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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "<hr style=\"height:2px;\">\n\n# Demo: Probabilistic neural network training for denoising of synthetic 2D data\n\nThis notebook demonstrates training a probabilistic CARE model for a 2D denoising task, using provided synthetic training data. \nNote that training a neural network for actual use should be done on more (representative) data and with more training time.\n\nMore documentation is available at http://csbdeep.bioimagecomputing.com/doc/.", "_____no_output_____" ] ], [ [ "from __future__ import print_function, unicode_literals, absolute_import, division\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\n\nfrom tifffile import imread\nfrom csbdeep.utils import download_and_extract_zip_file, axes_dict, plot_some, plot_history\nfrom csbdeep.utils.tf import limit_gpu_memory\nfrom csbdeep.io import load_training_data\nfrom csbdeep.models import Config, CARE", "_____no_output_____" ] ], [ [ "The TensorFlow backend uses all available GPU memory by default, hence it can be useful to limit it:", "_____no_output_____" ] ], [ [ "# limit_gpu_memory(fraction=1/2)", "_____no_output_____" ] ], [ [ "<hr style=\"height:2px;\">\n\n# Training data\n\nDownload and read provided training data, use 10% as validation data.", "_____no_output_____" ] ], [ [ "download_and_extract_zip_file (\n url = 'http://csbdeep.bioimagecomputing.com/example_data/synthetic_disks.zip',\n targetdir = 'data',\n)", "_____no_output_____" ], [ "(X,Y), (X_val,Y_val), axes = load_training_data('data/synthetic_disks/data.npz', validation_split=0.1, verbose=True)\n\nc = axes_dict(axes)['C']\nn_channel_in, n_channel_out = X.shape[c], Y.shape[c]", "_____no_output_____" ], [ "plt.figure(figsize=(12,5))\nplot_some(X_val[:5],Y_val[:5])\nplt.suptitle('5 example validation patches (top row: source, bottom row: target)');", "_____no_output_____" ] ], [ [ "<hr style=\"height:2px;\">\n\n# CARE model\n\nBefore we construct the actual CARE model, we have to define its configuration via a `Config` object, which includes \n* parameters of the underlying neural network,\n* the learning rate,\n* the number of parameter updates per epoch,\n* the loss function, and\n* whether the model is probabilistic or not.\n\nThe defaults should be sensible in many cases, so a change should only be necessary if the training process fails. \n\nFor a probabilistic model, we have to explicitly set `probabilistic=True`.\n\n---\n\n<span style=\"color:red;font-weight:bold;\">Important</span>: Note that for this notebook we use a very small number of update steps per epoch for immediate feedback, whereas this number should be increased considerably (e.g. `train_steps_per_epoch=400`) to obtain a well-trained model.", "_____no_output_____" ] ], [ [ "config = Config(axes, n_channel_in, n_channel_out, probabilistic=True, train_steps_per_epoch=30)\nprint(config)\nvars(config)", "_____no_output_____" ] ], [ [ "We now create a CARE model with the chosen configuration:", "_____no_output_____" ] ], [ [ "model = CARE(config, 'my_model', basedir='models')", "_____no_output_____" ] ], [ [ "<hr style=\"height:2px;\">\n\n# Training\n\nTraining the model will likely take some time. We recommend to monitor the progress with [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) (example below), which allows you to inspect the losses during training.\nFurthermore, you can look at the predictions for some of the validation images, which can be helpful to recognize problems early on.\n\nYou can start TensorBoard from the current working directory with `tensorboard --logdir=.`\nThen connect to [http://localhost:6006/](http://localhost:6006/) with your browser.\n\n", "_____no_output_____" ] ], [ [ "history = model.train(X,Y, validation_data=(X_val,Y_val))", "_____no_output_____" ] ], [ [ "Plot final training history (available in TensorBoard during training):", "_____no_output_____" ] ], [ [ "print(sorted(list(history.history.keys())))\nplt.figure(figsize=(16,5))\nplot_history(history,['loss','val_loss'],['mse','val_mse','mae','val_mae']);", "_____no_output_____" ] ], [ [ "<hr style=\"height:2px;\">\n\n# Evaluation\n\nExample results for validation images.", "_____no_output_____" ] ], [ [ "plt.figure(figsize=(12,10))\n_P = model.keras_model.predict(X_val[:5])\n_P_mean = _P[...,:(_P.shape[-1]//2)]\n_P_scale = _P[...,(_P.shape[-1]//2):]\nplot_some(X_val[:5],Y_val[:5],_P_mean,_P_scale,pmax=99.5)\nplt.suptitle('5 example validation patches\\n' \n 'first row: input (source), ' \n 'second row: target (ground truth), '\n 'third row: predicted Laplace mean, '\n 'forth row: predicted Laplace scale');", "_____no_output_____" ] ], [ [ "<hr style=\"height:2px;\">\n\n# Export model to be used with CSBDeep **Fiji** plugins and **KNIME** workflows\n\nSee https://github.com/CSBDeep/CSBDeep_website/wiki/Your-Model-in-Fiji for details.", "_____no_output_____" ] ], [ [ "model.export_TF()", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n%matplotlib inline", "_____no_output_____" ] ], [ [ "## COMP 3122 - Artificial Intelligence with Python\n__Week 6 lab__\n\n### [github.com/kamrik/ML1](https://github.com/kamrik/ML1)", "_____no_output_____" ], [ "## Plan for today\n - Demo of subplots and imshow\n - Lab exercise on GitHub in `exercises/week6_lab.ipynb`", "_____no_output_____" ] ] ]

[ "code", "markdown" ]

[ [ "code" ], [ "markdown", "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Imports", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom numpy import pi, cos, sin, array", "_____no_output_____" ], [ "from scipy import signal\nfrom scipy.linalg import toeplitz, inv", "_____no_output_____" ], [ "import matplotlib.pyplot as plt\nplt.style.use('dark_background')\nnp.set_printoptions(precision=3, suppress=True)", "_____no_output_____" ], [ "from warnings import filterwarnings\nfilterwarnings('ignore', category=UserWarning)", "_____no_output_____" ] ], [ [ "### Useful functions", "_____no_output_____" ] ], [ [ "def calculate_error_rms(x, x_est):\n error = x[:len(x_est)] - x_est\n error_rms = np.linalg.norm(error)/len(error)\n return error_rms.round(5)", "_____no_output_____" ] ], [ [ "## Zero Forcing Equalizer", "_____no_output_____" ] ], [ [ "x = np.random.choice([-1, 1], 40)\nchannel = array([.1, -.1, 0.05, 1, .05])\n\ny = np.convolve(x, channel)", "_____no_output_____" ], [ "_, ax = plt.subplots(1, 2, figsize=(10, 4))\nax[0].stem(x)\nax[1].stem(y);", "_____no_output_____" ], [ "# 6 tap equalizer\ntaps = 6\nY = toeplitz(y[taps-1:taps-1+taps], np.flip(y[:taps]))\nzerof = inv(Y)@x[:taps]\nzerof", "_____no_output_____" ], [ "x_zerof = np.convolve(y, zerof, 'valid') # x_est is shorter than x because of 'valid'\nplt.stem(x_zerof);", "_____no_output_____" ], [ "calculate_error_rms(x, x_zerof)", "_____no_output_____" ] ], [ [ "## Least Squares Error Equalizer", "_____no_output_____" ] ], [ [ "taps = 6 \nL = 30 # number of input samples used\nY = toeplitz(y[taps-1: taps-1+L ] ,np.flip(y[:taps]))\nlse = inv(Y.T @ Y)@Y.T @ x[:L] #.T is fine because Y is a real matrix, else use Y.conj().T\nlse", "_____no_output_____" ], [ "x_lse = np.convolve(y, lse, 'valid')\nplt.stem(x_lse);", "_____no_output_____" ], [ "calculate_error_rms(x, x_lse)", "_____no_output_____" ] ], [ [ "### Channel Estimation", "_____no_output_____" ] ], [ [ "L = 30 \ntaps = 5\nX = toeplitz( x[:L], np.zeros(6) )\n\nh_est = inv(X.T@X)@X.T@y[:L]\nh_est", "_____no_output_____" ] ], [ [ "## MMSE Equalizer", "_____no_output_____" ] ], [ [ "x_var = 1\nnoise_var = 1e-4 # SNR = 40 dB\nchannel = array([.1, -.1, .05, .9+0.1j, .05], complex) # L = 5\nN = 5 # Taps in equalizer\nL = len(channel)\nD = N + L - 4 ", "_____no_output_____" ], [ "# Calculate H \ncol = np.zeros(N, complex)\ncol[0] = channel[0]\n\nrow = np.zeros(N+L-1, complex)\nrow[:L] = channel\n\nH = toeplitz(col, row)\nH.shape", "_____no_output_____" ], [ "# Calculate R_y\nR_x = x_var * np.eye(N+L-1)\nR_v = noise_var * np.eye(N)\nR_y = H @ R_x @ H.conj().T + R_v", "_____no_output_____" ], [ "xD_x = np.zeros(N+L-1)\nxD_x[D] = x_var\n\nC = xD_x @ H.conj().T @ inv(R_y)\nC", "_____no_output_____" ], [ "corrected_channel = np.convolve(channel , C)\ncorrected_channel", "_____no_output_____" ], [ "plt.stem(channel.real, markerfmt='o', label='real')\nplt.stem(channel.imag, markerfmt='x', label='imag')\nplt.legend();", "_____no_output_____" ], [ "symbols = 2000\nx = (np.random.choice([-1, 1], symbols) + 1j*np.random.choice([-1, 1], symbols))*np.sqrt(x_var/2)\nnoise = (np.random.choice([-1, 1], symbols) + 1j*np.random.choice([-1, 1], symbols))*np.sqrt(noise_var/2)\n\ny = np.convolve(x, channel, 'full') # This is not the same as signal.lfilter\ny = y[:len(x)]\nobservations = y + noise", "_____no_output_____" ] ], [ [ "A quick note about how `np.convolve()` and `signal.lfilter()` are related.\n1. \n```\ny_full = np.convolve(x, channel, 'full') # size: 2004\n```\n2. \n```\ny_same = np.convolve(x, channel, 'same') # size: 2000, \n```\nThis is same as `y_full[2:-2]` (drops 2 samples in the beginning and end)\n3. \n```\ny_filt = signal.lfilter(channel, 1, x) # size: 2000\n```\nThis is same as `y_full[:-4]` (drops 4 samples at end)\n", "_____no_output_____" ] ], [ [ "estimation = signal.lfilter(C, 1, observations)\n\nerror = estimation[D:] - x[:-D]\nevm_db = 10*np.log10(np.var(error)/x_var)\nsnr_db = 10*np.log10(np.var(y)/noise_var)\nevm_rms = 100*np.sqrt( np.var(error)/x_var )\n\nprint(f'EVM: {evm_db:.2f} dB , {evm_rms:.4f} (% rms) \\nSNR: {snr_db:.1f} dB')", "EVM: -27.36 dB , 4.2832 (% rms) \nSNR: 39.3 dB\n" ], [ "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,8))\nax1.scatter(observations.real, observations.imag)\nax1.grid(linestyle='dashed')\nax1.set_title('Observations Y[n]')\n\nax2.scatter(estimation[D:].real, estimation[D:].imag);\nax2.grid(linestyle='dashed');\nax2.set_title('Estimate of x with EVM={:.2f}dB'.format(evm_db));\n\nax3.stem(corrected_channel.real, markerfmt='o', label='real')\nax3.stem(corrected_channel.imag, markerfmt='x', label='imag')\nax3.legend()\nax3.set_title('Corrected channel')\n\nw, h = signal.freqz(channel, 1)\nax4.plot(w/(2*pi), 20*np.log10(abs(h)), label='Original channel')\nw, h = signal.freqz(corrected_channel, 1)\nax4.plot(w/(2*pi), 20*np.log10(abs(h)), label='Equalized channel');\nax4.set_title('Magnitude Responses')\nax4.legend()\nax4.grid(linestyle='dashed');", "_____no_output_____" ] ], [ [ "## Least Mean Squares (LMS) ", "_____no_output_____" ] ], [ [ "x_len = 1500\nx = np.random.choice([-1, 1], x_len) + 1j*np.random.choice([-1, 1],x_len)\nchannel = array([-.19j, .14, 1+.1j, -.16, .11j+.03])\ny = signal.lfilter(channel, 1, x)", "_____no_output_____" ], [ "_, (ax1, ax2) = plt.subplots(2, 1)\nax1.stem(x[:80].imag)\nax2.stem(y[:80].imag);", "_____no_output_____" ], [ "taps = 13\nequalizer = np.zeros(taps, complex)\nequalizer[taps//2] = 1 # All pass filter, just delays the input\n\nchannel_delay = np.argmax(channel)\nequalizer_delay = np.argmax(equalizer)\napprox_delay = channel_delay + equalizer_delay\nprint(f'Initial approximate delay : {approx_delay} ({channel_delay} + {equalizer_delay})')\n\nu = 0.005 # Learning rate\nY = np.zeros_like(equalizer, complex)\nestimate = np.zeros_like(y, complex)\nerror = np.zeros_like(y, complex)\n\n# Adaptation loop\nfor n, sample in enumerate(y):\n Y = np.roll(Y, 1) \n Y[0] = sample\n estimate[n] = np.dot(Y, equalizer)\n \n if n >= approx_delay+5:\n training_symbol = x[n-approx_delay]\n error[n] = training_symbol - estimate[n]\n equalizer += 2*u*error[n]*Y.conjugate()\n #print('{}: {} \\t {:.2f} {}'.format(n, training_symbol, estimate[n], equalizer[:2]))\n \n \nplt.plot(abs(error))\nplt.xlabel('LMS iterations')\nplt.title('Absolute value of error')\nplt.grid();", "Initial approximate delay : 8 (2 + 6)\n" ], [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\nax1.scatter(y[10:].real, y[10:].imag)\nax1.grid(linestyle='dashed')\nax2.scatter(estimate[200:].real, estimate[200:].imag)\nax2.grid(linestyle='dashed');", "_____no_output_____" ] ], [ [ "## Decision feedback equalizers", "_____no_output_____" ] ], [ [ "# QPSK signal\nx_len = 200\nqpsk = np.random.choice([-1, 1], x_len) + 1j*np.random.choice([-1, 1], x_len)\nchannel = array([0.05, 0.1j, 1.0, 0.20, -0.15j, 0.1])\ny = signal.lfilter(channel, 1, qpsk)", "_____no_output_____" ], [ "# Feed forward filter\n# feed_forward_filter = array([0, 0, 0, 1, 0]) # Pass through\nfeed_forward_filter = array([ 0.004, 0.011j, -0.06, -0.1j, 1]) # From MMSE, see next section\n\nfeed_forward = signal.lfilter(feed_forward_filter, 1, y)", "_____no_output_____" ], [ "# Decision feedback loop\npost_cursors = 3\nfeedback_coeffs = -channel[-post_cursors:] # Negative of post-cursors\nfb_pipe = np.zeros_like(feedback_coeffs, complex)\n\nestimate = np.zeros_like(y, complex)\nlast_estimate = 0\n\nfor n, ff_out in enumerate(feed_forward):\n decision = np.sign(last_estimate.real) + 1j*np.sign(last_estimate.imag)\n fb_pipe = np.roll(fb_pipe, 1)\n fb_pipe[0] = decision\n \n last_estimate = ff_out + np.dot(fb_pipe, feedback_coeffs)\n estimate[n] = last_estimate\n ", "_____no_output_____" ], [ "# EVM \nideal = np.sign(y[5:].real) + 1j*np.sign(y[5:].imag)\nerror = y[5:] - ideal\nevm_observation = 10*np.log10(error.var()/ideal.var())\n\nprint('EVM of observation: {:.2f} dB'.format(evm_observation))", "EVM of observation: -10.67 dB\n" ], [ "ideal = np.sign( estimate[5:].real) + 1j*np.sign(estimate[5:].imag)\nerror = estimate[5:] - ideal\nevm_estimate = 10*np.log10(error.var()/ideal.var())\n\nprint('EVM of estimate: {:.2f} dB'.format(evm_estimate))", "EVM of estimate: -23.84 dB\n" ], [ "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\nax1.scatter(y[5:].real, y[5:].imag)\nax1.set_title('Observation y[n]')\nax1.grid()\n\nax2.scatter(estimate[10:].real, estimate[10:].imag)\nax2.set_title('Estimate of x[n]')\nax2.grid();", "_____no_output_____" ] ], [ [ "### MMSE Optimal Coefficient Calculation", "_____no_output_____" ] ], [ [ "x_var = 1\nnoise_var = 0 # Noise-free\nchannel = array([0.05, 0.1j, 1]) # After removing post-cursors (using feedback loop)\nL = len(channel) # number of taps in channel\n\nN = 5 # taps in feed forward FIR\nD = N + L - 2", "_____no_output_____" ], [ "# Calculate H \ncol = np.zeros(N, complex)\ncol[0] = channel[0]\n\nrow = np.zeros(N+L-1, complex)\nrow[:L] = channel\n\nH = toeplitz(col, row)\nH.shape", "_____no_output_____" ], [ "# Calculate covariance matrices\nR_x = x_var*np.eye(N+L-1)\nR_v = noise_var*np.eye(N)\nR_y = H @ R_x @ H.conj().T + R_v", "_____no_output_____" ], [ "xD_x = np.zeros(N+L-1)\nxD_x[D] = x_var\n\nequalizer = xD_x @ H.conj().T @ inv(R_y)\nequalizer", "_____no_output_____" ] ], [ [ "### Combine LMS and Decision Feedback", "_____no_output_____" ] ], [ [ "x_len = 1000\nqpsk = np.random.choice([1, -1], x_len) + 1j*np.random.choice([1, -1], x_len)\nchannel = array([0.05, 0.1j, 1.0, 0.2, -0.15j, 0.1])\ny = signal.lfilter(channel, 1, qpsk)", "_____no_output_____" ], [ "# Initialize feed forward equalizer\nequalizer = array([ 0.004, 0.0109j, -0.06, -0.1j, 1.0]) # MMSE (see prev section)\nN = len(equalizer)\nchannel_delay = np.argmax(channel)\nequalizer_delay = np.argmax(equalizer)\ntotal_delay = channel_delay + equalizer_delay\n\nY = np.zeros_like(equalizer, complex)\nequalizer_output = np.zeros_like(y, complex)", "_____no_output_____" ], [ "# Adaptive loop\npost_cursors = 3\nfb_coeffs = -channel[-post_cursors:]\nfb_pipe = np.zeros_like(fb_coeffs, complex)\n\nerror = np.zeros_like(y, complex)\nestimate = np.zeros_like(y, complex)\n\nu = 0.005 # LMS learning rate\nlast_estimate = 0\nfor n, obs in enumerate(y):\n Y = np.roll(Y, 1)\n Y[0] = obs\n equalizer_output[n] = np.dot(Y, equalizer)\n \n decision = np.sign(last_estimate.real) + 1j*np.sign(last_estimate.imag)\n fb_pipe = np.roll(fb_pipe, 1)\n fb_pipe[0] = decision\n estimate[n] = equalizer_output[n] + np.dot(fb_coeffs, fb_pipe)\n last_estimate = estimate[n]\n \n if n > total_delay : \n training_symbol = qpsk[n-total_delay]\n error[n] = training_symbol - estimate[n]\n \n equalizer += 2*u*error[n]*Y.conj()\n fb_coeffs += 2*u*error[n]*fb_pipe.conj()\n \nplt.plot(abs(error));", "_____no_output_____" ], [ "# EVM \nideal = np.sign(y[5:].real) + 1j*np.sign(y[5:].imag)\ny_error = y[5:] - ideal\nevm_observation = 10*np.log10(y_error.var()/ideal.var())\nprint('EVM of observation: {:.2f} dB'.format(evm_observation))\n\n# Check EVM after error has gone down\nideal = np.sign( estimate[600:].real) + 1j*np.sign(estimate[600:].imag)\nestimate_error = estimate[600:] - ideal\nevm_estimate = 10*np.log10(estimate_error.var()/ideal.var())\nprint('EVM of estimate: {:.2f} dB'.format(evm_estimate))", "EVM of observation: -10.78 dB\nEVM of estimate: -59.91 dB\n" ], [ "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\nax1.scatter(y[5:].real, y[5:].imag)\nax1.set_title('Observation y[n]')\nax1.grid()\n\nax2.scatter(estimate[600:].real, estimate[600:].imag)\nax2.set_title('Estimate of x[n]')\nax2.grid();", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import os, glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport nibabel as nb\nimport nibabel.freesurfer.mghformat as mgh\nfrom nibabel.freesurfer.io import read_geometry\nfrom nilearn.plotting.surf_plotting import load_surf_data\nimport myvis\nfrom brainstat.stats.terms import FixedEffect\nfrom brainstat.stats.SLM import SLM\nimport zipfile", "_____no_output_____" ] ], [ [ "### 0. get data demographics with pandas", "_____no_output_____" ] ], [ [ "datadir = '../data'\n\nzipped_data = os.path.join(datadir, 'thickness.zip')\nwith zipfile.ZipFile(zipped_data, 'r') as zip_ref:\n zip_ref.extractall(datadir)\n\ndfname = os.path.join(datadir, 'thickness.csv')\ndf = pd.read_csv(dfname)\n\nidx = np.array(df[\"ID2\"])\nage = np.array(df[\"AGE\"])\niq = np.array(df[\"IQ\"])\n\n", "_____no_output_____" ] ], [ [ "### 1. load thickness data ", "_____no_output_____" ] ], [ [ "j = 0\nfor Sidx in idx:\n \n # load mgh files for left hem \n for file_L in glob.glob('../data/thickness/%s*_lh2fsaverage5_20.mgh' \n % (Sidx)):\n \n S_Lmgh = mgh.load(file_L).get_fdata()\n S_Larr = np.array(S_Lmgh)[:,:,0]\n \n # load mgh files for right hem \n for file_R in glob.glob('../data/thickness/%s*_rh2fsaverage5_20.mgh' \n % (Sidx)):\n \n S_Rmgh = mgh.load(file_R).get_fdata()\n S_Rarr = np.array(S_Rmgh)[:,:,0]\n\n # concatenate left & right thickness for each subject \n Sidx_thickness = np.concatenate((S_Larr, S_Rarr)).T\n\n if j == 0:\n thickness = Sidx_thickness\n else:\n thickness = np.concatenate((thickness, Sidx_thickness), axis=0)\n j += 1 ", "_____no_output_____" ], [ "Mean_thickness = thickness.mean(axis=0)\nprint(\"all thicknes data loaded shape: \", thickness.shape)\nprint(\"Mean thickness shape: \", Mean_thickness.shape)\n", "all thicknes data loaded shape: (259, 20484)\nMean thickness shape: (20484,)\n" ], [ "fig01 = plt.imshow(thickness, extent=[0,20484,0,259], aspect='auto')\n", "_____no_output_____" ] ], [ [ "### 2. plot mean thickness along the cortex", "_____no_output_____" ] ], [ [ "Fs_Mesh_L = read_geometry(os.path.join(datadir, 'fsaverage5/lh.pial'))\nFs_Mesh_R = read_geometry(os.path.join(datadir, 'fsaverage5/rh.pial'))\n\nFs_Bg_Map_L = load_surf_data(os.path.join(datadir, 'fsaverage5/lh.sulc'))\nFs_Bg_Map_R = load_surf_data(os.path.join(datadir, 'fsaverage5/rh.sulc'))\n\nMask_Left = nb.freesurfer.io.read_label((os.path.join(\n datadir,'fsaverage5/lh.cortex.label')))\nMask_Right = nb.freesurfer.io.read_label((os.path.join(\n datadir,'fsaverage5/rh.cortex.label')))\n\nsurf_mesh = {}\nsurf_mesh['coords'] = np.concatenate((Fs_Mesh_L[0], Fs_Mesh_R[0]))\nsurf_mesh['tri'] = np.concatenate((Fs_Mesh_L[1], Fs_Mesh_R[1]))\nbg_map = np.concatenate((Fs_Bg_Map_L, Fs_Bg_Map_R))\nmedial_wall = np.concatenate((Mask_Left, 10242 + Mask_Right))\n", "_____no_output_____" ], [ "fig02 = myvis.plot_surfstat(surf_mesh, bg_map, Mean_thickness, \n mask = medial_wall,\n cmap = 'viridis', vmin = 1.5, vmax = 4)", "_____no_output_____" ] ], [ [ "### 3. build the stats model", "_____no_output_____" ] ], [ [ "term_intercept = FixedEffect(1, names=\"intercept\")\nterm_age = FixedEffect(age, \"age\")\nterm_iq = FixedEffect(iq, \"iq\")\nmodel = term_intercept + term_age \n\nslm = SLM(model, -age, surf=surf_mesh)\nslm.fit(thickness)\ntvals = slm.t.flatten()\npvals = slm.fdr()\n\nprint(\"t-values: \", tvals) # These are the t-values of the model.\nprint(\"p-values: \", pvals) # These are the p-values of the model.\n", "t-values: [ 3.70352445 9.00643325 11.51625374 ... 2.42269373 2.80816754\n 3.23542035]\np-values: [1.70069476e-04 8.81888145e-17 5.71164388e-24 ... 9.66039843e-03\n 3.29548858e-03 8.66342267e-04]\n" ], [ "fig03 = myvis.plot_surfstat(surf_mesh, bg_map, tvals,\n mask = medial_wall, cmap = 'gnuplot',\n vmin = tvals.min(), vmax = tvals.max())\n\n", "_____no_output_____" ], [ "fig04 = myvis.plot_surfstat(surf_mesh, bg_map, pvals,\n mask = medial_wall, cmap = 'YlOrRd',\n vmin = 0, vmax = 0.05)\n", "_____no_output_____" ] ] ]

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[ [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "\n#x range: +- 200\n#z range: 500, 2000\n#vx range: +- 10\n#vz range: -30, 10\n\n#m range: 8000, 12000\nimport pandas as pd\nimport numpy as np\nimport scipy as sp\nfrom scipy import integrate as spint\nimport matplotlib.pyplot as plt\n\nfrom math import sin, cos, asin\n\nfrom rocket import Rocket", "_____no_output_____" ], [ "r = Rocket(*[-30., 1000., 10., 20., 10000.])", "_____no_output_____" ], [ "r", "_____no_output_____" ], [ "r.s_0", "_____no_output_____" ], [ "r.propagate(r.s_0, (0,0))", "_____no_output_____" ], [ "\nt = 0.\ns = r.s_0\ndt = 0.1\nhistory = [(t, *s)]\n\nwhile t < r.t_char:\n t = t+dt\n s = r.propagate(s, (0.,0.), dt)\n history.append((t, *s))", "_____no_output_____" ], [ "t", "_____no_output_____" ], [ "df = pd.DataFrame.from_records(history, columns=['t','x','z','vx','vz','m'])", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "plt.plot(df.t, df.z)\nplt.grid()", "_____no_output_____" ], [ "plt.plot(df.t[df.z>0], df.z[df.z>0])", "_____no_output_____" ], [ "plt.plot(df.x[df.z>0], df.z[df.z>0])\nax = plt.gca()\nax.set_aspect(1)\nplt.grid()", "_____no_output_____" ], [ "plt.plot(((df.vx**2+df.vz**2)**0.5)[df.z>0], df.z[df.z>0])\nplt.grid()\nplt.xlabel(\"speed (m/s)\")\nplt.ylabel(\"z (m)\")", "_____no_output_____" ], [ "plt.plot(df.vx[df.z>0], df.z[df.z>0])\nplt.grid()\nplt.xlabel(\"vx (m/s)\")\nplt.ylabel(\"z (m)\")\nplt.show()\n\nplt.plot(df.vz[df.z>0], df.z[df.z>0])\nplt.grid()\nplt.xlabel(\"vz (m/s)\")\nplt.ylabel(\"z (m)\")\nplt.show()", "_____no_output_____" ], [ "#attempt to solve the mass optimal control problem\ndef bvp_dynamics(t, s, p):\n #t (time) #not needed\n #s (state) #(x,z,vx,vz,m,lambdam)\n #p (parameters) #(lx0, lz0, lvx0, lvz0)\n \n #implicitly assumes alpha = 1\n \n \n (x, z, vx, vz, m, lm) = s\n (lx0, lz0, lvx0, lvz0) = p\n \n lv_mag = (lvx0**2+lvz0**2)**0.5\n \n \n #determine u1\n S1 = 1 - (lv_mag*r.c2)/m - lm\n if len(S1) == 1:\n u1 = (lambda s: 1 if s<0 else 0)(S1)\n else:\n u1 = np.array([(lambda s: 1 if s<0 else 0)(s1) for s1 in S1])\n \n #determine u2, ith\n ith1 = -(1/lv_mag)*(lvx0)\n ith2 = -(1/lv_mag)*(lvz0)\n \n #construct dynamics\n ds = [None]*6\n \n ds[0] = vx\n ds[1] = vz\n \n ds[2] = (u1*(ith1/m))*r.c1\n ds[3] = (u1*(ith2/m))*r.c1 - r.g\n \n ds[4] = u1*(-r.c1/r.c2)\n \n \n ds[5] = ((u1/m**2)*r.c1)*((lvx0-lx0*t)*ith1 + (lvz0-lz0*t)*ith2)\n \n return ds\n \ndef bvp_boundary(sa, sb, p):\n \n res = []\n \n #initial conditions:\n res.append(sa[0] - r.x_0 )\n res.append(sa[1] - r.z_0 )\n res.append(sa[2] - r.vx_0)\n res.append(sa[3] - r.vz_0)\n res.append(sa[4] - r.m_0 )\n \n #final conditions\n res.append(sb[0] - 0)\n res.append(sb[1] - 0)\n res.append(sb[2] - 0)\n res.append(sb[3] - 0)\n res.append(sb[5] - 0) #lambda m = 0 in final time\n \n return np.array(res)\n ", "_____no_output_____" ], [ "np.diff(x_g)/(t_guess[1]-t_guess[0])", "_____no_output_____" ], [ "t_guess = np.linspace(0, (2*r.z_0/r.g)/2)\n\nx_g = np.linspace(r.x_0, 0)\nz_g = np.linspace(r.z_0, 0)\nvx_g = np.linspace(r.vx_0, 0)\nvz_g = np.linspace(r.vz_0, 0)\nm_g = np.linspace(r.m_0, 0.7*r.m_0)\nlm_g = np.linspace(10,1)\n\n\n\nstate_guess = np.array([x_g, z_g, vx_g, vz_g, m_g, lm_g])", "_____no_output_____" ], [ "r.vx_0", "_____no_output_____" ], [ "state_guess.shape", "_____no_output_____" ], [ "p_guess = np.array([-10,-10,10,10])", "_____no_output_____" ], [ "bvp_dynamics(0, state_guess, p_guess)", "_____no_output_____" ], [ "bvp_boundary(r.s_0, [0,0,0,0,7500,0], [1,1,1,1])", "_____no_output_____" ], [ "sol = spint.solve_bvp(bvp_dynamics, bvp_boundary, x = t_guess, y = state_guess, p=p_guess, verbose=2)", "_____no_output_____" ], [ "sol", "_____no_output_____" ], [ "t_sol = sol.x", "_____no_output_____" ], [ "(x_sol, z_sol, vx_sol, vz_sol, m_sol, lm_sol) = sol.y", "_____no_output_____" ], [ "plt.plot(t_sol, vz_sol)\nplt.grid()", "_____no_output_____" ], [ "plt.plot(t_guess, m_g)\nplt.grid()", "_____no_output_____" ], [ "vz_sol-vz_g", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "print(\"hello World\")", "hello World\n" ], [ "x=5", "_____no_output_____" ], [ "print(x)", "5\n" ], [ "print(\"shift+enter = run\")", "shift+enter = run\n" ], [ "print(\"ctrl + enter = run the same cell\")", "ctrl + enter = run the same cell\n" ] ], [ [ "# This is a Level 1 Heading\nThis is some paragraph text that describes the code below:", "_____no_output_____" ] ], [ [ "print(\"this is the code the was describes above\")", "this is the code the was describes above\n" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# SNOW partitioning parallel\nThe filter is used to perform SNOW algorithm in parallel and serial mode to save computational time and memory requirement respectively. [SNOW](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.023307) algorithm converts a binary image in to partitioned regions while avoiding oversegmentation. SNOW_partitioning_parallel speeds up this process by decomposing the domain into several subdomains and either process them in different cores in parallel to save time or one by one in single core to save memory requirements. ", "_____no_output_____" ], [ "#### Import Modules", "_____no_output_____" ] ], [ [ "import numpy as np\nimport porespy as ps\nfrom porespy.tools import randomize_colors\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\ngs = gridspec.GridSpec(2, 4)\ngs.update(wspace=0.5)\nnp.random.seed(10)\nps.visualization.set_mpl_style()", "_____no_output_____" ] ], [ [ "#### Create a random image of overlapping spheres", "_____no_output_____" ] ], [ [ "im = ps.generators.overlapping_spheres([1000, 1000], r=10, porosity=0.5)\nfig, ax = plt.subplots()\nax.imshow(im, origin='lower');", "_____no_output_____" ] ], [ [ "#### Apply SNOW_partitioning_parallel on the binary image ", "_____no_output_____" ] ], [ [ "snow_out = ps.filters.snow_partitioning_parallel(\n im=im, divs=2, r_max=5, sigma=0.4)", "_____no_output_____" ] ], [ [ "#### Plot output results ", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(1, 3, figsize=[9, 3])\nax[0].imshow(snow_out.im);\nax[1].imshow(snow_out.dt);\nax[2].imshow(randomize_colors(snow_out.regions));\nax[0].set_title('Binary Image');\nax[1].set_title('Euclidean Distance Transform')\nax[2].set_title('Segmented Image');", "_____no_output_____" ], [ "print(f\"Number of regions: {snow_out.regions.max()}\")", "Number of regions: 1006\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Blog 4: Spectral Clustering\n\nIn this blog post, we'll explore several algorithms to cluster data points. \n\nSome notation:\n- Boldface capital letters like $\\mathbf{A}$ are matrices.\n- Boldface lowercase letters like $\\mathbf{v}$ are vectors.\n\n\n## The clustering problem\nWe're aiming to solve the problem of assigning labels to observations based on the distribution of their features. In this exercise, the features are represented as the matrix $\\mathbf{X}$, with each row representing a data point and each data point being in $\\mathbb{R}^2$. We are also dealing with the simple case of 2 clusters for now. What we do generalize is the shape of the distribution in $\\mathbb{R}^2$. But first, let's look at a simple 2-cluster distribution. ", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom sklearn import datasets\nfrom matplotlib import pyplot as plt", "_____no_output_____" ], [ "n = 200\nnp.random.seed(1111)\nX, y = datasets.make_blobs(n_samples=n, shuffle=True, random_state=None, centers = 2, cluster_std = 2.0)\nplt.scatter(X[:,0], X[:,1])", "_____no_output_____" ] ], [ [ "For this simple distribution, we can use k-means clustering. Intuitively, this minimizes the distances between each cluster's members and its \"center of gravity\", and works well for roughly circular blobs. ", "_____no_output_____" ] ], [ [ "from sklearn.cluster import KMeans\nkm = KMeans(n_clusters = 2)\nkm.fit(X)\n\nplt.scatter(X[:,0], X[:,1], c = km.predict(X))", "_____no_output_____" ] ], [ [ "### Generalizing the distribution\nNow let's look at this distribution of data points. ", "_____no_output_____" ] ], [ [ "np.random.seed(1234)\nn = 200\nX, y = datasets.make_moons(n_samples=n, shuffle=True, noise=0.05, random_state=None)\nplt.scatter(X[:,0], X[:,1])", "_____no_output_____" ] ], [ [ "The two clusters are still obvious by sight, but k-means clustering fails. ", "_____no_output_____" ] ], [ [ "km = KMeans(n_clusters = 2)\nkm.fit(X)\nplt.scatter(X[:,0], X[:,1], c = km.predict(X))", "_____no_output_____" ] ], [ [ "## Constructing a similarity matrix A\n\nAn important ingredient in all of the clustering algorithms in this post is the similarity matrix *similarity matrix* $\\mathbf{A}$. A is a symmetric square matrix with n rows and columns. `A[i,j]` should be equal to `1` if and only if `X[i]` is close to `X[j]`. To quantify closeness, we will have a threshold distance `epsilon`, set to 0.4 for now. The diagonal entries `A[i,i]` should all be equal to zero.", "_____no_output_____" ] ], [ [ "from sklearn.metrics import pairwise_distances", "_____no_output_____" ], [ "epsilon = 0.4\n\ndist = pairwise_distances(X)\nA = np.array(dist < epsilon).astype('int')\nnp.fill_diagonal(A,0)", "_____no_output_____" ], [ "A", "_____no_output_____" ] ], [ [ "## Norm cut objective function\n\nNow that we have encoded the pairwise distances of the data points in $\\mathbf{A}$, we can define the clustering problem as a minimization problem. Intuitively, the parameter space of this minimization problem should be the possible cluster assignments (a vector of n 0s and 1s), and the objective function should decrease when our assignment yields more proximity within each cluster, and increase when our assignment yields more proximity between different clusters. \n\nWe thus define the *binary norm cut objective* of the similarity matrix $\\mathbf{A}$ :\n\n$$N_{\\mathbf{A}}(C_0, C_1)\\equiv \\mathbf{cut}(C_0, C_1)\\left(\\frac{1}{\\mathbf{vol}(C_0)} + \\frac{1}{\\mathbf{vol}(C_1)}\\right)\\;.$$\n\nIn this expression, \n- $C_0$ and $C_1$ are the clusters assigned.\n- $\\mathbf{cut}(C_0, C_1) \\equiv \\sum_{i \\in C_0, j \\in C_1} a_{ij}$ (the \"*cut*\") is the number of ones in $\\mathbf{A}$ connecting $C_0$ to cluster $C_1$.\n- $\\mathbf{vol}(C_0) \\equiv \\sum_{i \\in C_0}d_i$, where $d_i = \\sum_{j = 1}^n a_{ij}$ is the *degree* of row $i$. This volume term measures the size and connectedness within the closter $C_0$.