For the Mouse was swimming away from her as hard as it could go, and making quite a commotion in the pool as it went. So she called softly after it, รขย€ยœMouse dear! Do come back again, and we wonรขย€ย™t talk about cats or dogs either, if you donรขย€ย™t like them!รขย€ย When the Mouse heard this, it turned round and swam slowly back to her: its face was quite pale (with passion, Alice thought), and it said in a low trembling voice, รขย€ยœLet us get to the shore, and then Iรขย€ย™ll tell you my history, and youรขย€ย™ll understand why it is I hate cats and dogs.รขย€ย It was high time to go, for the pool was getting quite crowded with the birds and animals that had fallen into it: there were a Duck and a Dodo, a Lory and an Eaglet, and several other curious creatures. Alice led the way, and the whole party swam to the shore. CHAPTER III. A Caucus-Race and a Long Tale They were indeed a queer-looking party that assembled on the bankรขย€ย”the birds with draggled feathers, the animals with their fur clinging close to them, and all dripping wet, cross, and uncomfortable. The first question of course was, how to get dry again: they had a consultation about this, and after a few minutes it seemed quite natural to Alice to find herself talking familiarly with them, as if she had known them all her life. Indeed, she had quite a long argument with the Lory, who at last turned sulky, and would only say, รขย€ยœI am older than you, and must know better;รขย€ย and this Alice would not allow without knowing how old it was, and, as the Lory positively refused to tell its age, there was no more to be said. At last the Mouse, who seemed to be a person of authority among them, called out, รขย€ยœSit down, all of you, and listen to me! _Iรขย€ย™ll_ soon make you dry enough!รขย€ย They all sat down at once, in a large ring, with the Mouse in the middle. Alice kept her eyes anxiously fixed on it, for she felt sure she would catch a bad cold if she did not get dry very soon. รขย€ยœAhem!รขย€ย said the Mouse with an important air, รขย€ยœare you all ready? This is the driest thing I know. Silence all round, if you please! รขย€ย˜William the Conqueror, whose cause was favoured by the pope, was soon submitted to by the English, who wanted leaders, and had been of late much accustomed to usurpation and conquest. Edwin and Morcar, the earls of Mercia and Northumbriaรขย€ย”รขย€ย™รขย€ย รขย€ยœUgh!รขย€ย said the Lory, with a shiver. รขย€ยœI beg your pardon!รขย€ย said the Mouse, frowning, but very politely: รขย€ยœDid you speak?รขย€ย รขย€ยœNot I!รขย€ย said the Lory hastily. รขย€ยœI thought you did,รขย€ย said the Mouse. รขย€ยœรขย€ย”I proceed. รขย€ย˜Edwin and Morcar, the earls of Mercia and Northumbria, declared for him: and even Stigand, the patriotic archbishop of Canterbury, found it advisableรขย€ย”รขย€ย™รขย€ย รขย€ยœFound _what_?รขย€ย said the Duck. รขย€ยœFound _it_,รขย€ย the Mouse replied rather crossly: รขย€ยœof course you know what รขย€ย˜itรขย€ย™ means.รขย€ย รขย€ยœI know what รขย€ย˜itรขย€ย™ means well enough, when _I_ find a thing,รขย€ย said the Duck: รขย€ยœitรขย€ย™s generally a frog or a worm. The question is, what did the archbishop find?รขย€ย The Mouse did not notice this question, but hurriedly went on, รขย€ยœรขย€ย˜รขย€ย”found it advisable to go with Edgar Atheling to meet William and offer him the crown. Williamรขย€ย™s conduct at first was moderate. But the insolence of his Normansรขย€ย”รขย€ย™ How are you getting on now, my dear?รขย€ย it continued, turning to Alice as it spoke. รขย€ยœAs wet as ever,รขย€ย said Alice in a melancholy tone: รขย€ยœit doesnรขย€ย™t seem to dry me at all.รขย€ย รขย€ยœIn that case,รขย€ย said the Dodo solemnly, rising to its feet, รขย€ยœI move that the meeting adjourn, for the immediate adoption of more energetic remediesรขย€ย”รขย€ย รขย€ยœSpeak English!รขย€ย said the Eaglet. รขย€ยœI donรขย€ย™t know the meaning of half those long words, and, whatรขย€ย™s more, I donรขย€ย™t believe you do either!รขย€ย And the Eaglet bent down its head to hide a smile: some of the other birds tittered audibly. รขย€ยœWhat I was going to say,รขย€ย said the Dodo in an offended tone, รขย€ยœwas, that the best thing to get us dry would be a Caucus-race.รขย€ย รขย€ยœWhat _is_ a Caucus-race?รขย€ย said Alice; not that she wanted much to know, but the Dodo had paused as if it thought that _somebody_ ought to speak, and no one else seemed inclined to say anything. รขย€ยœWhy,รขย€ย said the Dodo, รขย€ยœthe best way to explain it is to do it.รขย€ย (And, as you might like to try the thing yourself, some winter day, I will tell you how the Dodo managed it.) First it marked out a race-course, in a sort of circle, (รขย€ยœthe exact shape doesnรขย€ย™t matter,รขย€ย it said,) and then all the party were placed along the course, here and there. There was no รขย€ยœOne, two, three, and away,รขย€ย but they began running when they liked, and left off when they liked, so that it was not easy to know when the race was over. However, when they had been running half an hour or so, and were quite dry again, the Dodo suddenly called out รขย€ยœThe race is over!รขย€ย and they all crowded round it, panting, and asking, รขย€ยœBut who has won?รขย€ย This question the Dodo could not answer without a great deal of thought, and it sat for a long time with one finger pressed upon its forehead (the position in which you usually see Shakespeare, in the pictures of him), while the rest waited in silence. At last the Dodo said, รขย€ยœ_Everybody_ has won, and all must have prizes.รขย€ย รขย€ยœBut who is to give the prizes?รขย€ย quite a chorus of voices asked. รขย€ยœWhy, _she_, of course,รขย€ย said the Dodo, pointing to Alice with one finger; and the whole party at once crowded round her, calling out in a confused way, รขย€ยœPrizes! Prizes!รขย€ย Alice had no idea what to do, and in despair she put her hand in her pocket, and pulled out a box of comfits, (luckily the salt water had not got into it), and handed them round as prizes. There was exactly one a-piece, all round. รขย€ยœBut she must have a prize herself, you know,รขย€ย said the Mouse. รขย€ยœOf course,รขย€ย the Dodo replied very gravely. รขย€ยœWhat else have you got in your pocket?รขย€ย he went on, turning to Alice. รขย€ยœOnly a thimble,รขย€ย said Alice sadly. รขย€ยœHand it over here,รขย€ย said the Dodo. Then they all crowded round her once more, while the Dodo solemnly presented the thimble, saying รขย€ยœWe beg your acceptance of this elegant thimble;รขย€ย and, when it had finished this short speech, they all cheered. Alice thought the whole thing very absurd, but they all looked so grave that she did not dare to laugh; and, as she could not think of anything to say, she simply bowed, and took the thimble, looking as solemn as she could. The next thing was to eat the comfits: this caused some noise and confusion, as the large birds complained that they could not taste theirs, and the small ones choked and had to be patted on the back. However, it was over at last, and they sat down again in a ring, and begged the Mouse to tell them something more. รขย€ยœYou promised to tell me your history, you know,รขย€ย said Alice, รขย€ยœand why it is you hateรขย€ย”C and D,รขย€ย she added in a whisper, half afraid that it would be offended again. รขย€ยœMine is a long and a sad tale!รขย€ย said the Mouse, turning to Alice, and sighing. รขย€ยœIt _is_ a long tail, certainly,รขย€ย said Alice, looking down with wonder at the Mouseรขย€ย™s tail; รขย€ยœbut why do you call it sad?รขย€ย And she kept on puzzling about it while the Mouse was speaking, so that her idea of the tale was something like this:รขย€ย” รขย€ยœFury said to a mouse, That he met in the house, รขย€ย˜Let us both go to law: _I_ will prosecute _you_.รขย€ย”Come, Iรขย€ย™ll take no denial; We must have a trial: For really this morning Iรขย€ย™ve nothing to do.รขย€ย™ Said the mouse to the cur, รขย€ย˜Such a trial, dear sir, With no jury or judge, would be wasting our breath.รขย€ย™ รขย€ย˜Iรขย€ย™ll be judge, Iรขย€ย™ll be jury,รขย€ย™ Said cunning old Fury: รขย€ย˜Iรขย€ย™ll try the whole cause, and condemn you to death.รขย€ย™รขย€ย รขย€ยœYou are not attending!รขย€ย said the Mouse to Alice severely. รขย€ยœWhat are you thinking of?รขย€ย รขย€ยœI beg your pardon,รขย€ย said Alice very humbly: รขย€ยœyou had got to the fifth bend, I think?รขย€ย รขย€ยœI had _not!_รขย€ย cried the Mouse, sharply and very angrily. รขย€ยœA knot!รขย€ย said Alice, always ready to make herself useful, and looking anxiously about her. รขย€ยœOh, do let me help to undo it!รขย€ย รขย€ยœI shall do nothing of the sort,รขย€ย said the Mouse, getting up and walking away. รขย€ยœYou insult me by talking such nonsense!รขย€ย รขย€ยœI didnรขย€ย™t mean it!รขย€ย pleaded poor Alice. รขย€ยœBut youรขย€ย™re so easily offended, you know!รขย€ย The Mouse only growled in reply. รขย€ยœPlease come back and finish your story!รขย€ย Alice called after it; and the others all joined in chorus, รขย€ยœYes, please do!รขย€ย but the Mouse only shook its head impatiently, and walked a little quicker. รขย€ยœWhat a pity it wouldnรขย€ย™t stay!รขย€ย sighed the Lory, as soon as it was quite out of sight; and an old Crab took the opportunity of saying to her daughter รขย€ยœAh, my dear! Let this be a lesson to you never to lose _your_ temper!รขย€ย รขย€ยœHold your tongue, Ma!รขย€ย said the young Crab, a little snappishly. รขย€ยœYouรขย€ย™re enough to try the patience of an oyster!รขย€ย รขย€ยœI wish I had our Dinah here, I know I do!รขย€ย said Alice aloud, addressing nobody in particular. รขย€ยœSheรขย€ย™d soon fetch it back!รขย€ย รขย€ยœAnd who is Dinah, if I might venture to ask the question?รขย€ย said the Lory. Alice replied eagerly, for she was always ready to talk about her pet: รขย€ยœDinahรขย€ย™s our cat. And sheรขย€ย™s such a capital one for catching mice you canรขย€ย™t think! And oh, I wish you could see her after the birds! Why, sheรขย€ย™ll eat a little bird as soon as look at it!รขย€ย This speech caused a remarkable sensation among the party. Some of the birds hurried off at once: one old Magpie began wrapping itself up very carefully, remarking, รขย€ยœI really must be getting home; the night-air doesnรขย€ย™t suit my throat!รขย€ย and a Canary called out in a trembling voice to its children, รขย€ยœCome away, my dears! Itรขย€ย™s high time you were all in bed!รขย€ย On various pretexts they all moved off, and Alice was soon left alone. รขย€ยœI wish I hadnรขย€ย™t mentioned Dinah!รขย€ย she said to herself in a melancholy tone. รขย€ยœNobody seems to like her, down here, and Iรขย€ย™m sure sheรขย€ย™s the best cat in the world! Oh, my dear Dinah! I wonder if I shall ever see you any more!รขย€ย And here poor Alice began to cry again, for she felt very lonely and low-spirited. In a little while, however, she again heard a little pattering of footsteps in the distance, and she looked up eagerly, half hoping that the Mouse had changed his mind, and was coming back to finish his story. CHAPTER IV. The Rabbit Sends in a Little Bill It was the White Rabbit, trotting slowly back again, and looking anxiously about as it went, as if it had lost something; and she heard it muttering to itself รขย€ยœThe Duchess! The Duchess! Oh my dear paws! Oh my fur and whiskers! Sheรขย€ย™ll get me executed, as sure as ferrets are ferrets! Where _can_ I have dropped them, I wonder?รขย€ย Alice guessed in a moment that it was looking for the fan and the pair of white kid gloves, and she very good-naturedly began hunting about for them, but they were nowhere to be seenรขย€ย”everything seemed to have changed since her swim in the pool, and the great hall, with the glass table and the little door, had vanished completely. Very soon the Rabbit noticed Alice, as she went hunting about, and called out to her in an angry tone, รขย€ยœWhy, Mary Ann, what _are_ you doing out here? Run home this moment, and fetch me a pair of gloves and a fan! Quick, now!รขย€ย And Alice was so much frightened that she ran off at once in the direction it pointed to, without trying to explain the mistake it had made. รขย€ยœHe took me for his housemaid,รขย€ย she said to herself as she ran. รขย€ยœHow surprised heรขย€ย™ll be when he finds out who I am! But Iรขย€ย™d better take him his fan and glovesรขย€ย”that is, if I can find them.รขย€ย As she said this, she came upon a neat little house, on the door of which was a bright brass plate with the name รขย€ยœW. RABBIT,รขย€ย engraved upon it. She went in without knocking, and hurried upstairs, in great fear lest she should meet the real Mary Ann, and be turned out of the house before she had found the fan and gloves. รขย€ยœHow queer it seems,รขย€ย Alice said to herself, รขย€ยœto be going messages for a rabbit! I suppose Dinahรขย€ย™ll be sending me on messages next!รขย€ย And she began fancying the sort of thing that would happen: รขย€ยœรขย€ย˜Miss Alice! Come here directly, and get ready for your walk!รขย€ย™ รขย€ย˜Coming in a minute, nurse! But Iรขย€ย™ve got to see that the mouse doesnรขย€ย™t get out.รขย€ย™ Only I donรขย€ย™t think,รขย€ย Alice went on, รขย€ยœthat theyรขย€ย™d let Dinah stop in the house if it began ordering people about like that!รขย€ย By this time she had found her way into a tidy little room with a table in the window, and on it (as she had hoped) a fan and two or three pairs of tiny white kid gloves: she took up the fan and a pair of the gloves, and was just going to leave the room, when her eye fell upon a little bottle that stood near the looking-glass. There was no label this time with the words รขย€ยœDRINK ME,รขย€ย but nevertheless she uncorked it and put it to her lips. รขย€ยœI know _something_ interesting is sure to happen,รขย€ย she said to herself, รขย€ยœwhenever I eat or drink anything; so Iรขย€ย™ll just see what this bottle does. I do hope itรขย€ย™ll make me grow large again, for really Iรขย€ย™m quite tired of being such a tiny little thing!รขย€ย It did so indeed, and much sooner than she had expected: before she had drunk half the bottle, she found her head pressing against the ceiling, and had to stoop to save her neck from being broken. She hastily put down the bottle, saying to herself รขย€ยœThatรขย€ย™s quite enoughรขย€ย”I hope I shanรขย€ย™t grow any moreรขย€ย”As it is, I canรขย€ย™t get out at the doorรขย€ย”I do wish I hadnรขย€ย™t drunk quite so much!รขย€ย Alas! it was too late to wish that! She went on growing, and growing, and very soon had to kneel down on the floor: in another minute there was not even room for this, and she tried the effect of lying down with one elbow against the door, and the other arm curled round her head. Still she went on growing, and, as a last resource, she put one arm out of the window, and one foot up the chimney, and said to herself รขย€ยœNow I can do no more, whatever happens. What _will_ become of me?รขย€ย Luckily for Alice, the little magic bottle had now had its full effect, and she grew no larger: still it was very uncomfortable, and, as there seemed to be no sort of chance of her ever getting out of the room again, no wonder she felt unhappy. รขย€ยœIt was much pleasanter at home,รขย€ย thought poor Alice, รขย€ยœwhen one wasnรขย€ย™t always growing larger and smaller, and being ordered about by mice and rabbits. I almost wish I hadnรขย€ย™t gone down that rabbit-holeรขย€ย”and yetรขย€ย”and yetรขย€ย”itรขย€ย™s rather curious, you know, this sort of life! I do wonder what _can_ have happened to me! When I used to read fairy-tales, I fancied that kind of thing never happened, and now here I am in the middle of one! There ought to be a book written about me, that there ought! And when I grow up, Iรขย€ย™ll write oneรขย€ย”but Iรขย€ย™m grown up now,รขย€ย she added in a sorrowful tone; รขย€ยœat least thereรขย€ย™s no room to grow up any more _here_.รขย€ย รขย€ยœBut then,รขย€ย thought Alice, รขย€ยœshall I _never_ get any older than I am now? Thatรขย€ย™ll be a comfort, one wayรขย€ย”never to be an old womanรขย€ย”but thenรขย€ย”always to have lessons to learn! Oh, I shouldnรขย€ย™t like _that!_รขย€ย รขย€ยœOh, you foolish Alice!รขย€ย she answered herself. รขย€ยœHow can you learn lessons in here? Why, thereรขย€ย™s hardly room for _you_, and no room at all for any lesson-books!รขย€ย And so she went on, taking first one side and then the other, and making quite a conversation of it altogether; but after a few minutes she heard a voice outside, and stopped to listen. รขย€ยœMary Ann! Mary Ann!รขย€ย said the voice. รขย€ยœFetch me my gloves this moment!รขย€ย Then came a little pattering of feet on the stairs. Alice knew it was the Rabbit coming to look for her, and she trembled till she shook the house, quite forgetting that she was now about a thousand times as large as the Rabbit, and had no reason to be afraid of it. Presently the Rabbit came up to the door, and tried to open it; but, as the door opened inwards, and Aliceรขย€ย™s elbow was pressed hard against it, that attempt proved a failure. Alice heard it say to itself รขย€ยœThen Iรขย€ย™ll go round and get in at the window.รขย€ย รขย€ยœ_That_ you wonรขย€ย™t!รขย€ย thought Alice, and, after waiting till she fancied she heard the Rabbit just under the window, she suddenly spread out her hand, and made a snatch in the air. She did not get hold of anything, but she heard a little shriek and a fall, and a crash of broken glass, from which she concluded that it was just possible it had fallen into a cucumber-frame, or something of the sort. Next came an angry voiceรขย€ย”the Rabbitรขย€ย™sรขย€ย”รขย€ยœPat! Pat! Where are you?รขย€ย And then a voice she had never heard before, รขย€ยœSure then Iรขย€ย™m here! Digging for apples, yer honour!รขย€ย รขย€ยœDigging for apples, indeed!รขย€ย said the Rabbit angrily. รขย€ยœHere! Come and help me out of _this!_รขย€ย (Sounds of more broken glass.) รขย€ยœNow tell me, Pat, whatรขย€ย™s that in the window?รขย€ย รขย€ยœSure, itรขย€ย™s an arm, yer honour!รขย€ย (He pronounced it รขย€ยœarrum.รขย€ย) รขย€ยœAn arm, you goose! Who ever saw one that size? Why, it fills the whole window!รขย€ย รขย€ยœSure, it does, yer honour: but itรขย€ย™s an arm for all that.รขย€ย รขย€ยœWell, itรขย€ย™s got no business there, at any rate: go and take it away!รขย€ย There was a long silence after this, and Alice could only hear whispers now and then; such as, รขย€ยœSure, I donรขย€ย™t like it, yer honour, at all, at all!รขย€ย รขย€ยœDo as I tell you, you coward!รขย€ย and at last she spread out her hand again, and made another snatch in the air. This time there were _two_ little shrieks, and more sounds of broken glass. รขย€ยœWhat a number of cucumber-frames there must be!รขย€ย thought Alice. รขย€ยœI wonder what theyรขย€ย™ll do next! As for pulling me out of the window, I only wish they _could!_ Iรขย€ย™m sure _I_ donรขย€ย™t want to stay in here any longer!รขย€ย She waited for some time without hearing anything more: at last came a rumbling of little cartwheels, and the sound of a good many voices all talking together: she made out the words: รขย€ยœWhereรขย€ย™s the other ladder?รขย€ย”Why, I hadnรขย€ย™t to bring but one; Billรขย€ย™s got the otherรขย€ย”Bill! fetch it here, lad!รขย€ย”Here, put รขย€ย™em up at this cornerรขย€ย”No, tie รขย€ย™em together firstรขย€ย”they donรขย€ย™t reach half high enough yetรขย€ย”Oh! theyรขย€ย™ll do well enough; donรขย€ย™t be particularรขย€ย”Here, Bill! catch hold of this ropeรขย€ย”Will the roof bear?รขย€ย”Mind that loose slateรขย€ย”Oh, itรขย€ย™s coming down! Heads below!รขย€ย (a loud crash)รขย€ย”รขย€ยœNow, who did that?รขย€ย”It was Bill, I fancyรขย€ย”Whoรขย€ย™s to go down the chimney?รขย€ย”Nay, _I_ shanรขย€ย™t! _You_ do it!รขย€ย”_That_ I wonรขย€ย™t, then!รขย€ย”Billรขย€ย™s to go downรขย€ย”Here, Bill! the master says youรขย€ย™re to go down the chimney!รขย€ย รขย€ยœOh! So Billรขย€ย™s got to come down the chimney, has he?รขย€ย said Alice to herself. รขย€ยœShy, they seem to put everything upon Bill! I wouldnรขย€ย™t be in Billรขย€ย™s place for a good deal: this fireplace is narrow, to be sure; but I _think_ I can kick a little!รขย€ย She drew her foot as far down the chimney as she could, and waited till she heard a little animal (she couldnรขย€ย™t guess of what sort it was) scratching and scrambling about in the chimney close above her: then, saying to herself รขย€ยœThis is Bill,รขย€ย she gave one sharp kick, and waited to see what would happen next. The first thing she heard was a general chorus of รขย€ยœThere goes Bill!รขย€ย then the Rabbitรขย€ย™s voice alongรขย€ย”รขย€ยœCatch him, you by the hedge!รขย€ย then silence, and then another confusion of voicesรขย€ย”รขย€ยœHold up his headรขย€ย”Brandy nowรขย€ย”Donรขย€ย™t choke himรขย€ย”How was it, old fellow? What happened to you? Tell us all about it!รขย€ย Last came a little feeble, squeaking voice, (รขย€ยœThatรขย€ย™s Bill,รขย€ย thought Alice,) รขย€ยœWell, I hardly knowรขย€ย”No more, thank ye; Iรขย€ย™m better nowรขย€ย”but Iรขย€ย™m a deal too flustered to tell youรขย€ย”all I know is, something comes at me like a Jack-in-the-box, and up I goes like a sky-rocket!รขย€ย รขย€ยœSo you did, old fellow!รขย€ย said the others. รขย€ยœWe must burn the house down!รขย€ย said the Rabbitรขย€ย™s voice; and Alice called out as loud as she could, รขย€ยœIf you do, Iรขย€ย™ll set Dinah at you!รขย€ย There was a dead silence instantly, and Alice thought to herself, รขย€ยœI wonder what they _will_ do next! If they had any sense, theyรขย€ย™d take the roof off.รขย€ย After a minute or two, they began moving about again, and Alice heard the Rabbit say, รขย€ยœA barrowful will do, to begin with.รขย€ย รขย€ยœA barrowful of _what?_รขย€ย thought Alice; but she had not long to doubt, for the next moment a shower of little pebbles came rattling in at the window, and some of them hit her in the face. รขย€ยœIรขย€ย™ll put a stop to this,รขย€ย she said to herself, and shouted out, รขย€ยœYouรขย€ย™d better not do that again!รขย€ย which produced another dead silence. Alice noticed with some surprise that the pebbles were all turning into little cakes as they lay on the floor, and a bright idea came into her head. รขย€ยœIf I eat one of these cakes,รขย€ย she thought, รขย€ยœitรขย€ย™s sure to make _some_ change in my size; and as it canรขย€ย™t possibly make me larger, it must make me smaller, I suppose.รขย€ย So she swallowed one of the cakes, and was delighted to find that she began shrinking directly. As soon as she was small enough to get through the door, she ran out of the house, and found quite a crowd of little animals and birds waiting outside. The poor little Lizard, Bill, was in the middle, being held up by two guinea-pigs, who were giving it something out of a bottle. They all made a rush at Alice the moment she appeared; but she ran off as hard as she could, and soon found herself safe in a thick wood. รขย€ยœThe first thing Iรขย€ย™ve got to do,รขย€ย said Alice to herself, as she wandered about in the wood, รขย€ยœis to grow to my right size again; and the second thing is to find my way into that lovely garden. I think that will be the best plan.รขย€ย It sounded an excellent plan, no doubt, and very neatly and simply arranged; the only difficulty was, that she had not the smallest idea how to set about it; and while she was peering about anxiously among the trees, a little sharp bark just over her head made her look up in a great hurry. An enormous puppy was looking down at her with large round eyes, and feebly stretching out one paw, trying to touch her. รขย€ยœPoor little thing!รขย€ย said Alice, in a coaxing tone, and she tried hard to whistle to it; but she was terribly frightened all the time at the thought that it might be hungry, in which case it would be very likely to eat her up in spite of all her coaxing. Hardly knowing what she did, she picked up a little bit of stick, and held it out to the puppy; whereupon the puppy jumped into the air off all its feet at once, with a yelp of delight, and rushed at the stick, and made believe to worry it; then Alice dodged behind a great thistle, to keep herself from being run over; and the moment she appeared on the other side, the puppy made another rush at the stick, and tumbled head over heels in its hurry to get hold of it; then Alice, thinking it was very like having a game of play with a cart-horse, and expecting every moment to be trampled under its feet, ran round the thistle again; then the puppy began a series of short charges at the stick, running a very little way forwards each time and a long way back, and barking hoarsely all the while, till at last it sat down a good way off, panting, with its tongue hanging out of its mouth, and its great eyes half shut. This seemed to Alice a good opportunity for making her escape; so she set off at once, and ran till she was quite tired and out of breath, and till the puppyรขย€ย™s bark sounded quite faint in the distance. รขย€ยœAnd yet what a dear little puppy it was!รขย€ย said Alice, as she leant against a buttercup to rest herself, and fanned herself with one of the leaves: รขย€ยœI should have liked teaching it tricks very much, ifรขย€ย”if Iรขย€ย™d only been the right size to do it! Oh dear! Iรขย€ย™d nearly forgotten that Iรขย€ย™ve got to grow up again! Let me seeรขย€ย”how _is_ it to be managed? I suppose I ought to eat or drink something or other; but the great question is, what?รขย€ย The great question certainly was, what? Alice looked all round her at the flowers and the blades of grass, but she did not see anything that looked like the right thing to eat or drink under the circumstances. There was a large mushroom growing near her, about the same height as herself; and when she had looked under it, and on both sides of it, and behind it, it occurred to her that she might as well look and see what was on the top of it. She stretched herself up on tiptoe, and peeped over the edge of the mushroom, and her eyes immediately met those of a large blue caterpillar, that was sitting on the top with its arms folded, quietly smoking a long hookah, and taking not the smallest notice of her or of anything else. CHAPTER V. Advice from a Caterpillar The Caterpillar and Alice looked at each other for some time in silence: at last the Caterpillar took the hookah out of its mouth, and addressed her in a languid, sleepy voice. รขย€ยœWho are _you?_รขย€ย said the Caterpillar. This was not an encouraging opening for a conversation. Alice replied, rather shyly, รขย€ยœIรขย€ย”I hardly know, sir, just at presentรขย€ย”at least I know who I _was_ when I got up this morning, but I think I must have been changed several times since then.รขย€ย รขย€ยœWhat do you mean by that?รขย€ย said the Caterpillar sternly. รขย€ยœExplain yourself!รขย€ย รขย€ยœI canรขย€ย™t explain _myself_, Iรขย€ย™m afraid, sir,รขย€ย said Alice, รขย€ยœbecause Iรขย€ย™m not myself, you see.รขย€ย รขย€ยœI donรขย€ย™t see,รขย€ย said the Caterpillar. รขย€ยœIรขย€ย™m afraid I canรขย€ย™t put it more clearly,รขย€ย Alice replied very politely, รขย€ยœfor I canรขย€ย™t understand it myself to begin with; and being so many different sizes in a day is very confusing.รขย€ย รขย€ยœIt isnรขย€ย™t,รขย€ย said the Caterpillar. รขย€ยœWell, perhaps you havenรขย€ย™t found it so yet,รขย€ย said Alice; รขย€ยœbut when you have to turn into a chrysalisรขย€ย”you will some day, you knowรขย€ย”and then after that into a butterfly, I should think youรขย€ย™ll feel it a little queer, wonรขย€ย™t you?รขย€ย รขย€ยœNot a bit,รขย€ย said the Caterpillar. รขย€ยœWell, perhaps your feelings may be different,รขย€ย said Alice; รขย€ยœall I know is, it would feel very queer to _me_.รขย€ย รขย€ยœYou!รขย€ย said the Caterpillar contemptuously. รขย€ยœWho are _you?_รขย€ย Which brought them back again to the beginning of the conversation. Alice felt a little irritated at the Caterpillarรขย€ย™s making such _very_ short remarks, and she drew herself up and said, very gravely, รขย€ยœI think, you ought to tell me who _you_ are, first.รขย€ย รขย€ยœWhy?รขย€ย said the Caterpillar. Here was another puzzling question; and as Alice could not think of any good reason, and as the Caterpillar seemed to be in a _very_ unpleasant state of mind, she turned away. รขย€ยœCome back!รขย€ย the Caterpillar called after her. รขย€ยœIรขย€ย™ve something important to say!รขย€ย This sounded promising, certainly: Alice turned and came back again. รขย€ยœKeep your temper,รขย€ย said the Caterpillar. รขย€ยœIs that all?รขย€ย said Alice, swallowing down her anger as well as she could. รขย€ยœNo,รขย€ย said the Caterpillar. Alice thought she might as well wait, as she had nothing else to do, and perhaps after all it might tell her something worth hearing. For some minutes it puffed away without speaking, but at last it unfolded its arms, took the hookah out of its mouth again, and said, รขย€ยœSo you think youรขย€ย™re changed, do you?รขย€ย รขย€ยœIรขย€ย™m afraid I am, sir,รขย€ย said Alice; รขย€ยœI canรขย€ย™t remember things as I usedรขย€ย”and I donรขย€ย™t keep the same size for ten minutes together!รขย€ย รขย€ยœCanรขย€ย™t remember _what_ things?รขย€ย said the Caterpillar. รขย€ยœWell, Iรขย€ย™ve tried to say รขย€ยœHow doth the little busy bee,รขย€ย but it all came different!รขย€ย Alice replied in a very melancholy voice. รขย€ยœRepeat, รขย€ยœ_You are old, Father William_,รขย€ย™รขย€ย said the Caterpillar. Alice folded her hands, and began:รขย€ย” รขย€ยœYou are old, Father William,รขย€ย the young man said, รขย€ยœAnd your hair has become very white; And yet you incessantly stand on your headรขย€ย” Do you think, at your age, it is right?รขย€ย รขย€ยœIn my youth,รขย€ย Father William replied to his son, รขย€ยœI feared it might injure the brain; But, now that Iรขย€ย™m perfectly sure I have none, Why, I do it again and again.รขย€ย รขย€ยœYou are old,รขย€ย said the youth, รขย€ยœas I mentioned before, And have grown most uncommonly fat; Yet you turned a back-somersault in at the doorรขย€ย” Pray, what is the reason of that?รขย€ย รขย€ยœIn my youth,รขย€ย said the sage, as he shook his grey locks, รขย€ยœI kept all my limbs very supple By the use of this ointmentรขย€ย”one shilling the boxรขย€ย” Allow me to sell you a couple?รขย€ย รขย€ยœYou are old,รขย€ย said the youth, รขย€ยœand your jaws are too weak For anything tougher than suet; Yet you finished the goose, with the bones and the beakรขย€ย” Pray, how did you manage to do it?รขย€ย รขย€ยœIn my youth,รขย€ย said his father, รขย€ยœI took to the law, And argued each case with my wife; And the muscular strength, which it gave to my jaw, Has lasted the rest of my life.รขย€ย รขย€ยœYou are old,รขย€ย said the youth, รขย€ยœone would hardly suppose That your eye was as steady as ever; Yet you balanced an eel on the end of your noseรขย€ย” What made you so awfully clever?รขย€ย รขย€ยœI have answered three questions, and that is enough,รขย€ย Said his father; รขย€ยœdonรขย€ย™t give yourself airs! Do you think I can listen all day to such stuff? Be off, or Iรขย€ย™ll kick you down stairs!รขย€ย รขย€ยœThat is not said right,รขย€ย said the Caterpillar. รขย€ยœNot _quite_ right, Iรขย€ย™m afraid,รขย€ย said Alice, timidly; รขย€ยœsome of the words have got altered.รขย€ย รขย€ยœIt is wrong from beginning to end,รขย€ย said the Caterpillar decidedly, and there was silence for some minutes. The Caterpillar was the first to speak. รขย€ยœWhat size do you want to be?รขย€ย it asked. รขย€ยœOh, Iรขย€ย™m not particular as to size,รขย€ย Alice hastily replied; รขย€ยœonly one doesnรขย€ย™t like changing so often, you know.รขย€ย รขย€ยœI _donรขย€ย™t_ know,รขย€ย said the Caterpillar. Alice said nothing: she had never been so much contradicted in her life before, and she felt that she was losing her temper. รขย€ยœAre you content now?รขย€ย said the Caterpillar. รขย€ยœWell, I should like to be a _little_ larger, sir, if you wouldnรขย€ย™t mind,รขย€ย said Alice: รขย€ยœthree inches is such a wretched height to be.รขย€ย รขย€ยœIt is a very good height indeed!รขย€ย said the Caterpillar angrily, rearing itself upright as it spoke (it was exactly three inches high). รขย€ยœBut Iรขย€ย™m not used to it!รขย€ย pleaded poor Alice in a piteous tone. And she thought of herself, รขย€ยœI wish the creatures wouldnรขย€ย™t be so easily offended!รขย€ย รขย€ยœYouรขย€ย™ll get used to it in time,รขย€ย said the Caterpillar; and it put the hookah into its mouth and began smoking again. This time Alice waited patiently until it chose to speak again. In a minute or two the Caterpillar took the hookah out of its mouth and yawned once or twice, and shook itself. Then it got down off the mushroom, and crawled away in the grass, merely remarking as it went, รขย€ยœOne side will make you grow taller, and the other side will make you grow shorter.รขย€ย รขย€ยœOne side of _what?_ The other side of _what?_รขย€ย thought Alice to herself. รขย€ยœOf the mushroom,รขย€ย said the Caterpillar, just as if she had asked it aloud; and in another moment it was out of sight. Alice remained looking thoughtfully at the mushroom for a minute, trying to make out which were the two sides of it; and as it was perfectly round, she found this a very difficult question. However, at last she stretched her arms round it as far as they would go, and broke off a bit of the edge with each hand. รขย€ยœAnd now which is which?รขย€ย she said to herself, and nibbled a little of the right-hand bit to try the effect: the next moment she felt a violent blow underneath her chin: it had struck her foot! She was a good deal frightened by this very sudden change, but she felt that there was no time to be lost, as she was shrinking rapidly; so she set to work at once to eat some of the other bit. Her chin was pressed so closely against her foot, that there was hardly room to open her mouth; but she did it at last, and managed to swallow a morsel of the lefthand bit. * * * * * * * * * * * * * * * * * * * * รขย€ยœCome, my headรขย€ย™s free at last!รขย€ย said Alice in a tone of delight, which changed into alarm in another moment, when she found that her shoulders were nowhere to be found: all she could see, when she looked down, was an immense length of neck, which seemed to rise like a stalk out of a sea of green leaves that lay far below her. รขย€ยœWhat _can_ all that green stuff be?รขย€ย said Alice. รขย€ยœAnd where _have_ my shoulders got to? And oh, my poor hands, how is it I canรขย€ย™t see you?รขย€ย She was moving them about as she spoke, but no result seemed to follow, except a little shaking among the distant green leaves. As there seemed to be no chance of getting her hands up to her head, she tried to get her head down to them, and was delighted to find that her neck would bend about easily in any direction, like a serpent. She had just succeeded in curving it down into a graceful zigzag, and was going to dive in among the leaves, which she found to be nothing but the tops of the trees under which she had been wandering, when a sharp hiss made her draw back in a hurry: a large pigeon had flown into her face, and was beating her violently with its wings. รขย€ยœSerpent!รขย€ย screamed the Pigeon. รขย€ยœIรขย€ย™m _not_ a serpent!รขย€ย said Alice indignantly. รขย€ยœLet me alone!รขย€ย รขย€ยœSerpent, I say again!รขย€ย repeated the Pigeon, but in a more subdued tone, and added with a kind of sob, รขย€ยœIรขย€ย™ve tried every way, and nothing seems to suit them!รขย€ย รขย€ยœI havenรขย€ย™t the least idea what youรขย€ย™re talking about,รขย€ย said Alice. รขย€ยœIรขย€ย™ve tried the roots of trees, and Iรขย€ย™ve tried banks, and Iรขย€ย™ve tried hedges,รขย€ย the Pigeon went on, without attending to her; รขย€ยœbut those serpents! Thereรขย€ย™s no pleasing them!รขย€ย Alice was more and more puzzled, but she thought there was no use in saying anything more till the Pigeon had finished. รขย€ยœAs if it wasnรขย€ย™t trouble enough hatching the eggs,รขย€ย said the Pigeon; รขย€ยœbut I must be on the look-out for serpents night and day! Why, I havenรขย€ย™t had a wink of sleep these three weeks!รขย€ย รขย€ยœIรขย€ย™m very sorry youรขย€ย™ve been annoyed,รขย€ย said Alice, who was beginning to see its meaning. รขย€ยœAnd just as Iรขย€ย™d taken the highest tree in the wood,รขย€ย continued the Pigeon, raising its voice to a shriek, รขย€ยœand just as I was thinking I should be free of them at last, they must needs come wriggling down from the sky! Ugh, Serpent!รขย€ย รขย€ยœBut Iรขย€ย™m _not_ a serpent, I tell you!รขย€ย said Alice. รขย€ยœIรขย€ย™m aรขย€ย”Iรขย€ย™m aรขย€ย”รขย€ย รขย€ยœWell! _What_ are you?รขย€ย said the Pigeon. รขย€ยœI can see youรขย€ย™re trying to invent something!รขย€ย รขย€ยœIรขย€ย”Iรขย€ย™m a little girl,รขย€ย said Alice, rather doubtfully, as she remembered the number of changes she had gone through that day. รขย€ยœA likely story indeed!รขย€ย said the Pigeon in a tone of the deepest contempt. รขย€ยœIรขย€ย™ve seen a good many little girls in my time, but never _one_ with such a neck as that! No, no! Youรขย€ย™re a serpent; and thereรขย€ย™s no use denying it. I suppose youรขย€ย™ll be telling me next that you never tasted an egg!รขย€ย รขย€ยœI _have_ tasted eggs, certainly,รขย€ย said Alice, who was a very truthful child; รขย€ยœbut little girls eat eggs quite as much as serpents do, you know.รขย€ย รขย€ยœI donรขย€ย™t believe it,รขย€ย said the Pigeon; รขย€ยœbut if they do, why then theyรขย€ย™re a kind of serpent, thatรขย€ย™s all I can say.รขย€ย This was such a new idea to Alice, that she was quite silent for a minute or two, which gave the Pigeon the opportunity of adding, รขย€ยœYouรขย€ย™re looking for eggs, I know _that_ well enough; and what does it matter to me whether youรขย€ย™re a little girl or a serpent?รขย€ย รขย€ยœIt matters a good deal to _me_,รขย€ย said Alice hastily; รขย€ยœbut Iรขย€ย™m not looking for eggs, as it happens; and if I was, I shouldnรขย€ย™t want _yours_: I donรขย€ย™t like them raw.รขย€ย รขย€ยœWell, be off, then!รขย€ย said the Pigeon in a sulky tone, as it settled down again into its nest. Alice crouched down among the trees as well as she could, for her neck kept getting entangled among the branches, and every now and then she had to stop and untwist it. After a while she remembered that she still held the pieces of mushroom in her hands, and she set to work very carefully, nibbling first at one and then at the other, and growing sometimes taller and sometimes shorter, until she had succeeded in bringing herself down to her usual height. It was so long since she had been anything near the right size, that it felt quite strange at first; but she got used to it in a few minutes, and began talking to herself, as usual. รขย€ยœCome, thereรขย€ย™s half my plan done now! How puzzling all these changes are! Iรขย€ย™m never sure what Iรขย€ย™m going to be, from one minute to another! However, Iรขย€ย™ve got back to my right size: the next thing is, to get into that beautiful gardenรขย€ย”how _is_ that to be done, I wonder?รขย€ย As she said this, she came suddenly upon an open place, with a little house in it about four feet high. รขย€ยœWhoever lives there,รขย€ย thought Alice, รขย€ยœitรขย€ย™ll never do to come upon them _this_ size: why, I should frighten them out of their wits!รขย€ย So she began nibbling at the righthand bit again, and did not venture to go near the house till she had brought herself down to nine inches high. CHAPTER VI. Pig and Pepper For a minute or two she stood looking at the house, and wondering what to do next, when suddenly a footman in livery came running out of the woodรขย€ย”(she considered him to be a footman because he was in livery: otherwise, judging by his face only, she would have called him a fish)รขย€ย”and rapped loudly at the door with his knuckles. It was opened by another footman in livery, with a round face, and large eyes like a frog; and both footmen, Alice noticed, had powdered hair that curled all over their heads. She felt very curious to know what it was all about, and crept a little way out of the wood to listen. The Fish-Footman began by producing from under his arm a great letter, nearly as large as himself, and this he handed over to the other, saying, in a solemn tone, รขย€ยœFor the Duchess. An invitation from the Queen to play croquet.รขย€ย The Frog-Footman repeated, in the same solemn tone, only changing the order of the words a little, รขย€ยœFrom the Queen. An invitation for the Duchess to play croquet.รขย€ย Then they both bowed low, and their curls got entangled together. Alice laughed so much at this, that she had to run back into the wood for fear of their hearing her; and when she next peeped out the Fish-Footman was gone, and the other was sitting on the ground near the door, staring stupidly up into the sky. Alice went timidly up to the door, and knocked. รขย€ยœThereรขย€ย™s no sort of use in knocking,รขย€ย said the Footman, รขย€ยœand that for two reasons. First, because Iรขย€ย™m on the same side of the door as you are; secondly, because theyรขย€ย™re making such a noise inside, no one could possibly hear you.รขย€ย And certainly there _was_ a most extraordinary noise going on withinรขย€ย”a constant howling and sneezing, and every now and then a great crash, as if a dish or kettle had been broken to pieces. รขย€ยœPlease, then,รขย€ย said Alice, รขย€ยœhow am I to get in?รขย€ย รขย€ยœThere might be some sense in your knocking,รขย€ย the Footman went on without attending to her, รขย€ยœif we had the door between us. For instance, if you were _inside_, you might knock, and I could let you out, you know.รขย€ย He was looking up into the sky all the time he was speaking, and this Alice thought decidedly uncivil. รขย€ยœBut perhaps he canรขย€ย™t help it,รขย€ย she said to herself; รขย€ยœhis eyes are so _very_ nearly at the top of his head. But at any rate he might answer questions.รขย€ย”How am I to get in?รขย€ย she repeated, aloud. รขย€ยœI shall sit here,รขย€ย the Footman remarked, รขย€ยœtill tomorrowรขย€ย”รขย€ย At this moment the door of the house opened, and a large plate came skimming out, straight at the Footmanรขย€ย™s head: it just grazed his nose, and broke to pieces against one of the trees behind him. รขย€ยœรขย€ย”or next day, maybe,รขย€ย the Footman continued in the same tone, exactly as if nothing had happened. รขย€ยœHow am I to get in?รขย€ย asked Alice again, in a louder tone. รขย€ยœ_Are_ you to get in at all?รขย€ย said the Footman. รขย€ยœThatรขย€ย™s the first question, you know.รขย€ย It was, no doubt: only Alice did not like to be told so. รขย€ยœItรขย€ย™s really dreadful,รขย€ย she muttered to herself, รขย€ยœthe way all the creatures argue. Itรขย€ย™s enough to drive one crazy!รขย€ย The Footman seemed to think this a good opportunity for repeating his remark, with variations. รขย€ยœI shall sit here,รขย€ย he said, รขย€ยœon and off, for days and days.รขย€ย รขย€ยœBut what am _I_ to do?รขย€ย said Alice. รขย€ยœAnything you like,รขย€ย said the Footman, and began whistling. รขย€ยœOh, thereรขย€ย™s no use in talking to him,รขย€ย said Alice desperately: รขย€ยœheรขย€ย™s perfectly idiotic!รขย€ย And she opened the door and went in. The door led right into a large kitchen, which was full of smoke from one end to the other: the Duchess was sitting on a three-legged stool in the middle, nursing a baby; the cook was leaning over the fire, stirring a large cauldron which seemed to be full of soup. รขย€ยœThereรขย€ย™s certainly too much pepper in that soup!รขย€ย Alice said to herself, as well as she could for sneezing. There was certainly too much of it in the air. Even the Duchess sneezed occasionally; and as for the baby, it was sneezing and howling alternately without a momentรขย€ย™s pause. The only things in the kitchen that did not sneeze, were the cook, and a large cat which was sitting on the hearth and grinning from ear to ear. รขย€ยœPlease would you tell me,รขย€ย said Alice, a little timidly, for she was not quite sure whether it was good manners for her to speak first, รขย€ยœwhy your cat grins like that?รขย€ย รขย€ยœItรขย€ย™s a Cheshire cat,รขย€ย said the Duchess, รขย€ยœand thatรขย€ย™s why. Pig!รขย€ย She said the last word with such sudden violence that Alice quite jumped; but she saw in another moment that it was addressed to the baby, and not to her, so she took courage, and went on again:รขย€ย” รขย€ยœI didnรขย€ย™t know that Cheshire cats always grinned; in fact, I didnรขย€ย™t know that cats _could_ grin.รขย€ย รขย€ยœThey all can,รขย€ย said the Duchess; รขย€ยœand most of รขย€ย™em do.รขย€ย รขย€ยœI donรขย€ย™t know of any that do,รขย€ย Alice said very politely, feeling quite pleased to have got into a conversation. รขย€ยœYou donรขย€ย™t know much,รขย€ย said the Duchess; รขย€ยœand thatรขย€ย™s a fact.รขย€ย Alice did not at all like the tone of this remark, and thought it would be as well to introduce some other subject of conversation. While she was trying to fix on one, the cook took the cauldron of soup off the fire, and at once set to work throwing everything within her reach at the Duchess and the babyรขย€ย”the fire-irons came first; then followed a shower of saucepans, plates, and dishes. The Duchess took no notice of them even when they hit her; and the baby was howling so much already, that it was quite impossible to say whether the blows hurt it or not. รขย€ยœOh, _please_ mind what youรขย€ย™re doing!รขย€ย cried Alice, jumping up and down in an agony of terror. รขย€ยœOh, there goes his _precious_ nose!รขย€ย as an unusually large saucepan flew close by it, and very nearly carried it off. รขย€ยœIf everybody minded their own business,รขย€ย the Duchess said in a hoarse growl, รขย€ยœthe world would go round a deal faster than it does.รขย€ย รขย€ยœWhich would _not_ be an advantage,รขย€ย said Alice, who felt very glad to get an opportunity of showing off a little of her knowledge. รขย€ยœJust think of what work it would make with the day and night! You see the earth takes twenty-four hours to turn round on its axisรขย€ย”รขย€ย รขย€ยœTalking of axes,รขย€ย said the Duchess, รขย€ยœchop off her head!รขย€ย Alice glanced rather anxiously at the cook, to see if she meant to take the hint; but the cook was busily stirring the soup, and seemed not to be listening, so she went on again: รขย€ยœTwenty-four hours, I _think_; or is it twelve? Iรขย€ย”รขย€ย รขย€ยœOh, donรขย€ย™t bother _me_,รขย€ย said the Duchess; รขย€ยœI never could abide figures!รขย€ย And with that she began nursing her child again, singing a sort of lullaby to it as she did so, and giving it a violent shake at the end of every line: รขย€ยœSpeak roughly to your little boy, And beat him when he sneezes: He only does it to annoy, Because he knows it teases.รขย€ย CHORUS. (In which the cook and the baby joined): รขย€ยœWow! wow! wow!รขย€ย While the Duchess sang the second verse of the song, she kept tossing the baby violently up and down, and the poor little thing howled so, that Alice could hardly hear the words:รขย€ย” รขย€ยœI speak severely to my boy, I beat him when he sneezes; For he can thoroughly enjoy The pepper when he pleases!รขย€ย CHORUS. รขย€ยœWow! wow! wow!รขย€ย รขย€ยœHere! you may nurse it a bit, if you like!รขย€ย the Duchess said to Alice, flinging the baby at her as she spoke. รขย€ยœI must go and get ready to play croquet with the Queen,รขย€ย and she hurried out of the room. The cook threw a frying-pan after her as she went out, but it just missed her. Alice caught the baby with some difficulty, as it was a queer-shaped little creature, and held out its arms and legs in all directions, รขย€ยœjust like a star-fish,รขย€ย thought Alice. The poor little thing was snorting like a steam-engine when she caught it, and kept doubling itself up and straightening itself out again, so that altogether, for the first minute or two, it was as much as she could do to hold it. As soon as she had made out the proper way of nursing it, (which was to twist it up into a sort of knot, and then keep tight hold of its right ear and left foot, so as to prevent its undoing itself,) she carried it out into the open air. รขย€ยœIf I donรขย€ย™t take this child away with me,รขย€ย thought Alice, รขย€ยœtheyรขย€ย™re sure to kill it in a day or two: wouldnรขย€ย™t it be murder to leave it behind?รขย€ย She said the last words out loud, and the little thing grunted in reply (it had left off sneezing by this time). รขย€ยœDonรขย€ย™t grunt,รขย€ย said Alice; รขย€ยœthatรขย€ย™s not at all a proper way of expressing yourself.รขย€ย The baby grunted again, and Alice looked very anxiously into its face to see what was the matter with it. There could be no doubt that it had a _very_ turn-up nose, much more like a snout than a real nose; also its eyes were getting extremely small for a baby: altogether Alice did not like the look of the thing at all. รขย€ยœBut perhaps it was only sobbing,รขย€ย she thought, and looked into its eyes again, to see if there were any tears. No, there were no tears. รขย€ยœIf youรขย€ย™re going to turn into a pig, my dear,รขย€ย said Alice, seriously, รขย€ยœIรขย€ย™ll have nothing more to do with you. Mind now!รขย€ย The poor little thing sobbed again (or grunted, it was impossible to say which), and they went on for some while in silence. Alice was just beginning to think to herself, รขย€ยœNow, what am I to do with this creature when I get it home?รขย€ย when it grunted again, so violently, that she looked down into its face in some alarm. This time there could be _no_ mistake about it: it was neither more nor less than a pig, and she felt that it would be quite absurd for her to carry it further. So she set the little creature down, and felt quite relieved to see it trot away quietly into the wood. รขย€ยœIf it had grown up,รขย€ย she said to herself, รขย€ยœit would have made a dreadfully ugly child: but it makes rather a handsome pig, I think.รขย€ย And she began thinking over other children she knew, who might do very well as pigs, and was just saying to herself, รขย€ยœif one only knew the right way to change themรขย€ย”รขย€ย when she was a little startled by seeing the Cheshire Cat sitting on a bough of a tree a few yards off. The Cat only grinned when it saw Alice. It looked good-natured, she thought: still it had _very_ long claws and a great many teeth, so she felt that it ought to be treated with respect. รขย€ยœCheshire Puss,รขย€ย she began, rather timidly, as she did not at all know whether it would like the name: however, it only grinned a little wider. รขย€ยœCome, itรขย€ย™s pleased so far,รขย€ย thought Alice, and she went on. รขย€ยœWould you tell me, please, which way I ought to go from here?รขย€ย รขย€ยœThat depends a good deal on where you want to get to,รขย€ย said the Cat. รขย€ยœI donรขย€ย™t much care whereรขย€ย”รขย€ย said Alice. รขย€ยœThen it doesnรขย€ย™t matter which way you go,รขย€ย said the Cat. รขย€ยœรขย€ย”so long as I get _somewhere_,รขย€ย Alice added as an explanation. รขย€ยœOh, youรขย€ย™re sure to do that,รขย€ย said the Cat, รขย€ยœif you only walk long enough.รขย€ย Alice felt that this could not be denied, so she tried another question. รขย€ยœWhat sort of people live about here?รขย€ย รขย€ยœIn _that_ direction,รขย€ย the Cat said, waving its right paw round, รขย€ยœlives a Hatter: and in _that_ direction,รขย€ย waving the other paw, รขย€ยœlives a March Hare. Visit either you like: theyรขย€ย™re both mad.รขย€ย รขย€ยœBut I donรขย€ย™t want to go among mad people,รขย€ย Alice remarked. รขย€ยœOh, you canรขย€ย™t help that,รขย€ย said the Cat: รขย€ยœweรขย€ย™re all mad here. Iรขย€ย™m mad. Youรขย€ย™re mad.รขย€ย รขย€ยœHow do you know Iรขย€ย™m mad?รขย€ย said Alice. รขย€ยœYou must be,รขย€ย said the Cat, รขย€ยœor you wouldnรขย€ย™t have come here.รขย€ย Alice didnรขย€ย™t think that proved it at all; however, she went on รขย€ยœAnd how do you know that youรขย€ย™re mad?รขย€ย รขย€ยœTo begin with,รขย€ย said the Cat, รขย€ยœa dogรขย€ย™s not mad. You grant that?รขย€ย รขย€ยœI suppose so,รขย€ย said Alice. รขย€ยœWell, then,รขย€ย the Cat went on, รขย€ยœyou see, a dog growls when itรขย€ย™s angry, and wags its tail when itรขย€ย™s pleased. Now _I_ growl when Iรขย€ย™m pleased, and wag my tail when Iรขย€ย™m angry. Therefore Iรขย€ย™m mad.รขย€ย รขย€ยœ_I_ call it purring, not growling,รขย€ย said Alice. รขย€ยœCall it what you like,รขย€ย said the Cat. รขย€ยœDo you play croquet with the Queen to-day?รขย€ย รขย€ยœI should like it very much,รขย€ย said Alice, รขย€ยœbut I havenรขย€ย™t been invited yet.รขย€ย รขย€ยœYouรขย€ย™ll see me there,รขย€ย said the Cat, and vanished. Alice was not much surprised at this, she was getting so used to queer things happening. While she was looking at the place where it had been, it suddenly appeared again. รขย€ยœBy-the-bye, what became of the baby?รขย€ย said the Cat. รขย€ยœIรขย€ย™d nearly forgotten to ask.รขย€ย รขย€ยœIt turned into a pig,รขย€ย Alice quietly said, just as if it had come back in a natural way. รขย€ยœI thought it would,รขย€ย said the Cat, and vanished again. Alice waited a little, half expecting to see it again, but it did not appear, and after a minute or two she walked on in the direction in which the March Hare was said to live. รขย€ยœIรขย€ย™ve seen hatters before,รขย€ย she said to herself; รขย€ยœthe March Hare will be much the most interesting, and perhaps as this is May it wonรขย€ย™t be raving madรขย€ย”at least not so mad as it was in March.รขย€ย As she said this, she looked up, and there was the Cat again, sitting on a branch of a tree. รขย€ยœDid you say pig, or fig?รขย€ย said the Cat. รขย€ยœI said pig,รขย€ย replied Alice; รขย€ยœand I wish you wouldnรขย€ย™t keep appearing and vanishing so suddenly: you make one quite giddy.รขย€ย รขย€ยœAll right,รขย€ย said the Cat; and this time it vanished quite slowly, beginning with the end of the tail, and ending with the grin, which remained some time after the rest of it had gone. รขย€ยœWell! Iรขย€ย™ve often seen a cat without a grin,รขย€ย thought Alice; รขย€ยœbut a grin without a cat! Itรขย€ย™s the most curious thing I ever saw in my life!รขย€ย She had not gone much farther before she came in sight of the house of the March Hare: she thought it must be the right house, because the chimneys were shaped like ears and the roof was thatched with fur. It was so large a house, that she did not like to go nearer till she had nibbled some more of the lefthand bit of mushroom, and raised herself to about two feet high: even then she walked up towards it rather timidly, saying to herself รขย€ยœSuppose it should be raving mad after all! I almost wish Iรขย€ย™d gone to see the Hatter instead!รขย€ย CHAPTER VII. A Mad Tea-Party There was a table set out under a tree in front of the house, and the March Hare and the Hatter were having tea at it: a Dormouse was sitting between them, fast asleep, and the other two were using it as a cushion, resting their elbows on it, and talking over its head. รขย€ยœVery uncomfortable for the Dormouse,รขย€ย thought Alice; รขย€ยœonly, as itรขย€ย™s asleep, I suppose it doesnรขย€ย™t mind.รขย€ย The table was a large one, but the three were all crowded together at one corner of it: รขย€ยœNo room! No room!รขย€ย they cried out when they saw Alice coming. รขย€ยœThereรขย€ย™s _plenty_ of room!รขย€ย said Alice indignantly, and she sat down in a large arm-chair at one end of the table. รขย€ยœHave some wine,รขย€ย the March Hare said in an encouraging tone. Alice looked all round the table, but there was nothing on it but tea. รขย€ยœI donรขย€ย™t see any wine,รขย€ย she remarked. รขย€ยœThere isnรขย€ย™t any,รขย€ย said the March Hare. รขย€ยœThen it wasnรขย€ย™t very civil of you to offer it,รขย€ย said Alice angrily. รขย€ยœIt wasnรขย€ย™t very civil of you to sit down without being invited,รขย€ย said the March Hare. รขย€ยœI didnรขย€ย™t know it was _your_ table,รขย€ย said Alice; รขย€ยœitรขย€ย™s laid for a great many more than three.รขย€ย รขย€ยœYour hair wants cutting,รขย€ย said the Hatter. He had been looking at Alice for some time with great curiosity, and this was his first speech. รขย€ยœYou should learn not to make personal remarks,รขย€ย Alice said with some severity; รขย€ยœitรขย€ย™s very rude.รขย€ย The Hatter opened his eyes very wide on hearing this; but all he _said_ was, รขย€ยœWhy is a raven like a writing-desk?รขย€ย รขย€ยœCome, we shall have some fun now!รขย€ย thought Alice. รขย€ยœIรขย€ย™m glad theyรขย€ย™ve begun asking riddles.รขย€ย”I believe I can guess that,รขย€ย she added aloud. รขย€ยœDo you mean that you think you can find out the answer to it?รขย€ย said the March Hare. รขย€ยœExactly so,รขย€ย said Alice. รขย€ยœThen you should say what you mean,รขย€ย the March Hare went on. รขย€ยœI do,รขย€ย Alice hastily replied; รขย€ยœat leastรขย€ย”at least I mean what I sayรขย€ย”thatรขย€ย™s the same thing, you know.รขย€ย รขย€ยœNot the same thing a bit!รขย€ย said the Hatter. รขย€ยœYou might just as well say that รขย€ย˜I see what I eatรขย€ย™ is the same thing as รขย€ย˜I eat what I seeรขย€ย™!รขย€ย รขย€ยœYou might just as well say,รขย€ย added the March Hare, รขย€ยœthat รขย€ย˜I like what I getรขย€ย™ is the same thing as รขย€ย˜I get what I likeรขย€ย™!รขย€ย รขย€ยœYou might just as well say,รขย€ย added the Dormouse, who seemed to be talking in his sleep, รขย€ยœthat รขย€ย˜I breathe when I sleepรขย€ย™ is the same thing as รขย€ย˜I sleep when I breatheรขย€ย™!รขย€ย รขย€ยœIt _is_ the same thing with you,รขย€ย said the Hatter, and here the conversation dropped, and the party sat silent for a minute, while Alice thought over all she could remember about ravens and writing-desks, which wasnรขย€ย™t much. The Hatter was the first to break the silence. รขย€ยœWhat day of the month is it?รขย€ย he said, turning to Alice: he had taken his watch out of his pocket, and was looking at it uneasily, shaking it every now and then, and holding it to his ear. Alice considered a little, and then said รขย€ยœThe fourth.รขย€ย รขย€ยœTwo days wrong!รขย€ย sighed the Hatter. รขย€ยœI told you butter wouldnรขย€ย™t suit the works!รขย€ย he added looking angrily at the March Hare. รขย€ยœIt was the _best_ butter,รขย€ย the March Hare meekly replied. รขย€ยœYes, but some crumbs must have got in as well,รขย€ย the Hatter grumbled: รขย€ยœyou shouldnรขย€ย™t have put it in with the bread-knife.รขย€ย The March Hare took the watch and looked at it gloomily: then he dipped it into his cup of tea, and looked at it again: but he could think of nothing better to say than his first remark, รขย€ยœIt was the _best_ butter, you know.รขย€ย Alice had been looking over his shoulder with some curiosity. รขย€ยœWhat a funny watch!รขย€ย she remarked. รขย€ยœIt tells the day of the month, and doesnรขย€ย™t tell what oรขย€ย™clock it is!รขย€ย รขย€ยœWhy should it?รขย€ย muttered the Hatter. รขย€ยœDoes _your_ watch tell you what year it is?รขย€ย รขย€ยœOf course not,รขย€ย Alice replied very readily: รขย€ยœbut thatรขย€ย™s because it stays the same year for such a long time together.รขย€ย รขย€ยœWhich is just the case with _mine_,รขย€ย said the Hatter. Alice felt dreadfully puzzled. The Hatterรขย€ย™s remark seemed to have no sort of meaning in it, and yet it was certainly English. รขย€ยœI donรขย€ย™t quite understand you,รขย€ย she said, as politely as she could. รขย€ยœThe Dormouse is asleep again,รขย€ย said the Hatter, and he poured a little hot tea upon its nose. The Dormouse shook its head impatiently, and said, without opening its eyes, รขย€ยœOf course, of course; just what I was going to remark myself.รขย€ย รขย€ยœHave you guessed the riddle yet?รขย€ย the Hatter said, turning to Alice again. รขย€ยœNo, I give it up,รขย€ย Alice replied: รขย€ยœwhatรขย€ย™s the answer?รขย€ย รขย€ยœI havenรขย€ย™t the slightest idea,รขย€ย said the Hatter. รขย€ยœNor I,รขย€ย said the March Hare. Alice sighed wearily. รขย€ยœI think you might do something better with the time,รขย€ย she said, รขย€ยœthan waste it in asking riddles that have no answers.รขย€ย รขย€ยœIf you knew Time as well as I do,รขย€ย said the Hatter, รขย€ยœyou wouldnรขย€ย™t talk about wasting _it_. Itรขย€ย™s _him_.รขย€ย รขย€ยœI donรขย€ย™t know what you mean,รขย€ย said Alice. รขย€ยœOf course you donรขย€ย™t!รขย€ย the Hatter said, tossing his head contemptuously. รขย€ยœI dare say you never even spoke to Time!รขย€ย รขย€ยœPerhaps not,รขย€ย Alice cautiously replied: รขย€ยœbut I know I have to beat time when I learn music.รขย€ย รขย€ยœAh! that accounts for it,รขย€ย said the Hatter. รขย€ยœHe wonรขย€ย™t stand beating. Now, if you only kept on good terms with him, heรขย€ย™d do almost anything you liked with the clock. For instance, suppose it were nine oรขย€ย™clock in the morning, just time to begin lessons: youรขย€ย™d only have to whisper a hint to Time, and round goes the clock in a twinkling! Half-past one, time for dinner!รขย€ย (รขย€ยœI only wish it was,รขย€ย the March Hare said to itself in a whisper.) รขย€ยœThat would be grand, certainly,รขย€ย said Alice thoughtfully: รขย€ยœbut thenรขย€ย”I shouldnรขย€ย™t be hungry for it, you know.รขย€ย รขย€ยœNot at first, perhaps,รขย€ย said the Hatter: รขย€ยœbut you could keep it to half-past one as long as you liked.รขย€ย รขย€ยœIs that the way _you_ manage?รขย€ย Alice asked. The Hatter shook his head mournfully. รขย€ยœNot I!รขย€ย he replied. รขย€ยœWe quarrelled last Marchรขย€ย”just before _he_ went mad, you knowรขย€ย”รขย€ย (pointing with his tea spoon at the March Hare,) รขย€ยœรขย€ย”it was at the great concert given by the Queen of Hearts, and I had to sing รขย€ย˜Twinkle, twinkle, little bat! How I wonder what youรขย€ย™re at!รขย€ย™ You know the song, perhaps?รขย€ย รขย€ยœIรขย€ย™ve heard something like it,รขย€ย said Alice. รขย€ยœIt goes on, you know,รขย€ย the Hatter continued, รขย€ยœin this way:รขย€ย” รขย€ย˜Up above the world you fly, Like a tea-tray in the sky. Twinkle, twinkleรขย€ย”รขย€ย™รขย€ย Here the Dormouse shook itself, and began singing in its sleep รขย€ยœ_Twinkle, twinkle, twinkle, twinkle_รขย€ย”รขย€ย and went on so long that they had to pinch it to make it stop. รขย€ยœWell, Iรขย€ย™d hardly finished the first verse,รขย€ย said the Hatter, รขย€ยœwhen the Queen jumped up and bawled out, รขย€ย˜Heรขย€ย™s murdering the time! Off with his head!