\n\n#### The cut term\nThe function `cut(A,y)` below computes the cut term. We first construct a `diff` matrix ($n$ by $n$), whose $i,j$-th term is an indicator for points i and j being in different clusters under classification `y`. We then elementwise-multiply it with the similarity matrix.", "_____no_output_____" ] ], [ [ "def cut(A,y):\n # diff[i,j] is 1 iff y[i] is in a different group than y[j]\n diff = np.array([y != i for i in y], dtype='int')\n # return sum of entries in A where diff is 1, divide by 2 due to double counting\n return np.sum(np.multiply(diff,A))/2\n", "_____no_output_____" ] ], [ [ "We first test it with the true labels, `y`.", "_____no_output_____" ] ], [ [ "cut(A,y)", "_____no_output_____" ] ], [ [ "...and then with randomly generated labels", "_____no_output_____" ] ], [ [ "randn = np.random.randint(0,2,n)\ncut(A,randn)", "_____no_output_____" ] ], [ [ "As expected, the true labels yield a lower cut term than the fake labels. ", "_____no_output_____" ], [ "#### The Volume Term \n\nNow we compute the *volume* of each cluster. The volume of the cluster is just the sum of degrees of the cluster's elements. Remember we want to minimize $\\frac{1}{\\mathbf{vol}(C_0)}$ so we want to maximize the volume. ", "_____no_output_____" ] ], [ [ "def vols(A,y):\n # sum the rows of A where the row belongs to the cluster\n v0 = np.sum(A[y==0,])\n v1 = np.sum(A[y==1,])\n return v0, v1", "_____no_output_____" ] ], [ [ "Now we have all the ingredients for the norm cut objective.", "_____no_output_____" ] ], [ [ "def normcut(A,y):\n v0, v1 = vols(A,y)\n return cut(A,y) * (1/v0 + 1/v1)", "_____no_output_____" ], [ "print(vols(A,y))\nprint(normcut(A,y))", "(2299, 2217)\n0.011518412331615225\n" ] ], [ [ "Again, the true labels `y` yield a much lower minimizing value than the random labels.", "_____no_output_____" ] ], [ [ "normcut(A,randn)", "_____no_output_____" ] ], [ [ "## Part C\n\nUnfortunately, with what we have, the parameter space (the possible set of labels) is too large for a computationally efficient algorithm. This is why we need some linear algebra magic to give us another formula for the norm cut objective:\n\n$$\\mathbf{N}_{\\mathbf{A}}(C_0, C_1) = \\frac{\\mathbf{z}^T (\\mathbf{D} - \\mathbf{A})\\mathbf{z}}{\\mathbf{z}^T\\mathbf{D}\\mathbf{z}}\\;,$$\n\nwhere\n- $\\mathbf{D}$ is the diagonal matrix with nonzero entries $d_{ii} = d_i$, and where $d_i = \\sum_{j = 1}^n a_i$ is the degree.\n- and $\\mathbf{z}$ is a vector such that\n$$\nz_i = \n\\begin{cases}\n \\frac{1}{\\mathbf{vol}(C_0)} &\\quad \\text{if } y_i = 0 \\\\ \n -\\frac{1}{\\mathbf{vol}(C_1)} &\\quad \\text{if } y_i = 1 \\\\ \n\\end{cases}\n$$\n\n\nSince the volume term is just a function of `A` and `y`, $\\mathbf{z}$ encodes all the information in `A` and `y` through the volume term and the sign. We define the function `transform(A,y)` to compute the appropriate $\\mathbf{z}$ vector. \n", "_____no_output_____" ] ], [ [ "def transform(A,y):\n # compute volumes\n v0, v1 = vols(A,y)\n # initialize z to be array of same shape as y, then fill depending on y\n z = np.where(y==0, 1/v0, -1/v1)\n return z", "_____no_output_____" ], [ "z = transform(A,y)\n# degree matrix: row sums placed on diagonal\n# the \"at\" sign is the matrix product\nD = np.diag([email protected](n)) \nnormcut_formula = (z@(D-A)@z)/(z@D@z)\nnormcut_formula", "_____no_output_____" ] ], [ [ "We check that the value of the norm cut function is numerically close by either method.", "_____no_output_____" ] ], [ [ "np.isclose(normcut(A,y), normcut_formula)", "_____no_output_____" ] ], [ [ "We can also check the identity $\\mathbf{z}^T\\mathbf{D}\\mathbb{1} = 0$, where $\\mathbb{1}$ is the vector of `n` ones. This identity effectively says that $\\mathbf{z}$ should contain roughly as many positive as negative entries, i.e. as many labels in each cluster.\n", "_____no_output_____" ] ], [ [ "D = np.diag([email protected](n))\nnp.isclose((z@[email protected](n)),0)", "_____no_output_____" ] ], [ [ "We denote the objective function\n\n$$ R_\\mathbf{A}(\\mathbf{z})\\equiv \\frac{\\mathbf{z}^T (\\mathbf{D} - \\mathbf{A})\\mathbf{z}}{\\mathbf{z}^T\\mathbf{D}\\mathbf{z}} $$\n\nWe can minimize this function subject to the condition $\\mathbf{z}^T\\mathbf{D}\\mathbb{1} = 0$, which says that the clusters ar equally sized. We can guarantee the condition holds if, instead of minimizing over $\\mathbf{z}$, we minimize the orthogonal complement of $\\mathbf{z}$ relative to $\\mathbf{D}\\mathbb{1}$. The `orth_obj` function computes this. Then we use the `minimize` function from `scipy.optimize` to minimize the function `orth_obj` with respect to $\\mathbf{z}$. ", "_____no_output_____" ] ], [ [ "def orth(u, v):\n return (u @ v) / (v @ v) * v\n\ne = np.ones(n) \n\nd = D @ e\n\ndef orth_obj(z):\n z_o = z - orth(z, d)\n return (z_o @ (D - A) @ z_o)/(z_o @ D @ z_o)", "_____no_output_____" ], [ "from scipy.optimize import minimize", "_____no_output_____" ], [ "z_min = minimize(fun=orth_obj, x0=np.ones(n)).x", "_____no_output_____" ] ], [ [ "By construction, the sign of `z_min[i]` corresponds to the cluster label of data point `i`. We plot the points below, coloring it by the sign of `z_min`.", "_____no_output_____" ] ], [ [ "plt.scatter(X[:,0], X[:,1], c = (z_min >= 0))", "_____no_output_____" ] ], [ [ "## Part F\n\nExplicitly minimizing the orthogonal objective is extremely slow, but thankfully find a solution using eigenvalues and eigenvectors.\n\nThe Rayleigh-Ritz Theorem implies that the minimizing $\\mathbf{z}$ is a solution to the eigenvalue problem \n\n$$ \\mathbf{D}^{-1}(\\mathbf{D} - \\mathbf{A}) \\mathbf{z} = \\lambda \\mathbf{z}\\;, \\quad \\mathbf{z}^T\\mathbb{1} = 0\\;.$$\n\nSince $\\mathbb{1}$ is the eigenvector with smallest eigenvalue, the vector $\\mathbf{z}$ that we want must be the eigenvector with the second-smallest eigenvalue. \n\nWe construct the *Laplacian* matrix of $\\mathbf{A}$, $\\mathbf{L} = \\mathbf{D}^{-1}(\\mathbf{D} - \\mathbf{A})$, and find the eigenvector corresponding to its second-smallest eigenvalue, `z_eig`. ", "_____no_output_____" ] ], [ [ "L = np.linalg.inv(D)@(D-A)", "_____no_output_____" ], [ "Lam, U = np.linalg.eig(L)\nz_eig = U[:,1]", "_____no_output_____" ] ], [ [ "Now we color the point according to the sign of `z_eig`. Looks pretty good. ", "_____no_output_____" ] ], [ [ "plt.scatter(X[:,0], X[:,1], c = z_eig<0)", "_____no_output_____" ] ], [ [ "Finally, we can define `spectral_clustering(X, epsilon)` which takes in the input data `X` and the distance threshold `epsilon`, performs spectral clustering, and returns an array of labels indicating whether data point `i` is in group `0` or group `1`. \n", "_____no_output_____" ] ], [ [ "def spectral_clustering(X, epsilon):\n '''\n Given input X (n by 2 array) and distance threshold epsilon, \n performs spectral clustering, and\n returns n by 1 labels of cluster classification. \n '''\n A = np.array(pairwise_distances(X) < epsilon).astype('int')\n np.fill_diagonal(A,0)\n D = np.diag([email protected](X.shape[0]))\n L = np.linalg.inv(D)@(D-A)\n Lam, U = np.linalg.eig(L)\n z_eig = U[:,1]\n labels = np.array(z_eig<0, dtype='int')\n return labels", "_____no_output_____" ], [ "spectral_clustering(X,0.4)", "_____no_output_____" ] ], [ [ "Now we can run som experiments, making the problem harder by increasing the noise parameter, and increase the computation by increasing `n`.", "_____no_output_____" ] ], [ [ "np.random.seed(123)\nfig, axs = plt.subplots(3, figsize = (8,20))\nnoises = [0.05, 0.1, 0.2]\nfor i in range(3):\n X, y = datasets.make_moons(n_samples=1000, shuffle=True, noise=noises[i], random_state=None)\n axs[i].scatter(X[:,0], X[:,1], c = spectral_clustering(X,0.4))\n axs[i].set_title(label = \"noise = \" + str(noises[i]))\n", "_____no_output_____" ] ], [ [ "How does it perform on a different distribution?", "_____no_output_____" ] ], [ [ "n = 1000\nX, y = datasets.make_circles(n_samples=n, shuffle=True, noise=0.05, random_state=None, factor = 0.4)\nplt.scatter(X[:,0], X[:,1])", "_____no_output_____" ], [ "# k-means fails\nkm = KMeans(n_clusters = 2)\nkm.fit(X)\nplt.scatter(X[:,0], X[:,1], c = km.predict(X))", "_____no_output_____" ] ], [ [ "By adjusting the distance parameter `epsilon`, we can find a way to cluster the two circular blobs. We do run into singularity issues for some values of `epsilon`, but otherwise the results are plotted below. ", "_____no_output_____" ] ], [ [ "fig, axs = plt.subplots(11, figsize=(8,50))\nfor i in range(3,11):\n epsilon = i/10\n try:\n axs[i].scatter(X[:,0], X[:,1], c = spectral_clustering(X,epsilon))\n axs[i].set_title(label = \"epsilon = \" + str(epsilon))\n except:\n print(\"Error when epsilon = \", epsilon)", "_____no_output_____" ] ], [ [ "`epsilon` = 0.4 and 0.5 both work for this particular dataset.", "_____no_output_____" ] ] ]

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[ [ [ "# GIT\n\nDocumented shell script which makes explicit all the hooks I desire before doing a commit.", "_____no_output_____" ], [ "# HOOK: Documentation\n\nCrucial for reproducibility and therefore our first hook.", "_____no_output_____" ], [ "# HOOK: Tests\n\nCI happens on github. I do not like to spend time on writing tests (nobody does), but I do most of my dev-work in notebooks anyway. I preserve those notebooks, which at the end of a development session are supposed to work, as tests. In this way the work done lives on as a test of the future codebase. \n\nWhen a notebook becomes obsolete, I am forced to delete or adapt, which I think is a good thing.", "_____no_output_____" ], [ "# Actual Git actions", "_____no_output_____" ] ] ]

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[ [ [ "<div id=\"container_build\">\n<h2>3. Build and push the container</h2>\n</div>\n (. . .)", "_____no_output_____" ], [ "[Go to ECR in the AWS console](https://console.aws.amazon.com/ecr/home?region=us-east-1) and check if our new repository called was created and the image was pushed to it.\n\n", "_____no_output_____" ], [ "----\n**TODO Add local test**\n\n```\nSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1-5onks\",\"framework_module\":\"custom_lightgbm_framework.training:main\",\"hosts\":[\"algo-1-5onks\"],\"hyperparameters\":{},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"ContentType\":\"text/csv\",\"TrainingInputMode\":\"File\"},\"validation\":{\"ContentType\":\"text/csv\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"sagemaker-custom-2020-08-02-03-57-03-742\",\"log_level\":20,\"master_hostname\":\"algo-1-5onks\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-725879053979/sagemaker-custom/code/sourcedir.tar.gz\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1-5onks\",\"hosts\":[\"algo-1-5onks\"]},\"user_entry_point\":\"train.py\"}\n```\n\nLook:\n\nhttps://github.com/aws/sagemaker-training-toolkit/blob/74722fab9c9a9138b350df2cf54a204e2ad790c4/src/sagemaker_training/environment.py#L311\n\n```\n!sudo rm -rf train_tests && mkdir -p train_tests\nwith open(\"train_tests/vars.env\", \"w\") as f:\n f.write(\"AWS_ACCOUNT_ID=%s\\n\" % account_id)\n f.write(\"IMAGE_TAG=%s\\n\" % image_tag)\n f.write(\"AWS_DEFAULT_REGION=%s\\n\" % region)\n \n (...)\n \n f.close()\n\n!cat tests/vars.env\n\n```\n\nPass env vars to docker:\n\nhttps://docs.docker.com/engine/reference/commandline/run/#set-environment-variables--e---env---env-file\n\n!docker run --env-file vars.env \\<IMG> train\n\n!docker run --env-file vars.env sagemaker-training-containers/framework-container:latest train ", "_____no_output_____" ], [ "<div id=\"testing\">\n<h2>4. Training with Amazon SageMaker</h2>\n</div>\n\n(. . .)", "_____no_output_____" ], [ "**TODO trigger Docker image build in CodePipeline**\n\nUse CodeBuild local", "_____no_output_____" ] ] ]

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[ [ [ "import pandas as pd\nimport data_util\nfrom tqdm.notebook import tqdm\n#from tqdm import tqdm_notebook as tqdm\nfrom data_generator import DataGenerator\nfrom state_util import StateUtil\nfrom tec_an import TecAn\nimport numpy as np\nfrom data_util import *\nfrom sklearn_model_hyper import *\n\n\nimport numpy as np\nimport math\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.stats as scs\nimport scikitplot as skplt\n\nfrom tensorflow.keras.layers import InputLayer, BatchNormalization, GlobalMaxPool1D, Bidirectional, Dense, Flatten, Conv2D, LeakyReLU, Dropout, LSTM, GRU, Input\nfrom tensorflow.keras import Model, Sequential\nfrom tensorflow.keras import datasets, layers, models\nfrom tensorflow.keras import regularizers\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom imblearn.over_sampling import RandomOverSampler\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport tensorflow.keras as keras\nimport random\nfrom catboost import CatBoost\nfrom sklearn.ensemble import RandomForestClassifier\n\n\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import accuracy_score, f1_score, precision_score\n", "_____no_output_____" ], [ "import pandas as pd\nimport data_util\nfrom tqdm import tqdm_notebook as tqdm\nfrom data_generator import DataGenerator\nfrom state_util import StateUtil\nfrom tec_an import TecAn\nimport numpy as np\n\n", "_____no_output_____" ], [ "path = \"./data/\"\ntrainX_raw, trainY_raw = load_data(\"simple_full_\", \"train\", path)\nvalX_raw, valY_raw = load_data(\"backtest\", \"train\", path)\n\ntrainX_balanced, trainY_balanced = get_balanced_set(trainX_raw, trainY_raw)\n\nX_train, Y_train = trainX_balanced, trainY_balanced\n\nvalX, valY = valX_raw, valY_raw\n\nfeatures = trainX_raw.shape[-1]\n\nprint(\"{}\".format(trainX_raw.shape))\n", "(4586, 16)\n" ], [ "%%time\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nparams = {\n 'random_state' : [42],\n 'n_estimators': [10, 30, 500, 1000, 2500],\n #'max_features': [1,4,5,6,7,8, 10, 30, 500],\n 'max_depth' : [4,5,6,7,8, 10, 50, 100],\n 'criterion' :['gini', 'entropy']\n}\n\n\nclfR = RandomForestClassifier()\n\nclf_grid = gridSearch(X_train, Y_train, clfR, params, make_scorer(accuracy_score))\n\n#5501154734411087", "{'criterion': 'entropy', 'max_depth': 100, 'n_estimators': 30, 'random_state': 42}\n0.5782909930715935\nCPU times: user 58min 15s, sys: 20.6 s, total: 58min 35s\nWall time: 58min 38s\n" ], [ "print(clf_grid.best_params_)", "{'criterion': 'entropy', 'max_depth': 100, 'n_estimators': 30, 'random_state': 42}\n" ], [ "\nmodel = make_pipeline(StandardScaler(), RandomForestClassifier(**clf_grid.best_params_))\n\nmodel.fit(X_train, Y_train)\n\neval_data(model, X_train, Y_train)", " precision recall f1-score support\n\n class 0 1.00 1.00 1.00 2165\n class 1 1.00 1.00 1.00 2165\n\n accuracy 1.00 4330\n macro avg 1.00 1.00 1.00 4330\nweighted avg 1.00 1.00 1.00 4330\n\n" ], [ "from sklearn.pipeline import Pipeline\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.svm import LinearSVC\n\n#model = clf_grid.best_estimator_\n\nclf = Pipeline([\n ('feature_selection', SelectFromModel(\n LinearSVC()\n )\n ),\n ('classification', RandomForestClassifier())\n])\n\nclf.fit(X_train, Y_train)\n\neval_data(clf, X_train, Y_train)", " precision recall f1-score support\n\n class 0 1.00 1.00 1.00 2165\n class 1 1.00 1.00 1.00 2165\n\n accuracy 1.00 4330\n macro avg 1.00 1.00 1.00 4330\nweighted avg 1.00 1.00 1.00 4330\n\n" ], [ "#val_x_norm = normalizer(valX).numpy()\n\neval_data(model, valX, valY)\n\n#0.4957874270900843", " precision recall f1-score support\n\n class 0 0.50 0.62 0.55 924\n class 1 0.48 0.36 0.41 901\n\n accuracy 0.49 1825\n macro avg 0.49 0.49 0.48 1825\nweighted avg 0.49 0.49 0.48 1825\n\n" ], [ "from joblib import dump, load\ndump(model, 'model/RandomForestClassifier_accuracy_score') ", "_____no_output_____" ], [ "valX_raw, valY_raw = load_data(\"backtest\", \"train\", path)\nvalX_raw, valY_raw = load_data(\"\", \"val\", path)\n\nprint(\"{}\".format(valX_raw.shape))\n\neval_data(model, valX_raw, valY_raw)\n", "(459, 16)\n precision recall f1-score support\n\n class 0 0.50 0.58 0.54 219\n class 1 0.55 0.47 0.50 240\n\n accuracy 0.52 459\n macro avg 0.52 0.52 0.52 459\nweighted avg 0.52 0.52 0.52 459\n\n" ], [ "import time\nimport numpy as np\n\nforest = clf_grid.best_estimator_\n\nstart_time = time.time()\nimportances = forest.feature_importances_\nstd = np.std([\n tree.feature_importances_ for tree in forest.estimators_], axis=0)\nelapsed_time = time.time() - start_time\n\nprint(f\"Elapsed time to compute the importances: \"\n f\"{elapsed_time:.3f} seconds\")", "Elapsed time to compute the importances: 0.008 seconds\n" ], [ "import pandas as pd\n\nfeature_names = ['F{}'.format(i) for i in range(features)]\n\nforest_importances = pd.Series(importances, index=feature_names)\n\nfig, ax = plt.subplots()\nforest_importances.plot.bar(yerr=std, ax=ax)\nax.set_title(\"Feature importances using MDI\")\nax.set_ylabel(\"Mean decrease in impurity\")\nfig.tight_layout()", "_____no_output_____" ] ] ]

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[ [ [ "Topic: Challenge Set 1 (MTA Subway Turnstile Data - \n\nSubject: Explore MTA turnstile data\n\nDate: 09/29/2018\n\nName: Brenner Heintz\n", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport random\nimport itertools\nimport calendar\nimport datetime as dt\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\n%matplotlib inline", "_____no_output_____" ], [ "%xmode", "Exception reporting mode: Verbose\n" ], [ "import matplotlib.style as style\nstyle.use('fivethirtyeight')", "_____no_output_____" ], [ "sns.set_context('notebook', font_scale=1.2)", "_____no_output_____" ], [ "%config InlineBackend.figure_format = 'svg'", "_____no_output_____" ] ], [ [ "Downloaded data from: http://web.mta.info/developers/turnstile.html\n\nDownloaded 3 weeks of data:\nSaturday, September 22, 2018\nSaturday, September 15, 2018\nSaturday, September 08, 2018\n\nDocumentation at: http://web.mta.info/developers/resources/nyct/turnstile/ts_Field_Description.txt\n\nMap of the MTA system: http://web.mta.info/maps/submap.html", "_____no_output_____" ], [ "**Challenge 1**\n\nOpen up a new IPython notebook\n\nDownload a few MTA turnstile data files \n\nOpen up a file, use csv reader to read it and ensure there is a column for each feature (C/A, UNIT, SCP, STATION). These are the first four columns. ", "_____no_output_____" ] ], [ [ "df1 = pd.read_csv('http://web.mta.info/developers/data/nyct/turnstile/turnstile_180908.txt')", "_____no_output_____" ], [ "df2 = pd.read_csv('http://web.mta.info/developers/data/nyct/turnstile/turnstile_180915.txt')", "_____no_output_____" ], [ "df3 = pd.read_csv('http://web.mta.info/developers/data/nyct/turnstile/turnstile_180922.txt')", "_____no_output_____" ], [ "frames = [df1, df2, df3]\ndf = pd.concat(frames)", "_____no_output_____" ], [ "df.info(2)", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 592752 entries, 0 to 199101\nData columns (total 11 columns):\nC/A 592752 non-null object\nUNIT 592752 non-null object\nSCP 592752 non-null object\nSTATION 592752 non-null object\nLINENAME 592752 non-null object\nDIVISION 592752 non-null object\nDATE 592752 non-null object\nTIME 592752 non-null object\nDESC 592752 non-null object\nENTRIES 592752 non-null int64\nEXITS 592752 non-null int64\ndtypes: int64(2), object(9)\nmemory usage: 54.3+ MB\n" ], [ "df.head(2)", "_____no_output_____" ] ], [ [ "**Challenge 2**\n\n\"Let's turn this into a time series. Create a new column that specifies the date and time of each entry.\"", "_____no_output_____" ] ], [ [ "df['DATE'] = pd.to_datetime(df['DATE'], format='%m/%d/%Y')\n# df['DATETIME'] = pd.to_datetime(df.DATE + ' ' + df.TIME, format='%m/%d/%Y')", "_____no_output_____" ] ], [ [ "**Challenge 3**\n\nThese counts are for every n hours. (What is n?) We want total daily entries.", "_____no_output_____" ] ], [ [ "df['STATION_KEY'] = df['C/A'] + ' ' + df['UNIT'] + ' ' + df['STATION']", "_____no_output_____" ], [ "df['EXITS'] = df['EXITS ']\ndf.drop('EXITS ', axis=1, inplace=True)", "_____no_output_____" ], [ "# Reset index because index was duplicated on all 3 original dataframes\ndf.reset_index(inplace=True)", "_____no_output_____" ], [ "df['ENTRY_DIFFS'] = df.groupby(['STATION_KEY','SCP'])['ENTRIES'].diff(periods=-1)*-1", "_____no_output_____" ], [ "df['EXIT_DIFFS'] = df.groupby(['STATION_KEY','SCP'])['EXITS'].diff(periods=-1)*-1", "_____no_output_____" ], [ "df['TOTAL'] = df['ENTRY_DIFFS'] + df['EXIT_DIFFS']", "_____no_output_____" ], [ "df = df[(df['ENTRY_DIFFS'] < 2E5) \n & (df['ENTRY_DIFFS'] > 0) \n & (df['EXIT_DIFFS'] < 2E5)\n & (df['EXIT_DIFFS'] > 0)]", "_____no_output_____" ], [ "df.head(1)", "_____no_output_____" ], [ "df.groupby(['STATION_KEY', 'SCP','DATE'])['ENTRY_DIFFS'].sum()", "_____no_output_____" ] ], [ [ "**Challenge 4**\n\nNow plot the daily time series for a turnstile.", "_____no_output_____" ] ], [ [ "x = df.groupby(['STATION_KEY', 'SCP','DATE'])['ENTRY_DIFFS'].sum()", "_____no_output_____" ], [ "x = pd.DataFrame(x)", "_____no_output_____" ], [ "x.reset_index(inplace=True)", "_____no_output_____" ], [ "x.head(2)", "_____no_output_____" ], [ "x_values = x[(x['SCP']=='02-00-00') & (x['STATION_KEY']=='A002 R051 59 ST')]['DATE']", "_____no_output_____" ], [ "y_values = x[(x['SCP']=='02-00-00') & (x['STATION_KEY']=='A002 R051 59 ST')]['ENTRY_DIFFS']", "_____no_output_____" ], [ "y_values = y_values.astype(int)", "_____no_output_____" ], [ "fig, ax = plt.subplots()\n\nfig.set_size_inches(8,4)\n\nfig.autofmt_xdate()\nax.xaxis.set_major_locator(mdates.WeekdayLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%b. %d'))\n\nax.set_xlabel('Date')\nax.set_ylabel('Daily Traffic')\nax.set_title('Daily Turnstile Entries')\n\nplt.tight_layout()\n\nplt.plot(x_values,y_values, linewidth=1.5, color='r')", "_____no_output_____" ] ], [ [ "**Challenge 5**\n\n\nWe want to combine the numbers together -- for each ControlArea/UNIT/STATION combo, for each day, add the counts from each turnstile belonging to that combo.", "_____no_output_____" ] ], [ [ "df.head(3)", "_____no_output_____" ], [ "df['UNIT'].nunique()", "_____no_output_____" ], [ "df.groupby(['C/A', 'UNIT', 'SCP', 'DATE']).sum()", "_____no_output_____" ] ], [ [ "**Challenge 6**\n\nSimilarly, combine everything in each station, and come up with a time series of [(date1, count1),(date2,count2),...] type of time series for each STATION, by adding up all the turnstiles in a station.", "_____no_output_____" ] ], [ [ "station_df = df.groupby(['STATION', 'DATE']).sum()", "_____no_output_____" ], [ "station_df.reset_index(inplace=True)", "_____no_output_____" ] ], [ [ "**Challenge 7**\n\nPlot the time series (either daily or your preferred level of granularity) for a station.", "_____no_output_____" ] ], [ [ "x_values = station_df[station_df['STATION'] == '1 AV']['DATE']", "_____no_output_____" ], [ "y_values = station_df[station_df['STATION'] == '1 AV']['TOTAL']\ny_values = y_values.astype(int)", "_____no_output_____" ], [ "fig, ax = plt.subplots()\n\nfig.set_size_inches(8,4)\n\nfig.autofmt_xdate()\nax.xaxis.set_major_locator(mdates.WeekdayLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%b. %d'))\n\nax.set_xlabel('Date')\nax.set_ylabel('Daily Traffic')\nax.set_title('Daily Turnstile Entries (1st Ave Station)')\n\n# plt.tight_layout()\n\ndates = mdates.date2num(x_values)\nplt.plot_date(dates, y_values, fmt='-', color='purple', linewidth=2);", "_____no_output_____" ] ], [ [ "**Challenge 8**\n\nSelect a station and find the total daily counts for this station. Then plot those daily counts for each week separately.\n\nTo clarify: if I have 10 weeks of data on the 28th st 6 station, I will add 10 lines to the same figure (e.g. running plt.plot(week_count_list) once for each week). Each plot will have 7 points of data.", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots()\n\nfig.set_size_inches(8,4)\n\nfig.autofmt_xdate()\nax.xaxis.set_major_locator(mdates.WeekdayLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%b. %d'))\n\nax.set_xlabel('Date')\nax.set_ylabel('Daily Entries')\nax.set_title('Daily Turnstile Entries (First Ave Station)')\n\nplt.plot(x_values[:8],y_values[:8], linewidth=1.5, color='r')\nplt.plot(x_values[8:16],y_values[8:16], linewidth=1.5, color='g')\nplt.plot(x_values[16:24],y_values[16:24], linewidth=1.5, color='b')\nplt.legend(labels=['Week 1', 'Week 2', 'Week 3'], loc='best')", "_____no_output_____" ] ], [ [ "**Challenge 9**\n\n\nOver multiple weeks, sum total ridership for each station and sort them, so you can find out the stations with the highest traffic during the time you investigate\n\n", "_____no_output_____" ] ], [ [ "total_ridership_counts = df.groupby('STATION').sum()", "_____no_output_____" ], [ "total_ridership_counts.reset_index(inplace=True)", "_____no_output_____" ], [ "total_ridership_counts.head(3)", "_____no_output_____" ] ], [ [ "**Challenge 10**\n\nMake a single list of these total ridership values and plot it with\n\nplt.hist(total_ridership_counts)", "_____no_output_____" ] ], [ [ "y_vals = total_ridership_counts['TOTAL']", "_____no_output_____" ], [ "fig, ax = plt.subplots()\n\nfig.set_size_inches(8,4)\n\nax.set_xlabel('Entries')\nax.set_ylabel('Number of Stations')\nax.set_title('Histogram of Total Entries')\n\nax.set_xlim(0,3000000)\n\nplt.ticklabel_format(style='plain', axis='x')\nplt.hist(y_vals, bins=30);", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Notebook 1: Homology matrix generation from genome sequences\n\nIn this notebook, I will be applying the notebooks accompanying the paper by Norsigian et al., 2020. (doi:10.1038/s41596-019-0254-3.) I will apply this for the P. thermo model we've been working on and the M10EXG strain that we used to validate our model with.\n\nThis is the the first notebook in the tutorial to create homology matrix from genome sequences.There are four major steps in this notebook\n1. Download the genome annotation (GenBank files) from NCBI, and generate fasta files (protein &nucleotide) from them\n2. Perform BLASTp to find homologous proteins in strains of interest\n3. Use best bidirectional hits to create gene presence/absence matrix\n4. Supplementary for best practice: use BLASTn to check if we have missed any unannotated open reading frames and retain these genes in orthology matrix as well as guide future manual curation", "_____no_output_____" ] ], [ [ "#import packages needed\nimport pandas as pd\nfrom glob import glob", "_____no_output_____" ], [ "from Bio import Entrez, SeqIO", "_____no_output_____" ], [ "import sys", "_____no_output_____" ], [ "import cobra", "_____no_output_____" ], [ "import decimal", "_____no_output_____" ] ], [ [ "__NOTE__ to be able to import the Entrez and SeqIO, I need to change the folder name from 'bio' to 'Bio' and then it'll work. \n\nC:\\Users\\vivmol\\AppData\\Local\\Continuum\\anaconda3\\envs\\g-thermo\\Lib\\site-packages\n\nSo be careful whenever i install Biopython again that this needs to be fixed.", "_____no_output_____" ], [ "Here I will be working with strains in the faculative anaerobic clade of the genus. I will also add genomes that are obligate aerobes to see if that could highlight to us what changed between these species that made them become obligate aerobes. ", "_____no_output_____" ] ], [ [ "# Load the information on the five strains we will be working with in this tutorial\nStrainsOfInterest=pd.read_excel('Strain Information.xlsx')\nStrainsOfInterest", "_____no_output_____" ], [ "#The Reference Genome is as Described in the Base Reconstruction; here the reference is \nreferenceStrainID='NCIMB11955'\ntargetStrainIDs=list(StrainsOfInterest['NCBI ID'])", "_____no_output_____" ] ], [ [ "## 1. Download genome annotations (GenBank files) to generate fasta files ", "_____no_output_____" ], [ "### Dowload genomes from NCBI\nDownload the genome annotations (GenBank files) from NCBI for strains of interest. ", "_____no_output_____" ] ], [ [ "# define a function to download the annotated genebank files from NCBI\ndef dl_genome(id, folder='genomes'): # be sure get CORRECT ID\n files=glob('%s/*.gb'%folder)\n out_file = '%s/%s.gb'%(folder, id)\n\n if out_file in files:\n print (out_file, 'already downloaded')\n return\n else:\n print ('downloading %s from NCBI'%id)\n \n from Bio import Entrez\n Entrez.email = \"[email protected]\" #Insert email here for NCBI\n handle = Entrez.efetch(db=\"nucleotide\", id=id, rettype=\"gb\", retmode=\"text\")\n fout = open(out_file,'w')\n fout.write(handle.read())\n fout.close()", "_____no_output_____" ], [ "# execute the above function, and download the GenBank files for 8 P. thermo strains\nfor strain in targetStrainIDs:\n dl_genome(strain, folder='genomes')", "downloading CP016552.1 from NCBI\ndownloading CP020030.1 from NCBI\ndownloading CP012712.1 from NCBI\ndownloading CP002835.1 from NCBI\ndownloading CP001638 from NCBI\ndownloading CP008903.1 from NCBI\n" ], [ "#also download the reference strain info\ndl_genome(referenceStrainID, folder='genomes')", "downloading CP016622.1 from NCBI\n" ] ], [ [ "### Examine the Downloaded Strains", "_____no_output_____" ] ], [ [ "# define a function to gather information of the downloaded strains from the GenBank files\ndef get_strain_info(folder='genomes'):\n files = glob('%s/*.gb'%folder)\n strain_info = []\n \n for file in files:\n handle = open(file)\n record = SeqIO.read(handle, \"genbank\")\n \n for f in record.features:\n if f.type=='source':\n info = {}\n info['file'] = file\n info['id'] = file.split('\\\\')[-1].split('.')[0]\n for q in f.qualifiers.keys():\n info[q] = '|'.join(f.qualifiers[q])\n strain_info.append(info)\n return pd.DataFrame(strain_info)", "_____no_output_____" ], [ "# information on the downloaded strain\nget_strain_info(folder='genomes')", "_____no_output_____" ] ], [ [ "### Generate FASTA files for both Protein and Nucleotide Pipelines\nFrom the GenBank file, we can extract sequence and annoation information to generate fasta files for the protein and nucleotide analyses. The resulting fasta files will then be used in step 2 as input for BLAST ", "_____no_output_____" ] ], [ [ "# define a function to parse the Genbank file to generate fasta files for both protein and nucleotide sequences\ndef parse_genome(id, type='prot', in_folder='genomes', out_folder='prots', overwrite=1):\n\n in_file = '%s/%s.gb'%(in_folder, id)\n out_file='%s/%s.fa'%(out_folder, id)\n files =glob('%s/*.fa'%out_folder)\n \n if out_file in files and overwrite==0:\n print (out_file, 'already parsed')\n return\n else:\n print ('parsing %s'%id)\n \n handle = open(in_file)\n \n fout = open(out_file,'w')\n x = 0\n \n records = SeqIO.parse(handle, \"genbank\")\n for record in records:\n for f in record.features:\n if f.type=='CDS':\n seq=f.extract(record.seq)\n \n if type=='nucl':\n seq=str(seq)\n else:\n seq=str(seq.translate())\n \n if 'locus_tag' in f.qualifiers.keys():\n locus = f.qualifiers['locus_tag'][0]\n elif 'gene' in f.qualifiers.keys():\n locus = f.qualifiers['gene'][0]\n else:\n locus = 'gene_%i'%x\n x+=1\n fout.write('>%s\\n%s\\n'%(locus, seq))\n fout.close()", "_____no_output_____" ], [ "# Generate fasta files for 5 strains of interest\nfor strain in targetStrainIDs:\n parse_genome(strain, type='prot', in_folder='genomes', out_folder='prots')\n parse_genome(strain, type='nucl', in_folder='genomes', out_folder='nucl')\n", "parsing 2501416905\nparsing 2501416905\n" ], [ "#Also generate fasta files for the reference strain\nparse_genome(referenceStrainID, type='nucl', in_folder='genomes', out_folder='nucl')\nparse_genome(referenceStrainID, type='prots', in_folder='genomes', out_folder='prots')", "parsing NCIMB11955\nparsing NCIMB11955\n" ] ], [ [ "## 2. Perform BLAST to find homologous proteins in strains of interest", "_____no_output_____" ], [ "### Make BLAST DB for each of the target strains for both Protein and Nucleotide Pipelines\n\nIn this tutorial, we will run both BLASTp for proteins and BLSATn for nucleotides. BLASTp will be used as the main approach to identify homologous proteins in reference strain and other strains of interest, while BLASTn will be used as a supplementary method to check for any unannotated genes", "_____no_output_____" ] ], [ [ "# Define a function to make blast database for either protein of nucleotide\ndef make_blast_db(id,folder='prots',db_type='prot'):\n import os\n \n out_file ='%s/%s.fa.pin'%(folder, id)\n files =glob('%s/*.fa.pin'%folder)\n \n if out_file in files:\n print (id, 'already has a blast db')\n return\n if db_type=='nucl':\n ext='fna'\n else:\n ext='fa'\n\n cmd_line='makeblastdb -in %s/%s.