รขย€ย™รขย€ย รขย€ยœHow dreadfully savage!รขย€ย exclaimed Alice. รขย€ยœAnd ever since that,รขย€ย the Hatter went on in a mournful tone, รขย€ยœhe wonรขย€ย™t do a thing I ask! Itรขย€ย™s always six oรขย€ย™clock now.รขย€ย A bright idea came into Aliceรขย€ย™s head. รขย€ยœIs that the reason so many tea-things are put out here?รขย€ย she asked. รขย€ยœYes, thatรขย€ย™s it,รขย€ย said the Hatter with a sigh: รขย€ยœitรขย€ย™s always tea-time, and weรขย€ย™ve no time to wash the things between whiles.รขย€ย รขย€ยœThen you keep moving round, I suppose?รขย€ย said Alice. รขย€ยœExactly so,รขย€ย said the Hatter: รขย€ยœas the things get used up.รขย€ย รขย€ยœBut what happens when you come to the beginning again?รขย€ย Alice ventured to ask. รขย€ยœSuppose we change the subject,รขย€ย the March Hare interrupted, yawning. รขย€ยœIรขย€ย™m getting tired of this. I vote the young lady tells us a story.รขย€ย รขย€ยœIรขย€ย™m afraid I donรขย€ย™t know one,รขย€ย said Alice, rather alarmed at the proposal. รขย€ยœThen the Dormouse shall!รขย€ย they both cried. รขย€ยœWake up, Dormouse!รขย€ย And they pinched it on both sides at once. The Dormouse slowly opened his eyes. รขย€ยœI wasnรขย€ย™t asleep,รขย€ย he said in a hoarse, feeble voice: รขย€ยœI heard every word you fellows were saying.รขย€ย รขย€ยœTell us a story!รขย€ย said the March Hare. รขย€ยœYes, please do!รขย€ย pleaded Alice. รขย€ยœAnd be quick about it,รขย€ย added the Hatter, รขย€ยœor youรขย€ย™ll be asleep again before itรขย€ย™s done.รขย€ย รขย€ยœOnce upon a time there were three little sisters,รขย€ย the Dormouse began in a great hurry; รขย€ยœand their names were Elsie, Lacie, and Tillie; and they lived at the bottom of a wellรขย€ย”รขย€ย รขย€ยœWhat did they live on?รขย€ย said Alice, who always took a great interest in questions of eating and drinking. รขย€ยœThey lived on treacle,รขย€ย said the Dormouse, after thinking a minute or two. รขย€ยœThey couldnรขย€ย™t have done that, you know,รขย€ย Alice gently remarked; รขย€ยœtheyรขย€ย™d have been ill.รขย€ย รขย€ยœSo they were,รขย€ย said the Dormouse; รขย€ยœ_very_ ill.รขย€ย Alice tried to fancy to herself what such an extraordinary ways of living would be like, but it puzzled her too much, so she went on: รขย€ยœBut why did they live at the bottom of a well?รขย€ย รขย€ยœTake some more tea,รขย€ย the March Hare said to Alice, very earnestly. รขย€ยœIรขย€ย™ve had nothing yet,รขย€ย Alice replied in an offended tone, รขย€ยœso I canรขย€ย™t take more.รขย€ย รขย€ยœYou mean you canรขย€ย™t take _less_,รขย€ย said the Hatter: รขย€ยœitรขย€ย™s very easy to take _more_ than nothing.รขย€ย รขย€ยœNobody asked _your_ opinion,รขย€ย said Alice. รขย€ยœWhoรขย€ย™s making personal remarks now?รขย€ย the Hatter asked triumphantly. Alice did not quite know what to say to this: so she helped herself to some tea and bread-and-butter, and then turned to the Dormouse, and repeated her question. รขย€ยœWhy did they live at the bottom of a well?รขย€ย The Dormouse again took a minute or two to think about it, and then said, รขย€ยœIt was a treacle-well.รขย€ย รขย€ยœThereรขย€ย™s no such thing!รขย€ย Alice was beginning very angrily, but the Hatter and the March Hare went รขย€ยœSh! sh!รขย€ย and the Dormouse sulkily remarked, รขย€ยœIf you canรขย€ย™t be civil, youรขย€ย™d better finish the story for yourself.รขย€ย รขย€ยœNo, please go on!รขย€ย Alice said very humbly; รขย€ยœI wonรขย€ย™t interrupt again. I dare say there may be _one_.รขย€ย รขย€ยœOne, indeed!รขย€ย said the Dormouse indignantly. However, he consented to go on. รขย€ยœAnd so these three little sistersรขย€ย”they were learning to draw, you knowรขย€ย”รขย€ย รขย€ยœWhat did they draw?รขย€ย said Alice, quite forgetting her promise. รขย€ยœTreacle,รขย€ย said the Dormouse, without considering at all this time. รขย€ยœI want a clean cup,รขย€ย interrupted the Hatter: รขย€ยœletรขย€ย™s all move one place on.รขย€ย He moved on as he spoke, and the Dormouse followed him: the March Hare moved into the Dormouseรขย€ย™s place, and Alice rather unwillingly took the place of the March Hare. The Hatter was the only one who got any advantage from the change: and Alice was a good deal worse off than before, as the March Hare had just upset the milk-jug into his plate. Alice did not wish to offend the Dormouse again, so she began very cautiously: รขย€ยœBut I donรขย€ย™t understand. Where did they draw the treacle from?รขย€ย รขย€ยœYou can draw water out of a water-well,รขย€ย said the Hatter; รขย€ยœso I should think you could draw treacle out of a treacle-wellรขย€ย”eh, stupid?รขย€ย รขย€ยœBut they were _in_ the well,รขย€ย Alice said to the Dormouse, not choosing to notice this last remark. รขย€ยœOf course they were,รขย€ย said the Dormouse; รขย€ยœรขย€ย”well in.รขย€ย This answer so confused poor Alice, that she let the Dormouse go on for some time without interrupting it. รขย€ยœThey were learning to draw,รขย€ย the Dormouse went on, yawning and rubbing its eyes, for it was getting very sleepy; รขย€ยœand they drew all manner of thingsรขย€ย”everything that begins with an Mรขย€ย”รขย€ย รขย€ยœWhy with an M?รขย€ย said Alice. รขย€ยœWhy not?รขย€ย said the March Hare. Alice was silent. The Dormouse had closed its eyes by this time, and was going off into a doze; but, on being pinched by the Hatter, it woke up again with a little shriek, and went on: รขย€ยœรขย€ย”that begins with an M, such as mouse-traps, and the moon, and memory, and muchnessรขย€ย”you know you say things are รขย€ยœmuch of a muchnessรขย€ยรขย€ย”did you ever see such a thing as a drawing of a muchness?รขย€ย รขย€ยœReally, now you ask me,รขย€ย said Alice, very much confused, รขย€ยœI donรขย€ย™t thinkรขย€ย”รขย€ย รขย€ยœThen you shouldnรขย€ย™t talk,รขย€ย said the Hatter. This piece of rudeness was more than Alice could bear: she got up in great disgust, and walked off; the Dormouse fell asleep instantly, and neither of the others took the least notice of her going, though she looked back once or twice, half hoping that they would call after her: the last time she saw them, they were trying to put the Dormouse into the teapot. รขย€ยœAt any rate Iรขย€ย™ll never go _there_ again!รขย€ย said Alice as she picked her way through the wood. รขย€ยœItรขย€ย™s the stupidest tea-party I ever was at in all my life!รขย€ย Just as she said this, she noticed that one of the trees had a door leading right into it. รขย€ยœThatรขย€ย™s very curious!รขย€ย she thought. รขย€ยœBut everythingรขย€ย™s curious today. I think I may as well go in at once.รขย€ย And in she went. Once more she found herself in the long hall, and close to the little glass table. รขย€ยœNow, Iรขย€ย™ll manage better this time,รขย€ย she said to herself, and began by taking the little golden key, and unlocking the door that led into the garden. Then she went to work nibbling at the mushroom (she had kept a piece of it in her pocket) till she was about a foot high: then she walked down the little passage: and _then_รขย€ย”she found herself at last in the beautiful garden, among the bright flower-beds and the cool fountains. CHAPTER VIII. The Queenรขย€ย™s Croquet-Ground A large rose-tree stood near the entrance of the garden: the roses growing on it were white, but there were three gardeners at it, busily painting them red. Alice thought this a very curious thing, and she went nearer to watch them, and just as she came up to them she heard one of them say, รขย€ยœLook out now, Five! Donรขย€ย™t go splashing paint over me like that!รขย€ย รขย€ยœI couldnรขย€ย™t help it,รขย€ย said Five, in a sulky tone; รขย€ยœSeven jogged my elbow.รขย€ย On which Seven looked up and said, รขย€ยœThatรขย€ย™s right, Five! Always lay the blame on others!รขย€ย รขย€ยœ_Youรขย€ย™d_ better not talk!รขย€ย said Five. รขย€ยœI heard the Queen say only yesterday you deserved to be beheaded!รขย€ย รขย€ยœWhat for?รขย€ย said the one who had spoken first. รขย€ยœThatรขย€ย™s none of _your_ business, Two!รขย€ย said Seven. รขย€ยœYes, it _is_ his business!รขย€ย said Five, รขย€ยœand Iรขย€ย™ll tell himรขย€ย”it was for bringing the cook tulip-roots instead of onions.รขย€ย Seven flung down his brush, and had just begun รขย€ยœWell, of all the unjust thingsรขย€ย”รขย€ย when his eye chanced to fall upon Alice, as she stood watching them, and he checked himself suddenly: the others looked round also, and all of them bowed low. รขย€ยœWould you tell me,รขย€ย said Alice, a little timidly, รขย€ยœwhy you are painting those roses?รขย€ย Five and Seven said nothing, but looked at Two. Two began in a low voice, รขย€ยœWhy the fact is, you see, Miss, this here ought to have been a _red_ rose-tree, and we put a white one in by mistake; and if the Queen was to find it out, we should all have our heads cut off, you know. So you see, Miss, weรขย€ย™re doing our best, afore she comes, toรขย€ย”รขย€ย At this moment Five, who had been anxiously looking across the garden, called out รขย€ยœThe Queen! The Queen!รขย€ย and the three gardeners instantly threw themselves flat upon their faces. There was a sound of many footsteps, and Alice looked round, eager to see the Queen. First came ten soldiers carrying clubs; these were all shaped like the three gardeners, oblong and flat, with their hands and feet at the corners: next the ten courtiers; these were ornamented all over with diamonds, and walked two and two, as the soldiers did. After these came the royal children; there were ten of them, and the little dears came jumping merrily along hand in hand, in couples: they were all ornamented with hearts. Next came the guests, mostly Kings and Queens, and among them Alice recognised the White Rabbit: it was talking in a hurried nervous manner, smiling at everything that was said, and went by without noticing her. Then followed the Knave of Hearts, carrying the Kingรขย€ย™s crown on a crimson velvet cushion; and, last of all this grand procession, came THE KING AND QUEEN OF HEARTS. Alice was rather doubtful whether she ought not to lie down on her face like the three gardeners, but she could not remember ever having heard of such a rule at processions; รขย€ยœand besides, what would be the use of a procession,รขย€ย thought she, รขย€ยœif people had all to lie down upon their faces, so that they couldnรขย€ย™t see it?รขย€ย So she stood still where she was, and waited. When the procession came opposite to Alice, they all stopped and looked at her, and the Queen said severely รขย€ยœWho is this?รขย€ย She said it to the Knave of Hearts, who only bowed and smiled in reply. รขย€ยœIdiot!รขย€ย said the Queen, tossing her head impatiently; and, turning to Alice, she went on, รขย€ยœWhatรขย€ย™s your name, child?รขย€ย รขย€ยœMy name is Alice, so please your Majesty,รขย€ย said Alice very politely; but she added, to herself, รขย€ยœWhy, theyรขย€ย™re only a pack of cards, after all. I neednรขย€ย™t be afraid of them!รขย€ย รขย€ยœAnd who are _these?_รขย€ย said the Queen, pointing to the three gardeners who were lying round the rose-tree; for, you see, as they were lying on their faces, and the pattern on their backs was the same as the rest of the pack, she could not tell whether they were gardeners, or soldiers, or courtiers, or three of her own children. รขย€ยœHow should _I_ know?รขย€ย said Alice, surprised at her own courage. รขย€ยœItรขย€ย™s no business of _mine_.รขย€ย The Queen turned crimson with fury, and, after glaring at her for a moment like a wild beast, screamed รขย€ยœOff with her head! Offรขย€ย”รขย€ย รขย€ยœNonsense!รขย€ย said Alice, very loudly and decidedly, and the Queen was silent. The King laid his hand upon her arm, and timidly said รขย€ยœConsider, my dear: she is only a child!รขย€ย The Queen turned angrily away from him, and said to the Knave รขย€ยœTurn them over!รขย€ย The Knave did so, very carefully, with one foot. รขย€ยœGet up!รขย€ย said the Queen, in a shrill, loud voice, and the three gardeners instantly jumped up, and began bowing to the King, the Queen, the royal children, and everybody else. รขย€ยœLeave off that!รขย€ย screamed the Queen. รขย€ยœYou make me giddy.รขย€ย And then, turning to the rose-tree, she went on, รขย€ยœWhat _have_ you been doing here?รขย€ย รขย€ยœMay it please your Majesty,รขย€ย said Two, in a very humble tone, going down on one knee as he spoke, รขย€ยœwe were tryingรขย€ย”รขย€ย รขย€ยœ_I_ see!รขย€ย said the Queen, who had meanwhile been examining the roses. รขย€ยœOff with their heads!รขย€ย and the procession moved on, three of the soldiers remaining behind to execute the unfortunate gardeners, who ran to Alice for protection. รขย€ยœYou shanรขย€ย™t be beheaded!รขย€ย said Alice, and she put them into a large flower-pot that stood near. The three soldiers wandered about for a minute or two, looking for them, and then quietly marched off after the others. รขย€ยœAre their heads off?รขย€ย shouted the Queen. รขย€ยœTheir heads are gone, if it please your Majesty!รขย€ย the soldiers shouted in reply. รขย€ยœThatรขย€ย™s right!รขย€ย shouted the Queen. รขย€ยœCan you play croquet?รขย€ย The soldiers were silent, and looked at Alice, as the question was evidently meant for her. รขย€ยœYes!รขย€ย shouted Alice. รขย€ยœCome on, then!รขย€ย roared the Queen, and Alice joined the procession, wondering very much what would happen next. รขย€ยœItรขย€ย™sรขย€ย”itรขย€ย™s a very fine day!รขย€ย said a timid voice at her side. She was walking by the White Rabbit, who was peeping anxiously into her face. รขย€ยœVery,รขย€ย said Alice: รขย€ยœรขย€ย”whereรขย€ย™s the Duchess?รขย€ย รขย€ยœHush! Hush!รขย€ย said the Rabbit in a low, hurried tone. He looked anxiously over his shoulder as he spoke, and then raised himself upon tiptoe, put his mouth close to her ear, and whispered รขย€ยœSheรขย€ย™s under sentence of execution.รขย€ย รขย€ยœWhat for?รขย€ย said Alice. รขย€ยœDid you say รขย€ย˜What a pity!รขย€ย™?รขย€ย the Rabbit asked. รขย€ยœNo, I didnรขย€ย™t,รขย€ย said Alice: รขย€ยœI donรขย€ย™t think itรขย€ย™s at all a pity. I said รขย€ย˜What for?รขย€ย™รขย€ย รขย€ยœShe boxed the Queenรขย€ย™s earsรขย€ย”รขย€ย the Rabbit began. Alice gave a little scream of laughter. รขย€ยœOh, hush!รขย€ย the Rabbit whispered in a frightened tone. รขย€ยœThe Queen will hear you! You see, she came rather late, and the Queen saidรขย€ย”รขย€ย รขย€ยœGet to your places!รขย€ย shouted the Queen in a voice of thunder, and people began running about in all directions, tumbling up against each other; however, they got settled down in a minute or two, and the game began. Alice thought she had never seen such a curious croquet-ground in her life; it was all ridges and furrows; the balls were live hedgehogs, the mallets live flamingoes, and the soldiers had to double themselves up and to stand on their hands and feet, to make the arches. The chief difficulty Alice found at first was in managing her flamingo: she succeeded in getting its body tucked away, comfortably enough, under her arm, with its legs hanging down, but generally, just as she had got its neck nicely straightened out, and was going to give the hedgehog a blow with its head, it _would_ twist itself round and look up in her face, with such a puzzled expression that she could not help bursting out laughing: and when she had got its head down, and was going to begin again, it was very provoking to find that the hedgehog had unrolled itself, and was in the act of crawling away: besides all this, there was generally a ridge or furrow in the way wherever she wanted to send the hedgehog to, and, as the doubled-up soldiers were always getting up and walking off to other parts of the ground, Alice soon came to the conclusion that it was a very difficult game indeed. The players all played at once without waiting for turns, quarrelling all the while, and fighting for the hedgehogs; and in a very short time the Queen was in a furious passion, and went stamping about, and shouting รขย€ยœOff with his head!รขย€ย or รขย€ยœOff with her head!รขย€ย about once in a minute. Alice began to feel very uneasy: to be sure, she had not as yet had any dispute with the Queen, but she knew that it might happen any minute, รขย€ยœand then,รขย€ย thought she, รขย€ยœwhat would become of me? Theyรขย€ย™re dreadfully fond of beheading people here; the great wonder is, that thereรขย€ย™s any one left alive!รขย€ย She was looking about for some way of escape, and wondering whether she could get away without being seen, when she noticed a curious appearance in the air: it puzzled her very much at first, but, after watching it a minute or two, she made it out to be a grin, and she said to herself รขย€ยœItรขย€ย™s the Cheshire Cat: now I shall have somebody to talk to.รขย€ย รขย€ยœHow are you getting on?รขย€ย said the Cat, as soon as there was mouth enough for it to speak with. Alice waited till the eyes appeared, and then nodded. รขย€ยœItรขย€ย™s no use speaking to it,รขย€ย she thought, รขย€ยœtill its ears have come, or at least one of them.รขย€ย In another minute the whole head appeared, and then Alice put down her flamingo, and began an account of the game, feeling very glad she had someone to listen to her. The Cat seemed to think that there was enough of it now in sight, and no more of it appeared. รขย€ยœI donรขย€ย™t think they play at all fairly,รขย€ย Alice began, in rather a complaining tone, รขย€ยœand they all quarrel so dreadfully one canรขย€ย™t hear oneself speakรขย€ย”and they donรขย€ย™t seem to have any rules in particular; at least, if there are, nobody attends to themรขย€ย”and youรขย€ย™ve no idea how confusing it is all the things being alive; for instance, thereรขย€ย™s the arch Iรขย€ย™ve got to go through next walking about at the other end of the groundรขย€ย”and I should have croqueted the Queenรขย€ย™s hedgehog just now, only it ran away when it saw mine coming!รขย€ย รขย€ยœHow do you like the Queen?รขย€ย said the Cat in a low voice. รขย€ยœNot at all,รขย€ย said Alice: รขย€ยœsheรขย€ย™s so extremelyรขย€ย”รขย€ย Just then she noticed that the Queen was close behind her, listening: so she went on, รขย€ยœรขย€ย”likely to win, that itรขย€ย™s hardly worth while finishing the game.รขย€ย The Queen smiled and passed on. รขย€ยœWho _are_ you talking to?รขย€ย said the King, going up to Alice, and looking at the Catรขย€ย™s head with great curiosity. รขย€ยœItรขย€ย™s a friend of mineรขย€ย”a Cheshire Cat,รขย€ย said Alice: รขย€ยœallow me to introduce it.รขย€ย รขย€ยœI donรขย€ย™t like the look of it at all,รขย€ย said the King: รขย€ยœhowever, it may kiss my hand if it likes.รขย€ย รขย€ยœIรขย€ย™d rather not,รขย€ย the Cat remarked. รขย€ยœDonรขย€ย™t be impertinent,รขย€ย said the King, รขย€ยœand donรขย€ย™t look at me like that!รขย€ย He got behind Alice as he spoke. รขย€ยœA cat may look at a king,รขย€ย said Alice. รขย€ยœIรขย€ย™ve read that in some book, but I donรขย€ย™t remember where.รขย€ย รขย€ยœWell, it must be removed,รขย€ย said the King very decidedly, and he called the Queen, who was passing at the moment, รขย€ยœMy dear! I wish you would have this cat removed!รขย€ย The Queen had only one way of settling all difficulties, great or small. รขย€ยœOff with his head!รขย€ย she said, without even looking round. รขย€ยœIรขย€ย™ll fetch the executioner myself,รขย€ย said the King eagerly, and he hurried off. Alice thought she might as well go back, and see how the game was going on, as she heard the Queenรขย€ย™s voice in the distance, screaming with passion. She had already heard her sentence three of the players to be executed for having missed their turns, and she did not like the look of things at all, as the game was in such confusion that she never knew whether it was her turn or not. So she went in search of her hedgehog. The hedgehog was engaged in a fight with another hedgehog, which seemed to Alice an excellent opportunity for croqueting one of them with the other: the only difficulty was, that her flamingo was gone across to the other side of the garden, where Alice could see it trying in a helpless sort of way to fly up into a tree. By the time she had caught the flamingo and brought it back, the fight was over, and both the hedgehogs were out of sight: รขย€ยœbut it doesnรขย€ย™t matter much,รขย€ย thought Alice, รขย€ยœas all the arches are gone from this side of the ground.รขย€ย So she tucked it away under her arm, that it might not escape again, and went back for a little more conversation with her friend. When she got back to the Cheshire Cat, she was surprised to find quite a large crowd collected round it: there was a dispute going on between the executioner, the King, and the Queen, who were all talking at once, while all the rest were quite silent, and looked very uncomfortable. The moment Alice appeared, she was appealed to by all three to settle the question, and they repeated their arguments to her, though, as they all spoke at once, she found it very hard indeed to make out exactly what they said. The executionerรขย€ย™s argument was, that you couldnรขย€ย™t cut off a head unless there was a body to cut it off from: that he had never had to do such a thing before, and he wasnรขย€ย™t going to begin at _his_ time of life. The Kingรขย€ย™s argument was, that anything that had a head could be beheaded, and that you werenรขย€ย™t to talk nonsense. The Queenรขย€ย™s argument was, that if something wasnรขย€ย™t done about it in less than no time sheรขย€ย™d have everybody executed, all round. (It was this last remark that had made the whole party look so grave and anxious.) Alice could think of nothing else to say but รขย€ยœIt belongs to the Duchess: youรขย€ย™d better ask _her_ about it.รขย€ย รขย€ยœSheรขย€ย™s in prison,รขย€ย the Queen said to the executioner: รขย€ยœfetch her here.รขย€ย And the executioner went off like an arrow. The Catรขย€ย™s head began fading away the moment he was gone, and, by the time he had come back with the Duchess, it had entirely disappeared; so the King and the executioner ran wildly up and down looking for it, while the rest of the party went back to the game. CHAPTER IX. The Mock Turtleรขย€ย™s Story รขย€ยœYou canรขย€ย™t think how glad I am to see you again, you dear old thing!รขย€ย said the Duchess, as she tucked her arm affectionately into Aliceรขย€ย™s, and they walked off together. Alice was very glad to find her in such a pleasant temper, and thought to herself that perhaps it was only the pepper that had made her so savage when they met in the kitchen. รขย€ยœWhen _Iรขย€ย™m_ a Duchess,รขย€ย she said to herself, (not in a very hopeful tone though), รขย€ยœI wonรขย€ย™t have any pepper in my kitchen _at all_. Soup does very well withoutรขย€ย”Maybe itรขย€ย™s always pepper that makes people hot-tempered,รขย€ย she went on, very much pleased at having found out a new kind of rule, รขย€ยœand vinegar that makes them sourรขย€ย”and camomile that makes them bitterรขย€ย”andรขย€ย”and barley-sugar and such things that make children sweet-tempered. I only wish people knew _that_: then they wouldnรขย€ย™t be so stingy about it, you knowรขย€ย”รขย€ย She had quite forgotten the Duchess by this time, and was a little startled when she heard her voice close to her ear. รขย€ยœYouรขย€ย™re thinking about something, my dear, and that makes you forget to talk. I canรขย€ย™t tell you just now what the moral of that is, but I shall remember it in a bit.รขย€ย รขย€ยœPerhaps it hasnรขย€ย™t one,รขย€ย Alice ventured to remark. รขย€ยœTut, tut, child!รขย€ย said the Duchess. รขย€ยœEverythingรขย€ย™s got a moral, if only you can find it.รขย€ย And she squeezed herself up closer to Aliceรขย€ย™s side as she spoke. Alice did not much like keeping so close to her: first, because the Duchess was _very_ ugly; and secondly, because she was exactly the right height to rest her chin upon Aliceรขย€ย™s shoulder, and it was an uncomfortably sharp chin. However, she did not like to be rude, so she bore it as well as she could. รขย€ยœThe gameรขย€ย™s going on rather better now,รขย€ย she said, by way of keeping up the conversation a little. รขย€ยœรขย€ย™Tis so,รขย€ย said the Duchess: รขย€ยœand the moral of that isรขย€ย”รขย€ย˜Oh, รขย€ย™tis love, รขย€ย™tis love, that makes the world go round!รขย€ย™รขย€ย รขย€ยœSomebody said,รขย€ย Alice whispered, รขย€ยœthat itรขย€ย™s done by everybody minding their own business!รขย€ย รขย€ยœAh, well! It means much the same thing,รขย€ย said the Duchess, digging her sharp little chin into Aliceรขย€ย™s shoulder as she added, รขย€ยœand the moral of _that_ isรขย€ย”รขย€ย˜Take care of the sense, and the sounds will take care of themselves.รขย€ย™รขย€ย รขย€ยœHow fond she is of finding morals in things!รขย€ย Alice thought to herself. รขย€ยœI dare say youรขย€ย™re wondering why I donรขย€ย™t put my arm round your waist,รขย€ย the Duchess said after a pause: รขย€ยœthe reason is, that Iรขย€ย™m doubtful about the temper of your flamingo. Shall I try the experiment?รขย€ย รขย€ยœHe might bite,รขย€ย Alice cautiously replied, not feeling at all anxious to have the experiment tried. รขย€ยœVery true,รขย€ย said the Duchess: รขย€ยœflamingoes and mustard both bite. And the moral of that isรขย€ย”รขย€ย˜Birds of a feather flock together.รขย€ย™รขย€ย รขย€ยœOnly mustard isnรขย€ย™t a bird,รขย€ย Alice remarked. รขย€ยœRight, as usual,รขย€ย said the Duchess: รขย€ยœwhat a clear way you have of putting things!รขย€ย รขย€ยœItรขย€ย™s a mineral, I _think_,รขย€ย said Alice. รขย€ยœOf course it is,รขย€ย said the Duchess, who seemed ready to agree to everything that Alice said; รขย€ยœthereรขย€ย™s a large mustard-mine near here. And the moral of that isรขย€ย”รขย€ย˜The more there is of mine, the less there is of yours.รขย€ย™รขย€ย รขย€ยœOh, I know!รขย€ย exclaimed Alice, who had not attended to this last remark, รขย€ยœitรขย€ย™s a vegetable. It doesnรขย€ย™t look like one, but it is.รขย€ย รขย€ยœI quite agree with you,รขย€ย said the Duchess; รขย€ยœand the moral of that isรขย€ย”รขย€ย˜Be what you would seem to beรขย€ย™รขย€ย”or if youรขย€ย™d like it put more simplyรขย€ย”รขย€ย˜Never imagine yourself not to be otherwise than what it might appear to others that what you were or might have been was not otherwise than what you had been would have appeared to them to be otherwise.รขย€ย™รขย€ย รขย€ยœI think I should understand that better,รขย€ย Alice said very politely, รขย€ยœif I had it written down: but I canรขย€ย™t quite follow it as you say it.รขย€ย รขย€ยœThatรขย€ย™s nothing to what I could say if I chose,รขย€ย the Duchess replied, in a pleased tone. รขย€ยœPray donรขย€ย™t trouble yourself to say it any longer than that,รขย€ย said Alice. รขย€ยœOh, donรขย€ย™t talk about trouble!รขย€ย said the Duchess. รขย€ยœI make you a present of everything Iรขย€ย™ve said as yet.รขย€ย รขย€ยœA cheap sort of present!รขย€ย thought Alice. รขย€ยœIรขย€ย™m glad they donรขย€ย™t give birthday presents like that!รขย€ย But she did not venture to say it out loud. รขย€ยœThinking again?รขย€ย the Duchess asked, with another dig of her sharp little chin. รขย€ยœIรขย€ย™ve a right to think,รขย€ย said Alice sharply, for she was beginning to feel a little worried. รขย€ยœJust about as much right,รขย€ย said the Duchess, รขย€ยœas pigs have to fly; and the mรขย€ย”รขย€ย But here, to Aliceรขย€ย™s great surprise, the Duchessรขย€ย™s voice died away, even in the middle of her favourite word รขย€ย˜moral,รขย€ย™ and the arm that was linked into hers began to tremble. Alice looked up, and there stood the Queen in front of them, with her arms folded, frowning like a thunderstorm. รขย€ยœA fine day, your Majesty!รขย€ย the Duchess began in a low, weak voice. รขย€ยœNow, I give you fair warning,รขย€ย shouted the Queen, stamping on the ground as she spoke; รขย€ยœeither you or your head must be off, and that in about half no time! Take your choice!รขย€ย The Duchess took her choice, and was gone in a moment. รขย€ยœLetรขย€ย™s go on with the game,รขย€ย the Queen said to Alice; and Alice was too much frightened to say a word, but slowly followed her back to the croquet-ground. The other guests had taken advantage of the Queenรขย€ย™s absence, and were resting in the shade: however, the moment they saw her, they hurried back to the game, the Queen merely remarking that a momentรขย€ย™s delay would cost them their lives. All the time they were playing the Queen never left off quarrelling with the other players, and shouting รขย€ยœOff with his head!รขย€ย or รขย€ยœOff with her head!รขย€ย Those whom she sentenced were taken into custody by the soldiers, who of course had to leave off being arches to do this, so that by the end of half an hour or so there were no arches left, and all the players, except the King, the Queen, and Alice, were in custody and under sentence of execution. Then the Queen left off, quite out of breath, and said to Alice, รขย€ยœHave you seen the Mock Turtle yet?รขย€ย รขย€ยœNo,รขย€ย said Alice. รขย€ยœI donรขย€ย™t even know what a Mock Turtle is.รขย€ย รขย€ยœItรขย€ย™s the thing Mock Turtle Soup is made from,รขย€ย said the Queen. รขย€ยœI never saw one, or heard of one,รขย€ย said Alice. รขย€ยœCome on, then,รขย€ย said the Queen, รขย€ยœand he shall tell you his history,รขย€ย As they walked off together, Alice heard the King say in a low voice, to the company generally, รขย€ยœYou are all pardoned.รขย€ย รขย€ยœCome, _thatรขย€ย™s_ a good thing!รขย€ย she said to herself, for she had felt quite unhappy at the number of executions the Queen had ordered. They very soon came upon a Gryphon, lying fast asleep in the sun. (If you donรขย€ย™t know what a Gryphon is, look at the picture.) รขย€ยœUp, lazy thing!รขย€ย said the Queen, รขย€ยœand take this young lady to see the Mock Turtle, and to hear his history. I must go back and see after some executions I have ordered;รขย€ย and she walked off, leaving Alice alone with the Gryphon. Alice did not quite like the look of the creature, but on the whole she thought it would be quite as safe to stay with it as to go after that savage Queen: so she waited. The Gryphon sat up and rubbed its eyes: then it watched the Queen till she was out of sight: then it chuckled. รขย€ยœWhat fun!รขย€ย said the Gryphon, half to itself, half to Alice. รขย€ยœWhat _is_ the fun?รขย€ย said Alice. รขย€ยœWhy, _she_,รขย€ย said the Gryphon. รขย€ยœItรขย€ย™s all her fancy, that: they never executes nobody, you know. Come on!รขย€ย รขย€ยœEverybody says รขย€ย˜come on!รขย€ย™ here,รขย€ย thought Alice, as she went slowly after it: รขย€ยœI never was so ordered about in all my life, never!รขย€ย They had not gone far before they saw the Mock Turtle in the distance, sitting sad and lonely on a little ledge of rock, and, as they came nearer, Alice could hear him sighing as if his heart would break. She pitied him deeply. รขย€ยœWhat is his sorrow?รขย€ย she asked the Gryphon, and the Gryphon answered, very nearly in the same words as before, รขย€ยœItรขย€ย™s all his fancy, that: he hasnรขย€ย™t got no sorrow, you know. Come on!รขย€ย So they went up to the Mock Turtle, who looked at them with large eyes full of tears, but said nothing. รขย€ยœThis here young lady,รขย€ย said the Gryphon, รขย€ยœshe wants for to know your history, she do.รขย€ย รขย€ยœIรขย€ย™ll tell it her,รขย€ย said the Mock Turtle in a deep, hollow tone: รขย€ยœsit down, both of you, and donรขย€ย™t speak a word till Iรขย€ย™ve finished.รขย€ย So they sat down, and nobody spoke for some minutes. Alice thought to herself, รขย€ยœI donรขย€ย™t see how he can _ever_ finish, if he doesnรขย€ย™t begin.รขย€ย But she waited patiently. รขย€ยœOnce,รขย€ย said the Mock Turtle at last, with a deep sigh, รขย€ยœI was a real Turtle.รขย€ย These words were followed by a very long silence, broken only by an occasional exclamation of รขย€ยœHjckrrh!รขย€ย from the Gryphon, and the constant heavy sobbing of the Mock Turtle. Alice was very nearly getting up and saying, รขย€ยœThank you, sir, for your interesting story,รขย€ย but she could not help thinking there _must_ be more to come, so she sat still and said nothing. รขย€ยœWhen we were little,รขย€ย the Mock Turtle went on at last, more calmly, though still sobbing a little now and then, รขย€ยœwe went to school in the sea. The master was an old Turtleรขย€ย”we used to call him Tortoiseรขย€ย”รขย€ย รขย€ยœWhy did you call him Tortoise, if he wasnรขย€ย™t one?รขย€ย Alice asked. รขย€ยœWe called him Tortoise because he taught us,รขย€ย said the Mock Turtle angrily: รขย€ยœreally you are very dull!รขย€ย รขย€ยœYou ought to be ashamed of yourself for asking such a simple question,รขย€ย added the Gryphon; and then they both sat silent and looked at poor Alice, who felt ready to sink into the earth. At last the Gryphon said to the Mock Turtle, รขย€ยœDrive on, old fellow! Donรขย€ย™t be all day about it!รขย€ย and he went on in these words: รขย€ยœYes, we went to school in the sea, though you maynรขย€ย™t believe itรขย€ย”รขย€ย รขย€ยœI never said I didnรขย€ย™t!รขย€ย interrupted Alice. รขย€ยœYou did,รขย€ย said the Mock Turtle. รขย€ยœHold your tongue!รขย€ย added the Gryphon, before Alice could speak again. The Mock Turtle went on. รขย€ยœWe had the best of educationsรขย€ย”in fact, we went to school every dayรขย€ย”รขย€ย รขย€ยœ_Iรขย€ย™ve_ been to a day-school, too,รขย€ย said Alice; รขย€ยœyou neednรขย€ย™t be so proud as all that.รขย€ย รขย€ยœWith extras?รขย€ย asked the Mock Turtle a little anxiously. รขย€ยœYes,รขย€ย said Alice, รขย€ยœwe learned French and music.รขย€ย รขย€ยœAnd washing?รขย€ย said the Mock Turtle. รขย€ยœCertainly not!รขย€ย said Alice indignantly. รขย€ยœAh! then yours wasnรขย€ย™t a really good school,รขย€ย said the Mock Turtle in a tone of great relief. รขย€ยœNow at _ours_ they had at the end of the bill, รขย€ย˜French, music, _and washing_รขย€ย”extra.รขย€ย™รขย€ย รขย€ยœYou couldnรขย€ย™t have wanted it much,รขย€ย said Alice; รขย€ยœliving at the bottom of the sea.รขย€ย รขย€ยœI couldnรขย€ย™t afford to learn it.รขย€ย said the Mock Turtle with a sigh. รขย€ยœI only took the regular course.รขย€ย รขย€ยœWhat was that?รขย€ย inquired Alice. รขย€ยœReeling and Writhing, of course, to begin with,รขย€ย the Mock Turtle replied; รขย€ยœand then the different branches of Arithmeticรขย€ย”Ambition, Distraction, Uglification, and Derision.รขย€ย รขย€ยœI never heard of รขย€ย˜Uglification,รขย€ย™รขย€ย Alice ventured to say. รขย€ยœWhat is it?รขย€ย The Gryphon lifted up both its paws in surprise. รขย€ยœWhat! Never heard of uglifying!รขย€ย it exclaimed. รขย€ยœYou know what to beautify is, I suppose?รขย€ย รขย€ยœYes,รขย€ย said Alice doubtfully: รขย€ยœit meansรขย€ย”toรขย€ย”makeรขย€ย”anythingรขย€ย”prettier.รขย€ย รขย€ยœWell, then,รขย€ย the Gryphon went on, รขย€ยœif you donรขย€ย™t know what to uglify is, you _are_ a simpleton.รขย€ย Alice did not feel encouraged to ask any more questions about it, so she turned to the Mock Turtle, and said รขย€ยœWhat else had you to learn?รขย€ย รขย€ยœWell, there was Mystery,รขย€ย the Mock Turtle replied, counting off the subjects on his flappers, รขย€ยœรขย€ย”Mystery, ancient and modern, with Seaography: then Drawlingรขย€ย”the Drawling-master was an old conger-eel, that used to come once a week: _he_ taught us Drawling, Stretching, and Fainting in Coils.รขย€ย รขย€ยœWhat was _that_ like?รขย€ย said Alice. รขย€ยœWell, I canรขย€ย™t show it you myself,รขย€ย the Mock Turtle said: รขย€ยœIรขย€ย™m too stiff. And the Gryphon never learnt it.รขย€ย รขย€ยœHadnรขย€ย™t time,รขย€ย said the Gryphon: รขย€ยœI went to the Classics master, though. He was an old crab, _he_ was.รขย€ย รขย€ยœI never went to him,รขย€ย the Mock Turtle said with a sigh: รขย€ยœhe taught Laughing and Grief, they used to say.รขย€ย รขย€ยœSo he did, so he did,รขย€ย said the Gryphon, sighing in his turn; and both creatures hid their faces in their paws. รขย€ยœAnd how many hours a day did you do lessons?รขย€ย said Alice, in a hurry to change the subject. รขย€ยœTen hours the first day,รขย€ย said the Mock Turtle: รขย€ยœnine the next, and so on.รขย€ย รขย€ยœWhat a curious plan!รขย€ย exclaimed Alice. รขย€ยœThatรขย€ย™s the reason theyรขย€ย™re called lessons,รขย€ย the Gryphon remarked: รขย€ยœbecause they lessen from day to day.รขย€ย This was quite a new idea to Alice, and she thought it over a little before she made her next remark. รขย€ยœThen the eleventh day must have been a holiday?รขย€ย รขย€ยœOf course it was,รขย€ย said the Mock Turtle. รขย€ยœAnd how did you manage on the twelfth?รขย€ย Alice went on eagerly. รขย€ยœThatรขย€ย™s enough about lessons,รขย€ย the Gryphon interrupted in a very decided tone: รขย€ยœtell her something about the games now.รขย€ย CHAPTER X. The Lobster Quadrille The Mock Turtle sighed deeply, and drew the back of one flapper across his eyes. He looked at Alice, and tried to speak, but for a minute or two sobs choked his voice. รขย€ยœSame as if he had a bone in his throat,รขย€ย said the Gryphon: and it set to work shaking him and punching him in the back. At last the Mock Turtle recovered his voice, and, with tears running down his cheeks, he went on again:รขย€ย” รขย€ยœYou may not have lived much under the seaรขย€ย”รขย€ย (รขย€ยœI havenรขย€ย™t,รขย€ย said Alice)รขย€ย”รขย€ยœand perhaps you were never even introduced to a lobsterรขย€ย”รขย€ย (Alice began to say รขย€ยœI once tastedรขย€ย”รขย€ย but checked herself hastily, and said รขย€ยœNo, neverรขย€ย) รขย€ยœรขย€ย”so you can have no idea what a delightful thing a Lobster Quadrille is!รขย€ย รขย€ยœNo, indeed,รขย€ย said Alice. รขย€ยœWhat sort of a dance is it?รขย€ย รขย€ยœWhy,รขย€ย said the Gryphon, รขย€ยœyou first form into a line along the sea-shoreรขย€ย”รขย€ย รขย€ยœTwo lines!รขย€ย cried the Mock Turtle. รขย€ยœSeals, turtles, salmon, and so on; then, when youรขย€ย™ve cleared all the jelly-fish out of the wayรขย€ย”รขย€ย รขย€ยœ_That_ generally takes some time,รขย€ย interrupted the Gryphon. รขย€ยœรขย€ย”you advance twiceรขย€ย”รขย€ย รขย€ยœEach with a lobster as a partner!รขย€ย cried the Gryphon. รขย€ยœOf course,รขย€ย the Mock Turtle said: รขย€ยœadvance twice, set to partnersรขย€ย”รขย€ย รขย€ยœรขย€ย”change lobsters, and retire in same order,รขย€ย continued the Gryphon. รขย€ยœThen, you know,รขย€ย the Mock Turtle went on, รขย€ยœyou throw theรขย€ย”รขย€ย รขย€ยœThe lobsters!รขย€ย shouted the Gryphon, with a bound into the air. รขย€ยœรขย€ย”as far out to sea as you canรขย€ย”รขย€ย รขย€ยœSwim after them!รขย€ย screamed the Gryphon. รขย€ยœTurn a somersault in the sea!รขย€ย cried the Mock Turtle, capering wildly about. รขย€ยœChange lobsters again!รขย€ย yelled the Gryphon at the top of its voice. รขย€ยœBack to land again, and thatรขย€ย™s all the first figure,รขย€ย said the Mock Turtle, suddenly dropping his voice; and the two creatures, who had been jumping about like mad things all this time, sat down again very sadly and quietly, and looked at Alice. รขย€ยœIt must be a very pretty dance,รขย€ย said Alice timidly. รขย€ยœWould you like to see a little of it?รขย€ย said the Mock Turtle. รขย€ยœVery much indeed,รขย€ย said Alice. รขย€ยœCome, letรขย€ย™s try the first figure!รขย€ย said the Mock Turtle to the Gryphon. รขย€ยœWe can do without lobsters, you know. Which shall sing?รขย€ย รขย€ยœOh, _you_ sing,รขย€ย said the Gryphon. รขย€ยœIรขย€ย™ve forgotten the words.รขย€ย So they began solemnly dancing round and round Alice, every now and then treading on her toes when they passed too close, and waving their forepaws to mark the time, while the Mock Turtle sang this, very slowly and sadly:รขย€ย” รขย€ยœWill you walk a little faster?รขย€ย said a whiting to a snail. รขย€ยœThereรขย€ย™s a porpoise close behind us, and heรขย€ย™s treading on my tail. See how eagerly the lobsters and the turtles all advance! They are waiting on the shingleรขย€ย”will you come and join the dance? Will you, wonรขย€ย™t you, will you, wonรขย€ย™t you, will you join the dance? Will you, wonรขย€ย™t you, will you, wonรขย€ย™t you, wonรขย€ย™t you join the dance? รขย€ยœYou can really have no notion how delightful it will be When they take us up and throw us, with the lobsters, out to sea!รขย€ย But the snail replied รขย€ยœToo far, too far!รขย€ย and gave a look askanceรขย€ย” Said he thanked the whiting kindly, but he would not join the dance. Would not, could not, would not, could not, would not join the dance. Would not, could not, would not, could not, could not join the dance. รขย€ยœWhat matters it how far we go?รขย€ย his scaly friend replied. รขย€ยœThere is another shore, you know, upon the other side. The further off from England the nearer is to Franceรขย€ย” Then turn not pale, beloved snail, but come and join the dance. Will you, wonรขย€ย™t you, will you, wonรขย€ย™t you, will you join the dance? Will you, wonรขย€ย™t you, will you, wonรขย€ย™t you, wonรขย€ย™t you join the dance?รขย€ย รขย€ยœThank you, itรขย€ย™s a very interesting dance to watch,รขย€ย said Alice, feeling very glad that it was over at last: รขย€ยœand I do so like that curious song about the whiting!รขย€ย รขย€ยœOh, as to the whiting,รขย€ย said the Mock Turtle, รขย€ยœtheyรขย€ย”youรขย€ย™ve seen them, of course?รขย€ย รขย€ยœYes,รขย€ย said Alice, รขย€ยœIรขย€ย™ve often seen them at dinnรขย€ย”รขย€ย she checked herself hastily. รขย€ยœI donรขย€ย™t know where Dinn may be,รขย€ย said the Mock Turtle, รขย€ยœbut if youรขย€ย™ve seen them so often, of course you know what theyรขย€ย™re like.รขย€ย รขย€ยœI believe so,รขย€ย Alice replied thoughtfully. รขย€ยœThey have their tails in their mouthsรขย€ย”and theyรขย€ย™re all over crumbs.รขย€ย รขย€ยœYouรขย€ย™re wrong about the crumbs,รขย€ย said the Mock Turtle: รขย€ยœcrumbs would all wash off in the sea. But they _have_ their tails in their mouths; and the reason isรขย€ย”รขย€ย here the Mock Turtle yawned and shut his eyes.รขย€ย”รขย€ยœTell her about the reason and all that,รขย€ย he said to the Gryphon. รขย€ยœThe reason is,รขย€ย said the Gryphon, รขย€ยœthat they _would_ go with the lobsters to the dance. So they got thrown out to sea. So they had to fall a long way. So they got their tails fast in their mouths. So they couldnรขย€ย™t get them out again. Thatรขย€ย™s all.รขย€ย รขย€ยœThank you,รขย€ย said Alice, รขย€ยœitรขย€ย™s very interesting. I never knew so much about a whiting before.รขย€ย รขย€ยœI can tell you more than that, if you like,รขย€ย said the Gryphon. รขย€ยœDo you know why itรขย€ย™s called a whiting?รขย€ย รขย€ยœI never thought about it,รขย€ย said Alice. รขย€ยœWhy?รขย€ย รขย€ยœ_It does the boots and shoes_,รขย€ย the Gryphon replied very solemnly. Alice was thoroughly puzzled. รขย€ยœDoes the boots and shoes!รขย€ย she repeated in a wondering tone. รขย€ยœWhy, what are _your_ shoes done with?รขย€ย said the Gryphon. รขย€ยœI mean, what makes them so shiny?รขย€ย Alice looked down at them, and considered a little before she gave her answer. รขย€ยœTheyรขย€ย™re done with blacking, I believe.รขย€ย รขย€ยœBoots and shoes under the sea,รขย€ย the Gryphon went on in a deep voice, รขย€ยœare done with a whiting. Now you know.รขย€ย รขย€ยœAnd what are they made of?รขย€ย Alice asked in a tone of great curiosity. รขย€ยœSoles and eels, of course,รขย€ย the Gryphon replied rather impatiently: รขย€ยœany shrimp could have told you that.รขย€ย รขย€ยœIf Iรขย€ย™d been the whiting,รขย€ย said Alice, whose thoughts were still running on the song, รขย€ยœIรขย€ย™d have said to the porpoise, รขย€ย˜Keep back, please: we donรขย€ย™t want _you_ with us!รขย€ย™รขย€ย รขย€ยœThey were obliged to have him with them,รขย€ย the Mock Turtle said: รขย€ยœno wise fish would go anywhere without a porpoise.รขย€ย รขย€ยœWouldnรขย€ย™t it really?รขย€ย said Alice in a tone of great surprise. รขย€ยœOf course not,รขย€ย said the Mock Turtle: รขย€ยœwhy, if a fish came to _me_, and told me he was going a journey, I should say รขย€ย˜With what porpoise?รขย€ย™รขย€ย รขย€ยœDonรขย€ย™t you mean รขย€ย˜purposeรขย€ย™?รขย€ย said Alice. รขย€ยœI mean what I say,รขย€ย the Mock Turtle replied in an offended tone. And the Gryphon added รขย€ยœCome, letรขย€ย™s hear some of _your_ adventures.รขย€ย รขย€ยœI could tell you my adventuresรขย€ย”beginning from this morning,รขย€ย said Alice a little timidly: รขย€ยœbut itรขย€ย™s no use going back to yesterday, because I was a different person then.รขย€ย รขย€ยœExplain all that,รขย€ย said the Mock Turtle. รขย€ยœNo, no! The adventures first,รขย€ย said the Gryphon in an impatient tone: รขย€ยœexplanations take such a dreadful time.รขย€ย So Alice began telling them her adventures from the time when she first saw the White Rabbit. She was a little nervous about it just at first, the two creatures got so close to her, one on each side, and opened their eyes and mouths so _very_ wide, but she gained courage as she went on. Her listeners were perfectly quiet till she got to the part about her repeating รขย€ยœ_You are old, Father William_,รขย€ย to the Caterpillar, and the words all coming different, and then the Mock Turtle drew a long breath, and said รขย€ยœThatรขย€ย™s very curious.รขย€ย รขย€ยœItรขย€ย™s all about as curious as it can be,รขย€ย said the Gryphon. รขย€ยœIt all came different!รขย€ย the Mock Turtle repeated thoughtfully. รขย€ยœI should like to hear her try and repeat something now. Tell her to begin.รขย€ย He looked at the Gryphon as if he thought it had some kind of authority over Alice. รขย€ยœStand up and repeat รขย€ย˜รขย€ย™_Tis the voice of the sluggard_,รขย€ย™รขย€ย said the Gryphon. รขย€ยœHow the creatures order one about, and make one repeat lessons!รขย€ย thought Alice; รขย€ยœI might as well be at school at once.รขย€ย However, she got up, and began to repeat it, but her head was so full of the Lobster Quadrille, that she hardly knew what she was saying, and the words came very queer indeed:รขย€ย” รขย€ยœรขย€ย™Tis the voice of the Lobster; I heard him declare, รขย€ยœYou have baked me too brown, I must sugar my hair.รขย€ย As a duck with its eyelids, so he with his nose Trims his belt and his buttons, and turns out his toes.รขย€ย [later editions continued as follows When the sands are all dry, he is gay as a lark, And will talk in contemptuous tones of the Shark, But, when the tide rises and sharks are around, His voice has a timid and tremulous sound.] รขย€ยœThatรขย€ย™s different from what _I_ used to say when I was a child,รขย€ย said the Gryphon. รขย€ยœWell, I never heard it before,รขย€ย said the Mock Turtle; รขย€ยœbut it sounds uncommon nonsense.รขย€ย Alice said nothing; she had sat down with her face in her hands, wondering if anything would _ever_ happen in a natural way again. รขย€ยœI should like to have it explained,รขย€ย said the Mock Turtle. รขย€ยœShe canรขย€ย™t explain it,รขย€ย said the Gryphon hastily. รขย€ยœGo on with the next verse.รขย€ย รขย€ยœBut about his toes?รขย€ย the Mock Turtle persisted. รขย€ยœHow _could_ he turn them out with his nose, you know?รขย€ย รขย€ยœItรขย€ย™s the first position in dancing.รขย€ย Alice said; but was dreadfully puzzled by the whole thing, and longed to change the subject. รขย€ยœGo on with the next verse,รขย€ย the Gryphon repeated impatiently: รขย€ยœit begins รขย€ย˜_I passed by his garden_.รขย€ย™รขย€ย Alice did not dare to disobey, though she felt sure it would all come wrong, and she went on in a trembling voice:รขย€ย” รขย€ยœI passed by his garden, and marked, with one eye, How the Owl and the Panther were sharing a pieรขย€ย”รขย€ย [later editions continued as follows The Panther took pie-crust, and gravy, and meat, While the Owl had the dish as its share of the treat. When the pie was all finished, the Owl, as a boon, Was kindly permitted to pocket the spoon: While the Panther received knife and fork with a growl, And concluded the banquetรขย€ย”] รขย€ยœWhat _is_ the use of repeating all that stuff,รขย€ย the Mock Turtle interrupted, รขย€ยœif you donรขย€ย™t explain it as you go on? Itรขย€ย™s by far the most confusing thing _I_ ever heard!รขย€ย รขย€ยœYes, I think youรขย€ย™d better leave off,รขย€ย said the Gryphon: and Alice was only too glad to do so. รขย€ยœShall we try another figure of the Lobster Quadrille?รขย€ย the Gryphon went on. รขย€ยœOr would you like the Mock Turtle to sing you a song?รขย€ย รขย€ยœOh, a song, please, if the Mock Turtle would be so kind,รขย€ย Alice replied, so eagerly that the Gryphon said, in a rather offended tone, รขย€ยœHm! No accounting for tastes! Sing her รขย€ย˜_Turtle Soup_,รขย€ย™ will you, old fellow?รขย€ย The Mock Turtle sighed deeply, and began, in a voice sometimes choked with sobs, to sing this:รขย€ย” รขย€ยœBeautiful Soup, so rich and green, Waiting in a hot tureen! Who for such dainties would not stoop? Soup of the evening, beautiful Soup! Soup of the evening, beautiful Soup! Beauรขย€ย”ootiful Sooรขย€ย”oop! Beauรขย€ย”ootiful Sooรขย€ย”oop! Sooรขย€ย”oop of the eรขย€ย”eรขย€ย”evening, Beautiful, beautiful Soup! รขย€ยœBeautiful Soup! Who cares for fish, Game, or any other dish? Who would not give all else for two p ennyworth only of beautiful Soup? Pennyworth only of beautiful Soup? Beauรขย€ย”ootiful Sooรขย€ย”oop! Beauรขย€ย”ootiful Sooรขย€ย”oop! Sooรขย€ย”oop of the eรขย€ย”eรขย€ย”evening, Beautiful, beautiรขย€ย”FUL SOUP!รขย€ย รขย€ยœChorus again!รขย€ย cried the Gryphon, and the Mock Turtle had just begun to repeat it, when a cry of รขย€ยœThe trialรขย€ย™s beginning!รขย€ย was heard in the distance. รขย€ยœCome on!รขย€ย cried the Gryphon, and, taking Alice by the hand, it hurried off, without waiting for the end of the song. รขย€ยœWhat trial is it?รขย€ย Alice panted as she ran; but the Gryphon only answered รขย€ยœCome on!รขย€ย and ran the faster, while more and more faintly came, carried on the breeze that followed them, the melancholy words:รขย€ย” รขย€ยœSooรขย€ย”oop of the eรขย€ย”eรขย€ย”evening, Beautiful, beautiful Soup!รขย€ย CHAPTER XI. Who Stole the Tarts? The King and Queen of Hearts were seated on their throne when they arrived, with a great crowd assembled about themรขย€ย”all sorts of little birds and beasts, as well as the whole pack of cards: the Knave was standing before them, in chains, with a soldier on each side to guard him; and near the King was the White Rabbit, with a trumpet in one hand, and a scroll of parchment in the other. In the very middle of the court was a table, with a large dish of tarts upon it: they looked so good, that it made Alice quite hungry to look at themรขย€ย”รขย€ยœI wish theyรขย€ย™d get the trial done,รขย€ย she thought, รขย€ยœand hand round the refreshments!รขย€ย But there seemed to be no chance of this, so she began looking at everything about her, to pass away the time. Alice had never been in a court of justice before, but she had read about them in books, and she was quite pleased to find that she knew the name of nearly everything there. รขย€ยœThatรขย€ย™s the judge,รขย€ย she said to herself, รขย€ยœbecause of his great wig.รขย€ย The judge, by the way, was the King; and as he wore his crown over the wig, (look at the frontispiece if you want to see how he did it,) he did not look at all comfortable, and it was certainly not becoming. รขย€ยœAnd thatรขย€ย™s the jury-box,รขย€ย thought Alice, รขย€ยœand those twelve creatures,รขย€ย (she was obliged to say รขย€ยœcreatures,รขย€ย you see, because some of them were animals, and some were birds,) รขย€ยœI suppose they are the jurors.รขย€ย She said this last word two or three times over to herself, being rather proud of it: for she thought, and rightly too, that very few little girls of her age knew the meaning of it at all. However, รขย€ยœjury-menรขย€ย would have done just as well. The twelve jurors were all writing very busily on slates. รขย€ยœWhat are they doing?รขย€ย Alice whispered to the Gryphon. รขย€ยœThey canรขย€ย™t have anything to put down yet, before the trialรขย€ย™s begun.รขย€ย รขย€ยœTheyรขย€ย™re putting down their names,รขย€ย the Gryphon whispered in reply, รขย€ยœfor fear they should forget them before the end of the trial.รขย€ย รขย€ยœStupid things!รขย€ย Alice began in a loud, indignant voice, but she stopped hastily, for the White Rabbit cried out, รขย€ยœSilence in the court!รขย€ย and the King put on his spectacles and looked anxiously round, to make out who was talking. Alice could see, as well as if she were looking over their shoulders, that all the jurors were writing down รขย€ยœstupid things!รขย€ย on their slates, and she could even make out that one of them didnรขย€ย™t know how to spell รขย€ยœstupid,รขย€ย and that he had to ask his neighbour to tell him. รขย€ยœA nice muddle their slatesรขย€ย™ll be in before the trialรขย€ย™s over!รขย€ย thought Alice. One of the jurors had a pencil that squeaked. This of course, Alice could _not_ stand, and she went round the court and got behind him, and very soon found an opportunity of taking it away. She did it so quickly that the poor little juror (it was Bill, the Lizard) could not make out at all what had become of it; so, after hunting all about for it, he was obliged to write with one finger for the rest of the day; and this was of very little use, as it left no mark on the slate. รขย€ยœHerald, read the accusation!รขย€ย said the King. On this the White Rabbit blew three blasts on the trumpet, and then unrolled the parchment scroll, and read as follows:รขย€ย” รขย€ยœThe Queen of Hearts, she made some tarts, All on a summer day: The Knave of Hearts, he stole those tarts, And took them quite away!รขย€ย รขย€ยœConsider your verdict,รขย€ย the King said to the jury. รขย€ยœNot yet, not yet!รขย€ย the Rabbit hastily interrupted. รขย€ยœThereรขย€ย™s a great deal to come before that!รขย€ย รขย€ยœCall the first witness,รขย€ย said the King; and the White Rabbit blew three blasts on the trumpet, and called out, รขย€ยœFirst witness!รขย€ย The first witness was the Hatter. He came in with a teacup in one hand and a piece of bread-and-butter in the other. รขย€ยœI beg pardon, your Majesty,รขย€ย he began, รขย€ยœfor bringing these in: but I hadnรขย€ย™t quite finished my tea when I was sent for.รขย€ย รขย€ยœYou ought to have finished,รขย€ย said the King. รขย€ยœWhen did you begin?รขย€ย The Hatter looked at the March Hare, who had followed him into the court, arm-in-arm with the Dormouse. รขย€ยœFourteenth of March, I _think_ it was,รขย€ย he said. รขย€ยœFifteenth,รขย€ย said the March Hare. รขย€ยœSixteenth,รขย€ย added the Dormouse. รขย€ยœWrite that down,รขย€ย the King said to the jury, and the jury eagerly wrote down all three dates on their slates, and then added them up, and reduced the answer to shillings and pence. รขย€ยœTake off your hat,รขย€ย the King said to the Hatter. รขย€ยœIt isnรขย€ย™t mine,รขย€ย said the Hatter. รขย€ยœ_Stolen!_รขย€ย the King exclaimed, turning to the jury, who instantly made a memorandum of the fact. รขย€ยœI keep them to sell,รขย€ย the Hatter added as an explanation; รขย€ยœIรขย€ย™ve none of my own. Iรขย€ย™m a hatter.รขย€ย Here the Queen put on her spectacles, and began staring at the Hatter, who turned pale and fidgeted. รขย€ยœGive your evidence,รขย€ย said the King; รขย€ยœand donรขย€ย™t be nervous, or Iรขย€ย™ll have you executed on the spot.รขย€ย This did not seem to encourage the witness at all: he kept shifting from one foot to the other, looking uneasily at the Queen, and in his confusion he bit a large piece out of his teacup instead of the bread-and-butter. Just at this moment Alice felt a very curious sensation, which puzzled her a good deal until she made out what it was: she was beginning to grow larger again, and she thought at first she would get up and leave the court; but on second thoughts she decided to remain where she was as long as there was room for her. รขย€ยœI wish you wouldnรขย€ย™t squeeze so.รขย€ย said the Dormouse, who was sitting next to her. รขย€ยœI can hardly breathe.รขย€ย รขย€ยœI canรขย€ย™t help it,รขย€ย said Alice very meekly: รขย€ยœIรขย€ย™m growing.รขย€ย รขย€ยœYouรขย€ย™ve no right to grow _here_,รขย€ย said the Dormouse. รขย€ยœDonรขย€ย™t talk nonsense,รขย€ย said Alice more boldly: รขย€ยœyou know youรขย€ย™re growing too.รขย€ย รขย€ยœYes, but _I_ grow at a reasonable pace,รขย€ย said the Dormouse: รขย€ยœnot in that ridiculous fashion.รขย€ย And he got up very sulkily and crossed over to the other side of the court. All this time the Queen had never left off staring at the Hatter, and, just as the Dormouse crossed the court, she said to one of the officers of the court, รขย€ยœBring me the list of the singers in the last concert!รขย€ย on which the wretched Hatter trembled so, that he shook both his shoes off. รขย€ยœGive your evidence,รขย€ย the King repeated angrily, รขย€ยœor Iรขย€ย™ll have you executed, whether youรขย€ย™re nervous or not.รขย€ย รขย€ยœIรขย€ย™m a poor man, your Majesty,รขย€ย the Hatter began, in a trembling voice, รขย€ยœรขย€ย”and I hadnรขย€ย™t begun my teaรขย€ย”not above a week or soรขย€ย”and what with the bread-and-butter getting so thinรขย€ย”and the twinkling of the teaรขย€ย”รขย€ย รขย€ยœThe twinkling of the _what?_รขย€ย said the King. รขย€ยœIt _began_ with the tea,รขย€ย the Hatter replied. รขย€ยœOf course twinkling begins with a T!รขย€ย said the King sharply. รขย€ยœDo you take me for a dunce? Go on!รขย€ย รขย€ยœIรขย€ย™m a poor man,รขย€ย the Hatter went on, รขย€ยœand most things twinkled after thatรขย€ย”only the March Hare saidรขย€ย”รขย€ย รขย€ยœI didnรขย€ย™t!รขย€ย the March Hare interrupted in a great hurry. รขย€ยœYou did!รขย€ย said the Hatter. รขย€ยœI deny it!รขย€ย said the March Hare. รขย€ยœHe denies it,รขย€ย said the King: รขย€ยœleave out that part.รขย€ย รขย€ยœWell, at any rate, the Dormouse saidรขย€ย”รขย€ย the Hatter went on, looking anxiously round to see if he would deny it too: but the Dormouse denied nothing, being fast asleep. รขย€ยœAfter that,รขย€ย continued the Hatter, รขย€ยœI cut some more bread-and-butterรขย€ย”รขย€ย รขย€ยœBut what did the Dormouse say?รขย€ย one of the jury asked. รขย€ยœThat I canรขย€ย™t remember,รขย€ย said the Hatter. รขย€ยœYou _must_ remember,รขย€ย remarked the King, รขย€ยœor Iรขย€ย™ll have you executed.รขย€ย The miserable Hatter dropped his teacup and bread-and-butter, and went down on one knee. รขย€ยœIรขย€ย™m a poor man, your Majesty,รขย€ย he began. รขย€ยœYouรขย€ย™re a _very_ poor _speaker_,รขย€ย said the King. Here one of the guinea-pigs cheered, and was immediately suppressed by the officers of the court. (As that is rather a hard word, I will just explain to you how it was done. They had a large canvas bag, which tied up at the mouth with strings: into this they slipped the guinea-pig, head first, and then sat upon it.) รขย€ยœIรขย€ย™m glad Iรขย€ย™ve seen that done,รขย€ย thought Alice. รขย€ยœIรขย€ย™ve so often read in the newspapers, at the end of trials, รขย€ยœThere was some attempts at applause, which was immediately suppressed by the officers of the court,รขย€ย and I never understood what it meant till now.รขย€ย รขย€ยœIf thatรขย€ย™s all you know about it, you may stand down,รขย€ย continued the King. รขย€ยœI canรขย€ย™t go no lower,รขย€ย said the Hatter: รขย€ยœIรขย€ย™m on the floor, as it is.รขย€ย รขย€ยœThen you may _sit_ down,รขย€ย the King replied. Here the other guinea-pig cheered, and was suppressed. รขย€ยœCome, that finished the guinea-pigs!รขย€ย thought Alice. รขย€ยœNow we shall get on better.รขย€ย รขย€ยœIรขย€ย™d rather finish my tea,รขย€ย said the Hatter, with an anxious look at the Queen, who was reading the list of singers. รขย€ยœYou may go,รขย€ย said the King, and the Hatter hurriedly left the court, without even waiting to put his shoes on. รขย€ยœรขย€ย”and just take his head off outside,รขย€ย the Queen added to one of the officers: but the Hatter was out of sight before the officer could get to the door. รขย€ยœCall the next witness!รขย€ย said the King. The next witness was the Duchessรขย€ย™s cook. She carried the pepper-box in her hand, and Alice guessed who it was, even before she got into the court, by the way the people near the door began sneezing all at once. รขย€ยœGive your evidence,รขย€ย said the King. รขย€ยœShanรขย€ย™t,รขย€ย said the cook. The King looked anxiously at the White Rabbit, who said in a low voice, รขย€ยœYour Majesty must cross-examine _this_ witness.รขย€ย รขย€ยœWell, if I must, I must,รขย€ย the King said, with a melancholy air, and, after folding his arms and frowning at the cook till his eyes were nearly out of sight, he said in a deep voice, รขย€ยœWhat are tarts made of?รขย€ย รขย€ยœPepper, mostly,รขย€ย said the cook. รขย€ยœTreacle,รขย€ย said a sleepy voice behind her. รขย€ยœCollar that Dormouse,รขย€ย the Queen shrieked out. รขย€ยœBehead that Dormouse! Turn that Dormouse out of court! Suppress him! Pinch him! Off with his whiskers!รขย€ย For some minutes the whole court was in confusion, getting the Dormouse turned out, and, by the time they had settled down again, the cook had disappeared. รขย€ยœNever mind!รขย€ย said the King, with an air of great relief. รขย€ยœCall the next witness.รขย€ย And he added in an undertone to the Queen, รขย€ยœReally, my dear, _you_ must cross-examine the next witness. It quite makes my forehead ache!รขย€ย Alice watched the White Rabbit as he fumbled over the list, feeling very curious to see what the next witness would be like, รขย€ยœรขย€ย”for they havenรขย€ย™t got much evidence _yet_,รขย€ย she said to herself. Imagine her surprise, when the White Rabbit read out, at the top of his shrill little voice, the name รขย€ยœAlice!รขย€ย CHAPTER XII. Aliceรขย€ย™s Evidence รขย€ยœHere!รขย€ย cried Alice, quite forgetting in the flurry of the moment how large she had grown in the last few minutes, and she jumped up in such a hurry that she tipped over the jury-box with the edge of her skirt, upsetting all the jurymen on to the heads of the crowd below, and there they lay sprawling about, reminding her very much of a globe of goldfish she had accidentally upset the week before. รขย€ยœOh, I _beg_ your pardon!