%s -dbtype %s' %(folder, id, ext, db_type)\n \n print ('making blast db with following command line...')\n print (cmd_line)\n os.system(cmd_line)", "_____no_output_____" ], [ "sys.path.append('..\\\\..\\\\..\\\\..\\\\..\\\\..\\\\Program Files\\\\NCBI\\\\blast-2.10.1+\\\\bin')", "_____no_output_____" ], [ "# make protein sequence databases \n# Because we are performing bi-directional blast, we make databases from both reference strain and strains of interest\nfor strain in targetStrainIDs:\n make_blast_db(strain,folder='prots',db_type='prot')\nmake_blast_db(referenceStrainID,folder='prots',db_type='prot')", "making blast db with following command line...\nmakeblastdb -in prots/2501416905.fa -dbtype prot\nmaking blast db with following command line...\nmakeblastdb -in prots/NCIMB11955.fa -dbtype prot\n" ] ], [ [ "### Define functions to run protein BLAST and get sequence lengths\n- BLASTp will be the main approach used here to identify homologous proteins between strains \n- Aside from sequence similarity, we also want to ensure the coverage of sequence mapping is sufficient. Therefore, we need to identiy the sequence length for each protein and compare it with the alignment length.", "_____no_output_____" ] ], [ [ "# define a function to run BLASTp\ndef run_blastp(seq,db,in_folder='prots', out_folder='bbh', out=None,outfmt=6,evalue=0.001,threads=1):\n import os\n if out==None:\n out='%s/%s_vs_%s.txt'%(out_folder, seq, db)\n print(out)\n \n files =glob('%s/*.txt'%out_folder)\n if out in files:\n print (seq, 'already blasted')\n return\n \n print ('blasting %s vs %s'%(seq, db))\n \n db = '%s/%s.fa'%(in_folder, db)\n seq = '%s/%s.fa'%(in_folder, seq)\n cmd_line='blastp -db %s -query %s -out %s -evalue %s -outfmt %s -num_threads %i' \\\n %(db, seq, out, evalue, outfmt, threads)\n \n print ('running blastp with following command line...')\n print (cmd_line)\n os.system(cmd_line)\n return out", "_____no_output_____" ], [ "# define a function to get sequence length \n\ndef get_gene_lens(query, in_folder='prots'):\n\n file = '%s/%s.fa'%(in_folder, query)\n handle = open(file)\n records = SeqIO.parse(handle, \"fasta\")\n out = []\n \n for record in records:\n out.append({'gene':record.name, 'gene_length':len(record.seq)})\n \n out = pd.DataFrame(out)\n return out", "_____no_output_____" ] ], [ [ "## 3. Use Bi-Directional BLASTp Best Hits to create gene presence/absence matrix", "_____no_output_____" ], [ "### Obtain Bi-Directional BLASTp Best Hits\n\nFrom the above BLASTp results, we can obtain Bi-Directional BLASTp Best Hits to identify homologous proteins. Note beside gene similarity score, the coverage of alignment is also used to filter mapping results. ", "_____no_output_____" ] ], [ [ "# define a function to get Bi-Directional BLASTp Best Hits\ndef get_bbh(query, subject, in_folder='bbh'): \n \n #Utilize the defined protein BLAST function\n run_blastp(query, subject)\n run_blastp(subject, query)\n \n query_lengths = get_gene_lens(query, in_folder='prots')\n subject_lengths = get_gene_lens(subject, in_folder='prots')\n \n #Define the output file of this BLAST\n out_file = '%s/%s_vs_%s_parsed.csv'%(in_folder,query, subject)\n files=glob('%s/*_parsed.csv'%in_folder)\n \n #Combine the results of the protein BLAST into a dataframe\n print ('parsing BBHs for', query, subject)\n cols = ['gene', 'subject', 'PID', 'alnLength', 'mismatchCount', 'gapOpenCount', 'queryStart', 'queryEnd', 'subjectStart', 'subjectEnd', 'eVal', 'bitScore']\n bbh=pd.read_csv('%s/%s_vs_%s.txt'%(in_folder,query, subject), sep='\\t', names=cols)\n bbh = pd.merge(bbh, query_lengths) \n bbh['COV'] = bbh['alnLength']/bbh['gene_length']\n \n bbh2=pd.read_csv('%s/%s_vs_%s.txt'%(in_folder,subject, query), sep='\\t', names=cols)\n bbh2 = pd.merge(bbh2, subject_lengths) \n bbh2['COV'] = bbh2['alnLength']/bbh2['gene_length']\n out = pd.DataFrame()\n \n # Filter the genes based on coverage\n bbh = bbh[bbh.COV>=0.25]\n bbh2 = bbh2[bbh2.COV>=0.25]\n \n #Delineate the best hits from the BLAST\n for g in bbh.gene.unique():\n res = bbh[bbh.gene==g]\n if len(res)==0:\n continue\n best_hit = res.loc[res.PID.idxmax()]\n best_gene = best_hit.subject\n res2 = bbh2[bbh2.gene==best_gene]\n if len(res2)==0:\n continue\n best_hit2 = res2.loc[res2.PID.idxmax()]\n best_gene2 = best_hit2.subject\n if g==best_gene2:\n best_hit['BBH'] = '<=>'\n else:\n best_hit['BBH'] = '->'\n out=pd.concat([out, pd.DataFrame(best_hit).transpose()])\n \n #Save the final file to a designated CSV file \n out.to_csv(out_file)", "_____no_output_____" ], [ "# Execute the BLAST for each target strain against the reference strain, save results to 'bbh' i.e. \"bidirectional best\n# hits\" folder to create\n# homology matrix\n\nfor strain in targetStrainIDs:\n get_bbh(referenceStrainID,strain, in_folder='bbh')", "bbh/NCIMB11955_vs_2501416905.txt\nblasting NCIMB11955 vs 2501416905\nrunning blastp with following command line...\nblastp -db prots/2501416905.fa -query prots/NCIMB11955.fa -out bbh/NCIMB11955_vs_2501416905.txt -evalue 0.001 -outfmt 6 -num_threads 1\nbbh/2501416905_vs_NCIMB11955.txt\nblasting 2501416905 vs NCIMB11955\nrunning blastp with following command line...\nblastp -db prots/NCIMB11955.fa -query prots/2501416905.fa -out bbh/2501416905_vs_NCIMB11955.txt -evalue 0.001 -outfmt 6 -num_threads 1\nparsing BBHs for NCIMB11955 2501416905\n" ] ], [ [ "### Parse the BLAST Results into one Homology Matrix of the Reconstruction Genes\n\nFor the homology matrix, want to find, for each gene in the reference annotation, is there one in the other strains. And then later filter this down to metabolic genes. ", "_____no_output_____" ] ], [ [ "#Load all the BLAST files between the reference strain and target strains\n\nblast_files=glob('%s/*_parsed.csv'%'bbh')\n\nfor blast in blast_files:\n bbh=pd.read_csv(blast)\n print (blast,bbh.shape) ", "bbh\\NCIMB11955_vs_2501416905_parsed.csv (3520, 16)\n" ] ], [ [ "In this section of the notebook, I will deviate from the published tutorial. In the tutorial, they map the orthologous genes onto the curated model of the reference genome. In reality, we are curious as to how homologous the genomes are to one another, and how many metabolic genes the different strains have in common. So we need to compare the orthologues to the reference genome and not the reference model. I'll try to adapt the scripts so that we can get a homology matrix that captures this.", "_____no_output_____" ], [ "Make a single dataframe where one column is all the genes in the reference organism, and the other column is a different strain and contains the PID.\n\nThen you can count for each column, the number of genes which have a PID above 80% (selected threshold) and compare it to the total number of genes.", "_____no_output_____" ] ], [ [ "#import all the csv files\ncompare = pd.read_csv('bbh/NCIMB11955_vs_2501416905_parsed.csv')", "_____no_output_____" ], [ "#filter out all other columns that i won't use later\ncompare = compare[['gene', 'PID']]", "_____no_output_____" ], [ "#list of all ORFs found in the reference genome\nwith open('prots/NCIMB11955.fa') as fasta_file: # Will close handle cleanly\n NCIMB_ids = []\n for seq_record in SeqIO.parse(fasta_file, 'fasta'): # (generator)\n NCIMB_ids.append(seq_record.id)", "_____no_output_____" ], [ "comparison = pd.DataFrame({'gene': NCIMB_ids})", "_____no_output_____" ], [ "comparison = pd.merge(comparison, compare, on='gene',how=\"outer\")", "_____no_output_____" ], [ "strains = ['NCIMB11955', 'M10EXG']", "_____no_output_____" ], [ "comparison.columns = strains", "_____no_output_____" ] ], [ [ "Now that we have a dataframe that compares the PID scores for each gene in the reference genome to the other strains, we can start to 'sum up' what percentage of the genes in the reference have a matched gene in the strain. \n\nFor this, we will set a threshold of 80% sequence identity between genes to be counted as a true homologue. One can decide to change this arbitrarily set value if they like. \n\nThe reference genome has 3708 ORFs annotated to it. ", "_____no_output_____" ] ], [ [ "columns = list(comparison) \n \nfor i in columns: #iterate through the columns\n if i in 'NCIMB11955': # skip reference column\n continue\n else:\n #now go through each row in this column\n common_genes = []\n for index,row in comparison.iterrows():\n value = row[i]\n if value > 90: #selected threshold level\n common_genes.append(1)\n elif value < 90:\n common_genes.append(0)\n homology = sum(common_genes)\n fraction = 100*homology/3708\n print(i,': ', fraction) ", "M10EXG : 88.9967637540453\n" ] ], [ [ "Try the same as above, but use the E-value as the threshold instead.", "_____no_output_____" ] ], [ [ "#import all the csv files\ncompare = pd.read_csv('bbh/NCIMB11955_vs_2501416905_parsed.csv')", "_____no_output_____" ], [ "#filter out all other columns that i won't use later\ncompare_eval = compare[['gene', 'eVal']]", "_____no_output_____" ], [ "#make dataframe for first comparison, and then add on the rest\ncomparison_eval = pd.DataFrame({'gene': NCIMB_ids})", "_____no_output_____" ], [ "comparison_eval = pd.merge(comparison_eval, compare_eval, on='gene',how=\"outer\")", "_____no_output_____" ], [ "strains = ['NCIMB11955', 'M10EXG']", "_____no_output_____" ], [ "comparison_eval.columns = strains", "_____no_output_____" ], [ "columns = list(comparison_eval) \n \nfor i in columns: #iterate through the columns\n if i in 'NCIMB11955': # skip reference column\n continue\n else:\n #now go through each row in this column\n common_genes = []\n for index,row in comparison_eval.iterrows():\n value = row[i]\n if value < 5E-5 : #selected threshold level\n common_genes.append(1)\n elif value >5E-5:\n continue\n homology = sum(common_genes)\n fraction = (homology/3708)*100\n print(i,': ', fraction)\n ", "M10EXG : 94.47141316073355\n" ] ], [ [ "Now that we have done the above, we can start to look at the overlap between the strains, so that we can create a venn diagram of total ORFs that are unique to each strain but also ovelap.\n", "_____no_output_____" ], [ "__Approach__\n- Import the fasta files in the prots folder: this is a list of all the ORFs in each strain. \n\n- make a list, per strain, of all genes for that strain that fit the comparison threshold (for this use Eval < 1E-10 for now) i.e. all the genes that these two strains have in common\n\n- then make an overlap of the all the lists: should give for each strain which are unique and which overlap with one another.\n", "_____no_output_____" ] ], [ [ "#import total strain lists, for each strain\nwith open('prots/2501416905.fa') as fasta_file: # Will close handle cleanly\n M10EXG_ids = []\n for seq_record in SeqIO.parse(fasta_file, 'fasta'): # (generator)\n M10EXG_ids.append(seq_record.id)", "_____no_output_____" ] ], [ [ "Next is to make a dataframe for the comparison of the reference (here NCIMB11955) to the other strain. The list should then contain the gene names of the reference organism, as well as the strain being mapped against. ", "_____no_output_____" ] ], [ [ "compare_eval", "_____no_output_____" ], [ "ol_reference = [] #the overlapping genes, with the reference strain ID\nol_strain =[] #the overlapping genes, with the reference strain ID\nfor index,row in compare.iterrows():\n value = row['eVal']\n if value > 1E-5: #selected threshold level\n continue #we don't want to save these anywhere \n elif value < 1E-5:\n ol_reference.append(row['gene'])\n ol_strain.append(row['subject'])\nNCIMB_M10EXG_OL = pd.DataFrame({'NCIMB11955': ol_reference, 'M10EXG': ol_strain})", "_____no_output_____" ], [ "# so NCIMB_M10EXG_OL is now a dataframe that maps each gene in the NCIMB to a gene in M10EXG", "_____no_output_____" ], [ "len(NCIMB_M10EXG_OL)", "_____no_output_____" ] ], [ [ "In the example above, you then see which 3498 genes of the target strain match the reference strain.\nWe can then see how many genes each strain has by themselves and from that make the venndiagram.", "_____no_output_____" ] ], [ [ "len(NCIMB_ids)", "_____no_output_____" ] ], [ [ "So NCIMB has 259 unique ORFs. ", "_____no_output_____" ], [ "In the above, we had NCIMB as the reference strain. TO find how many true unique genes there are in M10EXG, we would need to repeat the above analysis but now with M10EXG as reference strain. That will be done below.", "_____no_output_____" ] ], [ [ "StrainsOfInterest=pd.read_excel('Strain InformationB.xlsx')\nStrainsOfInterest", "_____no_output_____" ], [ "#switch reference and target here\nreferenceStrainID='2501416905'\ntargetStrainIDs=list(StrainsOfInterest['NCBI ID'])", "_____no_output_____" ], [ "for strain in targetStrainIDs:\n get_bbh(referenceStrainID,strain, in_folder='bbh')", "bbh/2501416905_vs_NCIMB11955.txt\nblasting 2501416905 vs NCIMB11955\nrunning blastp with following command line...\nblastp -db prots/NCIMB11955.fa -query prots/2501416905.fa -out bbh/2501416905_vs_NCIMB11955.txt -evalue 0.001 -outfmt 6 -num_threads 1\nbbh/NCIMB11955_vs_2501416905.txt\nblasting NCIMB11955 vs 2501416905\nrunning blastp with following command line...\nblastp -db prots/2501416905.fa -query prots/NCIMB11955.fa -out bbh/NCIMB11955_vs_2501416905.txt -evalue 0.001 -outfmt 6 -num_threads 1\nparsing BBHs for 2501416905 NCIMB11955\n" ] ], [ [ "Now import this file and make it into a dataframe so we can find the unique genes.", "_____no_output_____" ] ], [ [ "#import all the csv files\ncompare = pd.read_csv('bbh/2501416905_vs_NCIMB11955_parsed.csv')", "_____no_output_____" ], [ "#filter out all other columns that i won't use later\ncompare_eval = compare[['gene', 'eVal', 'subject']]", "_____no_output_____" ], [ "strains = ['M10EXG', 'eVal', 'NCIMB11955']", "_____no_output_____" ], [ "compare_eval.columns = strains", "_____no_output_____" ] ], [ [ "Now that we have done the above, we can slook at the overlap from M10EXg to the NCIMB strain. \n", "_____no_output_____" ], [ "__Approach__\n- make a list, per strain, of all genes for that strain that fit the comparison threshold (for this use Eval < 1E-5 for now) i.e. all the genes that these two strains have in common\n", "_____no_output_____" ] ], [ [ "ol_reference = [] #the overlapping genes, with the reference strain ID\nol_strain =[] #the overlapping genes, with the reference strain ID\nfor index,row in compare_eval.iterrows():\n value = row['eVal']\n if value > 1E-5: #selected threshold level\n continue #we don't want to save these anywhere \n elif value < 1E-5:\n ol_reference.append(row['M10EXG'])\n ol_strain.append(row['NCIMB11955'])\nM10EXG_NCIMB_OL = pd.DataFrame({'M10EXG': ol_reference, 'NCIMB': ol_strain})", "_____no_output_____" ], [ "# so M10EXG_NCIMB_OL is now a dataframe that maps each gene in the M10EXG to a gene in NCIMB11955", "_____no_output_____" ], [ "len(M10EXG_NCIMB_OL)", "_____no_output_____" ] ], [ [ "There are 5 genes less mapped as overlap, but this is within the error range.", "_____no_output_____" ] ], [ [ "len(M10EXG_ids)", "_____no_output_____" ] ], [ [ "So we would have 234 unique genes in M10EXG. ", "_____no_output_____" ], [ "So overall, we have 3498 genes that overlap, 259 unique in NCIMB and 234 unique in M10EXG. This very closely matches the results of the KBASE pipeline we did, which is good.", "_____no_output_____" ], [ "## Filter for metabolic genes\nNow that we know the overlap in the total protein, we want to find out what percentage of the metabolic genes overlap between the strains. So first I will filter all the genes from the target organism into the ones that are metabolic genes. And then apply this list to the list of bi-directional hits, so see how many of the metabolic genes overlap. ", "_____no_output_____" ], [ "I can't get the definition to work, So I will just write a script that will make a dataframe linking each gene to a possible EC code. Then we can filter the bbh-file made previously for these metabolic genes and get the answer that way.", "_____no_output_____" ] ], [ [ "genes = []\nEC = []\nx = 0\nfor seq_record in SeqIO.parse(\"genomes/NCIMB11955.gb\", \"genbank\"):\n for f in seq_record.features:\n if f.type=='CDS':\n if 'locus_tag' in f.qualifiers.keys():\n locus = f.qualifiers['locus_tag'][0]\n elif 'gene' in f.qualifiers.keys():\n locus = f.qualifiers['gene'][0]\n else:\n locus = 'gene_%i'%x\n x+=1\n try:\n synonyms = f.qualifiers['gene_synonym']\n #here it will check that it has one of the gene_synonyms as ec code, i.e. is metabolic\n check = []\n for a in synonyms:\n if a[0].isdigit():\n ec = a\n check.append(1)\n else:\n continue\n if sum(check) > 0:\n \n genes.append(locus)\n EC.append(a)\n except KeyError:\n continue\n", "_____no_output_____" ], [ "NCIMB_met = pd.DataFrame({'Gene': genes, 'EC': EC})", "_____no_output_____" ] ], [ [ "Now I'll do the same for the M10EXG genome/annotation.", "_____no_output_____" ] ], [ [ "genes = []\nEC = []\nx = 0\nfor seq_record in SeqIO.parse(\"genomes/2501416905.gb\", \"genbank\"):\n for f in seq_record.features:\n if f.type=='CDS':\n if 'locus_tag' in f.qualifiers.keys():\n locus = f.qualifiers['locus_tag'][0]\n elif 'gene' in f.qualifiers.keys():\n locus = f.qualifiers['gene'][0]\n else:\n locus = 'gene_%i'%x\n x+=1\n try:\n synonyms = f.qualifiers['gene_synonym']\n #here it will check that it has one of the gene_synonyms as ec code, i.e. is metabolic\n check = []\n for a in synonyms:\n if a[0].isdigit():\n ec = a\n check.append(1)\n else:\n continue\n if sum(check) > 0:\n \n genes.append(locus)\n EC.append(a)\n except KeyError:\n continue\n", "_____no_output_____" ], [ "M10EXG_met = pd.DataFrame({'Gene': genes, 'EC': EC})", "_____no_output_____" ], [ "len(NCIMB_met)", "_____no_output_____" ], [ "len(M10EXG_met)", "_____no_output_____" ] ], [ [ "So, this shows we have 1424 metabolic genes in the reference strain and 1417 in the M10EXG strain. Note, there are ofcourse many hypothetical proteins annotated in the genome. Those are ignored. \nNow, I will have to filter the list of BBH based on just the genes that are metabolic. \n\nApproach:\n- NCIMB_M10EXG_OL has all the CDS that are considered hits here (with the threshold of 1E-5 set). I will iterate through each row, and make a new dataframe which contains the filtered set of genes for comparing.\n\nNOTE: at the end of comparing with NCIMC11955 as reference, I will do the same with the M10EXG as reference, to find which genes are unique there as well.", "_____no_output_____" ] ], [ [ "NCIMB = []\nM10EXG = []\nfor row, index in NCIMB_M10EXG_OL.iterrows():\n ref_gene = index['NCIMB11955']\n try:\n NCIMB_met.loc[NCIMB_met[\"Gene\"] == ref_gene,'EC'].values[0] #if it is a metaoblic gene this will give a hit\n NCIMB.append(index['NCIMB11955'])\n M10EXG.append(index['M10EXG'])\n except IndexError: #if the hit isn't metabolic, we can ignore it\n continue", "_____no_output_____" ], [ "#make data frame\nOL_metabolic = pd.DataFrame({'NCIMB11955':NCIMB, 'M10EXG':M10EXG})", "_____no_output_____" ], [ "len(OL_metabolic)", "_____no_output_____" ] ], [ [ "So we have 1384 metabolic genes that overlap. That means there are 40 genes unique to NCIMB, and for M10EXG we need to check how many are still unmatched.", "_____no_output_____" ], [ "Finally, I will make a list of the unique metabolic genes, that can be supplied in supplementary, and given a short look through if anything unexpected appears.\n\nApproach:\n- NCIMB_met and M10EXG_met has all the metabolic genes with their corresponding EC code. I will filter out the significant hits here for the NCIMB strain first. I need to repeat the above analysis for M10EXG to be able to see how many unique it has properly. (will be done further in this notebook)\n- then try to find some more information about each E.C. code, e.g. from KEGG database I've Used before? I can look into the type of reactions (i.e. category) and/or the extact reaction name.", "_____no_output_____" ] ], [ [ "genes = []\nECs =[]\nfor row, index in NCIMB_met.iterrows():\n gene = index['Gene']\n try:\n OL_metabolic.loc[OL_metabolic[\"NCIMB11955\"] == gene,'M10EXG'].values[0] #If the gene is in the overlap dataframe it will give an output\n continue\n except IndexError: #i.e. if the gene doesn't have an overlap in M10EXG\n genes.append(gene)\n ECs.append(index['EC'])\n \n#make dataframe\nNCIMB_unique = pd.DataFrame({'Unique gene':genes, 'EC':ECs}) ", "_____no_output_____" ], [ "len(NCIMB_unique)", "_____no_output_____" ] ], [ [ "Now we know that for NCIMB, there are 40 unique metabolic genes and 1384 overlapping genes. I will do the same as above but for the M10EXG strain to find how many are unique in that strain when that is used as reference sequence.", "_____no_output_____" ] ], [ [ "M10EXG = []\nNCIMB = []\nfor row, index in M10EXG_NCIMB_OL.iterrows():\n ref_gene = index['M10EXG']\n try:\n M10EXG_met.loc[M10EXG_met[\"Gene\"] == ref_gene,'EC'].values[0] #if it is a metaoblic gene this will give a hit\n M10EXG.append(index['M10EXG'])\n NCIMB.append(index['NCIMB'])\n except IndexError: #if the hit isn't metabolic, we can ignore it\n continue", "_____no_output_____" ], [ "#make data frame\nOL_metabolic_M10EXG = pd.DataFrame({'M10EXG':M10EXG, 'NCIMB11955':NCIMB})", "_____no_output_____" ], [ "len(OL_metabolic_M10EXG)", "_____no_output_____" ] ], [ [ "Again here there is a difference of a few genes, but falls within an error range if you consider the size of the total metabolic gene set. So this is fine. Next, we will make a dataframe of the metabolic genes that are unique to M10EXG.", "_____no_output_____" ] ], [ [ "genes = []\nECs =[]\nfor row, index in M10EXG_met.iterrows():\n gene = index['Gene']\n try:\n OL_metabolic_M10EXG.loc[OL_metabolic_M10EXG[\"M10EXG\"] == gene,'NCIMB11955'].values[0] #If the gene is in the overlap dataframe it will give an output\n continue\n except IndexError: #i.e. if the gene doesn't have an overlap in M10EXG\n genes.append(gene)\n ECs.append(index['EC'])\n \n#make dataframe\nM10EXG_unique = pd.DataFrame({'Unique gene':genes, 'EC':ECs})", "_____no_output_____" ], [ "len(M10EXG_unique)", "_____no_output_____" ] ], [ [ "So we have 29 unique genes in M10EXG, and 40 in our strain. ", "_____no_output_____" ], [ "Now we have two dataframes, one for each strain, with the unique metabolic genes, we can try to find a bit more information about them. I will try to get a name for the reaction, as well as what pathway they are a part of. \n\nFirst I will prepare a dataframe from the KEGG Site that contains the information about which pathway the EC code belongs to.", "_____no_output_____" ] ], [ [ "df = pd.read_csv('http://rest.kegg.jp/link/ec/pathway', header=None, sep = '\\t')", "_____no_output_____" ], [ "df.columns = ['Pathway', 'EC'] #rename the columns", "_____no_output_____" ], [ "#remove all 'path:' and 'rn:'\ndf['Pathway'] = df['Pathway'].str.replace(r'path:ec', '')", "_____no_output_____" ], [ "df['EC'] = df['EC'].str.replace(r'ec:', '')", "_____no_output_____" ], [ "#remove the rows with 'path_map' to prevent duplication\ndf = df[~df['Pathway'].str.contains(\"map\")]", "_____no_output_____" ], [ "#now link the pathway code to the name of the pathway it is involved in ", "_____no_output_____" ], [ "df_groups = pd.read_csv('http://rest.kegg.jp/list/pathway', header=None, sep = '\\t')", "_____no_output_____" ], [ "df_groups.columns = ['Pathway', 'Name']", "_____no_output_____" ], [ "df_groups['Pathway'] = df_groups['Pathway'].str.replace(r'path:map', '')", "_____no_output_____" ], [ "#now filter out the IDs I dont want to include\n#i want to remove all rows below number 153\ndf_groups = df_groups[0:154]", "_____no_output_____" ], [ "#then merge the df and df_groups together\ndf = pd.merge(df_groups,df,on='Pathway',how='left')", "_____no_output_____" ] ], [ [ "Now I can map the EC code from the unique metabolic reaction lists to these pathway classes.", "_____no_output_____" ] ], [ [ "pathway =[]\nfor index, row in NCIMB_unique.iterrows():\n ec = row['EC']\n types =[]\n found = df.loc[df[\"EC\"] == ec]\n for indexa, rowa in found.iterrows() :\n types.append(rowa['Name']) \n pathway.append(types)\nNCIMB_unique['Pathway'] = pathway", "_____no_output_____" ], [ "pathway =[]\nfor index, row in M10EXG_unique.iterrows():\n ec = row['EC']\n types =[]\n found = df.loc[df[\"EC\"] == ec]\n for indexa, rowa in found.iterrows() :\n types.append(rowa['Name']) \n pathway.append(types)\nM10EXG_unique['Pathway'] = pathway", "_____no_output_____" ] ], [ [ "Now I'll also add a column which looks at the KO of the ec codes recognized so that we get a bit more information about which reactions are unique in each strain.\n\nFirst I will prepare a dataframe that contains the EC codes linked to the KO ontology terms for these annotations. That we can use to map the unique reactions to.\nThere will probably still be some that are not mapped, those we will need to check by hand.", "_____no_output_____" ] ], [ [ "df = pd.read_csv('http://rest.kegg.jp/link/ec/ko', header=None, sep = '\\t')", "_____no_output_____" ], [ "df.columns = ['KO', 'EC'] #rename the columns", "_____no_output_____" ], [ "#remove all 'ko:' and 'ec:'\ndf['KO'] = df['KO'].str.replace(r'ko:', '')", "_____no_output_____" ], [ "df['EC'] = df['EC'].str.replace(r'ec:', '')", "_____no_output_____" ], [ "#now import the list of KO terms with more meaningful description\nko = pd.read_csv('http://rest.kegg.jp/list/ko', header=None, sep = '\\t')", "_____no_output_____" ], [ "ko.columns = ['KO', 'Name']", "_____no_output_____" ], [ "ko['KO'] = ko['KO'].str.replace(r'ko:', '')", "_____no_output_____" ], [ "#link the two dataframes together\ndf = pd.merge(df,ko,on='KO',how='left')", "_____no_output_____" ] ], [ [ "Now we can map the EC code to a KO term.", "_____no_output_____" ] ], [ [ "ko_term =[]\nfor index, row in NCIMB_unique.iterrows():\n ec = row['EC']\n types =[]\n found = df.loc[df[\"EC\"] == ec]\n for indexa, rowa in found.iterrows() :\n types.append(rowa['Name']) \n ko_term.append(types)\nNCIMB_unique['KO'] = ko_term", "_____no_output_____" ], [ "ko_term =[]\nfor index, row in M10EXG_unique.iterrows():\n ec = row['EC']\n types =[]\n found = df.loc[df[\"EC\"] == ec]\n for indexa, rowa in found.iterrows() :\n types.append(rowa['Name']) \n ko_term.append(types)\nM10EXG_unique['KO'] = ko_term", "_____no_output_____" ] ], [ [ "Now I will export these two tables and need to some some manual inspection of them.", "_____no_output_____" ] ], [ [ "M10EXG_unique.to_csv('M10EXG_unique.csv')", "_____no_output_____" ], [ "NCIMB_unique.to_csv('NCIMB_unique.csv')", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Introduction\n\nIn this exercise you'll apply more advanced encodings to encode the categorical variables ito improve your classifier model. The encodings you will implement are:\n\n- Count Encoding\n- Target Encoding\n- Leave-one-out Encoding\n- CatBoost Encoding\n- Feature embedding with SVD \n\nYou'll refit the classifier after each encoding to check its performance on hold-out data. First, run the next cell to repeat the work you did in the last exercise.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nfrom sklearn import preprocessing, metrics\nimport lightgbm as lgb\n\n# Set up code checking\n# This can take a few seconds, thanks for your patience\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.feature_engineering.ex2 import *\n\nclicks = pd.read_parquet('../input/feature-engineering-data/baseline_data.pqt')", "_____no_output_____" ] ], [ [ "Here I'll define a couple functions to help test the new encodings.", "_____no_output_____" ] ], [ [ "def get_data_splits(dataframe, valid_fraction=0.1):\n \"\"\" Splits a dataframe into train, validation, and test sets. First, orders by \n the column 'click_time'. Set the size of the validation and test sets with\n the valid_fraction keyword argument.\n \"\"\"\n\n dataframe = dataframe.sort_values('click_time')\n valid_rows = int(len(dataframe) * valid_fraction)\n train = dataframe[:-valid_rows * 2]\n # valid size == test size, last two sections of the data\n valid = dataframe[-valid_rows * 2:-valid_rows]\n test = dataframe[-valid_rows:]\n \n return train, valid, test\n\ndef train_model(train, valid, test=None, feature_cols=None):\n if feature_cols is None:\n feature_cols = train.columns.drop(['click_time', 'attributed_time',\n 'is_attributed'])\n dtrain = lgb.Dataset(train[feature_cols], label=train['is_attributed'])\n dvalid = lgb.Dataset(valid[feature_cols], label=valid['is_attributed'])\n \n param = {'num_leaves': 64, 'objective': 'binary', \n 'metric': 'auc', 'seed': 7}\n num_round = 1000\n print(\"Training model!\")\n bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid], \n early_stopping_rounds=20, verbose_eval=False)\n \n valid_pred = bst.predict(valid[feature_cols])\n valid_score = metrics.roc_auc_score(valid['is_attributed'], valid_pred)\n print(f\"Validation AUC score: {valid_score}\")\n \n if test is not None: \n test_pred = bst.predict(test[feature_cols])\n test_score = metrics.roc_auc_score(test['is_attributed'], test_pred)\n return bst, valid_score, test_score\n else:\n return bst, valid_score", "_____no_output_____" ] ], [ [ "Run this cell to get a baseline score. If your encodings do better than this, you can keep them.", "_____no_output_____" ] ], [ [ "print(\"Baseline model\")\ntrain, valid, test = get_data_splits(clicks)\n_ = train_model(train, valid)", "_____no_output_____" ] ], [ [ "### 1) Categorical encodings and leakage\n\nThese encodings are all based on statistics calculated from the dataset like counts and means. Considering this, what data should you be using to calculate the encodings?\n\nUncomment the following line after you've decided your answer.", "_____no_output_____" ] ], [ [ "q_1.solution()", "_____no_output_____" ] ], [ [ "### 2) Count encodings\n\nHere, encode the categorical features `['ip', 'app', 'device', 'os', 'channel']` using the count of each value in the data set. Using `CountEncoder` from the `category_encoders` library, fit the encoding using the categorical feature columns defined in `cat_features`. Then apply the encodings to the train and validation sets, adding them as new columns with names suffixed `\"_count\"`.", "_____no_output_____" ] ], [ [ "import category_encoders as ce\n\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\ntrain, valid, test = get_data_splits(clicks)\n\n# Create the count encoder\ncount_enc = ____\n\n# Learn encoding from the training set\n____\n\n# Apply encoding to the train and validation sets as new columns\n# Make sure to add `_count` as a suffix to the new columns\ntrain_encoded = ____\nvalid_encoded = ____\n\nq_2.check()", "_____no_output_____" ], [ "# Uncomment if you need some guidance\nq_2.hint()\nq_2.solution()", "_____no_output_____" ], [ "#%%RM_IF(PROD)%%\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\ntrain, valid, test = get_data_splits(clicks)\n\n# Create the count encoder\ncount_enc = ce.CountEncoder(cols=cat_features)\n\n# Learn encoding from the training set\ncount_enc.fit(train[cat_features])\n\n# Apply encoding to the train and validation sets\ntrain_encoded = train.join(count_enc.transform(train[cat_features]).add_suffix('_count'))\nvalid_encoded = valid.join(count_enc.transform(valid[cat_features]).add_suffix('_count'))\n\nq_2.assert_check_passed()", "_____no_output_____" ], [ "# Train the model on the encoded datasets\n# This can take around 30 seconds to complete\n_ = train_model(train_encoded, valid_encoded)", "_____no_output_____" ] ], [ [ "Count encoding improved our model's score!", "_____no_output_____" ], [ "### 3) Why is count encoding effective?\nAt first glance, it could be surprising that Count Encoding helps make accurate models. \nWhy do you think is count encoding is a good idea, or how does it improve the model score?\n\nUncomment the following line after you've decided your answer.", "_____no_output_____" ] ], [ [ "q_3.solution()", "_____no_output_____" ] ], [ [ "### 4) Target encoding\n\nHere you'll try some supervised encodings that use the labels (the targets) to transform categorical features. The first one is target encoding. Create the target encoder from the `category_encoders` library. Then, learn the encodings from the training dataset, apply the encodings to all the datasets and retrain the model.", "_____no_output_____" ] ], [ [ "cat_features = ['ip', 'app', 'device', 'os', 'channel']\ntrain, valid, test = get_data_splits(clicks)\n\n# Create the target encoder. You can find this easily by using tab completion.\n# Start typing ce. the press Tab to bring up a list of classes and functions.\ntarget_enc = ____\n\n# Learn encoding from the training set. Use the 'is_attributed' column as the target.\n____\n\n# Apply encoding to the train and validation sets as new columns\n# Make sure to add `_target` as a suffix to the new columns\ntrain_encoded = ____\nvalid_encoded = ____\n\nq_4.check()", "_____no_output_____" ], [ "# Uncomment these if you need some guidance\n#q_4.hint()\n#q_4.solution()", "_____no_output_____" ], [ "#%%RM_IF(PROD)%%\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\ntarget_enc = ce.TargetEncoder(cols=cat_features)\n\ntrain, valid, test = get_data_splits(clicks)\ntarget_enc.fit(train[cat_features], train['is_attributed'])\n\ntrain_encoded = train.join(target_enc.transform(train[cat_features]).add_suffix('_target'))\nvalid_encoded = valid.join(target_enc.transform(valid[cat_features]).add_suffix('_target'))\n\nq_4.assert_check_passed()", "_____no_output_____" ], [ "_ = train_model(train_encoded, valid_encoded)", "_____no_output_____" ] ], [ [ "### 5) Try removing IP encoding\n\nTry leaving `ip` out of the encoded features and retrain the model with target encoding again. You should find that the score increases and is above the baseline score! Why do you think the score is below baseline when we encode the IP address but above baseline when we don't?\n\nUncomment the following line after you've decided your answer.", "_____no_output_____" ] ], [ [ "# q_5.solution()", "_____no_output_____" ] ], [ [ "### 6) CatBoost Encoding\n\nThe CatBoost encoder is supposed to working well with the LightGBM model. Encode the categorical features with `CatBoostEncoder` and train the model on the encoded data again.", "_____no_output_____" ] ], [ [ "train, valid, test = get_data_splits(clicks)\n\n# Create the CatBoost encoder\ncb_enc = ____\n\n# Learn encoding from the training set\n____\n\n# Apply encoding to the train and validation sets as new columns\n# Make sure to add `_cb` as a suffix to the new columns\ntrain_encoded = ____\nvalid_encoded = ____\nq_6.check()", "_____no_output_____" ], [ "# Uncomment these if you need some guidance\n#q_6.hint()\n#q_6.solution()", "_____no_output_____" ], [ "#%%RM_IF(PROD)%%\ncat_features = ['app', 'device', 'os', 'channel']\ntrain, valid, _ = get_data_splits(clicks)\n\ncb_enc = ce.CatBoostEncoder(cols=cat_features, random_state=7)\n\n# Learn encodings on the train set\ncb_enc.fit(train[cat_features], train['is_attributed'])\n\n# Apply encodings to each set\ntrain_encoded = train.join(cb_enc.transform(train[cat_features]).add_suffix('_cb'))\nvalid_encoded = valid.join(cb_enc.transform(valid[cat_features]).add_suffix('_cb'))\n\nq_6.assert_check_passed()", "_____no_output_____" ], [ "_ = train_model(train, valid)", "_____no_output_____" ] ], [ [ "The CatBoost encodings work the best, so we'll keep those.", "_____no_output_____" ] ], [ [ "encoded = cb_enc.transform(clicks[cat_features])\nfor col in encoded:\n clicks.insert(len(clicks.columns), col + '_cb', encoded[col])", "_____no_output_____" ] ], [ [ "# Keep Going\n\nNow you are ready to **[generating completely new features](#$NEXT_NOTEBOOK_URL$)** from the data itself.", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "##### Copyright 2018 The TensorFlow Authors.", "_____no_output_____" ] ], [ [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.", "_____no_output_____" ] ], [ [ "# Eager execution basics", "_____no_output_____" ], [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n <td>\n <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r1/tutorials/eager/eager_basics.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/eager/eager_basics.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n </td>\n</table>", "_____no_output_____" ], [ "This is an introductory tutorial for using TensorFlow. It will cover:\n\n* Importing required packages\n* Creating and using Tensors\n* Using GPU acceleration\n* Datasets", "_____no_output_____" ], [ "## Import TensorFlow\n\nTo get started, import the `tensorflow` module and enable eager execution.\nEager execution enables a more interactive frontend to TensorFlow, the details of which we will discuss much later.", "_____no_output_____" ] ], [ [ "from __future__ import absolute_import, division, print_function, unicode_literals\n\ntry:\n # %tensorflow_version only exists in Colab.\n %tensorflow_version 2.x\nexcept Exception:\n pass\nimport tensorflow.compat.v1 as tf\n", "_____no_output_____" ] ], [ [ "## Tensors\n\nA Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `Tensor` objects have a data type and a shape. Additionally, Tensors can reside in accelerator (like GPU) memory. TensorFlow offers a rich library of operations ([tf.add](https://www.tensorflow.org/api_docs/python/tf/add), [tf.matmul](https://www.tensorflow.org/api_docs/python/tf/matmul), [tf.linalg.inv](https://www.tensorflow.org/api_docs/python/tf/linalg/inv) etc.) that consume and produce Tensors. These operations automatically convert native Python types. For example:\n", "_____no_output_____" ] ], [ [ "print(tf.add(1, 2))\nprint(tf.add([1, 2], [3, 4]))\nprint(tf.square(5))\nprint(tf.reduce_sum([1, 2, 3]))\nprint(tf.encode_base64(\"hello world\"))\n\n# Operator overloading is also supported\nprint(tf.square(2) + tf.square(3))", "_____no_output_____" ] ], [ [ "Each Tensor has a shape and a datatype", "_____no_output_____" ] ], [ [ "x = tf.matmul([[1]], [[2, 3]])\nprint(x.shape)\nprint(x.dtype)", "_____no_output_____" ] ], [ [ "The most obvious differences between NumPy arrays and TensorFlow Tensors are:\n\n1. Tensors can be backed by accelerator memory (like GPU, TPU).\n2. Tensors are immutable.", "_____no_output_____" ], [ "### NumPy Compatibility\n\nConversion between TensorFlow Tensors and NumPy ndarrays is quite simple as:\n\n* TensorFlow operations automatically convert NumPy ndarrays to Tensors.\n* NumPy operations automatically convert Tensors to NumPy ndarrays.\n\nTensors can be explicitly converted to NumPy ndarrays by invoking the `.numpy()` method on them.\nThese conversions are typically cheap as the array and Tensor share the underlying memory representation if possible. However, sharing the underlying representation isn't always possible since the Tensor may be hosted in GPU memory while NumPy arrays are always backed by host memory, and the conversion will thus involve a copy from GPU to host memory.", "_____no_output_____" ] ], [ [ "import numpy as np\n\nndarray = np.ones([3, 3])\n\nprint(\"TensorFlow operations convert numpy arrays to Tensors automatically\")\ntensor = tf.multiply(ndarray, 42)\nprint(tensor)\n\n\nprint(\"And NumPy operations convert Tensors to numpy arrays automatically\")\nprint(np.add(tensor, 1))\n\nprint(\"The .numpy() method explicitly converts a Tensor to a numpy array\")\nprint(tensor.numpy())", "_____no_output_____" ] ], [ [ "## GPU acceleration\n\nMany TensorFlow operations can be accelerated by using the GPU for computation. Without any annotations, TensorFlow automatically decides whether to use the GPU or CPU for an operation (and copies the tensor between CPU and GPU memory if necessary). Tensors produced by an operation are typically backed by the memory of the device on which the operation executed. For example:", "_____no_output_____" ] ], [ [ "x = tf.random.uniform([3, 3])\n\nprint(\"Is there a GPU available: \"),\nprint(tf.test.is_gpu_available())\n\nprint(\"Is the Tensor on GPU #0: \"),\nprint(x.device.endswith('GPU:0'))", "_____no_output_____" ] ], [ [ "### Device Names\n\nThe `Tensor.device` property provides a fully qualified string name of the device hosting the contents of the tensor. This name encodes many details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of a TensorFlow program. The string ends with `GPU:<N>` if the tensor is placed on the `N`-th GPU on the host.", "_____no_output_____" ], [ "\n\n### Explicit Device Placement\n\nThe term \"placement\" in TensorFlow refers to how individual operations are assigned (placed on) a device for execution. As mentioned above, when there is no explicit guidance provided, TensorFlow automatically decides which device to execute an operation, and copies Tensors to that device if needed. However, TensorFlow operations can be explicitly placed on specific devices using the `tf.device` context manager. For example:", "_____no_output_____" ] ], [ [ "import time\n\ndef time_matmul(x):\n start = time.time()\n for loop in range(10):\n tf.matmul(x, x)\n\n result = time.time()-start\n\n print(\"10 loops: {:0.2f}ms\".format(1000*result))\n\n\n# Force execution on CPU\nprint(\"On CPU:\")\nwith tf.device(\"CPU:0\"):\n x = tf.random_uniform([1000, 1000])\n assert x.device.endswith(\"CPU:0\")\n time_matmul(x)\n\n# Force execution on GPU #0 if available\nif tf.test.is_gpu_available():\n with tf.device(\"GPU:0\"): # Or GPU:1 for the 2nd GPU, GPU:2 for the 3rd etc.\n x = tf.random_uniform([1000, 1000])\n assert x.device.endswith(\"GPU:0\")\n time_matmul(x)", "_____no_output_____" ] ], [ [ "## Datasets\n\nThis section demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/r1/guide/datasets) to build pipelines to feed data to your model. It covers:\n\n* Creating a `Dataset`.\n* Iteration over a `Dataset` with eager execution enabled.\n\nWe recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n\nIf you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\nYou can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\nAs a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/r1/guide/datasets) is not relevant when eager execution is enabled.", "_____no_output_____" ], [ "### Create a source `Dataset`\n\nCreate a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/r1/guide/datasets#reading_input_data) for more information.", "_____no_output_____" ] ], [ [ "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n\n# Create a CSV file\nimport tempfile\n_, filename = tempfile.mkstemp()\n\nwith open(filename, 'w') as f:\n f.write(\"\"\"Line 1\nLine 2\nLine 3\n \"\"\")\n\nds_file = tf.data.TextLineDataset(filename)", "_____no_output_____" ] ], [ [ "### Apply transformations\n\nUse the transformations functions like [`map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), [`batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch), [`shuffle`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) etc. to apply transformations to the records of the dataset. See the [API documentation for `tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) for details.", "_____no_output_____" ] ], [ [ "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n\nds_file = ds_file.batch(2)", "_____no_output_____" ] ], [ [ "### Iterate\n\nWhen eager execution is enabled `Dataset` objects support iteration.\nIf you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls.", "_____no_output_____" ] ], [ [ "print('Elements of ds_tensors:')\nfor x in ds_tensors:\n print(x)\n\nprint('\\nElements in ds_file:')\nfor x in ds_file:\n print(x)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\nimport sys\nimport os\nimport argparse\nimport time\nimport numpy as np\nsys.path.append('../')\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport torch.optim.lr_scheduler as lr_scheduler\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\nimport easydict as edict\n\nfrom lib import models, datasets\nimport math", "_____no_output_____" ], [ "args = edict\nargs.resume = '../checkpoint/pretrain_models/ckpt_instance_cifar10_wrn-28-2_82.12.pth.tar'\nargs.cache = '../checkpoint/train_features_labels_cache/instance_wrn-28-2.pth.tar'\nargs.save_path = '../checkpoint/pseudos/instance_knn_wrn-28-2'\nos.makedirs(args.save_path, exist_ok=True)\n\nargs.num_class = 10\nargs.rng_seed = 0", "_____no_output_____" ], [ "ckpt = torch.load(args.cache)", "_____no_output_____" ], [ "ckpt = torch.load(args.cache)\ntrain_features = ckpt['train_features']\ntrain_labels = ckpt['train_labels']\n\nprint(train_features.dtype, train_labels.dtype)\nprint(train_features.shape, train_labels.shape)", "torch.float32 torch.int64\ntorch.Size([50000, 128]) torch.Size([50000])\n" ] ], [ [ "# use cpu because the following computation need a lot of memory", "_____no_output_____" ] ], [ [ "device = 'cpu'\ntrain_features, train_labels = train_features.to(device), train_labels.to(device)", "_____no_output_____" ], [ "num_train_data = train_labels.shape[0]\nnum_class = torch.max(train_labels) + 1\n\ntorch.manual_seed(args.rng_seed)\ntorch.cuda.manual_seed_all(args.rng_seed)\nperm = torch.randperm(num_train_data).to(device)\nprint(perm)", "tensor([36044, 49165, 37807, ..., 42128, 15898, 31476])\n" ] ], [ [ "# soft label", "_____no_output_____" ] ], [ [ "fig = plt.figure(dpi=200)\nfor num_labeled_data in [50, 100, 250, 500, 1000, 2000, 4000, 8000]:\n index_labeled = []\n index_unlabeled = []\n data_per_class = num_labeled_data // args.num_class\n for c in range(10):\n indexes_c = perm[train_labels[perm] == c]\n index_labeled.append(indexes_c[:data_per_class])\n index_unlabeled.append(indexes_c[data_per_class:])\n index_labeled = torch.cat(index_labeled)\n index_unlabeled = torch.cat(index_unlabeled)\n \n# index_labeled = perm[:num_labeled_data]\n# index_unlabeled = perm[num_labeled_data:]\n\n # calculate similarity matrix\n dist = torch.mm(train_features, train_features[index_labeled].t())\n dist[index_labeled, torch.arange(num_labeled_data)] = 0\n\n K = min(num_labeled_data, 200)\n yd, yi = dist.topk(K, dim=1, largest=True, sorted=True)\n candidates = train_labels.view(1,-1).expand(num_train_data, -1)\n retrieval = torch.gather(candidates, 1, index_labeled[yi])\n\n retrieval_one_hot = torch.zeros(num_train_data * K, num_class).to(device)\n retrieval_one_hot.scatter_(1, retrieval.view(-1, 1), 1)\n\n temperature = 0.1\n\n yd_transform = (yd / temperature).exp_()\n probs = torch.sum(torch.mul(retrieval_one_hot.view(num_train_data, -1 , num_class), yd_transform.view(num_train_data, -1, 1)), 1)\n probs.div_(probs.sum(dim=1, keepdim=True))\n probs_sorted, predictions = probs.sort(1, True)\n correct = predictions.eq(train_labels.data.view(-1,1))\n\n confidence = probs_sorted[:, 0] # - probs_sorted[:, 1]\n \n correct = correct[index_unlabeled, :]\n confidence = confidence[index_unlabeled]\n \n n = confidence.shape[0]\n arange = 1 + np.arange(n)\n idx = confidence.sort(descending=True)[1]\n correct_sorted = correct[idx, 0].numpy()\n accuracies = np.cumsum(correct_sorted) / arange\n xs = arange / n\n\n plt.plot(xs, accuracies, label='num_labeled_data={}'.format(num_labeled_data))\n \n # save pseudo labels\n unlabeled_probs_top1, unlabeled_indexes_top1 = probs_sorted[:, 0][index_unlabeled].sort(0, True)\n pseudo_indexes = index_unlabeled[unlabeled_indexes_top1]\n pseudo_labels = predictions[index_unlabeled, 0][unlabeled_indexes_top1]\n pseudo_probs = probs[index_unlabeled][unlabeled_indexes_top1]\n assert torch.all(pseudo_labels == pseudo_probs.max(1)[1])\n \n save_dict = {\n 'pseudo_indexes': pseudo_indexes,\n 'pseudo_labels': pseudo_labels,\n 'pseudo_probs': pseudo_probs,\n 'labeled_indexes': index_labeled,\n 'unlabeled_indexes': index_unlabeled,\n }\n torch.save(save_dict, os.path.join(args.save_path, '{}.pth.tar'.format(num_labeled_data)))\n \n acc = (pseudo_labels == train_labels[pseudo_indexes]).float().mean().item()\n print('num_labeled={:4}, acc={:2.2f}, AUC={:2.2f}'.format(num_labeled_data, acc*100, accuracies.mean() * 100))\n \nplt.xlabel('ratio of data')\nplt.ylabel('top1 accuracy')\n# plt.xticks(np.arange(0, 1.05, 0.1))\n# plt.yticks(np.arange(0.36, 1.01, 0.05))\nplt.grid()\nlegend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n\nplt.show()", "tensor([24681, 42151, 48978, 41040, 36909, 8628, 24936, 35926, 15934, 8801,\n 36293, 28026, 3814, 34981, 21135, 16904, 20152, 3486, 11894, 29780,\n 23932, 33744, 41766, 42979, 49518, 11341, 6091, 48161, 36335, 29858,\n 36044, 25569, 46340, 8832, 38677, 37807, 4480, 18517, 8409, 15769,\n 49165, 43846, 13449, 34615, 38862, 17849, 49031, 4115, 29909, 34515])\nnum_labeled= 50, acc=38.82, AUC=54.82\ntensor([24681, 42151, 48978, 41040, 36909, 10197, 38770, 40649, 43279, 27934,\n 8628, 24936, 35926, 15934, 8801, 27814, 37390, 12841, 22270, 10107,\n 36293, 28026, 3814, 34981, 21135, 46451, 48404, 8366, 32477, 48870,\n 16904, 20152, 3486, 11894, 29780, 47462, 34297, 1271, 28738, 12147,\n 23932, 33744, 41766, 42979, 49518, 18912, 32018, 17323, 1974, 27259,\n 11341, 6091, 48161, 36335, 29858, 17315, 22879, 43307, 23185, 10318,\n 36044, 25569, 46340, 8832, 38677, 36881, 11751, 9278, 1836, 8557,\n 37807, 4480, 18517, 8409, 15769, 440, 45506, 22942, 37635, 49162,\n 49165, 43846, 13449, 34615, 38862, 11524, 21361, 18397, 10903, 33335,\n 17849, 49031, 4115, 29909, 34515, 1456, 32156, 21390, 25936, 46645])\nnum_labeled= 100, acc=45.43, AUC=62.99\ntensor([24681, 42151, 48978, 41040, 36909, 10197, 38770, 40649, 43279, 27934,\n 48148, 38536, 37693, 33617, 27768, 36763, 31048, 2345, 15097, 49656,\n 20265, 10365, 49091, 36836, 35285, 8628, 24936, 35926, 15934, 8801,\n 27814, 37390, 12841, 22270, 10107, 43669, 36935, 9806, 2389, 17223,\n 40808, 45946, 31854, 49487, 578, 31363, 2318, 2765, 29571, 15248,\n 36293, 28026, 3814, 34981, 21135, 46451, 48404, 8366, 32477, 48870,\n 38750, 45437, 11815, 34305, 22797, 27326, 38971, 30453, 9585, 21701,\n 47668, 41469, 300, 44482, 17954, 16904, 20152, 3486, 11894, 29780,\n 47462, 34297, 1271, 28738, 12147, 40307, 44267, 11855, 22489, 31199,\n 45646, 6936, 35292, 6714, 14575, 47132, 14125, 5086, 9343, 16013,\n 23932, 33744, 41766, 42979, 49518, 18912, 32018, 17323, 1974, 27259,\n 10654, 20054, 15256, 46803, 27812, 39173, 11810, 27935, 6983, 15578,\n 25954, 17146, 38810, 32496, 46321, 11341, 6091, 48161, 36335, 29858,\n 17315, 22879, 43307, 23185, 10318, 16247, 35949, 31833, 26102, 6222,\n 28877, 38784, 29549, 32795, 31568, 20322, 42243, 39846, 34133, 26785,\n 36044, 25569, 46340, 8832, 38677, 36881, 11751, 9278, 1836, 8557,\n 39511, 31396, 35620, 9481, 37289, 24394, 16822, 37564, 47653, 28188,\n 45944, 35444, 20432, 21644, 24946, 37807, 4480, 18517, 8409, 15769,\n 440, 45506, 22942, 37635, 49162, 10081, 37293, 29962, 40230, 26420,\n 42284, 47996, 30720, 3054, 21691, 10422, 43896, 33469, 45230, 22687,\n 49165, 43846, 13449, 34615, 38862, 11524, 21361, 18397, 10903, 33335,\n 17560, 11906, 23965, 8483, 41980, 891, 34910, 26038, 28874, 17625,\n 22948, 8489, 11265, 36893, 6346, 17849, 49031, 4115, 29909, 34515,\n 1456, 32156, 21390, 25936, 46645, 3489, 45235, 3126, 13128, 40168,\n 18175, 40819, 28019, 39620, 29182, 14458, 46401, 16051, 16394, 3737])\nnum_labeled= 250, acc=58.77, AUC=77.08\ntensor([24681, 42151, 48978, 41040, 36909, 10197, 38770, 40649, 43279, 27934,\n 48148, 38536, 37693, 33617, 27768, 36763, 31048, 2345, 15097, 49656,\n 20265, 10365, 49091, 36836, 35285, 42465, 44296, 15003, 8209, 38023,\n 31880, 32206, 29039, 14945, 31888, 29837, 1950, 7251, 16637, 10343,\n 32460, 44820, 3295, 7168, 3092, 46523, 19682, 49635, 12477, 43592,\n 8628, 24936, 35926, 15934, 8801, 27814, 37390, 12841, 22270, 10107,\n 43669, 36935, 9806, 2389, 17223, 40808, 45946, 31854, 49487, 578,\n 31363, 2318, 2765, 29571, 15248, 27576, 7540, 1985, 2762, 48358,\n 19594, 47745, 37081, 47495, 31208, 11301, 49554, 11418, 19382, 47839,\n 32147, 9476, 30495, 40431, 39933, 30439, 35339, 43609, 13277, 30945,\n 36293, 28026, 3814, 34981, 21135, 46451, 48404, 8366, 32477, 48870,\n 38750, 45437, 11815, 34305, 22797, 27326, 38971, 30453, 9585, 21701,\n 47668, 41469, 300, 44482, 17954, 38329, 45218, 39478, 3196, 39983,\n 39687, 41411, 22897, 32181, 37797, 28057, 35824, 34053, 20645, 35161,\n 36011, 21725, 48101, 22322, 34836, 16783, 9386, 28856, 33987, 21163,\n 16904, 20152, 3486, 11894, 29780, 47462, 34297, 1271, 28738, 12147,\n 40307, 44267, 11855, 22489, 31199, 45646, 6936, 35292, 6714, 14575,\n 47132, 14125, 5086, 9343, 16013, 30947, 48088, 48762, 11743, 34946,\n 3320, 49791, 6930, 47963, 45082, 33427, 26336, 23584, 9865, 16808,\n 47306, 9073, 44093, 45471, 17137, 27683, 49609, 9591, 2770, 16432,\n 23932, 33744, 41766, 42979, 49518, 18912, 32018, 17323, 1974, 27259,\n 10654, 20054, 15256, 46803, 27812, 39173, 11810, 27935, 6983, 15578,\n 25954, 17146, 38810, 32496, 46321, 712, 17420, 37731, 38015, 48513,\n 20564, 3460, 3360, 381, 14656, 30589, 11499, 16383, 86, 41003,\n 47345, 17877, 1814, 25666, 6875, 9141, 23255, 6303, 33823, 11510,\n 11341, 6091, 48161, 36335, 29858, 17315, 22879, 43307, 23185, 10318,\n 16247, 35949, 31833, 26102, 6222, 28877, 38784, 29549, 32795, 31568,\n 20322, 42243, 39846, 34133, 26785, 32333, 751, 3167, 6253, 49920,\n 16935, 49866, 48991, 39167, 35254, 47913, 30735, 29392, 32516, 36016,\n 19177, 38632, 20860, 28831, 26382, 26072, 16212, 40662, 48181, 34411,\n 36044, 25569, 46340, 8832, 38677, 36881, 11751, 9278, 1836, 8557,\n 39511, 31396, 35620, 9481, 37289, 24394, 16822, 37564, 47653, 28188,\n 45944, 35444, 20432, 21644, 24946, 23479, 15385, 40549, 31140, 28169,\n 11124, 29296, 36749, 7339, 33690, 5182, 14694, 4644, 13500, 34013,\n 36586, 23708, 25417, 42099, 9814, 26089, 5596, 17351, 45518, 48895,\n 37807, 4480, 18517, 8409, 15769, 440, 45506, 22942, 37635, 49162,\n 10081, 37293, 29962, 40230, 26420, 42284, 47996, 30720, 3054, 21691,\n 10422, 43896, 33469, 45230, 22687, 7833, 19956, 5778, 21601, 37273,\n 14301, 40517, 20072, 19779, 34904, 589, 10873, 23612, 21108, 7831,\n 18874, 7836, 9082, 3343, 8980, 37573, 41000, 7849, 27054, 47189,\n 49165, 43846, 13449, 34615, 38862, 11524, 21361, 18397, 10903, 33335,\n 17560, 11906, 23965, 8483, 41980, 891, 34910, 26038, 28874, 17625,\n 22948, 8489, 11265, 36893, 6346, 11842, 21202, 16315, 24760, 27243,\n 4742, 27976, 31390, 34977, 17335, 45620, 8968, 26923, 18444, 44681,\n 11091, 12126, 3094, 23215, 13893, 47978, 29766, 16336, 36405, 12646,\n 17849, 49031, 4115, 29909, 34515, 1456, 32156, 21390, 25936, 46645,\n 3489, 45235, 3126, 13128, 40168, 18175, 40819, 28019, 39620, 29182,\n 14458, 46401, 16051, 16394, 3737, 26511, 26805, 18120, 14610, 30722,\n 35974, 24086, 44058, 146, 45840, 18030, 47269, 5138, 48196, 44182,\n 15244, 10930, 10793, 23551, 41181, 5605, 28932, 3276, 19831, 14695])\nnum_labeled= 500, acc=67.35, AUC=84.90\ntensor([24681, 42151, 48978, 41040, 36909, 10197, 38770, 40649, 43279, 27934,\n 48148, 38536, 37693, 33617, 27768, 36763, 31048, 2345, 15097, 49656,\n 20265, 10365, 49091, 36836, 35285, 42465, 44296, 15003, 8209, 38023,\n 31880, 32206, 29039, 14945, 31888, 29837, 1950, 7251, 16637, 10343,\n 32460, 44820, 3295, 7168, 3092, 46523, 19682, 49635, 12477, 43592,\n 30240, 46458, 49169, 15546, 5775, 24420, 47520, 6952, 42010, 10115,\n 14778, 47740, 44771, 35626, 15594, 46750, 20568, 34313, 3909, 5346,\n 38312, 4239, 37191, 34578, 12767, 44082, 14052, 38326, 36358, 14307,\n 9373, 32378, 47242, 27961, 25097, 3515, 35665, 30114, 39959, 8096,\n 17101, 30400, 31991, 10811, 2413, 28866, 41222, 43266, 40928, 49556,\n 8628, 24936, 35926, 15934, 8801, 27814, 37390, 12841, 22270, 10107,\n 43669, 36935, 9806, 2389, 17223, 40808, 45946, 31854, 49487, 578,\n 31363, 2318, 2765, 29571, 15248, 27576, 7540, 1985, 2762, 48358,\n 19594, 47745, 37081, 47495, 31208, 11301, 49554, 11418, 19382, 47839,\n 32147, 9476, 30495, 40431, 39933, 30439, 35339, 43609, 13277, 30945,\n 8690, 23921, 1502, 35694, 29808, 2180, 12719, 16765, 42443, 25534,\n 15779, 27037, 1946, 17861, 21238, 14035, 45675, 11831, 45001, 22067,\n 11850, 1170, 25915, 25071, 43369, 28326, 14805, 31445, 12620, 41075,\n 9266, 9809, 45475, 31209, 34179, 13843, 16410, 26315, 42237, 22553,\n 39769, 44954, 25842, 7358, 1006, 12123, 184, 17294, 22063, 590,\n 36293, 28026, 3814, 34981, 21135, 46451, 48404, 8366, 32477, 48870,\n 38750, 45437, 11815, 34305, 22797, 27326, 38971, 30453, 9585, 21701,\n 47668, 41469, 300, 44482, 17954, 38329, 45218, 39478, 3196, 39983,\n 39687, 41411, 22897, 32181, 37797, 28057, 35824, 34053, 20645, 35161,\n 36011, 21725, 48101, 22322, 34836, 16783, 9386, 28856, 33987, 21163,\n 28158, 45428, 17217, 22012, 26219, 9002, 48288, 30836, 13557, 14404,\n 10160, 23930, 28933, 45662, 11779, 3368, 8438, 26266, 10155, 30454,\n 47710, 49101, 17745, 13195, 46053, 830, 15160, 6520, 4257, 40865,\n 4598, 13985, 21168, 6648, 48427, 7555, 24978, 31495, 2191, 35482,\n 25859, 34651, 35107, 49248, 46575, 19964, 36242, 19874, 46983, 18464,\n 16904, 20152, 3486, 11894, 29780, 47462, 34297, 1271, 28738, 12147,\n 40307, 44267, 11855, 22489, 31199, 45646, 6936, 35292, 6714, 14575,\n 47132, 14125, 5086, 9343, 16013, 30947, 48088, 48762, 11743, 34946,\n 3320, 49791, 6930, 47963, 45082, 33427, 26336, 23584, 9865, 16808,\n 47306, 9073, 44093, 45471, 17137, 27683, 49609, 9591, 2770, 16432,\n 4266, 19727, 5918, 17897, 36269, 10753, 23197, 7603, 38614, 23995,\n 42134, 36588, 21049, 14030, 19049, 43978, 2412, 24154, 20144, 28055,\n 34016, 43850, 8088, 36767, 19617, 48390, 14942, 6585, 34768, 32160,\n 9024, 27938, 38887, 16586, 6981, 31913, 7132, 14107, 43633, 47683,\n 6422, 6466, 49325, 16035, 37410, 15967, 8738, 33980, 1120, 2986,\n 23932, 33744, 41766, 42979, 49518, 18912, 32018, 17323, 1974, 27259,\n 10654, 20054, 15256, 46803, 27812, 39173, 11810, 27935, 6983, 15578,\n 25954, 17146, 38810, 32496, 46321, 712, 17420, 37731, 38015, 48513,\n 20564, 3460, 3360, 381, 14656, 30589, 11499, 16383, 86, 41003,\n 47345, 17877, 1814, 25666, 6875, 9141, 23255, 6303, 33823, 11510,\n 9359, 45076, 49252, 28315, 41554, 23603, 36156, 6009, 47921, 45286,\n 18264, 31515, 47482, 34808, 41216, 34316, 43524, 43239, 40550, 8257,\n 22386, 1158, 19358, 21323, 24900, 49812, 27500, 30094, 19006, 543,\n 31998, 32997, 44519, 9963, 25435, 32757, 38375, 15864, 14073, 11794,\n 30802, 39803, 19711, 4137, 41286, 20870, 11190, 42575, 38598, 12160,\n 11341, 6091, 48161, 36335, 29858, 17315, 22879, 43307, 23185, 10318,\n 16247, 35949, 31833, 26102, 6222, 28877, 38784, 29549, 32795, 31568,\n 20322, 42243, 39846, 34133, 26785, 32333, 751, 3167, 6253, 49920,\n 16935, 49866, 48991, 39167, 35254, 47913, 30735, 29392, 32516, 36016,\n 19177, 38632, 20860, 28831, 26382, 26072, 16212, 40662, 48181, 34411,\n 20043, 48422, 27950, 12839, 43425, 45828, 6519, 23067, 38680, 18074,\n 39029, 8730, 42518, 32910, 8198, 17780, 5100, 25287, 34739, 28079,\n 38692, 45501, 27377, 44838, 21767, 38099, 25272, 10072, 13100, 42950,\n 48220, 11928, 12567, 22473, 39718, 43446, 30279, 44268, 33138, 26365,\n 41130, 34879, 3580, 43851, 49334, 23046, 42092, 44112, 1618, 13598,\n 36044, 25569, 46340, 8832, 38677, 36881, 11751, 9278, 1836, 8557,\n 39511, 31396, 35620, 9481, 37289, 24394, 16822, 37564, 47653, 28188,\n 45944, 35444, 20432, 21644, 24946, 23479, 15385, 40549, 31140, 28169,\n 11124, 29296, 36749, 7339, 33690, 5182, 14694, 4644, 13500, 34013,\n 36586, 23708, 25417, 42099, 9814, 26089, 5596, 17351, 45518, 48895,\n 42836, 2978, 931, 37134, 9784, 43269, 35098, 2605, 37956, 34564,\n 8564, 7348, 45280, 48627, 8991, 39657, 72, 25294, 10815, 1584,\n 33787, 48401, 34388, 47873, 35614, 29292, 9742, 863, 6273, 10837,\n 18126, 21636, 33201, 33548, 40748, 17480, 45208, 16442, 10786, 14513,\n 4062, 16546, 2777, 24266, 17172, 40466, 12765, 7538, 39823, 24808,\n 37807, 4480, 18517, 8409, 15769, 440, 45506, 22942, 37635, 49162,\n 10081, 37293, 29962, 40230, 26420, 42284, 47996, 30720, 3054, 21691,\n 10422, 43896, 33469, 45230, 22687, 7833, 19956, 5778, 21601, 37273,\n 14301, 40517, 20072, 19779, 34904, 589, 10873, 23612, 21108, 7831,\n 18874, 7836, 9082, 3343, 8980, 37573, 41000, 7849, 27054, 47189,\n 15364, 31967, 10077, 17616, 1273, 35056, 18601, 3358, 20186, 31777,\n 20202, 45480, 42290, 41877, 48551, 6776, 5254, 37474, 33648, 34733,\n 18405, 20436, 35238, 30991, 5989, 42814, 29245, 39432, 48649, 42423,\n 34428, 26208, 7606, 9271, 45699, 44986, 36495, 27565, 11278, 16348,\n 47572, 30592, 13394, 29899, 40198, 43355, 6372, 11, 33773, 29185,\n 49165, 43846, 13449, 34615, 38862, 11524, 21361, 18397, 10903, 33335,\n 17560, 11906, 23965, 8483, 41980, 891, 34910, 26038, 28874, 17625,\n 22948, 8489, 11265, 36893, 6346, 11842, 21202, 16315, 24760, 27243,\n 4742, 27976, 31390, 34977, 17335, 45620, 8968, 26923, 18444, 44681,\n 11091, 12126, 3094, 23215, 13893, 47978, 29766, 16336, 36405, 12646,\n 9474, 17180, 34582, 20650, 9188, 21840, 3333, 24632, 10682, 31424,\n 48164, 4225, 40571, 627, 44534, 14402, 33713, 1782, 35352, 12610,\n 1506, 15490, 22172, 46668, 5273, 4995, 8241, 3425, 20239, 31930,\n 2160, 11667, 44384, 9514, 40789, 259, 46363, 30509, 19028, 22477,\n 485, 42502, 15951, 39365, 12041, 5600, 13616, 18221, 31846, 34120,\n 17849, 49031, 4115, 29909, 34515, 1456, 32156, 21390, 25936, 46645,\n 3489, 45235, 3126, 13128, 40168, 18175, 40819, 28019, 39620, 29182,\n 14458, 46401, 16051, 16394, 3737, 26511, 26805, 18120, 14610, 30722,\n 35974, 24086, 44058, 146, 45840, 18030, 47269, 5138, 48196, 44182,\n 15244, 10930, 10793, 23551, 41181, 5605, 28932, 3276, 19831, 14695,\n 26136, 30056, 29384, 47028, 46830, 48340, 39876, 24089, 49534, 28120,\n 19199, 24477, 43137, 27365, 26681, 39585, 7418, 6438, 43619, 39522,\n 11594, 40742, 13155, 34549, 24712, 29628, 39427, 14265, 4638, 17888,\n 34863, 22935, 5499, 53, 1390, 8479, 38402, 45813, 31216, 40083,\n 27439, 11700, 26659, 24188, 3363, 39361, 23975, 11210, 23166, 703])\n" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Introduction\nThe aim of this project is to conduct a sentiment analysis using python and twitter's API. The topic in my case is Expo 2020.\n\n**What is Expo 2020?**\n> Initiated in London 1851. It is a global gathering aimed to find solutions to challenges imposed by the current times. Aims to create enriching and immersive experience. World expo traverses through different cities each time. It also revolves around certain themes. Currently, World expo is taking place in Dubai, UAE. Between Oct, 2021 and Mar, 2022. For more information please visit [expo2020dubai](https://www.expo2020dubai.com/)\n\n**The goal of this project**\n> As discussed above the main goal is to conduct a sentiment analysis and learn the basics of data science and big data projects. Since expo 2020 is considered an educational exhibition that revolves around modern day problems and is hosted currently in the middle east. The aim is to measure the awareness of the arab society - *By arab society we mean anyone who posts their opinions in arabic*\n\n\n\nThis part is about discovering and collecting as much data as possible that is relating to the topic at hand. I will be needing tweets that are written in Arabic. \nEach step is further illustrated in its own markdown.