รขย€ย she exclaimed in a tone of great dismay, and began picking them up again as quickly as she could, for the accident of the goldfish kept running in her head, and she had a vague sort of idea that they must be collected at once and put back into the jury-box, or they would die. รขย€ยœThe trial cannot proceed,รขย€ย said the King in a very grave voice, รขย€ยœuntil all the jurymen are back in their proper placesรขย€ย”_all_,รขย€ย he repeated with great emphasis, looking hard at Alice as he said so. Alice looked at the jury-box, and saw that, in her haste, she had put the Lizard in head downwards, and the poor little thing was waving its tail about in a melancholy way, being quite unable to move. She soon got it out again, and put it right; รขย€ยœnot that it signifies much,รขย€ย she said to herself; รขย€ยœI should think it would be _quite_ as much use in the trial one way up as the other.รขย€ย As soon as the jury had a little recovered from the shock of being upset, and their slates and pencils had been found and handed back to them, they set to work very diligently to write out a history of the accident, all except the Lizard, who seemed too much overcome to do anything but sit with its mouth open, gazing up into the roof of the court. รขย€ยœWhat do you know about this business?รขย€ย the King said to Alice. รขย€ยœNothing,รขย€ย said Alice. รขย€ยœNothing _whatever?_รขย€ย persisted the King. รขย€ยœNothing whatever,รขย€ย said Alice. รขย€ยœThatรขย€ย™s very important,รขย€ย the King said, turning to the jury. They were just beginning to write this down on their slates, when the White Rabbit interrupted: รขย€ยœ_Un_important, your Majesty means, of course,รขย€ย he said in a very respectful tone, but frowning and making faces at him as he spoke. รขย€ยœ_Un_important, of course, I meant,รขย€ย the King hastily said, and went on to himself in an undertone, รขย€ยœimportantรขย€ย”unimportantรขย€ย”unimportantรขย€ย”importantรขย€ย”รขย€ย as if he were trying which word sounded best. Some of the jury wrote it down รขย€ยœimportant,รขย€ย and some รขย€ยœunimportant.รขย€ย Alice could see this, as she was near enough to look over their slates; รขย€ยœbut it doesnรขย€ย™t matter a bit,รขย€ย she thought to herself. At this moment the King, who had been for some time busily writing in his note-book, cackled out รขย€ยœSilence!รขย€ย and read out from his book, รขย€ยœRule Forty-two. _All persons more than a mile high to leave the court_.รขย€ย Everybody looked at Alice. รขย€ยœ_Iรขย€ย™m_ not a mile high,รขย€ย said Alice. รขย€ยœYou are,รขย€ย said the King. รขย€ยœNearly two miles high,รขย€ย added the Queen. รขย€ยœWell, I shanรขย€ย™t go, at any rate,รขย€ย said Alice: รขย€ยœbesides, thatรขย€ย™s not a regular rule: you invented it just now.รขย€ย รขย€ยœItรขย€ย™s the oldest rule in the book,รขย€ย said the King. รขย€ยœThen it ought to be Number One,รขย€ย said Alice. The King turned pale, and shut his note-book hastily. รขย€ยœConsider your verdict,รขย€ย he said to the jury, in a low, trembling voice. รขย€ยœThereรขย€ย™s more evidence to come yet, please your Majesty,รขย€ย said the White Rabbit, jumping up in a great hurry; รขย€ยœthis paper has just been picked up.รขย€ย รขย€ยœWhatรขย€ย™s in it?รขย€ย said the Queen. รขย€ยœI havenรขย€ย™t opened it yet,รขย€ย said the White Rabbit, รขย€ยœbut it seems to be a letter, written by the prisoner toรขย€ย”to somebody.รขย€ย รขย€ยœIt must have been that,รขย€ย said the King, รขย€ยœunless it was written to nobody, which isnรขย€ย™t usual, you know.รขย€ย รขย€ยœWho is it directed to?รขย€ย said one of the jurymen. รขย€ยœIt isnรขย€ย™t directed at all,รขย€ย said the White Rabbit; รขย€ยœin fact, thereรขย€ย™s nothing written on the _outside_.รขย€ย He unfolded the paper as he spoke, and added รขย€ยœIt isnรขย€ย™t a letter, after all: itรขย€ย™s a set of verses.รขย€ย รขย€ยœAre they in the prisonerรขย€ย™s handwriting?รขย€ย asked another of the jurymen. รขย€ยœNo, theyรขย€ย™re not,รขย€ย said the White Rabbit, รขย€ยœand thatรขย€ย™s the queerest thing about it.รขย€ย (The jury all looked puzzled.) รขย€ยœHe must have imitated somebody elseรขย€ย™s hand,รขย€ย said the King. (The jury all brightened up again.) รขย€ยœPlease your Majesty,รขย€ย said the Knave, รขย€ยœI didnรขย€ย™t write it, and they canรขย€ย™t prove I did: thereรขย€ย™s no name signed at the end.รขย€ย รขย€ยœIf you didnรขย€ย™t sign it,รขย€ย said the King, รขย€ยœthat only makes the matter worse. You _must_ have meant some mischief, or else youรขย€ย™d have signed your name like an honest man.รขย€ย There was a general clapping of hands at this: it was the first really clever thing the King had said that day. รขย€ยœThat _proves_ his guilt,รขย€ย said the Queen. รขย€ยœIt proves nothing of the sort!รขย€ย said Alice. รขย€ยœWhy, you donรขย€ย™t even know what theyรขย€ย™re about!รขย€ย รขย€ยœRead them,รขย€ย said the King. The White Rabbit put on his spectacles. รขย€ยœWhere shall I begin, please your Majesty?รขย€ย he asked. รขย€ยœBegin at the beginning,รขย€ย the King said gravely, รขย€ยœand go on till you come to the end: then stop.รขย€ย These were the verses the White Rabbit read:รขย€ย” รขย€ยœThey told me you had been to her, And mentioned me to him: She gave me a good character, But said I could not swim. He sent them word I had not gone (We know it to be true): If she should push the matter on, What would become of you? I gave her one, they gave him two, You gave us three or more; They all returned from him to you, Though they were mine before. If I or she should chance to be Involved in this affair, He trusts to you to set them free, Exactly as we were. My notion was that you had been (Before she had this fit) An obstacle that came between Him, and ourselves, and it. Donรขย€ย™t let him know she liked them best, For this must ever be A secret, kept from all the rest, Between yourself and me.รขย€ย รขย€ยœThatรขย€ย™s the most important piece of evidence weรขย€ย™ve heard yet,รขย€ย said the King, rubbing his hands; รขย€ยœso now let the juryรขย€ย”รขย€ย รขย€ยœIf any one of them can explain it,รขย€ย said Alice, (she had grown so large in the last few minutes that she wasnรขย€ย™t a bit afraid of interrupting him,) รขย€ยœIรขย€ย™ll give him sixpence. _I_ donรขย€ย™t believe thereรขย€ย™s an atom of meaning in it.รขย€ย The jury all wrote down on their slates, รขย€ยœ_She_ doesnรขย€ย™t believe thereรขย€ย™s an atom of meaning in it,รขย€ย but none of them attempted to explain the paper. รขย€ยœIf thereรขย€ย™s no meaning in it,รขย€ย said the King, รขย€ยœthat saves a world of trouble, you know, as we neednรขย€ย™t try to find any. And yet I donรขย€ย™t know,รขย€ย he went on, spreading out the verses on his knee, and looking at them with one eye; รขย€ยœI seem to see some meaning in them, after all. รขย€ยœรขย€ย”_said I could not swim_รขย€ย”รขย€ย you canรขย€ย™t swim, can you?รขย€ย he added, turning to the Knave. The Knave shook his head sadly. รขย€ยœDo I look like it?รขย€ย he said. (Which he certainly did _not_, being made entirely of cardboard.) รขย€ยœAll right, so far,รขย€ย said the King, and he went on muttering over the verses to himself: รขย€ยœรขย€ย˜_We know it to be true_รขย€ย”รขย€ย™ thatรขย€ย™s the jury, of courseรขย€ย”รขย€ย˜_I gave her one, they gave him two_รขย€ย”รขย€ย™ why, that must be what he did with the tarts, you knowรขย€ย”รขย€ย รขย€ยœBut, it goes on รขย€ย˜_they all returned from him to you_,รขย€ย™รขย€ย said Alice. รขย€ยœWhy, there they are!รขย€ย said the King triumphantly, pointing to the tarts on the table. รขย€ยœNothing can be clearer than _that_. Then againรขย€ย”รขย€ย˜_before she had this fit_รขย€ย”รขย€ย™ you never had fits, my dear, I think?รขย€ย he said to the Queen. รขย€ยœNever!รขย€ย said the Queen furiously, throwing an inkstand at the Lizard as she spoke. (The unfortunate little Bill had left off writing on his slate with one finger, as he found it made no mark; but he now hastily began again, using the ink, that was trickling down his face, as long as it lasted.) รขย€ยœThen the words donรขย€ย™t _fit_ you,รขย€ย said the King, looking round the court with a smile. There was a dead silence. รขย€ยœItรขย€ย™s a pun!รขย€ย the King added in an offended tone, and everybody laughed, รขย€ยœLet the jury consider their verdict,รขย€ย the King said, for about the twentieth time that day. รขย€ยœNo, no!รขย€ย said the Queen. รขย€ยœSentence firstรขย€ย”verdict afterwards.รขย€ย รขย€ยœStuff and nonsense!รขย€ย said Alice loudly. รขย€ยœThe idea of having the sentence first!รขย€ย รขย€ยœHold your tongue!รขย€ย said the Queen, turning purple. รขย€ยœI wonรขย€ย™t!รขย€ย said Alice. รขย€ยœOff with her head!รขย€ย the Queen shouted at the top of her voice. Nobody moved. รขย€ยœWho cares for you?รขย€ย said Alice, (she had grown to her full size by this time.) รขย€ยœYouรขย€ย™re nothing but a pack of cards!รขย€ย At this the whole pack rose up into the air, and came flying down upon her: she gave a little scream, half of fright and half of anger, and tried to beat them off, and found herself lying on the bank, with her head in the lap of her sister, who was gently brushing away some dead leaves that had fluttered down from the trees upon her face. รขย€ยœWake up, Alice dear!รขย€ย said her sister; รขย€ยœWhy, what a long sleep youรขย€ย™ve had!รขย€ย รขย€ยœOh, Iรขย€ย™ve had such a curious dream!รขย€ย said Alice, and she told her sister, as well as she could remember them, all these strange Adventures of hers that you have just been reading about; and when she had finished, her sister kissed her, and said, รขย€ยœIt _was_ a curious dream, dear, certainly: but now run in to your tea; itรขย€ย™s getting late.รขย€ย So Alice got up and ran off, thinking while she ran, as well she might, what a wonderful dream it had been. But her sister sat still just as she left her, leaning her head on her hand, watching the setting sun, and thinking of little Alice and all her wonderful Adventures, till she too began dreaming after a fashion, and this was her dream:รขย€ย” First, she dreamed of little Alice herself, and once again the tiny hands were clasped upon her knee, and the bright eager eyes were looking up into hersรขย€ย”she could hear the very tones of her voice, and see that queer little toss of her head to keep back the wandering hair that _would_ always get into her eyesรขย€ย”and still as she listened, or seemed to listen, the whole place around her became alive with the strange creatures of her little sisterรขย€ย™s dream. The long grass rustled at her feet as the White Rabbit hurried byรขย€ย”the frightened Mouse splashed his way through the neighbouring poolรขย€ย”she could hear the rattle of the teacups as the March Hare and his friends shared their never-ending meal, and the shrill voice of the Queen ordering off her unfortunate guests to executionรขย€ย”once more the pig-baby was sneezing on the Duchessรขย€ย™s knee, while plates and dishes crashed around itรขย€ย”once more the shriek of the Gryphon, the squeaking of the Lizardรขย€ย™s slate-pencil, and the choking of the suppressed guinea-pigs, filled the air, mixed up with the distant sobs of the miserable Mock Turtle. So she sat on, with closed eyes, and half believed herself in Wonderland, though she knew she had but to open them again, and all would change to dull realityรขย€ย”the grass would be only rustling in the wind, and the pool rippling to the waving of the reedsรขย€ย”the rattling teacups would change to tinkling sheep-bells, and the Queenรขย€ย™s shrill cries to the voice of the shepherd boyรขย€ย”and the sneeze of the baby, the shriek of the Gryphon, and all the other queer noises, would change (she knew) to the confused clamour of the busy farm-yardรขย€ย”while the lowing of the cattle in the distance would take the place of the Mock Turtleรขย€ย™s heavy sobs. Lastly, she pictured to herself how this same little sister of hers would, in the after-time, be herself a grown woman; and how she would keep, through all her riper years, the simple and loving heart of her childhood: and how she would gather about her other little children, and make _their_ eyes bright and eager with many a strange tale, perhaps even with the dream of Wonderland of long ago: and how she would feel with all their simple sorrows, and find a pleasure in all their simple joys, remembering her own child-life, and the happy summer days. THE END End of Project Gutenbergรขย€ย™s Aliceรขย€ย™s Adventures in Wonderland, by Lewis Carroll *** END OF THIS PROJECT GUTENBERG EBOOK ALICEรขย€ย™S ADVENTURES IN WONDERLAND *** ***** This file should be named 11-0.txt or 11-0.zip ***** This and all associated files of various formats will be found in: http://www.gutenberg.org/1/11/ Produced by Arthur DiBianca and David Widger Updated editions will replace the previous one--the old editions will be renamed. Creating the works from print editions not protected by U.S. copyright law means that no one owns a United States copyright in these works, so the Foundation (and you!) can copy and distribute it in the United States without permission and without paying copyright royalties. Special rules, set forth in the General Terms of Use part of this license, apply to copying and distributing Project Gutenberg-tm electronic works to protect the PROJECT GUTENBERG-tm concept and trademark. 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Most people start at our Web site which has the main PG search facility: www.gutenberg.org This Web site includes information about Project Gutenberg-tm, including how to make donations to the Project Gutenberg Literary Archive Foundation, how to help produce our new eBooks, and how to subscribe to our email newsletter to hear about new eBooks. ###Markdown Define a function to plot word frequencies ###Code def plot_word_frequency(words, top_n=10): word_freq = FreqDist(words) labels = [element[0] for element in word_freq.most_common(top_n)] counts = [element[1] for element in word_freq.most_common(top_n)] plot = sns.barplot(labels, counts) return plot ###Output _____no_output_____ ###Markdown Plot words frequencies present in the gutenberg corpus ###Code alice_words = alice.text.split() plot_word_frequency(alice_words, 15) ###Output _____no_output_____ ###Markdown Stopwords Import stopwords from nltk ###Code from nltk.corpus import stopwords ###Output _____no_output_____ ###Markdown Look at the list of stopwords ###Code import nltk nltk.download('stopwords') print(stopwords.words('english')) ###Output ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"] ###Markdown Let's remove stopwords from the following piece of text. ###Code sample_text = "the great aim of education is not knowledge but action" ###Output _____no_output_____ ###Markdown Break text into words ###Code sample_words = sample_text.split() print(sample_words) ###Output ['the', 'great', 'aim', 'of', 'education', 'is', 'not', 'knowledge', 'but', 'action'] ###Markdown Remove stopwords ###Code sample_words = [word for word in sample_words if word not in stopwords.words('english')] print(sample_words) ###Output ['great', 'aim', 'education', 'knowledge', 'action'] ###Markdown Join words back to sentence ###Code sample_text = " ".join(sample_words) print(sample_text) ###Output great aim education knowledge action ###Markdown Removing stopwords in the genesis corpus ###Code no_stops = [word for word in alice_words if word not in stopwords.words("english")] plot_word_frequency(no_stops, 10) ###Output _____no_output_____
01 Machine Learning/scikit_examples_jupyter/gaussian_process/plot_gpr_noisy_targets.ipynb
###Markdown =========================================================Gaussian Processes regression: basic introductory example=========================================================A simple one-dimensional regression example computed in two different ways:1. A noise-free case2. A noisy case with known noise-level per datapointIn both cases, the kernel's parameters are estimated using the maximumlikelihood principle.The figures illustrate the interpolating property of the Gaussian Processmodel as well as its probabilistic nature in the form of a pointwise 95%confidence interval.Note that the parameter ``alpha`` is applied as a Tikhonovregularization of the assumed covariance between the training points. ###Code print(__doc__) # Author: Vincent Dubourg <[email protected]> # Jake Vanderplas <[email protected]> # Jan Hendrik Metzen <[email protected]>s # License: BSD 3 clause import numpy as np from matplotlib import pyplot as plt from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C np.random.seed(1) def f(x): """The function to predict.""" return x * np.sin(x) # ---------------------------------------------------------------------- # First the noiseless case X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T # Observations y = f(X).ravel() # Mesh the input space for evaluations of the real function, the prediction and # its MSE x = np.atleast_2d(np.linspace(0, 10, 1000)).T # Instantiate a Gaussian Process model kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2)) gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9) # Fit to data using Maximum Likelihood Estimation of the parameters gp.fit(X, y) # Make the prediction on the meshed x-axis (ask for MSE as well) y_pred, sigma = gp.predict(x, return_std=True) # Plot the function, the prediction and the 95% confidence interval based on # the MSE plt.figure() plt.plot(x, f(x), 'r:', label=r'$f(x) = x\,\sin(x)$') plt.plot(X, y, 'r.', markersize=10, label='Observations') plt.plot(x, y_pred, 'b-', label='Prediction') plt.fill(np.concatenate([x, x[::-1]]), np.concatenate([y_pred - 1.9600 * sigma, (y_pred + 1.9600 * sigma)[::-1]]), alpha=.5, fc='b', ec='None', label='95% confidence interval') plt.xlabel('$x$') plt.ylabel('$f(x)$') plt.ylim(-10, 20) plt.legend(loc='upper left') # ---------------------------------------------------------------------- # now the noisy case X = np.linspace(0.1, 9.9, 20) X = np.atleast_2d(X).T # Observations and noise y = f(X).ravel() dy = 0.5 + 1.0 * np.random.random(y.shape) noise = np.random.normal(0, dy) y += noise # Instantiate a Gaussian Process model gp = GaussianProcessRegressor(kernel=kernel, alpha=dy ** 2, n_restarts_optimizer=10) # Fit to data using Maximum Likelihood Estimation of the parameters gp.fit(X, y) # Make the prediction on the meshed x-axis (ask for MSE as well) y_pred, sigma = gp.predict(x, return_std=True) # Plot the function, the prediction and the 95% confidence interval based on # the MSE plt.figure() plt.plot(x, f(x), 'r:', label=r'$f(x) = x\,\sin(x)$') plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label='Observations') plt.plot(x, y_pred, 'b-', label='Prediction') plt.fill(np.concatenate([x, x[::-1]]), np.concatenate([y_pred - 1.9600 * sigma, (y_pred + 1.9600 * sigma)[::-1]]), alpha=.5, fc='b', ec='None', label='95% confidence interval') plt.xlabel('$x$') plt.ylabel('$f(x)$') plt.ylim(-10, 20) plt.legend(loc='upper left') plt.show() ###Output _____no_output_____
salomon_exp/FAST_AI.ipynb
###Markdown **Fast AI Experements Based on [this](https://towardsdatascience.com/transfer-learning-using-the-fastai-library-d686b238213e) blog**trained models in this [google Drive Folder](https://drive.google.com/open?id=1bW0UjVudEarP5qTToxwDtcIKXX9iuM9L) ###Code # from google.colab import drive # drive.mount('/content/drive/') ###Output _____no_output_____ ###Markdown get models from drive ###Code #DATASET # !unzip drive/My\ Drive/ammi-2020-convnets.zip # #PSUDO DATASET # # # # !unzip drive/My\ Drive/data/random.zip -d here # # GET SAVED MODELS HERE # !mkdir models/ # !cp -r drive/My\ Drive/data/models/* models/. # PUSH TRAINED MODELS TO GOOGLE DRIVE # !cp -r models/* drive/My\ Drive/data/models/. # !pip install pretrainedmodels # !pip uninstall torch torchvision -y # !pip install torch==1.4.0 torchvision==0.5.0 ###Output _____no_output_____ ###Markdown Importing Fast AI library ###Code import os import pretrainedmodels from tqdm import tqdm from fastai import * from fastai.vision import * import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.metrics import auc,roc_curve from math import floor ###Output _____no_output_____ ###Markdown Looking at the data ###Code # train_path = "./train/train" # test_path = "./test/test/0" data_path = "./data/train/train" test_path = "./data/test/test" extraimage_path = "./data/extraimages/extraimages" # !ls def get_labels(file_path): dir_name = os.path.dirname(file_path) split_dir_name = dir_name.split("/") dir_levels = len(split_dir_name) label = split_dir_name[dir_levels - 1] return(label) # get_labels("./train/train/cgm/train-cgm-528.jpg") from glob import glob imagePatches = glob("./data/train/train/*/*.*", recursive=True) test_imagePatches = glob("./data/extraimages/extraimages/*.*", recursive=True) imagePatches[0:10] path="" transform_kwargs = {"do_flip": True, "flip_vert": True, "max_rotate": 180, "max_zoom": 1.1, "max_lighting": 0.2, "max_warp": 0.2, "p_affine": 0.75, "p_lighting": 0.7} tfms = get_transforms(**transform_kwargs) data = ImageDataBunch.from_name_func(path, imagePatches, label_func=get_labels, size=448, bs=16,num_workers=2,ds_tfms=tfms,valid_pct=0.0 ).normalize(imagenet_stats) data.show_batch(rows=3, figsize=(8,8)) data,4525+1131 ###Output _____no_output_____ ###Markdown Transfer learning using a pre-trained model: ResNet 50 ###Code model_name = 'se_resnext101_32x4d_2' model_name = 'se_resnext101_32x4d_2' def get_cadene_model(pretrained=True, model_name='se_resnext101_32x4d'): if pretrained: arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet') else: arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained=None) return arch learn = cnn_learner(data, get_cadene_model, metrics=[error_rate,accuracy]) learn.lr_find() learn.recorder.plot() # learn.apply_dropout(0.2) learn.fit_one_cycle(15) # learn.apply_dropout(0.2) learn.fit_one_cycle(15) learn.save(model_name) ###Output _____no_output_____
notebooks/knowledgebase_comparison.ipynb
###Markdown Data Structuring and Pruning ###Code ## python 2.7 setup # !pip install pathlib ## we need a python2 version of pyupset # !pip uninstall -y pyupset # !pip install https://github.com/agitter/py-upset/archive/master.zip # !pip install seaborn from __future__ import division # where are data files loaded %env DATAPATH=/util/elastic/ PAPER_SOURCES=['brca.json','cgi.json', 'civic.json', 'jax.json', 'molecularmatch.json', 'oncokb.json', 'pmkb.json'] # Load datasets import json import pathlib import os data_path = pathlib.Path(os.getenv('DATAPATH','/Users/awagner/Workspace/git/g2p-aggregator/data/local/0.8')) harvested_sources = dict() for path in [list(data_path.glob(file))[0] for file in PAPER_SOURCES]: source = path.parts[-1].split('.')[0] with path.open() as json_data: # harvested_sources[source] = json.load(json_data) <- this should work, but doesn't due to malformed json # what follows is a hack to reassemble into proper JSON object associations = list() for line in json_data: associations.append(json.loads(line)) # resume intended function harvested_sources[source] = associations # Standardize structure and merge files all_associations = list() for source in harvested_sources: for entry in harvested_sources[source]: entry['raw'] = entry.pop(source) all_associations.append(entry) len(all_associations) from collections import Counter def report_groups(associations): groups = Counter() for association in associations: groups[association['source']] += 1 total = sum(groups.values()) for group in sorted(groups): print("{}: {} ({:.1f}%)".format(group, groups[group], groups[group] / len(harvested_sources[group]) * 100)) print("Total: {} ({:.1f}%)".format(total, total / len(all_associations) * 100)) report_groups(all_associations) # Associations with more than 1 feature multi_featured = [x for x in all_associations if len(x['features']) > 1] len(multi_featured) / len(all_associations) report_groups(multi_featured) # Associations with feature name lists listed_feature_names = [x for x in all_associations if isinstance(x['feature_names'], list)] len(listed_feature_names) / len(all_associations) report_groups(listed_feature_names) len([x for x in listed_feature_names if len(x['feature_names']) >1 ]) ###Output _____no_output_____ ###Markdown Feature coordinate filteringWhat follows is a detailed look at associations without start and end coordinates after normalization, and a set of regular expression filters to separate out these associations into chunks that can be annotated with gene- or exon-level coordinates, as appropriate. ###Code # Associations with coordinate features coord_featured = list() no_coord_featured = list() for association in all_associations: c = 0 for feature in association['features']: if ('start' in feature) and ('end') in feature: coord_featured.append(association) break else: c+=1 if c == len(association['features']): no_coord_featured.append(association) report_groups(coord_featured) report_groups(no_coord_featured) # First association has feature, but no end coord harvested_sources['cgi'][0]['features'] # Associations with partial coordinate features partial_coord_featured = list() no_partial_coord_featured = list() for association in all_associations: c = 0 for feature in association['features']: if ('start' in feature): partial_coord_featured.append(association) break else: c+=1 if c == len(association['features']): no_partial_coord_featured.append(association) report_groups(no_partial_coord_featured) def get_feature_names(associations): return (list(map(lambda x: x['feature_names'], associations))) feature_names = get_feature_names(no_partial_coord_featured) no_partial_coord_featured_no_feature_names = [x for x in no_partial_coord_featured if x['feature_names'] is None] no_partial_coord_featured_with_feature_names = [x for x in no_partial_coord_featured if x['feature_names'] is not None] report_groups(no_partial_coord_featured_no_feature_names) # All of these have exactly 1 gene name len([x['genes'] for x in no_partial_coord_featured_no_feature_names if len(x['genes']) == 1]) report_groups(no_partial_coord_featured_with_feature_names) import re def test_curls(associations): # utility to generate curl commands names = [] for a in associations: for f in a['features']: parts = re.split(' +|:',f['description'].strip()) names.append(tuple(parts)) names = list(set(names)) feature_lookups = [t for t in names if len(t) > 1] if len(feature_lookups) > 0: print '# curl commands to find feature location' for t in feature_lookups: print "curl -s 'http://myvariant.info/v1/query?q={}%20{}' | jq '.hits[0] | {{name: \"{} {}\", referenceName: \"GRCh37\", chromosome: .chrom, start: .hg19.start, end: .hg19.end, ref: .vcf.ref, alt: .vcf.alt }}'".format(t[0],t[1], t[0],t[1]) gene_lookups = [t for t in names if len(t) == 1] if len(gene_lookups) > 0: print '# curl commands to find gene location' for t in gene_lookups: print "curl -s 'http://mygene.info/v3/query?q={}&fields=genomic_pos_hg19' | jq .hits[0].genomic_pos_hg19".format(t[0]) def feature_filter(re_obj, associations): # report matches and return non-matches found = list(filter(lambda x: re_obj.search(x['feature_names']) is not None, associations)) not_found = list(filter(lambda x: re_obj.search(x['feature_names']) is None, associations)) report_groups(found) # comment following line to suppress curl test commands test_curls(found) return(not_found, found) amp_re = re.compile(r'(amplification)|(loss)|(amp)', re.IGNORECASE) (remainder, found) = feature_filter(amp_re, no_partial_coord_featured_with_feature_names) fusion_re = re.compile(r'(\w{2,}-\w{2,})|(fusion)', re.IGNORECASE) (r2, found) = feature_filter(fusion_re, remainder) ppm_re = re.compile(r'\w+(:| )[a-z]\d+[a-z]?(fs\*?)?$', re.IGNORECASE) (r3, found) = feature_filter(ppm_re, r2) indel_re = re.compile(r'\w+(:| )\w+(ins\w+)|(del($|ins\w+))|(dup$)') (r4, found) = feature_filter(indel_re, r3) bucket_re = re.compile(r'[A-Z0-9]+( (in)?act)?( oncogenic)? mut((ant)|(ation))?$') (r5,found) = feature_filter(bucket_re, r4) exon_re = re.compile(r'exon', re.IGNORECASE) (r6,found) = feature_filter(exon_re, r5) expression_re = re.compile(r'(exp)|(^\w+ (pos(itive)?)|(neg(ative)?)|(biallelic inactivation)$)|(truncating)|(deletion)', re.IGNORECASE) (r7, found) = feature_filter(expression_re, r6) report_groups(r7) get_feature_names([x for x in r7 if x['source'] == 'cgi']) ###Output _____no_output_____ ###Markdown Knowledgebase Comparison Genes ###Code from collections import defaultdict def genes_by_source(associations): source_genes = defaultdict(set) for association in associations: source_genes[association['source']].update(association['genes']) return source_genes s = genes_by_source(all_associations) import pyupset as pyu import pandas as pd %matplotlib inline def plot_overlap(set_dict): d = {g: pd.DataFrame(list(set_dict[g])) for g in set_dict} pyu.plot(d, inters_size_bounds=(3, 400000)) # omitting BRCA (only 2 genes) s = {k: v for k, v in s.items() if k != 'brca'} plot_overlap(s) # Genes observed in all knowledgebases # # features provenance provenance = {'unknown_provenance':0} unknown_provenance = [] for association in all_associations: c = 0 for feature in association['features']: if 'provenance_rule' in feature: if feature['provenance_rule'] not in provenance: provenance[feature['provenance_rule']] = 0 provenance[feature['provenance_rule']] += 1 else: provenance['unknown_provenance'] += 1 unknown_provenance.append([association['source'], feature]) provenance for unknown in unknown_provenance: print unknown[0], unknown[1]['description'] ###Output cgi FLT3-ITD cgi FLT3-ITD cgi FLT3-ITD cgi FLT3-ITD cgi MAP2K1 (Q56P,P124S,P124L;C121S) cgi FLT3-ITD cgi JAK1 (S646F;R683) cgi MLL2 oncogenic mutation cgi MET (Y1230C;Y1235D) jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRCA2 del jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRAF V600E/K jax BRCA2 del jax BRCA2 del jax BRCA2 del civic HLA-C COPY-NEUTRAL LOSS OF HETEROZYGOSITY molecularmatch MET MET c.2888-52_2927delGGGGCCCATGATAGCCGTCTTTAACAAGCTCTTTCTTTCTCTCTGTTTTAAGATCTGGGCAGTGAATTAGTTCGCTACGATGCAAGAGTACAinsCC molecularmatch PGR ER/PR positive molecularmatch PGR ER/PR positive molecularmatch MET MET c.2888-6_2888-2delTTTAAinsG molecularmatch MET MET c.2888-15_2915delTCTCTCTGTTTTAAGATCTGGGCAGTGAATTAGTTCGCTACGAinsT molecularmatch PGR ER/PR positive molecularmatch PGR ER/PR positive molecularmatch PGR ER/PR positive molecularmatch MET MET c.2888-28_2888-3delCAAGCTCTTTCTTTCTCTCTGTTTTAinsAAAC molecularmatch MET MET c.2888-17_2888-1delTTTCTCTCTGTTTTAAGinsAA molecularmatch MET MET c.3028+2_3028+4delTATinsACC molecularmatch VEGF molecularmatch BRCA molecularmatch VEGFR molecularmatch VEGF molecularmatch VEGFR molecularmatch PGR ER/PR positive molecularmatch PGR ER/PR positive molecularmatch MET MET c.2888-5_2890delTTAAGATCinsATA molecularmatch BRCA1 BRCA1 Q1756fs molecularmatch PDGFR molecularmatch VEGFR molecularmatch PDGFR molecularmatch PGR ER/PR positive molecularmatch VEGF molecularmatch MET MET c.2888-33_2888-7delTTTAACAAGCTCTTTCTTTCTCTCTGTinsTTAAAACTG molecularmatch PDGFR molecularmatch VEGFR oncokb HLA-B Truncating Mutations oncokb HLA-A 596_619splice oncokb Other Biomarkers Microsatellite Instability-High oncokb WHSC1L1 Amplification oncokb Other Biomarkers Microsatellite Instability-High oncokb FAM58A Truncating Mutations oncokb HLA-A Truncating Mutations oncokb Other Biomarkers Microsatellite Instability-High
geolocalization-and-clustering.ipynb
###Markdown Geolocalization analysis and data vizualization__Scope__: Optimize leaflet distribution viewing customers on the map Import libraries ###Code import pandas as pd # data Extract Transform Load import numpy as np # linear algebra import matplotlib.pyplot as plt # plotting import matplotlib.image as mpimg # plotting %matplotlib inline import sys # system operations # machine learning libs from sklearn.preprocessing import normalize from sklearn.cluster import AgglomerativeClustering import scipy.cluster.hierarchy as shc ###Output _____no_output_____ ###Markdown Load Data Frame ###Code main_path =sys.path[0] path_data ="/data_geovisitors/onlygeo_with_ip.csv" df = pd.read_csv(main_path+path_data,';') df.head() # change columns name df[df.columns[0]] = 'date' df[df.columns[1]] = 'time' df[df.columns[2]] = 'geolock' ###Output _____no_output_____ ###Markdown Get the map for your boundries. Insert the max and min of latitude and longitude that I choosed in this site and download the map. With OpenStreetMap.org I download the map that I want for my coordinates. Select Layers / OVNKarte option at right for more address into the map.**Sites**.- Tutorial how download the map:https://medium.com/@abuqassim115/thanks-for-your-response-frank-fb869824ede2- OpenStreetMap.orghttps://www.openstreetmap.org/exportmap=5/51.500/-0.100**Examples**.- North-Italy zonehttps://www.openstreetmap.org/map=6/43.077/8.262- Specific italian city : Montebellunahttps://www.openstreetmap.org/map=13/45.7745/12.0216Take the coordinates data in the Data Frame only from the previous set boundries. From street address to coordinateshttps://developers-dot-devsite-v2-prod.appspot.com/maps/documentation/utils/geocoder Data Structure ###Code path_map_img = main_path+"/img/only-map/" global cities cities = { 'padova':{'map_path':path_map_img+'padova.png', 'lat_max':45.4476, 'lat_min':45.3657, 'lng_max':11.9868, 'lng_min':11.7942, 'coordinate_store':{'lat':45.412749, 'lng':11.919453 } }, 'montebelluna':{'map_path':path_map_img+'montebelluna.png', 'lat_max':45.7951, 'lat_min':45.7544, 'lng_max':12.0811, 'lng_min':12.0063, 'coordinate_store':{'lat':45.779023, 'lng':12.06014 } } } def extract_boundries(data_cities,city_name): """ extract latitude and longitude min/max from the data set with dictionary keys """ lat_max = data_cities[city_name]['lat_max'] lat_min = data_cities[city_name]['lat_min'] lng_max = data_cities[city_name]['lng_max'] lng_min = data_cities[city_name]['lng_min'] return([lat_max,lat_min,lng_max,lng_min]) def filter_df_for_plot(df,data_cities,city_name): """ filter dataframe with the boundries of the city map """ boundries=extract_boundries(data_cities,city_name) df_filtered = df[ (df['LAT']< boundries[0]) & (df['LAT']>= boundries[1]) & (df['LNG']< boundries[2]) & (df['LNG']>= boundries[3])] return df_filtered def create_bbox(boundries): # BBox serves for the plotting size figures BBox = ((boundries[3], boundries[2], boundries[1], boundries[0])) return BBox path_to_save_imgs = main_path+"/img/map-with-points/" def plot_map(city_name,df=df,data_cities=cities): # set boundries boundries = extract_boundries(data_cities,city_name) bbox = create_bbox(boundries) # store coordinates X_store_coordinates = data_cities[city_name]['coordinate_store']['lng'] Y_store_coordinates = data_cities[city_name]['coordinate_store']['lat'] # load background img IMG=plt.imread(path_map_img+city_name+'.png') # create figure fig, ax = plt.subplots() # plot ax.scatter(df.LNG, df.LAT, zorder=1, alpha=0.5 , c='r', s=10) ax.scatter(X_store_coordinates,Y_store_coordinates,c='b', s=50) # set figure boundries ax.set_xlim(bbox[0],bbox[1]) ax.set_ylim(bbox[2],bbox[3]) # estetics plt.title(" Client map of {0} ".format(city_name[:1].upper()+city_name[1:])) plt.xlabel('longitude') plt.ylabel('latitude') # show ax.imshow(IMG, zorder=0, extent = bbox, aspect= 'auto') # save fig.savefig(path_to_save_imgs+city_name+'.png', dpi=300, bbox_inches='tight') def main_plot(city_name,data_cities=cities, dataframe=df): """go to cities dictionary extract boundries latitude min/max and longitude min/max filter the dataframe of cli ents with the boundries extract img path and plot over it each client and the store position """ dataframe = filter_df_for_plot(dataframe, data_cities ,city_name) plot_map(city_name,dataframe) main_plot('padova') main_plot('montebelluna') ###Output _____no_output_____ ###Markdown CLUSTERING ###Code def filter_df_for_clustering(df,city_name, data_cities=cities): boundries=extract_boundries(data_cities,city_name) df_filtered = df[ (df['LAT']< boundries[0]) & (df['LAT']>= boundries[1]) & (df['LNG']< boundries[2]) & (df['LNG']>= boundries[3])] df_filtered2 = df_filtered[['LAT', 'LNG']] return df_filtered2 def hierarchical_clustering(city_name,df,N_cluster=5,data_cities=cities): # machine learning cluster = AgglomerativeClustering(n_clusters= N_cluster, affinity='euclidean', linkage='ward') cluster.fit_predict(df) # SETTINGs point_dimention = 4 # [ 0.1 - 100 ] opacity = 0.8 # [ 0.01 - 1 ] # PLOT plt.figure(figsize=(50, 20)) # set boundries boundries = extract_boundries(data_cities,city_name) bbox = create_bbox(boundries) # store coordinates X_store_coordinates = data_cities[city_name]['coordinate_store']['lng'] Y_store_coordinates = data_cities[city_name]['coordinate_store']['lat'] # load background img IMG=plt.imread(path_map_img+city_name+'.png') # create figure fig, ax = plt.subplots() # plot ax.scatter(np.array(df['LNG']),np.array(df['LAT']), alpha= opacity , c=cluster.labels_, cmap='gist_rainbow_r',marker='o', s = point_dimention) ax.scatter(X_store_coordinates,Y_store_coordinates, c ='r', s=30) # set figure boundries ax.set_xlim(bbox[0],bbox[1]) ax.set_ylim(bbox[2],bbox[3]) # estetics plt.title(" Clusters of client map of {0} ".format(city_name[:1].upper()+city_name[1:])) plt.xlabel('longitude') plt.ylabel('latitude') # show ax.imshow(IMG, zorder=0, extent = bbox, aspect= 'auto') # save fig.savefig(path_to_save_imgs+city_name+'_cluster.png', dpi=1200, bbox_inches='tight') def main_clustering(city_name,N_cluster=20,data_cities=cities,dataframe=df): dataframe=filter_df_for_clustering(df, city_name,data_cities) hierarchical_clustering(city_name,dataframe,N_cluster) main_clustering('padova',5) main_clustering('montebelluna',5) ###Output _____no_output_____
Module4/Module4 - Lab2CopyLab3.ipynb
###Markdown DAT210x - Programming with Python for DS Module4- Lab2 ###Code import math import pandas as pd import matplotlib.pyplot as plt import matplotlib from sklearn import preprocessing from sklearn.decomposition import PCA # Look pretty... # matplotlib.style.use('ggplot') plt.style.use('ggplot') ###Output _____no_output_____ ###Markdown Some Boilerplate Code For your convenience, we've included some boilerplate code here which will help you out. You aren't expected to know how to write this code on your own at this point, but it'll assist with your visualizations. We've added some notes to the code in case you're interested in knowing what it's doing: A Note on SKLearn's `.transform()` calls: Any time you perform a transformation on your data, you lose the column header names because the output of SciKit-Learn's `.transform()` method is an NDArray and not a daraframe.This actually makes a lot of sense because there are essentially two types of transformations:- Those that adjust the scale of your features, and- Those that change alter the number of features, perhaps even changing their values entirely.An example of adjusting the scale of a feature would be changing centimeters to inches. Changing the feature entirely would be like using PCA to reduce 300 columns to 30. In either case, the original column's units have either been altered or no longer exist at all, so it's up to you to assign names to your columns after any transformation, if you'd like to store the resulting NDArray back into a dataframe. ###Code def scaleFeaturesDF(df): # Feature scaling is a type of transformation that only changes the # scale, but not number of features. Because of this, we can still # use the original dataset's column names... so long as we keep in # mind that the _units_ have been altered: scaled = preprocessing.StandardScaler().fit_transform(df) scaled = pd.DataFrame(scaled, columns=df.columns) print("New Variances:\n", scaled.var()) print("New Describe:\n", scaled.describe()) return scaled ###Output _____no_output_____ ###Markdown SKLearn contains many methods for transforming your features by scaling them, a type of pre-processing): - `RobustScaler` - `Normalizer` - `MinMaxScaler` - `MaxAbsScaler` - `StandardScaler` - ...http://scikit-learn.org/stable/modules/classes.htmlmodule-sklearn.preprocessingHowever in order to be effective at PCA, there are a few requirements that must be met, and which will drive the selection of your scaler. PCA requires your data is standardized -- in other words, it's _mean_ should equal 0, and it should have unit variance.SKLearn's regular `Normalizer()` doesn't zero out the mean of your data, it only clamps it, so it could be inappropriate to use depending on your data. `MinMaxScaler` and `MaxAbsScaler` both fail to set a unit variance, so you won't be using them here either. `RobustScaler` can work, again depending on your data (watch for outliers!). So for this assignment, you're going to use the `StandardScaler`. Get familiar with it by visiting these two websites:- http://scikit-learn.org/stable/modules/preprocessing.htmlpreprocessing-scaler- http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.htmlsklearn.preprocessing.StandardScaler Lastly, some code to help with visualizations: ###Code def drawVectors(transformed_features, components_, columns, plt, scaled): if not scaled: return plt.axes() # No cheating ;-) num_columns = len(columns) # This funtion will project your *original* feature (columns) # onto your principal component feature-space, so that you can # visualize how "important" each one was in the # multi-dimensional scaling # Scale the principal components by the max value in # the transformed set belonging to that component xvector = components_[0] * max(transformed_features[:,0]) yvector = components_[1] * max(transformed_features[:,1]) ## visualize projections # Sort each column by it's length. These are your *original* # columns, not the principal components. important_features = { columns[i] : math.sqrt(xvector[i]**2 + yvector[i]**2) for i in range(num_columns) } important_features = sorted(zip(important_features.values(), important_features.keys()), reverse=True) print("Features by importance:\n", important_features) ax = plt.axes() for i in range(num_columns): # Use an arrow to project each original feature as a # labeled vector on your principal component axes plt.arrow(0, 0, xvector[i], yvector[i], color='b', width=0.0005, head_width=0.02, alpha=0.75) plt.text(xvector[i]*1.2, yvector[i]*1.2, list(columns)[i], color='b', alpha=0.75) return ax ###Output _____no_output_____ ###Markdown And Now, The Assignment ###Code # Do * NOT * alter this line, until instructed! scaleFeatures = True ###Output _____no_output_____ ###Markdown Load up the dataset specified on the lab instructions page and remove any and all _rows_ that have a NaN in them. You should be a pro at this by now ;-)**QUESTION**: Should the `id` column be included in your dataset as a feature? ###Code # .. your code here .. df=pd.read_csv('Datasets/kidney_disease.csv',sep=',',index_col=0) df[['pcv','wc','rc']]=df[['pcv','wc','rc']].apply(pd.to_numeric,errors='coerce') df.dropna(inplace=True) df=pd.get_dummies(df,columns=['rbc', 'pc', 'pcc', 'ba', 'htn', 'dm', 'cad', 'appet', 'pe', 'ane']) print(df.dtypes) print(df.describe) ###Output age float64 bp float64 sg float64 al float64 su float64 bgr float64 bu float64 sc float64 sod float64 pot float64 hemo float64 pcv float64 wc float64 rc float64 classification object rbc_abnormal uint8 rbc_normal uint8 pc_abnormal uint8 pc_normal uint8 pcc_notpresent uint8 pcc_present uint8 ba_notpresent uint8 ba_present uint8 htn_no uint8 htn_yes uint8 dm_no uint8 dm_yes uint8 cad_no uint8 cad_yes uint8 appet_good uint8 appet_poor uint8 pe_no uint8 pe_yes uint8 ane_no uint8 ane_yes uint8 dtype: object <bound method NDFrame.describe of age bp sg al su bgr bu sc sod pot ... \ id ... 3 48.0 70.0 1.005 4.0 0.0 117.0 56.0 3.8 111.0 2.5 ... 9 53.0 90.0 1.020 2.0 0.0 70.0 107.0 7.2 114.0 3.7 ... 11 63.0 70.0 1.010 3.0 0.0 380.0 60.0 2.7 131.0 4.2 ... 14 68.0 80.0 1.010 3.0 2.0 157.0 90.0 4.1 130.0 6.4 ... 20 61.0 80.0 1.015 2.0 0.0 173.0 148.0 3.9 135.0 5.2 ... 22 48.0 80.0 1.025 4.0 0.0 95.0 163.0 7.7 136.0 3.8 ... 27 69.0 70.0 1.010 3.0 4.0 264.0 87.0 2.7 130.0 4.0 ... 48 73.0 70.0 1.005 0.0 0.0 70.0 32.0 0.9 125.0 4.0 ... 58 73.0 80.0 1.020 2.0 0.0 253.0 142.0 4.6 138.0 5.8 ... 71 46.0 60.0 1.010 1.0 0.0 163.0 92.0 3.3 141.0 4.0 ... 74 56.0 90.0 1.015 2.0 0.0 129.0 107.0 6.7 131.0 4.8 ... 76 48.0 80.0 1.005 4.0 0.0 133.0 139.0 8.5 132.0 5.5 ... 84 59.0 70.0 1.010 3.0 0.0 76.0 186.0 15.0 135.0 7.6 ... 90 63.0 100.0 1.010 2.0 2.0 280.0 35.0 3.2 143.0 3.5 ... 91 56.0 70.0 1.015 4.0 1.0 210.0 26.0 1.7 136.0 3.8 ... 92 71.0 70.0 1.010 3.0 0.0 219.0 82.0 3.6 133.0 4.4 ... 93 73.0 100.0 1.010 3.0 2.0 295.0 90.0 5.6 140.0 2.9 ... 127 71.0 60.0 1.015 4.0 0.0 118.0 125.0 5.3 136.0 4.9 ... 128 52.0 90.0 1.015 4.0 3.0 224.0 166.0 5.6 133.0 47.0 ... 130 50.0 90.0 1.010 2.0 0.0 128.0 208.0 9.2 134.0 4.8 ... 133 70.0 100.0 1.015 4.0 0.0 118.0 125.0 5.3 136.0 4.9 ... 144 60.0 90.0 1.010 2.0 0.0 105.0 53.0 2.3 136.0 5.2 ... 147 60.0 60.0 1.010 3.0 1.0 288.0 36.0 1.7 130.0 3.0 ... 153 55.0 90.0 1.010 2.0 1.0 273.0 235.0 14.2 132.0 3.4 ... 157 62.0 70.0 1.025 3.0 0.0 122.0 42.0 1.7 136.0 4.7 ... 159 59.0 80.0 1.010 1.0 0.0 303.0 35.0 1.3 122.0 3.5 ... 171 83.0 70.0 1.020 3.0 0.0 102.0 60.0 2.6 115.0 5.7 ... 176 21.0 90.0 1.010 4.0 0.0 107.0 40.0 1.7 125.0 3.5 ... 181 45.0 70.0 1.025 2.0 0.0 117.0 52.0 2.2 136.0 3.8 ... 189 64.0 60.0 1.010 4.0 1.0 239.0 58.0 4.3 137.0 5.4 ... .. ... ... ... ... ... ... ... ... ... ... ... 368 30.0 80.0 1.025 0.0 0.0 82.0 42.0 0.7 146.0 5.0 ... 369 75.0 70.0 1.020 0.0 0.0 107.0 48.0 0.8 144.0 3.5 ... 370 69.0 70.0 1.020 0.0 0.0 83.0 42.0 1.2 139.0 3.7 ... 371 28.0 60.0 1.025 0.0 0.0 79.0 50.0 0.5 145.0 5.0 ... 372 72.0 60.0 1.020 0.0 0.0 109.0 26.0 0.9 150.0 4.9 ... 373 61.0 70.0 1.025 0.0 0.0 133.0 38.0 1.0 142.0 3.6 ... 374 79.0 80.0 1.025 0.0 0.0 111.0 44.0 1.2 146.0 3.6 ... 375 70.0 80.0 1.020 0.0 0.0 74.0 41.0 0.5 143.0 4.5 ... 376 58.0 70.0 1.025 0.0 0.0 88.0 16.0 1.1 147.0 3.5 ... 377 64.0 70.0 1.020 0.0 0.0 97.0 27.0 0.7 145.0 4.8 ... 379 62.0 80.0 1.025 0.0 0.0 78.0 45.0 0.6 138.0 3.5 ... 380 59.0 60.0 1.020 0.0 0.0 113.0 23.0 1.1 139.0 3.5 ... 382 48.0 80.0 1.025 0.0 0.0 75.0 22.0 0.8 137.0 5.0 ... 383 80.0 80.0 1.025 0.0 0.0 119.0 46.0 0.7 141.0 4.9 ... 384 57.0 60.0 1.020 0.0 0.0 132.0 18.0 1.1 150.0 4.7 ... 385 63.0 70.0 1.020 0.0 0.0 113.0 25.0 0.6 146.0 4.9 ... 386 46.0 70.0 1.025 0.0 0.0 100.0 47.0 0.5 142.0 3.5 ... 387 15.0 80.0 1.025 0.0 0.0 93.0 17.0 0.9 136.0 3.9 ... 388 51.0 80.0 1.020 0.0 0.0 94.0 15.0 1.2 144.0 3.7 ... 389 41.0 80.0 1.025 0.0 0.0 112.0 48.0 0.7 140.0 5.0 ... 390 52.0 80.0 1.025 0.0 0.0 99.0 25.0 0.8 135.0 3.7 ... 391 36.0 80.0 1.025 0.0 0.0 85.0 16.0 1.1 142.0 4.1 ... 392 57.0 80.0 1.020 0.0 0.0 133.0 48.0 1.2 147.0 4.3 ... 393 43.0 60.0 1.025 0.0 0.0 117.0 45.0 0.7 141.0 4.4 ... 394 50.0 80.0 1.020 0.0 0.0 137.0 46.0 0.8 139.0 5.0 ... 395 55.0 80.0 1.020 0.0 0.0 140.0 49.0 0.5 150.0 4.9 ... 396 42.0 70.0 1.025 0.0 0.0 75.0 31.0 1.2 141.0 3.5 ... 397 12.0 80.0 1.020 0.0 0.0 100.0 26.0 0.6 137.0 4.4 ... 398 17.0 60.0 1.025 0.0 0.0 114.0 50.0 1.0 135.0 4.9 ... 399 58.0 80.0 1.025 0.0 0.0 131.0 18.0 1.1 141.0 3.5 ... dm_no dm_yes cad_no cad_yes appet_good appet_poor pe_no pe_yes \ id 3 1 0 1 0 0 1 0 1 9 0 1 1 0 0 1 1 0 11 0 1 1 0 0 1 0 1 14 0 1 0 1 0 1 0 1 20 0 1 0 1 0 1 0 1 22 1 0 1 0 1 0 1 0 27 0 1 0 1 1 0 0 1 48 0 1 1 0 1 0 0 1 58 0 1 0 1 1 0 1 0 71 0 1 1 0 1 0 1 0 74 1 0 1 0 1 0 1 0 76 0 1 1 0 1 0 0 1 84 1 0 1 0 0 1 0 1 90 1 0 0 1 1 0 1 0 91 1 0 1 0 1 0 1 0 92 0 1 0 1 1 0 1 0 93 0 1 0 1 0 1 1 0 127 0 1 1 0 0 1 0 1 128 0 1 1 0 1 0 1 0 130 1 0 1 0 0 1 0 1 133 1 0 1 0 1 0 1 0 144 1 0 1 0 1 0 1 0 147 1 0 1 0 0 1 1 0 153 0 1 1 0 0 1 0 1 157 0 1 1 0 1 0 1 0 159 0 1 1 0 0 1 1 0 171 1 0 1 0 0 1 1 0 176 1 0 1 0 1 0 1 0 181 1 0 1 0 1 0 1 0 189 0 1 1 0 0 1 0 1 .. ... ... ... ... ... ... ... ... 368 1 0 1 0 1 0 1 0 369 1 0 1 0 1 0 1 0 370 1 0 1 0 1 0 1 0 371 1 0 1 0 1 0 1 0 372 1 0 1 0 1 0 1 0 373 1 0 1 0 1 0 1 0 374 1 0 1 0 1 0 1 0 375 1 0 1 0 1 0 1 0 376 1 0 1 0 1 0 1 0 377 1 0 1 0 1 0 1 0 379 1 0 1 0 1 0 1 0 380 1 0 1 0 1 0 1 0 382 1 0 1 0 1 0 1 0 383 1 0 1 0 1 0 1 0 384 1 0 1 0 1 0 1 0 385 1 0 1 0 1 0 1 0 386 1 0 1 0 1 0 1 0 387 1 0 1 0 1 0 1 0 388 1 0 1 0 1 0 1 0 389 1 0 1 0 1 0 1 0 390 1 0 1 0 1 0 1 0 391 1 0 1 0 1 0 1 0 392 1 0 1 0 1 0 1 0 393 1 0 1 0 1 0 1 0 394 1 0 1 0 1 0 1 0 395 1 0 1 0 1 0 1 0 396 1 0 1 0 1 0 1 0 397 1 0 1 0 1 0 1 0 398 1 0 1 0 1 0 1 0 399 1 0 1 0 1 0 1 0 ane_no ane_yes id 3 0 1 9 0 1 11 1 0 14 1 0 20 0 1 22 0 1 27 1 0 48 1 0 58 1 0 71 1 0 74 1 0 76 1 0 84 0 1 90 1 0 91 1 0 92 1 0 93 1 0 127 1 0 128 0 1 130 0 1 133 1 0 144 1 0 147 0 1 153 0 1 157 1 0 159 1 0 171 0 1 176 0 1 181 1 0 189 1 0 .. ... ... 368 1 0 369 1 0 370 1 0 371 1 0 372 1 0 373 1 0 374 1 0 375 1 0 376 1 0 377 1 0 379 1 0 380 1 0 382 1 0 383 1 0 384 1 0 385 1 0 386 1 0 387 1 0 388 1 0 389 1 0 390 1 0 391 1 0 392 1 0 393 1 0 394 1 0 395 1 0 396 1 0 397 1 0 398 1 0 399 1 0 [158 rows x 35 columns]> ###Markdown Let's build some color-coded labels; the actual label feature will be removed prior to executing PCA, since it's unsupervised. You're only labeling by color so you can see the effects of PCA: Use an indexer to select only the following columns: `['bgr','wc','rc']` ###Code # .. your code here .. #df=df[['bgr','wc','rc']] labels = ['red' if i=='ckd' else 'green' for i in df.classification] df.drop(['classification'],axis=1,inplace=True) print(df.dtypes) print(df.describe) ###Output age float64 bp float64 sg float64 al float64 su float64 bgr float64 bu float64 sc float64 sod float64 pot float64 hemo float64 pcv float64 wc float64 rc float64 rbc_abnormal uint8 rbc_normal uint8 pc_abnormal uint8 pc_normal uint8 pcc_notpresent uint8 pcc_present uint8 ba_notpresent uint8 ba_present uint8 htn_no uint8 htn_yes uint8 dm_no uint8 dm_yes uint8 cad_no uint8 cad_yes uint8 appet_good uint8 appet_poor uint8 pe_no uint8 pe_yes uint8 ane_no uint8 ane_yes uint8 dtype: object <bound method NDFrame.describe of age bp sg al su bgr bu sc sod pot ... \ id ... 3 48.0 70.0 1.005 4.0 0.0 117.0 56.0 3.8 111.0 2.5 ... 9 53.0 90.0 1.020 2.0 0.0 70.0 107.0 7.2 114.0 3.7 ... 11 63.0 70.0 1.010 3.0 0.0 380.0 60.0 2.7 131.0 4.2 ... 14 68.0 80.0 1.010 3.0 2.0 157.0 90.0 4.1 130.0 6.4 ... 20 61.0 80.0 1.015 2.0 0.0 173.0 148.0 3.9 135.0 5.2 ... 22 48.0 80.0 1.025 4.0 0.0 95.0 163.0 7.7 136.0 3.8 ... 27 69.0 70.0 1.010 3.0 4.0 264.0 87.0 2.7 130.0 4.0 ... 48 73.0 70.0 1.005 0.0 0.0 70.0 32.0 0.9 125.0 4.0 ... 58 73.0 80.0 1.020 2.0 0.0 253.0 142.0 4.6 138.0 5.8 ... 71 46.0 60.0 1.010 1.0 0.0 163.0 92.0 3.3 141.0 4.0 ... 74 56.0 90.0 1.015 2.0 0.0 129.0 107.0 6.7 131.0 4.8 ... 76 48.0 80.0 1.005 4.0 0.0 133.0 139.0 8.5 132.0 5.5 ... 84 59.0 70.0 1.010 3.0 0.0 76.0 186.0 15.0 135.0 7.6 ... 90 63.0 100.0 1.010 2.0 2.0 280.0 35.0 3.2 143.0 3.5 ... 91 56.0 70.0 1.015 4.0 1.0 210.0 26.0 1.7 136.0 3.8 ... 92 71.0 70.0 1.010 3.0 0.0 219.0 82.0 3.6 133.0 4.4 ... 93 73.0 100.0 1.010 3.0 2.0 295.0 90.0 5.6 140.0 2.9 ... 127 71.0 60.0 1.015 4.0 0.0 118.0 125.0 5.3 136.0 4.9 ... 128 52.0 90.0 1.015 4.0 3.0 224.0 166.0 5.6 133.0 47.0 ... 130 50.0 90.0 1.010 2.0 0.0 128.0 208.0 9.2 134.0 4.8 ... 133 70.0 100.0 1.015 4.0 0.0 118.0 125.0 5.3 136.0 4.9 ... 144 60.0 90.0 1.010 2.0 0.0 105.0 53.0 2.3 136.0 5.2 ... 147 60.0 60.0 1.010 3.0 1.0 288.0 36.0 1.7 130.0 3.0 ... 153 55.0 90.0 1.010 2.0 1.0 273.0 235.0 14.2 132.0 3.4 ... 157 62.0 70.0 1.025 3.0 0.0 122.0 42.0 1.7 136.0 4.7 ... 159 59.0 80.0 1.010 1.0 0.0 303.0 35.0 1.3 122.0 3.5 ... 171 83.0 70.0 1.020 3.0 0.0 102.0 60.0 2.6 115.0 5.7 ... 176 21.0 90.0 1.010 4.0 0.0 107.0 40.0 1.7 125.0 3.5 ... 181 45.0 70.0 1.025 2.0 0.0 117.0 52.0 2.2 136.0 3.8 ... 189 64.0 60.0 1.010 4.0 1.0 239.0 58.0 4.3 137.0 5.4 ... .. ... ... ... ... ... ... ... ... ... ... ... 368 30.0 80.0 1.025 0.0 0.0 82.0 42.0 0.7 146.0 5.0 ... 369 75.0 70.0 1.020 0.0 0.0 107.0 48.0 0.8 144.0 3.5 ... 370 69.0 70.0 1.020 0.0 0.0 83.0 42.0 1.2 139.0 3.7 ... 371 28.0 60.0 1.025 0.0 0.0 79.0 50.0 0.5 145.0 5.0 ... 372 72.0 60.0 1.020 0.0 0.0 109.0 26.0 0.9 150.0 4.9 ... 373 61.0 70.0 1.025 0.0 0.0 133.0 38.0 1.0 142.0 3.6 ... 374 79.0 80.0 1.025 0.0 0.0 111.0 44.0 1.2 146.0 3.6 ... 375 70.0 80.0 1.020 0.0 0.0 74.0 41.0 0.5 143.0 4.5 ... 376 58.0 70.0 1.025 0.0 0.0 88.0 16.0 1.1 147.0 3.5 ... 377 64.0 70.0 1.020 0.0 0.0 97.0 27.0 0.7 145.0 4.8 ... 379 62.0 80.0 1.025 0.0 0.0 78.0 45.0 0.6 138.0 3.5 ... 380 59.0 60.0 1.020 0.0 0.0 113.0 23.0 1.1 139.0 3.5 ... 382 48.0 80.0 1.025 0.0 0.0 75.0 22.0 0.8 137.0 5.0 ... 383 80.0 80.0 1.025 0.0 0.0 119.0 46.0 0.7 141.0 4.9 ... 384 57.0 60.0 1.020 0.0 0.0 132.0 18.0 1.1 150.0 4.7 ... 385 63.0 70.0 1.020 0.0 0.0 113.0 25.0 0.6 146.0 4.9 ... 386 46.0 70.0 1.025 0.0 0.0 100.0 47.0 0.5 142.0 3.5 ... 387 15.0 80.0 1.025 0.0 0.0 93.0 17.0 0.9 136.0 3.9 ... 388 51.0 80.0 1.020 0.0 0.0 94.0 15.0 1.2 144.0 3.7 ... 389 41.0 80.0 1.025 0.0 0.0 112.0 48.0 0.7 140.0 5.0 ... 390 52.0 80.0 1.025 0.0 0.0 99.0 25.0 0.8 135.0 3.7 ... 391 36.0 80.0 1.025 0.0 0.0 85.0 16.0 1.1 142.0 4.1 ... 392 57.0 80.0 1.020 0.0 0.0 133.0 48.0 1.2 147.0 4.3 ... 393 43.0 60.0 1.025 0.0 0.0 117.0 45.0 0.7 141.0 4.4 ... 394 50.0 80.0 1.020 0.0 0.0 137.0 46.0 0.8 139.0 5.0 ... 395 55.0 80.0 1.020 0.0 0.0 140.0 49.0 0.5 150.0 4.9 ... 396 42.0 70.0 1.025 0.0 0.0 75.0 31.0 1.2 141.0 3.5 ... 397 12.0 80.0 1.020 0.0 0.0 100.0 26.0 0.6 137.0 4.4 ... 398 17.0 60.0 1.025 0.0 0.0 114.0 50.0 1.0 135.0 4.9 ... 399 58.0 80.0 1.025 0.0 0.0 131.0 18.0 1.1 141.0 3.5 ... dm_no dm_yes cad_no cad_yes appet_good appet_poor pe_no pe_yes \ id 3 1 0 1 0 0 1 0 1 9 0 1 1 0 0 1 1 0 11 0 1 1 0 0 1 0 1 14 0 1 0 1 0 1 0 1 20 0 1 0 1 0 1 0 1 22 1 0 1 0 1 0 1 0 27 0 1 0 1 1 0 0 1 48 0 1 1 0 1 0 0 1 58 0 1 0 1 1 0 1 0 71 0 1 1 0 1 0 1 0 74 1 0 1 0 1 0 1 0 76 0 1 1 0 1 0 0 1 84 1 0 1 0 0 1 0 1 90 1 0 0 1 1 0 1 0 91 1 0 1 0 1 0 1 0 92 0 1 0 1 1 0 1 0 93 0 1 0 1 0 1 1 0 127 0 1 1 0 0 1 0 1 128 0 1 1 0 1 0 1 0 130 1 0 1 0 0 1 0 1 133 1 0 1 0 1 0 1 0 144 1 0 1 0 1 0 1 0 147 1 0 1 0 0 1 1 0 153 0 1 1 0 0 1 0 1 157 0 1 1 0 1 0 1 0 159 0 1 1 0 0 1 1 0 171 1 0 1 0 0 1 1 0 176 1 0 1 0 1 0 1 0 181 1 0 1 0 1 0 1 0 189 0 1 1 0 0 1 0 1 .. ... ... ... ... ... ... ... ... 368 1 0 1 0 1 0 1 0 369 1 0 1 0 1 0 1 0 370 1 0 1 0 1 0 1 0 371 1 0 1 0 1 0 1 0 372 1 0 1 0 1 0 1 0 373 1 0 1 0 1 0 1 0 374 1 0 1 0 1 0 1 0 375 1 0 1 0 1 0 1 0 376 1 0 1 0 1 0 1 0 377 1 0 1 0 1 0 1 0 379 1 0 1 0 1 0 1 0 380 1 0 1 0 1 0 1 0 382 1 0 1 0 1 0 1 0 383 1 0 1 0 1 0 1 0 384 1 0 1 0 1 0 1 0 385 1 0 1 0 1 0 1 0 386 1 0 1 0 1 0 1 0 387 1 0 1 0 1 0 1 0 388 1 0 1 0 1 0 1 0 389 1 0 1 0 1 0 1 0 390 1 0 1 0 1 0 1 0 391 1 0 1 0 1 0 1 0 392 1 0 1 0 1 0 1 0 393 1 0 1 0 1 0 1 0 394 1 0 1 0 1 0 1 0 395 1 0 1 0 1 0 1 0 396 1 0 1 0 1 0 1 0 397 1 0 1 0 1 0 1 0 398 1 0 1 0 1 0 1 0 399 1 0 1 0 1 0 1 0 ane_no ane_yes id 3 0 1 9 0 1 11 1 0 14 1 0 20 0 1 22 0 1 27 1 0 48 1 0 58 1 0 71 1 0 74 1 0 76 1 0 84 0 1 90 1 0 91 1 0 92 1 0 93 1 0 127 1 0 128 0 1 130 0 1 133 1 0 144 1 0 147 0 1 153 0 1 157 1 0 159 1 0 171 0 1 176 0 1 181 1 0 189 1 0 .. ... ... 368 1 0 369 1 0 370 1 0 371 1 0 372 1 0 373 1 0 374 1 0 375 1 0 376 1 0 377 1 0 379 1 0 380 1 0 382 1 0 383 1 0 384 1 0 385 1 0 386 1 0 387 1 0 388 1 0 389 1 0 390 1 0 391 1 0 392 1 0 393 1 0 394 1 0 395 1 0 396 1 0 397 1 0 398 1 0 399 1 0 [158 rows x 34 columns]> ###Markdown Either take a look at the dataset's webpage in the attribute info section of UCI's [Chronic Kidney Disease]() page,: https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease or alternatively, you can actually look at the first few rows of your dataframe using `.head()`. What kind of data type should these three columns be? Compare what you see with the results when you print out your dataframe's `dtypes`.If Pandas did not properly detect and convert your columns to the data types you expected, use an appropriate command to coerce these features to the right type. ###Code df.reset_index(drop=True) # .. your code here .. ###Output _____no_output_____ ###Markdown PCA Operates based on variance. The variable with the greatest variance will dominate. Examine your data using a command that will check the variance of every feature in your dataset, and then print out the results. Also print out the results of running `.describe` on your dataset._Hint:_ If you do not see all three variables: `'bgr'`, `'wc'`, and `'rc'`, then it's likely you probably did not complete the previous step properly. ###Code # .. your code here .. df.var(axis=0) print (df.var()) print(df.describe()) ###Output age 2.406297e+02 bp 1.248891e+02 sg 3.023865e-05 al 1.996936e+00 su 6.616141e-01 bgr 4.217182e+03 bu 2.246322e+03 sc 9.471717e+00 sod 5.609143e+01 pot 1.208501e+01 hemo 8.307100e+00 pcv 8.290402e+01 wc 9.777380e+06 rc 1.039104e+00 rbc_abnormal 1.015883e-01 rbc_normal 1.015883e-01 pc_abnormal 1.508103e-01 pc_normal 1.508103e-01 pcc_notpresent 8.127066e-02 pcc_present 8.127066e-02 ba_notpresent 7.062807e-02 ba_present 7.062807e-02 htn_no 1.699589e-01 htn_yes 1.699589e-01 dm_no 1.467387e-01 dm_yes 1.467387e-01 cad_no 6.518584e-02 cad_yes 6.518584e-02 appet_good 1.064662e-01 appet_poor 1.064662e-01 pe_no 1.112634e-01 pe_yes 1.112634e-01 ane_no 9.159074e-02 ane_yes 9.159074e-02 dtype: float64 age bp sg al su bgr \ count 158.000000 158.000000 158.000000 158.000000 158.000000 158.000000 mean 49.563291 74.050633 1.019873 0.797468 0.253165 131.341772 std 15.512244 11.175381 0.005499 1.413130 0.813397 64.939832 min 6.000000 50.000000 1.005000 0.000000 0.000000 70.000000 25% 39.250000 60.000000 1.020000 0.000000 0.000000 97.000000 50% 50.500000 80.000000 1.020000 0.000000 0.000000 115.500000 75% 60.000000 80.000000 1.025000 1.000000 0.000000 131.750000 max 83.000000 110.000000 1.025000 4.000000 5.000000 490.000000 bu sc sod pot ... dm_no \ count 158.000000 158.000000 158.000000 158.000000 ... 158.000000 mean 52.575949 2.188608 138.848101 4.636709 ... 0.822785 std 47.395382 3.077615 7.489421 3.476351 ... 0.383065 min 10.000000 0.400000 111.000000 2.500000 ... 0.000000 25% 26.000000 0.700000 135.000000 3.700000 ... 1.000000 50% 39.500000 1.100000 139.000000 4.500000 ... 1.000000 75% 49.750000 1.600000 144.000000 4.900000 ... 1.000000 max 309.000000 15.200000 150.000000 47.000000 ... 1.000000 dm_yes cad_no cad_yes appet_good appet_poor pe_no \ count 158.000000 158.000000 158.000000 158.000000 158.000000 158.000000 mean 0.177215 0.930380 0.069620 0.879747 0.120253 0.873418 std 0.383065 0.255315 0.255315 0.326292 0.326292 0.333562 min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 25% 0.000000 1.000000 0.000000 1.000000 0.000000 1.000000 50% 0.000000 1.000000 0.000000 1.000000 0.000000 1.000000 75% 0.000000 1.000000 0.000000 1.000000 0.000000 1.000000 max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 pe_yes ane_no ane_yes count 158.000000 158.000000 158.000000 mean 0.126582 0.898734 0.101266 std 0.333562 0.302640 0.302640 min 0.000000 0.000000 0.000000 25% 0.000000 1.000000 0.000000 50% 0.000000 1.000000 0.000000 75% 0.000000 1.000000 0.000000 max 1.000000 1.000000 1.000000 [8 rows x 34 columns] ###Markdown Below, we assume your dataframe's variable is named `df`. If it isn't, make the appropriate changes. But do not alter the code in `scaleFeaturesDF()` just yet! ###Code # .. your (possible) code adjustment here .. if scaleFeatures: df = scaleFeaturesDF(df) ###Output New Variances: age 1.006369 bp 1.006369 sg 1.006369 al 1.006369 su 1.006369 bgr 1.006369 bu 1.006369 sc 1.006369 sod 1.006369 pot 1.006369 hemo 1.006369 pcv 1.006369 wc 1.006369 rc 1.006369 rbc_abnormal 1.006369 rbc_normal 1.006369 pc_abnormal 1.006369 pc_normal 1.006369 pcc_notpresent 1.006369 pcc_present 1.006369 ba_notpresent 1.006369 ba_present 1.006369 htn_no 1.006369 htn_yes 1.006369 dm_no 1.006369 dm_yes 1.006369 cad_no 1.006369 cad_yes 1.006369 appet_good 1.006369 appet_poor 1.006369 pe_no 1.006369 pe_yes 1.006369 ane_no 1.006369 ane_yes 1.006369 dtype: float64 New Describe: age bp sg al su \ count 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 mean 1.032929e-16 7.406171e-16 -1.624580e-15 -7.757508e-16 -2.108018e-18 std 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 min -2.817246e+00 -2.158952e+00 -2.713365e+00 -5.661221e-01 -3.122333e-01 25% -6.669624e-01 -1.261282e+00 2.309247e-02 -5.661221e-01 -3.122333e-01 50% 6.057713e-02 5.340564e-01 2.309247e-02 -5.661221e-01 -3.122333e-01 75% 6.749439e-01 5.340564e-01 9.352451e-01 1.437770e-01 -3.122333e-01 max 2.162358e+00 3.227064e+00 9.352451e-01 2.273474e+00 5.854375e+00 bgr bu sc sod pot \ count 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 mean -9.755075e-17 -2.578809e-16 7.869935e-17 8.119384e-16 4.321438e-17 std 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 min -9.475974e-01 -9.011706e-01 -5.830146e-01 -3.730148e+00 -6.165957e-01 25% -5.305059e-01 -5.625116e-01 -4.852266e-01 -5.154386e-01 -2.703085e-01 50% -2.447210e-01 -2.767680e-01 -3.548426e-01 2.034626e-02 -3.945044e-02 75% 6.306235e-03 -5.981458e-02 -1.918626e-01 6.900774e-01 7.597862e-02 max 5.540492e+00 5.427520e+00 4.241194e+00 1.493755e+00 1.222489e+01 ... dm_no dm_yes cad_no cad_yes \ count ... 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 mean ... -5.417607e-16 3.478230e-16 8.207218e-16 -3.941994e-16 std ... 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 min ... -2.154729e+00 -4.640955e-01 -3.655631e+00 -2.735506e-01 25% ... 4.640955e-01 -4.640955e-01 2.735506e-01 -2.735506e-01 50% ... 4.640955e-01 -4.640955e-01 2.735506e-01 -2.735506e-01 75% ... 4.640955e-01 -4.640955e-01 2.735506e-01 -2.735506e-01 max ... 4.640955e-01 2.154729e+00 2.735506e-01 3.655631e+00 appet_good appet_poor pe_no pe_yes ane_no \ count 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 1.580000e+02 mean 3.913887e-16 -3.927941e-16 -8.291539e-16 8.291539e-16 -1.686415e-16 std 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 1.003180e+00 min -2.704772e+00 -3.697170e-01 -2.626785e+00 -3.806935e-01 -2.979094e+00 25% 3.697170e-01 -3.697170e-01 3.806935e-01 -3.806935e-01 3.356725e-01 50% 3.697170e-01 -3.697170e-01 3.806935e-01 -3.806935e-01 3.356725e-01 75% 3.697170e-01 -3.697170e-01 3.806935e-01 -3.806935e-01 3.356725e-01 max 3.697170e-01 2.704772e+00 3.806935e-01 2.626785e+00 3.356725e-01 ane_yes count 1.580000e+02 mean 7.869935e-17 std 1.003180e+00 min -3.356725e-01 25% -3.356725e-01 50% -3.356725e-01 75% -3.356725e-01 max 2.979094e+00 [8 rows x 34 columns] ###Markdown Run PCA on your dataset, reducing it to 2 principal components. Make sure your PCA model is saved in a variable called `'pca'`, and that the results of your transformation are saved in another variable `'T'`: ###Code # .. your code here .. pca=PCA(n_components=2) T=pca.fit_transform(df) print(T) ###Output [[ 7.40571153e+00 -5.43388620e+00] [ 8.21313912e+00 -3.80916306e+00] [ 8.15781916e+00 4.97229631e-01] [ 1.05963543e+01 2.28891097e+00] [ 9.44702510e+00 -9.76322819e-02] [ 3.77608141e+00 -3.45653656e+00] [ 6.49399598e+00 4.77553145e+00] [ 3.49232725e+00 3.13413817e-01] [ 5.48091667e+00 4.36358285e+00] [ 2.38110879e+00 7.78254699e-01] [ 3.78273710e+00 1.89743461e-01] [ 6.28105664e+00 2.03976907e+00] [ 7.55897683e+00 -5.56544088e+00] [ 3.32183735e+00 5.56899126e+00] [ 5.55330990e-04 1.92658101e+00] [ 6.86332695e+00 3.95825182e+00] [ 8.92564807e+00 3.40946867e+00] [ 4.78638553e+00 -9.40723269e-01] [ 7.00235753e+00 -1.20924925e+00] [ 8.42680121e+00 -4.85097558e+00] [ 8.37737011e-01 3.22885751e-02] [ 8.85103841e-01 6.32233656e-01] [ 6.68421732e+00 -3.65490242e+00] [ 1.03247654e+01 -2.33331878e+00] [ 2.33083423e+00 7.85873746e-01] [ 3.38481712e+00 1.17320895e+00] [ 4.09016492e+00 -4.16636781e+00] [ 4.99981257e+00 -2.82356773e+00] [ 1.82169880e+00 -1.83891419e+00] [ 8.26189613e+00 2.42970136e+00] [ 3.96521233e+00 8.42418341e-02] [ 9.49945240e+00 -3.17136233e+00] [ 5.51199747e+00 5.91597921e-01] [ 6.76825936e+00 1.37282577e+00] [ 6.32211702e+00 3.45686662e-01] [ 6.99373821e+00 1.69729966e+00] [ 6.05942715e+00 6.84686503e+00] [ 7.59670737e+00 3.54254699e+00] [ 5.40408814e+00 -7.49134080e-01] [ 1.06381223e+01 1.92596929e+00] [ 7.58188529e+00 -1.62995970e+00] [ 5.75522682e+00 6.74173638e+00] [ 1.16951367e+01 -5.37017954e+00] [ -1.94981286e+00 -1.29001306e-01] [ -2.80834881e+00 -2.30787608e-01] [ -2.30522508e+00 -1.83484938e-01] [ -2.21135861e+00 -1.03562125e-02] [ -2.13870571e+00 -3.20327133e-01] [ -2.54147495e+00 7.40153104e-03] [ -2.05731469e+00 2.05665001e-01] [ -2.15707089e+00 -3.55132017e-01] [ -2.05285730e+00 -1.23056736e-01] [ -2.09389860e+00 -2.64420415e-01] [ -2.05795198e+00 -1.18488273e-02] [ -1.95793336e+00 -1.39098633e-02] [ -1.91942913e+00 3.22227267e-03] [ -1.89546100e+00 -1.42922386e-01] [ -2.00982458e+00 2.98350772e-02] [ -2.10445805e+00 6.59456185e-02] [ -1.61948036e+00 -4.38051970e-02] [ -2.13021073e+00 2.10313506e-02] [ -2.43512392e+00 -1.56487923e-01] [ -2.20146993e+00 -3.09958104e-01] [ -2.08632334e+00 -3.45438264e-01] [ -2.00545018e+00 1.30168596e-02] [ -1.94121848e+00 3.75241748e-02] [ -2.25003619e+00 -2.26135203e-01] [ -2.23172229e+00 -5.43342843e-02] [ -2.64233808e+00 -5.43148249e-02] [ -2.19445729e+00 1.23780302e-01] [ -1.93288432e+00 -4.31248977e-01] [ -2.76734833e+00 -8.60162606e-02] [ -2.13992544e+00 1.74491303e-03] [ -2.22919689e+00 1.48581605e-01] [ -2.29802335e+00 -1.16493396e-01] [ -2.08125395e+00 -1.14113704e-01] [ -2.27218271e+00 -2.52438362e-01] [ -2.25770213e+00 -8.73750467e-03] [ -1.92928241e+00 -5.19525141e-01] [ -2.15719963e+00 3.91631017e-01] [ -2.54321627e+00 -1.24682685e-01] [ -1.83904746e+00 -3.62063479e-01] [ -1.99601098e+00 -6.46447304e-02] [ -2.27004031e+00 1.84540868e-01] [ -2.12099276e+00 1.91474622e-01] [ -2.04889138e+00 -4.57212208e-01] [ -2.02988164e+00 -1.61769061e-01] [ -2.40440995e+00 1.72349774e-01] [ -2.26544342e+00 1.30745986e-01] [ -2.10930633e+00 -1.22478821e-01] [ -1.72853478e+00 -1.92531771e-01] [ -2.51057409e+00 1.37203549e-01] [ -2.38837064e+00 1.52247389e-01] [ -1.97804942e+00 6.02226890e-02] [ -2.12108536e+00 -4.06412432e-02] [ -2.19268435e+00 4.03858924e-02] [ -2.12639496e+00 -1.05328954e-01] [ -2.51139840e+00 -8.89576140e-02] [ -2.61952642e+00 -2.83735262e-01] [ -2.06762213e+00 8.93641217e-02] [ -2.47904852e+00 -2.04284206e-01] [ -1.97898261e+00 8.80490733e-02] [ -2.58014240e+00 -1.55372312e-01] [ -2.14490397e+00 3.50479377e-01] [ -2.03469317e+00 -5.00345592e-01] [ -2.44686707e+00 -2.70070962e-01] [ -1.99983809e+00 1.34707703e-01] [ -2.45650899e+00 -1.38818446e-01] [ -2.62777095e+00 -2.20276536e-01] [ -2.10305205e+00 1.65222580e-01] [ -2.51974313e+00 -4.42034607e-01] [ -2.68972408e+00 -3.70924741e-02] [ -2.41048050e+00 5.48213644e-02] [ -1.97682326e+00 -4.31661750e-01] [ -2.22754814e+00 -2.06798676e-01] [ -2.39884352e+00 -1.20890339e-01] [ -2.53781498e+00 -1.85094472e-01] [ -2.52815467e+00 -2.24385081e-01] [ -2.84341316e+00 -1.43166055e-01] [ -1.89370089e+00 -6.48894674e-02] [ -2.10052830e+00 -1.07745494e-01] [ -2.43227046e+00 2.22563631e-01] [ -2.46288235e+00 -3.44173993e-01] [ -2.50635779e+00 -2.68966370e-01] [ -2.14066663e+00 -3.33028890e-01] [ -1.76809173e+00 -1.34140538e-03] [ -2.26583474e+00 -5.14253937e-02] [ -2.62509631e+00 2.17879240e-01] [ -2.50150190e+00 -2.72286172e-01] [ -1.73887567e+00 1.04731256e-01] [ -2.15176001e+00 5.26705470e-03] [ -2.88609089e+00 -2.94294522e-01] [ -2.41262736e+00 3.91677206e-01] [ -1.99411501e+00 5.50868754e-02] [ -2.27884891e+00 2.97550099e-01] [ -2.11112498e+00 5.93694365e-02] [ -2.72364376e+00 1.47822636e-01] [ -2.10204383e+00 9.68749142e-02] [ -2.45819360e+00 -8.05055737e-02] [ -2.29819942e+00 9.96795834e-02] [ -2.79668484e+00 -9.84203940e-02] [ -1.98500997e+00 1.77477480e-01] [ -2.03622362e+00 2.26390727e-01] [ -2.40386966e+00 3.20505738e-01] [ -2.65716046e+00 -5.97016544e-02] [ -2.63654354e+00 -3.96608855e-01] [ -2.39786617e+00 1.25304143e-01] [ -2.58518289e+00 -3.92378192e-02] [ -2.32266447e+00 -1.18845803e-01] [ -2.66443425e+00 -1.57266081e-01] [ -2.04312611e+00 2.37434965e-01] [ -2.42242966e+00 -1.35655438e-01] [ -1.59091936e+00 -5.79741856e-02] [ -2.10943378e+00 3.29180376e-01] [ -2.89780419e+00 -1.28982091e-01] [ -2.38689593e+00 -3.50490868e-01] [ -2.50728620e+00 -4.82294059e-01] [ -2.59080024e+00 3.06285529e-01]] ###Markdown Now, plot the transformed data as a scatter plot. Recall that transforming the data will result in a NumPy NDArray. You can either use MatPlotLib to graph it directly, or you can convert it back to DataFrame and have Pandas do it for you.Since we've already demonstrated how to plot directly with MatPlotLib in `Module4/assignment1.ipynb`, this time we'll show you how to convert your transformed data back into to a Pandas Dataframe and have Pandas plot it from there. ###Code # Since we transformed via PCA, we no longer have column names; but we know we # are in `principal-component` space, so we'll just define the coordinates accordingly: ax = drawVectors(T, pca.components_, df.columns.values, plt, scaleFeatures) T = pd.DataFrame(T) T.columns = ['component1', 'component2'] T.plot.scatter(x='component1', y='component2', marker='o', c=labels, alpha=0.75, ax=ax) plt.show() ###Output Features by importance: [(2.9696150394993355, 'ane_no'), (2.969615039499335, 'ane_yes'), (2.758875925894981, 'bgr'), (2.715504955394058, 'dm_yes'), (2.7155049553940573, 'dm_no'), (2.6589320941392356, 'pcv'), (2.6455721001098698, 'hemo'), (2.602112662848505, 'al'), (2.5934172193046887, 'htn_no'), (2.593417219304688, 'htn_yes'), (2.576730565999865, 'su'), (2.485944023344342, 'cad_no'), (2.4859440233443415, 'cad_yes'), (2.484132428132541, 'sc'), (2.4794711137049363, 'pc_normal'), (2.479471113704936, 'pc_abnormal'), (2.465109416186053, 'appet_poor'), (2.4651094161860527, 'appet_good'), (2.45756650608509, 'bu'), (2.3937269696125707, 'sg'), (2.3881975796285304, 'rc'), (2.2105782461310133, 'pe_yes'), (2.2105782461310133, 'pe_no'), (2.182818062607649, 'sod'), (2.0059261508175275, 'rbc_normal'), (2.0059261508175275, 'rbc_abnormal'), (1.9861731688066917, 'ba_present'), (1.9861731688066917, 'ba_notpresent'), (1.9842911319071956, 'pcc_present'), (1.9842911319071956, 'pcc_notpresent'), (1.2846796771566322, 'age'), (1.0541946042166217, 'bp'), (0.9193845987448057, 'wc'), (0.5640412875529994, 'pot')]
adopt/kuwait-make-strata.ipynb
###Markdown CREATE CAMPAIGN ###Code from adopt.facebook.update import Instruction from adopt.malaria import run_instructions def create_campaign(name): params = { "name": name, "objective": "MESSAGES", "status": "PAUSED", "special_ad_categories": [], } return Instruction("campaign", "create", params) # create_campaign_for_user(USER, CAMPAIGN, db_conf) run_instructions([create_campaign(AD_CAMPAIGN)], state) # cid = next(c for c in state.campaigns if c['name'] == AD_CAMPAIGN)['id'] # run_instructions([Instruction("campaign", "update", {"status": "PAUSED"}, cid)], state) ###Output _____no_output_____ ###Markdown BASIC CONF ###Code c = {'optimization_goal': 'REPLIES', 'destination_type': 'MESSENGER', 'adset_hours': 48, 'budget': 1500000.0, 'min_budget': 100.0, 'opt_window': 5*24, 'end_date': '2021-05-21', 'proportional': True, 'page_id': PAGE_ID, 'instagram_id': None, 'ad_account': AD_ACCOUNT, 'ad_campaign': AD_CAMPAIGN} config = CampaignConf(**c) create_campaign_confs(CAMPAIGNID, "opt", [config._asdict()], db_conf) ###Output _____no_output_____ ###Markdown AUDIENCES ###Code from adopt.marketing import dict_from_nested_type import typedjson import json audiences = [ { "name": f"vlab-vacc-{COUNTRY_CODE}-nationality-a", "shortcodes": SURVEY_SHORTCODES, "subtype": "LOOKALIKE", "lookalike": { "name": f"vlab-vacc-{COUNTRY_CODE}-nationality-a-lookalike", "target": 1100, "spec": { "country": COUNTRY_CODE, "starting_ratio": 0.0, "ratio": 0.2 } }, "question_targeting": {"op": "equal", "vars": [ {"type": "response", "value": "nationality"}, {"type": "constant", "value": "A"} ]} }, { "name": f"vlab-vacc-{COUNTRY_CODE}-nationality-b", "shortcodes": SURVEY_SHORTCODES, "subtype": "LOOKALIKE", "lookalike": { "name": f"vlab-vacc-{COUNTRY_CODE}-nationality-b-lookalike", "target": 1100, "spec": { "country": COUNTRY_CODE, "starting_ratio": 0.0, "ratio": 0.2 } }, "question_targeting": {"op": "equal", "vars": [ {"type": "response", "value": "nationality"}, {"type": "constant", "value": "B"} ]} }, { "name": RESPONDENT_AUDIENCE, "shortcodes": [INITIAL_SHORTCODE], "subtype": "CUSTOM" }, ] audience_confs = [typedjson.decode(AudienceConf, c) for c in audiences] confs = [dict_from_nested_type(a) for a in audience_confs] create_campaign_confs(CAMPAIGNID, "audience", confs, db_conf) ###Output _____no_output_____ ###Markdown CREATIVES ###Code from mena.strata import generate_creative_confs images = {i['name']: i for i in state.account.get_ad_images(fields=['name', 'hash'])} creative_confs, image_confs = generate_creative_confs(CREATIVE_FILE, INITIAL_SHORTCODE, images) create_campaign_confs(CAMPAIGNID, "creative", creative_confs, db_conf) ###Output _____no_output_____ ###Markdown STRATA ###Code from mena.strata import get_adsets, extraction_confs, hyphen_case from itertools import product from mena.strata import format_group_product template_state = CampaignState(userinfo.token, get_api(env, userinfo.token), AD_ACCOUNT, TEMPLATE_CAMPAIGN) a, g, l = get_adsets(template_state, extraction_confs) variables = [ { "name": "age", "source": "facebook", "conf": a}, { "name": "gender", "source": "facebook", "conf": g}, { "name": "location", "source": "facebook", "conf": l}, { "name": "nationality", "source": "survey", "conf": { "levels": [{"name": "A", "audiences": [f"vlab-vacc-{COUNTRY_CODE}-nationality-a-lookalike"], "excluded_audiences": [f"vlab-vacc-{COUNTRY_CODE}-nationality-b-lookalike"], "question_targeting": {"op": "or", "vars": [ {"op": "equal", "vars": [ {"type": "response", "value": "nationality"}, {"type": "constant", "value": "A"}]}, ]}}, {"name": "B", "audiences": [], "excluded_audiences": [], "question_targeting": {"op": "equal", "vars": [ {"type": "response", "value": "nationality"}, {"type": "constant", "value": "B"} ]}}, ]}} ] share_lookup = pd.read_csv(DISTRIBUTION_FILE, header=[0,1,2], index_col=[0]) share = share_lookup.T.reset_index().melt(id_vars=DISTRIBUTION_VARS, var_name='location', value_name='percentage') groups = product(*[[(v['name'], v['source'], l) for l in v['conf']['levels']] for v in variables]) groups = [format_group_product(g, share) for g in groups] ALL_CREATIVES = [t['name'] for t in image_confs] def make_stratum(id_, quota, c): return { 'id': id_, 'metadata': {**c['metadata'], **EXTRA_METADATA}, 'facebook_targeting': c['facebook_targeting'], 'creatives': ALL_CREATIVES, 'audiences': c['audiences'], 'excluded_audiences': [*c['excluded_audiences'], RESPONDENT_AUDIENCE], 'quota': float(quota), 'shortcodes': SURVEY_SHORTCODES, 'question_targeting': c['question_targeting']} from adopt.marketing import StratumConf, QuestionTargeting import typedjson strata = [make_stratum(*g) for g in groups] strata_data = [dict_from_nested_type(typedjson.decode(StratumConf, c)) for c in strata] create_campaign_confs(CAMPAIGNID, "stratum", strata_data, db_conf) ###Output _____no_output_____ ###Markdown TESTING ###Code mal = load_basics(CAMPAIGNID, env) mal.state.ads mal.state.custom_audiences %%time from adopt.malaria import update_ads_for_campaign, update_audience_for_campaign # instructions, report = update_ads_for_campaign(mal) instructions, report = update_audience_for_campaign(mal) len(instructions) [i.params['session'] for i in instructions] import facebook_business from adopt.malaria import run_instructions run_instructions(instructions, mal.state) import pandas as pd from adopt.campaign_queries import get_last_adopt_report rdf = pd.DataFrame(get_last_adopt_report(CAMPAIGNID, "FACEBOOK_ADOPT", mal.db_conf)).T # get the frequency! mal.state.insights ###Output _____no_output_____
something-learned/Mathematics/hackermath/Module_1c_linear_regression_ridge.ipynb
###Markdown Linear Regression (Ridge)So far we have been looking at solving for vector $x$ when there is a known matrix $A$ and vector $b$, such that$$ Ax = b $$The first approach is solving for one (or none) unique solution when $n$ dimensions and $p$ feature when $ n = p + 1 $ i.e. $n \times n$ matrixThe second approach is using OLS - ordinary least squares linear regression, when $ n > p + 1 $ Overfitting in OLSOrdinary least squares estimation leads to an overdetermined (over-fitted) solution, which fits well with in-sample we have but does not generalise well when we extend it to outside the sample Lets take the OLS cars example: Our sample was 7 cars for which we had $price$ and $kmpl$ data. However, our entire data is a population is a total of 42 cars. We want to see how well does this OLS for 7 cars do when we extend it to the entire set of 42 cars. ###Code import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline plt.style.use('fivethirtyeight') plt.rcParams['figure.figsize'] = (10, 6) pop = pd.read_csv('data/cars_small.csv') pop.shape pop.head() sample_rows = [35,17,11,25,12,22,13] sample = pop.loc[sample_rows,:] sample ###Output _____no_output_____ ###Markdown Lets plot the entire population (n = 42) and the sample (n =7) and our original prediction line.$$ price = 1662 - 62 * kmpl ~~~~ \textit{(sample = 7)}$$ ###Code # Plot the population and the sample plt.scatter(pop.kmpl, pop.price, s = 150, alpha = 0.5 ) plt.scatter(sample.kmpl, sample.price, s = 150, alpha = 0.5, c = 'r') # Plot the OLS Line - Sample beta_0_s, beta_1_s = 1662, -62 x = np.arange(min(pop.kmpl),max(pop.kmpl),1) plt.xlabel('kmpl') plt.ylabel('price') y_s = beta_0_s + beta_1_s * x plt.text(x[-1], y_s[-1], 'sample') plt.plot(x, y_s, '-') ###Output _____no_output_____ ###Markdown Let us find the best-fit OLS line for the population ###Code def ols (df): n = df.shape[0] x0 = np.ones(n) x1 = df.kmpl X = np.c_[x0, x1] X = np.asmatrix(X) y = np.transpose(np.asmatrix(df.price)) X_T = np.transpose(X) X_pseudo = np.linalg.inv(X_T * X) * X_T beta = X_pseudo * y return beta ols(sample) ols(pop) ###Output _____no_output_____ ###Markdown So the two OLS lines are:$$ price = 1662 - 62 * kmpl ~~~~ \textit{(sample = 7)}$$ $$ price = 1158 - 36 * kmpl ~~~~ \textit{(population = 42)}$$ Let us plot this data: ###Code # Plot the population and the sample plt.scatter(pop.kmpl, pop.price, s = 150, alpha = 0.5 ) plt.scatter(sample.kmpl, sample.price, s = 150, alpha = 0.5, c = 'r') # Plot the OLS Line - sample and population beta_0_s, beta_1_s = 1662, -62 beta_0_p, beta_1_p = 1158, -36 x = np.arange(min(pop.kmpl),max(pop.kmpl),1) plt.xlabel('kmpl') plt.ylabel('price') y_s = beta_0_s + beta_1_s * x plt.text(x[-1], y_s[-1], 'sample') y_p = beta_0_p + beta_1_p * x plt.text(x[-1], y_p[-1], 'population') plt.plot(x, y_s, '-') plt.plot(x, y_p, '-') ###Output _____no_output_____ ###Markdown Understanding OverfittingThe reason overfitting is happening is because our orginal line is really dependent on the selection of the sample of 7 observations. If we change our sample, we would get a different answer every time! ###Code # Randomly select 7 cars from this dataset sample_random = pop.sample(n=7) sample_random ###Output _____no_output_____ ###Markdown Let us write some code to randomly draw a sample of 7 and do it $z$ times and see the OLS lines and coefficients ###Code ols(sample_random) def random_cars_ols (z): beta = [] for i in range(z): # Select a sample and run OLS sample_random = pop.sample(n=7) b = ols(sample_random) beta.append([b[0,0], b[1,0]]) # Get the OLS line x = np.arange(min(pop.kmpl), max(pop.kmpl), 1) y = b[0,0] + b[1,0] *x # Set the plotting area plt.subplot(1, 2, 1) plt.tight_layout() a = round(1/np.log(z), 2) # Plot the OLS line plt.plot(x,y, '-', linewidth = 1.0, c = 'b', alpha = a) plt.xlabel('kmpl') plt.ylabel('price') plt.ylim(0,1000) # Plot the intercept and coefficients plt.subplot(1,2,2) plt.scatter(beta[i][1],beta[i][0], s = 50, alpha = a) plt.xlim(-120,60) plt.ylim(-500,3000) plt.xlabel('beta_1') plt.ylabel('beta_0') # Plot the Popultaion line plt.subplot(1, 2, 1) beta_0_p, beta_1_p = 1158, -36 x = np.arange(min(pop.kmpl),max(pop.kmpl),1) y_p = beta_0_p + beta_1_p * x plt.plot(x, y_p, '-', linewidth =4, c = 'r') ###Output _____no_output_____ ###Markdown Let us do this 500 times, $ z = 500 $ ###Code random_cars_ols(500) ###Output _____no_output_____ ###Markdown L2 Regularization - Ridge RegressionNow to prevent our $\beta $ from going all over the place to fit the line, we can need to constrain the constraint $\beta$$$ \beta^{T} \beta < C $$For OLS our error term was: $$ E_{ols}(\beta)= \frac {1}{n} (y-X\beta)^{T}(y-X\beta) $$So now we add another constraint on the $\beta$ to our minimization function$$ E_{reg}(\beta)= \frac {1}{n} (y-X\beta)^{T}(y-X\beta) + \frac {\alpha}{n} \beta^{T}\beta$$To get the minimum for this error function, we need to differentiate by $\beta^T$$$ \nabla E_{reg}(\beta) = 0 $$$$ \nabla E_{reg}(\beta) ={\frac {dE_{reg}(\beta)}{d\beta^T}} = \frac {2}{n} X^T(X\betaโˆ’y) + \frac {\alpha}{n} \beta= 0 $$$$ X^T X\beta + \alpha \beta= X^T y $$So our $\beta$ for a regularized function is$$ \beta_{reg} = (X^T X + \alpha I)^{-1}X^Ty$$When $ \alpha = 0 $, then it becomes OLS$$ \beta_{ols} = (X^T X)^{-1}X^Ty$$ Direct Calculation ###Code def ridge (df, alpha): n = df.shape[0] x0 = np.ones(n) x1 = df.kmpl X = np.c_[x0, x1] X = np.asmatrix(X) y = np.asmatrix(df.price.values.reshape(-1,1)) X_T = np.transpose(X) I = np.identity(2) beta = np.linalg.inv(X_T * X + alpha * I ) * X_T * y return beta ###Output _____no_output_____ ###Markdown Let us run this with slpha = 0, which is OLS ###Code ridge(sample, 0) ###Output _____no_output_____ ###Markdown Lets increase alpha to constraint the plot and see the result ###Code def ridge_plot(df, alphas, func): plt.scatter(df.kmpl, df.price, s = 150, alpha = 0.5) plt.xlabel('kmpl') plt.ylabel('price') # Plot the Ridge line for a in alphas: beta = func(df, a) x = np.arange(min(df.kmpl), max(df.kmpl), 1) y = beta[0,0] + beta[1,0] * x plt.plot(x,y, '-', linewidth = 1, c = 'b') plt.text(x[-1], y[-1], '%s' % a, size = "smaller") ridge_plot(sample, [0, 0.005, 0.01, 0.02, 0.03, 0.05, 0.1], ridge) ###Output _____no_output_____ ###Markdown ExercisesRun a Ridge Linear Regression:$$ price = \beta_{0} + \beta_{1} kmpl + \beta_{2} bhp + \beta_{2} kmpl^2 + \beta_{2} bhp/kmpl $$Run the Ridge Regression using Pseudo Inverse? Plot the Ridge Regression for different values of $\alpha$ ###Code ###Output _____no_output_____ ###Markdown Plot the overfitting by taking $n = 20$ samples? Plot the overfitting by taking $n = 42$ (entire population)? Using sklearn ###Code from sklearn import linear_model def ridge_sklearn(df, alpha): y = df.price X = df.kmpl.values.reshape(-1,1) X = np.c_[np.ones((X.shape[0],1)),X] model = linear_model.Ridge(alpha = alpha, fit_intercept = False) model.fit(X,y) beta = np.array([model.coef_]).T return beta ridge_sklearn(pop, 0) ridge_plot(sample, [0, 0.005, 0.01, 0.02, 0.03, 0.05, 0.1], ridge_sklearn) ###Output _____no_output_____ ###Markdown Let us now run the see how this ridge regression helps in reducing overplotting ###Code def random_cars_ridge (z, alpha, func): beta = [] for i in range(z): # Select a sample and run OLS sample_random = pop.sample(n=7) b = func(sample_random, alpha) beta.append([b[0,0], b[1,0]]) # Get the OLS line x = np.arange(min(pop.kmpl), max(pop.kmpl), 1) y = b[0,0] + b[1,0] *x # Set the plotting area plt.subplot(1, 2, 1) plt.tight_layout() a = round(1/np.log(z), 2) # Plot the OLS line plt.plot(x,y, '-', linewidth = 1, c = 'b', alpha = a) plt.xlabel('kmpl') plt.ylabel('price') plt.ylim(0,1000) # Plot the intercept and coefficients plt.subplot(1,2,2) plt.scatter(beta[i][1],beta[i][0], s = 50, alpha = a) plt.xlim(-120,60) plt.ylim(-500,3000) plt.xlabel('beta_1') plt.ylabel('beta_0') # Plot the Population line plt.subplot(1, 2, 1) beta_0_p, beta_1_p = 1158, -36 x = np.arange(min(pop.kmpl),max(pop.kmpl),1) y_p = beta_0_p + beta_1_p * x plt.plot(x, y_p, '-', linewidth =4, c = 'r') random_cars_ridge (500, 0.02, ridge) ###Output _____no_output_____
lab4/lab4_colour_palette.ipynb
###Markdown Lab 4.1 - Wykrywanie palety kolorรณw**Wykonanie rozwiฤ…zaล„: Marcin Przewiฤ™ลบlikowski**https://github.com/mprzewie/ml_basics_course ###Code import numpy as np import matplotlib.pyplot as plt import cv2 import os from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.metrics import silhouette_samples ###Output _____no_output_____ ###Markdown Wybierzmy ล‚adne, kolorowe zdjฤ™cie. Postarajmy siฤ™ by miaล‚ kilka dominijฤ…cych barw oraz jakieล› anomalie (np. maล‚y balonik na tle parku, albo samotny X-Wing na tle galaktyki). Warunek kilku dominujฤ…cych barw jest tylko jednym z powodรณw, dla ktรณrych warto siฤ™gnฤ…ฤ‡ po klasykฤ™ komiksu :) ###Code image = cv2.imread("ds_1.jpg") image = cv2.resize(image, (500, 800)) image = image[:,:,[2,1,0]] image = image / 255 plt.figure(figsize=(6, 9)) plt.imshow(image) plt.title("Okล‚adka jednego z najlepszych komiksรณw wszechczasรณw") plt.axis("off") plt.show() ###Output _____no_output_____ ###Markdown Potraktujmy kaลผdy jego piksel jako obserwacjฤ™ w przestrzeni 3-D (po jednym wymiarze na kaลผdy z kolorรณw). Zdecydujmy czy usuwamy ze zbioru duplikaty (piksele o takich samych wartoล›ciach RGB) - nasz wybรณr wpล‚ynie na finalny wynik. ###Code all_pixels = image.reshape(-1, 3) unique_pixels = np.unique(all_pixels, axis=0) ###Output _____no_output_____ ###Markdown Bฤ™de pracowaฤ‡ na wszystkich pikselach. Wykonajmy na takim zbiorze klasteryzacjฤ™ k-means, z nastฤ™pujฤ…cymi zaล‚oลผeniami:* jako ล›rodkรณw klastrรณw uลผywamy istniejฤ…ce elementy zbioru, a nie ich ล›rednie (czyli jest to w praktyce k-medoids) - nie chcemy znaleลบฤ‡ kolorรณw, ktรณre nie wystฤ…piล‚y na zdjฤ™ciu;* dobieramy wartoล›ฤ‡ staล‚ej k uลผywajฤ…c dowolnej zaproponowanej przez siebie metody. ###Code kmeanses = [] fig = plt.figure(figsize=(15, 25)) for n_clusters in range(1, 11): kmeans = KMeans(n_clusters).fit(all_pixels) # zamieniam znalezione centra klastrรณw na punkty najbardziej im podobne z datasetu, # ลผeby otrzymaฤ‡ K-Medoids new_cluster_centers = [] for c in kmeans.cluster_centers_: differences = unique_pixels - c differences_summed = (differences ** 2).sum(axis=1) min_difference = differences[np.argmin(differences_summed)] new_cluster_centers.append(c + min_difference) new_cluster_centers = np.array(new_cluster_centers) kmeans.cluster_centers_ = new_cluster_centers kmeanses.append(kmeans) cluster_indices = kmeans.predict(all_pixels) all_pixels_clustered = kmeans.cluster_centers_[cluster_indices].reshape(image.shape) plt.subplot(5, 2, n_clusters) plt.title(f"n_clusters = {n_clusters}") plt.imshow(all_pixels_clustered) plt.axis("off") plt.tight_layout() plt.show() plt.title("Kwadratowa suma odlegล‚oล›ci punktรณw od swoich klastrรณw w zaleลผnoล›ci od liczby klastrรณw") plt.plot(np.arange(len(kmeanses)), [k.inertia_ for k in kmeanses]) plt.xlabel("Liczba klastrรณw") plt.xlabel("Suma kwadratowej odlegล‚oล›ci") plt.show() ###Output _____no_output_____ ###Markdown Z wykresu widaฤ‡ (z "elbow method"), ลผe od $k=4$, zmiany w ล›rednich odlegล‚oล›ciach punktรณw od ich klastrรณw nie sฤ… juลผ tak duลผe, jak przy mniejszej liczbie klastrรณw.Dodatkowo wizualizujฤ…c klasteryzacje, $k \geq 5$ wydajฤ… siฤ™ dawaฤ‡ ล‚adne wizualne wyniki. Uลผyjฤ™ wiฤ™c dalej kmeans wytrenowanego dla $k=5$ ###Code kmeans = kmeanses[4] ###Output _____no_output_____ ###Markdown Prezentujemy uzyskanฤ… paletฤ™. ###Code plt.title("Paleta kolorรณw znalezionych w k-means") plt.imshow(np.array([kmeans.cluster_centers_])) plt.show() sampled_pixels = unique_pixels[np.random.randint(0, len(unique_pixels), 10000)] sampled_pixels_clusters = kmeans.predict(sampled_pixels) clusters = kmeans.cluster_centers_ sampled_pixels_clustered = clusters[sampled_pixels_clusters] fig = plt.figure(figsize=(10, 10)) fig.suptitle("Przykล‚adowe piksele z obrazka (po lewej) i kolory, do ktรณrych zostaล‚y zmapowane przez k-means (po prawej)") for i, c in enumerate(clusters): plt.subplot(1, len(clusters), i +1) pixels_of_cluster = sampled_pixels[sampled_pixels_clusters == i][:10] pixels_clustered = sampled_pixels_clustered[sampled_pixels_clusters == i][:10] original_and_clustered = np.hstack([ pixels_of_cluster, pixels_clustered ]).reshape(-1, 2, 3) plt.axis("off") plt.imshow(original_and_clustered) plt.show() ###Output _____no_output_____ ###Markdown Wizualizujemy samฤ… klasteryzacjฤ™ (np. rzutujemy punkty ze zbioru na 2D uลผywajฤ…c PCA, kaลผdemu z nich ล›rodek malujemy na pierwotny kolor, a obwรณdkฤ™ na kolor klastra do ktรณrego byล‚ przyporzฤ…dkowany). ###Code pca = PCA().fit(all_pixels) sampled_pixels_pcad = pca.transform(sampled_pixels) clusters_pcad = pca.transform(clusters) fig = plt.figure(figsize=(10,10)) fig.suptitle("Wizualizacja klasteryzacji bez centrรณw klastrรณw") for i, (c, c_p) in enumerate(zip(clusters, clusters_pcad)): n_points = 20 pixels_of_cluster = sampled_pixels[sampled_pixels_clusters == i][:n_points] pixels_pcad = sampled_pixels_pcad[sampled_pixels_clusters == i][:n_points] plt.scatter(pixels_pcad[:,0], pixels_pcad[:,1], c=[c for _ in pixels_pcad], s=400) plt.scatter(pixels_pcad[:,0], pixels_pcad[:,1], c=pixels_of_cluster, s=150) plt.show() ###Output _____no_output_____ ###Markdown Nastฤ™pnie na tej samej wizualizacji 2D pokazujemy centra znalezionych klastrรณw oraz wartoล›ฤ‡ miary Silhouette dla kaลผdego z punktรณw (jest zawsze z zakresu -1 do 1, moลผna to zwizualizowaฤ‡ skalฤ… szaroล›ci). Jaki kolor miaล‚y oryginalnie punkty o najwyลผszym Silhouette, a jakie te o najniลผszym? Czy miara ta nadaje siฤ™ do wykrywania punktรณw - anomalii? Poniewaลผ $silhouette$ liczy siฤ™ bardzo dล‚ugo na peล‚nym zbiorze punktรณw, liczฤ™ je tylko na stworzonej wczesniej prรณbce. ###Code sampled_pixels_scores = silhouette_samples(sampled_pixels, sampled_pixels_clusters) fig = plt.figure(figsize=(15,15)) fig.suptitle("Wizualizacja klasteryzacji z centrami klastrรณw i wartoล›ciฤ… $silhouette$") for i, (c, c_p) in enumerate(zip(clusters, clusters_pcad)): n_points = 20 pixels_of_cluster = sampled_pixels[sampled_pixels_clusters == i][:n_points] pixels_pcad = sampled_pixels_pcad[sampled_pixels_clusters == i][:n_points] pixels_scores = sampled_pixels_scores[sampled_pixels_clusters == i][:n_points] plt.scatter(pixels_pcad[:,0], pixels_pcad[:,1], c=[c for _ in pixels_pcad], s=1100) plt.scatter(pixels_pcad[:,0], pixels_pcad[:,1], c="white", s=800) for (p_c, p_p, p_s) in zip(pixels_of_cluster, pixels_pcad, pixels_scores): plt.scatter([p_p[0]], [p_p[1]], c=[p_c], s=600, marker=f"${'%.2f' % p_s}$") plt.scatter([c_p[0]], [c_p[1]], c="white", marker="D", s=800 ) plt.scatter([c_p[0]], [c_p[1]], c=[c], marker="D", s=500 ) plt.show() ###Output _____no_output_____
_notebooks/2022-05-23-NLPKaggleComp.ipynb
###Markdown Quickly trying out a NLP model for Kaggle Competition- toc: true- branch: master- badges: true- hide_binder_badge: true- hide_deepnote_badge: true- comments: true- author: Kurian Benoy- categories: [kaggle, fastaicourse, NLP, huggingface] - hide: false- search_exclude: false This is my attempt to see how well we can build a NLP model for [Natural Language Processing with Disaster Tweets](https://www.kaggle.com/competitions/nlp-getting-started/overview).According to competition you are required to :> In this competition, youโ€™re challenged to build a machine learning model that predicts which Tweets are about real disasters and which oneโ€™s arenโ€™t. Youโ€™ll have access to a dataset of 10,000 tweets that were hand classified. If this is your first time working on an NLP problem, we've created a quick tutorial to get you up and running. Downloading Data ###Code creds = '' from pathlib import Path cred_path = Path("~/.kaggle/kaggle.json").expanduser() if not cred_path.exists(): cred_path.parent.mkdir(exist_ok=True) cred_path.write_text(creds) cred_path.chmod(0o600) ! kaggle competitions download -c nlp-getting-started #hide-output ! unzip nlp-getting-started.zip import pandas as pd df = pd.read_csv("train.csv") df.head() df.describe(include="object") #hide_output df["input"] = df["text"] ###Output _____no_output_____ ###Markdown Tokenization ###Code from datasets import Dataset, DatasetDict ds = Dataset.from_pandas(df) ds model_nm = "microsoft/deberta-v3-small" #hide-output from transformers import AutoModelForSequenceClassification, AutoTokenizer tokz = AutoTokenizer.from_pretrained(model_nm) def tok_func(x): return tokz(x["input"]) tok_ds = ds.map(tok_func, batched=True) #collapse_output row = tok_ds[0] row["input"], row["input_ids"] tok_ds = tok_ds.rename_columns({"target": "labels"}) tok_ds #collapse_output tok_ds[0] ###Output _____no_output_____ ###Markdown Validation, Traning, Testing ###Code eval_df = pd.read_csv("test.csv") eval_df.head() eval_df.describe(include="object") model_dataset = tok_ds.train_test_split(0.25, seed=34) model_dataset eval_df["input"] = eval_df["text"] eval_ds = Dataset.from_pandas(eval_df).map(tok_func, batched=True) ###Output _____no_output_____ ###Markdown Training Models ###Code from transformers import TrainingArguments, Trainer, DataCollatorWithPadding bs = 128 epochs = 4 data_collator = DataCollatorWithPadding(tokenizer=tokz) training_args = TrainingArguments("test-trainer") model = AutoModelForSequenceClassification.from_pretrained(model_nm, num_labels=2) trainer = Trainer( model, training_args, train_dataset=model_dataset['train'], eval_dataset=model_dataset['test'], data_collator=data_collator, tokenizer=tokz, ) trainer.train() preds = trainer.predict(eval_ds).predictions.astype(float) preds ###Output The following columns in the test set don't have a corresponding argument in `DebertaV2ForSequenceClassification.forward` and have been ignored: location, text, id, input, keyword. If location, text, id, input, keyword are not expected by `DebertaV2ForSequenceClassification.forward`, you can safely ignore this message. ***** Running Prediction ***** Num examples = 3263 Batch size = 8
Real_and_Fake_Face_Detection.ipynb
###Markdown IntroductionGreetings from the Kaggle bot! This is an automatically-generated kernel with starter code demonstrating how to read in the data and begin exploring. If you're inspired to dig deeper, click the blue "Fork Notebook" button at the top of this kernel to begin editing. Exploratory AnalysisTo begin this exploratory analysis, first import libraries and define functions for plotting the data using `matplotlib`. Depending on the data, not all plots will be made. (Hey, I'm just a simple kerneling bot, not a Kaggle Competitions Grandmaster!) ###Code from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # plotting import numpy as np # linear algebra import os # accessing directory structure import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) ###Output _____no_output_____ ###Markdown There is 0 csv file in the current version of the dataset: ###Code print(os.listdir('../input')) ###Output _____no_output_____ ###Markdown The next hidden code cells define functions for plotting data. Click on the "Code" button in the published kernel to reveal the hidden code. ###Code # Distribution graphs (histogram/bar graph) of column data def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow): nunique = df.nunique() df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values nRow, nCol = df.shape columnNames = list(df) nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k') for i in range(min(nCol, nGraphShown)): plt.subplot(nGraphRow, nGraphPerRow, i + 1) columnDf = df.iloc[:, i] if (not np.issubdtype(type(columnDf.iloc[0]), np.number)): valueCounts = columnDf.value_counts() valueCounts.plot.bar() else: columnDf.hist() plt.ylabel('counts') plt.xticks(rotation = 90) plt.title(f'{columnNames[i]} (column {i})') plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0) plt.show() # Correlation matrix def plotCorrelationMatrix(df, graphWidth): filename = df.dataframeName df = df.dropna('columns') # drop columns with NaN df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values if df.shape[1] < 2: print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2') return corr = df.corr() plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k') corrMat = plt.matshow(corr, fignum = 1) plt.xticks(range(len(corr.columns)), corr.columns, rotation=90) plt.yticks(range(len(corr.columns)), corr.columns) plt.gca().xaxis.tick_bottom() plt.colorbar(corrMat) plt.title(f'Correlation Matrix for {filename}', fontsize=15) plt.show() # Scatter and density plots def plotScatterMatrix(df, plotSize, textSize): df = df.select_dtypes(include =[np.number]) # keep only numerical columns # Remove rows and columns that would lead to df being singular df = df.dropna('columns') df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values columnNames = list(df) if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots columnNames = columnNames[:10] df = df[columnNames] ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde') corrs = df.corr().values for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)): ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize) plt.suptitle('Scatter and Density Plot') plt.show() ###Output _____no_output_____
task_20.ipynb
###Markdown ะกะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ , ัะพัั‚ะพัั‰ะธะน ะธะท 10 ัะปัƒั‡ะฐะนะฝั‹ั… ั†ะตะปั‹ั… ั‡ะธัะตะป ะธะท ะดะธะฐะฟะฐะทะพะฝะฐ ะพั‚ 1 ะดะพ 20, ะทะฐั‚ะตะผ ัะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ ัะพัั‚ะพัั‰ะธะน ะธะท 5 ัะปะตะผะตะฝั‚ะพะฒ, ะฝะฐั‡ะธะฝะฐั ัะพ 2 ะฟะพ ะฟะพั€ัะดะบัƒ. ะ’ั‹ะฒะตะดะธั‚ะต ะทะฝะฐั‡ะตะฝะธั ะพะฑะพะธั… ะผะฐััะธะฒะพะฒ ะฟั€ะธ ะฟะพะผะพั‰ะธ ั„ัƒะฝะบั†ะธะธ print() ###Code arr_1 = np.random.randint(1, 21, 10) arr_2 = arr_1[1:6] print(arr_1) print(arr_2) ###Output [16 13 12 7 5 3 16 9 4 18] [13 12 7 5 3] ###Markdown ะกะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ , ัะพัั‚ะพัั‰ะธะน ะธะท 10 ัะปัƒั‡ะฐะนะฝั‹ั… ั†ะตะปั‹ั… ั‡ะธัะตะป ะธะท ะดะธะฐะฟะฐะทะพะฝะฐ ะพั‚ 0 ะดะพ 20, ะทะฐั‚ะตะผ ัะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ ัะพัั‚ะพัั‰ะธะน ะธะท ัะปะตะผะตะฝั‚ะพะฒ, ะฝะฐั‡ะธะฝะฐั ั ั‚ั€ะตั‚ัŒะตะณะพ ะฟะพ ะฟะพั€ัะดะบัƒ ะดะพ ะฟะพัะปะตะดะฝะตะณะพ. ะ’ั‹ะฒะตะดะธั‚ะต ะทะฝะฐั‡ะตะฝะธั ะพะฑะพะธั… ะผะฐััะธะฒะพะฒ ะฟั€ะธ ะฟะพะผะพั‰ะธ ั„ัƒะฝะบั†ะธะธ print() ###Code arr_1 = np.random.randint(1, 21, 10) arr_2 = arr_1[2:] print(arr_1) print(arr_2) ###Output [ 2 8 5 7 18 6 3 12 16 5] [ 5 7 18 6 3 12 16 5] ###Markdown ะกะพะทะดะฐะนั‚ะต ะดะฒัƒะผะตั€ะฝั‹ะน ะผะฐััะธะฒ , ัะพัั‚ะพัั‰ะธะน ะธะท ัะปัƒั‡ะฐะนะฝั‹ั… ั†ะตะปั‹ั… ั‡ะธัะตะป ะธะท ะดะธะฐะฟะฐะทะพะฝะฐ ะพั‚ 3 ะดะพ 11, ะฒ ะบะพั‚ะพั€ะพะผ 4 ัั‚ั€ะพะบะธ ะธ 3 ัั‚ะพะปะฑั†ะฐ. ะ—ะฐั‚ะตะผ ัะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ ัะพัั‚ะพัั‰ะธะน ะธะท ัะปะตะผะตะฝั‚ะพะฒ , ะฒั‚ะพั€ะพะน ะธ ั‚ั€ะตั‚ัŒะตะน ัั‚ั€ะพะบะธ ะธ ะฟะตั€ะฒะพะณะพ ะธ ะฒั‚ะพั€ะพะณะพ ัั‚ะพะปะฑั†ะฐ ะฟะพ ะฟะพั€ัะดะบัƒ. ะ’ั‹ะฒะตะดะธั‚ะต ะทะฝะฐั‡ะตะฝะธั ะพะฑะพะธั… ะผะฐััะธะฒะพะฒ ะฟั€ะธ ะฟะพะผะพั‰ะธ ั„ัƒะฝะบั†ะธะธ print() ###Code arr_1 = np.random.randint(3, 12, 12).reshape(4, 3) arr_2 = arr_1[1:3, :2] print(arr_1) print(arr_2) ###Output [[11 10 8] [ 5 9 3] [ 8 11 8] [ 3 3 4]] [[ 5 9] [ 8 11]] ###Markdown ะกะพะทะดะฐะนั‚ะต ะดะฒัƒะผะตั€ะฝั‹ะน ะผะฐััะธะฒ , ัะพัั‚ะพัั‰ะธะน ะธะท ัะปัƒั‡ะฐะนะฝั‹ั… ั†ะตะปั‹ั… ั‡ะธัะตะป ะธะท ะดะธะฐะฟะฐะทะพะฝะฐ ะพั‚ 0 ะดะพ 9, ะฒ ะบะพั‚ะพั€ะพะผ 4 ัั‚ั€ะพะบะธ ะธ 6 ัั‚ะพะปะฑั†ะพะฒ. ะ—ะฐั‚ะตะผ ัะพะทะดะฐะนั‚ะต ะผะฐััะธะฒ ัะพัั‚ะพัั‰ะธะน ะธะท ัะปะตะผะตะฝั‚ะพะฒ , ะบะพั‚ะพั€ั‹ะต ะฑะพะปัŒัˆะต ั‡ะธัะปะฐ 3. ะ’ั‹ะฒะตะดะธั‚ะต ะทะฝะฐั‡ะตะฝะธั ะพะฑะพะธั… ะผะฐััะธะฒะพะฒ ะฟั€ะธ ะฟะพะผะพั‰ะธ ั„ัƒะฝะบั†ะธะธ print() ###Code arr_1 = np.random.randint(0, 10, 24).reshape(4, 6) arr_2 = arr_1[arr_1>3] print(arr_1) print(arr_2) ###Output [[5 4 8 4 9 6] [8 1 6 6 7 9] [5 9 2 5 9 3] [9 3 0 1 0 1]] [5 4 8 4 9 6 8 6 6 7 9 5 9 5 9 9]
tsa/jose/TSA_COURSE_NOTEBOOKS/05-Time-Series-Analysis-with-Statsmodels/.ipynb_checkpoints/02-EWMA-Exponentially-Weighted-Moving-Average-checkpoint.ipynb
###Markdown ______Copyright Pierian DataFor more information, visit us at www.pieriandata.com MA Moving AveragesIn this section we'll compare Simple Moving Averages to Exponentially Weighted Moving Averages in terms of complexity and performance.Related Functions:pandas.DataFrame.rolling(window)&nbsp;&nbsp;Provides rolling window calculationspandas.DataFrame.ewm(span)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Provides exponential weighted functions Perform standard imports and load the datasetFor these examples we'll use the International Airline Passengers dataset, which gives monthly totals in thousands from January 1949 to December 1960. ###Code import pandas as pd import numpy as np %matplotlib inline airline = pd.read_csv('../Data/airline_passengers.csv',index_col='Month',parse_dates=True) airline.dropna(inplace=True) airline.head() ###Output _____no_output_____ ###Markdown ___ SMA Simple Moving AverageWe've already shown how to create a simple moving average by applying a mean function to a rolling window.For a quick review: ###Code airline['6-month-SMA'] = airline['Thousands of Passengers'].rolling(window=6).mean() airline['12-month-SMA'] = airline['Thousands of Passengers'].rolling(window=12).mean() airline.head(15) airline.plot(); ###Output _____no_output_____ ###Markdown ___ EWMA Exponentially Weighted Moving Average We just showed how to calculate the SMA based on some window. However, basic SMA has some weaknesses:* Smaller windows will lead to more noise, rather than signal* It will always lag by the size of the window* It will never reach to full peak or valley of the data due to the averaging.* Does not really inform you about possible future behavior, all it really does is describe trends in your data.* Extreme historical values can skew your SMA significantlyTo help fix some of these issues, we can use an EWMA (Exponentially weighted moving average). EWMA will allow us to reduce the lag effect from SMA and it will put more weight on values that occured more recently (by applying more weight to the more recent values, thus the name). The amount of weight applied to the most recent values will depend on the actual parameters used in the EWMA and the number of periods given a window size.[Full details on Mathematics behind this can be found here](http://pandas.pydata.org/pandas-docs/stable/user_guide/computation.htmlexponentially-weighted-windows).Here is the shorter version of the explanation behind EWMA.The formula for EWMA is: $y_t = \frac{\sum\limits_{i=0}^t w_i x_{t-i}}{\sum\limits_{i=0}^t w_i}$ Where $x_t$ is the input value, $w_i$ is the applied weight (Note how it can change from $i=0$ to $t$), and $y_t$ is the output.Now the question is, how to we define the weight term $w_i$?This depends on the adjust parameter you provide to the .ewm() method.When adjust=True (default) is used, weighted averages are calculated using weights equal to $w_i = (1 - \alpha)^i$which gives $y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ...+ (1 - \alpha)^t x_{0}}{1 + (1 - \alpha) + (1 - \alpha)^2 + ...+ (1 - \alpha)^t}$ When adjust=False is specified, moving averages are calculated as: $\begin{split}y_0 &= x_0 \\y_t &= (1 - \alpha) y_{t-1} + \alpha x_t,\end{split}$which is equivalent to using weights: \begin{split}w_i = \begin{cases} \alpha (1 - \alpha)^i & \text{if } i < t \\ (1 - \alpha)^i & \text{if } i = t.\end{cases}\end{split} When adjust=True we have $y_0=x_0$ and from the last representation above we have $y_t=\alpha x_t+(1โˆ’ฮฑ)y_{tโˆ’1}$, therefore there is an assumption that $x_0$ is not an ordinary value but rather an exponentially weighted moment of the infinite series up to that point.For the smoothing factor $\alpha$ one must have $0alpha directly, itโ€™s often easier to think about either the span, center of mass (com) or half-life of an EW moment: \begin{split}\alpha = \begin{cases} \frac{2}{s + 1}, & \text{for span}\ s \geq 1\\ \frac{1}{1 + c}, & \text{for center of mass}\ c \geq 0\\ 1 - \exp^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0 \end{cases}\end{split} * Span corresponds to what is commonly called an โ€œN-day EW moving averageโ€.* Center of mass has a more physical interpretation and can be thought of in terms of span: $c=(sโˆ’1)/2$* Half-life is the period of time for the exponential weight to reduce to one half.* Alpha specifies the smoothing factor directly.We have to pass precisely one of the above into the .ewm() function. For our data we'll use span=12. ###Code airline['EWMA12'] = airline['Thousands of Passengers'].ewm(span=12,adjust=False).mean() airline[['Thousands of Passengers','EWMA12']].plot(); ###Output _____no_output_____ ###Markdown Comparing SMA to EWMA ###Code airline[['Thousands of Passengers','EWMA12','12-month-SMA']].plot(figsize=(12,8)).autoscale(axis='x',tight=True); ###Output _____no_output_____
code_lou_demos/pandas_intro.ipynb
###Markdown Code Louisville intro to Pandas Pandas is a library that allows us to deal with data in a dataframe format. This is really useful for being able to quickly do data analysis. When importing, it is conventional to import as 'pd' which allows it to later be referenced by typing 'pd' rather than 'pandas' ###Code import pandas as pd import os ###Output _____no_output_____ ###Markdown I'm using the os to find the current working directory. ###Code os.getcwd() ###Output _____no_output_____ ###Markdown I downloaded the data from here: http://greaterlouisvilleproject.org/deep-drivers-of-change/education/ and saved it in my current working directory. Now I'm using Pandas' read_excel command to read it in and save it as a dataframe named edu_df. Then I used the `.head()` method to show the first 6 rows of the dataframe. Pandas also has a `pd.read_csv()` command. You can specify either an absolute file path, or more commonly a relative file path, e.g. `pd.read_csv('data/my_data.csv')` ###Code edu_df = pd.read_excel('GLP-Codebook.xlsx', 'Edu County', index_col=None, na_values=['NA']) edu_df.head(n = 6) ###Output _____no_output_____ ###Markdown Using `.tail()` shows the last n rows of the dataframe. ###Code edu_df.tail(n = 6) ###Output _____no_output_____ ###Markdown More generally, `.shape` will give the dimensions of the dataframe ###Code edu_df.shape ###Output _____no_output_____ ###Markdown And passing the pandas dataframe to the list function will produce a list of all the column names. ###Code list(edu_df) ###Output _____no_output_____ ###Markdown You can use the `.iloc` method to pull data based on its index location. ###Code edu_df.iloc[3, 8] ###Output _____no_output_____ ###Markdown While calling `.iloc[3, 8]` will give you Louisville's child poverty rate in 2005, I don't recommend doing it this way. It's better to select by column name and then filter down to the row(s) you want. It's way too easy to make a mistake with numerical indices. Selecting by column name and filtering data based is covered a bit later in this intro. You can pull more than one row and column using the `:` operator. The first index is included, while the second one is excluded, so in the example below asking for rows `1:4` includes row 1 but not row 4 ###Code edu_df.iloc[1:4, 1:9] ###Output _____no_output_____ ###Markdown The `:` operator can also be used to select everything up to a certain index - again exclusive of the index you use. Sp :4 gives rows 0 to 3. ###Code edu_df.iloc[:4, :9] ###Output _____no_output_____ ###Markdown Pandas makes it easy to get summary statistics for the whole dataframe. Note though, that Pandas guesses what kind of data it is dealing with. A mean year of 2010.5 doesn't make much sense. ###Code edu_df.describe() ###Output _____no_output_____ ###Markdown The type of a variable can be changed using the `.astype()` method. Here we make year a categorical variable, and it drops out of the `edu_df.describe()` output because it no longer matches the rest of the dataframe. ###Code edu_df['year'] = edu_df['year'].astype('category') edu_df.describe() ###Output _____no_output_____ ###Markdown But we can select it on its own to describe it. ###Code edu_df['year'].describe() ###Output _____no_output_____ ###Markdown The `.describe()` method returns something different depending on the type of data it's passed ###Code edu_df['child_per'].describe() ###Output _____no_output_____ ###Markdown Pandas also makes it easy to create new variables by performing mathematical operations on already existing variables. ###Code edu_df['bach_plus_race_gap'] = edu_df['bach_plus_per_white'] - edu_df['bach_plus_per_black'] edu_df.head(n = 6) ###Output _____no_output_____ ###Markdown Filtering data can be done by using brackets. So suppose we just want Louisville in the year 2005. We can filter to that, and then select the column for under age 5 child poverty. This is a better idea than using `.iloc()` because it won't silently break if the underlying dataframe changes and it's harder to make a mistake with column names and variable values (city == "Louisville) than with index values. ###Code filtered_df = edu_df[(edu_df.city == "Louisville") & (edu_df.year == 2005)] filtered_df['under_5_per'] ###Output _____no_output_____ ###Markdown We can also combine these operation to avoid creating a new dataframe. ###Code edu_df[(edu_df.city == "Louisville") & (edu_df.year == 2005)]['under_5_per'] ###Output _____no_output_____ ###Markdown Joining Data Pandas also allows us to merge datasets together relatively painlessly. To start with, we'll need another dataset. Let's read in another sheet from the same excel document. ###Code jobs_df = pd.read_excel('GLP-Codebook.xlsx', 'Jobs County', index_col=None, na_values=['NA']) jobs_df.head(n = 6) ###Output _____no_output_____ ###Markdown Pandas has a `merge()` function that takes the name of the two dataframe, the type of join (left, right, inner, outer) and the names of the columns to join on. ###Code df = pd.merge(edu_df, jobs_df, how='outer', left_on=['FIPS','year'], right_on = ['FIPS','year']) df.head(n = 6) ###Output _____no_output_____ ###Markdown Notice that pandas even renamed duplicated columns. So city was in both datasets and now there is city_x and city_y. ###Code list(df) ###Output _____no_output_____ ###Markdown That's more columns than we need for this example workbook. Pandas .filter method can be used to select a subset of the columns ###Code df_sel = df.filter(items = ['city_x', 'year', 'current_x', 'child_per', 'per_25_64_bach_plus', 'per_high_wage']) df_sel.head(n = 6) ###Output _____no_output_____ ###Markdown In this data, current is an indicator variable that takes 1 if the city is from the current peer city list, and 0 otherwise. There is an older peer city list called baseline. If you work for the city, you should select to keep the `baseline_x` column and then filter to when that variable is equal to 1. ###Code df_sel = df_sel[(df_sel.current_x == 1)] df_sel['current_x'].describe() ###Output _____no_output_____ ###Markdown Some of these names are kind of unwieldy. Let's rename things. And we already showed how to select columsn, but now that `current_x` only takes only the value of 1, we can drop it from the dataframe. ###Code df_sel = df_sel.rename(columns = {"city_x": "city", "per_25_64_bach_plus" :"bach", "child_per":"child_pov", "per_high_wage":"high_wage_jobs"}) df_sel = df_sel.drop('current_x', axis = 1) list(df_sel) ###Output _____no_output_____ ###Markdown Pandas easily allows us to look for correlations across all of the data using the .corr() method. ###Code df_sel.corr() ###Output _____no_output_____ ###Markdown Reshaping Data A common operation in data science is to transform data from wide to long and vice versa. The data is currently in a long format. It's 204 rows and 5 columns. ###Code df_sel.shape df_T = df_sel.T df_T df_wide = df_sel.pivot(index = 'city', columns = 'year') df_wide df_wide.shape ###Output _____no_output_____ ###Markdown Pivoting the dataframe resulted in a dataframe with a hierarchical index. So now calling data at the top level can select more than one column. ###Code df_wide['child_pov'] ###Output _____no_output_____ ###Markdown And we can call down multiple index levels, making it easy to select all our cities for 2016 ###Code df_wide['child_pov'][2016] ###Output _____no_output_____ ###Markdown You can also sort by using the .sort_values ###Code df_wide['child_pov'][2016].sort_values ###Output _____no_output_____ ###Markdown Except that sorted by city - by default it used the first column. Which is okay if we want them in alphabetical order, but what about order by child poverty? ###Code df_wide['child_pov'].sort_values(by=[2016], ascending = False) ###Output _____no_output_____ ###Markdown Reshaping hierarchical data frames is difficult, so I'm going to cut down to just the child poverty data ###Code df_wide = df_wide['child_pov'] df_wide.head(n = 6) ###Output _____no_output_____ ###Markdown One final note before reshaping, you can use .T to transpose a dataframe. ###Code df_wide.T ###Output _____no_output_____ ###Markdown Pandas has an index that isn't strictly part of the dataframe. By default it's 0, 1, 2, 3, etc. HOwever, when I cast the data from long to wide, I set the index to city values. Now we need to undo that before melting/gathering the data. ###Code df_wide.reset_index(level=0, inplace = True) df_wide.head(n = 6) ###Output _____no_output_____ ###Markdown And now we're ready to put our data back into long format using `.melt()` ###Code df_long = pd.melt(frame = df_wide, col_level = 0, id_vars = ['city'], value_vars = [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016], value_name = "child_pov") df_long ###Output _____no_output_____
sample_volume.ipynb
###Markdown Demonstration of DCBC evaluation usage in volume spaceThis notebook shows an example of a Distance controlled boundary coefficient (DCBC) evaluation of a striatum parcellation using the Multi-domain task battery (MDTB) functional dataset (glm7). Installation and RequirmentsEnsure Python version >= 3.6 and pip installed on your system.- Install Python at https://www.python.org/downloads/- Install pip: https://pip.pypa.io/en/stable/installing/ Dependencies`pip install nibabel scipy numpy sklearn matplotlib` UsageBelow is a quick sample script of using the DCBC evaluation for evaluating `Power 2011` left hemisphere. ###Code import numpy as np import scipy as sp from DCBC_vol import compute_dist, compute_DCBC import nibabel as nb import mat73 from plotting import plot_single # Load mask voxel index vol_ind = mat73.loadmat('D:/data/sc2/encoding/glm7/striatum_avrgDataStruct.mat')['volIndx'] vol_ind = vol_ind.astype(int) ###Output data type not supported: struct, uint64 ###Markdown Now, we load the parcellation that we want to evaluate from the file given the volxel indices of the mask.Note: we first need to transfer the vol index to 3d coordinates by F-order. This is because MATLAB uses row-order to perform linear indexing, while numpy uses colume order. ###Code # Load parcellation given the mask file or voxel indices parcel_mni = nb.load('D:/data/sc2/encoding/glm7/spect/masked_par_choi_7.nii.gz').get_fdata() coord = np.unravel_index(vol_ind - 1, parcel_mni.shape, 'F') # Note: the linear indexing in numpy is column-order parcels = np.rint(parcel_mni[coord[0], coord[1], coord[2]]) print(parcels) ###Output [5. 0. 5. ... 0. 6. 0.] ###Markdown We also need a pairwise distance matrix of all mask voxel indices for DCBC calcluation. ###Code # Compute the distance matrix between voxel pairs using the mask file, numpy default C-order coord = np.asarray(coord).transpose() dist = compute_dist(coord, 2) print(dist) ###Output [[ 0. 38.05259518 12.16552506 ... 69.3108938 67.94115101 68.93475176] [38.05259518 0. 26. ... 51.57518783 51.2249939 50.99019514] [12.16552506 26. 0. ... 62.09669879 60.95900262 61.6116872 ] ... [69.3108938 51.57518783 62.09669879 ... 0. 2. 2. ] [67.94115101 51.2249939 60.95900262 ... 2. 0. 2.82842712] [68.93475176 50.99019514 61.6116872 ... 2. 2.82842712 0. ]] ###Markdown Here, we load subject functional data for DCBC evaluation and several experiment settings. ###Code # Load functional profile (betas) and several parameters for evaluation settings T = mat73.loadmat('D:/data/sc2/encoding/glm7/striatum_avrgDataStruct.mat')['T'] returnsubj = [2,3,4,6,8,9,10,12,14,15,17,18,19,20,21,22,24,25,26,27,28,29,30,31] session, maxDist, binWidth = 1, 90, 5 ###Output data type not supported: struct, uint64 ###Markdown Now, we start the real DCBC evaluation on the given parcellation using selected subjects and given experiment settings. So here we set the bin width = 5 mm, the maximum distance between any pair of voxels is 90 mm. We only use subjects session 1 data. ###Code wcorr_array, bcorr_array, dcbc_array = np.array([]), np.array([]), np.array([]) for sub in returnsubj: data = T['data'][(T['SN'] == sub) & (T['sess'] == session)].transpose() R = compute_DCBC(maxDist=maxDist, func=data, dist=dist, binWidth=binWidth, parcellation=parcels) wcorr_array = np.append(wcorr_array, R['corr_within']) bcorr_array = np.append(bcorr_array, R['corr_between']) dcbc_array = np.append(dcbc_array, R['DCBC']) # print(wcorr_array, bcorr_array, dcbc_array) ###Output _____no_output_____ ###Markdown After we finishe the DCBC evalaution for all subjects, we plot the un-weighted within- and between-correlation curves. A bigger gap between two curves means the given parcellation has higher quality to functionally separate the brain regions. Otherwise, the parcellation cannot functionally separate the brain obviously. In the extrame, the two curves are the same for random parcellations. ###Code %matplotlib inline plot_single(within=wcorr_array, between=bcorr_array, maxDist=90, binWidth=5, subjects=returnsubj, within_color='k', between_color='r') print(dcbc_array) ###Output _____no_output_____
Monte_carlo Simulation on AMD Stocks/amd stocks simulation.ipynb
###Markdown OVERVIEW---* Data Visualization of AMD stock price.* Plotting ACF AND PACF.* Growth Factor of AMD stock price.* Seasonal Decomposition of data.* Monte Carlo simulation of AMD stock price. ###Code #VIZ LIBRARY import pandas as pd from pandas import plotting import pandas_datareader as wb import numpy as np from tqdm.notebook import tqdm as tqdm import seaborn as sns import matplotlib.pyplot as plt from matplotlib import pyplot plt.style.use('fivethirtyeight') sns.set_style('whitegrid') #CLASSICAL STATS import scipy from scipy.stats import norm import statsmodels from scipy import signal import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.seasonal import seasonal_decompose #METRICS from sklearn.metrics import accuracy_score, confusion_matrix,classification_report, r2_score,mean_absolute_error,mean_squared_error import warnings warnings.filterwarnings('ignore') #Setting monte-carlo asset data ticker = 'AMD' t_intervals, iteration = 30, 25 #Simulating the movement of stocks for 30 days with 25 different possibilities df = pd.DataFrame() #Get the data from source df[ticker] = wb.DataReader(ticker, data_source='yahoo', start='2018-1-1')['Adj Close'] ###Output _____no_output_____ ###Markdown DATA BASIC INFORMATION--- ###Code #show dataframe df.head(10).T #show feature datatype df.info() print('MIN STOCK PRICE: ', df.AMD.min()) print('MAX STOCK PRICE: ', df.AMD.max()) print('MEAN OF STOCK PRICE: ', df.AMD.mean()) print('MEDIAN OF STOCK PRICE: ', df.AMD.median()) ###Output MIN STOCK PRICE: 9.529999732971191 MAX STOCK PRICE: 58.900001525878906 MEAN OF STOCK PRICE: 29.04399999285501 MEDIAN OF STOCK PRICE: 27.8100004196167 ###Markdown EDA--- ###Code #show fig plt.figure(figsize=(13,4)) plt.title('STOCK PRICE VS TIME') plt.plot(df.index, df.AMD, lw=2, marker='o', markersize=2, color='steelblue') plt.xlabel('Date') plt.ylabel('Price') ###Output _____no_output_____ ###Markdown INSIGHT---* The graph is highly non-linear and just by looking at it, we can't identy the pattern or trend of stock price. I think if we decompose it, we can see more interesting details. ###Code #Applying Seasonal decomposse dec = seasonal_decompose(df.AMD, freq=1, model='multiplicative') fig,ax = plt.subplots(3,1, figsize=(10,5)) sns.lineplot(dec.trend.index, dec.trend.values, ax=ax[0]) sns.lineplot(dec.seasonal.index, dec.seasonal.values, ax=ax[1]) sns.lineplot(dec.resid.index, dec.resid.values, ax=ax[2]) for i, res in enumerate(['TREND', 'SEASONAL', 'RESIDUAL']): ax[i].set_title(res) plt.tight_layout(1) ###Output _____no_output_____ ###Markdown DISTRIBUTION OF PRICE--- ###Code df['range'] = pd.cut(df.AMD, [0,10,20,30,40,50,60]).values #show distribution fig, ax = plt.subplots(1,2, figsize=(15,5)) sns.barplot(x=df.groupby('range')['AMD'].count().index, y=df.groupby('range')['AMD'].count().values, ax=ax[0]) sns.distplot(df.AMD, bins=40) plt.suptitle('DISTRIBUTION OF PRICE', fontsize=20) ax[0].set_xlabel('Range') ax[0].set_ylabel('Frequency') ax[1].set_xlabel('Range') ###Output _____no_output_____ ###Markdown INSIGHTS---* As we can see from the plot abaove, prices from 10-30 are very frequent.* The Stock price distribution is positively skewed, which mean the measures are dispersed.* The Distribution may be expressed as (Mean > Median > Mode). GROWTH FACTOR OF STOCK PRICE--- ###Code plt.figure(figsize=(14,5)) plt.title('GROWTH FACTOR PLOT OF AMD STOCK PRICE', fontsize=18) plt.plot(df.AMD.index, (df.AMD / df.AMD.shift().fillna(0)), lw=2, color='salmon') ###Output _____no_output_____ ###Markdown ACF AND PACF--- ###Code fig, ax = plt.subplots(1,2, figsize=(14,4)) plot_acf(df.AMD, lags=7, ax=ax[0]) plot_pacf(df.AMD, lags=7, ax=ax[1]) plt.show() ###Output _____no_output_____ ###Markdown INSIGHTS---* The autocorrelation function shows a veryslow decay, which means that the future values have a very high correlation with its past values.* The partial autocorrelation function shows a high correlation with the first lag and lesser correlation with the second and third lag. MONTE CARLO SIMULATION--- ###Code #dropping the range feature, because i dont need them anymore df.drop('range', axis=1, inplace=True) #log returns of data log_returns = np.log(1 + df.pct_change()) #show fig log returns plt.figure(figsize=(10,4)) plt.title('LOG NORMAL RETURNS OF PRICES') sns.lineplot(log_returns.index, log_returns.AMD,lw=1, color='violet') plt.legend('') #Setting up the drift and random component mean_ = log_returns.mean() var = log_returns.var() stdev = log_returns.std() drift = mean_ - (0.5 *var) daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, iteration))) S0 = df.iloc[-1] #Empty daily returns price_list = np.zeros_like(daily_returns) price_list[0] = S0 #appliying montecarlo simulation for i in range(1 , t_intervals): price_list[i] = price_list[i-1] * daily_returns[i] #Show the result of 30 days simulation plt.figure(figsize = (10,4)) plt.plot(price_list, lw=1) plt.title('30 DAYS SIMULATION WITH 25 DIFFERENT POSSIBILITIES') ###Output _____no_output_____
notebook/19B_Pystan.ipynb