\n", "_____no_output_____" ] ], [ [ "import tweepy\nimport pandas as pd\nimport numpy as np\nimport configparser\nimport matplotlib.pyplot as plt\nimport random", "_____no_output_____" ] ], [ [ "### 1. Configuration and Authentication \n---\nThis is the setup part and API authentication. Prior to using the API it is necessary to create a developer account, the account grants you two levels of access. A user level and an application/project level. I will be using **configparser** to ensure my API keys are not visible. I suggest you do the same. The following is how to set up the configuration process.\n\n\n1. create a project from the developer's portal\n2. generate your API and access keys\n3. save them in a 'config.ini' file in the following format:\n \n ``` ini\n [twitter]\n CONSUMER_KEY = 'YOUR CONSUMER KEY'\n CONSUMER_SECRET = 'YOUR CONSUMER SECRET'\n ACCESS_TOKEN = 'YOUR ACCESS TOKEN'\n ACCESS_TOKEN_SECRET = 'YOUR ACCESS TOKEN SECRET' \n ```\n \n4. install configparser by running `pip install configparser`\n\n> **Note:** If you don't plan on using the config parser make sure you remove the import and change the next cell accordingly. To eliminate any errors. Make sure you adhere to the same variable names!", "_____no_output_____" ] ], [ [ "# read the file from 'config.ini' \nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n# API Variables\nCONSUMER_KEY = config['twitter']['CONSUMER_KEY']\nCONSUMER_SECRET = config['twitter']['CONSUMER_SECRET']\nACCESS_TOKEN = config['twitter']['ACCESS_TOKEN']\nACCESS_TOKEN_SECRET = config['twitter']['ACCESS_TOKEN_SECRET']", "_____no_output_____" ], [ "# authenticate using tweepy\ndef twitter_setup():\n auth = tweepy.OAuth1UserHandler(CONSUMER_KEY, CONSUMER_SECRET) # project access\n auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET) # user access\n\n api = tweepy.API(auth = auth)\n return api\n\nextractor = twitter_setup() ", "_____no_output_____" ] ], [ [ "### 2. Data Collection\n---\nAfter setting up the credentials and authenticating the project. I can start extracting data using **tweepy's** API. The aim is to search different terms and different hashtags in order to collect as much entries as the API allows for. There are many limitations since I have the `Elevated Access`. The main ones is that the search API only allows search to go back 7 days. One way this was managed was starting the project early. However, the ideal goal is to be able to search the entire archive. Nonetheless, the project aims to measure the opinion at the current time. Therefore, the regular search will suffice!\n\nI have created a function that when called extracts tweets depending on a local list. This list can have as many search queires as anyone would like. Also, The function parses the needed information and stores it in a data frame. Upon each iteration it appends to the previous data frame. This is beneficial when storing the data in .csv files.", "_____no_output_____" ] ], [ [ "def extract_tweets():\n tweets = [] # main data frame\n data = [] # temporary data frame\n columns_header = ['ID', 'Tweet', 'Timestamp', 'Likes', 'Retweets', 'Length']\n search_terms = ['@expo2020dubai -filter:retweets',\n '#expo2020 -filter:retweets',\n '#اكسبو -filter:retweets',\n 'اكسبو دبي -filter:retweets'] # search terms\n\n # fetch the tweets once prior to the iteration to append things correctly\n collected_tweets = tweepy.Cursor(extractor.search_tweets, q='expo dubai -filter:retweets', lang='ar', tweet_mode='extended').items(600)\n\n for tweet in collected_tweets:\n data.append([tweet.id, tweet.full_text, tweet.created_at,tweet.favorite_count, tweet.retweet_count, len(tweet.full_text)])\n\n tweets = pd.DataFrame(data=data, columns=columns_header) # store in original data frame\n\n for term in search_terms:\n data = []\n collected_tweets = tweepy.Cursor(extractor.search_tweets, q=term, lang='ar', tweet_mode='extended').items(600)\n\n for tweet in collected_tweets:\n data.append([tweet.id, tweet.full_text, tweet.created_at, tweet.favorite_count, tweet.retweet_count, len(tweet.full_text)])\n\n df = pd.DataFrame(data=data, columns=columns_header)\n frames = [tweets, df] \n tweets = pd.concat(frames) # append the data frame to the previous one\n\n # since we are appending data frames the index value changes each time\n # here the goal is to create a new index that is incremented by one \n tweets.insert(0, 'index', range(0, len(tweets))) \n tweets = tweets.set_index('index')\n\n # random number to ensure files don't get overwritten\n tweets.to_csv('Tweets\\\\tweets155.csv')\n \n return tweets", "_____no_output_____" ], [ "tweets = extract_tweets()", "_____no_output_____" ] ], [ [ "### 3. Preliminary Data Exploration", "_____no_output_____" ] ], [ [ "display(tweets.head())\ndisplay(tweets.tail())\nprint('total of collected tweets is ', len(tweets))", "_____no_output_____" ], [ "\ntweets.info()\n", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 706 entries, 0 to 705\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 ID 706 non-null int64 \n 1 Tweet 706 non-null object \n 2 Timestamp 706 non-null datetime64[ns, UTC]\n 3 Likes 706 non-null int64 \n 4 Retweets 706 non-null int64 \n 5 Length 706 non-null int64 \ndtypes: datetime64[ns, UTC](1), int64(4), object(1)\nmemory usage: 38.6+ KB\n" ] ], [ [ "### 4. Data Visualization", "_____no_output_____" ] ], [ [ "plt.plot(tweets['Timestamp'], tweets['Likes'])\nplt.gcf().autofmt_xdate()\nplt.show()", "_____no_output_____" ], [ "plt.scatter(tweets['Length'], tweets['Likes'], color='pink')\nplt.show()\n", "_____no_output_____" ], [ "# tweets1 = pd.read_csv('tweets1.csv')\n# tweets2 = pd.read_csv('tweets3.csv')\n# tweets3 = pd.read_csv('tweets327.csv')\n# tweets4 = pd.read_csv('tweets1439.csv')\n# tweets5 = pd.read_csv('tweets526.csv')\n# tweets6 = pd.read_csv('tweets546.csv')\n# tweets7 = pd.read_csv('tweets129.csv')\n# tweets8 = pd.read_csv('tweets1700.csv')\n\n# tweets1 = tweets1.drop(['Unnamed: 0'], axis=1)\n# tweets2 = tweets2.drop(['Unnamed: 0'], axis=1)\n# tweets3 = tweets3.drop(['index'], axis=1)\n# tweets4 = tweets4.drop(['index'], axis=1)\n# tweets5 = tweets5.drop(['index'], axis=1)\n# tweets6 = tweets6.drop(['index'], axis=1)\n# tweets7 = tweets7.drop(['index'], axis=1)\n# tweets8 = tweets8.drop(['index'], axis=1)\n\n\n\n# frames = [tweets1, tweets2, tweets3, tweets4, tweets5, tweets6, tweets7, tweets8]\n# final_tweets = pd.concat(frames) # append the data frame to the previous one\n# final_tweets.insert(0, 'index', range(0, len(final_tweets)))\n# final_tweets = final_tweets.set_index('index')\n\n# display(final_tweets.head())\n# display(final_tweets.tail())\n\n# final_tweets.to_csv('dirty_tweets_updated.csv')\n", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "FTL" ]

[ "FTL" ]

[ "FTL" ]

[ [ [ "import pandas as pd\nimport numpy as np\n\nfrom datetime import datetime\n\n%matplotlib inline\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns", "_____no_output_____" ], [ "mpl.rcParams['figure.figsize'] = (16, 9)\npd.set_option('display.max_rows', 500)\nsns.set(style = \"darkgrid\")", "_____no_output_____" ], [ "df_plot = pd.read_csv('../data/processed/Covid_flat_table.csv', sep = ';')", "_____no_output_____" ], [ "df_plot.head()", "_____no_output_____" ], [ "plt.figure()\nax = df_plot.set_index('date').plot()\nax.set_yscale('log')", "_____no_output_____" ] ], [ [ "## Plotly", "_____no_output_____" ] ], [ [ "import plotly.graph_objects as go\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html", "_____no_output_____" ], [ "country_list = df_plot[1:].columns", "_____no_output_____" ], [ "fig = go.Figure()\nfor each in country_list:\n \n fig.add_trace(go.Scatter(\n x = df_plot.date, \n y = df_plot[each], \n mode = 'markers+lines', \n name = each,\n line_width = 2,\n marker_size = 4,\n opacity = 0.9\n ))\n \n\nfig.update_layout(\n xaxis_title = 'Time',\n yaxis_title = \"Confirmed infected people (source johns hopkins, log-scale)\"\n)\n\nfig.update_yaxes(type = 'log')\nfig.update_layout(xaxis_rangeslider_visible = True)\nfig.show(renderer = 'chrome')", "_____no_output_____" ], [ "option_list = []\nfor each in country_list:\n label_dict = {}\n label_dict['label'] = each\n label_dict['value'] = each\n option_list.append(label_dict)", "_____no_output_____" ], [ "app = dash.Dash()\napp.layout = html.Div([\n html.Label('Multi-Select Country'),\n dcc.Dropdown(\n id = 'country_drop_down',\n options = option_list,\n value = ['Canada', 'India'],\n multi = True\n ),\n \n dcc.Graph(figure = fig, id = 'main_window_slope')\n])", "_____no_output_____" ], [ "from dash.dependencies import Input, Output\n\[email protected](\n Output('main_window_slope', 'figure'),\n [Input('country_drop_down', 'value')]\n)\n\ndef update_figure(country_list):\n traces = []\n for each in country_list:\n traces.append(dict(\n x = df_plot.date, \n y = df_plot[each], \n mode = 'markers+lines', \n name = each,\n line_width = 2,\n marker_size = 4,\n opacity = 0.9\n ))\n return {\n 'data': traces,\n 'layout': dict(\n width = 1280,\n height = 720,\n xaxis_title = \"Time\",\n yaxis_title = \"Confirmed infected people (source johns hopkins, log-scale)\", \n )\n }\n ", "_____no_output_____" ], [ "app.run_server(debug = True, use_reloader = False)", "Dash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\nDash is running on http://127.0.0.1:8050/\n\n * Serving Flask app \"__main__\" (lazy loading)\n * Environment: production\n WARNING: This is a development server. Do not use it in a production deployment.\n Use a production WSGI server instead.\n * Debug mode: on\n" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/dlminvestments/cloudml-template/blob/master/Copy_of_get_started.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "##### Copyright 2019 The TensorFlow Authors.", "_____no_output_____" ] ], [ [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.", "_____no_output_____" ] ], [ [ "# Get started with TensorBoard\n\n<table class=\"tfo-notebook-buttons\" align=\"left\">\n <td>\n <a target=\"_blank\" href=\"https://www.tensorflow.org/tensorboard/get_started\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/get_started.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://github.com/tensorflow/tensorboard/blob/master/docs/get_started.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n </td>\n</table>", "_____no_output_____" ], [ "In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.\n\nThis quickstart will show how to quickly get started with TensorBoard. The remaining guides in this website provide more details on specific capabilities, many of which are not included here. ", "_____no_output_____" ] ], [ [ "# Load the TensorBoard notebook extension\n%load_ext tensorboard", "_____no_output_____" ], [ "import tensorflow as tf\nimport datetime", "_____no_output_____" ], [ "# Clear any logs from previous runs\n!rm -rf ./logs/ ", "_____no_output_____" ] ], [ [ "Using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset as the example, normalize the data and write a function that creates a simple Keras model for classifying the images into 10 classes.", "_____no_output_____" ] ], [ [ "mnist = tf.keras.datasets.mnist\n\n(x_train, y_train),(x_test, y_test) = mnist.load_data()\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\ndef create_model():\n return tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape=(28, 28)),\n tf.keras.layers.Dense(512, activation='relu'),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, activation='softmax')\n ])", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11493376/11490434 [==============================] - 0s 0us/step\n" ] ], [ [ "## Using TensorBoard with Keras Model.fit()", "_____no_output_____" ], [ "When training with Keras's [Model.fit()](https://www.tensorflow.org/api_docs/python/tf/keras/models/Model#fit), adding the `tf.keras.callbacks.TensorBoard` callback ensures that logs are created and stored. Additionally, enable histogram computation every epoch with `histogram_freq=1` (this is off by default)\n\nPlace the logs in a timestamped subdirectory to allow easy selection of different training runs.", "_____no_output_____" ] ], [ [ "model = create_model()\nmodel.compile(optimizer='adam',\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\nlog_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\ntensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\nmodel.fit(x=x_train, \n y=y_train, \n epochs=5, \n validation_data=(x_test, y_test), \n callbacks=[tensorboard_callback])", "Train on 60000 samples, validate on 10000 samples\nEpoch 1/5\n60000/60000 [==============================] - 15s 246us/sample - loss: 0.2217 - accuracy: 0.9343 - val_loss: 0.1019 - val_accuracy: 0.9685\nEpoch 2/5\n60000/60000 [==============================] - 14s 229us/sample - loss: 0.0975 - accuracy: 0.9698 - val_loss: 0.0787 - val_accuracy: 0.9758\nEpoch 3/5\n60000/60000 [==============================] - 14s 231us/sample - loss: 0.0718 - accuracy: 0.9771 - val_loss: 0.0698 - val_accuracy: 0.9781\nEpoch 4/5\n60000/60000 [==============================] - 14s 227us/sample - loss: 0.0540 - accuracy: 0.9820 - val_loss: 0.0685 - val_accuracy: 0.9795\nEpoch 5/5\n60000/60000 [==============================] - 14s 228us/sample - loss: 0.0433 - accuracy: 0.9862 - val_loss: 0.0623 - val_accuracy: 0.9823\n" ] ], [ [ "Start TensorBoard through the command line or within a notebook experience. The two interfaces are generally the same. In notebooks, use the `%tensorboard` line magic. On the command line, run the same command without \"%\".", "_____no_output_____" ] ], [ [ "%tensorboard --logdir logs/fit", "_____no_output_____" ] ], [ [ "<img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/quickstart_model_fit.png?raw=1\"/>", "_____no_output_____" ], [ "A brief overview of the dashboards shown (tabs in top navigation bar):\n\n* The **Scalars** dashboard shows how the loss and metrics change with every epoch. You can use it to also track training speed, learning rate, and other scalar values.\n* The **Graphs** dashboard helps you visualize your model. In this case, the Keras graph of layers is shown which can help you ensure it is built correctly. \n* The **Distributions** and **Histograms** dashboards show the distribution of a Tensor over time. This can be useful to visualize weights and biases and verify that they are changing in an expected way.\n\nAdditional TensorBoard plugins are automatically enabled when you log other types of data. For example, the Keras TensorBoard callback lets you log images and embeddings as well. You can see what other plugins are available in TensorBoard by clicking on the \"inactive\" dropdown towards the top right.", "_____no_output_____" ], [ "## Using TensorBoard with other methods\n", "_____no_output_____" ], [ "When training with methods such as [`tf.GradientTape()`](https://www.tensorflow.org/api_docs/python/tf/GradientTape), use `tf.summary` to log the required information.\n\nUse the same dataset as above, but convert it to `tf.data.Dataset` to take advantage of batching capabilities:", "_____no_output_____" ] ], [ [ "train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\ntest_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n\ntrain_dataset = train_dataset.shuffle(60000).batch(64)\ntest_dataset = test_dataset.batch(64)", "_____no_output_____" ] ], [ [ "The training code follows the [advanced quickstart](https://www.tensorflow.org/tutorials/quickstart/advanced) tutorial, but shows how to log metrics to TensorBoard. Choose loss and optimizer:", "_____no_output_____" ] ], [ [ "loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\noptimizer = tf.keras.optimizers.Adam()", "_____no_output_____" ] ], [ [ "Create stateful metrics that can be used to accumulate values during training and logged at any point:", "_____no_output_____" ] ], [ [ "# Define our metrics\ntrain_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32)\ntrain_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('train_accuracy')\ntest_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32)\ntest_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('test_accuracy')", "_____no_output_____" ] ], [ [ "Define the training and test functions:", "_____no_output_____" ] ], [ [ "def train_step(model, optimizer, x_train, y_train):\n with tf.GradientTape() as tape:\n predictions = model(x_train, training=True)\n loss = loss_object(y_train, predictions)\n grads = tape.gradient(loss, model.trainable_variables)\n optimizer.apply_gradients(zip(grads, model.trainable_variables))\n\n train_loss(loss)\n train_accuracy(y_train, predictions)\n\ndef test_step(model, x_test, y_test):\n predictions = model(x_test)\n loss = loss_object(y_test, predictions)\n\n test_loss(loss)\n test_accuracy(y_test, predictions)", "_____no_output_____" ] ], [ [ "Set up summary writers to write the summaries to disk in a different logs directory:", "_____no_output_____" ] ], [ [ "current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\ntrain_log_dir = 'logs/gradient_tape/' + current_time + '/train'\ntest_log_dir = 'logs/gradient_tape/' + current_time + '/test'\ntrain_summary_writer = tf.summary.create_file_writer(train_log_dir)\ntest_summary_writer = tf.summary.create_file_writer(test_log_dir)", "_____no_output_____" ] ], [ [ "Start training. Use `tf.summary.scalar()` to log metrics (loss and accuracy) during training/testing within the scope of the summary writers to write the summaries to disk. You have control over which metrics to log and how often to do it. Other `tf.summary` functions enable logging other types of data.", "_____no_output_____" ] ], [ [ "model = create_model() # reset our model\n\nEPOCHS = 5\n\nfor epoch in range(EPOCHS):\n for (x_train, y_train) in train_dataset:\n train_step(model, optimizer, x_train, y_train)\n with train_summary_writer.as_default():\n tf.summary.scalar('loss', train_loss.result(), step=epoch)\n tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch)\n\n for (x_test, y_test) in test_dataset:\n test_step(model, x_test, y_test)\n with test_summary_writer.as_default():\n tf.summary.scalar('loss', test_loss.result(), step=epoch)\n tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch)\n \n template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'\n print (template.format(epoch+1,\n train_loss.result(), \n train_accuracy.result()*100,\n test_loss.result(), \n test_accuracy.result()*100))\n\n # Reset metrics every epoch\n train_loss.reset_states()\n test_loss.reset_states()\n train_accuracy.reset_states()\n test_accuracy.reset_states()", "Epoch 1, Loss: 0.24321186542510986, Accuracy: 92.84333801269531, Test Loss: 0.13006582856178284, Test Accuracy: 95.9000015258789\nEpoch 2, Loss: 0.10446818172931671, Accuracy: 96.84833526611328, Test Loss: 0.08867532759904861, Test Accuracy: 97.1199951171875\nEpoch 3, Loss: 0.07096975296735764, Accuracy: 97.80166625976562, Test Loss: 0.07875105738639832, Test Accuracy: 97.48999786376953\nEpoch 4, Loss: 0.05380449816584587, Accuracy: 98.34166717529297, Test Loss: 0.07712937891483307, Test Accuracy: 97.56999969482422\nEpoch 5, Loss: 0.041443776339292526, Accuracy: 98.71833038330078, Test Loss: 0.07514958828687668, Test Accuracy: 97.5\n" ] ], [ [ "Open TensorBoard again, this time pointing it at the new log directory. We could have also started TensorBoard to monitor training while it progresses.", "_____no_output_____" ] ], [ [ "%tensorboard --logdir logs/gradient_tape", "_____no_output_____" ] ], [ [ "<img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/quickstart_gradient_tape.png?raw=1\"/>", "_____no_output_____" ], [ "That's it! You have now seen how to use TensorBoard both through the Keras callback and through `tf.summary` for more custom scenarios. ", "_____no_output_____" ], [ "## TensorBoard.dev: Host and share your ML experiment results\n\n[TensorBoard.dev](https://tensorboard.dev) is a free public service that enables you to upload your TensorBoard logs and get a permalink that can be shared with everyone in academic papers, blog posts, social media, etc. This can enable better reproducibility and collaboration.\n\nTo use TensorBoard.dev, run the following command:\n", "_____no_output_____" ] ], [ [ "!tensorboard dev upload \\\n --logdir logs/fit \\\n --name \"(optional) My latest experiment\" \\\n --description \"(optional) Simple comparison of several hyperparameters\" \\\n --one_shot", "2020-12-17 00:52:27.342972: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n\n***** TensorBoard Uploader *****\n\nThis will upload your TensorBoard logs to https://tensorboard.dev/ from\nthe following directory:\n\nlogs/fit\n\nThis TensorBoard will be visible to everyone. Do not upload sensitive\ndata.\n\nYour use of this service is subject to Google's Terms of Service\n<https://policies.google.com/terms> and Privacy Policy\n<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n<https://tensorboard.dev/policy/terms/>.\n\nThis notice will not be shown again while you are logged into the uploader.\nTo log out, run `tensorboard dev auth revoke`.\n\nContinue? (yes/NO) Yes\n\nPlease visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=373649185512-8v619h5kft38l4456nm2dj4ubeqsrvh6.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email&state=IdEQglmQZEYpNFRPpVZ7G44Y0MLdJI&prompt=consent&access_type=offline\nEnter the authorization code: " ] ], [ [ "Note that this invocation uses the exclamation prefix (`!`) to invoke the shell\nrather than the percent prefix (`%`) to invoke the colab magic. When invoking this command from the command line there is no need for either prefix.\n\nView an example [here](https://tensorboard.dev/experiment/EDZb7XgKSBKo6Gznh3i8hg/#scalars).\n\nFor more details on how to use TensorBoard.dev, see https://tensorboard.dev/#get-started", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Commonly Available Datasets", "_____no_output_____" ], [ "### MNIST Dataset\n\nDataset of 70,000 Handwritten Digits (28X28 Images)\n\nOriginal Dataset : http://yann.lecun.com/exdb/mnist/", "_____no_output_____" ] ], [ [ "from sklearn.datasets import load_digits", "_____no_output_____" ], [ "import matplotlib.pyplot as plt", "_____no_output_____" ], [ "mnist = load_digits()", "_____no_output_____" ], [ "X = mnist.data\nY = mnist.target", "_____no_output_____" ], [ "print(X.shape)\nprint(Y.shape)", "(1797, 64)\n(1797,)\n" ], [ "example = X[42]\nprint(Y[42])", "1\n" ], [ "img = example.reshape((8,8))", "_____no_output_____" ], [ "print(img)", "[[ 0. 0. 0. 0. 12. 5. 0. 0.]\n [ 0. 0. 0. 2. 16. 12. 0. 0.]\n [ 0. 0. 1. 12. 16. 11. 0. 0.]\n [ 0. 2. 12. 16. 16. 10. 0. 0.]\n [ 0. 6. 11. 5. 15. 6. 0. 0.]\n [ 0. 0. 0. 1. 16. 9. 0. 0.]\n [ 0. 0. 0. 2. 16. 11. 0. 0.]\n [ 0. 0. 0. 3. 16. 8. 0. 0.]]\n" ], [ "plt.imshow(img,cmap=\"gray\")\nplt.show()", "_____no_output_____" ] ], [ [ "### Boston Dataset\nHousing Prices Dataset", "_____no_output_____" ] ], [ [ "from sklearn.datasets import load_boston\nboston = load_boston()", "_____no_output_____" ], [ "X = boston.data\nY = boston.target", "_____no_output_____" ], [ "print(X.shape)", "(506, 13)\n" ], [ "print(Y.shape)", "(506,)\n" ] ], [ [ "#### Loading & Visualising MNIST Dataset using Pandas & Matplotlib", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "df = pd.read_csv(\"../Datasets/MNIST-2/mnist_train.csv\")", "_____no_output_____" ], [ "df.shape", "_____no_output_____" ], [ "df.head(n=3)", "_____no_output_____" ], [ "print(type(df))", "<class 'pandas.core.frame.DataFrame'>\n" ], [ "data = df.values\nnp.random.shuffle(data)", "_____no_output_____" ], [ "print(type(data))", "<class 'numpy.ndarray'>\n" ], [ "print(data.shape)\n", "(42000, 785)\n" ], [ "X = data[ : ,1: ]\nY = data[ : ,0]", "_____no_output_____" ], [ "print(X.shape,Y.shape)", "(42000, 784) (42000,)\n" ], [ "## Try to visualise one image", "_____no_output_____" ], [ "def drawImg(X,Y,i):\n plt.imshow(X[i].reshape(28,28),cmap='gray')\n plt.title(\"Label \"+ str(Y[i]))\n plt.show()\n \n \nfor i in range(1):\n drawImg(X,Y,i)", "_____no_output_____" ], [ "## Split this dataset => \n\nsplit = int(0.80*X.shape[0])\nprint(split)", "33600\n" ], [ "X_train,Y_train = X[ :split, :], Y[:split]\nX_test,Y_test = X[split: , :], Y[split: ]\n\nprint(X_train.shape,Y_train.shape)\n\nprint(X_test.shape,Y_test.shape)", "(33600, 784) (33600,)\n(8400, 784) (8400,)\n" ], [ "# Randomization\nimport numpy as np\na = np.array([1,2,3,4,5])\nnp.random.shuffle(a)\n\nprint(a)", "[5 2 3 4 1]\n" ], [ "# Randomly shuffle a 2 D array\na = np.array([[1,2,3],\n [4,5,6],\n [7,8,9]])\n\nnp.random.shuffle(a)\nprint(a)", "[[1 2 3]\n [7 8 9]\n [4 5 6]]\n" ], [ "# Try to plot a visualisation (Grid of first 25 images 5 X 5)\n\nplt.figure(figsize=(10,10))\nfor i in range(25):\n plt.subplot(5,5,i+1)\n plt.imshow(X_train[i].reshape(28,28),cmap='gray')\n plt.title(Y_train[i])\n plt.axis(\"off\")\n ", "_____no_output_____" ], [ "# last thing \nfrom sklearn.model_selection import train_test_split\n\nXT,Xt,YT,Yt = train_test_split(X,Y,test_size=0.2,random_state=5)\nprint(XT.shape,YT.shape)\nprint(Xt.shape,Yt.shape)", "(33600, 784) (33600,)\n(8400, 784) (8400,)\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Python fundamentals\n\nA quick introduction to the [Python programming language](https://www.python.org/) and [Jupyter notebooks](https://jupyter.org/). ([We're using Python 3, not Python 2](https://pythonclock.org/).)", "_____no_output_____" ], [ "### Basic data types and the print() function", "_____no_output_____" ] ], [ [ "# variable assignment\n# https://www.digitalocean.com/community/tutorials/how-to-use-variables-in-python-3\n\n# strings -- enclose in single or double quotes, just make sure they match\nmy_name = 'Cody'\n\n# numbers\nint_num = 6\nfloat_num = 6.4\n\n# the print function\nprint(8)\nprint('Hello!')\nprint(my_name)\nprint(int_num)\nprint(float_num)\n\n# booleans\nprint(True)\nprint(False)\nprint(4 > 6)\nprint(6 == 6)\nprint('ell' in 'Hello')", "_____no_output_____" ] ], [ [ "### Basic math\n\nYou can do [basic math](https://www.digitalocean.com/community/tutorials/how-to-do-math-in-python-3-with-operators) with Python. (You can also do [more advanced math](https://docs.python.org/3/library/math.html).)", "_____no_output_____" ] ], [ [ "# addition\nadd_eq = 4 + 2\n\n# subtraction\nsub_eq = 4 - 2\n\n# multiplication\nmult_eq = 4 * 2\n\n# division\ndiv_eq = 4 / 2\n\n# etc.", "_____no_output_____" ] ], [ [ "### Lists\n\nA comma-separated collection of items between square brackets: `[]`. Python keeps track of the order of things inside a list.", "_____no_output_____" ] ], [ [ "# create a list: name, hometown, age\n# an item's position in the list is the key thing\ncody = ['Cody', 'Midvale, WY', 32]\n\n# create another list of mixed data\nmy_list = [1, 2, 3, 'hello', True, ['a', 'b', 'c']]\n\n# use len() to get the number of items in the list\nmy_list_count = len(my_list)\n\nprint('There are', my_list_count, 'items in my list.')\n\n# use square brackets [] to access items in a list\n# (counting starts at zero in Python)\n\n# get the first item\nfirst_item = my_list[0]\nprint(first_item)\n\n# you can do negative indexing to get items from the end of your list\n\n# get the last item\nlast_item = my_list[-1]\nprint(last_item)\n\n# Use colons to get a range of items in a list\n\n# get the first two items\n# the last number in a list slice is the first list item that's ~not~ included in the result\nmy_range = my_list[0:2]\nprint(my_range)\n\n# if you leave the last number off, it takes the item at the first number's index and everything afterward\n# get everything from the third item onward\nmy_open_range = my_list[2:]\nprint(my_open_range)\n\n# Use append() to add things to a list\nmy_list.append(5)\nprint(my_list)\n\n# Use pop() to remove items from the end of a list\nmy_list.pop()\nprint(my_list)\n\n# use join() to join items from a list into a string with a delimiter of your choosing\nletter_list = ['a', 'b', 'c']\njoined_list = '-'.join(letter_list)\nprint(joined_list)", "_____no_output_____" ] ], [ [ "### Dictionaries\n\nA data structure that maps _keys_ to _values_ inside curly brackets: `{}`. Items in the dictionary are separated by commas. Python does not keep track of the order of items in a dictionary; if you need to keep track of insertion order, use an [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) instead.", "_____no_output_____" ] ], [ [ "my_dict = {'name': 'Cody', 'title': 'Training director', 'organization': 'IRE'}\n\n# Access items in a dictionary using square brackets and the key (typically a string)\nmy_name = my_dict['name']\nprint(my_name)\n\n# You can also use the `get()` method to retrieve values\n# you can optionally provide a second argument as the default value\n# if the key doesn't exist (otherwise defaults to `None`)\nmy_name = my_dict.get('name', 'Jefferson Humperdink')\nprint(my_name)\n\n# Use the .keys() method to get the keys of a dictionary\nprint(my_dict.keys())\n\n# Use the .values() method to get the values\nprint(my_dict.values())\n\n# add items to a dictionary using square brackets, the name of the key (typically a string)\n# and set the value like you'd set a variable, with =\nmy_dict['my_age'] = 32\nprint(my_dict)\n\n# delete an item from a dictionary with `del`\ndel my_dict['my_age']\nprint(my_dict)", "_____no_output_____" ] ], [ [ "### Commenting your code\n\nPython skips lines that begin with a hashtag # -- these lines are used to write comments to help explain the code to others (and to your future self).\n\nMulti-line comments are enclosed between triple quotes: \"\"\" \"\"\"", "_____no_output_____" ] ], [ [ "# this is a one-line comment\n\n\"\"\"\nThis is a \nmulti-line comment\n\n~~~\n\n\"\"\"", "_____no_output_____" ] ], [ [ "### Comparison operators\n\nWhen you want to [compare values](https://docs.python.org/3/reference/expressions.html#value-comparisons), you can use these symbols:\n\n- `<` means less than\n- `>` means greater than\n- `==` means equal\n- `>=` means greater than or equal\n- `<=` means less than or equal\n- `!=` means not equal", "_____no_output_____" ] ], [ [ "4 > 6\n\n'Hello!' == 'Hello!'\n\n(2 + 2) != (4 * 2)\n\n100.2 >= 100", "_____no_output_____" ] ], [ [ "### String functions\n\nPython has a number of built-in methods to work with strings. They're useful if, say, you're using Python to clean data. Here are a few of them:", "_____no_output_____" ], [ "#### _strip()_\n\nCall `strip()` on a string to remove whitespace from either side. It's like using the `=TRIM()` function in Excel.", "_____no_output_____" ] ], [ [ "whitespace_str = ' hello! '\nprint(whitespace_str.strip())", "_____no_output_____" ] ], [ [ "#### _upper()_ and _lower()_\n\nCall `.upper()` on a string to make the characters uppercase. Call `.lower()` on a string to make the characters lowercase. This can be useful when testing strings for equality.", "_____no_output_____" ] ], [ [ "my_name = 'Cody'\n\nmy_name_upper = my_name.upper()\nprint(my_name_upper)\n\nmy_name_lower = my_name.lower()\nprint(my_name_lower)", "_____no_output_____" ] ], [ [ "#### _replace()_\n\nUse `.replace()` to substitute bits of text.", "_____no_output_____" ] ], [ [ "company = 'Bausch & Lomb'\n\ncompany_no_ampersand = company.replace('&', 'and')\n\nprint(company_no_ampersand)", "_____no_output_____" ] ], [ [ "#### _split()_\n\nUse `.split()` to split a string on some delimiter. If you don't specify a delimiter, it uses a single space as the default.", "_____no_output_____" ] ], [ [ "date = '6/4/2011'\n\ndate_split = date.split('/')\n\nprint(date_split)", "_____no_output_____" ] ], [ [ "#### _zfill()_\n\nAmong other things, you can use `.zfill()` to add zero padding -- for instance, if you're working with ZIP code data that was saved as a number somewhere and you've lost the leading zeroes for that handful of ZIP codes that begin with 0.\n\n_Note: `.zfill()` is a string method, so if you want to apply it to a number, you'll need to first coerce it to a string with `str()`._", "_____no_output_____" ] ], [ [ "mangled_zip = '2301'\nfixed_zip = mangled_zip.zfill(5)\nprint(fixed_zip)\n\nnum_zip = 2301\nfixed_num_zip = str(num_zip).zfill(5)\nprint(fixed_num_zip)", "_____no_output_____" ] ], [ [ "#### _slicing_\n\nLike lists, strings are _iterables_, so you can use slicing to grab chunks.", "_____no_output_____" ] ], [ [ "my_string = 'supercalifragilisticexpialidocious'\n\nchunk = my_string[9:20]\n\nprint(chunk)", "_____no_output_____" ] ], [ [ "#### _startswith()_, _endswith()_ and _in_\n\nIf you need to test whether a string starts with a series of characters, use `.startswith()`. If you need to test whether a string ends with a series of characters, use `.endswith()`. If you need to test whether a string is part of another string -- or a list of strings -- use `.in()`.\n\nThese are case sensitive, so you'd typically `.upper()` or `.lower()` the strings you're comparing to ensure an apples-to-apples comparison.", "_____no_output_____" ] ], [ [ "str_to_test = 'hello'\n\nprint(str_to_test.startswith('hel'))\nprint(str_to_test.endswith('lo'))\nprint('el' in str_to_test)\nprint(str_to_test in ['hi', 'whatsup', 'salutations', 'hello'])", "_____no_output_____" ] ], [ [ "### String formatting\n\nUsing curly brackets with the [various options](https://pyformat.info/) available to the `.format()` method, you can create string templates for your data. Some examples:", "_____no_output_____" ] ], [ [ "# date in m/d/yyyy format\nin_date = '8/17/1982'\n\n# split out individual pieces of the date\n# using a shortcut method to assign variables to the resulting list\nmonth, day, year = in_date.split('/')\n\n# reshuffle as yyyy-mm-dd using .format()\n# use a formatting option (:0>2) to left-pad month/day numbers with a zero\nout_date = '{}-{:0>2}-{:0>2}'.format(year, month, day)\n\nprint(out_date)", "_____no_output_____" ], [ "# construct a greeting template\ngreeting = 'Hello, {}! My name is {}.'\nyour_name = 'Pat'\nmy_name = 'Cody'\n\nprint(greeting.format(your_name, my_name))", "_____no_output_____" ] ], [ [ "### Type coercion\n\nConsider:\n\n```python\n# this is a number, can't do string-y things to it\nage = 32\n\n# this is a string, can't do number-y things to it\nage = '32'\n```\nThere are several functions you can use to _coerce_ a value of one type to a value of another type. Here are a couple of them:\n\n- `int()` tries to convert to an integer\n- `str()` tries to convert to a string\n- `float()` tries to convert to a float", "_____no_output_____" ] ], [ [ "# two strings of numbers\nnum_1 = '100'\nnum_2 = '200'\n\n# what happens when you add them without coercing?\nconcat = num_1 + num_2\nprint(concat)\n\n# coerce to integer, then add them\nadded = int(num_1) + int(num_2)\nprint(added)", "_____no_output_____" ] ] ]