###Markdown PyStan====Install `PyStan` with```pip install pystan```The nice thing about `PyMC` is that everything is in Python. With `PyStan`, however, you need to use a domain specific language based on C++ syntax to specify the model and the data, which is less flexible and more work. However, in exchange you get an extremely powerful HMC package (only does HMC) that can be used in R and Python. Useful links- [Paper describing Stan](http://www.stat.columbia.edu/~gelman/research/unpublished/stan-resubmit-JSS1293.pdf)- [Stan home page](http://mc-stan.org/interfaces/)- [Stan Examples and Reference Manual](https://github.com/stan-dev/example-models/wiki)- [PyStan docs](http://pystan.readthedocs.org/en/latest/)- [PyStan GitHub page](https://github.com/stan-dev/pystan) Coin tossWe'll repeat the example of determining the bias of a coin from observed coin tosses. The likelihood is binomial, and we use a beta prior. ###Code coin_code = """ data { int<lower=0> n; // number of tosses int<lower=0> y; // number of heads } transformed data {} parameters { real<lower=0, upper=1> p; } transformed parameters {} model { p ~ beta(2, 2); y ~ binomial(n, p); } generated quantities {} """ coin_dat = { 'n': 100, 'y': 61, } ###Output _____no_output_____ ###Markdown Fit model ###Code sm = pystan.StanModel(model_code=coin_code) ###Output INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_7f1947cd2d39ae427cd7b6bb6e6ffd77 NOW. ###Markdown MAP ###Code op = sm.optimizing(data=coin_dat) op ###Output _____no_output_____ ###Markdown MCMC ###Code fit = sm.sampling(data=coin_dat) ###Output _____no_output_____ ###Markdown Loading from a fileThe string in coin_code can also be in a file - say `coin_code.stan` - then we can use it like so```pythonfit = pystan.stan(file='coin_code.stan', data=coin_dat, iter=1000, chains=1)``` ###Code print(fit) coin_dict = fit.extract() coin_dict.keys() # lp_ is the log posterior ###Output _____no_output_____ ###Markdown We can convert to a DataFrame if necessary ###Code df = pd.DataFrame(coin_dict) df.head(3) fit.plot('p'); plt.tight_layout() ###Output _____no_output_____ ###Markdown Estimating mean and standard deviation of normal distribution$$X \sim \mathcal{N}(\mu, \sigma^2)$$ ###Code norm_code = """ data { int<lower=0> n; real y[n]; } transformed data {} parameters { real<lower=0, upper=100> mu; real<lower=0, upper=10> sigma; } transformed parameters {} model { y ~ normal(mu, sigma); } generated quantities {} """ norm_dat = { 'n': 100, 'y': np.random.normal(10, 2, 100), } fit = pystan.stan(model_code=norm_code, data=norm_dat, iter=1000, chains=1) fit trace = fit.extract() plt.figure(figsize=(10,4)) plt.subplot(1,2,1); plt.hist(trace['mu'][:], 25, histtype='step'); plt.subplot(1,2,2); plt.hist(trace['sigma'][:], 25, histtype='step'); ###Output _____no_output_____ ###Markdown Optimization (finding MAP) ###Code sm = pystan.StanModel(model_code=norm_code) op = sm.optimizing(data=norm_dat) op ###Output INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_3318343d5265d1b4ebc1e443f0228954 NOW. ###Markdown Reusing fitted objects ###Code new_dat = { 'n': 100, 'y': np.random.normal(10, 2, 100), } fit2 = pystan.stan(fit=fit, data=new_dat, chains=1) fit2 ###Output _____no_output_____ ###Markdown Saving compiled modelsWe can also compile Stan models and save them to file, so as to reload them for later use without needing to recompile. ###Code def save(obj, filename): """Save compiled models for reuse.""" import pickle with open(filename, 'wb') as f: pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL) def load(filename): """Reload compiled models for reuse.""" import pickle return pickle.load(open(filename, 'rb')) model = pystan.StanModel(model_code=norm_code) save(model, 'norm_model.pic') new_model = load('norm_model.pic') fit4 = new_model.sampling(new_dat, chains=1) fit4 ###Output _____no_output_____ ###Markdown Estimating parameters of a linear regression modelWe will show how to estimate regression parameters using a simple linear model$$y \sim ax + b$$We can restate the linear model $$y = ax + b + \epsilon$$ as sampling from a probability distribution$$y \sim \mathcal{N}(ax + b, \sigma^2)$$We will assume the following priors$$a \sim \mathcal{N}(0, 100) \\b \sim \mathcal{N}(0, 100) \\\sigma \sim \mathcal{U}(0, 20)$$ ###Code lin_reg_code = """ data { int<lower=0> n; real x[n]; real y[n]; } transformed data {} parameters { real a; real b; real sigma; } transformed parameters { real mu[n]; for (i in 1:n) { mu[i] <- a*x[i] + b; } } model { sigma ~ uniform(0, 20); y ~ normal(mu, sigma); } generated quantities {} """ n = 11 _a = 6 _b = 2 x = np.linspace(0, 1, n) y = _a*x + _b + np.random.randn(n) lin_reg_dat = { 'n': n, 'x': x, 'y': y } fit = pystan.stan(model_code=lin_reg_code, data=lin_reg_dat, iter=1000, chains=1) fit fit.plot(['a', 'b']); plt.tight_layout() ###Output _____no_output_____ ###Markdown Simple Logistic modelWe have observations of height and weight and want to use a logistic model to guess the sex. ###Code # observed data df = pd.read_csv('HtWt.csv') df.head() log_reg_code = """ data { int<lower=0> n; int male[n]; real weight[n]; real height[n]; } transformed data {} parameters { real a; real b; real c; } transformed parameters {} model { a ~ normal(0, 10); b ~ normal(0, 10); c ~ normal(0, 10); for(i in 1:n) { male[i] ~ bernoulli(inv_logit(a*weight[i] + b*height[i] + c)); } } generated quantities {} """ log_reg_dat = { 'n': len(df), 'male': df.male, 'height': df.height, 'weight': df.weight } fit = pystan.stan(model_code=log_reg_code, data=log_reg_dat, iter=1000, chains=4) fit df_trace = pd.DataFrame(fit.extract(['c', 'b', 'a'])) pd.scatter_matrix(df_trace[:], diagonal='kde'); ###Output _____no_output_____
jupyter_notebooks/ditributions/plot_distributions.ipynb
###Markdown Normal distribution ###Code mu = 3.2 sigma = 0.3 data = norm.rvs(mu, sigma, size=3000, random_state=45) fig, ax = plt.subplots(figsize=(12,6)) sns.histplot(data=data, kde=True, palette='deep') ax.annotate(f'$\mu$={mu}\n$\sigma$={sigma}', xy=(3.85, 80), fontsize=15, ha='center', va='center') ax.set_title('Average weight of a newborn in kilos', fontsize=15) ax.xaxis.set_tick_params(labelsize=13) ax.get_yaxis().set_visible(False) plt.tight_layout() plt.savefig('../../assets/images/probability/toy_newborn_weight_distribution.png', bbox_inches='tight'); fig, ax = plt.subplots(1,2, figsize=(20,6)) x = np.arange(1.5, 4.9, 0.001) y = norm.pdf(x, mu, sigma) ax[0].plot(x, y, color='royalblue', alpha=0.9) x1 = np.arange(1.5, 3, 0.001) y1 = norm.pdf(x1, mu, sigma) ax[0].fill_between(x1, y1, 0, alpha=0.3, color='b') ax[1].plot(x, y, color='royalblue', alpha=0.9) x2 = np.arange(3, 3.5, 0.001) y2 = norm.pdf(x2, mu, sigma) ax[1].fill_between(x2, y2, 0, alpha=0.3, color='b') ax[0].set_title('Weight less than 3 kilos', fontsize=15) ax[1].set_title('Weight from 3 to 3.5 kilos', fontsize=15) for ax in ax: ax.xaxis.set_tick_params(labelsize=13) ax.get_yaxis().set_visible(False) plt.savefig('toy_newborn_weight_distribution_area.png', bbox_inches='tight'); fig, ax = plt.subplots(figsize=(10,6)) x = np.arange(1.5, 4.9, 0.001) y = norm.pdf(x, mu, sigma) ax.plot(x, y, color='royalblue', alpha=0.9) ax.axvline(mu, color='dimgray', linestyle='--') ax.axvline(mu+sigma, color='darkgrey', linestyle='--') ax.axvline(mu+sigma*2, color='darkgrey', linestyle='--') ax.axvline(mu+sigma*3, color='darkgrey', linestyle='--') ax.axvline(mu-sigma, color='darkgrey', linestyle='--') ax.axvline(mu-sigma*2, color='darkgrey', linestyle='--') ax.axvline(mu-sigma*3, color='darkgrey', linestyle='--') props = dict(boxstyle="round", fc='lightsteelblue', ec='ghostwhite') ax.annotate(s='', xy=(mu-sigma, 0.9), xytext=(mu+sigma, 0.9), fontsize=15, ha='center', va='center', arrowprops=dict(arrowstyle='<->', ) ) ax.text(mu, 1, '68.26%', fontsize=14, ha='center', va='center', bbox=props) ax.annotate(s='', xy=(mu-2*sigma, 0.55), xytext=(mu+2*sigma, 0.55), fontsize=15, ha='center', va='center', arrowprops=dict(arrowstyle='<->', ) ) ax.text(mu, 0.65, '95.44%', fontsize=14, ha='center', va='center', bbox=props) ax.annotate(s='', xy=(mu-3*sigma, 0.2), xytext=(mu+3*sigma, 0.2), fontsize=15, ha='center', va='center', arrowprops=dict(arrowstyle='<->', ) ) ax.text(mu, 0.3, '99.73%', fontsize=14, ha='center', va='center', bbox=props) ax.xaxis.set_tick_params(labelsize=13) ax.get_yaxis().set_visible(False) plt.savefig('toy_newborn_6_sigma.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Student's t-distribution ###Code fig, ax = plt.subplots(figsize=(12,6)) mu = 3.2 sigma = 0.3 x = np.arange(1.5, 4.9, 0.01) y = norm.pdf(x, loc=mu, scale=sigma) ax.plot(x, y, color='royalblue', alpha=0.9, label='Normal distribution') y2 = t.pdf(x, df=2, loc=mu, scale=sigma) ax.plot(x, y2, color='peru', alpha=0.9, label=r'$t$-distribution, 2 degrees of freedom') y3 = t.pdf(x, df=10, loc=mu, scale=sigma) ax.plot(x, y3, color='olive', alpha=0.9, label=r'$t$-distribution, 10 degrees of freedom') y4 = t.pdf(x, df=30, loc=mu, scale=sigma) ax.plot(x, y4, color='maroon', alpha=0.9, label=r'$t$-distribution, 30 degrees of freedom') ax.axvline(mu, color='darkgrey', linestyle='--') ax.set_title('PDF for the normal and t-distributions', fontsize=15) ax.xaxis.set_tick_params(labelsize=13) ax.get_yaxis().set_visible(False) plt.legend(fontsize=13) plt.tight_layout() plt.savefig('../../assets/images/probability/normal_and_t_distributions.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Chi-square distribution ###Code x = np.arange(0, 10, 0.01) with sns.axes_style('whitegrid'): fig, ax = plt.subplots(figsize=(12,6)) ax.set_ylim(0, 1) ax.set_xlim(0, 10) for df in range(1, 6): y = chi2.pdf(x, df=df, loc=0, scale=1) plt.plot(x, y, label = f'{df} degree of freedom') plt.legend(fontsize=13) plt.tight_layout() plt.savefig('../../assets/images/probability/chi_squared_distributions.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Binomial distribution ###Code with sns.axes_style('darkgrid'): fig, ax = plt.subplots(figsize=(12,6)) n = 200 p = 0.8 size = 1000 binomial = np.random.binomial(n, p, size) sns.histplot(data=binomial, palette='deep', bins=20) ax.set_title('Number of visitors out of 200 who enjoyed the movie', fontsize=15) ax.annotate(f'$n$={n}\n$p$={p}\n$N$={size}', xy=(146, 100), fontsize=15, ha='center', va='center') plt.tight_layout() plt.savefig('../../assets/images/probability/binomial_distribution.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Uniform distribution ###Code with sns.axes_style('darkgrid'): fig, ax = plt.subplots(figsize=(12,6)) uniform_discrete = np.random.randint(low=1, high=7, size=500) sns.histplot(data=uniform_discrete, palette='deep', bins=6) ax.set_title('Number of outcomes in 500 dice rollings', fontsize=15) plt.tight_layout() plt.savefig('../../assets/images/probability/uniform_distribution.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Geometric distribution ###Code geometric = [] failure = 0 n = 0 p = 0.2 while n < 2000: result = np.random.choice(['success', 'failure'], p=(p, 1-p)) if result == 'failure': failure += 1 else: geometric.append(failure) failure = 0 n += 1 with sns.axes_style('darkgrid'): fig, ax = plt.subplots(figsize=(12,6)) sns.histplot(data=geometric, palette='deep', bins=14) ax.annotate(f'$p$={p}\n$N$={n}', xy=(9, 550), fontsize=15, ha='center', va='center') ax.set_title('Number of customer engagements before the first sale', fontsize=15) plt.tight_layout() plt.savefig('../../assets/images/probability/geometric_distribution.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Negative binomial distribution ###Code with sns.axes_style('darkgrid'): fig, ax = plt.subplots(figsize=(12,6)) negative_binomial = np.random.default_rng().negative_binomial(10, 0.2, size=2000) sns.histplot(data=negative_binomial, palette='deep', bins=14) ax.annotate('$r$=10\n$p$=0.2\n$N$=2000', xy=(70, 280), fontsize=15, ha='center', va='center') ax.set_title('Number of unsuccessful customer engagements before 10 sales were made', fontsize=15) plt.tight_layout() plt.savefig('../../assets/images/probability/negative_binomial_distribution.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Poisson distribution ###Code with sns.axes_style('darkgrid'): fig, ax = plt.subplots(figsize=(12,6)) poisson = np.random.poisson(lam=5, size=2000) sns.histplot(data=poisson, palette='deep', bins=14) ax.annotate('$\lambda$=5\n$N$=2000', xy=(11, 150), fontsize=15, ha='center', va='center') ax.set_title('Number of received promo emails per week', fontsize=15) plt.tight_layout() plt.savefig('../../assets/images/probability/poisson_distribution.png', bbox_inches='tight'); ###Output _____no_output_____ ###Markdown Exponential distribution ###Code with sns.axes_style('darkgrid'): fig, ax1 = plt.subplots(figsize=(12,6)) ax2 = ax1.twinx() lam = 4/60 beta = 1/lam exponential = np.random.default_rng().exponential(beta, size=2000) x = np.arange(0, 110, 0.001) def expon_func(x, lam): f_x = lam*np.exp(-lam*x) return f_x sns.histplot(data=exponential, palette='deep', bins=14, ax=ax1) ax2.plot(x, expon_func(x, lam), color='maroon', label='Probability density function') ax1.annotate('$\lambda$=0.07\n$N$=2000', xy=(100, 280), fontsize=15, ha='center', va='center') ax1.set_title('Distribution of minutes spent before a new bus arrives', fontsize=15) plt.legend(fontsize=13) plt.tight_layout() plt.savefig('../../assets/images/probability/exponential_distribution.png', bbox_inches='tight'); ###Output _____no_output_____
Jupyter_Notebooks/.ipynb_checkpoints/Keras_101_Neuron_Number-checkpoint.ipynb
###Markdown We are going to discuss and hopefully demonstrate how many hidden layers and how many neurons should populate those layers. To begin lets import the necessary packages we will use for our demonstrations and discussions. ###Code import keras from sklearn.datasets import make_moons, make_circles import matplotlib.pyplot as plt import numpy as np ###Output _____no_output_____ ###Markdown We will now generate some classification data for us to play with. These datasets consist of the features x1 and x2 for which each point is assigned a class of 0 or 1. ###Code dataset_poly = make_moons(n_samples=300,noise=0.20, random_state=1) dataset_circle = make_circles(n_samples=300,noise=0.15,factor=0.2, random_state=1) features_poly = dataset_poly[0] labels_poly = dataset_poly[1] features_circle = dataset_circle[0] labels_circle = dataset_circle[1] features_poly[:,0] = (features_poly[:,0]+1.5)/3.0 features_poly[:,1] = (features_poly[:,1]+1.5)/3.0 x1_min = np.amin(features_poly[:,0]) x1_max = np.amax(features_poly[:,0]) x2_min = np.amin(features_poly[:,1]) x2_max = np.amax(features_poly[:,1]) features_circle[:,0] = (features_circle[:,0]+1.5)/3.0 features_circle[:,1] = (features_circle[:,1]+1.5)/3.0 ###Output _____no_output_____ ###Markdown By plotting the two datasets we can see that the shapes of the decision boundaries that seperate these two classes of data resemble that of a polynolial for the first and a circle for the second. ###Code plt.scatter(features_poly[:,0],features_poly[:,1],edgecolor="black",linewidth=2,c=labels_poly) plt.xlabel("x1") plt.ylabel("x2") plt.colorbar() plt.show() plt.scatter(features_circle[:,0],features_circle[:,1],edgecolor="black",linewidth=2,c=labels_circle) plt.xlabel("x1") plt.ylabel("x2") plt.colorbar() plt.show() ###Output _____no_output_____ ###Markdown We will begin by looking at the first polynomial dataset and looking at the most simple neural network possible, that of a single neuron, strictly speaking this is not a neual netowkr but a linear classifier however it does make up the simplest building block of the networks we will be looking at later. Here the two input features x1 and x2 feed into a single output neuron that classifies each datapoint input into the network as a 1 or 0. ###Code layers = [] layers.append(keras.layers.Dense(1, input_dim = 2, activation="sigmoid")) model = keras.Sequential(layers) model.compile(optimizer=keras.optimizers.Adam(lr=1), loss='binary_crossentropy', metrics=['binary_accuracy', 'categorical_accuracy']) history = model.fit(features_poly, labels_poly, batch_size=features_poly.shape[0],epochs=600, verbose=0) loss = history.history['loss'] epoch = np.arange(0, len(loss)) plt.plot(epoch,loss, label='Training Data Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() ###Output _____no_output_____ ###Markdown By plotting the resulting decision boundary after the training has converged we see that we do indeed have a linear classifier, after any amount of training we will always be left with a linear decision boundary seperating the two classifications of data. ###Code xx, yy = np.meshgrid(np.arange(x1_min,x1_max,0.01),np.arange(x2_min,x2_max,0.01)) z = model.predict(np.c_[xx.ravel(),yy.ravel()]) z = z.reshape(xx.shape) plt.contourf(xx,yy,z) plt.scatter(features[:,0],features[:,1],c=labels) plt.xlabel('x1') plt.ylabel('x2') plt.colorbar() plt.show() ###Output _____no_output_____ ###Markdown This single neuron makes up the most basic componant of a neural network, it is here we will begin our discussion of how to design our neural network. The second simplest network we can imagine consists of the same input and output as the first network but with a single hiddiden layer of one neuron between the input and output, at this point we have nothing more than a linear classifier. If you are not convinced (as you probably shouldn't be) then feel free to adapt the model above to observe the reuslting decision boundary. The task now is to descirbe how adding more neurons to a single hidden layer changes the shape and complexity of the decision boundary we can describe with our network. We will see that by increasing the number of neurons in a single hidden layer that then feeds into a single output neuron we are able to represent any single function that maps one finite space onto another. As each neuron in the hidden layer acts as a single linear classifier by adding more we feed more linear componants into the output neuron, the result is a sum of linear componants that allows any single function to be described. To demonstrate this lets add a second neuron into our hidden layer. ###Code layers = [] layers.append(keras.layers.Dense(2, input_dim = 2, activation="sigmoid")) layers.append(keras.layers.Dense(1, activation="sigmoid")) model = keras.Sequential(layers) model.compile(optimizer=keras.optimizers.Adam(lr=0.1), loss='binary_crossentropy', metrics=['binary_accuracy', 'categorical_accuracy']) history = model.fit(features_poly, labels_poly, batch_size=features_poly.shape[0],epochs=2000, verbose=0) loss = history.history['loss'] epoch = np.arange(0, len(loss)) plt.plot(epoch,loss, label='Training Data Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() xx, yy = np.meshgrid(np.arange(x1_min,x1_max,0.01),np.arange(x2_min,x2_max,0.01)) z = model.predict(np.c_[xx.ravel(),yy.ravel()]) z = z.reshape(xx.shape) plt.contourf(xx,yy,z) plt.scatter(features[:,0],features[:,1],c=labels) plt.xlabel('x1') plt.ylabel('x2') plt.colorbar() plt.show() ###Output _____no_output_____ ###Markdown It should be evident that the above decision boundary is essentially the summation of two linear decision boundaries through an output neuron thereby normalising the inputs. Now let's have a look at the shape of the data we are classifiying inorder to egt an idea of the model this data requires.We can see that the summation of three linear decision boundares would perfectly describe the decision boundary we require for our classification data, so let's add a third neuron to our network model and test this hypothesis. ###Code layers = [] layers.append(keras.layers.Dense(3, input_dim = 2, activation="sigmoid")) layers.append(keras.layers.Dense(1, activation="sigmoid")) model = keras.Sequential(layers) model.compile(optimizer=keras.optimizers.Adam(lr=0.1), loss='binary_crossentropy', metrics=['binary_accuracy', 'categorical_accuracy']) history = model.fit(features_poly, labels_poly, batch_size=features_poly.shape[0],epochs=3000, verbose=0) loss = history.history['loss'] epoch = np.arange(0, len(loss)) plt.plot(epoch,loss, label='Training Data Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() xx, yy = np.meshgrid(np.arange(x1_min,x1_max,0.01),np.arange(x2_min,x2_max,0.01)) z = model.predict(np.c_[xx.ravel(),yy.ravel()]) z = z.reshape(xx.shape) plt.contourf(xx,yy,z) plt.scatter(features[:,0],features[:,1],c=labels) plt.xlabel('x1') plt.ylabel('x2') plt.colorbar() plt.show() ###Output _____no_output_____ ###Markdown You can see that we do indeed get a decisioun boundary that describes a good generalisation of the classification between the two classes in our data set. You may be wondering the effect of adding more neurons to the single hidden layer of our network. We can add any number of neurons and the result will be a more complex decision boundary, a more complex decision boundary allows for those data points in the data that deviate from the general distribution of the data (outliers) to be incorperated into the decision boundary, this is because the loss function will indeed be reduced when the network fits these points even though they do as discused lay outide the general rule or decision boundary, this is termed overfitting (More on this later). ###Code layers = [] layers.append(keras.layers.Dense(36, input_dim = 2, activation="sigmoid")) layers.append(keras.layers.Dense(1, activation="sigmoid")) model = keras.Sequential(layers) model.compile(optimizer=keras.optimizers.Adam(lr=0.1), loss='binary_crossentropy', metrics=['binary_accuracy', 'categorical_accuracy']) history = model.fit(features_poly, labels_poly, batch_size=features_poly.shape[0],epochs=3000, verbose=0) loss = history.history['loss'] epoch = np.arange(0, len(loss)) plt.plot(epoch,loss, label='Training Data Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() xx, yy = np.meshgrid(np.arange(x1_min,x1_max,0.01),np.arange(x2_min,x2_max,0.01)) z = model.predict(np.c_[xx.ravel(),yy.ravel()]) z = z.reshape(xx.shape) plt.contourf(xx,yy,z) plt.scatter(features[:,0],features[:,1],c=labels) plt.xlabel('x1') plt.ylabel('x2') plt.colorbar() plt.show() ###Output _____no_output_____
notebooks/INTRO_DC_LayeredEarth.ipynb
###Markdown **Course website**: https://github.com/leomiquelutti/UFU-geofisica-1**Note**: This notebook is part of the course "Geofรญsica 1" of Geology program of the [Universidade Federal de Uberlรขndia](http://www.ufu.br/). All content can be freely used and adapted under the terms of the [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).![Creative Commons License](https://i.creativecommons.org/l/by/4.0/88x31.png)Agradecimentos especiais ao [Leonardo Uieda](www.leouieda.com) e ao [Grupo Geosci](http://geosci.xyz/) Esse documento que vocรช estรก usando รฉ um [Jupyter notebook](http://jupyter.org/). ร‰ um documento interativo que mistura texto (como esse), cรณdigo (como abaixo), e o resultado de executar o cรณdigo (nรบmeros, texto, figuras, videos, etc). ###Code from em_examples import DCLayers from IPython.display import display %matplotlib inline from matplotlib import rcParams rcParams['font.size'] = 14 ###Output _____no_output_____ ###Markdown InstruรงรตesO notebook te fornecerรก exemplos interativos que trabalham os temas abordados no questionรกrio. Utilize esses exemplos para responder as perguntas.As cรฉlulas com nรบmeros ao lado, como `In [1]:`, sรฃo cรณdigo [Python](http://python.org/). Algumas dessas cรฉlulas nรฃo produzem resultado e servem de preparaรงรฃo para os exemplos interativos. Outras, produzem grรกficos interativos. **Vocรช deve executar todas as cรฉlulas, uma de cada vez**, mesmo as que nรฃo produzem grรกficos.Para executar uma cรฉlula, clique em cima dela e aperte `Shift + Enter`. O foco (contorno verde ou cinza em torno da cรฉlula) deverรก passar para a cรฉlula abaixo. Para rodรก-la, aperte `Shift + Enter` novamente e assim por diante. Vocรช pode executar cรฉlulas de texto que nรฃo acontecerรก nada. Purpose Investigating DC Resistivity Data Using the widgets contained in this notebook we will explore the physical principals governing DC resistivity including the behavior of currents, electric field, electric potentials in a two layer earth. The measured data in a DC experiment are potential differences, we will demonstrate how these provide information about subsurface physical properties. Background: Computing Apparent ResistivityIn practice we cannot measure the potentials everywhere, we are limited to those locations where we place electrodes. For each source (current electrode pair) many potential differences are measured between M and N electrode pairs to characterize the overall distribution of potentials. The widget below allows you to visualize the potentials, electric fields, and current densities from a dipole source in a simple model with 2 layers. For different electrode configurations you can measure the potential differences and see the calculated apparent resistivities. In a uniform halfspace the potential differences can be computed by summing up the potentials at each measurement point from the different current sources based on the following equations:\begin{align} V_M = \frac{\rho I}{2 \pi} \left[ \frac{1}{AM} - \frac{1}{MB} \right] \\ V_N = \frac{\rho I}{2 \pi} \left[ \frac{1}{AN} - \frac{1}{NB} \right] \end{align} where $AM$, $MB$, $AN$, and $NB$ are the distances between the corresponding electrodes. The potential difference $\Delta V_{MN}$ in a dipole-dipole survey can therefore be expressed as follows,\begin{equation} \Delta V_{MN} = V_M - V_N = \rho I \underbrace{\frac{1}{2 \pi} \left[ \frac{1}{AM} - \frac{1}{MB} - \frac{1}{AN} + \frac{1}{NB} \right]}_{G}\end{equation}and the resistivity of the halfspace $\rho$ is equal to,$$ \rho = \frac{\Delta V_{MN}}{IG}$$In this equation $G$ is often referred to as the geometric factor. In the case where we are not in a uniform halfspace the above equation is used to compute the apparent resistivity ($\rho_a$) which is the resistivity of the uniform halfspace which best reproduces the measured potential difference.In the top plot the location of the A electrode is marked by the red +, the B electrode is marked by the blue -, and the M/N potential electrodes are marked by the black dots. The $V_M$ and $V_N$ potentials are printed just above and to the right of the black dots. The calculted apparent resistivity is shown in the grey box to the right. The bottom plot can show the resistivity model, the electric fields (e), potentials, or current densities (j) depending on which toggle button is selected. Some patience may be required for the plots to update after parameters have been changed. LayeredEarth app Parameters: - **A**: (+) Current electrode location - **B**: (-) Current electrode location - **M**: (+) Potential electrode location - **N**: (-) Potential electrode location - **$\rho_1$**: Resistivity of the first layer - **$\rho_2$**: Resistivity of the second layer - **h**: Thickness of the first layer - **Plot**: Choice of 2D plot (Model, Potential, Electric field, Currents) ###Code out = DCLayers.plot_layer_potentials_app() display(out) ###Output _____no_output_____
Data_Analysis/missing_value_handling.ipynb
###Markdown Working With Missing ValuesMissing Data can occur when no information is provided for one or more items or for a whole unit. Missing Data is a very big problem in real life scenario. Missing Data can also refer to as **NA(Not Available)** values in pandas. ###Code import pandas as pd import numpy as np import matplotlib.pyplot as plt dataframe = pd.read_csv('dataset/circle_employee.csv',index_col='user_id') # Load data dataframe.iloc[:,:6].head() ###Output _____no_output_____ ###Markdown Checking for missing values using isnull() and notnull()In order to check missing values in Pandas DataFrame, we use a function isnull() and notnull(). Both function help in checking whether a value is NaN or not. These function can also be used in Pandas Series in order to find null values in a series ###Code # using isnull() function dataframe.head(10).isnull() # using isnull() function dataframe.head(10).notnull() age = dataframe['age'] age.head(10).isnull() ###Output _____no_output_____ ###Markdown Filling missing values using fillna(), replace() and interpolate() :In order to fill null values in a datasets, we use fillna(), replace() and interpolate() function these function replace NaN values with some value of their own. All these function help in filling a null values in datasets of a DataFrame. - **Interpolate():** function is basically used to fill NA values in the dataframe but it uses various interpolation technique to fill the missing values rather than hard-coding the value. ###Code # fill null values on age column using Mean mean = age.mean() print(mean) age.fillna(mean).head(10) ###Output 28.302325581395348 ###Markdown Dropping missing values using dropna() :In order to drop a null values from a dataframe, we used dropna() function this fuction drop Rows/Columns of datasets with Null values in different ways. ###Code # using dropna() function age.dropna().head(10) ###Output _____no_output_____ ###Markdown DATA_ANALYSIS_WITH_PYTHON ###Code filename = "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv" headers = ["symboling","normalized-losses","make","fuel-type","aspiration", "num-of-doors","body-style", "drive-wheels","engine-location","wheel-base", "length","width","height","curb-weight","engine-type", "num-of-cylinders", "engine-size","fuel-system","bore","stroke","compression-ratio","horsepower", "peak-rpm","city-mpg","highway-mpg","price"] df = pd.read_csv(filename, names = headers) df.head() ###Output _____no_output_____ ###Markdown In the car dataset, missing data comes with the question mark "?". We replace "?" with NaN (Not a Number), which is Python's default missing value marker, for reasons of computational speed and convenience. Here we use the function: ###Code # replace "?" to NaN df.replace("?", np.nan, inplace = True) df.head(5) ###Output _____no_output_____ ###Markdown Evaluating for Missing DataThe missing values are converted to Python's default. We use Python's built-in functions to identify these missing values. There are two methods to detect missing data: .isnull() .notnull()The output is a boolean value indicating whether the value that is passed into the argument is in fact missing data. ###Code missing_data = df.isnull() missing_data.head(5) ###Output _____no_output_____ ###Markdown Count missing values in each columnUsing a for loop in Python, we can quickly figure out the number of missing values in each column. As mentioned above, "True" represents a missing value, "False" means the value is present in the dataset. In the body of the for loop the method ".value_counts()" counts the number of "True" values. ###Code for column in missing_data.columns.values.tolist(): print(column) print (missing_data[column].value_counts()) print("") ###Output symboling False 205 Name: symboling, dtype: int64 normalized-losses False 164 True 41 Name: normalized-losses, dtype: int64 make False 205 Name: make, dtype: int64 fuel-type False 205 Name: fuel-type, dtype: int64 aspiration False 205 Name: aspiration, dtype: int64 num-of-doors False 203 True 2 Name: num-of-doors, dtype: int64 body-style False 205 Name: body-style, dtype: int64 drive-wheels False 205 Name: drive-wheels, dtype: int64 engine-location False 205 Name: engine-location, dtype: int64 wheel-base False 205 Name: wheel-base, dtype: int64 length False 205 Name: length, dtype: int64 width False 205 Name: width, dtype: int64 height False 205 Name: height, dtype: int64 curb-weight False 205 Name: curb-weight, dtype: int64 engine-type False 205 Name: engine-type, dtype: int64 num-of-cylinders False 205 Name: num-of-cylinders, dtype: int64 engine-size False 205 Name: engine-size, dtype: int64 fuel-system False 205 Name: fuel-system, dtype: int64 bore False 201 True 4 Name: bore, dtype: int64 stroke False 201 True 4 Name: stroke, dtype: int64 compression-ratio False 205 Name: compression-ratio, dtype: int64 horsepower False 203 True 2 Name: horsepower, dtype: int64 peak-rpm False 203 True 2 Name: peak-rpm, dtype: int64 city-mpg False 205 Name: city-mpg, dtype: int64 highway-mpg False 205 Name: highway-mpg, dtype: int64 price False 201 True 4 Name: price, dtype: int64 ###Markdown Deal with missing dataHow to deal with missing data? 1. drop data a. drop the whole row b. drop the whole column 2. replace data a. replace it by mean b. replace it by frequency c. replace it based on other functions Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely. We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:Replace by mean: "normalized-losses": 41 missing data, replace them with mean "stroke": 4 missing data, replace them with mean "bore": 4 missing data, replace them with mean "horsepower": 2 missing data, replace them with mean "peak-rpm": 2 missing data, replace them with meanReplace by frequency: "num-of-doors": 2 missing data, replace them with "four". Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occurDrop the whole row: "price": 4 missing data, simply delete the whole row Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us Calculate the average of the column ###Code avg_norm_loss = df["normalized-losses"].astype("float").mean(axis=0) print("Average of normalized-losses:", avg_norm_loss) ###Output Average of normalized-losses: 122.0 ###Markdown Replace "NaN" by mean value in "normalized-losses" column ###Code df["normalized-losses"].replace(np.nan, avg_norm_loss, inplace=True) ###Output _____no_output_____ ###Markdown Calculate the mean value for 'bore' column and replace nan ###Code avg_bore=df['bore'].astype('float').mean(axis=0) print("Average of bore:", avg_bore) df["bore"].replace(np.nan, avg_bore, inplace=True) ###Output Average of bore: 3.3297512437810943 ###Markdown For Stroke ###Code avg_stroke = df["stroke"].astype("float").mean(axis = 0) print("Average of stroke:", avg_stroke) # replace NaN by mean value in "stroke" column df["stroke"].replace(np.nan, avg_stroke, inplace = True) ###Output Average of stroke: 3.255422885572139 ###Markdown For Horse Power ###Code avg_horsepower = df['horsepower'].astype('float').mean(axis=0) print("Average horsepower:", avg_horsepower) df['horsepower'].replace(np.nan, avg_horsepower, inplace=True) ###Output Average horsepower: 104.25615763546799 ###Markdown For Peak-RPM ###Code avg_peakrpm=df['peak-rpm'].astype('float').mean(axis=0) print("Average peak rpm:", avg_peakrpm) df['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True) ###Output Average peak rpm: 5125.369458128079 ###Markdown To see which values are present in a particular column, we can use the ".value_counts()" method: For Number of Doors ###Code df['num-of-doors'].value_counts() df['num-of-doors'].value_counts().idxmax() #calculate Most Common #replace the missing 'num-of-doors' values by the most frequent df["num-of-doors"].replace(np.nan, "four", inplace=True) ###Output _____no_output_____ ###Markdown For Price DataFinally, let's drop all rows that do not have price data: ###Code # simply drop whole row with NaN in "price" column df.dropna(subset=["price"], axis=0, inplace=True) # reset index, because we droped two rows df.reset_index(drop=True, inplace=True) df.head() ###Output _____no_output_____ ###Markdown Convert data types to proper format ###Code df.dtypes ###Output _____no_output_____ ###Markdown As we can see above, some columns are not of the correct data type. Numerical variables should have type 'float' or 'int', and variables with strings such as categories should have type 'object'. For example, 'bore' and 'stroke' variables are numerical values that describe the engines, so we should expect them to be of the type 'float' or 'int'; however, they are shown as type 'object'. We have to convert data types into a proper format for each column using the "astype()" method. ###Code df[["bore", "stroke"]] = df[["bore", "stroke"]].astype("float") df[["normalized-losses"]] = df[["normalized-losses"]].astype("int") df[["price"]] = df[["price"]].astype("float") df[["peak-rpm"]] = df[["peak-rpm"]].astype("float") df.dtypes ###Output _____no_output_____ ###Markdown Data StandardizationStandardization is the process of transforming data into a common format which allows the researcher to make the meaningful comparison.ExampleTransform mpg to L/100km:In our dataset, the fuel consumption columns "city-mpg" and "highway-mpg" are represented by mpg (miles per gallon) unit. Assume we are developing an application in a country that accept the fuel consumption with L/100km standardWe will need to apply data transformation to transform mpg into L/100km?The formula for unit conversion isL/100km = 235 / mpgWe can do many mathematical operations directly in Pandas. ###Code # Convert mpg to L/100km by mathematical operation (235 divided by mpg) df['city-L/100km'] = 235/df["city-mpg"] # check your transformed data df.head() # transform mpg to L/100km by mathematical operation (235 divided by mpg) df["highway-mpg"] = 235/df["highway-mpg"] # rename column name from "highway-mpg" to "highway-L/100km" df.rename(columns={'"highway-mpg"':'highway-L/100km'}, inplace=True) # check your transformed data df.head() ###Output _____no_output_____ ###Markdown Data Normalization Why normalization?Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1ExampleTo demonstrate normalization, let's say we want to scale the columns "length", "width" and "height"Target:would like to Normalize those variables so their value ranges from 0 to 1. ###Code # replace (original value) by (original value)/(maximum value) df['length'] = df['length']/df['length'].max() df['width'] = df['width']/df['width'].max() df['height'] = df['height']/df['height'].max() # show the scaled columns df[["length","width","height"]].head() ###Output _____no_output_____ ###Markdown Binning Why binning?Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.Example:In our dataset, "horsepower" is a real valued variable ranging from 48 to 288, it has 57 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three โ€˜bins' to simplify analysis?We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins ###Code df["horsepower"]=df["horsepower"].astype(int, copy=True) ###Output _____no_output_____ ###Markdown Lets plot the histogram of horspower, to see what the distribution of horsepower looks like. ###Code %matplotlib inline import matplotlib as plt from matplotlib import pyplot plt.pyplot.hist(df["horsepower"]) # set x/y labels and plot title plt.pyplot.xlabel("horsepower") plt.pyplot.ylabel("count") plt.pyplot.title("horsepower bins") ###Output _____no_output_____ ###Markdown We would like 3 bins of equal size bandwidth so we use numpy's linspace(start_value, end_value, numbers_generated function.Since we want to include the minimum value of horsepower we want to set start_value=min(df["horsepower"]).Since we want to include the maximum value of horsepower we want to set end_value=max(df["horsepower"]).Since we are building 3 bins of equal length, there should be 4 dividers, so numbers_generated=4.We build a bin array, with a minimum value to a maximum value, with bandwidth calculated above. The bins will be values used to determine when one bin ends and another begins. ###Code bins = np.linspace(min(df["horsepower"]), max(df["horsepower"]), 4) bins group_names = ['Low', 'Medium', 'High'] # set group df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True ) df[['horsepower','horsepower-binned']].head(20) df["horsepower-binned"].value_counts() %matplotlib inline import matplotlib as plt from matplotlib import pyplot pyplot.bar(group_names, df["horsepower-binned"].value_counts()) # set x/y labels and plot title plt.pyplot.xlabel("horsepower") plt.pyplot.ylabel("count") plt.pyplot.title("horsepower bins") ###Output _____no_output_____ ###Markdown Bins visualizationNormally, a histogram is used to visualize the distribution of bins we created above. ###Code %matplotlib inline import matplotlib as plt from matplotlib import pyplot a = (0,1,2) # draw historgram of attribute "horsepower" with bins = 3 plt.pyplot.hist(df["horsepower"], bins = 3) # set x/y labels and plot title plt.pyplot.xlabel("horsepower") plt.pyplot.ylabel("count") plt.pyplot.title("horsepower bins") ###Output _____no_output_____ ###Markdown Indicator variable (or dummy variable) What is an indicator variable?An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning.Why we use indicator variables?So we can use categorical variables for regression analysis in the later modules.ExampleWe see the column "fuel-type" has two unique values, "gas" or "diesel". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert "fuel-type" into indicator variables.We will use the panda's method 'get_dummies' to assign numerical values to different categories of fuel type. ###Code df.columns ###Output _____no_output_____ ###Markdown get indicator variables and assign it to data frame "dummy_variable_1" ###Code dummy_variable_1 = pd.get_dummies(df["fuel-type"]) dummy_variable_1.head() ###Output _____no_output_____ ###Markdown change column names for clarity ###Code dummy_variable_1.rename(columns={'fuel-type-diesel':'gas', 'fuel-type-diesel':'diesel'}, inplace=True) dummy_variable_1.head() # merge data frame "df" and "dummy_variable_1" df = pd.concat([df, dummy_variable_1], axis=1) # drop original column "fuel-type" from "df" df.drop("fuel-type", axis = 1, inplace=True) df.head() # get indicator variables of aspiration and assign it to data frame "dummy_variable_2" dummy_variable_2 = pd.get_dummies(df['aspiration']) # change column names for clarity dummy_variable_2.rename(columns={'std':'aspiration-std', 'turbo': 'aspiration-turbo'}, inplace=True) # show first 5 instances of data frame "dummy_variable_1" dummy_variable_2.head() #merge the new dataframe to the original datafram df = pd.concat([df, dummy_variable_2], axis=1) # drop original column "aspiration" from "df" df.drop('aspiration', axis = 1, inplace=True) df.head() ###Output _____no_output_____
scratch/deep-gaussian-processes.ipynb
###Markdown Sparse Gaussian Process ###Code import matplotlib as mpl; mpl.use('pgf') %matplotlib inline import numpy as np import tensorflow.compat.v1 as tf tf.disable_v2_behavior() # import tensorflow as tf import tensorflow_probability as tfp import matplotlib.pyplot as plt import seaborn as sns import pandas as pd from collections import defaultdict from matplotlib import animation from IPython.display import HTML from scribbles.gaussian_processes import gp_sample_custom, dataframe_from_gp_samples golden_ratio = 0.5 * (1 + np.sqrt(5)) golden_size = lambda width: (width, width / golden_ratio) width = 10 rc = { "figure.figsize": golden_size(width), "text.usetex": True, } sns.set(context="notebook", style="ticks", palette="colorblind", font="serif", rc=rc) # shortcuts tfd = tfp.distributions kernels = tfp.math.psd_kernels # constants n_train = 500 observation_noise_variance = 1e-1 n_features = 1 # dimensionality n_index_points = 256 # nbr of index points n_samples = 8 # nbr of GP prior samples jitter = 1e-2 kernel_cls = kernels.ExponentiatedQuadratic n_inducing_points = 20 n_epochs = 2000 batch_size = 50 seed = 42 # set random seed for reproducibility random_state = np.random.RandomState(seed) x_min, x_max = -1.0, 1.0 y_min, y_max = -3.0, 3.0 x_loc = -0.5 # index points X_q = np.linspace(x_min, x_max, n_index_points).reshape(-1, n_features) f = lambda x: np.sin(12.0*x) + 0.66*np.cos(25.0*x) X = x_loc + random_state.rand(n_train, n_features) eps = observation_noise_variance * random_state.randn(n_train, n_features) Y = np.squeeze(f(X) + eps) fig, ax = plt.subplots() ax.plot(X_q, f(X_q), label="true") ax.scatter(X, Y, marker='x', color='k', label="noisy observations") ax.legend() ax.set_xlim(x_loc, -x_loc) ax.set_xlabel('$x$') ax.set_ylabel('$y$') plt.show() class BatchIdentity(tf.keras.initializers.Identity): def __call__(self, shape, dtype=None): return super(BatchIdentity, self).__call__(shape[-2:], dtype=None) shape = (16, 5, 5) shape[:-2] tf.keras.backend.repeat(tf.keras.initializers.Identity(gain=1.0)(shape=(5, 5)), n=2) def identity_initializer(shape, dtype=None): *batch_shape, num_rows, num_columns = shape return tf.eye(num_rows, num_columns, batch_shape=batch_shape, dtype=dtype) class VGP(tf.keras.layers.Layer): def __init__(self, units, kernel_provider, num_inducing_points=64, mean_fn=None, jitter=1e-6, **kwargs): self.units = units # TODO: Maybe generalize to `event_shape`? self.num_inducing_points = num_inducing_points self.kernel_provider = kernel_provider self.mean_fn = mean_fn self.jitter = jitter super(VGP, self).__init__(**kwargs) def build(self, input_shape): input_dim = input_shape[-1] self.inducing_index_points = self.add_weight( name="inducing_index_points", shape=(self.units, self.num_inducing_points, input_dim), initializer=tf.keras.initializers.RandomUniform(-1, 1), # TODO: initialization trainable=True) self.variational_inducing_observations_loc = self.add_weight( name="variational_inducing_observations_loc", shape=(self.units, self.num_inducing_points), initializer='zeros', trainable=True) self.variational_inducing_observations_scale = self.add_weight( name="variational_inducing_observations_scale", shape=(self.units, self.num_inducing_points, self.num_inducing_points), initializer=identity_initializer, trainable=True) super(VGP, self).build(input_shape) def call(self, x): base = tfd.VariationalGaussianProcess( kernel=self.kernel_provider.kernel, index_points=x, inducing_index_points=self.inducing_index_points, variational_inducing_observations_loc=self.variational_inducing_observations_loc, variational_inducing_observations_scale=self.variational_inducing_observations_scale, mean_fn=self.mean_fn, predictive_noise_variance=1e-1, jitter=self.jitter ) # sum KL divergence between `units` independent processes self.add_loss(tf.reduce_sum(base.surrogate_posterior_kl_divergence_prior())) bijector = tfp.bijectors.Transpose(rightmost_transposed_ndims=2) qf = tfd.TransformedDistribution(tfd.Independent(base, reinterpreted_batch_ndims=1), bijector=bijector) return qf.sample() def compute_output_shape(self, input_shape): return (input_shape[0], self.units) class RBFKernelFn(tf.keras.layers.Layer): # TODO: automatic relevance determination def __init__(self, **kwargs): super(RBFKernelFn, self).__init__(**kwargs) dtype = kwargs.get('dtype', None) self.ln_amplitude = self.add_variable( initializer=tf.constant_initializer(0), dtype=dtype, name='amplitude') self.ln_length_scale = self.add_variable( initializer=tf.constant_initializer(0), dtype=dtype, name='length_scale') def call(self, x): # Never called -- this is just a layer so it can hold variables # in a way Keras understands. return x @property def kernel(self): return kernels.ExponentiatedQuadratic( amplitude=tf.exp(self.ln_amplitude), length_scale=tf.exp(self.ln_length_scale) ) model = tf.keras.models.Sequential([ VGP(16, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6), VGP(32, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6), VGP(1, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6) ]) model.losses model(X_q) model.losses kl = tf.reduce_sum(model.losses) / n_train kl f1 = VGP(16, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6)(X_q) f2 = VGP(32, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6)(f1) f3 = VGP(1, kernel_provider=RBFKernelFn(dtype="float64"), jitter=1e-6)(f2) f3 fig, ax = plt.subplots() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) ax.plot(X_q, f1.eval()) plt.show() m(X_q) inducing_index_points_initial = random_state.choice(X.squeeze(), size=(5, n_inducing_points)) \ .reshape(5, n_inducing_points, n_features) inducing_index_points_initial.shape inducing_index_points = tf.Variable(inducing_index_points_initial, name='inducing_index_points') variational_inducing_observations_loc = tf.Variable(np.zeros((5, n_inducing_points)), name='variational_inducing_observations_loc') variational_inducing_observations_scale = tf.Variable( tf.eye(n_inducing_points, batch_shape=(5,), dtype="float64"), name='variational_inducing_observations_scale') vgp = tfd.VariationalGaussianProcess( kernel=kernel, index_points=X_q, inducing_index_points=inducing_index_points, variational_inducing_observations_loc=variational_inducing_observations_loc, variational_inducing_observations_scale=variational_inducing_observations_scale, observation_noise_variance=0.0, jitter=jitter ) vgp.sample() bijector = tfp.bijectors.Transpose(rightmost_transposed_ndims=2) bijector res = tfd.TransformedDistribution(tfd.Independent(vgp, reinterpreted_batch_ndims=1), bijector=bijector) res res.sample() variational_inducing_observations_scale ?np.identity # amplitude = tf.exp(tf.Variable(np.float64(0)), name='amplitude') # length_scale = tf.exp(tf.Variable(np.float64(-1)), name='length_scale') # observation_noise_variance = tf.exp(tf.Variable(np.float64(-5)), name='observation_noise_variance') # kernel = kernel_cls(amplitude=amplitude, length_scale=length_scale) # gp = tfd.GaussianProcess( # kernel=kernel, # index_points=X, # observation_noise_variance=observation_noise_variance # ) # nll = - gp.log_prob(Y) # nll # optimizer = tf.train.AdamOptimizer(learning_rate=.05, beta1=.5, beta2=.99) # optimize = optimizer.minimize(nll) # history = defaultdict(list) # with tf.Session() as sess: # sess.run(tf.global_variables_initializer()) # for i in range(500): # (_, nll_value, amplitude_value, length_scale_value, # observation_noise_variance_value) = sess.run([optimize, nll, amplitude, length_scale, observation_noise_variance]) # history["nll"].append(nll_value) # history["amplitude"].append(amplitude_value) # history["length_scale"].append(length_scale_value) # history["observation_noise_variance"].append(observation_noise_variance_value) # fig, ax = plt.subplots() # sns.lineplot(x='amplitude', y='length_scale', # sort=False, data=pd.DataFrame(history), alpha=0.8, ax=ax) # ax.set_xlabel(r"amplitude $\sigma$") # ax.set_ylabel(r"lengthscale $\ell$") # plt.show() # kernel_history = kernel_cls(amplitude=history.get("amplitude"), length_scale=history.get("length_scale")) # gprm_history = tfd.GaussianProcessRegressionModel( # kernel=kernel_history, index_points=X_q, observation_index_points=X, observations=Y, # observation_noise_variance=history.get("observation_noise_variance"), jitter=jitter # ) # gprm_mean = gprm_history.mean() # gprm_stddev = gprm_history.stddev() # with tf.Session() as sess: # gprm_mean_value, gprm_stddev_value = sess.run([gprm_mean, gprm_stddev]) # fig, ax = plt.subplots() # ax.plot(X_q, gprm_mean_value[0]) # ax.fill_between(np.squeeze(X_q), # gprm_mean_value[0] - 2*gprm_stddev_value[0], # gprm_mean_value[0] + 2*gprm_stddev_value[0], alpha=0.1) # ax.scatter(X, Y, marker='x', color='k', label="noisy observations") # ax.set_xlabel('$x$') # ax.set_ylabel('$y$') # ax.set_ylim(y_min, y_max) # plt.show() # fig, ax = plt.subplots() # ax.plot(X_q, gprm_mean_value[-1]) # ax.fill_between(np.squeeze(X_q), # gprm_mean_value[-1] - 2*gprm_stddev_value[-1], # gprm_mean_value[-1] + 2*gprm_stddev_value[-1], alpha=0.1) # ax.scatter(X, Y, marker='x', color='k', label="noisy observations") # ax.set_xlabel('$x$') # ax.set_ylabel('$y$') # ax.set_ylim(y_min, y_max) # plt.show() amplitude = tf.exp(tf.Variable(np.float64(0)), name='amplitude') length_scale = tf.exp(tf.Variable(np.float64(-1)), name='length_scale') observation_noise_variance = tf.exp(tf.Variable(np.float64(-5)), name='observation_noise_variance') kernel = kernel_cls(amplitude=amplitude, length_scale=length_scale) inducing_index_points_initial = random_state.choice(X.squeeze(), size=(5, n_inducing_points)) \ .reshape(5, n_inducing_points, n_features) inducing_index_points_initial.shape .shape # bijector = tfp.bijectors.Chain([tfp.bijectors.CholeskyOuterProduct(), # ]) # bijector n_inducing_points = 20 inducing_index_points = tf.Variable(inducing_index_points_initial, name='inducing_index_points') # variational_inducing_observations_loc = tf.Variable(np.zeros(n_inducing_points), # name='variational_inducing_observations_loc') # variational_inducing_observations_scale = tf.Variable( # np.eye(n_inducing_points), name='variational_inducing_observations_scale') # variational_inducing_observations_scale = tfp.util.TransformedVariable( # np.eye(n_inducing_points), tfp.bijectors.FillTriangular(), name='variational_inducing_observations_scale' # ) # variational_inducing_observations_scale_flat = tf.Variable( # random_state.rand(n_inducing_points * (n_inducing_points + 1) // 2), # name='variational_inducing_observations_scale_flat') # variational_inducing_observations_scale = tfp.math.fill_triangular(variational_inducing_observations_scale_flat) dataset = tf.data.Dataset.from_tensor_slices((X, Y)) \ .shuffle(buffer_size=500) \ .batch(batch_size, drop_remainder=True) iterator = tf.data.make_initializable_iterator(dataset) X_batch, Y_batch = iterator.get_next() X_batch, Y_batch [variational_inducing_observations_loc, variational_inducing_observations_scale] = tfd.VariationalGaussianProcess.optimal_variational_posterior( kernel=kernel, inducing_index_points=inducing_index_points, observation_index_points=X, observations=Y, observation_noise_variance=observation_noise_variance ) vgp = tfd.VariationalGaussianProcess( kernel=kernel, index_points=X_batch, inducing_index_points=inducing_index_points, variational_inducing_observations_loc=variational_inducing_observations_loc, variational_inducing_observations_scale=variational_inducing_observations_scale, observation_noise_variance=observation_noise_variance, jitter=jitter ) vgp tfd.Independent(vgp, reinterpreted_batch_ndims=1) bij = transpose_lib.Transpose(rightmost_transposed_ndims=2) d = transformed_distribution_lib.TransformedDistribution(ind, bijector=bij) vgp.sample(index_points=X_batch) nelbo = vgp.variational_loss( observations=Y_batch, observation_index_points=X_batch, kl_weight=batch_size/n_train ) optimizer = tf.train.AdamOptimizer() optimize = optimizer.minimize(nelbo) steps_per_epoch = n_train // batch_size steps_per_epoch n_epochs = 100 history = defaultdict(list) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(n_epochs): sess.run(iterator.initializer) for j in range(steps_per_epoch): # sess.run(optimize) (_, nelbo_value, amplitude_value, length_scale_value, observation_noise_variance_value, inducing_index_points_value, variational_inducing_observations_loc_value, variational_inducing_observations_scale_value) = sess.run([optimize, nelbo, amplitude, length_scale, observation_noise_variance, inducing_index_points, variational_inducing_observations_loc, variational_inducing_observations_scale]) history["nelbo"].append(nelbo_value) history["amplitude"].append(amplitude_value) history["length_scale"].append(length_scale_value) history["observation_noise_variance"].append(observation_noise_variance_value) history["inducing_index_points"].append(inducing_index_points_value) history["variational_inducing_observations_loc"].append(variational_inducing_observations_loc_value) history["variational_inducing_observations_scale"].append(variational_inducing_observations_scale_value) inducing_index_points_history = np.stack(history["inducing_index_points"]) inducing_index_points_history.shape segments_min_history = np.dstack(np.broadcast_arrays(inducing_index_points_history, y_min)) segments_max_history = np.dstack([inducing_index_points_history, history["variational_inducing_observations_loc"]]) segments_history = np.stack([segments_max_history, segments_min_history], axis=-2) segments_history.shape kernel_history = kernel_cls(amplitude=history.get("amplitude"), length_scale=history.get("length_scale")) vgp_history = tfd.VariationalGaussianProcess( kernel=kernel_history, index_points=X_q, inducing_index_points=np.stack(history.get("inducing_index_points")), variational_inducing_observations_loc=np.stack(history.get("variational_inducing_observations_loc")), variational_inducing_observations_scale=np.stack(history.get("variational_inducing_observations_scale")), observation_noise_variance=history.get("observation_noise_variance") ) vgp_mean = vgp_history.mean() vgp_stddev = vgp_history.stddev() with tf.Session() as sess: vgp_mean_value, vgp_stddev_value = sess.run([vgp_mean[::10], vgp_stddev[::10]]) fig, ax = plt.subplots() ax.plot(X_q, gprm_mean_value[-1]) ax.fill_between(np.squeeze(X_q), gprm_mean_value[-1] - 2*gprm_stddev_value[-1], gprm_mean_value[-1] + 2*gprm_stddev_value[-1], alpha=0.1) ax.plot(X_q, vgp_mean_value[-1]) ax.fill_between(np.squeeze(X_q), vgp_mean_value[-1] - 2*vgp_stddev_value[-1], vgp_mean_value[-1] + 2*vgp_stddev_value[-1], alpha=0.1) ax.scatter(X, Y, marker='x', color='k', label="noisy observations") ax.vlines(history["inducing_index_points"][-1], ymin=y_min, ymax=history["variational_inducing_observations_loc"][-1], color='k', linewidth=1.0, alpha=0.4) ax.set_xlabel('$x$') ax.set_ylabel('$y$') ax.set_ylim(y_min, y_max) plt.show() fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw=dict(hspace=0.1)) ax1.scatter(X, Y, marker='x', color='k') ax1.plot(X_q, gprm_mean_value[-1]) ax1.fill_between(np.squeeze(X_q), gprm_mean_value[-1] - 2*gprm_stddev_value[-1], gprm_mean_value[-1] + 2*gprm_stddev_value[-1], alpha=0.1) line_mean, = ax1.plot(X_q, vgp_mean_value[-1], color="tab:orange") line_stddev_lower, = ax1.plot(X_q, vgp_mean_value[-1] - 2*vgp_stddev_value[-1], color="tab:orange", alpha=0.4) line_stddev_upper, = ax1.plot(X_q, vgp_mean_value[-1] + 2*vgp_stddev_value[-1], color="tab:orange", alpha=0.4) vlines_inducing_index_points = ax1.vlines(inducing_index_points_history[-1].squeeze(), ymax=history["variational_inducing_observations_loc"][-1], ymin=y_min, linewidth=1.0, alpha=0.4) ax1.set_ylabel(r'$y$') ax1.set_ylim(y_min, y_max) lines_inducing_index_points = ax2.plot(inducing_index_points_history.squeeze(), range(n_epochs), color='k', linewidth=1.0, alpha=0.4) ax2.set_xlabel(r"$x$") ax2.set_ylabel("epoch") plt.show() def animate(i): line_mean.set_data(X_q, vgp_mean_value[i]) line_stddev_lower.set_data(X_q, vgp_mean_value[i] - 2*vgp_stddev_value[i]) line_stddev_upper.set_data(X_q, vgp_mean_value[i] + 2*vgp_stddev_value[i]) vlines_inducing_index_points.set_segments(segments_history[i]) for j, line in enumerate(lines_inducing_index_points): line.set_data(inducing_index_points_history[:i, j], range(i)) ax2.relim() ax2.autoscale_view(scalex=False) return line_mean, line_stddev_lower, line_stddev_upper anim = animation.FuncAnimation(fig, animate, frames=n_epochs, interval=60, repeat_delay=5, blit=True) # HTML(anim.to_html5_video()) ###Output _____no_output_____
2-EDA/2-Pandas/Practica/02_Filtering_&_Sorting/Fictional Army/Fictional Army aula.ipynb
###Markdown Fictional Army - Filtering and Sorting ###Code import pandas as pd ###Output _____no_output_____ ###Markdown Step 2. This is the data given as a dictionary ###Code # Create an example dataframe about a fictional army raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35], 'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9], 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523], 'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345], 'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3], 'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1], 'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3], 'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']} ###Output _____no_output_____ ###Markdown Step 3. Create a dataframe and assign it to a variable called army. Don't forget to include the columns names in the order presented in the dictionary ('regiment', 'company', 'deaths'...) so that the column index order is consistent with the solutions. If omitted, pandas will order the columns alphabetically. ###Code army = pd.DataFrame(data=raw_data) army.head() ###Output _____no_output_____ ###Markdown Step 4. Set the 'origin' colum as the index of the dataframe ###Code army = army.set_index('origin') army.set_index('origin', inplace = True) army ###Output _____no_output_____ ###Markdown Step 5. Print only the column veterans ###Code army['veterans'] ###Output _____no_output_____ ###Markdown Step 6. Print the columns 'veterans' and 'deaths' ###Code army[['veterans', 'deaths']] army.loc[:, ['veterans', 'deaths']] ###Output _____no_output_____ ###Markdown Step 7. Print the name of all the columns. ###Code army.columns ###Output _____no_output_____ ###Markdown Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska ###Code army.loc['Maine': 'Alaska', ['deaths', 'size', 'deserters']] army.loc[['Maine','Iowa','Alaska'], ['deaths', 'size', 'deserters']] ###Output _____no_output_____ ###Markdown Step 9. Select the rows 3 to 7 and the columns 3 to 6 ###Code army.iloc[3:8, 3:7] ###Output _____no_output_____ ###Markdown Step 10. Select every row after the fourth row and all columns ###Code army[4:] army.iloc[4:,:] ###Output _____no_output_____ ###Markdown Step 11. Select every row up to the 4th row and all columns ###Code army.iloc[:4, :] ###Output _____no_output_____ ###Markdown Step 12. Select the 3rd column up to the 7th column ###Code army.iloc[:, 2:7] ###Output _____no_output_____ ###Markdown Step 13. Select rows where df.deaths is greater than 50 ###Code #army[army['deaths']>50] #display(army.loc[army['deaths']>50]) ''' mask = army.deaths > 50 mask print(mask) display(army) army.loc[mask] ''' #display(army) #army.loc[army.deaths > 50 , 'deserters' ] #army.loc[army['deaths'] > 50 , ['deserters', 'size'] ] army.loc[army['deaths']>50] ###Output _____no_output_____ ###Markdown Step 14. Select rows where df.deaths is greater than 500 or less than 50 ###Code mask = (army['deaths'] > 500) | (army['deaths'] < 50) army[mask] #army.loc[mask] ###Output _____no_output_____ ###Markdown Step 15. Select all the regiments not named "Dragoons" ###Code army['regiment'] != 'Dragoons' army.loc[army['regiment'] != 'Dragoons'] ###Output _____no_output_____ ###Markdown Step 16. Select the rows called Texas and Arizona ###Code army.loc[ ['Texas', 'Arizona'] ] army[(army.index == 'Texas') | (army.index == 'Arizona')] army[army.index.isin(['Texas', 'Arizona'])] ###Output _____no_output_____ ###Markdown Step 17. Select the third cell in the row named Arizona ###Code #display(army) army.iloc[:,2].loc["Arizona"] army.loc['Arizona'][2] army.iloc[army.index == 'Arizona', 2] army.loc[['Arizona'], army.columns[2]] ###Output _____no_output_____ ###Markdown Step 18. Select the third cell down in the column named deaths ###Code army['deaths'][-3] ###Output _____no_output_____
lab10/decomposition/plot_image_denoising.ipynb
###Markdown Image denoising using dictionary learningAn example comparing the effect of reconstructing noisy fragmentsof a raccoon face image using firstly online `DictionaryLearning` andvarious transform methods.The dictionary is fitted on the distorted left half of the image, andsubsequently used to reconstruct the right half. Note that even betterperformance could be achieved by fitting to an undistorted (i.e.noiseless) image, but here we start from the assumption that it is notavailable.A common practice for evaluating the results of image denoising is by lookingat the difference between the reconstruction and the original image. If thereconstruction is perfect this will look like Gaussian noise.It can be seen from the plots that the results of `omp` with twonon-zero coefficients is a bit less biased than when keeping only one(the edges look less prominent). It is in addition closer from the groundtruth in Frobenius norm.The result of `least_angle_regression` is much more strongly biased: thedifference is reminiscent of the local intensity value of the original image.Thresholding is clearly not useful for denoising, but it is here to show thatit can produce a suggestive output with very high speed, and thus be usefulfor other tasks such as object classification, where performance is notnecessarily related to visualisation. ###Code print(__doc__) from time import time import matplotlib.pyplot as plt import numpy as np import scipy as sp from sklearn.decomposition import MiniBatchDictionaryLearning from sklearn.feature_extraction.image import extract_patches_2d from sklearn.feature_extraction.image import reconstruct_from_patches_2d try: # SciPy >= 0.16 have face in misc from scipy.misc import face face = face(gray=True) except ImportError: face = sp.face(gray=True) # Convert from uint8 representation with values between 0 and 255 to # a floating point representation with values between 0 and 1. face = face / 255. # downsample for higher speed face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2] face /= 4.0 height, width = face.shape # Distort the right half of the image print('Distorting image...') distorted = face.copy() distorted[:, width // 2:] += 0.075 * np.random.randn(height, width // 2) # Extract all reference patches from the left half of the image print('Extracting reference patches...') t0 = time() patch_size = (7, 7) data = extract_patches_2d(distorted[:, :width // 2], patch_size) data = data.reshape(data.shape[0], -1) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) print('done in %.2fs.' % (time() - t0)) # ############################################################################# # Learn the dictionary from reference patches print('Learning the dictionary...') t0 = time() dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500) V = dico.fit(data).components_ dt = time() - t0 print('done in %.2fs.' % dt) plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(V[:100]): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('Dictionary learned from face patches\n' + 'Train time %.1fs on %d patches' % (dt, len(data)), fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) # ############################################################################# # Display the distorted image def show_with_diff(image, reference, title): """Helper function to display denoising""" plt.figure(figsize=(5, 3.3)) plt.subplot(1, 2, 1) plt.title('Image') plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.subplot(1, 2, 2) difference = image - reference plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference ** 2))) plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle(title, size=16) plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2) show_with_diff(distorted, face, 'Distorted image') # ############################################################################# # Extract noisy patches and reconstruct them using the dictionary print('Extracting noisy patches... ') t0 = time() data = extract_patches_2d(distorted[:, width // 2:], patch_size) data = data.reshape(data.shape[0], -1) intercept = np.mean(data, axis=0) data -= intercept print('done in %.2fs.' % (time() - t0)) transform_algorithms = [ ('Orthogonal Matching Pursuit\n1 atom', 'omp', {'transform_n_nonzero_coefs': 1}), ('Orthogonal Matching Pursuit\n2 atoms', 'omp', {'transform_n_nonzero_coefs': 2}), ('Least-angle regression\n5 atoms', 'lars', {'transform_n_nonzero_coefs': 5}), ('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})] reconstructions = {} for title, transform_algorithm, kwargs in transform_algorithms: print(title + '...') reconstructions[title] = face.copy() t0 = time() dico.set_params(transform_algorithm=transform_algorithm, **kwargs) code = dico.transform(data) patches = np.dot(code, V) patches += intercept patches = patches.reshape(len(data), *patch_size) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() reconstructions[title][:, width // 2:] = reconstruct_from_patches_2d( patches, (height, width // 2)) dt = time() - t0 print('done in %.2fs.' % dt) show_with_diff(reconstructions[title], face, title + ' (time: %.1fs)' % dt) plt.show() ###Output _____no_output_____
Addition.ipynb
###Markdown Heat maps addition ###Code %load_ext autoreload %autoreload import heat_maps import utils import itertools import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.style.use('ggplot') sns.set(color_codes=True) ###Output _____no_output_____ ###Markdown Load sample data ###Code hm1 = heat_maps.read('test_hm_1.bmp') hm2 = heat_maps.read('test_hm_2.bmp') print hm1.shape, hm2.shape utils.compare_2d(hm1, hm2, title_left='Heatmap A', title_right='Heatmap B') ###Output _____no_output_____ ###Markdown Fit B-spline surface ###Code tck1 = heat_maps.fit(hm1) tck2 = heat_maps.fit(hm2) ###Output _____no_output_____ ###Markdown Addition ###Code # compute average net of knot vectors in both directions (U & V) t_average = [ heat_maps.linearMean(tck1[0], tck2[0], [0.0, 319.0], order=4), heat_maps.linearMean(tck1[1], tck2[1], [0.0, 179.0], order=4) ] f, ax = plt.subplots(1, 2, figsize=(15, 5)) for i in range(2): ax[i].plot(tck1[i], 'b.') ax[i].plot(tck2[i], 'g.') ax[i].plot(t_average[i], 'r-') ax[0].set_title('Knots in direction U') ax[1].set_title('Knots in direction V') plt.show() # Fit surface above average knot net tck_hm1_mean = heat_maps.fit(hm1, t_average) tck_hm2_mean = heat_maps.fit(hm2, t_average) # add coefficients of both heat maps tkc_merged = [ t_average[0], t_average[1], tck_hm1_mean[2] + tck_hm2_mean[2], tck1[3], tck2[4] ] X, Y = np.arange(0.0, hm2.shape[1], 1.0), np.arange(0.0, hm2.shape[0], 1.0) hm_merged = heat_maps.approx(tkc_merged, X, Y) utils.compare_2d(hm1 + hm2, hm_merged, title_left='Values addition', title_right='Surface addition') utils.err(hm1+hm2, hm_merged) ###Output _____no_output_____
Teoria_HW3.ipynb
###Markdown Basically, we need to find the maximum sum of non-consecutive values.Below, the pseudocode of the algorithm. Input: an array v of N elementsOutpu: list of value to obtain the max sum of non consecutive value1 **Start**2 While v is not empty:3 list=[] empty list in which we will insert the values4 find max in v and consider it's index i5 list.append(v) Add the first element 6 delete v [i-1], v [i], v [i + 1] delete the found value and its neighbors7 **end**8 Return(list) This is the implemtented program: ###Code import numpy as np n=int(input("How many appointment requests are there? --->")) l = [] for i in range(1,n+1): x=int(input("Insert a value that correspond to the duration of the appointment --->")) l.append(x) print (l) #our list l_1=[] #list for new values while len(l)>0: m=max(l) #find max i=l.index(m) #max index index=[i-1,i,i+1] #consecutive values l_1.append(m) for j in indici: try: l.pop(j) #print(l) except IndexError: pass print(l_1) ###Output _____no_output_____
in-class/.ipynb_checkpoints/week_2_inclass_exercises_DEMO-checkpoint.ipynb
###Markdown Week 2 - Flow Control The following play critical roles: 1. Indentation - running blocks of code.2. Time Delays - pacing the speed of our code.3. For Loops - iterating through data4. For Loops through multiple but related lists5. For Loops through Dictionaries6. Conditional Statements 1. Indentation* Python is unique in requiring indentations.* Indentations signify the start and end of code that belongs together (code blocks).* Without proper indentation, your code won't do what you expect.* Not working as expected? Check if you have indented correctly! Basic Flow Example: A Counter ###Code ## Using a While loop build a counter that counts from 1 to 5. ## Print the counter numbers in statement that reads "The count is" whatever the count is. ## Once it reaches 5, it should print "Done counting to 5!" count = 1 while count <=5: print(f"The count is {count}") count = count +1 print("done counting to 5") ###Output The count is 1 The count is 2 The count is 3 The count is 4 The count is 5 done counting to 5 ###Markdown You just controlled flow using indentation and a while loop. How fast does our code run? ###Code import datetime as dt counter = 1 while counter <6: current = dt.datetime.now() # the exact current time print(f"The count is {counter} and this process ran at {current}") # counter = counter + 1 counter += 1 print("Done printing to 5") ###Output The count is 1 and this process ran at 2021-09-13 13:59:17.292169 The count is 2 and this process ran at 2021-09-13 13:59:17.292419 The count is 3 and this process ran at 2021-09-13 13:59:17.292441 The count is 4 and this process ran at 2021-09-13 13:59:17.292453 The count is 5 and this process ran at 2021-09-13 13:59:17.292464 Done printing to 5 ###Markdown 2. Time Delays**Delay timers** are critical when scraping data from websites for several reasons. The **two** most important reasons are:1. Sometimes your scraper clicks on links and must wait for the content to actually populated on the new page. Your script is likely to run faster than a page can load.2. You don't want your scraper to be mistaken for a hostile attack on a server. You have to slow down the scrapes. Step 1 - Import required libraries ###Code import time # time is required. we will use its sleep function # import datetime as dt ## we already imported this earlier, but you'd need it if starting fresh ###Output _____no_output_____ ###Markdown Let's add a 5-second delay: ###Code counter = 1 while counter <6: current = dt.datetime.now() # the exact current time print(f"The count is {counter} and this process ran at {current}") # counter = counter + 1 counter += 1 time.sleep(5) print("Done printing to 5") ###Output The count is 1 and this process ran at 2021-09-13 14:04:38.373754 The count is 2 and this process ran at 2021-09-13 14:04:43.378471 The count is 3 and this process ran at 2021-09-13 14:04:48.382150 The count is 4 and this process ran at 2021-09-13 14:04:53.387505 The count is 5 and this process ran at 2021-09-13 14:04:58.392683 Done printing to 5 ###Markdown RandomizeSoftware that tracks traffic to a server might grow suspicious about a hit every nth seconds.Let's **randomize** the time between hits by using ```randint``` from the ```random``` library.You might sometimes see me use ```randrange``` from the ```random``` library: ``` from random import randrange```. What's the difference?**Difference 1**```randrange``` is exclusive of the final range value.```randint``` is inclusive of the final range value.**Difference 2**```randrange``` allows you to add a step: ```randrange(start, end, step)``````randint ``` only has start and end: ```randint(start, end) ###Code from random import randint # import necessary library randint(0,10) from random import randrange ## import necessary library randrange(0, 10) # we've already imported random counter = 1 while counter <6: mysnoozer = randint(4, 10) current = dt.datetime.now() # the exact current time print(f"The count is {counter} and this process ran at {current}.\ snooze for {mysnoozer} seconds!") counter += 1 time.sleep(mysnoozer) print("Done printing to 5") pip install icecream from icecream import ic rent = 1000 food = 400 expenses = rent + food print(f"the total expenses is: {expenses} and rent is {rent} and food is {food} ") ic(rent) ic(expenses) ###Output ic| expenses: 1400 ###Markdown 3. For Loops For Loops are your best friend - most used Python expression for journalists: Iterate over:* data stored in a list and run some calculation on each value;* a list of URLs and visit each site to scrape data;* data stored in dictionary keys and values and return what you are looking for. A simple ```for loop``` example: Let's take **For Loops** for test drive: ###Code ## RUN THIS CELL - Use this list of CEO salaries from 1985 ceo_salaries_1985 = [150_000, 201_000, 110_000, 75_000, 92_000, 55_000] ceo_salaries_1985 ## Print each salary with in the following format: ## "A CEO earned [some value] in 1985." for each_salary in ceo_salaries_1985: print(f"A ceo earned ${each_salary:,} in 1985") ## Now update each salary to 2019 dollars. ## Print the following info: ## "A CEO's salary of [1985 salary] in 1985 is worth [updated salary] in 2019 dollars." ## The CPI for 1985 is 107.6 ## The 2019 CPI is 255.657 ## The formula is: updated_salary = (oldSalary/oldCPI) * currentCPI salaries_2019 = [] old_salary = [150_000, 201_000, 110_000, 75_000, 92_000, 55_000] for ceo in old_salary: updated_salary = (ceo/107.6) * 255.657 print(f"A CEO's salary of ${ceo:,} in 1985 is worth ${updated_salary:,.0f}\ in 2019 dollars.") salaries_2019.append(updated_salary) salaries_2019 ceo_salaries_1985 sals_2019 = [(salary/107.6) * 255.657 for salary in ceo_salaries_1985] sals_2019 ###Output _____no_output_____ ###Markdown 4. For Loops through multiple but related lists ###Code ## RUN THIS CELL - You scrape a site and each datapoint is stored in different lists first_names = ["Irene", "Ursula", "Elon", "Tim"] last_names = ["Rosenfeld", "Burns", "Musk", "Cook"] titles = ["Chairman and CEO", "Chairman and CEO", "CEO", "CEO"] companies = ["Kraft Foods", "Xerox", "Tesla", "Apple"] industries = ["Food and Beverage", "Process and Document Management", "Auto Manufacturing", "Consumer Technology"] ###Output _____no_output_____ ###Markdown Use ```zip()``` to zip lists together ###Code ## with zip ## also print what each type of data is. ceo_list = [] for item in zip(first_names, last_names, titles, companies, industries): print(item) print(type(item)) ceo_list.append(item) item ceo_list ## zip it and store in a list called ceo_list ## export to a pandas dataframe import pandas as pd df = pd.DataFrame(ceo_list) df.columns =["first_name", "last_name", "title", "company", "industry"] df ## export to a csv filename = "ceo_bios.csv" df.to_csv(filename, index = False, encoding ="UTF-8") ## recall that dictionaries are like columns and rows in a csv ## let's turn this csv into a dataframe pd.read_csv("ceo_bios.csv") ###Output _____no_output_____ ###Markdown Turn tuples into lists ###Code ## zip lists into a list of lists ceo_list = [] for item in zip(first_names, last_names, titles, companies, industries): print(list(item)) print(type(list(item))) ceo_list.append(list(item)) ###Output ['Irene', 'Rosenfeld', 'Chairman and CEO', 'Kraft Foods', 'Food and Beverage'] <class 'list'> ['Ursula', 'Burns', 'Chairman and CEO', 'Xerox', 'Process and Document Management'] <class 'list'> ['Elon', 'Musk', 'CEO', 'Tesla', 'Auto Manufacturing'] <class 'list'> ['Tim', 'Cook', 'CEO', 'Apple', 'Consumer Technology'] <class 'list'> ###Markdown 5. For Loops through Dictionaries ###Code ## You have a list of CEO salaries from 1969. sals_1969 = [47_000, 65_000, 39_000, 96_000] sals_1969 ## We need the value of these salaries updated for every decade till 2019 ## Here are the CPIs for each decade in list of dictionaries from 1969 to 2019. decades_cpi = [ {"year": 1979, "cpi": 72.6,}, {"year": 1989, "cpi": 124}, {"year": 1999, "cpi": 166.6}, {"year": 2009, "cpi": 214.537}, {"year": 2019, "cpi": 255.657} ] ## Show the contents of this list of dictionaries decades_cpi ## What datatype is decades_cpi type(decades_cpi) # Check what type of data each list item is within decades_cpi for item in decades_cpi: print(type(item)) ## Print out each value in this format: ## "key --> value" decades_cpi[0] for decade in decades_cpi: # print(decade) for key, value in decade.items(): print(f"{key} ---->{value}") print("********") value ###Output _____no_output_____ ###Markdown The key alternates between the strings "year" and "cpi" in this loop. How do we actually target the values for "year" and "cpi" and place them in our calculations? ###Code ## show it here: for decade in decades_cpi: that_year = decade.get("year") old_cpi = decade.get("cpi") print(old_cpi) old_cpi sals_1969 ## Loop through each salary and update its value for each decade sals_1969 updated_sals = [] cpi_69 = 36.7 ## iterate through list of salaries with for loop for salary in sals_1969: # ic(salary) ## iterate through list of dictionaries to retrive year and old cpi for decade in decades_cpi: that_year = decade.get("year") old_cpi = decade.get("cpi") # ic(that_year) # ic(old_cpi) ## calc updated salary for that decade updated_salary = (salary/cpi_69) * old_cpi # ic(updated_salary) updated_sals.append(updated_salary) len(updated_sals) updated_sals ###Output _____no_output_____ ###Markdown 6. Conditional Statements ###Code ## create a list of 10 random numbers anywhere from -100 to 100 ##name the list numbers ###Output _____no_output_____ ###Markdown Create conditional statements that tell us if the last number and the penultimate number are positive or negative. Print a sentence that reads:```"The last number [what is it?] is [positive or negative] while the penultimate number [what is it?] is [negative or positive]."``` ###Code ## if else statements ###Output _____no_output_____ ###Markdown Tenary Expression```variable = value1 if some_condition else value2``` ###Code ## ternary expression # as ternary expression ###Output _____no_output_____ ###Markdown Multiple Tenary Expression```variable = value1 if condition1 else value2 if condition2 else value3 ``` ###Code ## A simple example ''' write a simple if else statement that prints out x is greater than y, or y is greater than x or if they are equal. ''' ## Now as a ternary ###Output _____no_output_____ ###Markdown Conditionals as Tuples```("False: Does not meet condition", "True: Meets condition")[conditional expression]``` ###Code age = 20 ## conditional tuple ###Output _____no_output_____ ###Markdown ChallengeWrite a tenary expression to update the first conditional exercise above to deal Zeros. For example if the random list generates:```[46, 30, 31, -56, 18, 57, -90, 81, 0, 0]```It should print out:```The last number (0) is neither negative or positive at zero while the penultimate number (0) is neither negative or positive at zero.``` ###Code ## activate the list numbers = [46, 30, 31, -56, 18, 57, -90, 81, 0, 0] ## write your multiple ternary expression ###Output _____no_output_____
01-01.train_model.ipynb
###Markdown XXXXXXXX ###Code !git clone https://github.com/Kazuhito00/7-segment-display-reader ###Output Cloning into '7-segment-display-reader'... remote: Enumerating objects: 21908, done. remote: Counting objects: 100% (21908/21908), done. remote: Compressing objects: 100% (21889/21889), done. remote: Total 21908 (delta 39), reused 21869 (delta 16), pack-reused 0 Receiving objects: 100% (21908/21908), 62.92 MiB | 30.55 MiB/s, done. Resolving deltas: 100% (39/39), done. Checking out files: 100% (42000/42000), done. ###Markdown XXXXXXXX ###Code !git clone https://github.com/Kazuhito00/7seg-image-generator.git !python '7seg-image-generator/create_7segment_dataset_da(easy).py' \ --erase_debug_window \ --steps=4000 \ --start_count=10000000 ###Output 100% 4000/4000 [00:37<00:00, 107.80it/s] ###Markdown XXXXXXXX ###Code %cp -rf './7-segment-display-reader/01.dataset/00' './dataset' %cp -rf './7-segment-display-reader/01.dataset/01' './dataset' %cp -rf './7-segment-display-reader/01.dataset/02' './dataset' %cp -rf './7-segment-display-reader/01.dataset/03' './dataset' %cp -rf './7-segment-display-reader/01.dataset/04' './dataset' %cp -rf './7-segment-display-reader/01.dataset/05' './dataset' %cp -rf './7-segment-display-reader/01.dataset/06' './dataset' %cp -rf './7-segment-display-reader/01.dataset/07' './dataset' %cp -rf './7-segment-display-reader/01.dataset/08' './dataset' %cp -rf './7-segment-display-reader/01.dataset/09' './dataset' %cp -rf './7-segment-display-reader/01.dataset/11' './dataset' ###Output _____no_output_____ ###Markdown XXXXXXXX ###Code import os dataset_directory = './dataset' train_directory = './train' validation_directory = './validation' # ๅญฆ็ฟ’ใƒ‡ใƒผใ‚ฟๆ ผ็ดใƒ‡ใ‚ฃใƒฌใ‚ฏใƒˆใƒชไฝœๆˆใ€€โ€ปใ€Œdataset_directoryใ€ใจๅŒๆง˜ใฎๆง‹ๆˆ for dir_path in os.listdir(dataset_directory): os.makedirs(train_directory + '/' + dir_path, exist_ok=True) # ๆคœ่จผใƒ‡ใƒผใ‚ฟๆ ผ็ดใƒ‡ใ‚ฃใƒฌใ‚ฏใƒˆใƒชไฝœๆˆใ€€โ€ปใ€Œdataset_directoryใ€ใจๅŒๆง˜ใฎๆง‹ๆˆ for dir_path in os.listdir(dataset_directory): os.makedirs(validation_directory + '/' + dir_path, exist_ok=True) import os import random import numpy as np import tensorflow as tf seed = 42 random.seed(seed) np.random.seed(seed) tf.random.set_seed(seed) os.environ["PYTHONHASHSEED"] = str(seed) import glob import shutil import random train_ratio = 0.75 # ๅญฆ็ฟ’ใƒ‡ใƒผใ‚ฟใฎๅ‰ฒๅˆ random.seed(42) # ใ‚ณใƒ”ใƒผๅ…ƒใƒ‡ใ‚ฃใƒฌใ‚ฏใƒˆใƒชๅ–ๅพ— directory_list = glob.glob(dataset_directory + '/*') for temp_directory in directory_list: file_list = glob.glob(temp_directory + '/*') # ใƒ‡ใ‚ฃใƒฌใ‚ฏใƒˆใƒชใธใ‚ณใƒ”ใƒผ for index, filepath in enumerate(file_list): if index < int(len(file_list) * train_ratio): # ๅญฆ็ฟ’็”จใƒ‡ใƒผใ‚ฟ shutil.copy2(filepath, train_directory + '/' + os.path.basename(temp_directory)) else: # ๆคœ่จผ็”จใƒ‡ใƒผใ‚ฟ shutil.copy2(filepath, validation_directory + '/' + os.path.basename(temp_directory)) ###Output _____no_output_____ ###Markdown XXXXXXX ###Code !pip install -U albumentations # Albumentationsใ‚’็”จใ„ใŸใƒ‡ใƒผใ‚ฟๆ‹กๅผต่จญๅฎš import albumentations as A def preprocessing_augmentation_function(param_p = 0.0): transform = [ A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=10, p=param_p), A.MotionBlur(blur_limit=15, p=param_p), A.GlassBlur(sigma=0.15, max_delta=4, iterations=1, p=param_p), A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, brightness_by_max=True, p=param_p), A.RGBShift(r_shift_limit=10, g_shift_limit=10, b_shift_limit=10, p=param_p), A.Cutout(num_holes=8, max_h_size=8, max_w_size=8, fill_value=0, p=param_p), A.Cutout(num_holes=8, max_h_size=8, max_w_size=8, fill_value=255, p=param_p), ] augmentation_function = A.Compose(transform) def augmentation(x): augmentation_image = augmentation_function(image=x) return augmentation_image['image'] return augmentation from tensorflow.keras.preprocessing.image import ImageDataGenerator train_image_da_generator = ImageDataGenerator( rescale=1.0/255, preprocessing_function=preprocessing_augmentation_function(0.1), ) validation_image_generator = ImageDataGenerator(rescale=1.0/255) batch_size = 64 image_height, image_width = 96, 96 train_data_gen = train_image_da_generator.flow_from_directory( batch_size=batch_size, directory=train_directory, shuffle=True, target_size=(image_height, image_width), class_mode='categorical' ) validation_data_gen = validation_image_generator.flow_from_directory( batch_size=batch_size, directory=validation_directory, shuffle=False, target_size=(image_height, image_width), class_mode='categorical' ) base_model = tf.keras.applications.MobileNetV2(include_top=False, weights='imagenet', input_shape=(96, 96, 3), alpha=0.35) base_model.trainable = True x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output) output = tf.keras.layers.Dense(12, activation='softmax', name='last_output')(x) model = tf.keras.Model(inputs=base_model.inputs, outputs=output, name='model') model.compile( optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'] ) # ใƒขใƒ‡ใƒซใƒใ‚งใƒƒใ‚ฏใƒใ‚คใƒณใƒˆไฟๅญ˜็”จใ‚ณใƒผใƒซใƒใƒƒใ‚ฏ checkpoint_path = os.path.join(os.getcwd(), 'checkpoints', 'weights.hdf5') cp_callback = tf.keras.callbacks.ModelCheckpoint( checkpoint_path, verbose=1, save_best_only=True, mode='auto', save_weights_only=False, save_freq='epoch' ) # ่ฉ•ไพกๅ€คใฎๆ”นๅ–„ใŒ่ฆ‹ใ‚‰ใ‚Œใชใ„ๅ ดๅˆใซๅญฆ็ฟ’็Ž‡ใ‚’ๆธ›ใ‚‰ใ™ใ‚ณใƒผใƒซใƒใƒƒใ‚ฏ lrp_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1) # ๆ—ฉๆœŸๆ‰“ใกๅˆ‡ใ‚Š็”จใ‚ณใƒผใƒซใƒใƒƒใ‚ฏ es_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1) epochs = 100 history = model.fit( train_data_gen, epochs=epochs, validation_data=validation_data_gen, callbacks=[cp_callback, lrp_callback, es_callback] ) ###Output Epoch 1/100 1055/1055 [==============================] - ETA: 0s - loss: 0.7381 - accuracy: 0.7472 Epoch 00001: val_loss improved from inf to 1.44679, saving model to /content/checkpoints/weights.hdf5 ###Markdown XXXXXXXX ###Code evaluate_result = model.evaluate_generator(validation_data_gen) print('Validation Loss:' + str(evaluate_result[0])) print('Validation Accuracy:' + str(evaluate_result[1])) import matplotlib.pyplot as plt def plot_history(history): plt.figure(figsize=(19, 6)) # ็ฒพๅบฆใฎๅฑฅๆญดใ‚’ใƒ—ใƒญใƒƒใƒˆ plt.subplot(1, 2, 1) plt.title('accuracy') plt.plot(history.history['accuracy'],"-",label="accuracy") plt.plot(history.history['val_accuracy'],"-",label="val_accuracy") plt.xlabel('epoch') plt.ylabel('accuracy') plt.legend(loc="lower right") # ๆๅคฑใฎๅฑฅๆญดใ‚’ใƒ—ใƒญใƒƒใƒˆ plt.subplot(1, 2, 2) plt.title('loss') plt.plot(history.history['loss'],"-",label="loss",) plt.plot(history.history['val_loss'],"-",label="val_loss") plt.xlabel('epoch') plt.ylabel('loss') plt.legend(loc='upper right') plt.show() plot_history(history) import math import cv2 as cv import numpy as np def draw_images(images): column = 6 row = math.ceil(len(images) / column) plt.figure(figsize=(16, 11)) plt.subplots_adjust(wspace=0.4, hspace=0.6) for i, image in enumerate(images): debug_image = cv.imread(image[1]) plt.subplot(row, column, i+1) plt.title(str(image[0]), fontsize=10) plt.tick_params(color='white') plt.tick_params(labelbottom=False, labelleft=False, labelright=False, labeltop=False) plt.imshow(cv.cvtColor(debug_image, cv.COLOR_BGR2RGB)) plt.xlabel('', fontsize=15) plt.ylabel('', rotation=0, fontsize=15, labelpad=20) plt.show() Y_pred = model.predict_generator(validation_data_gen) y_pred = np.argmax(Y_pred, axis=1) incorrect_numbers = [] for true_num, pred_num, filepath in zip(validation_data_gen.classes, y_pred, validation_data_gen.filepaths): if true_num != pred_num: if pred_num == 10: incorrect_numbers.append(['-(10)', filepath]) elif pred_num == 11: incorrect_numbers.append(['N(11)', filepath]) else: incorrect_numbers.append([pred_num, filepath]) draw_images(incorrect_numbers) ###Output /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators. ###Markdown XXXXXXXX ###Code model_path = 'checkpoints/weights.hdf5' load_model = tf.keras.models.load_model(model_path) from IPython.display import Image, display_png from tensorflow.keras.preprocessing.image import img_to_array, load_img test_image = tf.keras.preprocessing.image.load_img('./validation/07/00007004.jpg', target_size=(96, 96)) display_png(test_image) test_image = img_to_array(test_image) test_image = test_image.reshape(-1, 96, 96, 3) test_image = test_image.astype('float32') test_image = test_image * 1.0/255 predict_result = load_model.predict(test_image) print(np.squeeze(predict_result)) print(np.argmax(np.squeeze(predict_result))) ###Output [2.2122640e-06 6.4582217e-08 4.0999529e-07 7.6501377e-10 1.3395091e-07 1.1328411e-06 2.9578780e-08 9.9999595e-01 2.8156240e-09 2.3762277e-08 1.2261952e-08 6.3104553e-09] 7 ###Markdown XXXXXXXX ###Code load_model.save('7seg_classifier.hdf5', include_optimizer=False) converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_quantized_model = converter.convert() open('7seg_classifier.tflite', 'wb').write(tflite_quantized_model) ###Output INFO:tensorflow:Assets written to: /tmp/tmpl9niaoo4/assets ###Markdown XXXXXXXX ###Code interpreter = tf.lite.Interpreter(model_path="7seg_classifier.tflite") interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() print(input_details) print(output_details) interpreter.set_tensor(input_details[0]['index'], test_image) interpreter.invoke() tflite_results = interpreter.get_tensor(output_details[0]['index']) print(np.squeeze(tflite_results)) print(np.argmax(np.squeeze(tflite_results))) ###Output [2.3053269e-06 5.3328641e-08 4.0264879e-07 8.3536766e-10 1.3598803e-07 1.3750700e-06 3.1720809e-08 9.9999571e-01 2.5853140e-09 2.5920599e-08 9.1866488e-09 5.2289262e-09] 7
past-team-code/Fall2018Team2/Final Solution - Old Iteration/Full_MVP_Sentiment_JK-2.ipynb
###Markdown Predict Prices using Sentiment and ARIMA Strategy:1. Predict Sentiment 2. Show relationship between sentiment and price3. Create article scoring based on sentiment and price prediction 1. Import and Preprocess Data 1.1 Import and pre-process articles ###Code import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import datetime as dt from pandas import DataFrame from datetime import datetime,tzinfo from pytz import timezone import time import pytz import csv plt.style.use('fivethirtyeight') %config InlineBackend.figure_format = 'retina' %matplotlib inline articles = pd.read_csv("./data/Classified Articles.csv") articles.head(1) articles["timeStamp"] = pd.to_datetime(articles['date'] + ' ' + articles['time']) articles = articles.set_index("timeStamp") articles.head(1) len(articles) min(articles.index) max(articles.index) ###Output _____no_output_____ ###Markdown 1.1.1 Import and Pre-process hand-labeled sentiment ###Code data = pd.read_csv("./data/Articles Reading Assignment.csv") data.head(1) data = data.dropna() #data["Sentiment"] += 1 #data["Sentiment"] /= 2 data["contents"] = ["" for i in range(len(data))] data["title"] = ["" for i in range(len(data))] data["date"] = ["" for i in range(len(data))] data["time"] = ["" for i in range(len(data))] data["marks"] = ["" for i in range(len(data))] for i, row in data.iterrows(): x = row["URL"] key_words = articles[articles["source_url"] == x][:1]["contents"].values[0] data.at[i, "contents"] = str(key_words) title = articles[articles["source_url"] == x][:1]["title"].values[0] data.at[i, "title"] = str(title) date = articles[articles["source_url"] == x][:1]["date"].values[0] data.at[i, "date"] = date time = articles[articles["source_url"] == x][:1]["time"].values[0] data.at[i, "time"] = time marks = articles[articles["source_url"] == x][:1]["marks"].values[0] data.at[i, "marks"] = marks data.head(1) data["timeStamp"] = pd.to_datetime(data['date'] + ' ' + data['time']) data = data.set_index("timeStamp") data.head(1) len(data.loc[data["Sentiment"] == data["marks"]]) len(data) data["Sentiment"] = pd.to_numeric(data["Sentiment"]) data["marks"] = pd.to_numeric(data["marks"]) data.head(1) ###Output _____no_output_____ ###Markdown 1.2 Import and pre-process Bitcoin price data ###Code # import data: bitcoin prices btc = pd.read_csv("./data/coinbaseUSD_1-min_data_2014-12-01_to_2018-03-27.csv") # preprocess bitcoin price data btc.Timestamp = pd.to_datetime(btc.Timestamp, unit='s') btc.Timestamp = btc.Timestamp.dt.tz_localize('UTC') btc['log_close'] = np.log(btc.Close) - np.log(btc.Close.shift(1)) btc['Date'] = pd.to_datetime(btc['Timestamp']).dt.date min_periods = 43200 # 60minutes*24hours*30days price=btc['Close'] # Calculate the sd and volatility mean=price.rolling(min_periods).mean() sd=price.rolling(min_periods).std() vol = price.rolling(min_periods).std() * np.sqrt(min_periods) btc['Average']=mean btc['Volatility']=vol btc['SD']=sd price_log=btc['log_close'] # Calculate the sd and volatility mean=price_log.rolling(min_periods).mean() sd=price_log.rolling(min_periods).std() vol = price_log.rolling(min_periods).std() * np.sqrt(min_periods) btc['Average_log']=mean btc['Volatility_log']=vol btc['SD_log']=sd index_1 = btc[btc.Date == datetime.date(dt.datetime.strptime('01/23/18', '%x'))].index[0] index_2 = btc[btc.Date == datetime.date(dt.datetime.strptime('03/27/18', '%x'))].index[0] btc_1= btc.loc[index_1:index_2] btc_1 = btc_1.set_index("Timestamp") max(articles.index) min(articles.index) ###Output _____no_output_____ ###Markdown Plot data ###Code btc_close = btc_1['Close'] plt.plot(btc_close) plt.show() btc_log_close = btc_1['log_close'] plt.plot(btc_log_close) plt.show() ###Output _____no_output_____ ###Markdown Create function to change Bitcoin-Price to any time frame 1.3 Create lags of responses and merge data from 1 and 2 ###Code # create response add response with multiple lags in seconds import datetime import time start = time.time() response = pd.DataFrame() benchmark_naive = pd.DataFrame() # 1 minute, 5 minutes, 10 minutes, 30 minutes, 60 minutes, 12 hours, 1 day, 2 days, 4 days colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d" : 345600} train_time = pd.to_datetime(articles.index) for colname in colnames: count = 0 stock_return = [] stock_return_naive = [] lag = colnames[colname] for i in train_time: count +=1 try: start_price = btc_1.Close.iloc[btc_1.index.get_loc(i,method = "nearest")] end_price = btc_1.Close.iloc[btc_1.index.get_loc((i+datetime.timedelta(0,lag)),method = "nearest")] stock_return.append(end_price/start_price-1) end_price_naive = btc_1.Close.iloc[btc_1.index.get_loc(i,method = "nearest")] start_price_naive = btc_1.Close.iloc[btc_1.index.get_loc((i-datetime.timedelta(0,lag)),method = "nearest")] stock_return_naive.append(end_price_naive/start_price_naive-1) #if lag ==86400: # print(start_price,i) #print(end_price,(i+datetime.timedelta(0,lag))) #print("") except: stock_return.append(0) stock_return_naive.append(0) print("exception raised") #if count ==10: #break response[colname] = stock_return benchmark_naive[colname] = stock_return_naive print("time elapsed:",round((time.time()-start)/60,1),"minutes") response.head(1) benchmark_naive.head(1) ###Output _____no_output_____ ###Markdown 1.4.1 Sentiment Assignment ###Code data.head(1) len(data) ###Output _____no_output_____ ###Markdown 1.4.1.1 NLP ###Code # create class which handles NLP tokenization, stemming/lemmatizing and tranformation to vector class nlp_validation_sets: def __init__(self,fold,validation,train_data,max_features=10,method_nlp = "stem",ngram = 1): self.fold = fold self.validation = validation self.max_features = max_features self.method_nlp = method_nlp self.ngram = ngram self.train_data = train_data self.stemmed_word_list = [] self.tokenized_word_list = [] self.stemmed_word_list_only_bad = [] self.tokenized_word_list_only_bad = [] self.stemmed_word_list_val = [] self.tokenized_word_list_val = [] self.stemmed_word_list_train = [] self.tokenized_word_list_train = [] def choose_w2v(self,method = "tfidf"): from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer if method == "tfidf": self.tfidf = TfidfVectorizer(max_features = self.max_features,ngram_range =(1,self.ngram), max_df = 1.0,min_df = 1) if method == "count": self.tfidf = CountVectorizer(max_features = self.max_features,ngram_range = (1,self.ngram), max_df = 1.0,min_df = 1) def fit(self): import time from nltk.stem.porter import PorterStemmer from nltk.corpus import stopwords import re start = time.time() stop_words_english = set(stopwords.words('english')) stem = PorterStemmer() for text in self.fold.contents: wordList = re.sub("[^\w]", " ",text).split() stem_words = [] token_words = [] for word in wordList: if not word.lower() in stop_words_english: stem_words.append(stem.stem(word.lower())) token_words.append(word.lower()) self.stemmed_word_list.append(" ".join(str(x) for x in stem_words)) self.tokenized_word_list.append(" ".join(str(x) for x in token_words)) for text in self.train_data.contents: wordList = re.sub("[^\w]", " ",text).split() stem_words = [] token_words = [] for word in wordList: if not word.lower() in stop_words_english: stem_words.append(stem.stem(word.lower())) token_words.append(word.lower()) self.stemmed_word_list_train.append(" ".join(str(x) for x in stem_words)) self.tokenized_word_list_train.append(" ".join(str(x) for x in token_words)) if self.method_nlp == "stem": self.tfidf.fit(self.stemmed_word_list_train) if self.method_nlp == "token": self.tfidf.fit(self.tokenized_word_list_train) for text in self.validation.contents: wordList = re.sub("[^\w]", " ",text).split() stem_words = [] token_words = [] for word in wordList: if not word.lower() in stop_words_english: stem_words.append(stem.stem(word.lower())) token_words.append(word.lower()) self.stemmed_word_list_val.append(" ".join(str(x) for x in stem_words)) self.tokenized_word_list_val.append(" ".join(str(x) for x in token_words)) print("time elapsed",round((time.time()-start)/60,1)) def transform_test(self): if self.method_nlp == "stem": return self.tfidf.transform(self.stemmed_word_list_val) if self.method_nlp == "token": return self.tfidf.transform(self.tokenized_word_list_val) def transform_train(self): return self.tfidf.transform(self.stemmed_word_list) from sklearn.model_selection import train_test_split X_train, X_test, Y_train_sen, Y_test_sen = train_test_split(data,data.Sentiment, test_size = 0.25, random_state = 42) X_train, X_test, Y_train_mar, Y_test_mar = train_test_split(data,data.marks, test_size = 0.25, random_state = 42) X_test.shape X_train.shape sentiment_nlp = nlp_validation_sets(fold = X_train,validation = X_test,train_data = X_train, max_features=100,method_nlp = "stem",ngram = 1) sentiment_nlp.choose_w2v(method = "tfidf") sentiment_nlp.fit() X = sentiment_nlp.transform_train() X.shape X_test = sentiment_nlp.transform_test() X_test.shape ###Output _____no_output_____ ###Markdown 1.4.1.2 Model fitting Marks ###Code from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score lr = LogisticRegression() lr.fit(X,Y_train_mar) pred = lr.predict(X_test) pred accuracy_score(Y_test_mar,pred) print("no information accuracy", np.mean(Y_test_mar)) confusion_matrix(pred,Y_test_mar) ###Output _____no_output_____ ###Markdown Hand-labeled Sentiment Random Forest ###Code from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV rf = RandomForestClassifier() n_estimators = [500] max_features = [.3,.5,.7,1.0] max_depth = [None] param_grid = {'n_estimators': n_estimators,'max_features': max_features,"max_depth":max_depth} grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 2, n_jobs = -1, verbose = 2) # Fit the random search model grid_search.fit(X, Y_train_sen) best_param = grid_search.best_params_ rf_d = RandomForestClassifier(n_estimators = best_param["n_estimators"],max_features = best_param["max_features"] ,max_depth = best_param["max_depth"]) rf_d.fit(X,Y_train_sen) pred = rf_d.predict(X_test) best_param pred accuracy_score(Y_test_sen,pred) print("no information accuracy", len(X_train.loc[X_train.Sentiment == 0])/len(X_train)) confusion_matrix(Y_test_sen,pred) ###Output _____no_output_____ ###Markdown Gradient Boosting ###Code #https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import GridSearchCV gbm = GradientBoostingClassifier() max_features = [0.3,0.6,0.8] subsample = [1] max_depth = [1,3,6] learning_rate = [.01] n_estimators=[50,100,150,200] param_grid = {'max_features': max_features,"max_depth":max_depth, "subsample":subsample,"learning_rate":learning_rate,"n_estimators":n_estimators} grid_search = GridSearchCV(estimator = gbm, param_grid = param_grid, cv = 2, n_jobs = -1, verbose = 2) # Fit the random search model grid_search.fit(X,Y_train_sen) best_param = grid_search.best_params_ gbm_d = GradientBoostingClassifier(n_estimators = best_param["n_estimators"],max_features = best_param["max_features"] ,max_depth = best_param["max_depth"],learning_rate = best_param["learning_rate"]) gbm_d.fit(X,Y_train_sen) pred = gbm_d.predict(X_test) best_param pred accuracy_score(Y_test_sen,pred) print("no information accuracy", len(X_train.loc[X_train.Sentiment == 0])/len(X_train)) confusion_matrix(Y_test_sen,pred) ###Output _____no_output_____ ###Markdown Prediction ###Code main_articles_nlp = nlp_validation_sets(fold = X_train,validation = articles,train_data = X_train, max_features=100,method_nlp = "stem",ngram = 1) main_articles_nlp.choose_w2v(method = "count") main_articles_nlp.fit() X = main_articles_nlp.transform_test() X.shape pred_main_articles = rf_d.predict(X) pred_main_articles.shape articles["predicted_sentiment"] = pred_main_articles articles.head(1) articles.to_csv("Classified Articles with Predicted Sentiment") ###Output _____no_output_____ ###Markdown 2. Relationship between Sentiment and Price 2.1 train test split ###Code articles.head(1) articles.shape response.shape response.head() colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d": 345600} response = response.set_index(articles.index) for i in colnames: articles[i] = response[i] #split = .75 #train = articles.iloc[0:(round(len(articles)*split)),:] #test = articles.iloc[-(round(len(articles)*(1-split))):] #train_response = response.iloc[0:(round(len(response)*split)),:] #test_response = response.iloc[-(round(len(response)*(1-split))):] #train_naive = benchmark_naive.iloc[0:(round(len(benchmark_naive)*split)),:] #test_naive = benchmark_naive.iloc[-(round(len(benchmark_naive)*(1-split))):] from sklearn.model_selection import train_test_split train, test, train_response, test_response = train_test_split(articles,response, test_size = 0.25, random_state = 42) train_naive,test_naive,a,b = train_test_split(benchmark_naive,response, test_size = 0.25, random_state = 42) train.shape train_response.shape test.shape articles.shape test_response.shape test.shape train_response.shape sns.boxplot(train_response["lag_1d"]) colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d": 345600} ###Output _____no_output_____ ###Markdown 2.2 Mean Difference for Sentiment ###Code articles.head(1) round(np.mean(articles.lag_30m)*100,4) round(np.mean(articles.lag_30m.loc[articles.predicted_sentiment==0.0])*100,4) round(np.mean(articles.lag_30m.loc[articles.predicted_sentiment==1.0])*100,4) round(np.mean(articles.lag_30m.loc[articles.predicted_sentiment==-1.0])*100,4) round(np.mean(articles.lag_30m.loc[articles.marks==0.0])*100,4) round(np.mean(articles.lag_30m.loc[articles.marks==1.0])*100,4) ###Output _____no_output_____ ###Markdown 2.3 Evaluate difference using linear regression ###Code import statsmodels.api as sm for j in colnames: lm = sm.OLS(articles[j],sm.add_constant(articles.predicted_sentiment)).fit() print("OLS for ",j) print(lm.summary()) print("") print("") import statsmodels.api as sm for j in colnames: lm = sm.OLS(articles[j],sm.add_constant(articles.marks)).fit() print("OLS for ",j) print(lm.summary()) print("") print("") ###Output OLS for lag_1m OLS Regression Results ============================================================================== Dep. Variable: lag_1m R-squared: 0.000 Model: OLS Adj. R-squared: -0.000 Method: Least Squares F-statistic: 0.5884 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.443 Time: 22:59:45 Log-Likelihood: 1.9797e+05 No. Observations: 40732 AIC: -3.959e+05 Df Residuals: 40730 BIC: -3.959e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -2.335e-05 1.77e-05 -1.320 0.187 -5.8e-05 1.13e-05 marks 1.595e-05 2.08e-05 0.767 0.443 -2.48e-05 5.67e-05 ============================================================================== Omnibus: 9475.151 Durbin-Watson: 1.842 Prob(Omnibus): 0.000 Jarque-Bera (JB): 267636.258 Skew: 0.495 Prob(JB): 0.00 Kurtosis: 15.519 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_5m OLS Regression Results ============================================================================== Dep. Variable: lag_5m R-squared: 0.000 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 9.921 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.00163 Time: 22:59:45 Log-Likelihood: 1.6592e+05 No. Observations: 40732 AIC: -3.318e+05 Df Residuals: 40730 BIC: -3.318e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0001 3.89e-05 -3.356 0.001 -0.000 -5.43e-05 marks 0.0001 4.57e-05 3.150 0.002 5.43e-05 0.000 ============================================================================== Omnibus: 9405.589 Durbin-Watson: 1.662 Prob(Omnibus): 0.000 Jarque-Bera (JB): 184538.855 Skew: 0.615 Prob(JB): 0.00 Kurtosis: 13.355 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_10m OLS Regression Results ============================================================================== Dep. Variable: lag_10m R-squared: 0.000 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 6.322 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.0119 Time: 22:59:45 Log-Likelihood: 1.5148e+05 No. Observations: 40732 AIC: -3.030e+05 Df Residuals: 40730 BIC: -3.029e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -9.132e-05 5.54e-05 -1.649 0.099 -0.000 1.72e-05 marks 0.0002 6.51e-05 2.514 0.012 3.61e-05 0.000 ============================================================================== Omnibus: 11248.319 Durbin-Watson: 1.568 Prob(Omnibus): 0.000 Jarque-Bera (JB): 251043.943 Skew: 0.802 Prob(JB): 0.00 Kurtosis: 15.056 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_30m OLS Regression Results ============================================================================== Dep. Variable: lag_30m R-squared: 0.000 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 2.647 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.104 Time: 22:59:45 Log-Likelihood: 1.2956e+05 No. Observations: 40732 AIC: -2.591e+05 Df Residuals: 40730 BIC: -2.591e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0001 9.49e-05 -1.148 0.251 -0.000 7.71e-05 marks 0.0002 0.000 1.627 0.104 -3.71e-05 0.000 ============================================================================== Omnibus: 8941.356 Durbin-Watson: 1.376 Prob(Omnibus): 0.000 Jarque-Bera (JB): 135923.786 Skew: 0.640 Prob(JB): 0.00 Kurtosis: 11.857 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_60m OLS Regression Results ============================================================================== Dep. Variable: lag_60m R-squared: 0.000 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 7.281 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.00697 Time: 22:59:45 Log-Likelihood: 1.1617e+05 No. Observations: 40732 AIC: -2.323e+05 Df Residuals: 40730 BIC: -2.323e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0001 0.000 -1.065 0.287 -0.000 0.000 marks 0.0004 0.000 2.698 0.007 0.000 0.001 ============================================================================== Omnibus: 5897.620 Durbin-Watson: 1.324 Prob(Omnibus): 0.000 Jarque-Bera (JB): 50146.005 Skew: 0.437 Prob(JB): 0.00 Kurtosis: 8.365 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_12h OLS Regression Results ============================================================================== Dep. Variable: lag_12h R-squared: 0.005 Model: OLS Adj. R-squared: 0.005 Method: Least Squares F-statistic: 211.0 Date: Sun, 02 Dec 2018 Prob (F-statistic): 1.09e-47 Time: 22:59:45 Log-Likelihood: 69379. No. Observations: 40732 AIC: -1.388e+05 Df Residuals: 40730 BIC: -1.387e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0027 0.000 -6.494 0.000 -0.004 -0.002 marks 0.0071 0.000 14.526 0.000 0.006 0.008 ============================================================================== Omnibus: 5945.965 Durbin-Watson: 1.042 Prob(Omnibus): 0.000 Jarque-Bera (JB): 31241.052 Skew: 0.605 Prob(JB): 0.00 Kurtosis: 7.116 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_1d OLS Regression Results ============================================================================== Dep. Variable: lag_1d R-squared: 0.001 Model: OLS Adj. R-squared: 0.001 Method: Least Squares F-statistic: 34.36 Date: Sun, 02 Dec 2018 Prob (F-statistic): 4.62e-09 Time: 22:59:45 Log-Likelihood: 55219. No. Observations: 40732 AIC: -1.104e+05 Df Residuals: 40730 BIC: -1.104e+05 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0008 0.001 -1.367 0.172 -0.002 0.000 marks 0.0041 0.001 5.862 0.000 0.003 0.005 ============================================================================== Omnibus: 4370.854 Durbin-Watson: 0.788 Prob(Omnibus): 0.000 Jarque-Bera (JB): 14077.125 Skew: 0.553 Prob(JB): 0.00 Kurtosis: 5.659 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_2d OLS Regression Results ============================================================================== Dep. Variable: lag_2d R-squared: 0.000 Model: OLS Adj. R-squared: -0.000 Method: Least Squares F-statistic: 0.2860 Date: Sun, 02 Dec 2018 Prob (F-statistic): 0.593 Time: 22:59:46 Log-Likelihood: 42943. No. Observations: 40732 AIC: -8.588e+04 Df Residuals: 40730 BIC: -8.587e+04 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 0.0015 0.001 1.912 0.056 -3.83e-05 0.003 marks -0.0005 0.001 -0.535 0.593 -0.002 0.001 ============================================================================== Omnibus: 2304.937 Durbin-Watson: 0.511 Prob(Omnibus): 0.000 Jarque-Bera (JB): 4945.843 Skew: 0.384 Prob(JB): 0.00 Kurtosis: 4.525 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS for lag_4d OLS Regression Results ============================================================================== Dep. Variable: lag_4d R-squared: 0.003 Model: OLS Adj. R-squared: 0.003 Method: Least Squares F-statistic: 123.2 Date: Sun, 02 Dec 2018 Prob (F-statistic): 1.39e-28 Time: 22:59:46 Log-Likelihood: 26699. No. Observations: 40732 AIC: -5.339e+04 Df Residuals: 40730 BIC: -5.338e+04 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const -0.0053 0.001 -4.505 0.000 -0.008 -0.003 marks 0.0155 0.001 11.099 0.000 0.013 0.018 ============================================================================== Omnibus: 367.505 Durbin-Watson: 0.348 Prob(Omnibus): 0.000 Jarque-Bera (JB): 398.400 Skew: 0.203 Prob(JB): 3.08e-87 Kurtosis: 3.265 Cond. No. 3.58 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ###Markdown what if we add past price as additional predictor? 3. Article Scoring 3.1 nlp features ###Code nlp = nlp_validation_sets(fold = train,validation = test,train_data = articles,max_features=100,method_nlp = "stem",ngram = 1) nlp.choose_w2v(method = "count") nlp.fit() X = nlp.transform_train() X_test = nlp.transform_test() X.shape X_test.shape ###Output _____no_output_____ ###Markdown 3.2 train models and evaluate models ###Code train_response.shape X.shape X_test.shape test_response.shape ###Output _____no_output_____ ###Markdown 3.3 benchmark: naive (use last price change as predictor for current price change) ###Code from sklearn.metrics import mean_squared_error colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d": 345600} for j in colnames: print("benchmark training error:",j,":",round(mean_squared_error(train_response[j],train_naive[j]),5)) for j in colnames: print("benchmark testing error:",j,":",round(mean_squared_error(test_response[j],test_naive[j]),5)) ###Output benchmark testing error: lag_1m : 1e-05 benchmark testing error: lag_5m : 3e-05 benchmark testing error: lag_10m : 6e-05 benchmark testing error: lag_30m : 0.00019 benchmark testing error: lag_60m : 0.0004 benchmark testing error: lag_12h : 0.00397 benchmark testing error: lag_1d : 0.00893 benchmark testing error: lag_2d : 0.01604 benchmark testing error: lag_4d : 0.02795 ###Markdown 3.4 benchmark: AR-1 linear regresion (use weighted past price change as predictor for current price change) ###Code from sklearn.linear_model import LinearRegression for j in colnames: reg = LinearRegression() reg.fit(train_naive,train_response[j]) #pred = reg.predict(train_naive) #print("linear regression training error:",j,round(mean_squared_error(pred,train_response[j])/ #mean_squared_error(train_response[j],train_naive[j]),5)) pred = reg.predict(test_naive) #print("") print("AR-1 linear regression test error:",j,round(mean_squared_error(pred,test_response[j])/ mean_squared_error(test_response[j],test_naive[j]),5)) ###Output AR-1 linear regression test error: lag_1m 0.52073 AR-1 linear regression test error: lag_5m 0.52643 AR-1 linear regression test error: lag_10m 0.49202 AR-1 linear regression test error: lag_30m 0.49134 AR-1 linear regression test error: lag_60m 0.46501 AR-1 linear regression test error: lag_12h 0.47748 AR-1 linear regression test error: lag_1d 0.42965 AR-1 linear regression test error: lag_2d 0.4483 AR-1 linear regression test error: lag_4d 0.56196 ###Markdown 3.5 linear regression with NLP features 2.3.1 NLP features only ###Code X = np.concatenate((X.toarray(),train[["predicted_sentiment"]].values),axis = 1) X.shape X_test = np.concatenate((X_test.toarray(),test[["predicted_sentiment"]].values),axis = 1) X_test.shape test_response.shape # linear regression: train #a = np.concatenate((X,train_dummies),axis = 1) #b = np.concatenate((X_test,test_dummies),axis = 1) for j in colnames: reg = LinearRegression() reg.fit(X,train_response[j]) #pred = reg.predict(X) #print("linear regression training error:",j,round(mean_squared_error(pred,train_response[j])/ # mean_squared_error(train_response[j],train_naive[j]),5)) pred = reg.predict(X_test) #print("") print("linear regression test error:",j,round(mean_squared_error(pred,test_response[j])/ mean_squared_error(test_response[j],test_naive[j]),5)) ###Output linear regression test error: lag_1m 0.52084 linear regression test error: lag_5m 0.52696 linear regression test error: lag_10m 0.49382 linear regression test error: lag_30m 0.49053 linear regression test error: lag_60m 0.46523 linear regression test error: lag_12h 0.48613 linear regression test error: lag_1d 0.43401 linear regression test error: lag_2d 0.44412 linear regression test error: lag_4d 0.55177 ###Markdown 3.6 lasso regression with NLP features (with 5-fold cross validation) ###Code #from: https://www.kaggle.com/floser/aw6-the-lasso-cross-validated #https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html from sklearn.preprocessing import scale from sklearn.linear_model import Lasso, LassoCV, Ridge, RidgeCV #from sklearn import cross_validation for j in colnames: alphas = [1,5,10,50,100,1000] lassocv = LassoCV(alphas=alphas, cv=5, max_iter=100000, normalize=True) lassocv.fit(X, train_response[j]) lasso = Lasso() lasso.set_params(alpha=lassocv.alpha_) #print("Alpha=", lassocv.alpha_) lasso.fit(X, train_response[j]) #print("mse = ",mean_squared_error(y_test, lasso.predict(X_test))) #print("best model coefficients:") #pd.Series(lasso.coef_, index=X.columns) #pred = lasso.predict(X) #print("lasso regression training error:",j,round(mean_squared_error(pred,train_response[j])/ #mean_squared_error(train_response[j],train_naive[j]),5)) pred = lasso.predict(X_test) print("lasso R squared:",j,lasso.score(X,train_response[j])) print("lasso regression test error:",j,round(mean_squared_error(pred,test_response[j])/ mean_squared_error(test_response[j],test_naive[j]),5)) ###Output lasso R squared: lag_1m 0.0 lasso regression test error: lag_1m 0.52028 lasso R squared: lag_5m 0.0 lasso regression test error: lag_5m 0.52693 lasso R squared: lag_10m 0.0 lasso regression test error: lag_10m 0.49379 lasso R squared: lag_30m 0.0 lasso regression test error: lag_30m 0.49205 lasso R squared: lag_60m 0.0 lasso regression test error: lag_60m 0.46784 lasso R squared: lag_12h 0.0 lasso regression test error: lag_12h 0.49192 lasso R squared: lag_1d 0.0 lasso regression test error: lag_1d 0.44055 lasso R squared: lag_2d 0.0 lasso regression test error: lag_2d 0.45166 lasso R squared: lag_4d 0.0 lasso regression test error: lag_4d 0.56981 ###Markdown 3.7 random forest regressor with NLP features ###Code #from: https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74 #https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html #from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import GridSearchCV start = time.time() colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d": 345600} model = ["lag_1d"] count = 0 for j in model: count+=1 #a = np.concatenate((train_dummies,np.array(train_naive[j]).reshape(len(train_naive[j]),1),X),axis = 1) #b = np.concatenate((test_dummies,np.array(test_naive[j]).reshape(len(test_naive[j]),1),X_test),axis = 1) rf = RandomForestRegressor() n_estimators = [500] max_features = [.3] max_depth = [None] param_grid = {'n_estimators': n_estimators,'max_features': max_features,"max_depth":max_depth} grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 2, n_jobs = -1, verbose = 2,scoring = "neg_mean_squared_error") #rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 4, #cv = 3, verbose=2, random_state=42, n_jobs = -1) # Fit the random search model grid_search.fit(X, train_response[j]) best_param = grid_search.best_params_ rf_d = RandomForestRegressor(n_estimators = best_param["n_estimators"],max_features = best_param["max_features"] ,max_depth = best_param["max_depth"]) rf_d.fit(X,train_response[j]) pred = rf_d.predict(X_test) pred_rf = rf_d.predict(X_test) print("random forest test error:",j,round(mean_squared_error(pred,test_response[j])/ mean_squared_error(test_response[j],test_naive[j]),5)) #if count>0: # break print("time elapsed:",round((time.time()-start)/60,1),"minutes") best_param import matplotlib.pyplot as plt plt.plot(pred_rf,test_response["lag_1d"],"o") plt.plot(test_naive["lag_1d"],test_response["lag_1d"],"o") import random rand = [] for x in range(10): rand.append(random.randint(0,len(pred_rf))) for i in rand: print("true value:",round(test_response["lag_4d"].iloc[i],2),"","predicted value:",round(pred_rf[i],2)) #print(i) for i in rand: print(test["title"].iloc[i]) #print(i) ###Output Use blockchain technology to prevent PNB like scam: Foreign data expert Unpacking Facebookโ€™s Bitcoin ban Bitcoinโ€™s Nosedive Hasnโ€™t Hurt Red-Hot Coin Offerings Nine suspects arrested over theft of bitcoin machines Global Cryptocurrency Markets Improve in February as Bitcoin Rises Again Rippleโ€™s XRP crypto token is more volatile than just about everything Bitcoin Has Triggered the Energy Arms Race Bitcoin, Ethereum, Bitcoin Cash, Ripple, Stellar, Litecoin, Cardano, NEO, EOS: Price Analysis, March 10 Supposed Bitcoin co-inventor sued for more than $10 billion in cryptocurrency Wall Street embraces bitcoin ###Markdown 3.8 gbm with cv ###Code #https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import GridSearchCV start = time.time() colnames = {"lag_1m":60,"lag_5m":300,"lag_10m":600,"lag_30m":1800,"lag_60m":3600,"lag_12h":43200,"lag_1d":86400, "lag_2d":172800,"lag_4d": 345600} model = ["lag_1d"] count = 0 for j in model: count+=1 #a = np.concatenate((train_dummies,np.array(train_naive[j]).reshape(len(train_naive[j]),1),X),axis = 1) #b = np.concatenate((test_dummies,np.array(test_naive[j]).reshape(len(test_naive[j]),1),X_test),axis = 1) gbm = GradientBoostingRegressor(n_estimators = 500,validation_fraction = .75, n_iter_no_change = 2) max_features = [0.6,0.8] subsample = [.7] max_depth = [1,5] learning_rate = [.01] n_estimators=[200] param_grid = {'max_features': max_features,"max_depth":max_depth, "subsample":subsample,"learning_rate":learning_rate} #grid_search = GridSearchCV(estimator = gbm, param_grid = param_grid, # cv = 2, n_jobs = -1, verbose = 2,scoring = "neg_mean_squared_error") #rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 4, #cv = 3, verbose=2, random_state=42, n_jobs = -1) # Fit the random search model #grid_search.fit(X, train_response[j]) #best_param = grid_search.best_params_ gbm_d = GradientBoostingRegressor( ) gbm_d.fit(X,train_response[j]) pred = gbm_d.predict(X_test) pred_gbm = gbm_d.predict(X_test) print("gbm test error:",j,round(mean_squared_error(pred,test_response[j])/ mean_squared_error(test_response[j],test_naive[j]),5)) #if count>0: # break print("time elapsed:",round((time.time()-start)/60,1),"minutes") best_param gbm_d.n_estimators #grid_search.cv_results_ import matplotlib.pyplot as plt plt.plot(pred,test_response["lag_1d"],"o") import random rand = [] for x in range(20): rand.append(random.randint(0,len(pred))) for i in rand: print("true value:",round(test_response["lag_4d"].iloc[i],2),"","predicted value:",round(pred[i],2)) #print(i) ###Output true value: -0.11 predicted value: 0.01 true value: 0.03 predicted value: -0.01 true value: 0.12 predicted value: 0.01 true value: -0.18 predicted value: -0.0 true value: -0.17 predicted value: 0.0 true value: -0.13 predicted value: -0.02 true value: -0.14 predicted value: 0.0 true value: -0.0 predicted value: 0.01 true value: 0.07 predicted value: -0.0 true value: 0.01 predicted value: 0.0 true value: 0.04 predicted value: 0.02 true value: -0.07 predicted value: -0.0 true value: 0.0 predicted value: 0.01 true value: -0.14 predicted value: 0.01 true value: 0.04 predicted value: 0.0 true value: -0.14 predicted value: -0.0 true value: -0.06 predicted value: 0.0 true value: 0.03 predicted value: 0.0 true value: 0.06 predicted value: -0.0 true value: -0.13 predicted value: -0.01 ###Markdown An error ocurred while starting the kernelOMP: Error 15: Initializing libomp.dylib, but found libiomp5.dylib already initialized.OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://openmp.llvm.org/ 3.9 Final Scoring ###Code pred_rf np.percentile(pred_rf,95) pred_rf[0] def create_score(prediction): min_ = np.percentile(abs(prediction),2) max_ = np.percentile(abs(prediction),98) new_list = [] for i in abs(prediction): if i < min_ or i> max_: new_list.append(10) else: new_list.append(round((i-min_)/(max_-min_)*10)) return new_list scoring = create_score(pred_rf) min(scoring) max(scoring) ###Output _____no_output_____ ###Markdown 4. Evaluation ###Code import matplotlib.pyplot as plt plt.plot(pred_rf,test_response["lag_1d"],"o") plt.xlabel('Predicted Daily Price Change') plt.ylabel('Actual Daily Price Change') import random rand = [] for x in range(10): rand.append(random.randint(0,len(pred_rf))) for i in rand: print("true value:",round(test_response["lag_4d"].iloc[i],2),"","predicted value:",round(pred_rf[i],2),"", "predicted scoring:",scoring[i]) print("") print(test["title"].iloc[i]) print("") print("") ###Output true value: 0.11 predicted value: 0.0 predicted scoring: 0.0 Craig Wright, who once claimed to be Bitcoin founder Satoshi Nakamoto, sued for $10B by his deceased partner Dave Kleiman's estate for stealing $5B in bitcoin (Russell Brandom/The Verge) true value: -0.21 predicted value: -0.02 predicted scoring: 2.0 Arun Jaitley has just killed Indiaโ€™s cryptocurrency party true value: -0.04 predicted value: -0.02 predicted scoring: 2.0 Why did Ethereum Drop so hard? Bitcoin is Correcting, and Cryptocurrency Markets Follow. true value: 0.3 predicted value: 0.16 predicted scoring: 10 Bitcoin slides below $6000; half its value lost in 2018 - Fox Business true value: -0.04 predicted value: 0.05 predicted scoring: 4.0 Dutch Court Finds Bitcoin A Legitimate โ€œTransferable Valueโ€ true value: 0.14 predicted value: 0.12 predicted scoring: 10 Bitcoin plunges below $10,000 as major crypto exchange to share user details with US tax authorities true value: 0.19 predicted value: 0.11 predicted scoring: 10 Expert Believes Bitcoin Price Crash Will Continue true value: 0.05 predicted value: -0.03 predicted scoring: 3.0 Experts Discuss: What Can Blockchain Really Do for Advertising? true value: 0.02 predicted value: 0.03 predicted scoring: 3.0 There's now a vibrator that will order you a pizza when you, um, finish true value: 0.04 predicted value: 0.04 predicted scoring: 4.0 Friend or Foe: Inside Polandโ€™s Strange War on Cryptocurrencies