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[ "ADSL" ]

[ "ADSL" ]

[ "ADSL" ]

[ [ [ "# Evaluation - Echo Chamber", "_____no_output_____" ], [ "The primary goal of this project is to provide users with recommendations that are different from those produced by an ALS recommendation system, but not too different. To evaluate the performance of the augmented, four metrics were developed to evaluate how different the movies recommended by the augmented model are from those recommened by the ALS model.\n\nThe <a href='#metric1'>first metric</a> looks a user's Top 100 ALS recommendations (based on predicted user ratings) and the Top 100 recommendations from the user's cluster (based on the cluster centroid's ratings). The proportion of the ALS recommended movies also found in the cluster recommendations should be high. A high proportion indicates that the user's cluster is representative of the user's movie preferences. \n\nThe <a href='#metric2'>second metric</a> looks a user's Top 100 ALS recommendations (based on predicted user ratings) and the top recommendations from the augmented model (based on the cluster centroid's ratings for the two clusters nearest to the user's cluster). The proportion of the ALS recommended movies also found in the augmented model recommendations should be low. A low proportion indicates that the recommendations from the augmented model differ from those produced by the ALS model.\n\nThe <a href='#metric3'>third metric</a> utilizes the distance between movies (based on the ALS item factors) to evaluate the extent to which the movies from the augmented model are qualitatively different from the movies from the ALS model. For this metric, the mean squared distance between the Top 100 movies from the ALS model (excluding the distance between a movie and itself) is calculated for each user in the sample. Likewise, the mean squared distance between each of the Top 100 ALS movies and each of the top movies from augmented model is calculated for each user in the sample. The difference between these mean squared distances for the sample are tested using a t-test. A negative and statistically significant t-statistic indicates the mean difference between the two sets of recommendations is greater than the mean difference within the ALS recommendations and, thus, the movies from the augmented model are qualitatively different from the movies from the ALS model.\n\nThe <a href='#metric4'>final metric</a> evaluates whether the movies recommended by the augmented model are too qualitatively different from the ALS recommendations. The metric tests the difference between two differences: the difference between ALS and the augmented model and the difference between ALS and recommendations from the two clusters furthest away from the user's cluster. These differences are calculated in the same way as described above for third metric. The difference in differences is tested using a t-test. A positive and statistically significant t-statistic indicates the difference between the ALS recommendations and those from the furthest clusters is greater than the distance between the ALS recommendations and the augmented model's recommendations. A greater distance is evidence that the augmented model recommendations are not too different from the ALS recommendations since the movies recommended by the furthest clusters are more different. \n\nThe performance of the augmented model is evaluated in the cells below by applying the above metrics to a sample of 1000 users from the MovieLens dataset. The links above can be used to skip to a particular metric in Sections 5 - 8. Sections 2 - 4 demonstrate the process for importing, sampling, filtering, and engineering the data for evaluation. ", "_____no_output_____" ], [ "## Local Code Imports", "_____no_output_____" ] ], [ [ "from src import model as mdl\nfrom src import custom as cm", "_____no_output_____" ] ], [ [ "## Code Imports", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nfrom joblib import load\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ] ], [ [ "# Data", "_____no_output_____" ], [ "## Import Data Files", "_____no_output_____" ] ], [ [ "user_fac = pd.read_csv('../data/processed/user_factors.csv', index_col='id')", "_____no_output_____" ], [ "item_fac = pd.read_csv(\n '../data/processed/item_factors_unstacked.csv', index_col='id')", "_____no_output_____" ] ], [ [ "## Build Rankings Matrix", "_____no_output_____" ], [ "The following cell multiplies the user factors and item factors calculated from the ALS model implementation to get the ALS movie ratings for each user (see the SparkALS.py file in the model folder for the ALS implementation code).", "_____no_output_____" ] ], [ [ "ALS_rankings_matrix = user_fac.to_numpy().dot(item_fac.T.to_numpy())", "_____no_output_____" ], [ "ALS_rankings_matrix.shape", "_____no_output_____" ] ], [ [ "## Sample Users", "_____no_output_____" ], [ "To sample users, an index of random values is generated. The index is then used to filter rows from the user factors object (user_fac) and the ALS rankings matrix. The sample is transformed from a numpy array to a dataframe, the columns set to be the movie IDs (take from the item_fac index), and then transposed so that each column represents a user and each row a movie. Lastly, the index is reset making the movieId a column in the dataframe. ", "_____no_output_____" ] ], [ [ "idx = np.random.randint(0, 243658, size=1000)", "_____no_output_____" ], [ "sample_user_facs = user_fac.to_numpy()[idx, :]", "_____no_output_____" ], [ "sample = ALS_rankings_matrix[idx, :]", "_____no_output_____" ], [ "sample_df = pd.DataFrame(sample)", "_____no_output_____" ], [ "sample_df.columns = item_fac.index", "_____no_output_____" ], [ "sample_T = sample_df.T", "_____no_output_____" ], [ "sample_T.reset_index(inplace=True)", "_____no_output_____" ] ], [ [ "## Filter for Most Rated Movies", "_____no_output_____" ], [ "The ALS and augmented models used in this project only recommend movies that have been rated by more than 50 users. The file most_rated.csv contains the movie ID, title, and genre for these often rated movies. Using the most rated movies, the sample is filtered to include only those movies with more than 50 user reviews. This is done so that the evaluation below is based on the same set of possible movie recommendations as used in the recommendation system. ", "_____no_output_____" ] ], [ [ "most_rated = pd.read_csv(\n '../data/processed/most_rated.csv', index_col='Unnamed: 0')", "_____no_output_____" ], [ "sample_redx = pd.merge(sample_T, most_rated, how='inner',\n left_on='id', right_on='movieId')", "_____no_output_____" ], [ "sample_redx.set_index('id', inplace=True)", "_____no_output_____" ], [ "sample_redx.drop(['movieId', 'title', 'genres'], axis=1, inplace=True)", "_____no_output_____" ] ], [ [ "# Get ALS Top 100 for Sample", "_____no_output_____" ], [ "The following cell creates a list of the Top 100 rated movies for each user based on the ALS model. ", "_____no_output_____" ] ], [ [ "als_top_100s = []\nfor idx, col in enumerate(sample_redx):\n top_100 = sample_redx[col].sort_values(ascending=False).head(100)\n top_100_df = pd.DataFrame(top_100)\n top_100_df.reset_index(inplace=True)\n als_top_100s.append(top_100_df)", "_____no_output_____" ] ], [ [ "# Get Cluster Top 100 for Sample", "_____no_output_____" ], [ "The augmented model gets recommendations from the two clusters nearest to the user's cluster. The following cells predict the cluster of each user in the sample, gets creates a dataframe with the movie ratings for each cluster, and generates a list of the Top 100 movies recommendations for each user in the sample from the user's predicted cluster. ", "_____no_output_____" ], [ "## Predict Users' Clusters", "_____no_output_____" ] ], [ [ "gbc = load('../models/fifp_classification.joblib')", "_____no_output_____" ], [ "preds = gbc.predict(sample_user_facs)", "_____no_output_____" ] ], [ [ "## Get Cluster Centroid Ratings", "_____no_output_____" ] ], [ [ "centroids = pd.read_csv(\n '../data/processed/centroids.csv', index_col='Unnamed: 0')\ncentroid_ratings_T_df = cm.get_centroid_ratings(centroids, item_fac)", "_____no_output_____" ], [ "centroid_ratings_T_df.reset_index(inplace=True)", "_____no_output_____" ], [ "centroid_ratings_redx = pd.merge(\n centroid_ratings_T_df, most_rated, how='inner', left_on='id', right_on='movieId')", "_____no_output_____" ], [ "centroid_ratings_redx.set_index('id', inplace=True)", "_____no_output_____" ], [ "centroid_ratings_redx.drop(\n ['movieId', 'title', 'genres'], axis=1, inplace=True)", "_____no_output_____" ] ], [ [ "## Get Cluster Top 100", "_____no_output_____" ] ], [ [ "cluster_top_100s = []\nfor cluster in preds:\n top_100 = centroid_ratings_redx[cluster].sort_values(\n ascending=False).head(100)\n top_100_df = pd.DataFrame(top_100)\n top_100_df.reset_index(inplace=True)\n cluster_top_100s.append(top_100_df)", "_____no_output_____" ] ], [ [ "<a id='metric1'></a>", "_____no_output_____" ], [ "# Evaluation Metric 1: Proportion of Top 100 Movies Shared between the ALS recommendations and the User's Cluster Centroid", "_____no_output_____" ], [ "The first metric evaluates the similarity between the top 100 ALS recommendations (based on predicted user ratings) and those generated by the user's cluster (based on the cluster centroid's ratings). The proportion of the ALS recommended movies also found in the cluster recommendations should be high indicating the user's cluster is representative of the user's movie preferences.", "_____no_output_____" ] ], [ [ "proportions1 = []\nfor i in range(len(cluster_top_100s)):\n als_set = set(als_top_100s[i].iloc[:, 0])\n cluster_set = set(cluster_top_100s[i].iloc[:, 0])\n intersection = als_set.intersection(cluster_set)\n n_in_common = len(intersection)\n proportion_in_common = (n_in_common/100)\n proportions1.append(proportion_in_common)", "_____no_output_____" ], [ "plt.figure(figsize=(12, 6))\nplt.title('Proportion of Movies Common to a User\\'s ALS Recommendations and a User\\'s Own Cluster\\'s Recommendations')\nplt.ylabel('Frequencies')\nplt.xlabel('Proportion of Movies in Common')\nplt.hist(proportions1, bins=20)\nplt.show;\nplt.savefig('../reports/figures/Metric1.png')", "_____no_output_____" ] ], [ [ "<a id='metric2'></a>", "_____no_output_____" ], [ "# Evaluation Metric 2: Proportion of Top 100 Movies Shared between the ALS and Augmented Recommendations", "_____no_output_____" ], [ "The second metric evaluates the similarity between the top 100 ALS recommendations (based on predicted user ratings) and the top recommendations from the augmented model. The proportion of the ALS recommended movies also found in the augmented model recommendations should be low indicating the recommendations from the augmented model differ from those produced by the ALS model.", "_____no_output_____" ] ], [ [ "cluster_distances = pd.read_csv(\n '../data/processed/cluster_distances_df.csv', index_col='Unnamed: 0')", "_____no_output_____" ], [ "cluster_distances.head()", "_____no_output_____" ], [ "cm.get_nearest_clusters(cluster_distances, '8')", "_____no_output_____" ], [ "proportions2 = []\nfor i in range(len(cluster_top_100s)):\n j = preds[i]\n nearest_clusters = cm.get_nearest_clusters(\n cluster_distances, '{}'.format(j))\n cluster1 = nearest_clusters[0]\n cluster2 = nearest_clusters[1]\n als_set = set(als_top_100s[i].iloc[:, 0])\n cluster1_set = set(cluster_top_100s[cluster1].iloc[:, 0])\n cluster2_set = set(cluster_top_100s[cluster2].iloc[:, 0])\n cluster_full = cluster1_set.union(cluster2_set)\n intersection = als_set.intersection(cluster_full)\n n_in_common = len(intersection)\n proportion_in_common = (n_in_common/100)\n proportions2.append(proportion_in_common)", "_____no_output_____" ], [ "plt.figure(figsize=(12, 6))\nplt.title('Proportion of Movies Common to a User\\'s ALS and Augmented Recommendations')\nplt.ylabel('Frequencies')\nplt.xlabel('Proportion of Movies in Common')\nplt.hist(proportions2, bins=20)\nplt.show;\nplt.savefig('../reports/figures/Metric2.png')", "_____no_output_____" ] ], [ [ "<a id='metric3'></a>", "_____no_output_____" ], [ "# Evaluation Metric 3: Distances Between ALS Recommended Movies and Nearest Cluster Recommended Movies", "_____no_output_____" ], [ "In addition to recommending a different set of movies than the ALS model, the goal of the augmented model is to recommend movies that are qualitatively more diverse. To test for differences in the qualities of movie recommended by each model, the third evaluation metric assesses the distances between movies. For each user, the mean squared distance between movies recommended by the ALS model is calculated. Then, the mean squared distance between movies recommended by ALS model and movies recommended by the augmented model is calculated for each user. For the sample, the difference in the two mean squared distance calculations is tested using a t-test. A negative and statistically significant t-statistic indicates the mean difference between the two sets of recommendations is greater than the mean difference within the ALS recommendations and, thus, the movies from the augmented model are qualitatively different from the movies from the ALS model.", "_____no_output_____" ] ], [ [ "movie_distances = cm.get_cluster_distances(item_fac)", "_____no_output_____" ], [ "movie_distances.columns = item_fac.index", "_____no_output_____" ], [ "movie_distances.index = item_fac.index", "_____no_output_____" ], [ "nearest_clusters_top = []\nfor i in range(len(cluster_top_100s)):\n j = preds[i]\n nearest_clusters = cm.get_nearest_clusters(\n cluster_distances, '{}'.format(j))\n cluster1 = nearest_clusters[0]\n cluster2 = nearest_clusters[1]\n cluster1_set = set(cluster_top_100s[cluster1].iloc[:, 0])\n cluster2_set = set(cluster_top_100s[cluster2].iloc[:, 0])\n cluster_full_set = cluster1_set.union(cluster2_set)\n cluster_full_list = list(cluster_full_set)\n nearest_clusters_top.append(cluster_full_list)", "_____no_output_____" ], [ "MSS_distances_within = []\nfor i in range(len(als_top_100s)):\n within_distances = []\n for j in als_top_100s[i].iloc[:, 0]:\n for k in als_top_100s[i].iloc[:, 0]:\n if j == k:\n pass\n else:\n within_distances.append(movie_distances[j][k])\n sq_within_distances = [x**2 for x in within_distances]\n MSS_distances = np.mean(sq_within_distances)\n MSS_distances_within.append(MSS_distances)", "_____no_output_____" ], [ "MSS_distances_between = []\nfor i in range(len(als_top_100s)):\n between_distances = []\n for j in als_top_100s[i].iloc[:, 0]:\n for k in nearest_clusters_top[i]:\n between_distances.append(movie_distances[j][k])\n sq_between_distances = [x**2 for x in between_distances]\n MSS_distances = np.mean(sq_between_distances)\n MSS_distances_between.append(MSS_distances)", "_____no_output_____" ], [ "stats.ttest_ind(MSS_distances_within, MSS_distances_between, equal_var=False)", "_____no_output_____" ] ], [ [ "<a id='metric4'></a>", "_____no_output_____" ], [ "# Evaluation: Difference in Distances Between ALS Recommended Movies and Nearest Cluster Recommended Movies and the Distances Between ALS Recommended Movies and Furthest Cluster Recommended Movies", "_____no_output_____" ], [ "The goal of the augmented model is to recommend movies that are qualitatively different, but not too different, from those provided by the ALS model. Recommendations that are too different will be ignored or, worse, could result in the user no longer using the recommendation service. \n\nTo test that the recommendations provided by the augmented model are not too different for the ALS recommendation, metric 4 compares the differences between the ALS and augmented recommendations to the difference between the ALS recommendations and those provided by the two clusters furthest from the user's cluster. Recommendations from the furthest clusters should be more different than recommendations from the nearest clusters (i.e., the augmented model). For the sample, the difference in differences is tested using a t-test. A positive and statistically significant t-statistic indicates the difference between the ALS recommendations and the furthest cluster recommendations is greater than the difference between the ALS recommendations and the augmented recommendations. If the difference is positive and statitically significant, then the augmented recommendations are not as different as the recommendations from the furthest clusters and therefore are not too different from the ALS recommendations. ", "_____no_output_____" ] ], [ [ "furthest_clusters_top = []\nfor i in range(len(cluster_top_100s)):\n j = preds[i]\n nearest_clusters = cm.get_furthest_clusters(\n cluster_distances, '{}'.format(j))\n cluster1 = nearest_clusters[0]\n cluster2 = nearest_clusters[1]\n cluster1_set = set(cluster_top_100s[cluster1].iloc[:, 0])\n cluster2_set = set(cluster_top_100s[cluster2].iloc[:, 0])\n cluster_full_set = cluster1_set.union(cluster2_set)\n cluster_full_list = list(cluster_full_set)\n furthest_clusters_top.append(cluster_full_list)", "_____no_output_____" ], [ "MSS_distances_between_furthest = []\nfor i in range(len(als_top_100s)):\n within_distances = []\n for j in als_top_100s[i].iloc[:, 0]:\n for k in furthest_clusters_top[i]:\n within_distances.append(movie_distances[j][k])\n sq_within_distances = [x**2 for x in within_distances]\n MSS_distances = np.mean(sq_within_distances)\n MSS_distances_between_furthest.append(MSS_distances)", "_____no_output_____" ], [ "within_between_differences = np.subtract(\n MSS_distances_between, MSS_distances_within)\nwithin_furthest_differences = np.subtract(\n MSS_distances_between_furthest, MSS_distances_within)", "_____no_output_____" ], [ "stats.ttest_ind(within_furthest_differences,\n within_between_differences, equal_var=False)", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "PyImageJ Tutorial\n===\n\nThis notebook covers how to use ImageJ as a library from Python. A major advantage of this approach is the ability to combine ImageJ with other tools available from the Python software ecosystem, including NumPy, SciPy, scikit-image, CellProfiler, OpenCV, ITK and more.\n\nThis notebook assumes familiarity with the ImageJ API. Detailed tutorials in that regard can be found in the other notebooks.", "_____no_output_____" ], [ "## 7 Visualizing large images\n\nBefore we begin: how much memory is Java using right now?", "_____no_output_____" ] ], [ [ "from scyjava import jimport\nRuntime = jimport('java.lang.Runtime')\ndef java_mem():\n rt = Runtime.getRuntime()\n mem_max = rt.maxMemory()\n mem_used = rt.totalMemory() - rt.freeMemory()\n return '{} of {} MB ({}%)'.format(int(mem_used)/2**20, int(mem_max/2**20), int(100*mem_used/mem_max))\n\njava_mem()", "_____no_output_____" ] ], [ [ "Now let's open an obnoxiously huge synthetic dataset:", "_____no_output_____" ] ], [ [ "big_data = ij.scifio().datasetIO().open('lotsofplanes&lengths=512,512,16,1000,10000&axes=X,Y,Channel,Z,Time.fake')", "_____no_output_____" ] ], [ [ "How many total samples does this image have?", "_____no_output_____" ] ], [ [ "import numpy as np\n\ndims = [big_data.dimension(d) for d in range(big_data.numDimensions())]\npix = np.prod(dims)\nstr(pix/2**40) + \" terapixels\"", "_____no_output_____" ] ], [ [ "And how much did memory usage in Java increase?", "_____no_output_____" ] ], [ [ "java_mem()", "_____no_output_____" ] ], [ [ "Let's visualize this beast. First, we define a function for slicing out a single plane:", "_____no_output_____" ] ], [ [ "def plane(image, pos):\n while image.numDimensions() > 2:\n image = ij.op().transform().hyperSliceView(image, image.numDimensions() - 1, pos[-1])\n pos.pop()\n return ij.py.from_java(image)\n\nij.py.show(plane(big_data, [0, 0, 0]))", "_____no_output_____" ] ], [ [ "But we can do better. Let's provide some interaction. First, a function to extract the _non-planar_ axes as a dict:", "_____no_output_____" ] ], [ [ "def axes(dataset):\n axes = {}\n for d in range(2, dataset.numDimensions()):\n axis = dataset.axis(d)\n label = axis.type().getLabel()\n length = dataset.dimension(d)\n axes[label] = length\n return axes\n\naxes(big_data)", "_____no_output_____" ], [ "import ipywidgets, matplotlib\n\nwidgets = {}\nfor label, length in axes(big_data).items():\n label = str(label) # HINT: Convert Java string to a python string to use with ipywidgets.\n widgets[label] = ipywidgets.IntSlider(description=label, max=length-1)\n\nwidgets", "_____no_output_____" ], [ "def f(**kwargs):\n matplotlib.pyplot.imshow(plane(big_data, list(kwargs.values())), cmap='gray')\nipywidgets.interact(f, **widgets);", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Exampe 2.1: Fourier series", "_____no_output_____" ] ], [ [ "import numpy as np # Import numpy\nfrom matplotlib import pyplot as plt # pyplot module for plotting", "_____no_output_____" ] ], [ [ "## Define waveform\nWe start by defining a square waveform. We will integrate the waveform over one period. We have, however, plotted several periods to illustrate the behaviour of the Fourier series. The blue line in the figure shows four periods of the time series that we will approximate, while the brown line shows the line that we will integrate (one period)", "_____no_output_____" ] ], [ [ "dt = 0.01 # Time step\nt = np.arange(0,10.01,dt) # time vector\nx = np.zeros(t.shape) # Initialize the x array\nx[t<5] = 1.0 # Set the value of x to one for t<5\n# Plot waveform\nplt.figure()\nplt.plot(np.hstack((t-20.0,t-10.0, t, t+10.0)),np.hstack((x,x,x,x))); # Plot four periods\nplt.plot(t,x); #Plot one period\nplt.ylim(-2, 2);\nplt.xlim(-20,20);\nplt.grid();\nplt.xlabel('$t$');\nplt.ylabel('$X(t)$');\n\n", "_____no_output_____" ] ], [ [ "## Alternative 1: Obtaining Fourier coefficients expressed by sin() and cos() by the trapezoidal rule\nFourier series expressed in terms of sinus and cosine functions are defined by\n\n$$ X(t) = a_{0} + \\sum_{k=1}^{\\infty} \\left( a_k cos\\left(\\frac{2\\pi k}{T}t \\right) + b_k sin\\left(\\frac{2\\pi k}{T}t \\right)\\right) $$\n\nHere $a_0$, $a_k$ and $b_k$ are Fourier coefficients given by\n\n$$a_0 = \\frac{1}{T} \\int_{0}^{T}X(t)dt$$\n\n$$a_k = \\frac{1}{T} \\int_{0}^{T}X(t)cos\\left(\\frac{2\\pi k}{T}t \\right)dt$$\n\n$$b_k = \\frac{1}{T} \\int_{0}^{T}X(t)sin\\left(\\frac{2\\pi k}{T}t \\right)dt$$\n\nThe integrals above can be solved analytically and by numerical integration. In this example, we will consider different methods for numerical integration to obtain the coefficients, and we use the trapezoidal rule in this section.", "_____no_output_____" ] ], [ [ "nterms = 50 # Number of Fourier coefficeints\nT = np.max(t) # The period of the waveform\na0 = 1/T*np.trapz(x,t) # Mean value\nak = np.zeros((nterms)) \nbk = np.zeros((nterms))\nfor k in range(nterms): # Integrate for all terms\n ak[k] = 1/T*np.trapz(x*np.cos(2.0*np.pi*(k+1.0)*t/T),t)\n bk[k] = 1/T*np.trapz(x*np.sin(2.0*np.pi*(k+1.0)*t/T),t)\n\n# Plot Fourier coeffecients\nfig, axs = plt.subplots(nrows=1, ncols=2, constrained_layout=True)\n\nax1 = axs[0]\nax1.plot(np.arange(1,nterms+1),ak)\nax1.set_ylim(-1, 1)\nax1.grid()\nax1.set_ylabel('$a_k$');\nax1.set_xlabel('$k$');\n\nax2 = axs[1]\nax2.plot(np.arange(1,nterms+1),bk)\nax2.set_ylim(-1, 1)\nax2.grid()\nax2.set_ylabel('$b_k$');\nax2.set_xlabel('$k$');\n\n", "_____no_output_____" ] ], [ [ "The mean value is 0,5, while the figures above show that the $a_k$ coefficients are all zero and that every second $b_k$ coefficient is zero and that the nonzero terms become smaller as $k$ increases.It is interesting to plot the Fourier approximation and see how its accuracy depends on the number of terms used in the approximation.", "_____no_output_____" ] ], [ [ "# Plot Fourier series approximation\ntp = np.linspace(-20,20,1000)\nX_Fourier = np.ones(tp.shape[0])*a0\nfor k in range(nterms):\n X_Fourier = X_Fourier + 2.0*(ak[k]*np.cos(2.0*np.pi*(k+1.0)*tp/T) + bk[k]*np.sin(2.0*np.pi*(k+1.0)*tp/T))\n\nplt.figure(figsize=(8,4))\nplt.plot(np.hstack((t-20.0,t-10.0, t, t+10.0)),np.hstack((x,x,x,x))); # Plot four periods\nplt.plot(tp,X_Fourier, label=('Fourier approximation Nterms='+str(nterms)));\nplt.ylim(-2, 2)\nplt.xlim(-20,20)\nplt.grid()\nplt.xlabel('$t$')\nplt.ylabel('$X(t)$')\nplt.legend();\n ", "_____no_output_____" ] ], [ [ "The figure above shows that the Fourier approximation fits the waveform reasonably well and that the approximation gets better as more terms are added to the approximation. Try to change the number of terms yourself and observe how the approximation improves. Also, note the high-frequency oscillations observed in the corners of the Fourier approximation. This is called the Gibbs phenomenon. Note that it is impossible to get rid of these oscillations and that a Fourier series will always slightly overshoot when approximating step changes.", "_____no_output_____" ], [ "## Alternative 2: Trapezoidal rule, complex Fourier series\nThis approach is the same as the example above. Still, now we use the complex format of the Fourier series, which is essentially only rewriting of the formula introducing Euler's formula $e^{i \\omega t} = cos(\\omega t) + i sin(\\omega t)$. We define \n\n$$x_k = a_k -ib_k $$\n\n$$ e^{-i\\left(\\frac{2\\pi kt}{T} \\right)} =cos\\left(\\frac{2\\pi k}{T}t \\right) - i sin\\left(\\frac{2\\pi k}{T}t \\right) $$\n\n\n$$x_k = \\frac{1}{T} \\int_{0}^{T}X(t) \\left( cos\\left(\\frac{2\\pi k}{T}t \\right) - i sin\\left(\\frac{2\\pi k}{T}t \\right) \\right)dt$$\n\n$$x_k = \\frac{1}{T} \\int_{0}^{T} X(t) e^{-i\\left(\\frac{2\\pi kt}{T} \\right)}dt$$\n\nNote that the mean value is obtained when $k=0$ and that both negative and positive $k$ values are necessary to cancel the imaginary part of the approximation. We will now calculate the Fourier coefficients using the trapezoidal rule.", "_____no_output_____" ] ], [ [ "nterms = t.shape[0] # The numer of terms in the Fourier series\nXk = np.zeros(nterms,dtype=complex)\nfor k in range(nterms):\n Xk[k] = 1/T*np.trapz(x*np.exp(-1j*2*np.pi/T*k*t),t)\n\n# Plot Fourier coeffecients\nfig, axs = plt.subplots(nrows=1, ncols=2, constrained_layout=True)\n\nax1 = axs[0]\nax1.plot(np.arange(0,nterms),np.real(Xk))\nax1.set_ylim(-1, 1)\nax1.set_xlim(0, 10)\nax1.grid()\nax1.set_ylabel('$Re(X_k)$');\nax1.set_xlabel('$k$');\n\nax2 = axs[1]\nax2.plot(np.arange(0,nterms),np.imag(Xk))\nax2.set_ylim(-1, 1)\nax2.set_xlim(0, 10)\nax2.grid()\nax2.set_ylabel('$Imag(X_k)$');\nax2.set_xlabel('$k$');\n \n \n ", "_____no_output_____" ] ], [ [ "The figures above and the defined relation $x_k=a_k-ib_k$ show that we get the same Fourier coefficients as the case above and that this is only a rewriting of the Fourier approximation.", "_____no_output_____" ], [ "## Alternative 3: Left rectangular rule, complex Fourier series\n The function is assumed constant and equal to the left side of the rectangle when integrating by the left rectangular rule. This is what is typically implemented in softwares as the descrete Fourier transform. The integral using the left rectangular rule can be expressed as\n \n $$x_k = \\frac{1}{T} \\int_{0}^{T} X(t) e^{-i\\left(\\frac{2\\pi kt}{T} \\right)}dt$$\n \n $$x_k = \\frac{1}{T} \\sum_{r=0}^{N-1} X_r e^{-i\\left(\\frac{2\\pi k r\\Delta t}{T} \\right)} \\Delta t$$\n \n $$x_k = \\frac{1}{N} \\sum_{r=0}^{N-1} X_r e^{-i\\left(\\frac{2\\pi k r}{N} \\right)}$$\n ", "_____no_output_____" ] ], [ [ "N = t.shape[0] # The numer of terms in the Fourier series\nXk = np.zeros(nterms,dtype=complex)\nfor k in range(N):\n Xk[k] = 1/N*np.matmul(x,np.exp(-1j*2*np.pi/N*k*np.arange(N)))\n\n# Plot Fourier coeffecients\nfig, axs = plt.subplots(nrows=1, ncols=2, constrained_layout=True)\n\nax1 = axs[0]\nax1.plot(np.arange(0,nterms),np.real(Xk))\nax1.set_ylim(-1, 1)\nax1.set_xlim(0, 10)\nax1.grid()\nax1.set_ylabel('$Re(X_k)$');\nax1.set_xlabel('$k$');\n\nax2 = axs[1]\nax2.plot(np.arange(0,nterms),np.imag(Xk))\nax2.set_ylim(-1, 1)\nax2.set_xlim(0, 10)\nax2.grid()\nax2.set_ylabel('$Imag(X_k)$');\nax2.set_xlabel('$k$');", "_____no_output_____" ] ], [ [ "## Alternative 4: The fast Fourier transform\nThe discrete Fourier transform can be implemented in a clever time-saving way. This implementation is called the fast Fourier transform and is implemented in many software and is available in the NumPy package. The Fourier coefficients can be obtained using the FFT as shown below.\n\n", "_____no_output_____" ] ], [ [ "N = t.shape[0] # The numer of terms in the Fourier series\nXk = np.fft.fft(x)/N\n\n# Plot Fourier coeffecients\nfig, axs = plt.subplots(nrows=1, ncols=2, constrained_layout=True)\n\nax1 = axs[0]\nax1.plot(np.arange(0,nterms),np.real(Xk))\nax1.set_ylim(-1, 1)\nax1.set_xlim(0, 10)\nax1.grid()\nax1.set_ylabel('$Re(X_k)$');\nax1.set_xlabel('$k$');\n\nax2 = axs[1]\nax2.plot(np.arange(0,nterms),np.imag(Xk))\nax2.set_ylim(-1, 1)\nax2.set_xlim(0, 10)\nax2.grid()\nax2.set_ylabel('$Imag(X_k)$');\nax2.set_xlabel('$k$');", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "\n# Transfer Learning Tutorial\n\n**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_\n\nIn this tutorial, you will learn how to train your network using\ntransfer learning. You can read more about the transfer learning at `cs231n\nnotes <https://cs231n.github.io/transfer-learning/>`__\n\nQuoting these notes,\n\n In practice, very few people train an entire Convolutional Network\n from scratch (with random initialization), because it is relatively\n rare to have a dataset of sufficient size. Instead, it is common to\n pretrain a ConvNet on a very large dataset (e.g. ImageNet, which\n contains 1.2 million images with 1000 categories), and then use the\n ConvNet either as an initialization or a fixed feature extractor for\n the task of interest.\n\nThese two major transfer learning scenarios look as follows:\n\n- **Finetuning the convnet**: Instead of random initializaion, we\n initialize the network with a pretrained network, like the one that is\n trained on imagenet 1000 dataset. Rest of the training looks as\n usual.\n- **ConvNet as fixed feature extractor**: Here, we will freeze the weights\n for all of the network except that of the final fully connected\n layer. This last fully connected layer is replaced with a new one\n with random weights and only this layer is trained.\n", "_____no_output_____" ] ], [ [ "import os\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\n\nimport torchvision\nimport torchvision.models as models\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nimport copy\n\nplt.ion() # interactive mode", "_____no_output_____" ] ], [ [ "## Load Data\n\nWe will use `torchvision` and `torch.utils.data` packages for loading the\ndata.\n\nThe problem we're going to solve today is to train a model to classify\n**ants** and **bees**. We have about 120 training images each for ants and bees.\nThere are 75 validation images for each class. Usually, this is a very\nsmall dataset to generalize upon, if trained from scratch. Since we\nare using transfer learning, we should be able to generalize reasonably\nwell.\n\nThis dataset is a very small subset of imagenet.\n\n.. Note ::\n Download the data from\n `here <https://download.pytorch.org/tutorial/hymenoptera_data.zip>`_\n and extract it to the current directory.\n", "_____no_output_____" ] ], [ [ "# Data augmentation and normalization for training\n# Just normalization for validation\ndata_transforms = {\n 'train': transforms.Compose([\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n 'val': transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n}\n\n\ndata_dir = '/media/storage/datasets/cat_vs_dog'\nimage_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n data_transforms[x])\n for x in ['train', 'val']}\n\ndataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n shuffle=True, num_workers=4)\n for x in ['train', 'val']}\n\ndataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\nclass_names = image_datasets['train'].classes\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")", "_____no_output_____" ] ], [ [ "### Visualize a few images\n\nLet's visualize a few training images so as to understand the data augmentations.\n", "_____no_output_____" ] ], [ [ "def imshow(inp, title=None):\n \"\"\"Imshow for Tensor.\"\"\"\n inp = inp.numpy().transpose((1, 2, 0)) # Convert [channel, height, width] --> [height, width, channel]\n mean = np.array([0.485, 0.456, 0.406])\n std = np.array([0.229, 0.224, 0.225])\n inp = std * inp + mean\n inp = np.clip(inp, 0, 1)\n plt.imshow(inp)\n if title is not None:\n plt.title(title)\n plt.pause(0.001) # pause a bit so that plots are updated\n\n\n# Get a batch of training data\ninputs, classes = next(iter(dataloaders['train']))\n\n# Make a grid from batch\nout = torchvision.utils.make_grid(inputs)\n\nimshow(out, title=[class_names[x] for x in classes])", "_____no_output_____" ] ], [ [ "Training the model\n------------------\n\nNow, let's write a general function to train a model. Here, we will\nillustrate:\n\n- Scheduling the learning rate\n- Saving the best model\n\nIn the following, parameter ``scheduler`` is an LR scheduler object from\n``torch.optim.lr_scheduler``.\n", "_____no_output_____" ] ], [ [ "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n since = time.time()\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_acc = 0.0\n\n for epoch in range(num_epochs):\n print(f'Epoch {epoch}/{num_epochs - 1}')\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n scheduler.step()\n model.train() # Set model to training mode\n else:\n model.eval() # Set model to evaluate mode\n\n running_loss = 0.0\n running_corrects = 0\n\n # Iterate over data.\n for inputs, labels in dataloaders[phase]:\n inputs = inputs.to(device)\n labels = labels.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n # track history if only in train\n with torch.set_grad_enabled(phase == 'train'):\n outputs = model(inputs)\n _, preds = torch.max(outputs, 1)\n loss = criterion(outputs, labels)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.item() * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n\n epoch_loss = running_loss / dataset_sizes[phase]\n epoch_acc = running_corrects.double() / dataset_sizes[phase]\n\n print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n\n # deep copy the model\n if phase == 'val' and epoch_acc > best_acc:\n best_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n\n print()\n\n time_elapsed = time.time() - since\n print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n print(f'Best val Acc: {best_acc:4f}')\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model", "_____no_output_____" ] ], [ [ "### Visualizing the model predictions\n\nGeneric function to display predictions for a few images\n", "_____no_output_____" ] ], [ [ "def visualize_model(model, num_images=6):\n was_training = model.training\n model.eval()\n images_so_far = 0\n fig = plt.figure()\n\n with torch.no_grad():\n for i, (inputs, labels) in enumerate(dataloaders['val']):\n inputs = inputs.to(device)\n labels = labels.to(device)\n\n outputs = model(inputs)\n _, preds = torch.max(outputs, 1)\n\n for j in range(inputs.size()[0]):\n images_so_far += 1\n ax = plt.subplot(num_images//2, 2, images_so_far)\n ax.axis('off')\n ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n imshow(inputs.cpu().data[j])\n\n if images_so_far == num_images:\n model.train(mode=was_training)\n return\n model.train(mode=was_training)", "_____no_output_____" ] ], [ [ "Finetuning the convnet\n----------------------\n\nLoad a pretrained model and reset final fully connected layer.\n\n\n", "_____no_output_____" ] ], [ [ "model_ft = models.resnet18(pretrained=True)\nnum_ftrs = model_ft.fc.in_features\nmodel_ft.fc = nn.Linear(num_ftrs, 2)\n\nmodel_ft = model_ft.to(device)\n\ncriterion = nn.CrossEntropyLoss()\n\n# Observe that all parameters are being optimized\noptimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n\n# Decay LR by a factor of 0.1 every 7 epochs\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)", "_____no_output_____" ] ], [ [ "### Train and evaluate\n\nIt should take around 15-25 min on CPU. On GPU though, it takes less than a\nminute.\n", "_____no_output_____" ] ], [ [ "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n num_epochs=25)", "_____no_output_____" ], [ "visualize_model(model_ft)", "_____no_output_____" ] ], [ [ "ConvNet as fixed feature extractor\n----------------------------------\n\nHere, we need to freeze all the network except the final layer. We need\nto set ``requires_grad == False`` to freeze the parameters so that the\ngradients are not computed in ``backward()``.\n\nYou can read more about this in the documentation\n`here <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.\n\n\n", "_____no_output_____" ] ], [ [ "model_conv = torchvision.models.resnet18(pretrained=True)\nfor param in model_conv.parameters():\n param.requires_grad = False\n\n# Parameters of newly constructed modules have requires_grad=True by default\nnum_ftrs = model_conv.fc.in_features\nmodel_conv.fc = nn.Linear(num_ftrs, 2)\n\nmodel_conv = model_conv.to(device)\n\ncriterion = nn.CrossEntropyLoss()\n\n# Observe that only parameters of final layer are being optimized as\n# opposed to before.\noptimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n\n# Decay LR by a factor of 0.1 every 7 epochs\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)", "_____no_output_____" ] ], [ [ "### Train and evaluate\n\nOn CPU this will take about half the time compared to previous scenario.\nThis is expected as gradients don't need to be computed for most of the\nnetwork. However, forward does need to be computed.\n\n\n", "_____no_output_____" ] ], [ [ "model_conv = train_model(model_conv, criterion, optimizer_conv,\n exp_lr_scheduler, num_epochs=25)", "_____no_output_____" ], [ "visualize_model(model_conv)\n\nplt.ioff()\nplt.show()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Plot estimated and observed clade frequencies\n\nPlot the overall observed clade frequencies compared to the estimated frequencies at each timepoint. The differences between these frequencies tells us something about the error in frequency estimation due to missing data from the near future.", "_____no_output_____" ] ], [ [ "from collections import defaultdict\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\n%matplotlib inline\n\nplt.style.use(\"huddlej\")", "_____no_output_____" ], [ "!pwd", "/Users/jlhudd/projects/nextstrain/flu-forecasting/analyses\n" ], [ "with open(\"../results/builds/h3n2/5_viruses_per_month/sample_0/2005-10-01--2015-10-01/timepoints/2015-10-01/segments/ha/frequencies.json\", \"r\") as fh:\n frequencies = json.load(fh)", "_____no_output_____" ], [ "with open(\"../results/builds/h3n2/5_viruses_per_month/sample_0/2005-10-01--2015-10-01/timepoints/2015-10-01/segments/ha/clades.json\", \"r\") as fh:\n clades = json.load(fh)", "_____no_output_____" ], [ "tips = pd.read_csv(\"../results/builds/h3n2/5_viruses_per_month/sample_0/2005-10-01--2015-10-01/standardized_tip_attributes.tsv\",\n sep=\"\\t\", parse_dates=[\"timepoint\"])\ntips = tips.loc[\n tips[\"segment\"] == \"ha\",\n [\"strain\", \"clade_membership\", \"timepoint\", \"frequency\"]\n].copy()", "_____no_output_____" ], [ "data_path = \"../results/builds/h3n2/5_viruses_per_month/sample_0/2005-10-01--2015-10-01/tips_to_clades.tsv\"", "_____no_output_____" ], [ "df = pd.read_csv(data_path, sep=\"\\t\", parse_dates=[\"timepoint\"])", "_____no_output_____" ], [ "# successful clade\nclade_name = \"d4aa5d5\"\n\n# unsuccessful clade\n#clade_name = \"5f0cf16\"", "_____no_output_____" ], [ "clade_tips = df[df[\"clade_membership\"] == clade_name][\"tip\"].unique()", "_____no_output_____" ], [ "clade_tips.shape", "_____no_output_____" ], [ "df[\"tip\"].unique().shape", "_____no_output_____" ], [ "clade_tips.shape", "_____no_output_____" ], [ "estimated_clade_frequencies = tips[tips[\"strain\"].isin(clade_tips)].groupby(\"timepoint\")[\"frequency\"].sum().reset_index()", "_____no_output_____" ], [ "estimated_clade_frequencies[\"timepoint_float\"] = estimated_clade_frequencies[\"timepoint\"].dt.year + (estimated_clade_frequencies[\"timepoint\"].dt.month - 1) / 12.0", "_____no_output_____" ], [ "estimated_clade_frequencies", "_____no_output_____" ], [ "clade_frequencies = np.zeros_like(frequencies[\"data\"][\"pivots\"])", "_____no_output_____" ], [ "for tip in clade_tips:\n clade_frequencies += frequencies[\"data\"][\"frequencies\"][tip]", "_____no_output_____" ], [ "clade_frequencies", "_____no_output_____" ], [ "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\nax.plot(frequencies[\"data\"][\"pivots\"], clade_frequencies, \"o-\")\nax.plot(estimated_clade_frequencies[\"timepoint_float\"], estimated_clade_frequencies[\"frequency\"], \"o\")\nax.set_xlabel(\"Date\")\nax.set_ylabel(\"Frequency\")\n#ax.set_ylim(0, 1)", "_____no_output_____" ], [ "tips[tips[\"strain\"].isin(clade_tips)]", "_____no_output_____" ], [ "found_clade_tips = tips[tips[\"strain\"].isin(clade_tips)][\"strain\"].unique()", "_____no_output_____" ], [ "set(clade_tips) - set(found_clade_tips)", "_____no_output_____" ], [ "tips[tips[\"strain\"] == \"A/Kenya/230/2012\"]", "_____no_output_____" ], [ "df[df[\"clade_membership\"] == \"5f0cf16\"]", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "\n<a id='python-done-right'></a>\n<div id=\"qe-notebook-header\" align=\"right\" style=\"text-align:right;\">\n <a href=\"https://quantecon.org/\" title=\"quantecon.org\">\n <img style=\"width:250px;display:inline;\" width=\"250px\" src=\"https://assets.quantecon.org/img/qe-menubar-logo.svg\" alt=\"QuantEcon\">\n </a>\n</div>", "_____no_output_____" ], [ "# Python Essentials", "_____no_output_____" ], [ "## Contents\n\n- [Python Essentials](#Python-Essentials) \n - [Overview](#Overview) \n - [Data Types](#Data-Types) \n - [Input and Output](#Input-and-Output) \n - [Iterating](#Iterating) \n - [Comparisons and Logical Operators](#Comparisons-and-Logical-Operators) \n - [More Functions](#More-Functions) \n - [Coding Style and PEP8](#Coding-Style-and-PEP8) \n - [Exercises](#Exercises) \n - [Solutions](#Solutions) ", "_____no_output_____" ], [ "## Overview\n\nWe have covered a lot of material quite quickly, with a focus on examples.\n\nNow let’s cover some core features of Python in a more systematic way.\n\nThis approach is less exciting but helps clear up some details.", "_____no_output_____" ], [ "## Data Types\n\n\n<a id='index-0'></a>\nComputer programs typically keep track of a range of data types.\n\nFor example, `1.5` is a floating point number, while `1` is an integer.\n\nPrograms need to distinguish between these two types for various reasons.\n\nOne is that they are stored in memory differently.\n\nAnother is that arithmetic operations are different\n\n- For example, floating point arithmetic is implemented on most machines by a\n specialized Floating Point Unit (FPU). \n\n\nIn general, floats are more informative but arithmetic operations on integers\nare faster and more accurate.\n\nPython provides numerous other built-in Python data types, some of which we’ve already met\n\n- strings, lists, etc. \n\n\nLet’s learn a bit more about them.", "_____no_output_____" ], [ "### Primitive Data Types\n\nOne simple data type is **Boolean values**, which can be either `True` or `False`", "_____no_output_____" ] ], [ [ "x = True\nx", "_____no_output_____" ] ], [ [ "We can check the type of any object in memory using the `type()` function.", "_____no_output_____" ] ], [ [ "type(x)", "_____no_output_____" ] ], [ [ "In the next line of code, the interpreter evaluates the expression on the right of = and binds y to this value", "_____no_output_____" ] ], [ [ "y = 100 < 10\ny", "_____no_output_____" ], [ "type(y)", "_____no_output_____" ] ], [ [ "In arithmetic expressions, `True` is converted to `1` and `False` is converted `0`.\n\nThis is called **Boolean arithmetic** and is often useful in programming.\n\nHere are some examples", "_____no_output_____" ] ], [ [ "x + y", "_____no_output_____" ], [ "x * y", "_____no_output_____" ], [ "True + True", "_____no_output_____" ], [ "bools = [True, True, False, True] # List of Boolean values\n\nsum(bools)", "_____no_output_____" ] ], [ [ "Complex numbers are another primitive data type in Python", "_____no_output_____" ] ], [ [ "x = complex(1, 2)\ny = complex(2, 1)\nprint(x * y)\n\ntype(x)", "_____no_output_____" ] ], [ [ "### Containers\n\nPython has several basic types for storing collections of (possibly heterogeneous) data.\n\nWe’ve [already discussed lists](https://python-programming.quantecon.org/python_by_example.html#lists-ref).\n\n\n<a id='index-1'></a>\nA related data type is **tuples**, which are “immutable” lists", "_____no_output_____" ] ], [ [ "x = ('a', 'b') # Parentheses instead of the square brackets\nx = 'a', 'b' # Or no brackets --- the meaning is identical\nx", "_____no_output_____" ], [ "type(x)", "_____no_output_____" ] ], [ [ "In Python, an object is called **immutable** if, once created, the object cannot be changed.\n\nConversely, an object is **mutable** if it can still be altered after creation.\n\nPython lists are mutable", "_____no_output_____" ] ], [ [ "x = [1, 2]\nx[0] = 10\nx", "_____no_output_____" ] ], [ [ "But tuples are not", "_____no_output_____" ] ], [ [ "x = (1, 2)\nx[0] = 10", "_____no_output_____" ] ], [ [ "We’ll say more about the role of mutable and immutable data a bit later.\n\nTuples (and lists) can be “unpacked” as follows", "_____no_output_____" ] ], [ [ "integers = (10, 20, 30)\nx, y, z = integers\nx", "_____no_output_____" ], [ "y", "_____no_output_____" ] ], [ [ "You’ve actually [seen an example of this](https://python-programming.quantecon.org/about_py.html#tuple-unpacking-example) already.\n\nTuple unpacking is convenient and we’ll use it often.", "_____no_output_____" ], [ "#### Slice Notation\n\n\n<a id='index-2'></a>\nTo access multiple elements of a list or tuple, you can use Python’s slice\nnotation.\n\nFor example,", "_____no_output_____" ] ], [ [ "a = [2, 4, 6, 8]\na[1:]", "_____no_output_____" ], [ "a[1:3]", "_____no_output_____" ] ], [ [ "The general rule is that `a[m:n]` returns `n - m` elements, starting at `a[m]`.\n\nNegative numbers are also permissible", "_____no_output_____" ] ], [ [ "a[-2:] # Last two elements of the list", "_____no_output_____" ] ], [ [ "The same slice notation works on tuples and strings", "_____no_output_____" ] ], [ [ "s = 'foobar'\ns[-3:] # Select the last three elements", "_____no_output_____" ] ], [ [ "#### Sets and Dictionaries\n\n\n<a id='index-4'></a>\nTwo other container types we should mention before moving on are [sets](https://docs.python.org/3/tutorial/datastructures.html#sets) and [dictionaries](https://docs.python.org/3/tutorial/datastructures.html#dictionaries).\n\nDictionaries are much like lists, except that the items are named instead of\nnumbered", "_____no_output_____" ] ], [ [ "d = {'name': 'Frodo', 'age': 33}\ntype(d)", "_____no_output_____" ], [ "d['age']", "_____no_output_____" ] ], [ [ "The names `'name'` and `'age'` are called the *keys*.\n\nThe objects that the keys are mapped to (`'Frodo'` and `33`) are called the `values`.\n\nSets are unordered collections without duplicates, and set methods provide the\nusual set-theoretic operations", "_____no_output_____" ] ], [ [ "s1 = {'a', 'b'}\ntype(s1)", "_____no_output_____" ], [ "s2 = {'b', 'c'}\ns1.issubset(s2)", "_____no_output_____" ], [ "s1.intersection(s2)", "_____no_output_____" ] ], [ [ "The `set()` function creates sets from sequences", "_____no_output_____" ] ], [ [ "s3 = set(('foo', 'bar', 'foo'))\ns3", "_____no_output_____" ] ], [ [ "## Input and Output\n\n\n<a id='index-5'></a>\nLet’s briefly review reading and writing to text files, starting with writing", "_____no_output_____" ] ], [ [ "f = open('newfile.txt', 'w') # Open 'newfile.txt' for writing\nf.write('Testing\\n') # Here '\\n' means new line\nf.write('Testing again')\nf.close()", "_____no_output_____" ] ], [ [ "Here\n\n- The built-in function `open()` creates a file object for writing to. \n- Both `write()` and `close()` are methods of file objects. \n\n\nWhere is this file that we’ve created?\n\nRecall that Python maintains a concept of the present working directory (pwd) that can be located from with Jupyter or IPython via", "_____no_output_____" ] ], [ [ "%pwd", "_____no_output_____" ] ], [ [ "If a path is not specified, then this is where Python writes to.\n\nWe can also use Python to read the contents of `newline.txt` as follows", "_____no_output_____" ] ], [ [ "f = open('newfile.txt', 'r')\nout = f.read()\nout", "_____no_output_____" ], [ "print(out)", "_____no_output_____" ] ], [ [ "### Paths\n\n\n<a id='index-6'></a>\nNote that if `newfile.txt` is not in the present working directory then this call to `open()` fails.\n\nIn this case, you can shift the file to the pwd or specify the [full path](https://en.wikipedia.org/wiki/Path_%28computing%29) to the file", "_____no_output_____" ], [ "```python3\nf = open('insert_full_path_to_file/newfile.txt', 'r')\n```\n", "_____no_output_____" ], [ "\n<a id='iterating-version-1'></a>", "_____no_output_____" ], [ "## Iterating\n\n\n<a id='index-7'></a>\nOne of the most important tasks in computing is stepping through a\nsequence of data and performing a given action.\n\nOne of Python’s strengths is its simple, flexible interface to this kind of iteration via\nthe `for` loop.", "_____no_output_____" ], [ "### Looping over Different Objects\n\nMany Python objects are “iterable”, in the sense that they can be looped over.\n\nTo give an example, let’s write the file us_cities.txt, which lists US cities and their population, to the present working directory.\n\n\n<a id='us-cities-data'></a>", "_____no_output_____" ] ], [ [ "%%file us_cities.txt\nnew york: 8244910\nlos angeles: 3819702\nchicago: 2707120\nhouston: 2145146\nphiladelphia: 1536471\nphoenix: 1469471\nsan antonio: 1359758\nsan diego: 1326179\ndallas: 1223229", "_____no_output_____" ] ], [ [ "Here %%file is an [IPython cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#cell-magics).\n\nSuppose that we want to make the information more readable, by capitalizing names and adding commas to mark thousands.\n\nThe program below reads the data in and makes the conversion:", "_____no_output_____" ] ], [ [ "data_file = open('us_cities.txt', 'r')\nfor line in data_file:\n city, population = line.split(':') # Tuple unpacking\n city = city.title() # Capitalize city names\n population = f'{int(population):,}' # Add commas to numbers\n print(city.ljust(15) + population)\ndata_file.close()", "_____no_output_____" ] ], [ [ "Here `format()` is a string method [used for inserting variables into strings](https://docs.python.org/3/library/string.html#formatspec).\n\nThe reformatting of each line is the result of three different string methods,\nthe details of which can be left till later.\n\nThe interesting part of this program for us is line 2, which shows that\n\n1. The file object `data_file` is iterable, in the sense that it can be placed to the right of `in` within a `for` loop. \n1. Iteration steps through each line in the file. \n\n\nThis leads to the clean, convenient syntax shown in our program.\n\nMany other kinds of objects are iterable, and we’ll discuss some of them later on.", "_____no_output_____" ], [ "### Looping without Indices\n\nOne thing you might have noticed is that Python tends to favor looping without explicit indexing.\n\nFor example,", "_____no_output_____" ] ], [ [ "x_values = [1, 2, 3] # Some iterable x\nfor x in x_values:\n print(x * x)", "_____no_output_____" ] ], [ [ "is preferred to", "_____no_output_____" ] ], [ [ "for i in range(len(x_values)):\n print(x_values[i] * x_values[i])", "_____no_output_____" ] ], [ [ "When you compare these two alternatives, you can see why the first one is preferred.\n\nPython provides some facilities to simplify looping without indices.\n\nOne is `zip()`, which is used for stepping through pairs from two sequences.\n\nFor example, try running the following code", "_____no_output_____" ] ], [ [ "countries = ('Japan', 'Korea', 'China')\ncities = ('Tokyo', 'Seoul', 'Beijing')\nfor country, city in zip(countries, cities):\n print(f'The capital of {country} is {city}')", "_____no_output_____" ] ], [ [ "The `zip()` function is also useful for creating dictionaries — for\nexample", "_____no_output_____" ] ], [ [ "names = ['Tom', 'John']\nmarks = ['E', 'F']\ndict(zip(names, marks))", "_____no_output_____" ] ], [ [ "If we actually need the index from a list, one option is to use `enumerate()`.\n\nTo understand what `enumerate()` does, consider the following example", "_____no_output_____" ] ], [ [ "letter_list = ['a', 'b', 'c']\nfor index, letter in enumerate(letter_list):\n print(f\"letter_list[{index}] = '{letter}'\")", "_____no_output_____" ] ], [ [ "### List Comprehensions\n\n\n<a id='index-8'></a>\nWe can also simplify the code for generating the list of random draws considerably by using something called a *list comprehension*.\n\n[List comprehensions](https://en.wikipedia.org/wiki/List_comprehension) are an elegant Python tool for creating lists.\n\nConsider the following example, where the list comprehension is on the\nright-hand side of the second line", "_____no_output_____" ] ], [ [ "animals = ['dog', 'cat', 'bird']\nplurals = [animal + 's' for animal in animals]\nplurals", "_____no_output_____" ] ], [ [ "Here’s another example", "_____no_output_____" ] ], [ [ "range(8)", "_____no_output_____" ], [ "doubles = [2 * x for x in range(8)]\ndoubles", "_____no_output_____" ] ], [ [ "## Comparisons and Logical Operators", "_____no_output_____" ], [ "### Comparisons\n\n\n<a id='index-9'></a>\nMany different kinds of expressions evaluate to one of the Boolean values (i.e., `True` or `False`).\n\nA common type is comparisons, such as", "_____no_output_____" ] ], [ [ "x, y = 1, 2\nx < y", "_____no_output_____" ], [ "x > y", "_____no_output_____" ] ], [ [ "One of the nice features of Python is that we can *chain* inequalities", "_____no_output_____" ] ], [ [ "1 < 2 < 3", "_____no_output_____" ], [ "1 <= 2 <= 3", "_____no_output_____" ] ], [ [ "As we saw earlier, when testing for equality we use `==`", "_____no_output_____" ] ], [ [ "x = 1 # Assignment\nx == 2 # Comparison", "_____no_output_____" ] ], [ [ "For “not equal” use `!=`", "_____no_output_____" ] ], [ [ "1 != 2", "_____no_output_____" ] ], [ [ "Note that when testing conditions, we can use **any** valid Python expression", "_____no_output_____" ] ], [ [ "x = 'yes' if 42 else 'no'\nx", "_____no_output_____" ], [ "x = 'yes' if [] else 'no'\nx", "_____no_output_____" ] ], [ [ "What’s going on here?\n\nThe rule is:\n\n- Expressions that evaluate to zero, empty sequences or containers (strings, lists, etc.) and `None` are all equivalent to `False`. \n - for example, `[]` and `()` are equivalent to `False` in an `if` clause \n- All other values are equivalent to `True`. \n - for example, `42` is equivalent to `True` in an `if` clause ", "_____no_output_____" ], [ "### Combining Expressions\n\n\n<a id='index-10'></a>\nWe can combine expressions using `and`, `or` and `not`.\n\nThese are the standard logical connectives (conjunction, disjunction and denial)", "_____no_output_____" ] ], [ [ "1 < 2 and 'f' in 'foo'", "_____no_output_____" ], [ "1 < 2 and 'g' in 'foo'", "_____no_output_____" ], [ "1 < 2 or 'g' in 'foo'", "_____no_output_____" ], [ "not True", "_____no_output_____" ], [ "not not True", "_____no_output_____" ] ], [ [ "Remember\n\n- `P and Q` is `True` if both are `True`, else `False` \n- `P or Q` is `False` if both are `False`, else `True` ", "_____no_output_____" ], [ "## More Functions\n\n\n<a id='index-11'></a>\nLet’s talk a bit more about functions, which are all important for good programming style.", "_____no_output_____" ], [ "### The Flexibility of Python Functions\n\nAs we discussed in the [previous lecture](https://python-programming.quantecon.org/python_by_example.html#python-by-example), Python functions are very flexible.\n\nIn particular\n\n- Any number of functions can be defined in a given file. \n- Functions can be (and often are) defined inside other functions. \n- Any object can be passed to a function as an argument, including other functions. \n- A function can return any kind of object, including functions. \n\n\nWe already [gave an example](https://python-programming.quantecon.org/functions.html#test-program-6) of how straightforward it is to pass a function to\na function.\n\nNote that a function can have arbitrarily many `return` statements (including zero).\n\nExecution of the function terminates when the first return is hit, allowing\ncode like the following example", "_____no_output_____" ] ], [ [ "def f(x):\n if x < 0:\n return 'negative'\n return 'nonnegative'", "_____no_output_____" ] ], [ [ "Functions without a return statement automatically return the special Python object `None`.", "_____no_output_____" ], [ "### Docstrings\n\n\n<a id='index-12'></a>\nPython has a system for adding comments to functions, modules, etc. called *docstrings*.\n\nThe nice thing about docstrings is that they are available at run-time.\n\nTry running this", "_____no_output_____" ] ], [ [ "def f(x):\n \"\"\"\n This function squares its argument\n \"\"\"\n return x**2", "_____no_output_____" ] ], [ [ "After running this code, the docstring is available", "_____no_output_____" ] ], [ [ "f?", "_____no_output_____" ] ], [ [ "```ipython\nType: function\nString Form:<function f at 0x2223320>\nFile: /home/john/temp/temp.py\nDefinition: f(x)\nDocstring: This function squares its argument\n```\n", "_____no_output_____" ] ], [ [ "f??", "_____no_output_____" ] ], [ [ "```ipython\nType: function\nString Form:<function f at 0x2223320>\nFile: /home/john/temp/temp.py\nDefinition: f(x)\nSource:\ndef f(x):\n \"\"\"\n This function squares its argument\n \"\"\"\n return x**2\n```\n", "_____no_output_____" ], [ "With one question mark we bring up the docstring, and with two we get the source code as well.", "_____no_output_____" ], [ "### One-Line Functions: `lambda`\n\n\n<a id='index-13'></a>\nThe `lambda` keyword is used to create simple functions on one line.\n\nFor example, the definitions", "_____no_output_____" ] ], [ [ "def f(x):\n return x**3", "_____no_output_____" ] ], [ [ "and", "_____no_output_____" ] ], [ [ "f = lambda x: x**3", "_____no_output_____" ] ], [ [ "are entirely equivalent.\n\nTo see why `lambda` is useful, suppose that we want to calculate $ \\int_0^2 x^3 dx $ (and have forgotten our high-school calculus).\n\nThe SciPy library has a function called `quad` that will do this calculation for us.\n\nThe syntax of the `quad` function is `quad(f, a, b)` where `f` is a function and `a` and `b` are numbers.\n\nTo create the function $ f(x) = x^3 $ we can use `lambda` as follows", "_____no_output_____" ] ], [ [ "from scipy.integrate import quad\n\nquad(lambda x: x**3, 0, 2)", "_____no_output_____" ] ], [ [ "Here the function created by `lambda` is said to be *anonymous* because it was never given a name.", "_____no_output_____" ], [ "### Keyword Arguments\n\n\n<a id='index-14'></a>\nIn a [previous lecture](https://python-programming.quantecon.org/python_by_example.html#python-by-example), you came across the statement", "_____no_output_____" ], [ "```python3\nplt.plot(x, 'b-', label=\"white noise\")\n```\n", "_____no_output_____" ], [ "In this call to Matplotlib’s `plot` function, notice that the last argument is passed in `name=argument` syntax.\n\nThis is called a *keyword argument*, with `label` being the keyword.\n\nNon-keyword arguments are called *positional arguments*, since their meaning\nis determined by order\n\n- `plot(x, 'b-', label=\"white noise\")` is different from `plot('b-', x, label=\"white noise\")` \n\n\nKeyword arguments are particularly useful when a function has a lot of arguments, in which case it’s hard to remember the right order.\n\nYou can adopt keyword arguments in user-defined functions with no difficulty.