04 - NLP - Applied Text Mining/Assignment 1.ipynb
###Markdown ---_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-text-mining/resources/d9pwm) course resource._--- Assignment 1In this assignment, you'll be working with messy medical data and using regex to extract relevant infromation from the data. Each line of the `dates.txt` file corresponds to a medical note. Each note has a date that needs to be extracted, but each date is encoded in one of many formats.The goal of this assignment is to correctly identify all of the different date variants encoded in this dataset and to properly normalize and sort the dates. Here is a list of some of the variants you might encounter in this dataset:* 04/20/2009; 04/20/09; 4/20/09; 4/3/09* Mar-20-2009; Mar 20, 2009; March 20, 2009; Mar. 20, 2009; Mar 20 2009;* 20 Mar 2009; 20 March 2009; 20 Mar. 2009; 20 March, 2009* Mar 20th, 2009; Mar 21st, 2009; Mar 22nd, 2009* Feb 2009; Sep 2009; Oct 2010* 6/2008; 12/2009* 2009; 2010Once you have extracted these date patterns from the text, the next step is to sort them in ascending chronological order accoring to the following rules:* Assume all dates in xx/xx/xx format are mm/dd/yy* Assume all dates where year is encoded in only two digits are years from the 1900's (e.g. 1/5/89 is January 5th, 1989)* If the day is missing (e.g. 9/2009), assume it is the first day of the month (e.g. September 1, 2009).* If the month is missing (e.g. 2010), assume it is the first of January of that year (e.g. January 1, 2010).* Watch out for potential typos as this is a raw, real-life derived dataset.With these rules in mind, find the correct date in each note and return a pandas Series in chronological order of the original Series' indices.For example if the original series was this: 0 1999 1 2010 2 1978 3 2015 4 1985Your function should return this: 0 2 1 4 2 0 3 1 4 3Your score will be calculated using [Kendall's tau](https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient), a correlation measure for ordinal data.*This function should return a Series of length 500 and dtype int.* ###Code def date_sorter(): import pandas as pd import re from calendar import month_name import dateutil.parser from datetime import datetime doc = [] with open('dates.txt') as file: for line in file: doc.append(line) df1 = pd.Series(doc) df = pd.DataFrame(df1, columns=['text']) print(df) pattern = "[,.]? \d{4}|".join(month_name[1:]) + "[,.]? \d{4}"; date_list=[] i=0 for word in df['text']: word=word.strip('\n') dte = re.findall(r'\d{1,2}\/\d{1,2}\/\d{2,4}|\d{1,2}\-\d{1,2}\-\d{2,4}|[A-Z][a-z]+\-\d{1,2}\-\d{4}|[A-Z][a-z]+[,.]? \d{2}[a-z]*,? \d{4}|\d{1,2} [A-Z][a-z,.]+ \d{4}|[A-Z][a-z]{2}[,.]? \d{4}|'+pattern+r'|\d{1,2}\/\d{4}|\d{4}',word) date_list.append(dte) i=i+1 date_list[271]=[date_list[271][1]] temp="" date_list[461]=[temp.join(date_list[461]).split(',')[1]] date_list[465]=[temp.join(date_list[465]).split(',')[2]] date_final=[] for text in date_list: date_final.append(text[0]) i=0 num=[] while (i < len(date_final)): num.append(i) temp="" temp=temp.join(date_final[i]) if(re.match(r'\d{4}',temp)) : temp = 'January 1, '+temp elif (re.match(r'\d{1,2}\/\d{4}',temp)) : date_split = temp.split('/') temp = date_split[0] + '/1/'+date_split[1] elif(re.match(r'[A-Z][a-z]+[,.]? \d{4}',temp)) : date_split = temp.split(' ') temp = date_split[0] + ' 1 '+date_split[1] date_final[i] = dateutil.parser.parse(temp).strftime("%m/%d/%Y") date_final[i] = datetime.strptime(date_final[i], "%m/%d/%Y").date() i = i+1 df['Date']=date_final print(df) df['rank']=num sorted_date= df.sort_values(by="Date") df1=sorted_date.index df_series=pd.Series(df1) return df_series date_sorter() ###Output text 0 03/25/93 Total time of visit (in minutes):\n 1 6/18/85 Primary Care Doctor:\n 2 sshe plans to move as of 7/8/71 In-Home Servic... 3 7 on 9/27/75 Audit C Score Current:\n 4 2/6/96 sleep studyPain Treatment Pain Level (N... 5 .Per 7/06/79 Movement D/O note:\n 6 4, 5/18/78 Patient's thoughts about current su... 7 10/24/89 CPT Code: 90801 - Psychiatric Diagnos... 8 3/7/86 SOS-10 Total Score:\n 9 (4/10/71)Score-1Audit C Score Current:\n 10 (5/11/85) Crt-1.96, BUN-26; AST/ALT-16/22; WBC... 11 4/09/75 SOS-10 Total Score:\n 12 8/01/98 Communication with referring physician... 13 1/26/72 Communication with referring physician... 14 5/24/1990 CPT Code: 90792: With medical servic... 15 1/25/2011 CPT Code: 90792: With medical servic... 16 4/12/82 Total time of visit (in minutes):\n 17 1; 10/13/1976 Audit C Score, Highest/Date:\n 18 4, 4/24/98 Relevant Drug History:\n 19 ) 59 yo unemployed w referred by Urgent Care f... 20 7/21/98 Total time of visit (in minutes):\n 21 10/21/79 SOS-10 Total Score:\n 22 3/03/90 CPT Code: 90792: With medical services\n 23 2/11/76 CPT Code: 90792: With medical services\n 24 07/25/1984 CPT Code: 90791: No medical services\n 25 4-13-82 Other Child Mental Health Outcomes Sca... 26 9/22/89 CPT Code: 90792: With medical services\n 27 9/02/76 CPT Code: 90791: No medical services\n 28 9/12/71 [report_end]\n 29 10/24/86 Communication with referring physicia... .. ... 470 y1983 Clinic Hospital, first hospitalization, ... 471 tProblems Urinary incontinence : mild urge inc... 472 .2010 - wife; nightmares and angry outbursts; ... 473 shx of TBI (1975) ISO MVA.Medical History:\n 474 sPatient reported losing three friends that pa... 475 TSH okay in 2015 Prior EKG:\n 476 1989 Family Psych History: Family History of S... 477 oEnjoys animals, had a dog x 14 yrs who died i... 478 eHistory of small right parietal subgaleal hem... 479 sIn KEP Psychiatryfor therapy and medications ... 480 1. Esophageal cancer, dx: 2013, on FOLFOX with... 481 y1974 (all)\n 482 h/o restraining order by sister/mother in 1990... 483 sTexas Medical Center; Oklahoma for 2 weeks; 1... 484 Death of former partner in 2004 by overdose as... 485 Was "average" student. "I didn't have too man... 486 Contemplating jumping off building - 1973 - di... 487 appendectomy s/p delivery 1992 Prior relevant ... 488 tProblems renal cell cancer : s/p nephrectomy ... 489 ran own business for 35 years, sold in 1985\n 490 Lab: B12 969 2007\n 491 )and 8mo in 2009\n 492 .Moved to USA in 1986. Suffered from malnutrit... 493 r1978\n 494 . Went to Emerson, in Newfane Alaska. Started ... 495 1979 Family Psych History: Family History of S... 496 therapist and friend died in ~2006 Parental/Ca... 497 2008 partial thyroidectomy\n 498 sPt describes a history of sexual abuse as a c... 499 . In 1980, patient was living in Naples and de... [500 rows x 1 columns] text Date 0 03/25/93 Total time of visit (in minutes):\n 1993-03-25 1 6/18/85 Primary Care Doctor:\n 1985-06-18 2 sshe plans to move as of 7/8/71 In-Home Servic... 1971-07-08 3 7 on 9/27/75 Audit C Score Current:\n 1975-09-27 4 2/6/96 sleep studyPain Treatment Pain Level (N... 1996-02-06 5 .Per 7/06/79 Movement D/O note:\n 1979-07-06 6 4, 5/18/78 Patient's thoughts about current su... 1978-05-18 7 10/24/89 CPT Code: 90801 - Psychiatric Diagnos... 1989-10-24 8 3/7/86 SOS-10 Total Score:\n 1986-03-07 9 (4/10/71)Score-1Audit C Score Current:\n 1971-04-10 10 (5/11/85) Crt-1.96, BUN-26; AST/ALT-16/22; WBC... 1985-05-11 11 4/09/75 SOS-10 Total Score:\n 1975-04-09 12 8/01/98 Communication with referring physician... 1998-08-01 13 1/26/72 Communication with referring physician... 1972-01-26 14 5/24/1990 CPT Code: 90792: With medical servic... 1990-05-24 15 1/25/2011 CPT Code: 90792: With medical servic... 2011-01-25 16 4/12/82 Total time of visit (in minutes):\n 1982-04-12 17 1; 10/13/1976 Audit C Score, Highest/Date:\n 1976-10-13 18 4, 4/24/98 Relevant Drug History:\n 1998-04-24 19 ) 59 yo unemployed w referred by Urgent Care f... 1977-05-21 20 7/21/98 Total time of visit (in minutes):\n 1998-07-21 21 10/21/79 SOS-10 Total Score:\n 1979-10-21 22 3/03/90 CPT Code: 90792: With medical services\n 1990-03-03 23 2/11/76 CPT Code: 90792: With medical services\n 1976-02-11 24 07/25/1984 CPT Code: 90791: No medical services\n 1984-07-25 25 4-13-82 Other Child Mental Health Outcomes Sca... 1982-04-13 26 9/22/89 CPT Code: 90792: With medical services\n 1989-09-22 27 9/02/76 CPT Code: 90791: No medical services\n 1976-09-02 28 9/12/71 [report_end]\n 1971-09-12 29 10/24/86 Communication with referring physicia... 1986-10-24 .. ... ... 470 y1983 Clinic Hospital, first hospitalization, ... 1983-01-01 471 tProblems Urinary incontinence : mild urge inc... 1999-01-01 472 .2010 - wife; nightmares and angry outbursts; ... 2010-01-01 473 shx of TBI (1975) ISO MVA.Medical History:\n 1975-01-01 474 sPatient reported losing three friends that pa... 1972-01-01 475 TSH okay in 2015 Prior EKG:\n 2015-01-01 476 1989 Family Psych History: Family History of S... 1989-01-01 477 oEnjoys animals, had a dog x 14 yrs who died i... 1994-01-01 478 eHistory of small right parietal subgaleal hem... 1993-01-01 479 sIn KEP Psychiatryfor therapy and medications ... 1996-01-01 480 1. Esophageal cancer, dx: 2013, on FOLFOX with... 2013-01-01 481 y1974 (all)\n 1974-01-01 482 h/o restraining order by sister/mother in 1990... 1990-01-01 483 sTexas Medical Center; Oklahoma for 2 weeks; 1... 1995-01-01 484 Death of former partner in 2004 by overdose as... 2004-01-01 485 Was "average" student. "I didn't have too man... 1987-01-01 486 Contemplating jumping off building - 1973 - di... 1973-01-01 487 appendectomy s/p delivery 1992 Prior relevant ... 1992-01-01 488 tProblems renal cell cancer : s/p nephrectomy ... 1977-01-01 489 ran own business for 35 years, sold in 1985\n 1985-01-01 490 Lab: B12 969 2007\n 2007-01-01 491 )and 8mo in 2009\n 2009-01-01 492 .Moved to USA in 1986. Suffered from malnutrit... 1986-01-01 493 r1978\n 1978-01-01 494 . Went to Emerson, in Newfane Alaska. Started ... 2002-01-01 495 1979 Family Psych History: Family History of S... 1979-01-01 496 therapist and friend died in ~2006 Parental/Ca... 2006-01-01 497 2008 partial thyroidectomy\n 2008-01-01 498 sPt describes a history of sexual abuse as a c... 2005-01-01 499 . In 1980, patient was living in Naples and de... 1980-01-01 [500 rows x 2 columns]
Style Transfer/Picture Change.ipynb
###Markdown Model Load ###Code from keras import backend as K target_image = K.constant(preprocess_image(target_image_path)) style_reference_image = K.constant(preprocess_image(style_reference_image_path)) # This placeholder will contain our generated image combination_image = K.placeholder((1, img_height, img_width, 3)) # We combine the 3 images into a single batch input_tensor = K.concatenate([target_image, style_reference_image, combination_image], axis=0) # We build the VGG19 network with our batch of 3 images as input. # The model will be loaded with pre-trained ImageNet weights. model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False) print('Model loaded.') def content_loss(base, combination): return K.sum(K.square(combination - base)) ###Output _____no_output_____ ###Markdown Set loss function ###Code def gram_matrix(x): features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1))) gram = K.dot(features, K.transpose(features)) return gram def style_loss(style, combination): S = gram_matrix(style) C = gram_matrix(combination) channels = 3 size = img_height * img_width return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2)) def total_variation_loss(x): a = K.square( x[:, :img_height - 1, :img_width - 1, :] - x[:, 1:, :img_width - 1, :]) b = K.square( x[:, :img_height - 1, :img_width - 1, :] - x[:, :img_height - 1, 1:, :]) return K.sum(K.pow(a + b, 1.25)) # Dict mapping layer names to activation tensors outputs_dict = dict([(layer.name, layer.output) for layer in model.layers]) # Name of layer used for content loss content_layer = 'block5_conv2' # Name of layers used for style loss style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1'] # Weights in the weighted average of the loss components total_variation_weight = 1e-4 style_weight = 1. content_weight = 0.025 # Define the loss by adding all components to a `loss` variable loss = K.variable(0.) layer_features = outputs_dict[content_layer] target_image_features = layer_features[0, :, :, :] combination_features = layer_features[2, :, :, :] loss += content_weight * content_loss(target_image_features, combination_features) for layer_name in style_layers: layer_features = outputs_dict[layer_name] style_reference_features = layer_features[1, :, :, :] combination_features = layer_features[2, :, :, :] sl = style_loss(style_reference_features, combination_features) loss += (style_weight / len(style_layers)) * sl loss += total_variation_weight * total_variation_loss(combination_image) # Get the gradients of the generated image wrt the loss grads = K.gradients(loss, combination_image)[0] # Function to fetch the values of the current loss and the current gradients fetch_loss_and_grads = K.function([combination_image], [loss, grads]) class Evaluator(object): def __init__(self): self.loss_value = None self.grads_values = None def loss(self, x): assert self.loss_value is None x = x.reshape((1, img_height, img_width, 3)) outs = fetch_loss_and_grads([x]) loss_value = outs[0] grad_values = outs[1].flatten().astype('float64') self.loss_value = loss_value self.grad_values = grad_values return self.loss_value def grads(self, x): assert self.loss_value is not None grad_values = np.copy(self.grad_values) self.loss_value = None self.grad_values = None return grad_values evaluator = Evaluator() ###Output _____no_output_____ ###Markdown Learning ###Code from scipy.optimize import fmin_l_bfgs_b from scipy.misc import imsave import time result_prefix = 'style_transfer_result' iterations = 10 # Run scipy-based optimization (L-BFGS) over the pixels of the generated image # so as to minimize the neural style loss. # This is our initial state: the target image. # Note that `scipy.optimize.fmin_l_bfgs_b` can only process flat vectors. x = preprocess_image(target_image_path) x = x.flatten() for i in range(iterations): print('Start of iteration', i) start_time = time.time() x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20) print('Current loss value:', min_val) # Save current generated image img = x.copy().reshape((img_height, img_width, 3)) img = deprocess_image(img) fname = result_prefix + '_at_iteration_%d.png' % i imsave(fname, img) end_time = time.time() print('Image saved as', fname) print('Iteration %d completed in %ds' % (i, end_time - start_time)) ###Output Start of iteration 0 ###Markdown View picture ###Code from matplotlib import pyplot as plt # Content image plt.imshow(load_img(target_image_path, target_size=(img_height, img_width))) plt.figure() # Style image plt.imshow(load_img(style_reference_image_path, target_size=(img_height, img_width))) plt.figure() # Generate image plt.imshow(img) plt.show() from scipy.optimize import fmin_l_bfgs_b from scipy.misc import imsave import time result_prefix = 'style_transfer_result' iterations = 20 # Run scipy-based optimization (L-BFGS) over the pixels of the generated image # so as to minimize the neural style loss. # This is our initial state: the target image. # Note that `scipy.optimize.fmin_l_bfgs_b` can only process flat vectors. x = preprocess_image('www.jpg') x = x.flatten() for i in range(5): print('Start of iteration', i) start_time = time.time() x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20) print('Current loss value:', min_val) # Save current generated image img = x.copy().reshape((img_height, img_width, 3)) img = deprocess_image(img) fname = result_prefix + '_at_iteration_%d.png' % i imsave(fname, img) end_time = time.time() print('Image saved as', fname) print('Iteration %d completed in %ds' % (i, end_time - start_time)) ###Output Start of iteration 0 Current loss value: 1.20559e+09 Image saved as style_transfer_result_at_iteration_0.png Iteration 0 completed in 191s Start of iteration 1 Current loss value: 5.43658e+08 Image saved as style_transfer_result_at_iteration_1.png Iteration 1 completed in 275s Start of iteration 2
kernels/sequential_testing.ipynb
###Markdown Sequential A/B TestingA/B testing (also known as split testing) is the process of comparing two versions of an asset and measuring the difference in performance.Involves conducting the test on 2 versions of a single variable at a time. It goes with the belief that not more than one factor should be varied at the same time.**Case Overview**:SmartAd is a mobile first advertiser agency. The company provides an additional service called Brand Impact Optimiser (BIO), a lightweight questionnaire, served with every campaign to determine the impact of the ad they design.The task at hand is to design a reliable hypothesis testing algorithm for the BIO service and determine whether the recent advertising campaign resulted in a significant lift in brand awareness.**Data**:The BIO data for this project is a โ€œYesโ€ and โ€œNoโ€ response of online users to the following question:`Q: Do you know the brand SmartAd?` Yes NoThe data has the following columns: **auction_id**, **experiment**, **date**, **hour**, **device_make**, **platform_os**, **browser**, **yes**, **no**. Table of Contents1. [Libraries](Libraries)2. [Dataset](Dataset)3. [Sample conditional SPRT](Sample-conditional-SPRT) 3.1 [conditional SPRT function](conditional-SPRT-function) 3.2 [Boundaries and Plots](Boundaries-and-Plots) 3.3 [Data Transformation](Data-Transformation) 3.4 [Data Summary plot and print functions](Data-Summary-plot-and-print-functions) 3.5 [Testing](Testing) 1. Libraries ###Code from pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive from google.colab import auth from oauth2client.client import GoogleCredentials import pandas as pd import numpy as np import math from scipy.stats import binom from math import * import seaborn as sns import matplotlib.pyplot as plt import json import warnings def ignore_warn(*args, **kwargs): pass warnings.warn = ignore_warn ###Output /usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead. import pandas.util.testing as tm ###Markdown 2. Dataset ###Code # function to fetch data def fetch_data(id, file_name): auth.authenticate_user() gauth = GoogleAuth() gauth.credentials = GoogleCredentials.get_application_default() drive = GoogleDrive(gauth) downloaded = drive.CreateFile({'id':id}) downloaded.GetContentFile(file_name) data=pd.read_csv(file_name) return data # fetch the data data = fetch_data('1YSn01vvlHKQaAIBtwIXRNd-oTaTuDN09', 'ABAdRecall.csv') data.head() ###Output _____no_output_____ ###Markdown 3. Sample conditional SPRT 3.1 conditional SPRT function ###Code def ConditionalSPRT(x,y,t1,alpha=0.05,beta=0.10,stop=None): if t1<=1: printLog('warning',"Odd ratio should exceed 1.") if (alpha >0.5) | (beta >0.5): printLog('warning',"Unrealistic values of alpha or beta were passed." +" You should have good reason to use large alpha & beta values") if stop!=None: stop=math.floor(n0) def comb(n, k): return factorial(n) // factorial(k) // factorial(n - k) def lchoose(b, j): a=[] if (type(j) is list) | (isinstance(j,np.ndarray)==True): if len(j)<2: j=j[0] if (type(j) is list) | (isinstance(j,np.ndarray)==True): for k in j: n=b if (0 <= k) & (k<= n): a.append(math.log(comb(n,k))) else: a.append(0) else: n=b k=j if (0 <= k) & (k<= n): a.append(math.log(comb(n,k))) else: a.append(0) return np.array(a) def g(x,r,n,t1,t0=1): return -math.log(h(x,r,n,t1))+math.log(h(x,r,n,t0)) def h(x,r,n,t=1): return f(r,n,t,offset=ftermlog(x,r,n,t)) def f(r,n,t,offset=0): upper=max(0,r-n) lower=min(n,r) rng=list(range(upper,lower+1)) return np.sum(fterm(rng,r,n,t,offset)) def fterm(j,r,n,t,offset=0): ftlog=ftermlog(j,r,n,t,offset) return np.array([math.exp(ex) for ex in ftlog]) def ftermlog(j,r,n,t,offset=0): xx=r-j lch=lchoose(n,j) lchdiff=lchoose(n,xx) lg=np.array(j)*math.log(t) lgsum=lch+lchdiff lgsum2=lgsum+lg lgdiff=lgsum2-offset return lgdiff def logf(r,n,t,offset=0): z=f(r,n,t,offset) if z>0: return math.log(z) else: return np.nan def clowerUpper(r,n,t1c,t0=1,alpha=0.05,beta=0.10): offset=ftermlog(math.ceil(r/2),r,n,t1c) z=logf(r,n,t1c,logf(r,n,t0,offset)+offset) a=-math.log(alpha/(1-beta)) b=math.log(beta/(1-alpha)) lower=b upper=1+a return (np.array([lower,upper])+z)/math.log(t1c/t0) l=math.log(beta/(1-alpha)) u=-math.log(alpha/(1-beta)) sample_size=min(len(x),len(y)) n=np.array(range(1,sample_size+1)) if stop!=None: n=np.array([z for z in n if z<=stop]) x1=np.cumsum(x[n-1]) r=x1+np.cumsum(y[n-1]) stats=np.array(list(map(g,x1, r, n, [t1]*len(x1)))) #recurcively calls g clu=list(map(clowerUpper,r,n,[t1]*len(r),[1]*len(r),[alpha]*len(r), [beta]*len(r))) limits=[] for v in clu: inArray=[] for vin in v: inArray.append(math.floor(vin)) limits.append(np.array(inArray)) limits=np.array(limits) k=np.where((stats>=u) | (stats<=l)) cvalues=stats[k] if cvalues.shape[0]<1: k= np.nan outcome='Unable to conclude.Needs more sample.' else: k=np.min(k) if stats[k]>=u: outcome=f'Exposed group produced a statistically significant increase.' else: outcome='There is no statistically significant difference between two test groups' if (stop!=None) & (k==np.nan): c1=clowerUpper(r,stop,t1,alpha,beta) c1=math.floor(np.mean(c1)-0.5) if x1[n0]<=c1: truncate_decision='h0' outcome='Maximum Limit Decision. The aproximate decision point shows their is no statistically significant difference between two test groups' else: truncate_decision='h1' outcome=f'Maximum Limit Decision. The aproximate decision point shows exposed group produced a statistically significant increase.' truncated=stop else: truncate_decision='Non' truncated=np.nan return (outcome,n, k,l,u,truncated,truncate_decision,x1,r,stats,limits) ###Output _____no_output_____ ###Markdown 3.2 Boundaries and Plots ###Code class SequentialTest: def __init__(t1 = 2, alpha = 0.05, beta = 0.1, stop = None): ''' initialise startup variables ''' if t1<=1: printLog('warning',"Odd ratio should exceed 1.") if (alpha >0.5) | (beta >0.5): printLog('warning',"Unrealistic values of alpha or beta were passed." +" You should have good reason to use large alpha & beta values") if stop!=None: stop=math.floor(n0) def computeBoundaries(self,alpha, beta): ''' This function shoud compute boundaries ''' a=math.log(beta/(1-alpha)) b=math.log((1 - beta)/alpha) return a, b def plotTest(self): ''' showing the cumulative statistical test (e.g., log probability ratio) and the upper and lower limits. ''' def plotBoundaries(self, exposed): '''cumulative sums of exposed successes, bounded by the critical limits. ''' # e_df = pd.DataFrame(exposed) # a = e_df.cumsum() # a.columns = ['value'] # sns.lineplot(x = a.index, y = a.value) b ###Output _____no_output_____ ###Markdown 3.3 Data Transformation ###Code def transform_data(df): ''' segment data into exposed and control groups consider that SmartAd runs the experment hourly, group data into hours. Hint: create new column to hold date+hour and use df.column.map(lambda x: pd.Timestamp(x,tz=None).strftime('%Y-%m-%d:%H')) create two dataframes with bernouli series 1 for posetive(yes) and 0 for negative(no) Hint: Given engagement(sum of yes and no until current observation as an array) and success (yes count as an array), the method generates random binomial distribution #Example engagement = np.array([5, 3, 3]) yes = np.array([2, 0, 3]) Output is "[1] 1 0 1 0 0 0 0 0 1 1 1", showing a binary array of 5+3+3 values of which 2 of the first 5 are ones, 0 of the next 3 are ones, and all 3 of the last 3 are ones where the position the ones is randomly distributed within each group. ''' # split dataset to control and exposed groups exposed = df.loc[df.experiment == 'exposed'] #exposed set control = df.loc[df.experiment == 'control'] #control set #datehour exposed['dateHour'] = pd.to_datetime(exposed.date) exposed.dateHour += pd.to_timedelta(exposed.hour, unit='h') exposed.dateHour = exposed.dateHour.map(lambda x: pd.Timestamp(x,tz=None).strftime('%Y-%m-%d:%H')) control['dateHour'] = pd.to_datetime(control.date) control.dateHour += pd.to_timedelta(control.hour, unit='h') control.dateHour = control.dateHour.map(lambda x: pd.Timestamp(x,tz=None).strftime('%Y-%m-%d:%H')) # groupby datehour df_exposed = exposed.groupby('dateHour').agg({'auction_id':'count', 'device_make':'count', 'platform_os':'count', 'browser':'count', 'yes':'sum', 'no':'sum'}) df_control = control.groupby('dateHour').agg({'auction_id':'count', 'device_make':'count', 'platform_os':'count', 'browser':'count', 'yes':'sum', 'no':'sum'}) # engagement df_exposed['engagement'] = df_exposed['yes'] + df_exposed['no'] df_control['engagement'] = df_control['yes'] + df_control['no'] # success df_exposed['success'] = df_exposed['yes'] df_control['success'] = df_control['yes'] # p of success global p_e, p_c p_e = sum(df_exposed['success']) / sum(df_exposed['engagement']) p_c = sum(df_control['success']) / sum(df_control['engagement']) # engagement and success to arrays then p engagement_e = df_exposed['engagement'].to_numpy() engagement_c = df_control['engagement'].to_numpy() # data generation e = np.random.choice([0, 1], size=((np.sum(engagement_e)),), p=[p_e, 1-p_e]) c = np.random.choice([0, 1], size=((np.sum(engagement_c)),), p=[p_c, 1-p_c]) return e,c ###Output _____no_output_____ ###Markdown 3.4 Data Summary plot and print functions ###Code def plotDataSummary(exposed, control): 'This function plots cummulated success' fig, ax = plt.subplots(figsize=(10,8)) kwargs = {'cumulative': True} sns.distplot(control.success, hist_kws=kwargs, kde_kws=kwargs, color = 'black') sns.distplot(exposed.success, hist_kws=kwargs, kde_kws=kwargs, color = 'green') plt.title('A histogram indicating cummulative distributions of success in the 2 groups black: control, green:exposed') plt.ylabel('frequency') plt.xlabel('cummulative success') def pretyPrintTestResult(self, test): '''This function print final test result. Json format is recommended. For example { "name": "", "engagementCountControl": , "engagementCountExposed": , "positiveCountControl": , "positiveCountExposed": , "ControlSuccessProbability": , "ExposedSuccessProbability": , "basePositiveRate": , "significanceSign": ".", "lift": , "oddRatio": , "exactSuccessOddRate":, "confidenceIntervalLevel": , "alpha": , "beta": , "power": , "criticalValue": , "lower critical(a)": "upper critical(b)": , "TotalObservation": }''' ###Output _____no_output_____ ###Markdown 3.5 Testing 3.5.1 Parameters ###Code 'statistical parameters for SPRT' alpha = 0.05 beta = 0.1 'Compute statistical lower and upper decision points such as a and b' st = SequentialTest() a, b = st.computeBoundaries(alpha = alpha, beta = beta) ##data processing here exposed,control=transform_data(data) # odd ratio odd_ratio=(p_e/(1-p_e))/(p_c/(1-p_c)) ###Output _____no_output_____ ###Markdown 3.5.2 Testing ###Code test = ConditionalSPRT(x = exposed,y = control,t1 = odd_ratio, alpha=alpha,beta=alpha) test[0] ###Output _____no_output_____ ###Markdown 3.5.3 Plots ###Code !pip install sprt import sprt ##plot data summary # plotDataSummary(exposed,control) # 'Print test result.' # pretyPrintTestResult(resultObject) # generate the requirements file !pip freeze > requirements.txt ###Output _____no_output_____
Chapter02/.ipynb_checkpoints/Sequential_method_to_build_a_neural_network-checkpoint.ipynb
###Markdown ###Code x = [[1,2],[3,4],[5,6],[7,8]] y = [[3],[7],[11],[15]] import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from torch.optim import SGD device = 'cuda' if torch.cuda.is_available() else "cpu" class MyDataset(Dataset): def __init__(self, x, y): super().__init_ import torch import torch.nn as nn import numpy as np from torch.utils.data import Dataset, DataLoader device = 'cuda' if torch.cuda.is_available() else 'cpu' class MyDataset(Dataset): def __init__(self, x, y): self.x = torch.tensor(x).float().to(device) self.y = torch.tensor(y).float().to(device) def __getitem__(self, ix): return self.x[ix], self.y[ix] def __len__(self): return len(self.x) ds = MyDataset(x, y) dl = DataLoader(ds, batch_size=2, shuffle=True) model = nn.Sequential( nn.Linear(2, 8), nn.ReLU(), nn.Linear(8, 1) ).to(device) !pip install torch_summary from torchsummary import summary summary(model, torch.zeros(1,2)); loss_func = nn.MSELoss() from torch.optim import SGD opt = SGD(model.parameters(), lr = 0.001) import time loss_history = [] start = time.time() for _ in range(50): for ix, iy in dl: opt.zero_grad() loss_value = loss_func(model(ix),iy) loss_value.backward() opt.step() loss_history.append(loss_value) end = time.time() print(end - start) val = [[8,9],[10,11],[1.5,2.5]] val = torch.tensor(val).float() model(val.to(device)) val.sum(-1) ###Output _____no_output_____
examples/detection/convert_detection_model.ipynb
###Markdown Download the pretrained model Download the model configuration file and checkpoint containing pretrained weights by using the following command. For improved performance, increase the non-max suppression score threshold in the downloaded config file from 1e-8 to something greater, like 0.1. ###Code config_path, checkpoint_path = download_detection_model(MODEL, 'data') ###Output _____no_output_____ ###Markdown Build the frozen graph ###Code frozen_graph, input_names, output_names = build_detection_graph( config=config_path, checkpoint=checkpoint_path, score_threshold=0.3, batch_size=1 ) ###Output W0901 09:08:58.797062 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/tf_trt_models-0.0-py3.6.egg/tf_trt_models/detection.py:179: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead. W0901 09:08:58.798357 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/tf_trt_models-0.0-py3.6.egg/tf_trt_models/detection.py:183: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. W0901 09:08:58.974009 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:381: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead. W0901 09:08:58.975014 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:113: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. W0901 09:08:58.989618 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/core/preprocessor.py:2412: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead. W0901 09:08:59.030214 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/meta_architectures/faster_rcnn_meta_arch.py:166: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. W0901 09:09:08.932466 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead. W0901 09:09:08.988511 140190800152384 deprecation.py:323] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/core/box_list_ops.py:141: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0901 09:09:09.545723 140190800152384 deprecation.py:506] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/utils/spatial_transform_ops.py:418: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead W0901 09:09:11.165808 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1634: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.flatten instead. W0901 09:09:14.836183 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:180: batch_gather (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2017-10-25. Instructions for updating: `tf.batch_gather` is deprecated, please use `tf.gather` with `batch_dims` instead. W0901 09:09:15.524989 140190800152384 deprecation.py:323] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:362: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.get_or_create_global_step W0901 09:09:15.531374 140190800152384 deprecation.py:323] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:518: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01. Instructions for updating: Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details W0901 09:09:15.533828 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name` 651 ops no flops stats due to incomplete shapes. 651 ops no flops stats due to incomplete shapes. W0901 09:09:22.164069 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:411: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead. W0901 09:09:27.135222 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. W0901 09:09:40.721538 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.compat.v1.graph_util.convert_variables_to_constants` W0901 09:09:40.722523 140190800152384 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:270: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.compat.v1.graph_util.extract_sub_graph` W0901 09:09:50.972707 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:288: The name tf.saved_model.builder.SavedModelBuilder is deprecated. Please use tf.compat.v1.saved_model.builder.SavedModelBuilder instead. W0901 09:09:50.974496 140190800152384 deprecation.py:323] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:291: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info. W0901 09:09:50.975758 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:297: The name tf.saved_model.signature_def_utils.build_signature_def is deprecated. Please use tf.compat.v1.saved_model.signature_def_utils.build_signature_def instead. W0901 09:09:50.976409 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:300: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead. W0901 09:09:50.977097 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:305: The name tf.saved_model.tag_constants.SERVING is deprecated. Please use tf.saved_model.SERVING instead. W0901 09:09:50.977633 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/exporter.py:307: The name tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY is deprecated. Please use tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY instead. W0901 09:09:53.562907 140190800152384 deprecation_wrapper.py:119] From /root/.local/lib/python3.6/site-packages/object_detection-0.1-py3.6.egg/object_detection/utils/config_util.py:188: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead. ###Markdown Optimize the model with TensorRT ###Code print(output_names) trt_graph = trt.create_inference_graph( input_graph_def=frozen_graph, outputs=output_names, max_batch_size=1, max_workspace_size_bytes=1 << 25, precision_mode='FP16', minimum_segment_size=50 ) save_path = os.path.join('.', 'model') if tf.gfile.Exists(save_path) == False: tf.gfile.MkDir(save_path) save_file_path = os.path.join(save_path, MODEL + '_trt_fp16.pb') with open(save_file_path, 'wb') as f: f.write(trt_graph.SerializeToString()) ###Output _____no_output_____ ###Markdown Create session and load graph ###Code tf_config = tf.ConfigProto() tf_config.gpu_options.allow_growth = True tf_sess = tf.Session(config=tf_config) tf.import_graph_def(trt_graph, name='') tf_input = tf_sess.graph.get_tensor_by_name(input_names[0] + ':0') tf_scores = tf_sess.graph.get_tensor_by_name('detection_scores:0') tf_boxes = tf_sess.graph.get_tensor_by_name('detection_boxes:0') tf_classes = tf_sess.graph.get_tensor_by_name('detection_classes:0') tf_num_detections = tf_sess.graph.get_tensor_by_name('num_detections:0') ###Output _____no_output_____ ###Markdown Load and Preprocess Image ###Code image = Image.open(IMAGE_PATH) plt.imshow(image) image_resized = np.array(image.resize((300, 300))) image = np.array(image) ###Output _____no_output_____ ###Markdown Run network on Image ###Code scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={ tf_input: image_resized[None, ...] }) boxes = boxes[0] # index by 0 to remove batch dimension scores = scores[0] classes = classes[0] num_detections = num_detections[0] ###Output _____no_output_____ ###Markdown Display Results ###Code fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.imshow(image) # plot boxes exceeding score threshold for i in range(num_detections.astype('int32')): # scale box to image coordinates box = boxes[i] * np.array([image.shape[0], image.shape[1], image.shape[0], image.shape[1]]) # display rectangle patch = patches.Rectangle((box[1], box[0]), box[3] - box[1], box[2] - box[0], color='g', alpha=0.3) ax.add_patch(patch) # display class index and score plt.text(x=box[1] + 10, y=box[2] - 10, s='%d (%0.2f) ' % (classes[i], scores[i]), color='w') plt.show() ###Output _____no_output_____ ###Markdown Benchmark ###Code num_samples = 50 t0 = time.time() for i in range(num_samples): scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={ tf_input: image_resized[None, ...] }) t1 = time.time() print('Average runtime: %f seconds' % (float(t1 - t0) / num_samples)) ###Output Average runtime: 0.644130 seconds ###Markdown Close session to release resources ###Code tf_sess.close() ###Output _____no_output_____
10_ml_clustering/notebooks/02_exercises.ipynb
###Markdown Clustering using `scikit-learn` ###Code import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn import preprocessing, cluster, metrics from sklearn.pipeline import Pipeline from scipy.spatial.distance import cdist, pdist IRIS_URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/bezdekIris.data' var_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] ###Output _____no_output_____
data_science/research/archive_exploration/01 - subject coocurrence.ipynb
###Markdown Archive dataThe Wellcome archive sits in a collections management system called CALM, which follows a rough set of standards and guidelines for storing archival records called [ISAD(G)](https://en.wikipedia.org/wiki/ISAD(G). The archive is comprised of _collections_, each of which has a hierarchical set of series, sections, subjects, items and pieces sitting underneath it. In the following notebooks I'm going to explore it and try to make as much sense of it as I can programatically.Let's start by loading in a few useful packages and defining some nice utils. ###Code %matplotlib inline import matplotlib.pyplot as plt import seaborn as sns sns.set_style('white') plt.rcParams['figure.figsize'] = (20, 20) import pandas as pd import numpy as np import networkx as nx from sklearn.cluster import AgglomerativeClustering from umap import UMAP from tqdm import tqdm_notebook as tqdm def flatten(input_list): return [item for sublist in input_list for item in sublist] def cartesian(*arrays): return np.array([x.reshape(-1) for x in np.meshgrid(*arrays)]).T def clean(subject): return subject.strip().lower().replace('<p>', '') ###Output _____no_output_____ ###Markdown let's load up our CALM data. The data has been exported in its entirety as a single `.json` where each line is a record. You can download the data yourself using [this script](https://github.com/wellcometrust/platform/blob/master/misc/download_oai_harvest.py). Stick the `.json` in the neighbouring `/data` directory to run the rest of the notebook seamlessly. ###Code df = pd.read_json('data/calm_records.json') len(df) df.astype(str).describe() ###Output _____no_output_____ ###Markdown Exploring individual columnsAt the moment I have no idea what kind of information CALM contains - lets look at the list of column names ###Code list(df) ###Output _____no_output_____ ###Markdown Here I'm looking through a sample of values in each column, choosing the columns to explore based on the their headings, a bit of contextual info from colleagues and the `df.describe()` above. ###Code df['Subject'] ###Output _____no_output_____ ###Markdown After much trial and error...Subjects look like an interesting avenue to explore further. Where subjects have _actually_ been filled in and the entry is not `None`, a list of subjects is returned. We can explore some of these subjects' subtleties by creating an adjacency matrix. We'll count the number of times each subject appears alongside every other subject and return a big $n \times n$ matrix, where $n$ is the total number of unique subjects. We can use this adjacency matrix for all sorts of stuff, but we have to build it first. To start, lets get a uniqur list of all subjects. This involves unpacking each sub-list and flattening them out into one long list, before finding the unique elements. We'll also use the `clean` function defined above to get rid of any irregularities which might become annoying later on. ###Code subjects = flatten(df['Subject'].dropna().tolist()) print(len(subjects)) subjects = list(set(map(clean, subjects))) print(len(subjects)) ###Output _____no_output_____ ###Markdown At this point it's often helpful to index our data, ie transform words into numbers. We'll create two dictionaries which map back and forth between the subjects and their corresponding indicies: ###Code index_to_subject = {index: subject for index, subject in enumerate(subjects)} subject_to_index = {subject: index for index, subject in enumerate(subjects)} ###Output _____no_output_____ ###Markdown Lets instantiate an empty numpy array which we'll then fill with our coocurrence data. Each column and each row will represent a subject - each cell (the intersection of a column and row) will therefore represent the 'strength' of the interaction between those subjects. As we haven't seen any interactions yet, we'll set every array element to 0. ###Code adjacency = np.empty((len(subjects), len(subjects)), dtype=np.uint16) ###Output _____no_output_____ ###Markdown To populate the matrix, we want to find every possible combination of subject in each sub-list from our original column, ie if we had the subjects`[Disease, Heart, Heart Diseases, Cardiology]`we would want to return `[['Disease', 'Disease'], ['Heart', 'Disease'], ['Heart Diseases', 'Disease'], ['Cardiology', 'Disease'], ['Disease', 'Heart'], ['Heart', 'Heart'], ['Heart Diseases', 'Heart'], ['Cardiology', 'Heart'], ['Disease', 'Heart Diseases'], ['Heart', 'Heart Diseases'], ['Heart Diseases', 'Heart Diseases'], ['Cardiology', 'Heart Diseases'], ['Disease', 'Cardiology'], ['Heart', 'Cardiology'], ['Heart Diseases', 'Cardiology'], ['Cardiology', 'Cardiology']]`The `cartesian()` function which I've defined above will do that for us. We then find the appropriate intersection in the matrix and add another unit of 'strength' to it. We'll do this for every row of subjects in the `['Subjects']` column. ###Code for row_of_subjects in tqdm(df['Subject'].dropna()): for subject_pair in cartesian(row_of_subjects, row_of_subjects): subject_index_1 = subject_to_index[clean(subject_pair[0])] subject_index_2 = subject_to_index[clean(subject_pair[1])] adjacency[subject_index_1, subject_index_2] += 1 ###Output _____no_output_____ ###Markdown We can do all sorts of fun stuff now - adjacency matrices are the foundation on which all of graph theory is built. However, because it's a bit more interesting, I'm going to start with some dimensionality reduction. We'll get to the graphy stuff later. Using [UMAP](https://github.com/lmcinnes/umap), we can squash the $n \times n$ dimensional matrix down into a $n \times m$ dimensional one, where $m$ is some arbitrary integer. Setting $m$ to 2 will then allow us to plot each subject as a point on a two dimensional plane. UMAP will try to preserve the 'distances' between subjects - in this case, that means that related or topically similar subjects will end up clustered together, and different subjects will move apart. ###Code embedding_2d = pd.DataFrame(UMAP(n_components=2) .fit_transform(adjacency)) embedding_2d.plot.scatter(x=0, y=1); ###Output _____no_output_____ ###Markdown We can isolate the clusters we've found above using a number of different methods - `scikit-learn` provides easy access to some very powerful algorithms. Here I'll use a technique called _agglomerative clustering_, and make a guess that 15 is an appropriate number of clusters to look for. ###Code n_clusters = 15 embedding_2d['labels'] = (AgglomerativeClustering(n_clusters) .fit_predict(embedding_2d.values)) embedding_2d.plot.scatter(x=0, y=1, c='labels', cmap='Paired'); ###Output _____no_output_____ ###Markdown We can now use the `index_to_subject` mapping that we created earlier to examine which subjects have been grouped together into clusters ###Code for i in range(n_clusters): print(str(i) + ' ' + '-'*80 + '\n') print(np.sort([index_to_subject[index] for index in embedding_2d[embedding_2d['labels'] == i].index.values])) print('\n') ###Output _____no_output_____
Assignemnt_3.ipynb
###Markdown Linear Algebra for ChE Assignment 3: Matrices We'll try to explore in greater dimensions now that you have a basic understanding of Python. ObjectivesYou will be able to:Be familiar with matrices and how they relate to linear equations.Basic matrix computations are performed.Matrix equations can be programmed and translated using Python. Discussion ###Code import numpy as np import matplotlib.pyplot as plt import scipy.linalg as la %matplotlib inline ###Output _____no_output_____ ###Markdown Matrices One of the most fundamental aspects of modern computing is the notation and use of matrices. Matrices are also useful representations of complicated equations or many interconnected equations, ranging from two-dimensional to hundreds of thousands of them. Let's assume you have two variables, A and B, in your equation $$A = \left\{ \begin{array}\ x + y \\ 4x - 10y \end{array}\right. \\B = \left\{ \begin{array}\ x+y+z \\ 3x -2y -z \\ -x + 4y +2z \end{array}\right. \\C = \left\{ \begin{array}\ w-2x+3y-4z \\ 3w- x -2y +z \\ \end{array}\right. $$ Assume you've already covered the fundamental format, types, and operations of matrices. We'll go ahead and do them in Python here. Declaring Matrices We'll express a system of linear equations as a matrix, much like we did in our previous laboratory exercise. The elements of a matrix are the things or numbers in matrices. Matrixes have a list/array-like structure in which these items are grouped and ordered in rows and columns. These elements are indexed according to their location in relation to their rows and columns, exactly like arrays. The equation below can be used to express this. A is a matrix whose elements are indicated by the symbol aij. The number of rows in the matrix is denoted by i whereas the number of columns is denoted by j.It's worth noting that a matrix's size is i x j $$A=\begin{bmatrix}a_{(0,0)}&a_{(0,1)}&\dots&a_{0,j-1)}\\a_{(1,0)}&a_{(1,1)}&\dots&a_{1,j-1)}\\\vdots&\vdots&ddot&\vdots&\\a_{(i-1,0)}&a_{(i-1,1)}&\dots&a_{(i-1,j-1)}\end{bmatrix}$$ We've previously gone over some of the different types of matrices as vectors, but in this lab assignment, we'll go over them again. Because you already know how to create vectors using form, dimensions, and size attributes, we'll use these features to study these matrices ###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([ [1,1], [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 Matrixes can be classified in a variety of ways. One may be based on their form, while the other could be based on their element values. We'll do our best to get through them. According to shape Row and Column Matrices In vector and matrix calculations, row and column matrices are frequent. They can also be used to represent the rows and columns of a larger vector space. A single column or row is used to depict row and column matrices. As a result, the form of row matrices is 1 x j, whereas the shape of column matrices is 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 (1,3) 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 Matrixes with the same row and column sizes are known as square matrices. If we can claim that a matrix is square, in order to find square matrices, we may change our matrix descriptor function. ###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 Null Matrix 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 Zero Matrix 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)) 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.]] Zero Square Matrix: [[0. 0.] [0. 0.]] Zero Rectangular Matrix: [[0. 0.] [0. 0.] [0. 0.]] ###Markdown Ones Matrix 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 Diagonal Matrix 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]) np.diag(d).shape == d.shape[0] == d.shape[1] ###Output _____no_output_____ ###Markdown Identity Matrix 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 Upper Triangular Matrix An upper triangular matrix is a matrix that has no values below the diagona ###Code np.array([ [1,2,3], [0,3,1], [0,0,5] ]) ###Output _____no_output_____ ###Markdown Lower Triangular Matrix 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. Given the linear combination below, try to create a corresponding matrix representing it :$$\theta = 5x + 3y = :$$ 2. Given the system of linear combinations below, try to encode it as a matrix. Also describe the matrix $$A = \left\{\begin{array}5x_1 + 2x_2 +x_3\\4x_2 - x_3\\10x_3\end{array}\right.$$ 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 Given the matrix below, display the output as a LaTeX makdown 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 # 2*np.ones(A.shape)+A ###Output _____no_output_____ ###Markdown Subtraction ###Code A-B 3-B == 3*np.ones(B.shape)-B ###Output _____no_output_____ ###Markdown Element-wise Multiplication ###Code A*B np.multiply(A,B) 2*A A@B alpha=10**-10 A/(alpha+B) np.add(A,B) ###Output _____no_output_____ ###Markdown Activity Task 1 Create a function named mat_desc() that througouhly describes a matrix, it should1. 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 matrixUse 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 ## Function area import numpy as np import matplotlib.pyplot as plt import scipy.linalg as la %matplotlib inline def mat_desc (matrix): if matrix.size > 0: if matrix.shape [0] == matrix.shape[1]: s = "Square." else: s = "Non-square." if np.all(matrix == np.identity(matrix.shape[0])): sp = "Identity Matrix." elif np.all(matrix == np.zeros(matrix.shape)): sp = "Zero Matrix." elif np.all(matrix == np.ones(matrix.shape)): sp = "Ones Matrix." else: sp = "None." print(f'Matrix:\n{matrix}\n\nShape:\t{matrix.shape}\nRank:\{matrix.ndim}\nSquare?: {s}\nSpecial Characteristics: {sp}') else: print('Matrix is Empty') ## Matrix declarations hi = np.array([ [3,1,2,4], [4,7,9,6], [9,1,6,7] ]) one = np.array([ [1,1,1,1,1], [1,1,1,1,1], [1,1,1,1,1], [1,1,1,1,1], ]) id = np.array([ [1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1] ]) ## Test Areas mat_desc(hi) mat_desc(one) mat_desc(id) ###Output Matrix: [[1 0 0 0] [0 1 0 0] [0 0 1 0] [0 0 0 1]] Shape: (4, 4) Rank:\2 Square?: Square. Special Characteristics: Identity Matrix. ###Markdown Task 2 Create a function named mat_operations() that takes in two matrices a input parameters it should1. 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 differen 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 # Function Area import numpy as np import matplotlib.pyplot as plt import scipy.linalg as la %matplotlib inline def mat_operations(matA,matB): op = input("Enter opration(+,-,*,/): ") if matA.shape == matB.shape: # proceed to operations if op == '+': return matA + matB elif op == '-': return matA - matB elif op == '*': return matA * matB elif op == '/': return matA/matB else: print("The matrices are viable but you have not inputted a correct operation.") else: print("Huhuhu.The matrices are not viable.") #Matrix declarations A = np.array([ [1,1,1,1], [1,1,1,1], [1,1,1,1], [1,1,1,1] ]) B = np.array([ [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], ]) C = np.array([ [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3] ]) D = np.array([ [4, 4, 4, 4], [4, 4, 4, 4], [4, 4, 4, 4] ]) mat_operations(A,C) mat_operations(D,C) mat_operations(A,B) ###Output Enter opration(+,-,*,/): -
notebooks/processing/01-signal-preprocessing.ipynb
###Markdown Signal Preprocessing Before the raw signal can be used by the machine learning algorithms it must be preprocessed. This notebook will perform the different preprocessing steps on some sample signals to visually verify they are functioning properly. ###Code import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np from sklearn.pipeline import Pipeline from src.data.generators import data_generator_levels, data_generator_signals from src.models.encoders.levelbinary import LevelBinary from src.models.encoders.levelmulti import LevelMulti from src.models.transformers.filter import Filter from src.models.transformers.baseline import Baseline from src.models.transformers.truncate import Truncate mpl.style.use('seaborn-notebook') plt.rcParams["figure.figsize"] = (12, 5) ###Output _____no_output_____ ###Markdown Encoding Target Values The target value for this project is a floating point concentation value. This value must be encoded into a class value in order to help analyze the results of each algorithm. The following code will geneate a sample with one in each of the target classes. The encoders are then used determine the class values. ###Code cvals = np.array([0.15, 0.50, 0.85]) xvalues, ylevel, blexps, ydata0 = data_generator_levels(cvals) signals = data_generator_signals(cvals, noise=0.0) ynormal = LevelBinary(targetmin=0.2, targetmax=0.8).transform(cvals) ymulti = LevelMulti(targetmin=0.2, targetmax=0.8).transform(cvals) print(cvals) print(ynormal) print(ymulti) ###Output [0.15 0.5 0.85] [1 0 1] [0 1 2] ###Markdown Preprocessing Pipeline The raw signal from each sample will be run through the different steps of the preprocessing pipeline. The following code and plots will show the output of these different steps to ensure the entire transformation produces the best input signal for the machine learning algorithms. The following table gives a brief description of each preprocessing step. | Step | Description | |:----------|:-------------------------------------------------------------------------------------| | filter | Applys Savitsky-Golay filter to raw data to smooth out the signal. | | baseline | Determines baseline signal from the input signal. | | correct | Performs baseline correction by subtracting off the baseline signal. | | truncate | Slices the input signal to only output the region of interest from the signal. | ###Code xmin = 200 xmax = 450 datapipeline = Pipeline([ ('filter', Filter(windowsize=15, polyorder=2)), ('baseline', Baseline(polyorder=3, weight=0.95, outbaseline=True)), ('correct', Baseline(polyorder=3, weight=0.95)), ('truncate', Truncate(xmin=xmin, xmax=xmax)) ]) ydata_fl = datapipeline.named_steps['filter'].transform(ydata0.copy()) ydata_bl = datapipeline.named_steps['baseline'].transform(ydata_fl.copy()) ydata_cs = datapipeline.named_steps['correct'].transform(ydata_fl.copy()) ydata_tr = datapipeline.named_steps['truncate'].transform(ydata_cs.copy()) ###Output _____no_output_____ ###Markdown Baseline Signal (Full Signal) The following set of plots will show the raw signal and the computed baseline signal for each sample. The pure signal used to generate the sample is also displayed. ###Code for i in range(3): fig, axs = plt.subplots() axs.plot(xvalues, signals[i], label='signal') axs.plot(xvalues, ydata0[i], label='raw') axs.plot(xvalues, ydata_bl[i], label='baseline') fig.suptitle('Sample:[{0}] Baseline:[{1:.4f}] Target:[{2:.4f}]'.format(i, blexps[i], ylevel[i])) plt.legend() ###Output _____no_output_____ ###Markdown Baseline Signal (Region of Interest) The following set of plots will show the different lines computed from the preprocessing pipeline in only the region of interest for this analysis. It will include the filtered/smoothed signal line since this line is visible is this chart. ###Code for i in range(3): fig, axs = plt.subplots() axs.plot(xvalues[xmin:xmax+1], signals[i, xmin:xmax+1], label='signal') axs.plot(xvalues[xmin:xmax+1], ydata0[i, xmin:xmax+1], label='raw') axs.plot(xvalues[xmin:xmax+1], ydata_fl[i, xmin:xmax+1], label='filter') axs.plot(xvalues[xmin:xmax+1], ydata_bl[i, xmin:xmax+1], label='baseline') fig.suptitle('Sample:[{0}] Baseline:[{1:.4f}] Target:[{2:.4f}]'.format(i, blexps[i], ylevel[i])) plt.legend() ###Output _____no_output_____ ###Markdown Baseline Corrected Signal (Region of Interest) The following set of plots will show the baseline corrected lines computed from the preprocessing pipeline in only the region of interest for this analysis. This data displayed in these charts will end up being the data that will be used by the machine learning algorithms. ###Code for i in range(3): fig, axs = plt.subplots() axs.plot(xvalues[xmin:xmax+1], signals[i, xmin:xmax+1], label='signal') axs.plot(xvalues[xmin:xmax+1], ydata_tr[i], label='corrected') fig.suptitle('Sample:[{0}] Baseline:[{1:.4f}] Target:[{2:.4f}]'.format(i, blexps[i], ylevel[i])) plt.legend() ###Output _____no_output_____
docs/introduction/getting-started/regression.ipynb
###Markdown Regression Regression is about predicting a numeric output for a given sample. A labeled regression sample is made up of a bunch of features and a number. The number is usually continuous, but it may also be discrete. We'll use the Trump approval rating dataset as an example. ###Code from river import datasets dataset = datasets.TrumpApproval() dataset ###Output _____no_output_____ ###Markdown This dataset is a streaming dataset which can be looped over. ###Code for x, y in dataset: pass ###Output _____no_output_____ ###Markdown Let's take a look at the first sample. ###Code x, y = next(iter(dataset)) x ###Output _____no_output_____ ###Markdown A regression model's goal is to learn to predict a numeric target `y` from a bunch of features `x`. We'll attempt to do this with a nearest neighbors model. ###Code from river import neighbors model = neighbors.KNNRegressor() model.predict_one(x) ###Output _____no_output_____ ###Markdown The model hasn't been trained on any data, and therefore outputs a default value of 0.The model can be trained on the sample, which will update the model's state. ###Code model = model.learn_one(x, y) ###Output _____no_output_____ ###Markdown If we try to make a prediction on the same sample, we can see that the output is different, because the model has learned something. ###Code model.predict_one(x) ###Output _____no_output_____ ###Markdown Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation. ###Code from river import metrics model = neighbors.KNNRegressor() metric = metrics.MAE() for x, y in dataset: y_pred = model.predict_one(x) model.learn_one(x, y) metric.update(y, y_pred) metric ###Output _____no_output_____ ###Markdown This is a common way to evaluate an online model. In fact, there is a dedicated `evaluate.progressive_val_score` function that does this for you. ###Code from river import evaluate model = neighbors.KNNRegressor() metric = metrics.MAE() evaluate.progressive_val_score(dataset, model, metric) ###Output _____no_output_____
Notebooks/Part(2.2) RDM.ipynb
###Markdown Load FaceNet Model ###Code model = load_model('Template_poisoning/model/facenet_keras.h5', compile= False) model.trainable= False #model.layers[-2].get_config() 'activation- linear' #model.layers[-1].get_config() 'batchnorm' # Read Images (160x160x3) def load_img(path, resize=None): img= Image.open(path) img= img.convert('RGB') if resize is not None: img= img.resize((resize, resize)) return np.asarray(img) def Centroid(vector): ''' After the random initialization, we first optimize the glasses using the adversaryโ€™s centroid in feature space(Xc) # Arguments Input: feature vector(batch_size x feature) ''' Xc= tf.math.reduce_mean(vector, axis=0) return tf.reshape(Xc, (1, vector.shape[-1])) ###Output _____no_output_____ ###Markdown Data Loading ###Code def load_data(dir): X=[] for i, sample in enumerate(os.listdir(dir)): image= load_img(os.path.join(dir, sample)) image = cv2.resize(image, (160, 160)) X.append(image/255.0) return np.array(X) X= load_data('Template_poisoning/Croped_data/adversary_images') Target_samples= load_data('Template_poisoning/Croped_data/target_images') X_ex= X.copy() # Copy of X print('Adversarial Batch:',X.shape) print('Target Batch:',Target_samples.shape) # GET Mask mask= load_img('Template_poisoning/final_mask.png') #Sacle(0-255), RGB mask= mask/255.0 mask.shape ###Output _____no_output_____ ###Markdown Get Predictions ###Code # img_tr= load_img('Template_poisoning/Croped_data/target_images/ben_afflek_0.jpg') # feature_tr= model.predict(img_tr[np.newaxis, :, :, :]) #Target= Generate_target(feature_tr, batch_size= X.shape[0]) Targetc= Centroid(model.predict(Target_samples)) Targetc.shape Xc= Centroid(model.predict(X)) #(1 x 128) print(Xc.shape) delta_x= np.random.uniform(low=0.0, high=1.0, size=X.shape) # Scale(0-1) delta_x.shape f, ax= plt.subplots(1, 5, figsize=(14, 4)) image= X*(1-mask)+ delta_x*mask for i in range(5): ax[i].imshow(image[i+5]) ax[i].set_xticks([]); ax[i].set_yticks([]) plt.show() del image ###Output _____no_output_____ ###Markdown Random Distance Method Conditions: * Target's cancelable biometric identity revealed * Target's Stolen Token= 6 ###Code def salt_2dim(X): samples, features= X.shape X_out= np.zeros((samples,features//2, 2)) for i, x in enumerate(X): X_out[i,:, 0]= x[:features//2] X_out[i,:, 1]= x[features//2:] return X_out def shuffle(X_vec, p=4, seed= 0, with_seed= True): for X in X_vec: j= 64+p for i in np.arange(p, j, p): x= X[(i-p):i] if with_seed: np.random.seed(seed) np.random.shuffle(x[:8]) return X_vec # For Tensors def shuffle_tesnsor(X_vec, p=4, seed= 0, with_seed= True): for X in X_vec: j= 64+p for i in np.arange(p, j, p): x= X[(i-p):i] if with_seed: tf.random.set_seed(seed) tf.random.shuffle(x) return X_vec def get_RDM(Fv, token=6, c=100): ''' INPUT--- Fv.shape: (None, feature) token: Uses token key PROCESS--- 1. Feature vector(Fv) multiplied by a large constant, say c = 100 due to its low dynamic range. 2. To increase the entropy of the template, fv is salted by ORing it with a random grid RG as fs = fv + RG. 3. Fv is divided into two equal parts. 4. A user-specific key (K) of dimension 1 ร— N is generated, which has randomly distributed non-integral values in the range [โˆ’100, 100]. 5. Computation of distance via random feature vectors. 6. In order to provide noninvertibility, median filtering is applied on distance vector D to generate transformed feature vector T f , where the intensity values are shuffled in p ร—1 neighborhood. T f is stored as the final transformed template. OUTPUT--- Out.shape: (None, feature//2) ''' #1 Fv*= c #2 np.random.seed(token) Fv+= np.random.randint(1, 256, size= Fv.shape) #3 Fv= salt_2dim(Fv) #4 np.random.seed(token) K= np.random.randint(-100, 101, size= (1, Fv.shape[-1])) #5 dist =(Fv- K)**2 dist= np.sqrt(np.sum(dist, 2)) #6 Tf= shuffle(dist.copy(), p=4, seed= token, with_seed= True) return Tf #for tensors def RDM_tf(Fv, token=6, c=100.0): #Fv= tf.Variable(Fv, dtype=tf.float64) Fv+=c tf.random.set_seed(token) Fv+= tf.random.uniform( Fv.shape, minval=1, maxval=256, dtype=tf.dtypes.float64, seed=None, name=None) Fv= tf.convert_to_tensor(salt_2dim(Fv)) tf.random.set_seed(token) K= tf.random.uniform((1, Fv.shape[-1]), minval=-101, maxval=101, dtype=tf.dtypes.float64, seed=None, name=None) dist =(Fv- K)**2 dist= tf.math.sqrt(tf.math.reduce_sum(dist, 2)) return shuffle_tesnsor(dist, p=4, seed= token, with_seed= True) Targetc.shape, Xc.shape Targetc= get_RDM(Targetc) Targetc= Generate_target(Targetc, batch_size= X.shape[0]) Targetc.shape Xc= get_RDM(Xc) Xc= Generate_target(Xc, batch_size=X.shape[0]) #(46 x 128) Xc.shape ###Output _____no_output_____ ###Markdown Back propagation and Loss Pipeline ###Code def loss_object(pred, label, delta= delta_x, direction= False): # Loss= euclidean distance + Delta_x pixel Variance dist= Euclidean_dist(pred, label) variance= Sample_variance(delta_x) if direction: sc= tf.math.subtract(1.0, tf.math.divide(1.0, label.shape[0])) #print(dist.shape, sc.shape) vector_mean= dist* tf.cast(sc, dist.dtype) #tf.math.multiply(dist, sc) target_dir= tf.math.multiply(vector_mean, dist) Loss= tf.math.add(target_dir, tf.cast(variance, dist.dtype)) return Loss Loss= tf.math.add(tf.cast(dist, variance.dtype), variance) return Loss def back_propagate(model, X, mask, delta_x, label, direction= False): with tf.GradientTape() as g: g.watch(delta_x) X_batch= Generate_sample(X, delta_x, mask) feature= tf.cast(model(X_batch), tf.float64) rdm_feature= RDM_tf(feature) loss= loss_object(pred= rdm_feature, label= label, delta= delta_x, direction= direction) # Get the gradients of the loss w.r.t to the input image. gradient = g.gradient(loss, delta_x) return gradient, tf.reduce_mean(loss).numpy() # Tf Variables X= tf.Variable(X, dtype=tf.float64) delta_x= tf.Variable(delta_x, dtype=tf.float64) mask= tf.Variable(mask, dtype=tf.float64) Xc= tf.Variable(Xc) Targetc= tf.Variable(Targetc) ###Output _____no_output_____ ###Markdown Modify mask wrt Adversarial Batch ###Code HIS= {} HIS['adv_cen_loss']=[] HIS['target_cen_loss']=[] epoch= 300 Lambda= 0.6 for ep in range(epoch): grad, loss= back_propagate(model, X, mask, delta_x, Xc) HIS['adv_cen_loss'].append(loss) # Gradient step delta_x= delta_x - Lambda*grad if ep%10 == 0: print('Epoch: {} Loss: {:.3f}'.format((ep+1), loss)) ###Output Epoch: 1 Loss: 1282.865 Epoch: 11 Loss: 1253.664 Epoch: 21 Loss: 1226.173 Epoch: 31 Loss: 1200.859 Epoch: 41 Loss: 1178.377 Epoch: 51 Loss: 1159.589 Epoch: 61 Loss: 1145.446 Epoch: 71 Loss: 1136.291 Epoch: 81 Loss: 1130.966 Epoch: 91 Loss: 1127.865 Epoch: 101 Loss: 1126.035 Epoch: 111 Loss: 1124.951 Epoch: 121 Loss: 1124.305 Epoch: 131 Loss: 1123.927 Epoch: 141 Loss: 1125.602 Epoch: 151 Loss: 1125.664 Epoch: 161 Loss: 1125.689 Epoch: 171 Loss: 1125.702 Epoch: 181 Loss: 1125.709 Epoch: 191 Loss: 1125.713 Epoch: 201 Loss: 1125.715 Epoch: 211 Loss: 1125.717 Epoch: 221 Loss: 1125.719 Epoch: 231 Loss: 1125.720 Epoch: 241 Loss: 1125.720 Epoch: 251 Loss: 1125.721 Epoch: 261 Loss: 1125.721 Epoch: 271 Loss: 1125.722 Epoch: 281 Loss: 1125.722 Epoch: 291 Loss: 1125.722 ###Markdown Modify mask wrt Targets Batch ###Code Lambda= 1 for ep in range(int(3*epoch)): grad, loss= back_propagate(model, X, mask, delta_x, Targetc, direction= True) HIS['target_cen_loss'].append(loss) # Gradient step delta_x= delta_x - Lambda*grad if ep== 0: delta_x0= tf.identity(delta_x) if ep== 1: delta_x1= tf.identity(delta_x) if ep== 2: delta_x2= tf.identity(delta_x) if ep== 3: delta_x2= tf.identity(delta_x) if ep== 170: Lambda= 0.1 if ep== 250: Lambda= 0.01 if ep== 450: Lambda= 0.00001 if ep%10 == 0: print('Epoch: {}, Loss: {}'.format((ep+1), loss)) adv_sample0=Generate_sample(X, delta_x, mask) adv_sample0=adv_sample0.numpy() adv_sample1=Generate_sample(X, delta_x1, mask) adv_sample1=adv_sample1.numpy() adv_sample1.shape adv_sample0=np.clip(adv_sample0, 0, 1) adv_sample1=np.clip(adv_sample1, 0, 1) f, ax= plt.subplots(1, 5, figsize=(14, 4)) for i in range(5): ax[i].imshow(adv_sample0[i+5]) ax[i].set_xticks([]); ax[i].set_yticks([]) plt.show() adv_feature= model.predict(X_ex) df_adv= pd.DataFrame(adv_feature) adv_modified_feature0= model.predict(adv_sample0) df_adv_modify0= pd.DataFrame(adv_modified_feature0) adv_modified_feature1= model.predict(adv_sample1) df_adv_modify1= pd.DataFrame(adv_modified_feature1) target_feature= model.predict(Target_samples) df_target= pd.DataFrame(target_feature) df_adv['target']= 'Adversarial_sample' df_adv_modify0['target']= 'Adversarial Pubertation initial step' df_adv_modify1['target']= 'Adversarial Pubertation final step' df_target['target']= 'Target_sample' df=pd.concat([df_target, df_adv_modify0,df_adv_modify1, df_adv], ignore_index= True) df.shape pca = PCA(n_components=2) # Fit pca to 'X' df1= pd.DataFrame(pca.fit_transform(df.drop(['target'], 1))) df1.shape df1['target']= df.target fig, ax = plt.subplots(figsize=(12, 6)) plt.grid(True) plt.xlabel('feature-1'); plt.ylabel('feature-2') sns.scatterplot(x=df1.iloc[:, 0] , y= df1.iloc[:, 1], hue = df1.iloc[:, 2], data= df1, palette='Set1', ax= ax) plt.show() np.save('Benchmark_RDM.npy', HIS) ###Output _____no_output_____
notebooks/Evaluations/Continuous_Timeseries/All_Depths_ORCA/Hoodsport/201905_Hindcast/2014_Hoodsport_Evaluations.ipynb
###Markdown This notebook contains Hovmoller plots that compare the model output over many different depths to the results from the ORCA Buoy data. ###Code import sys sys.path.append('/ocean/kflanaga/MEOPAR/analysis-keegan/notebooks/Tools') import numpy as np import matplotlib.pyplot as plt import os import pandas as pd import netCDF4 as nc import xarray as xr import datetime as dt from salishsea_tools import evaltools as et, viz_tools, places import gsw import matplotlib.gridspec as gridspec import matplotlib as mpl import matplotlib.dates as mdates import cmocean as cmo import scipy.interpolate as sinterp import math from scipy import io import pickle import cmocean import json import Keegan_eval_tools as ket from collections import OrderedDict from matplotlib.colors import LogNorm fs=16 mpl.rc('xtick', labelsize=fs) mpl.rc('ytick', labelsize=fs) mpl.rc('legend', fontsize=fs) mpl.rc('axes', titlesize=fs) mpl.rc('axes', labelsize=fs) mpl.rc('figure', titlesize=fs) mpl.rc('font', size=fs) mpl.rc('font', family='sans-serif', weight='normal', style='normal') import warnings #warnings.filterwarnings('ignore') from IPython.display import Markdown, display %matplotlib inline ptrcloc='/ocean/kflanaga/MEOPAR/savedData/201905_ptrc_data' modver='HC201905' #HC202007 is the other option. gridloc='/ocean/kflanaga/MEOPAR/savedData/201905_grid_data' ORCAloc='/ocean/kflanaga/MEOPAR/savedData/ORCAData' year=2019 mooring='Twanoh' # Parameters year = 2014 modver = "HC201905" mooring = "Hoodsport" ptrcloc = "/ocean/kflanaga/MEOPAR/savedData/201905_ptrc_data" gridloc = "/ocean/kflanaga/MEOPAR/savedData/201905_grid_data" ORCAloc = "/ocean/kflanaga/MEOPAR/savedData/ORCAData" orca_dict=io.loadmat(f'{ORCAloc}/{mooring}.mat') def ORCA_dd_to_dt(date_list): UTC=[] for yd in date_list: if np.isnan(yd) == True: UTC.append(float("NaN")) else: start = dt.datetime(1999,12,31) delta = dt.timedelta(yd) offset = start + delta time=offset.replace(microsecond=0) UTC.append(time) return UTC obs_tt=[] for i in range(len(orca_dict['Btime'][1])): obs_tt.append(np.nanmean(orca_dict['Btime'][:,i])) #I should also change this obs_tt thing I have here into datetimes YD_rounded=[] for yd in obs_tt: if np.isnan(yd) == True: YD_rounded.append(float("NaN")) else: YD_rounded.append(math.floor(yd)) obs_dep=[] for i in orca_dict['Bdepth']: obs_dep.append(np.nanmean(i)) grid=xr.open_mfdataset(gridloc+f'/ts_{modver}_{year}_{mooring}.nc') tt=np.array(grid.time_counter) mod_depth=np.array(grid.deptht) mod_votemper=(grid.votemper.isel(y=0,x=0)) mod_vosaline=(grid.vosaline.isel(y=0,x=0)) mod_votemper = (np.array(mod_votemper)) mod_votemper = np.ma.masked_equal(mod_votemper,0).T mod_vosaline = (np.array(mod_vosaline)) mod_vosaline = np.ma.masked_equal(mod_vosaline,0).T def Process_ORCA(orca_var,depths,dates,year): # Transpose the columns so that a yearday column can be added. df_1=pd.DataFrame(orca_var).transpose() df_YD=pd.DataFrame(dates,columns=['yearday']) df_1=pd.concat((df_1,df_YD),axis=1) #Group by yearday so that you can take the daily mean values. dfg=df_1.groupby(by='yearday') df_mean=dfg.mean() df_mean=df_mean.reset_index() # Convert the yeardays to datetime UTC UTC=ORCA_dd_to_dt(df_mean['yearday']) df_mean['yearday']=UTC # Select the range of dates that you would like. df_year=df_mean[(df_mean.yearday >= dt.datetime(year,1,1))&(df_mean.yearday <= dt.datetime(year,12,31))] df_year=df_year.set_index('yearday') #Add in any missing date values idx=pd.date_range(df_year.index[0],df_year.index[-1]) df_full=df_year.reindex(idx,fill_value=-1) #Transpose again so that you can add a depth column. df_full=df_full.transpose() df_full['depth']=obs_dep # Remove any rows that have NA values for depth. df_full=df_full.dropna(how='all',subset=['depth']) df_full=df_full.set_index('depth') #Mask any NA values and any negative values. df_final=np.ma.masked_invalid(np.array(df_full)) df_final=np.ma.masked_less(df_final,0) return df_final, df_full.index, df_full.columns ###Output _____no_output_____ ###Markdown Map of Buoy Location. ###Code lon,lat=places.PLACES[mooring]['lon lat'] fig, ax = plt.subplots(1,1,figsize = (6,6)) with nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc') as bathy: viz_tools.plot_coastline(ax, bathy, coords = 'map',isobath=.1) color=('firebrick') ax.plot(lon, lat,'o',color = 'firebrick', label=mooring) ax.set_ylim(47, 49) ax.legend(bbox_to_anchor=[1,.6,0.45,0]) ax.set_xlim(-124, -122); ax.set_title('Buoy Location'); ###Output _____no_output_____ ###Markdown Temperature ###Code df,dep,tim= Process_ORCA(orca_dict['Btemp'],obs_dep,YD_rounded,year) date_range=(dt.datetime(year,1,1),dt.datetime(year,12,31)) ax=ket.hovmoeller(df,dep,tim,(2,15),date_range,title='Observed Temperature Series', var_title='Temperature (C$^0$)',vmax=23,vmin=8,cmap=cmo.cm.thermal) ax=ket.hovmoeller(mod_votemper, mod_depth, tt, (2,15),date_range, title='Modeled Temperature Series', var_title='Temperature (C$^0$)',vmax=23,vmin=8,cmap=cmo.cm.thermal) ###Output /ocean/kflanaga/MEOPAR/analysis-keegan/notebooks/Tools/Keegan_eval_tools.py:816: UserWarning: 'set_params()' not defined for locator of type <class 'matplotlib.dates.AutoDateLocator'> plt.locator_params(axis="x", nbins=20) ###Markdown Salinity ###Code df,dep,tim= Process_ORCA(orca_dict['Bsal'],obs_dep,YD_rounded,year) ax=ket.hovmoeller(df,dep,tim,(2,15),date_range,title='Observed Absolute Salinity Series', var_title='SA (g/kg)',vmax=31,vmin=14,cmap=cmo.cm.haline) ax=ket.hovmoeller(mod_vosaline, mod_depth, tt, (2,15),date_range,title='Modeled Absolute Salinity Series', var_title='SA (g/kg)',vmax=31,vmin=14,cmap=cmo.cm.haline) grid.close() bio=xr.open_mfdataset(ptrcloc+f'/ts_{modver}_{year}_{mooring}.nc') tt=np.array(bio.time_counter) mod_depth=np.array(bio.deptht) mod_flagellatets=(bio.flagellates.isel(y=0,x=0)) mod_ciliates=(bio.ciliates.isel(y=0,x=0)) mod_diatoms=(bio.diatoms.isel(y=0,x=0)) mod_Chl = np.array((mod_flagellatets+mod_ciliates+mod_diatoms)*1.8) mod_Chl = np.ma.masked_equal(mod_Chl,0).T df,dep,tim= Process_ORCA(orca_dict['Bfluor'],obs_dep,YD_rounded,year) ax=ket.hovmoeller(df,dep,tim,(2,15),date_range,title='Observed Chlorophyll Series', var_title='Chlorophyll (mg Chl/m$^3$)',vmin=0,vmax=30,cmap=cmo.cm.algae) ax=ket.hovmoeller(mod_Chl, mod_depth, tt, (2,15),date_range,title='Modeled Chlorophyll Series', var_title='Chlorophyll (mg Chl/m$^3$)',vmin=0,vmax=30,cmap=cmo.cm.algae) bio.close() ###Output _____no_output_____
mnist_isfive.ipynb
###Markdown MNIST: Classification of the symbol 'five' ###Code from sklearn.datasets import fetch_mldata mnist = fetch_mldata('MNIST original') %matplotlib inline import matplotlib import matplotlib.pyplot as plt import numpy as np X, y = mnist['data'], mnist['target'] def show_digit(index): digit = X[index] digit_img = digit.reshape(28, 28) plt.imshow(digit_img, cmap=matplotlib.cm.binary, interpolation='nearest') plt.axis('off') plt.show() show_digit(36000) # Split up dataset in test and training data X_train, y_train, X_test, y_test = X[:60000], y[:60000], X[60000:], y[60000:] # and shuffle training data shuffle_index = np.random.permutation(60000) X_train, y_train = X[shuffle_index], y_train[shuffle_index] # Filter out non-fives y_train_5 = (y_train == 5) y_test_5 = (y_test == 5) from sklearn.linear_model import SGDClassifier sgd_clf = SGDClassifier(random_state=42) sgd_clf.fit(X_train, y_train_5) # Predict the five above sgd_clf.predict([X[36000]]) # And validate the model from sklearn.model_selection import cross_val_score y_train_accuracy = cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy') # and use a confusion matrix from sklearn.model_selection import cross_val_predict y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3) y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method='decision_function') from sklearn.metrics import confusion_matrix confusion_matrix(y_train_5, y_train_pred) from sklearn.metrics import precision_score, recall_score, f1_score print('SGDClassifier accuracy:', y_train_accuracy) print('SGDClassifier precision:', precision_score(y_train_5, y_train_pred)) print('SGDClassifier recall:', recall_score(y_train_5, y_train_pred)) print('SGDClassifier F1:', f1_score(y_train_5, y_train_pred)) from sklearn.metrics import roc_curve, roc_auc_score fpr, tpr, thresholds = roc_curve(y_train_5, y_scores) def plot_roc_curve(fpr, tpr, label=None): plt.plot(fpr, tpr, linewidth=2, label=label) plt.plot([0, 1], [0, 1], 'k--') plt.axis([0, 1, 0, 1]) plt.xlabel('False Positive Rate') plt.ylabel('False Negative Rate') plot_roc_curve(fpr, tpr) plt.show() from sklearn.ensemble import RandomForestClassifier forest_clf = RandomForestClassifier(random_state=42) y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3) y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method='predict_proba') y_scores_forest = y_probas_forest[:, 1] fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest) plt.plot(fpr, tpr, 'b:', label='SGD') plot_roc_curve(fpr_forest, tpr_forest, 'Random Forest') plt.legend(loc='lower right') plt.show() roc_auc_score(y_train_5, y_scores_forest) print('RandomForestClassifier precision:', precision_score(y_train_5, y_train_pred_forest)) print('RandomForestClassifier recall:', recall_score(y_train_5, y_train_pred_forest)) ###Output RandomForestClassifier precision: 0.982969432314 RandomForestClassifier recall: 0.830474082273
wrangle-lesson-api-review.ipynb
###Markdown API Review Create the spark session ###Code import pyspark spark = pyspark.sql.SparkSession.builder.getOrCreate() ###Output _____no_output_____ ###Markdown Create Dataframes ###Code import pandas as pd import numpy as np np.random.seed(123) pd_df = pd.DataFrame(dict(n=np.arange(20), group=np.random.choice(list("abc"), 20))) pd_df.head() ###Output _____no_output_____ ###Markdown Convert to a spark dataframe ###Code df = spark.createDataFrame(pd_df) #must run .show() to see the spark dataframe df.show(2) df.describe().show() from pydataset import data mpg = spark.createDataFrame(data("mpg")) mpg.show(2) ###Output +------------+-----+-----+----+---+----------+---+---+---+---+-------+ |manufacturer|model|displ|year|cyl| trans|drv|cty|hwy| fl| class| +------------+-----+-----+----+---+----------+---+---+---+---+-------+ | audi| a4| 1.8|1999| 4| auto(l5)| f| 18| 29| p|compact| | audi| a4| 1.8|1999| 4|manual(m5)| f| 21| 29| p|compact| +------------+-----+-----+----+---+----------+---+---+---+---+-------+ only showing top 2 rows ###Markdown Create ColumnsThis returns a column object: ###Code mpg.hwy ###Output _____no_output_____ ###Markdown To select the values in the column object, we follow it with show. And we can use .select to select multiple column objects. ###Code # select 3 columns and show 2 rows mpg.select(mpg.hwy, mpg.cty, mpg.model).show(2) # select 1 column, then select that column and add one to each of the values, return and show both columns. mpg.select(mpg.hwy, mpg.hwy + 1).show(2) # select & alias hwy column name mpg.select(mpg.hwy.alias("highway_mileage")).show(2) # create a var col1 to store the column object of hwy, aliased as highway_mileage col1 = mpg.hwy.alias("highway_mileage") # create a var col2 to store the column object of hwy divided by 2, aliased as highway_mileage_halved col2 = (mpg.hwy/2).alias("highway_mileage_halved") # select both, referencing the new variables, col1 and col2 mpg.select(col1, col2).show(1) from pyspark.sql.functions import col, expr col("hwy") avg_col = (col("hwy") + col("cty")) / 2 mpg.select( col("hwy").alias("highway_mileage"), mpg.cty.alias("city_mileage"), avg_col.alias("avg_mileage") ).show(2) ###Output +---------------+------------+-----------+ |highway_mileage|city_mileage|avg_mileage| +---------------+------------+-----------+ | 29| 18| 23.5| | 29| 21| 25.0| +---------------+------------+-----------+ only showing top 2 rows ###Markdown Another way to do what we did above, using expr() ... ###Code mpg.select( expr("hwy"), # the same as `col` expr("hwy + 1"), # an arithmetic expression expr("hwy AS highway_mileage"), # using an alias expr("hwy + 1 AS highway_incremented"), # a combination of the above ).show(5) ###Output +---+---------+---------------+-------------------+ |hwy|(hwy + 1)|highway_mileage|highway_incremented| +---+---------+---------------+-------------------+ | 29| 30| 29| 30| | 29| 30| 29| 30| | 31| 32| 31| 32| | 30| 31| 30| 31| | 26| 27| 26| 27| +---+---------+---------------+-------------------+ only showing top 5 rows ###Markdown Briging together all the different ways to accomplish the same task...select a column & alias it. ###Code mpg.select( mpg.hwy.alias("highway"), col("hwy").alias("highway"), expr("hwy").alias("highway"), expr("hwy AS highway"), ).show(5) ###Output +-------+-------+-------+-------+ |highway|highway|highway|highway| +-------+-------+-------+-------+ | 29| 29| 29| 29| | 29| 29| 29| 29| | 31| 31| 31| 31| | 30| 30| 30| 30| | 26| 26| 26| 26| +-------+-------+-------+-------+ only showing top 5 rows ###Markdown Spark SQL ###Code # register the table with spark mpg.createOrReplaceTempView("mpg") spark.sql( """ SELECT hwy, cty, (hwy + cty) / 2 as avg FROM mpg """ ).show(2) ###Output +---+---+----+ |hwy|cty| avg| +---+---+----+ | 29| 18|23.5| | 29| 21|25.0| +---+---+----+ only showing top 2 rows ###Markdown Type Casting ###Code mpg.dtypes mpg.printSchema() mpg.select(mpg.hwy.cast("string")).printSchema() # shows null because can't be converted. mpg.select(mpg.model, mpg.model.cast("int")).show(2) ###Output +-----+-----+ |model|model| +-----+-----+ | a4| null| | a4| null| +-----+-----+ only showing top 2 rows ###Markdown Built in Functions ###Code # avg and mean are aliases of each other from pyspark.sql.functions import concat, sum, avg, min, max, count, mean # from pyspark.sql.functions import * mpg.select( sum(mpg.hwy) / count(mpg.hwy).alias("average_1"), avg(mpg.hwy).alias("average_2"), min(mpg.hwy), max(mpg.hwy), ).show() mpg.select(concat(mpg.manufacturer, mpg.model)).show(5) ###Output +---------------------------+ |concat(manufacturer, model)| +---------------------------+ | audia4| | audia4| | audia4| | audia4| | audia4| +---------------------------+ only showing top 5 rows ###Markdown The function for string literals: lit ###Code from pyspark.sql.functions import lit mpg.select(concat(mpg.cyl, lit(" cylinders"))).show(5) ###Output +-----------------------+ |concat(cyl, cylinders)| +-----------------------+ | 4 cylinders| | 4 cylinders| | 4 cylinders| | 4 cylinders| | 6 cylinders| +-----------------------+ only showing top 5 rows ###Markdown More String Manipulation ###Code from pyspark.sql.functions import regexp_extract, regexp_replace textdf = spark.createDataFrame( pd.DataFrame( { "address": [ "600 Navarro St ste 600, San Antonio, TX 78205", "3130 Broadway St, San Antonio, TX 78209", "303 Pearl Pkwy, San Antonio, TX 78215", "1255 SW Loop 410, San Antonio, TX 78227", ] } ) ) textdf.show(truncate=False) ###Output +---------------------------------------------+ |address | +---------------------------------------------+ |600 Navarro St ste 600, San Antonio, TX 78205| |3130 Broadway St, San Antonio, TX 78209 | |303 Pearl Pkwy, San Antonio, TX 78215 | |1255 SW Loop 410, San Antonio, TX 78227 | +---------------------------------------------+ ###Markdown Using regexp_extract - extract at least one capture group and create new column of that. ###Code textdf.select( "address", regexp_extract("address", r"^(\d+)", 1).alias("street_no"), regexp_extract("address", r"^\d+\s([\w\s]+?),", 1).alias("street"), ).show(truncate=False) ###Output +---------------------------------------------+---------+------------------+ |address |street_no|street | +---------------------------------------------+---------+------------------+ |600 Navarro St ste 600, San Antonio, TX 78205|600 |Navarro St ste 600| |3130 Broadway St, San Antonio, TX 78209 |3130 |Broadway St | |303 Pearl Pkwy, San Antonio, TX 78215 |303 |Pearl Pkwy | |1255 SW Loop 410, San Antonio, TX 78227 |1255 |SW Loop 410 | +---------------------------------------------+---------+------------------+ ###Markdown regexp_replace lets us make substitutions based on a regular expression. ###Code textdf.select( "address", regexp_replace("address", r"^.*?,\s*", "").alias("city_state_zip"), ).show(truncate=False) ###Output +---------------------------------------------+---------------------+ |address |city_state_zip | +---------------------------------------------+---------------------+ |600 Navarro St ste 600, San Antonio, TX 78205|San Antonio, TX 78205| |3130 Broadway St, San Antonio, TX 78209 |San Antonio, TX 78209| |303 Pearl Pkwy, San Antonio, TX 78215 |San Antonio, TX 78215| |1255 SW Loop 410, San Antonio, TX 78227 |San Antonio, TX 78227| +---------------------------------------------+---------------------+ ###Markdown Filtering with .filter and .where ###Code mpg.filter(mpg.cyl == 4).where(mpg["class"] == "subcompact").show() ###Output +------------+-----------+-----+----+---+----------+---+---+---+---+----------+ |manufacturer| model|displ|year|cyl| trans|drv|cty|hwy| fl| class| +------------+-----------+-----+----+---+----------+---+---+---+---+----------+ | honda| civic| 1.6|1999| 4|manual(m5)| f| 28| 33| r|subcompact| | honda| civic| 1.6|1999| 4| auto(l4)| f| 24| 32| r|subcompact| | honda| civic| 1.6|1999| 4|manual(m5)| f| 25| 32| r|subcompact| | honda| civic| 1.6|1999| 4|manual(m5)| f| 23| 29| p|subcompact| | honda| civic| 1.6|1999| 4| auto(l4)| f| 24| 32| r|subcompact| | honda| civic| 1.8|2008| 4|manual(m5)| f| 26| 34| r|subcompact| | honda| civic| 1.8|2008| 4| auto(l5)| f| 25| 36| r|subcompact| | honda| civic| 1.8|2008| 4| auto(l5)| f| 24| 36| c|subcompact| | honda| civic| 2.0|2008| 4|manual(m6)| f| 21| 29| p|subcompact| | hyundai| tiburon| 2.0|1999| 4| auto(l4)| f| 19| 26| r|subcompact| | hyundai| tiburon| 2.0|1999| 4|manual(m5)| f| 19| 29| r|subcompact| | hyundai| tiburon| 2.0|2008| 4|manual(m5)| f| 20| 28| r|subcompact| | hyundai| tiburon| 2.0|2008| 4| auto(l4)| f| 20| 27| r|subcompact| | subaru|impreza awd| 2.2|1999| 4| auto(l4)| 4| 21| 26| r|subcompact| | subaru|impreza awd| 2.2|1999| 4|manual(m5)| 4| 19| 26| r|subcompact| | subaru|impreza awd| 2.5|1999| 4|manual(m5)| 4| 19| 26| r|subcompact| | subaru|impreza awd| 2.5|1999| 4| auto(l4)| 4| 19| 26| r|subcompact| | volkswagen| new beetle| 1.9|1999| 4|manual(m5)| f| 35| 44| d|subcompact| | volkswagen| new beetle| 1.9|1999| 4| auto(l4)| f| 29| 41| d|subcompact| | volkswagen| new beetle| 2.0|1999| 4|manual(m5)| f| 21| 29| r|subcompact| +------------+-----------+-----+----+---+----------+---+---+---+---+----------+ only showing top 20 rows ###Markdown Conditionals with When and Otherwise ###Code from pyspark.sql.functions import when mpg.select( mpg.displ, ( when(mpg.displ < 2, "small") .when(mpg.displ < 3, "medium") .otherwise("large") .alias("engine_size") ), ).show(10) ###Output +-----+-----------+ |displ|engine_size| +-----+-----------+ | 1.8| small| | 1.8| small| | 2.0| medium| | 2.0| medium| | 2.8| medium| | 2.8| medium| | 3.1| large| | 1.8| small| | 1.8| small| | 2.0| medium| +-----+-----------+ only showing top 10 rows ###Markdown Sorting & Ordering ###Code mpg.sort(mpg.hwy).show(8) from pyspark.sql.functions import asc, desc mpg.sort(mpg.hwy.desc()) # is the same as mpg.sort(col("hwy").desc()) # is the same as mpg.sort(desc("hwy")).show(5) mpg.sort(desc("class"), mpg.cyl.asc(), col("hwy").desc()).show() ###Output +------------+------------------+-----+----+---+----------+---+---+---+---+-----+ |manufacturer| model|displ|year|cyl| trans|drv|cty|hwy| fl|class| +------------+------------------+-----+----+---+----------+---+---+---+---+-----+ | subaru| forester awd| 2.5|2008| 4|manual(m5)| 4| 20| 27| r| suv| | subaru| forester awd| 2.5|2008| 4| auto(l4)| 4| 20| 26| r| suv| | subaru| forester awd| 2.5|1999| 4|manual(m5)| 4| 18| 25| r| suv| | subaru| forester awd| 2.5|2008| 4|manual(m5)| 4| 19| 25| p| suv| | subaru| forester awd| 2.5|1999| 4| auto(l4)| 4| 18| 24| r| suv| | subaru| forester awd| 2.5|2008| 4| auto(l4)| 4| 18| 23| p| suv| | toyota| 4runner 4wd| 2.7|1999| 4|manual(m5)| 4| 15| 20| r| suv| | toyota| 4runner 4wd| 2.7|1999| 4| auto(l4)| 4| 16| 20| r| suv| | jeep|grand cherokee 4wd| 3.0|2008| 6| auto(l5)| 4| 17| 22| d| suv| | nissan| pathfinder 4wd| 4.0|2008| 6| auto(l5)| 4| 14| 20| p| suv| | toyota| 4runner 4wd| 4.0|2008| 6| auto(l5)| 4| 16| 20| r| suv| | jeep|grand cherokee 4wd| 4.0|1999| 6| auto(l4)| 4| 15| 20| r| suv| | toyota| 4runner 4wd| 3.4|1999| 6| auto(l4)| 4| 15| 19| r| suv| | ford| explorer 4wd| 4.0|1999| 6|manual(m5)| 4| 15| 19| r| suv| | jeep|grand cherokee 4wd| 3.7|2008| 6| auto(l5)| 4| 15| 19| r| suv| | mercury| mountaineer 4wd| 4.0|2008| 6| auto(l5)| 4| 13| 19| r| suv| | ford| explorer 4wd| 4.0|2008| 6| auto(l5)| 4| 13| 19| r| suv| | nissan| pathfinder 4wd| 3.3|1999| 6| auto(l4)| 4| 14| 17| r| suv| | ford| explorer 4wd| 4.0|1999| 6| auto(l5)| 4| 14| 17| r| suv| | ford| explorer 4wd| 4.0|1999| 6| auto(l5)| 4| 14| 17| r| suv| +------------+------------------+-----+----+---+----------+---+---+---+---+-----+ only showing top 20 rows ###Markdown Grouping & Aggregating ###Code mpg.groupBy(mpg.cyl) mpg.groupBy(col("cyl")) mpg.groupBy("cyl") mpg.groupBy(mpg.cyl).agg(avg(mpg.cty), avg(mpg.hwy)).show() mpg.groupBy("cyl", "class").agg(avg(mpg.cty), avg(mpg.hwy)).show() ###Output +---+----------+------------------+------------------+ |cyl| class| avg(cty)| avg(hwy)| +---+----------+------------------+------------------+ | 5| compact| 21.0| 29.0| | 5|subcompact| 20.0| 28.5| | 6|subcompact| 17.0|24.714285714285715| | 6| pickup| 14.5| 17.9| | 4|subcompact|22.857142857142858| 30.80952380952381| | 8| suv|12.131578947368421|16.789473684210527| | 8| pickup| 11.8| 15.8| | 8| midsize| 16.0| 24.0| | 4| midsize| 20.5| 29.1875| | 8| 2seater| 15.4| 24.8| | 6| compact|16.923076923076923|25.307692307692307| | 6| minivan| 15.6| 22.2| | 4| compact| 21.375| 29.46875| | 8|subcompact| 14.8| 21.6| | 6| midsize|17.782608695652176| 26.26086956521739| | 4| minivan| 18.0| 24.0| | 4| pickup| 16.0|20.666666666666668| | 6| suv| 14.5| 18.5| | 4| suv| 18.0| 23.75| +---+----------+------------------+------------------+ ###Markdown Rollup will do the same aggregations, but also include overall totals. ###Code mpg.rollup("cyl").count().sort("cyl").show() ###Output +----+-----+ | cyl|count| +----+-----+ |null| 234| | 4| 81| | 5| 4| | 6| 79| | 8| 70| +----+-----+ ###Markdown Here the null value in cyl indicates the total count. ###Code mpg.rollup("cyl").agg(expr("avg(hwy)")).sort("cyl").show() ###Output +----+-----------------+ | cyl| avg(hwy)| +----+-----------------+ |null|23.44017094017094| | 4|28.80246913580247| | 5| 28.75| | 6|22.82278481012658| | 8|17.62857142857143| +----+-----------------+ ###Markdown Here the null value in cyl indicates the total count. ###Code mpg.rollup("cyl", "class").mean("hwy").sort(col("cyl"), col("class")).show() ###Output +----+----------+------------------+ | cyl| class| avg(hwy)| +----+----------+------------------+ |null| null| 23.44017094017094| | 4| null| 28.80246913580247| | 4| compact| 29.46875| | 4| midsize| 29.1875| | 4| minivan| 24.0| | 4| pickup|20.666666666666668| | 4|subcompact| 30.80952380952381| | 4| suv| 23.75| | 5| null| 28.75| | 5| compact| 29.0| | 5|subcompact| 28.5| | 6| null| 22.82278481012658| | 6| compact|25.307692307692307| | 6| midsize| 26.26086956521739| | 6| minivan| 22.2| | 6| pickup| 17.9| | 6|subcompact|24.714285714285715| | 6| suv| 18.5| | 8| null| 17.62857142857143| | 8| 2seater| 24.8| +----+----------+------------------+ only showing top 20 rows ###Markdown Crosstables & Pivot TablesCrosstab is a simple way to get counts. ###Code mpg.crosstab("class", "cyl").show() ###Output +----------+---+---+---+---+ | class_cyl| 4| 5| 6| 8| +----------+---+---+---+---+ | midsize| 16| 0| 23| 2| |subcompact| 21| 2| 7| 5| | 2seater| 0| 0| 0| 5| | pickup| 3| 0| 10| 20| | minivan| 1| 0| 10| 0| | suv| 8| 0| 16| 38| | compact| 32| 2| 13| 0| +----------+---+---+---+---+ ###Markdown We can use pivot to compute different aggregations than count. ###Code mpg.groupby("class").pivot("cyl").mean("hwy").show() ###Output +----------+------------------+----+------------------+------------------+ | class| 4| 5| 6| 8| +----------+------------------+----+------------------+------------------+ |subcompact| 30.80952380952381|28.5|24.714285714285715| 21.6| | compact| 29.46875|29.0|25.307692307692307| null| | minivan| 24.0|null| 22.2| null| | suv| 23.75|null| 18.5|16.789473684210527| | midsize| 29.1875|null| 26.26086956521739| 24.0| | pickup|20.666666666666668|null| 17.9| 15.8| | 2seater| null|null| null| 24.8| +----------+------------------+----+------------------+------------------+ ###Markdown Missing Values ###Code df = spark.createDataFrame( pd.DataFrame( {"x": [1, 2, np.nan, 4, 5, np.nan], "y": [np.nan, 0, 0, 3, 1, np.nan]} ) ) df.show() df.na.drop().show() df.na.fill(0).show() df.na.fill(0, subset="x").show() df.na.drop(subset="y").show() ###Output +---+---+ | x| y| +---+---+ |2.0|0.0| |NaN|0.0| |4.0|3.0| |5.0|1.0| +---+---+ ###Markdown Transformations of Dataframes ###Code # how is spark thinking about our df? mpg.explain() ###Output == Physical Plan == *(1) Scan ExistingRDD[manufacturer#146,model#147,displ#148,year#149L,cyl#150L,trans#151,drv#152,cty#153L,hwy#154L,fl#155,class#156] ###Markdown Only a single step above ^This one below shows another step after "Scan ExistingRDD", a "Project" that contains the names of the columns we are looking for. ###Code mpg.select(mpg.cyl, mpg.hwy).explain() ###Output == Physical Plan == *(1) Project [cyl#150L, hwy#154L] +- *(1) Scan ExistingRDD[manufacturer#146,model#147,displ#148,year#149L,cyl#150L,trans#151,drv#152,cty#153L,hwy#154L,fl#155,class#156] ###Markdown And now we are going to do a more advanced select calcluation, but this is still just a single step. ###Code mpg.select(((mpg.cyl + mpg.hwy) / 2).alias("avg_mpg")).explain() ###Output == Physical Plan == *(1) Project [(cast((cyl#150L + hwy#154L) as double) / 2.0) AS avg_mpg#1541] +- *(1) Scan ExistingRDD[manufacturer#146,model#147,displ#148,year#149L,cyl#150L,trans#151,drv#152,cty#153L,hwy#154L,fl#155,class#156] ###Markdown Notice that our filter below is also a single step. ###Code mpg.filter(mpg.cyl == 6).explain() mpg.select("cyl", "hwy").filter(expr("cyl = 6")).explain() mpg.filter(expr("cyl = 6")).select("cyl", "hwy").explain() ###Output == Physical Plan == *(1) Project [cyl#150L, hwy#154L] +- *(1) Filter (isnotnull(cyl#150L) AND (cyl#150L = 6)) +- *(1) Scan ExistingRDD[manufacturer#146,model#147,displ#148,year#149L,cyl#150L,trans#151,drv#152,cty#153L,hwy#154L,fl#155,class#156] == Physical Plan == *(1) Project [cyl#150L, hwy#154L] +- *(1) Filter (isnotnull(cyl#150L) AND (cyl#150L = 6)) +- *(1) Scan ExistingRDD[manufacturer#146,model#147,displ#148,year#149L,cyl#150L,trans#151,drv#152,cty#153L,hwy#154L,fl#155,class#156] ###Markdown More DF ManipulationsFor these examples, we'll be working with a dataset of observations of the weather in seattle. ###Code from vega_datasets import data weather = data.seattle_weather().assign(date=lambda df: df.date.astype(str)) weather = spark.createDataFrame(weather) weather.show(6) # print number of rows & columns print(weather.count(), "rows", len(weather.columns), "columns") # get the date range of the dataset. min_date, max_date = weather.select(min("date"), max("date")).first() min_date, max_date # compute temp average weather = weather.withColumn( "temp_avg", expr("ROUND(temp_min + temp_max) / 2") ).drop("temp_max", "temp_min") weather.show(6) ###Output +----------+-------------+----+-------+--------+ | date|precipitation|wind|weather|temp_avg| +----------+-------------+----+-------+--------+ |2012-01-01| 0.0| 4.7|drizzle| 9.0| |2012-01-02| 10.9| 4.5| rain| 6.5| |2012-01-03| 0.8| 2.3| rain| 9.5| |2012-01-04| 20.3| 4.7| rain| 9.0| |2012-01-05| 1.3| 6.1| rain| 6.0| |2012-01-06| 2.5| 2.2| rain| 3.5| +----------+-------------+----+-------+--------+ only showing top 6 rows ###Markdown Calculate total rainfall ###Code from pyspark.sql.functions import month, year, quarter ( weather.withColumn("month", month("date")) .groupBy("month") .agg(sum("precipitation").alias("total_rainfall")) .sort("month") .show() ) ###Output +-----+------------------+ |month| total_rainfall| +-----+------------------+ | 1|465.99999999999994| | 2| 422.0| | 3| 606.2| | 4| 375.4| | 5| 207.5| | 6| 132.9| | 7| 48.2| | 8| 163.7| | 9|235.49999999999997| | 10| 503.4| | 11| 642.5| | 12| 622.7000000000002| +-----+------------------+ ###Markdown Let's now take a look at the average temperature for each type of weather in December 2013: ###Code ( weather.filter(month("date") == 12) .filter(year("date") == 2013) .groupBy("weather") .agg(mean("temp_avg")) .show() ) ###Output +-------+-----------------+ |weather| avg(temp_avg)| +-------+-----------------+ | fog|7.555555555555555| | sun|2.977272727272727| +-------+-----------------+ ###Markdown Let's now find out how many days had freezing temperatures in each month of 2013. ###Code ( weather.filter(year("date") == 2013) .withColumn("freezing_temps", (weather.temp_avg <= 0).cast("int")) .withColumn("month", month("date")) .groupBy("month") .agg(sum("freezing_temps").alias("no_of_days_with_freezing_temps")) .sort("month") .show() ) ###Output +-----+------------------------------+ |month|no_of_days_with_freezing_temps| +-----+------------------------------+ | 1| 3| | 2| 0| | 3| 0| | 4| 0| | 5| 0| | 6| 0| | 7| 0| | 8| 0| | 9| 0| | 10| 0| | 11| 0| | 12| 5| +-----+------------------------------+ ###Markdown One last example, let's calculate the average temperature for each quarter of each year: ###Code ( weather.withColumn("quarter", quarter("date")) .withColumn("year", year("date")) .groupBy("year", "quarter") .agg(mean("temp_avg").alias("temp_avg")) .sort("year", "quarter") .show() ) ###Output +----+-------+------------------+ |year|quarter| temp_avg| +----+-------+------------------+ |2012| 1| 5.587912087912088| |2012| 2|12.675824175824175| |2012| 3| 18.375| |2012| 4| 8.581521739130435| |2013| 1| 6.405555555555556| |2013| 2|14.505494505494505| |2013| 3| 19.47826086956522| |2013| 4| 8.032608695652174| |2014| 1| 7.205555555555556| |2014| 2|14.296703296703297| |2014| 3|19.858695652173914| |2014| 4| 9.88586956521739| |2015| 1| 8.972222222222221| |2015| 2|15.258241758241759| |2015| 3|19.407608695652176| |2015| 4| 8.956521739130435| +----+-------+------------------+ ###Markdown We could use a pivot table instead: ###Code ( weather.withColumn("quarter", quarter("date")) .withColumn("year", year("date")) .groupBy("quarter") .pivot("year") .agg(expr("ROUND(MEAN(temp_avg), 2) AS temp_avg")) .sort("quarter") .show() ) ###Output +-------+-----+-----+-----+-----+ |quarter| 2012| 2013| 2014| 2015| +-------+-----+-----+-----+-----+ | 1| 5.59| 6.41| 7.21| 8.97| | 2|12.68|14.51| 14.3|15.26| | 3|18.38|19.48|19.86|19.41| | 4| 8.58| 8.03| 9.89| 8.96| +-------+-----+-----+-----+-----+ ###Markdown Joins We'll start by creating some data that we can join together: ###Code users = spark.createDataFrame( pd.DataFrame( { "id": [1, 2, 3, 4, 5, 6], "name": ["bob", "joe", "sally", "adam", "jane", "mike"], "role_id": [1, 2, 3, 3, np.nan, np.nan], } ) ) roles = spark.createDataFrame( pd.DataFrame( { "id": [1, 2, 3, 4], "name": ["admin", "author", "reviewer", "commenter"], } ) ) print("--- users ---") users.show() print("--- roles ---") roles.show() ###Output --- users --- +---+-----+-------+ | id| name|role_id| +---+-----+-------+ | 1| bob| 1.0| | 2| joe| 2.0| | 3|sally| 3.0| | 4| adam| 3.0| | 5| jane| NaN| | 6| mike| NaN| +---+-----+-------+ --- roles --- +---+---------+ | id| name| +---+---------+ | 1| admin| | 2| author| | 3| reviewer| | 4|commenter| +---+---------+ ###Markdown To join two dataframes together, we'll need to call the .join method on one of them and supply the other as an argument. In addition, we'll need to supply the condition on which we are joining. In our case, we are joining where the role_id column on the users table is equal to the id column on the roles table. ###Code users.join(roles, on=users.role_id == roles.id).show() ###Output +---+-----+-------+---+--------+ | id| name|role_id| id| name| +---+-----+-------+---+--------+ | 1| bob| 1.0| 1| admin| | 3|sally| 3.0| 3|reviewer| | 4| adam| 3.0| 3|reviewer| | 2| joe| 2.0| 2| author| +---+-----+-------+---+--------+ ###Markdown By default, spark will perform an inner join, meaning that records from both dataframes will have a match with the other. We can also specify either a left or a right join, which will keep all of the records from either the left or right side, even if those records don't have a match with the other dataframe. ###Code users.join(roles, on=users.role_id == roles.id, how="left").show() users.join(roles, on=users.role_id == roles.id, how="right").show() ###Output +----+-----+-------+---+---------+ | id| name|role_id| id| name| +----+-----+-------+---+---------+ | 1| bob| 1.0| 1| admin| |null| null| null| 4|commenter| | 3|sally| 3.0| 3| reviewer| | 4| adam| 3.0| 3| reviewer| | 2| joe| 2.0| 2| author| +----+-----+-------+---+---------+ ###Markdown Notice that examples above have a duplicate id column. There are several ways we could go about dealing with this:alias each dataframe + explicitly select columns after joining (this could also be implemented with spark SQL)rename duplicated columns before mergingdrop duplicated columns after the merge (.drop(right.id)) Wrangling In this lesson, we will acquire and prepare the data we will use in the rest of this module.- Acquiring Data- Data Prep- Train Test Split ###Code from pyspark.sql import SparkSession from pyspark.sql.functions import * spark = SparkSession.builder.getOrCreate() ###Output _____no_output_____ ###Markdown Acquisition Spark lets us read data in from a variety of data sources using what it calls a DataFrameReader. We can access the read property of our spark object and then set various options and read from a data source. Using Data Schemas ###Code df = spark.read.csv("source.csv", sep=",", header=True, inferSchema=True) df.show(7) df.printSchema() # can be done this way too. from pyspark.sql.types import StructType, StructField, StringType schema = StructType( [ StructField("source_id", StringType()), StructField("source_username", StringType()), ] ) spark.read.csv("source.csv", header=True, inferSchema=True).show(7) ###Output +---------+-------------------+ |source_id| source_username| +---------+-------------------+ | 100137| Merlene Blodgett| | 103582| Carmen Cura| | 106463| Richard Sanchez| | 119403| Betty De Hoyos| | 119555| Socorro Quiara| | 119868|Michelle San Miguel| | 120752| Eva T. Kleiber| +---------+-------------------+ only showing top 7 rows ###Markdown Writing Data ###Code # can write to jso, csv, etc. from pydataset import data mpg = spark.createDataFrame(data("mpg")) # write to Json mpg.write.json("mpg_json", mode = "overwrite") # write to csv mpg.write.csv("mpg_csv", mode = "overwrite") cases = spark.read.csv("case.csv", header = True, inferSchema = True) ###Output _____no_output_____ ###Markdown Data Prep ###Code cases.show(5) ###Output +----------+----------------+----------------+------------+---------+-------------------+-----------+----------------+--------------------+-----------+-----------+---------+--------------------+----------------+ | case_id|case_opened_date|case_closed_date|SLA_due_date|case_late| num_days_late|case_closed| dept_division|service_request_type| SLA_days|case_status|source_id| request_address|council_district| +----------+----------------+----------------+------------+---------+-------------------+-----------+----------------+--------------------+-----------+-----------+---------+--------------------+----------------+ |1014127332| 1/1/18 0:42| 1/1/18 12:29|9/26/20 0:42| NO| -998.5087616000001| YES|Field Operations| Stray Animal| 999.0| Closed| svcCRMLS|2315 EL PASO ST,...| 5| |1014127333| 1/1/18 0:46| 1/3/18 8:11| 1/5/18 8:30| NO|-2.0126041669999997| YES| Storm Water|Removal Of Obstru...|4.322222222| Closed| svcCRMSS|2215 GOLIAD RD, ...| 3| |1014127334| 1/1/18 0:48| 1/2/18 7:57| 1/5/18 8:30| NO| -3.022337963| YES| Storm Water|Removal Of Obstru...|4.320729167| Closed| svcCRMSS|102 PALFREY ST W...| 3| |1014127335| 1/1/18 1:29| 1/2/18 8:13|1/17/18 8:30| NO| -15.01148148| YES|Code Enforcement|Front Or Side Yar...|16.29188657| Closed| svcCRMSS|114 LA GARDE ST,...| 3| |1014127336| 1/1/18 1:34| 1/1/18 13:29| 1/1/18 4:34| YES|0.37216435200000003| YES|Field Operations|Animal Cruelty(Cr...| 0.125| Closed| svcCRMSS|734 CLEARVIEW DR...| 7| +----------+----------------+----------------+------------+---------+-------------------+-----------+----------------+--------------------+-----------+-----------+---------+--------------------+----------------+ only showing top 5 rows ###Markdown Column Renaming ###Code cases = cases.withColumnRenamed("SLA_due_date", "case_due_date") ###Output _____no_output_____ ###Markdown Data Types ###Code cases.printSchema() cases.withColumn("case_closed", expr('case_closed == "YES"')).withColumn("case_late", expr("case_late == 'YES'")) cases.select("case_closed", "case_late").show(7) cases.printSchema() ###Output root |-- case_id: integer (nullable = true) |-- case_opened_date: string (nullable = true) |-- case_closed_date: string (nullable = true) |-- case_due_date: string (nullable = true) |-- case_late: string (nullable = true) |-- num_days_late: double (nullable = true) |-- case_closed: string (nullable = true) |-- dept_division: string (nullable = true) |-- service_request_type: string (nullable = true) |-- SLA_days: double (nullable = true) |-- case_status: string (nullable = true) |-- source_id: string (nullable = true) |-- request_address: string (nullable = true) |-- council_district: integer (nullable = true) ###Markdown Data Transformations ###Code cases.groupBy('council_district').count().show() cases = cases.withColumn("council_district", col("council_district").cast("string")) cases.printSchema() # Convert datefeild to date data types. cases.select("case_opened_date", "case_closed_date", "case_due_date").show(7) fmt = "M/d/yy H:mm" cases = ( cases.withColumn("case_opened_date", to_timestamp("case_opened_date", fmt)) .withColumn("case_closed_date", to_timestamp("case_closed_date", fmt)) .withColumn("case_due_date", to_timestamp("case_due_date", fmt)) ) cases.select("case_opened_date", "case_closed_date", "case_due_date").show(7) ###Output +-------------------+-------------------+-------------------+ | case_opened_date| case_closed_date| case_due_date| +-------------------+-------------------+-------------------+ |2018-01-01 00:42:00|2018-01-01 12:29:00|2020-09-26 00:42:00| |2018-01-01 00:46:00|2018-01-03 08:11:00|2018-01-05 08:30:00| |2018-01-01 00:48:00|2018-01-02 07:57:00|2018-01-05 08:30:00| |2018-01-01 01:29:00|2018-01-02 08:13:00|2018-01-17 08:30:00| |2018-01-01 01:34:00|2018-01-01 13:29:00|2018-01-01 04:34:00| |2018-01-01 06:28:00|2018-01-01 14:38:00|2018-01-31 08:30:00| |2018-01-01 06:57:00|2018-01-02 15:32:00|2018-01-17 08:30:00| +-------------------+-------------------+-------------------+ only showing top 7 rows ###Markdown Data Transformations ###Code cases.select("request_address").show(7) cases = cases.withColumn("request_address", trim(lower(cases.request_address))) cases = cases.withColumn("num_weeks_late", expr("num_days_late / 7 AS num_weeks_late")) cases.select("num_days_late", "num_weeks_late").show(7) # cases = cases.withColumn("council_district", col("council_district").cast("int")) # %03d # Three digits if not fills leading spaces with 0. cases = cases.withColumn("council_district", format_string("%03d", col("council_district").cast("int"))) cases.select("council_district").show(7) cases.printSchema() ###Output root |-- case_id: integer (nullable = true) |-- case_opened_date: timestamp (nullable = true) |-- case_closed_date: timestamp (nullable = true) |-- case_due_date: timestamp (nullable = true) |-- case_late: string (nullable = true) |-- num_days_late: double (nullable = true) |-- case_closed: string (nullable = true) |-- dept_division: string (nullable = true) |-- service_request_type: string (nullable = true) |-- SLA_days: double (nullable = true) |-- case_status: string (nullable = true) |-- source_id: string (nullable = true) |-- request_address: string (nullable = true) |-- council_district: string (nullable = false) |-- num_weeks_late: double (nullable = true) ###Markdown New Features ###Code cases = cases.withColumn("zipcode", regexp_extract("request_address", r"\d+$", 0)) cases.select("zipcode").show(7) cases = ( cases.withColumn("case_age", datediff(current_timestamp(), "case_opened_date")) .withColumn("days_to_close", datediff("case_closed_date", "case_opened_date")) ) cases.select("case_age", "days_to_close").show(7) ###Output +--------+-------------+ |case_age|days_to_close| +--------+-------------+ | 1229| 0| | 1229| 2| | 1229| 1| | 1229| 1| | 1229| 0| | 1229| 0| | 1229| 1| +--------+-------------+ only showing top 7 rows ###Markdown Joining New Dataset ###Code dept = spark.read.csv('dept.csv', header = True, inferSchema = True) dept.show(7) dept.groupBy("dept_division").count().show(truncate = False) # Take a look at the unique values in each column cases.groupBy("dept_division").count().show(truncate = False) # You can also do this cases.groupBy("dept_division").count().show() == dept.groupBy("dept_division").count().show() # To join cases = ( cases.join(dept, "dept_division", "left") .drop(dept.dept_division) .drop(dept.dept_name) .drop(cases.dept_division) .withColumnRenamed("standardized_dept_name", "dept") .withColumn("dept_subject_to_SLA", col("dept_subject_to_SLA") == "YES") ) cases.show(2, vertical = True) ###Output -RECORD 0------------------------------------ case_id | 1014127332 case_opened_date | 2018-01-01 00:42:00 case_closed_date | 2018-01-01 12:29:00 case_due_date | 2020-09-26 00:42:00 case_late | NO num_days_late | -998.5087616000001 case_closed | YES service_request_type | Stray Animal SLA_days | 999.0 case_status | Closed source_id | svcCRMLS request_address | 2315 el paso st,... council_district | 005 num_weeks_late | -142.6441088 zipcode | 78207 case_age | 1229 days_to_close | 0 dept | Animal Care Services dept_subject_to_SLA | true -RECORD 1------------------------------------ case_id | 1014127333 case_opened_date | 2018-01-01 00:46:00 case_closed_date | 2018-01-03 08:11:00 case_due_date | 2018-01-05 08:30:00 case_late | NO num_days_late | -2.0126041669999997 case_closed | YES service_request_type | Removal Of Obstru... SLA_days | 4.322222222 case_status | Closed source_id | svcCRMSS request_address | 2215 goliad rd, ... council_district | 003 num_weeks_late | -0.28751488099999994 zipcode | 78223 case_age | 1229 days_to_close | 2 dept | Trans & Cap Impro... dept_subject_to_SLA | true only showing top 2 rows ###Markdown Data Splitting ###Code train, validate, test = cases.randomSplit([.7, .2, .1]) train.count() validate.count() test.count() ###Output _____no_output_____
Python for Finance - Code Files/109 Monte Carlo - Euler Discretization - Part I/Online Financial Data (APIs)/Python 3 APIs/MC - Euler Discretization - Part I - Solution_IEX.ipynb
###Markdown Monte Carlo - Euler Discretization - Part I *Suggested Answers follow (usually there are multiple ways to solve a problem in Python).* Download the data for Microsoft (โ€˜MSFTโ€™) from IEX for the period โ€˜2015-1-1โ€™ until '2017-3-21'. ###Code import numpy as np import pandas as pd from pandas_datareader import data as web from scipy.stats import norm import matplotlib.pyplot as plt %matplotlib inline ticker = 'MSFT' data = pd.DataFrame() data[ticker] = web.DataReader(ticker, data_source='iex', start='2015-1-1', end='2017-3-21')['close'] ###Output 5y ###Markdown Store the annual standard deviation of the log returns in a variable, called โ€œstdevโ€. ###Code log_returns = np.log(1 + data.pct_change()) log_returns.tail() data.plot(figsize=(10, 6)); stdev = log_returns.std() * 250 ** 0.5 stdev ###Output _____no_output_____ ###Markdown Set the risk free rate, r, equal to 2.5% (0.025). ###Code r = 0.025 ###Output _____no_output_____ ###Markdown To transform the object into an array, reassign stdev.values to stdev. ###Code type(stdev) stdev = stdev.values stdev ###Output _____no_output_____ ###Markdown Set the time horizon, T, equal to 1 year, the number of time intervals equal to 250, the iterations equal to 10,000. Create a variable, delta_t, equal to the quotient of T divided by the number of time intervals. ###Code T = 1.0 t_intervals = 250 delta_t = T / t_intervals iterations = 10000 ###Output _____no_output_____ ###Markdown Let Z equal a random matrix with dimension (time intervals + 1) by the number of iterations. ###Code Z = np.random.standard_normal((t_intervals + 1, iterations)) ###Output _____no_output_____ ###Markdown Use the .zeros_like() method to create another variable, S, with the same dimension as Z. S is the matrix to be filled with future stock price data. ###Code S = np.zeros_like(Z) ###Output _____no_output_____ ###Markdown Create a variable S0 equal to the last adjusted closing price of Microsoft. Use the โ€œilocโ€ method. ###Code S0 = data.iloc[-1] S[0] = S0 ###Output _____no_output_____ ###Markdown Use the following formula to create a loop within the range (1, t_intervals + 1) that reassigns values to S in time t. $$S_t = S_{t-1} \cdot exp((r - 0.5 \cdot stdev^2) \cdot delta_t + stdev \cdot delta_t^{0.5} \cdot Z_t)$$ ###Code for t in range(1, t_intervals + 1): S[t] = S[t-1] * np.exp((r - 0.5 * stdev ** 2) * delta_t + stdev * delta_t ** 0.5 * Z[t]) S S.shape ###Output _____no_output_____ ###Markdown Plot the first 10 of the 10,000 generated iterations on a graph. ###Code plt.figure(figsize=(10, 6)) plt.plot(S[:, :10]); ###Output _____no_output_____
examples/example_inverted_pendulum_kalman.ipynb
###Markdown System dynamics The system to be controlled is an inverted pendulum on a cart (see next Figure). The system is governed by the following differential equations:\begin{equation} \begin{aligned} (M+m)\ddot p + ml\ddot\phi \cos\phi - ml \dot \phi ^2 \sin \phi + b\dot p &= F \\ l \ddot \phi + \ddot p \cos \phi - g \sin\phi &= -f_\phi\dot \phi\end{aligned}\end{equation}Introducing the state vector $x=[p\; \dot p\; \phi\; \dot \phi]$ and the input $u=F$, the system dynamics are described in state-space by a set of an nonlinear ordinary differential equations: $\dot x = f(x,u)$ with\begin{equation}\begin{split} f(x,u) &= \begin{bmatrix} x_2\\ \frac{-mg \sin x_3\cos x_3 + mlx_4^3\sin x_3 + f_\phi m x_4 \cos x_3 - bx_2 + u }{M+(1-\cos^2 x_3)m}\\ x_3\\ \frac{(M+m)(g \sin x_3 - f_\phi x_4) - (lm x_4^2 \sin x_3 - bx_2 + u)\cos x_3}{l(M+(1-\cos^2 x_3)m)} \end{bmatrix}\\ \end{split} \end{equation}For MPC control design, the system is linearized about the upright (unstable) equilibrium point, i.e., about the point $x_{eq} = [0, \; 0\;, 0,\; 0]^\top$.The linearized system has form $\dot x = A_c x + B_c u$ with\begin{equation} A = \begin{bmatrix} 0& 1& 0& 0\\ 0& -\frac{b}{M}& -g\frac{m}{M}& f_\theta\frac{m}{M}\\ 0&0&0&1\\ 0&\frac{b}{Ml}& \frac{g(M+m)}{Ml}&-\frac{(M+m)f_\theta}{M l} \end{bmatrix},\qquad B= \begin{bmatrix} 0\\ \frac{1}{M}\\ 0\\ -\frac{1}{Ml}& \end{bmatrix} \end{equation} Next, the system is discretized with sampling time $T_s = 10\;\text{ms}$. Here we just use a Forward Euler dsicretization scheme for the sake of simplicity. ###Code # Constants # M = 0.5 m = 0.2 b = 0.1 ftheta = 0.1 l = 0.3 g = 9.81 Ts = 10e-3 # System dynamics: \dot x = f_ODE(t,x,u) def f_ODE(t,x,u): F = u v = x[1] theta = x[2] omega = x[3] der = np.zeros(4) der[0] = v der[1] = (m * l * np.sin(theta) * omega ** 2 - m * g * np.sin(theta) * np.cos(theta) + m * ftheta * np.cos(theta) * omega + F - b * v) / (M + m * (1 - np.cos(theta) ** 2)) der[2] = omega der[3] = ((M + m) * (g * np.sin(theta) - ftheta * omega) - m * l * omega ** 2 * np.sin(theta) * np.cos(theta) - (F - b * v) * np.cos(theta)) / (l * (M + m * (1 - np.cos(theta) ** 2))) return der # Linearized System Matrices Ac =np.array([[0, 1, 0, 0], [0, -b / M, -(g * m) / M, (ftheta * m) / M], [0, 0, 0, 1], [0, b / (M * l), (M * g + g * m) / (M * l), -(M * ftheta + ftheta * m) / (M * l)]]) Bc = np.array([ [0.0], [1.0 / M], [0.0], [-1 / (M * l)] ]) Cc = np.array([[1., 0., 0., 0.], [0., 0., 1., 0.]]) Dc = np.zeros((2, 1)) [nx, nu] = Bc.shape # number of states and number or inputs ny = np.shape(Cc)[0] # Simple forward euler discretization Ad = np.eye(nx) + Ac * Ts Bd = Bc * Ts Cd = Cc Dd = Dc # Standard deviation of the measurement noise on position and angle std_npos = 0.005 std_nphi = 0.005 # Reference input and states xref = np.array([0.3, 0.0, 0.0, 0.0]) # reference state uref = np.array([0.0]) # reference input uminus1 = np.array([0.0]) # input at time step negative one - used to penalize the first delta u at time instant 0. Could be the same as uref. # Constraints xmin = np.array([-10.0, -10.0, -100, -100]) xmax = np.array([10.0, 10.0, 100, 100]) umin = np.array([-20]) umax = np.array([20]) Dumin = np.array([-5]) Dumax = np.array([5]) # Objective function weights Qx = sparse.diags([1.0, 0, 5.0, 0]) # Quadratic cost for states x0, x1, ..., x_N-1 QxN = sparse.diags([1.0, 0, 5.0, 0]) # Quadratic cost for xN Qu = 0.0 * sparse.eye(1) # Quadratic cost for u0, u1, ...., u_N-1 QDu = 0.1 * sparse.eye(1) # Quadratic cost for Du0, Du1, ...., Du_N-1 # Initialize simulation system phi0 = 15*2*np.pi/360 x0 = np.array([0, 0, phi0, 0]) # initial state t0 = 0 system_dyn = ode(f_ODE).set_integrator('vode', method='bdf') system_dyn.set_initial_value(x0, t0) _ = system_dyn.set_f_params(0.0) # Prediction horizon Np = 150 Nc = 75 # Instantiate and initialize MPC controller K = MPCController(Ad, Bd, Np=Np, Nc=Nc, x0=x0, xref=xref, uminus1=uminus1, Qx=Qx, QxN=QxN, Qu=Qu, QDu=QDu, xmin=xmin, xmax=xmax, umin=umin, umax=umax, Dumin=Dumin, Dumax=Dumax) K.setup() # Basic Kalman filter design Q_kal = np.diag([0.1, 10, 0.1, 10]) R_kal = np.eye(ny) L,P,W = kalman_design_simple(Ad, Bd, Cd, Dd, Q_kal, R_kal, type='filter') x0_est = x0 KF = LinearStateEstimator(x0_est, Ad, Bd, Cd, Dd,L) # Simulate in closed loop [nx, nu] = Bd.shape # number of states and number or inputs len_sim = 10 # simulation length (s) nsim = int(len_sim / Ts) # simulation length(timesteps) x_vec = np.zeros((nsim, nx)) y_vec = np.zeros((nsim, ny)) y_meas_vec = np.zeros((nsim, ny)) y_est_vec = np.zeros((nsim, ny)) x_est_vec = np.zeros((nsim, nx)) x_ref_vec = np.zeros((nsim, nx)) u_vec = np.zeros((nsim, nu)) t_MPC_CPU = np.zeros((nsim,1)) t_vec = np.arange(0, nsim) * Ts time_start = time.time() x_step = x0 x_step_est = x0 t_step = t0 uMPC = uminus1 for i in range(nsim): # Output for step i # System y_step = Cd.dot(system_dyn.y) # y[i] from the system ymeas_step = y_step ymeas_step[0] += std_npos * np.random.randn() ymeas_step[1] += std_nphi * np.random.randn() # Estimator # MPC uMPC = K.output() # u[i] = k(\hat x[i]) possibly computed at time instant -1 # Save output for step i y_vec[i, :] = y_step # y[i] y_meas_vec[i, :] = ymeas_step # y_meas[i] x_vec[i, :] = system_dyn.y # x[i] y_est_vec[i, :] = KF.y # \hat y[i|i-1] x_est_vec[i, :] = KF.x # \hat x[i|i-1] x_ref_vec[i, :] = xref #xref_fun(t_step) u_vec[i, :] = uMPC # u[i] # Update to i+1 # System system_dyn.set_f_params(uMPC) # set current input value to uMPC system_dyn.integrate(system_dyn.t + Ts) # integrate system dynamics for a time step # Kalman filter: update and predict KF.update(ymeas_step) # \hat x[i|i] KF.predict(uMPC) # \hat x[i+1|i] # MPC update for step i+1 time_MPC_start = time.time() K.update(KF.x, uMPC) # update with measurement (and possibly pre-compute u[i+1]) t_MPC_CPU[i] = time.time() - time_MPC_start # Time update t_step += Ts time_sim = time.time() - time_start # Plot results fig, axes = plt.subplots(3, 1, figsize=(10, 10), sharex=True) axes[0].plot(t_vec, x_est_vec[:, 0], "b", label="p_est") axes[0].plot(t_vec, x_vec[:, 0], "k", label='p') axes[0].plot(t_vec, x_ref_vec[:,0], "r--", linewidth=4, label="p_ref") axes[0].set_ylabel("Position (m)") axes[1].plot(t_vec, x_est_vec[:, 2] * 360 / 2 / np.pi, "b", label="phi_est") axes[1].plot(t_vec, x_vec[:, 2] * 360 / 2 / np.pi, label="phi") axes[1].plot(t_vec, x_ref_vec[:,2] * 360 / 2 / np.pi, "r--", linewidth=4, label="phi_ref") axes[1].set_ylabel("Angle (deg)") axes[2].plot(t_vec, u_vec[:, 0], label="u") axes[2].plot(t_vec, uref * np.ones(np.shape(t_vec)), "r--", linewidth=4, label="u_ref") axes[2].set_ylabel("Force (N)") for ax in axes: ax.grid(True) ax.legend() # Histogram of the MPC CPU time fig,ax = plt.subplots(1,1, figsize=(5,5)) ax.hist(t_MPC_CPU*1000, bins=100) ax.grid(True) _ = ax.set_xlabel('MPC computation CPU time (ms)') ###Output _____no_output_____