\n\nThe next example illustrates the syntax", "_____no_output_____" ] ], [ [ "def f(x, a=1, b=1):\n return a + b * x", "_____no_output_____" ] ], [ [ "The keyword argument values we supplied in the definition of `f` become the default values", "_____no_output_____" ] ], [ [ "f(2)", "_____no_output_____" ] ], [ [ "They can be modified as follows", "_____no_output_____" ] ], [ [ "f(2, a=4, b=5)", "_____no_output_____" ] ], [ [ "## Coding Style and PEP8\n\n\n<a id='index-15'></a>\nTo learn more about the Python programming philosophy type `import this` at the prompt.\n\nAmong other things, Python strongly favors consistency in programming style.\n\nWe’ve all heard the saying about consistency and little minds.\n\nIn programming, as in mathematics, the opposite is true\n\n- A mathematical paper where the symbols $ \\cup $ and $ \\cap $ were\n reversed would be very hard to read, even if the author told you so on the\n first page. \n\n\nIn Python, the standard style is set out in [PEP8](https://www.python.org/dev/peps/pep-0008/).\n\n(Occasionally we’ll deviate from PEP8 in these lectures to better match mathematical notation)", "_____no_output_____" ], [ "## Exercises\n\nSolve the following exercises.\n\n(For some, the built-in function `sum()` comes in handy).\n\n\n<a id='pyess-ex1'></a>", "_____no_output_____" ], [ "### Exercise 1\n\nPart 1: Given two numeric lists or tuples `x_vals` and `y_vals` of equal length, compute\ntheir inner product using `zip()`.\n\nPart 2: In one line, count the number of even numbers in 0,…,99.\n\n- Hint: `x % 2` returns 0 if `x` is even, 1 otherwise. \n\n\nPart 3: Given `pairs = ((2, 5), (4, 2), (9, 8), (12, 10))`, count the number of pairs `(a, b)`\nsuch that both `a` and `b` are even.\n\n\n<a id='pyess-ex2'></a>", "_____no_output_____" ], [ "### Exercise 2\n\nConsider the polynomial\n\n\n<a id='equation-polynom0'></a>\n$$\np(x)\n= a_0 + a_1 x + a_2 x^2 + \\cdots a_n x^n\n= \\sum_{i=0}^n a_i x^i \\tag{5.1}\n$$\n\nWrite a function `p` such that `p(x, coeff)` that computes the value in [(5.1)](#equation-polynom0) given a point `x` and a list of coefficients `coeff`.\n\nTry to use `enumerate()` in your loop.\n\n\n<a id='pyess-ex3'></a>", "_____no_output_____" ], [ "### Exercise 3\n\nWrite a function that takes a string as an argument and returns the number of capital letters in the string.\n\nHint: `'foo'.upper()` returns `'FOO'`.\n\n\n<a id='pyess-ex4'></a>", "_____no_output_____" ], [ "### Exercise 4\n\nWrite a function that takes two sequences `seq_a` and `seq_b` as arguments and\nreturns `True` if every element in `seq_a` is also an element of `seq_b`, else\n`False`.\n\n- By “sequence” we mean a list, a tuple or a string. \n- Do the exercise without using [sets](https://docs.python.org/3/tutorial/datastructures.html#sets) and set methods. \n\n\n\n<a id='pyess-ex5'></a>", "_____no_output_____" ], [ "### Exercise 5\n\nWhen we cover the numerical libraries, we will see they include many\nalternatives for interpolation and function approximation.\n\nNevertheless, let’s write our own function approximation routine as an exercise.\n\nIn particular, without using any imports, write a function `linapprox` that takes as arguments\n\n- A function `f` mapping some interval $ [a, b] $ into $ \\mathbb R $. \n- Two scalars `a` and `b` providing the limits of this interval. \n- An integer `n` determining the number of grid points. \n- A number `x` satisfying `a <= x <= b`. \n\n\nand returns the [piecewise linear interpolation](https://en.wikipedia.org/wiki/Linear_interpolation) of `f` at `x`, based on `n` evenly spaced grid points `a = point[0] < point[1] < ... < point[n-1] = b`.\n\nAim for clarity, not efficiency.", "_____no_output_____" ], [ "### Exercise 6\n\nUsing list comprehension syntax, we can simplify the loop in the following\ncode.", "_____no_output_____" ] ], [ [ "import numpy as np\n\nn = 100\nϵ_values = []\nfor i in range(n):\n e = np.random.randn()\n ϵ_values.append(e)", "_____no_output_____" ] ], [ [ "## Solutions", "_____no_output_____" ], [ "### Exercise 1", "_____no_output_____" ], [ "#### Part 1 Solution:\n\nHere’s one possible solution", "_____no_output_____" ] ], [ [ "x_vals = [1, 2, 3]\ny_vals = [1, 1, 1]\nsum([x * y for x, y in zip(x_vals, y_vals)])", "_____no_output_____" ] ], [ [ "This also works", "_____no_output_____" ] ], [ [ "sum(x * y for x, y in zip(x_vals, y_vals))", "_____no_output_____" ] ], [ [ "#### Part 2 Solution:\n\nOne solution is", "_____no_output_____" ] ], [ [ "sum([x % 2 == 0 for x in range(100)])", "_____no_output_____" ] ], [ [ "This also works:", "_____no_output_____" ] ], [ [ "sum(x % 2 == 0 for x in range(100))", "_____no_output_____" ] ], [ [ "Some less natural alternatives that nonetheless help to illustrate the\nflexibility of list comprehensions are", "_____no_output_____" ] ], [ [ "len([x for x in range(100) if x % 2 == 0])", "_____no_output_____" ] ], [ [ "and", "_____no_output_____" ] ], [ [ "sum([1 for x in range(100) if x % 2 == 0])", "_____no_output_____" ] ], [ [ "#### Part 3 Solution\n\nHere’s one possibility", "_____no_output_____" ] ], [ [ "pairs = ((2, 5), (4, 2), (9, 8), (12, 10))\nsum([x % 2 == 0 and y % 2 == 0 for x, y in pairs])", "_____no_output_____" ] ], [ [ "### Exercise 2", "_____no_output_____" ] ], [ [ "def p(x, coeff):\n return sum(a * x**i for i, a in enumerate(coeff))", "_____no_output_____" ], [ "p(1, (2, 4))", "_____no_output_____" ] ], [ [ "### Exercise 3\n\nHere’s one solution:", "_____no_output_____" ] ], [ [ "def f(string):\n count = 0\n for letter in string:\n if letter == letter.upper() and letter.isalpha():\n count += 1\n return count\n\nf('The Rain in Spain')", "_____no_output_____" ] ], [ [ "An alternative, more pythonic solution:", "_____no_output_____" ] ], [ [ "def count_uppercase_chars(s):\n return sum([c.isupper() for c in s])\n\ncount_uppercase_chars('The Rain in Spain')", "_____no_output_____" ] ], [ [ "### Exercise 4\n\nHere’s a solution:", "_____no_output_____" ] ], [ [ "def f(seq_a, seq_b):\n is_subset = True\n for a in seq_a:\n if a not in seq_b:\n is_subset = False\n return is_subset\n\n# == test == #\n\nprint(f([1, 2], [1, 2, 3]))\nprint(f([1, 2, 3], [1, 2]))", "_____no_output_____" ] ], [ [ "Of course, if we use the `sets` data type then the solution is easier", "_____no_output_____" ] ], [ [ "def f(seq_a, seq_b):\n return set(seq_a).issubset(set(seq_b))", "_____no_output_____" ] ], [ [ "### Exercise 5", "_____no_output_____" ] ], [ [ "def linapprox(f, a, b, n, x):\n \"\"\"\n Evaluates the piecewise linear interpolant of f at x on the interval\n [a, b], with n evenly spaced grid points.\n\n Parameters\n ==========\n f : function\n The function to approximate\n\n x, a, b : scalars (floats or integers)\n Evaluation point and endpoints, with a <= x <= b\n\n n : integer\n Number of grid points\n\n Returns\n =======\n A float. The interpolant evaluated at x\n\n \"\"\"\n length_of_interval = b - a\n num_subintervals = n - 1\n step = length_of_interval / num_subintervals\n\n # === find first grid point larger than x === #\n point = a\n while point <= x:\n point += step\n\n # === x must lie between the gridpoints (point - step) and point === #\n u, v = point - step, point\n\n return f(u) + (x - u) * (f(v) - f(u)) / (v - u)", "_____no_output_____" ] ], [ [ "### Exercise 6\n\nHere’s one solution.", "_____no_output_____" ] ], [ [ "n = 100\nϵ_values = [np.random.randn() for i in range(n)]", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# `uarray` NumPy Compatability", "_____no_output_____" ] ], [ [ "from uarray import *\nimport numpy as np\nfrom numba import njit", "_____no_output_____" ] ], [ [ "## Original Expression", "_____no_output_____" ], [ "Let's look at this simple NumPy expression of calling the outer production of two values and then indexing it:", "_____no_output_____" ] ], [ [ "def some_fn(a, b):\n return np.multiply.outer(a, b)[5]", "_____no_output_____" ] ], [ [ "We can see that this does a lot of extra work, since we discard most of the results of the outer product after indexing. We can look at the time:", "_____no_output_____" ] ], [ [ "args = [np.arange(1000), np.arange(10)]", "_____no_output_____" ], [ "# NBVAL_IGNORE_OUTPUT\n%timeit some_fn(*args)", "27.5 µs ± 214 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] ], [ [ "## Uarray reduced", "_____no_output_____" ], [ "Now let's use uarray's `optimize` decorator to create an updated function that specifes the dimensionality of the arrays to produced an optimized form:", "_____no_output_____" ] ], [ [ "# enable_logging()", "_____no_output_____" ], [ "optimized_some_fn = optimize(args[0].shape, args[1].shape)(some_fn)", "_____no_output_____" ] ], [ [ "Now let's try our function out to see if it's faster:", "_____no_output_____" ] ], [ [ "# NBVAL_IGNORE_OUTPUT\n%timeit optimized_some_fn(*args)", "5.47 µs ± 48.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" ] ], [ [ "Yep about 10x as fast. Let's look at how this is done! First, we create an abstract representation of the array operations:", "_____no_output_____" ] ], [ [ "optimized_some_fn.__optimize_steps__['resulting_expr']", "_____no_output_____" ] ], [ [ "Then, we compile that to Python AST:", "_____no_output_____" ] ], [ [ "print(optimized_some_fn.__optimize_steps__['ast_as_source'])", "\n\ndef fn(a, b):\n i_5 = ()\n i_6 = 10\n i_1 = ((i_6,) + i_5)\n i_0 = np.empty(i_1)\n i_2 = 10\n for i_3 in range(i_2):\n i_4 = i_0[i_3]\n i_9 = 5\n i_10 = a\n i_13 = i_10[i_9]\n i_11 = i_3\n i_12 = b\n i_14 = i_12[i_11]\n i_4 = (i_13 * i_14)\n i_0[i_3] = i_4\n return i_0\n\n" ] ], [ [ "## Numba optimized", "_____no_output_____" ], [ "To give this an extra speed boost, we can compile the returned expression with Numba:", "_____no_output_____" ] ], [ [ "numba_optimized = njit(optimized_some_fn)", "_____no_output_____" ], [ "# NBVAL_IGNORE_OUTPUT\n# run once first to compile\nnumba_optimized(*args)\n \n%timeit numba_optimized(*args)", "876 ns ± 16.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] ], [ [ "Great, another speedup!", "_____no_output_____" ], [ "## Unkown dimensionality?", "_____no_output_____" ], [ "What if we want to produce a version of the function that works on any dimensional input? Or if we just want to actually defer to NumPy's implementation and not replace `outer`? We simply omit the `with_dim` methods and we get back an abstract representation that is compiled without any knowledge of the dimensionality:", "_____no_output_____" ] ], [ [ "dims_not_known = optimize(some_fn)", "_____no_output_____" ], [ "dims_not_known.__optimize_steps__['resulting_expr']", "_____no_output_____" ], [ "print(dims_not_known.__optimize_steps__['ast_as_source'])", "\n\ndef fn(a, b):\n i_18 = 5\n i_16 = a\n i_17 = b\n i_19 = np.multiply.outer(i_16, i_17)\n i_15 = i_19[i_18]\n return i_15\n\n" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import os\nimport tweepy\nimport pandas as pd", "_____no_output_____" ] ], [ [ "## Tweepy\n\nPara acessar a API do twitter, deve-se acessar a área de desenvolvedor, criar uma aplicação e solicitar as credenciais de acesso. As células abaixo demonstram como fazer a conexão na API do twitter usando a biblioteca python Tweepy e retornar a lista de tweets na timeline do usuário.\n\n[Documentação do tweepy](https://tweepy.readthedocs.io/)", "_____no_output_____" ] ], [ [ "consumer_key = os.environ['CONSUMER_API_KEY']\nconsumer_secret = os.environ['CONSUMER_API_SECRET']\naccess_token = os.environ['ACCESS_TOKEN']\naccess_token_secret = os.environ['ACCESS_TOKEN_SECRET']", "_____no_output_____" ], [ "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth)", "_____no_output_____" ] ], [ [ "Recuperar tweets da timeline", "_____no_output_____" ] ], [ [ "public_tweets = api.home_timeline()", "_____no_output_____" ] ], [ [ "Criar um dicionário vazio para incluir os tweets.\nPasso necessário para criar um dataframe.", "_____no_output_____" ] ], [ [ "tweets = {'id':[],'author':[], 'screen_name':[], 'tweet':[],'created_at':[],'language':[],'n_retweets':[],'n_likes':[]}", "_____no_output_____" ], [ "ct = 0\nnum_results = 1000\nresult_count = 0\nlast_id = None\nwhile result_count < num_results:\n gremio_tweets = api.search(q='gremio',lang='pt',since_id=last_id)\n for tweet in gremio_tweets:\n print(\"id: \"+tweet.id_str)\n print(\"Screen name: \"+tweet.author.screen_name)\n print(\"Autor: \"+tweet.author.name)\n print(\"Tweet: \"+tweet.text)\n print(\"Data de criação: \"+tweet.created_at.strftime(\"%d/%m/%Y, %H:%M:%S\"))\n print(\"Idioma: \"+tweet.lang)\n print(\"Retweets: \"+str(tweet.retweet_count))\n print(\"Curtidas: \"+str(tweet.favorite_count))\n tweets['id'].append(tweet.id)\n tweets['screen_name'].append(tweet.author.screen_name)\n tweets['author'].append(tweet.author.name)\n tweets['tweet'].append(tweet.text)\n tweets['created_at'].append(tweet.created_at)\n tweets['language'].append(tweet.lang)\n tweets['n_retweets'].append(tweet.retweet_count)\n tweets['n_likes'].append(tweet.favorite_count)\n print(\"==========================\")\n result_count += 1", "id: 1201655788508471298\nScreen name: RauenPaian\nAutor: IMORTAL 🖤💙🖤💙\nTweet: RT @SoccerGremio: A informação que nos chega, é que são apenas detalhes a serem finalizados entre Grêmio e Palmeiras por Raphael Veiga. Os…\nData de criação: 03/12/2019, 00:14:00\nIdioma: pt\nRetweets: 54\nCurtidas: 0\n==========================\nid: 1201655782686760960\nScreen name: regisschuch\nAutor: Régis\nTweet: Esperamos pela parte do @Gremio Que não contrate esse Egídio. JOGADOR muito ruim.\nData de criação: 03/12/2019, 00:13:58\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655773706833922\nScreen name: magrin00\nAutor: 🄼🄰🄶🅁🄸🄽ᶜʳᶠ ⚫🔴\nTweet: RT @TozzaFla: Fifa reconhece títulos mundiais de Flamengo, Grêmio, Santos e São Paulo | futebol internacional | Globoesporte https://t.co/A…\nData de criação: 03/12/2019, 00:13:56\nIdioma: pt\nRetweets: 232\nCurtidas: 0\n==========================\nid: 1201655770200317958\nScreen name: BaseFuracao\nAutor: Base Furacão\nTweet: Em atuação coletiva, 3-0 vs Boca.\nEm emoção e vibração, Remontada vs Grêmio. https://t.co/ebQwsoBUmo\nData de criação: 03/12/2019, 00:13:55\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655768107356164\nScreen name: Proerrd\nAutor: Vivi pereiraⓟ\nTweet: @di_dinelli @isastrevisan comissão rainha grêmio nadinha\nData de criação: 03/12/2019, 00:13:55\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655765859262465\nScreen name: jean_wosch\nAutor: Jean Wosch\nTweet: @omenguista @LibertadoresBR @AthleticoPR @Flamengo @Gremio @Palmeiras @SantosFC @SaoPauloFC Não reclama, se não pio… https://t.co/n60v1B2Mby\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655763921453056\nScreen name: avalyzinho\nAutor: nx.avaly 15/11\nTweet: RT @Jhorobert11: Obrigado senhor 🙏🏽🙏🏽\nFeliz por mais uma vitória e pelos 2 gols ⚽️⚽️ Isso é Grêmio 🇪🇪🇪🇪\nJR11 https://t.co/AEzxnn3GLt\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 20\nCurtidas: 0\n==========================\nid: 1201655762931666948\nScreen name: DanielZ80970238\nAutor: Daniel Zurita\nTweet: RT @TozzaFla: Fifa reconhece títulos mundiais de Flamengo, Grêmio, Santos e São Paulo | futebol internacional | Globoesporte https://t.co/A…\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 232\nCurtidas: 0\n==========================\nid: 1201655755826442241\nScreen name: BianoRL1\nAutor: @BianoRL\nTweet: RT @ejramorim: Gostaria de agradecer as atletas, comissão e direção por essa temporada, é um enorme prazer poder conviver e aprender diaria…\nData de criação: 03/12/2019, 00:13:52\nIdioma: pt\nRetweets: 3\nCurtidas: 0\n==========================\nid: 1201655731537268736\nScreen name: LuizCar74542646\nAutor: Luiz Carlos\nTweet: Tu tens razão, qual era a meia cancha do grêmio em 2017, campeão da libertadores, quem entrou e nunca mais saiu Art… https://t.co/VNcZjOWna6\nData de criação: 03/12/2019, 00:13:46\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655713065512960\nScreen name: ceciliaraisa\nAutor: O Chelsea kerrLUTE 😒💙😒💦\nTweet: @manamaiara @leilinha_1910 @tathiane_vidal pra não dar briga é melhor ele ir pro Grêmio\nData de criação: 03/12/2019, 00:13:42\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655702562967552\nScreen name: cxcarloos\nAutor: carrlos\nTweet: RT @SoccerGremio: A informação que nos chega, é que são apenas detalhes a serem finalizados entre Grêmio e Palmeiras por Raphael Veiga. Os…\nData de criação: 03/12/2019, 00:13:39\nIdioma: pt\nRetweets: 54\nCurtidas: 0\n==========================\nid: 1201655693784338433\nScreen name: willianselong_\nAutor: ₩illian\nTweet: @cesarspo @sandra_kunst @Gremio Vo passar por burro nada o comentário é meu e quem decide sou eu, evito ladainha de pessoas como fosse.\nData de criação: 03/12/2019, 00:13:37\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655691825553411\nScreen name: pmsilva37\nAutor: Coalito 🐨\nTweet: @carlydamasceno2 Com empate também, caso o Grêmio vença o Cruzeiro.\nFicaria 3 pontos na frente e pelo número de vit… https://t.co/hA7TYBW0Jk\nData de criação: 03/12/2019, 00:13:37\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655682900078592\nScreen name: VNJS18\nAutor: Vinicin\nTweet: RT @RDTRubroNegro: Flamengo pré Jorge Jesus não vencia:\n\n- A Liberta há 38 anos.\n\n- O Brasileiro há 10 anos.\n\n- Na Arena da Baixada há 45 a…\nData de criação: 03/12/2019, 00:13:35\nIdioma: pt\nRetweets: 597\nCurtidas: 0\n==========================\nid: 1201655788508471298\nScreen name: RauenPaian\nAutor: IMORTAL 🖤💙🖤💙\nTweet: RT @SoccerGremio: A informação que nos chega, é que são apenas detalhes a serem finalizados entre Grêmio e Palmeiras por Raphael Veiga. Os…\nData de criação: 03/12/2019, 00:14:00\nIdioma: pt\nRetweets: 54\nCurtidas: 0\n==========================\nid: 1201655782686760960\nScreen name: regisschuch\nAutor: Régis\nTweet: Esperamos pela parte do @Gremio Que não contrate esse Egídio. JOGADOR muito ruim.\nData de criação: 03/12/2019, 00:13:58\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655773706833922\nScreen name: magrin00\nAutor: 🄼🄰🄶🅁🄸🄽ᶜʳᶠ ⚫🔴\nTweet: RT @TozzaFla: Fifa reconhece títulos mundiais de Flamengo, Grêmio, Santos e São Paulo | futebol internacional | Globoesporte https://t.co/A…\nData de criação: 03/12/2019, 00:13:56\nIdioma: pt\nRetweets: 232\nCurtidas: 0\n==========================\nid: 1201655770200317958\nScreen name: BaseFuracao\nAutor: Base Furacão\nTweet: Em atuação coletiva, 3-0 vs Boca.\nEm emoção e vibração, Remontada vs Grêmio. https://t.co/ebQwsoBUmo\nData de criação: 03/12/2019, 00:13:55\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655768107356164\nScreen name: Proerrd\nAutor: Vivi pereiraⓟ\nTweet: @di_dinelli @isastrevisan comissão rainha grêmio nadinha\nData de criação: 03/12/2019, 00:13:55\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655765859262465\nScreen name: jean_wosch\nAutor: Jean Wosch\nTweet: @omenguista @LibertadoresBR @AthleticoPR @Flamengo @Gremio @Palmeiras @SantosFC @SaoPauloFC Não reclama, se não pio… https://t.co/n60v1B2Mby\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655763921453056\nScreen name: avalyzinho\nAutor: nx.avaly 15/11\nTweet: RT @Jhorobert11: Obrigado senhor 🙏🏽🙏🏽\nFeliz por mais uma vitória e pelos 2 gols ⚽️⚽️ Isso é Grêmio 🇪🇪🇪🇪\nJR11 https://t.co/AEzxnn3GLt\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 20\nCurtidas: 0\n==========================\nid: 1201655762931666948\nScreen name: DanielZ80970238\nAutor: Daniel Zurita\nTweet: RT @TozzaFla: Fifa reconhece títulos mundiais de Flamengo, Grêmio, Santos e São Paulo | futebol internacional | Globoesporte https://t.co/A…\nData de criação: 03/12/2019, 00:13:54\nIdioma: pt\nRetweets: 232\nCurtidas: 0\n==========================\nid: 1201655755826442241\nScreen name: BianoRL1\nAutor: @BianoRL\nTweet: RT @ejramorim: Gostaria de agradecer as atletas, comissão e direção por essa temporada, é um enorme prazer poder conviver e aprender diaria…\nData de criação: 03/12/2019, 00:13:52\nIdioma: pt\nRetweets: 3\nCurtidas: 0\n==========================\nid: 1201655731537268736\nScreen name: LuizCar74542646\nAutor: Luiz Carlos\nTweet: Tu tens razão, qual era a meia cancha do grêmio em 2017, campeão da libertadores, quem entrou e nunca mais saiu Art… https://t.co/VNcZjOWna6\nData de criação: 03/12/2019, 00:13:46\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655713065512960\nScreen name: ceciliaraisa\nAutor: O Chelsea kerrLUTE 😒💙😒💦\nTweet: @manamaiara @leilinha_1910 @tathiane_vidal pra não dar briga é melhor ele ir pro Grêmio\nData de criação: 03/12/2019, 00:13:42\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655702562967552\nScreen name: cxcarloos\nAutor: carrlos\nTweet: RT @SoccerGremio: A informação que nos chega, é que são apenas detalhes a serem finalizados entre Grêmio e Palmeiras por Raphael Veiga. Os…\nData de criação: 03/12/2019, 00:13:39\nIdioma: pt\nRetweets: 54\nCurtidas: 0\n==========================\nid: 1201655693784338433\nScreen name: willianselong_\nAutor: ₩illian\nTweet: @cesarspo @sandra_kunst @Gremio Vo passar por burro nada o comentário é meu e quem decide sou eu, evito ladainha de pessoas como fosse.\nData de criação: 03/12/2019, 00:13:37\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655691825553411\nScreen name: pmsilva37\nAutor: Coalito 🐨\nTweet: @carlydamasceno2 Com empate também, caso o Grêmio vença o Cruzeiro.\nFicaria 3 pontos na frente e pelo número de vit… https://t.co/hA7TYBW0Jk\nData de criação: 03/12/2019, 00:13:37\nIdioma: pt\nRetweets: 0\nCurtidas: 0\n==========================\nid: 1201655682900078592\nScreen name: VNJS18\nAutor: Vinicin\nTweet: RT @RDTRubroNegro: Flamengo pré Jorge Jesus não vencia:\n\n- A Liberta há 38 anos.\n\n- O Brasileiro há 10 anos.\n\n- Na Arena da Baixada há 45 a…\nData de criação: 03/12/2019, 00:13:35\nIdioma: pt\nRetweets: 597\nCurtidas: 0\n==========================\n" ] ], [ [ "Criação do dataframe", "_____no_output_____" ] ], [ [ "df = pd.DataFrame.from_dict(tweets)", "_____no_output_____" ], [ "df", "_____no_output_____" ] ], [ [ "Salvar o dataframe em csv para uso posterior.", "_____no_output_____" ] ], [ [ "df.to_csv('dataframe.csv')", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Assignment 2\n\nBefore working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n\nAn NOAA dataset has been stored in the file `data/C2A2_data/BinnedCsvs_d100/4e86d2106d0566c6ad9843d882e72791333b08be3d647dcae4f4b110.csv`. The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.\n\nEach row in the assignment datafile corresponds to a single observation.\n\nThe following variables are provided to you:\n\n* **id** : station identification code\n* **date** : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)\n* **element** : indicator of element type\n * TMAX : Maximum temperature (tenths of degrees C)\n * TMIN : Minimum temperature (tenths of degrees C)\n* **value** : data value for element (tenths of degrees C)\n\nFor this assignment, you must:\n\n1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.\n2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.\n4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.\n\nThe data you have been given is near **Singapore, Central Singapore Community Development Council, Singapore**, and the stations the data comes from are shown on the map below.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport mplleaflet\nimport pandas as pd\n\ndef leaflet_plot_stations(binsize, hashid):\n\n df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))\n\n station_locations_by_hash = df[df['hash'] == hashid]\n\n lons = station_locations_by_hash['LONGITUDE'].tolist()\n lats = station_locations_by_hash['LATITUDE'].tolist()\n\n plt.figure(figsize=(8,8))\n\n plt.scatter(lons, lats, c='r', alpha=0.7, s=200)\n\n return mplleaflet.display()\n\nleaflet_plot_stations(100,'4e86d2106d0566c6ad9843d882e72791333b08be3d647dcae4f4b110')", "_____no_output_____" ], [ "# Import useful libraries\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as dates\nimport matplotlib.ticker as ticker\nimport pandas as pd\nimport numpy as np\n\n%matplotlib notebook\n\n# Read the dataframe\ndf1 = pd.read_csv('data/C2A2_data/BinnedCsvs_d100/4e86d2106d0566c6ad9843d882e72791333b08be3d647dcae4f4b110.csv')\ndf1.head()", "_____no_output_____" ], [ "#How many records?\nlen(df1)", "_____no_output_____" ], [ "minimum = []\nmaximum = []\nmonth = []\n\n#remove February 29\ndf1 = df1[~(df1['Date'].str.endswith(r'02-29'))]\ntimes1 = pd.DatetimeIndex(df1['Date'])", "_____no_output_____" ], [ "#after removing Feb 29, how many records remaining\nlen(df1)", "_____no_output_____" ], [ "#Data for 2005-2014\ndf = df1[times1.year != 2015]\ntimes = pd.DatetimeIndex(df['Date'])\nfor j in df.groupby([times.month, times.day]):\n minimum.append(min(j[1]['Data_Value']))\n maximum.append(max(j[1]['Data_Value']))", "_____no_output_____" ], [ "#Data of 2015\ndf2015 = df1[times1.year == 2015]\ntimes2015 = pd.DatetimeIndex(df2015['Date'])\nminimum2015 = []\nmaximum2015 = []\nfor j in df2015.groupby([times2015.month, times2015.day]):\n minimum2015.append(min(j[1]['Data_Value']))\n maximum2015.append(max(j[1]['Data_Value']))", "_____no_output_____" ], [ "minaxis = []\nmaxaxis = []\nminvals = []\nmaxvals = []\nfor i in range(len(minimum)):\n if((minimum[i] - minimum2015[i]) > 0):\n minaxis.append(i)\n minvals.append(minimum2015[i])\n if((maximum[i] - maximum2015[i]) < 0):\n maxaxis.append(i)\n maxvals.append(maximum2015[i])", "_____no_output_____" ], [ "plt.figure()\ncolors = ['skyblue', 'lightcoral']\nplt.plot(minimum, c='skyblue', alpha = 0.5, label = 'Minimum Temperature (2005-14)')\nplt.plot(maximum, c ='lightcoral', alpha = 0.5, label = 'Maximum Temperature (2005-14)')\nplt.scatter(minaxis, minvals, s = 10, c = 'blue', label = 'Record Break Minimum (2015)')\nplt.scatter(maxaxis, maxvals, s = 10, c = 'red', label = 'Record Break Maximum (2015)')\nplt.gca().fill_between(range(len(minimum)), \n minimum, maximum, \n facecolor='lightgray', \n alpha=0.2)\n\nplt.ylim(0, 450)\nplt.legend(loc = 8, frameon=False, title='Temperature', fontsize=8)\nplt.xticks( np.linspace(15,15 + 30*11 , num = 12), (r'Jan', r'Feb', r'Mar', r'Apr', r'May', r'Jun', r'Jul', r'Aug', r'Sep', r'Oct', r'Nov', r'Dec') )\nplt.xlabel('Months')\nplt.ylabel('Temperature (tenths of degrees C)')\nplt.title(r'Temperature Summary Plot of Singapore (2005-2015)')\nplt.show()", "_____no_output_____" ], [ "plt.savefig('Temperature.png', transparent = True, bbox_inches = 'tight')", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Classical Machine Learning Approach\n\nIn this notebook we will be learning to\n 1. Create a Naive TF - IDF based Bag of Words representation of text.\n 2. Use classical ML models to solve text classification.\n 3. Use a One Vs Rest strategy to solve multi-label text classification.\n\n\n **HOT TIP** : *Save them as pickle for easy rendering for experiments*\n\n This Notebook uses code from https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Multi%20label%20text%20classification.ipynb\n", "_____no_output_____" ] ], [ [ "# Installing packages.\n!pip install contractions\n!pip install textsearch\n!pip install tqdm\n\n# Importing packages.\nimport nltk\nnltk.download('punkt')\nnltk.download('stopwords')\n%matplotlib inline\nimport re\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom nltk.corpus import stopwords\nstop_words = set(stopwords.words('english'))\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport pickle\nimport ast\nfrom sklearn.externals import joblib\nfrom datetime import datetime\nfrom sklearn.preprocessing import MultiLabelBinarizer", "Collecting contractions\n Downloading https://files.pythonhosted.org/packages/85/41/c3dfd5feb91a8d587ed1a59f553f07c05f95ad4e5d00ab78702fbf8fe48a/contractions-0.0.24-py2.py3-none-any.whl\nCollecting textsearch\n Downloading https://files.pythonhosted.org/packages/42/a8/03407021f9555043de5492a2bd7a35c56cc03c2510092b5ec018cae1bbf1/textsearch-0.0.17-py2.py3-none-any.whl\nCollecting Unidecode\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/d0/42/d9edfed04228bacea2d824904cae367ee9efd05e6cce7ceaaedd0b0ad964/Unidecode-1.1.1-py2.py3-none-any.whl (238kB)\n\u001b[K |████████████████████████████████| 245kB 4.0MB/s \n\u001b[?25hCollecting pyahocorasick\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/f4/9f/f0d8e8850e12829eea2e778f1c90e3c53a9a799b7f412082a5d21cd19ae1/pyahocorasick-1.4.0.tar.gz (312kB)\n\u001b[K |████████████████████████████████| 317kB 58.8MB/s \n\u001b[?25hBuilding wheels for collected packages: pyahocorasick\n Building wheel for pyahocorasick (setup.py) ... \u001b[?25l\u001b[?25hdone\n Created wheel for pyahocorasick: filename=pyahocorasick-1.4.0-cp36-cp36m-linux_x86_64.whl size=81698 sha256=f163f074e78b5cd47a2b71398341f597c7b77d8aa2b7781783d0cc381dc4a4a6\n Stored in directory: /root/.cache/pip/wheels/0a/90/61/87a55f5b459792fbb2b7ba6b31721b06ff5cf6bde541b40994\nSuccessfully built pyahocorasick\nInstalling collected packages: Unidecode, pyahocorasick, textsearch, contractions\nSuccessfully installed Unidecode-1.1.1 contractions-0.0.24 pyahocorasick-1.4.0 textsearch-0.0.17\nRequirement already satisfied: textsearch in /usr/local/lib/python3.6/dist-packages (0.0.17)\nRequirement already satisfied: Unidecode in /usr/local/lib/python3.6/dist-packages (from textsearch) (1.1.1)\nRequirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (from textsearch) (1.4.0)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.28.1)\n[nltk_data] Downloading package punkt to /root/nltk_data...\n[nltk_data] Unzipping tokenizers/punkt.zip.\n[nltk_data] Downloading package stopwords to /root/nltk_data...\n[nltk_data] Unzipping corpora/stopwords.zip.\n" ], [ "# Let's mount our G-Drive.\n\nfrom google.colab import drive\ndrive.mount('/content/drive', force_remount=True)", "Mounted at /content/drive\n" ], [ "# Data read and preparation.\n# Mentioning where is our data located on G-Drive. Make sure to rectify your path\npath = '/content/drive/My Drive/ICDMAI_Tutorial/notebook/'\ndata ='filtered_data/question_tag_text_mapping.pkl'\nml_model = path + 'ml_model/'", "_____no_output_____" ], [ "# Let us quickly load our question tag data\nquestion_tag = pd.read_pickle(path+data)\nquestion_tag.head(3)", "_____no_output_____" ] ], [ [ "### Creating one hot encoding from multilabelled tagged data", "_____no_output_____" ] ], [ [ "# In order to use one vs rest strategy we will need to one hot encoding each tag across all documents.\nmlb = MultiLabelBinarizer()\nquestion_tag['Tag_pop'] = question_tag['Tag']\nquestion_tag = question_tag.join(pd.DataFrame(mlb.fit_transform(question_tag.pop('Tag_pop')),\n columns=mlb.classes_,\n index=question_tag.index))\nquestion_tag.head(3)", "_____no_output_____" ], [ "# Creating a list of all existing 'Tags'\ndummy = question_tag.drop(['Id', 'OwnerUserId', 'CreationDate', 'ClosedDate', 'Score', 'Title','Body','Tag'], axis=1)\ncategories = list(dummy.columns.values)", "_____no_output_____" ] ], [ [ "### Text preprocessing", "_____no_output_____" ] ], [ [ "# Let us createa a very basic text preprocessor which we will use for cleaning text.\ndef clean_text(text):\n text = text.lower()\n text = re.sub(r\"what's\", \"what is \", text)\n text = re.sub(r\"\\'s\", \" \", text)\n text = re.sub(r\"\\'ve\", \" have \", text)\n text = re.sub(r\"can't\", \"can not \", text)\n text = re.sub(r\"n't\", \" not \", text)\n text = re.sub(r\"i'm\", \"i am \", text)\n text = re.sub(r\"\\'re\", \" are \", text)\n text = re.sub(r\"\\'d\", \" would \", text)\n text = re.sub(r\"\\'ll\", \" will \", text)\n text = re.sub(r\"\\'scuse\", \" excuse \", text)\n text = re.sub('\\W', ' ', text)\n text = re.sub('\\s+', ' ', text)\n text = text.strip(' ')\n return text\n\nquestion_tag['Body'] = question_tag['Body'].map(lambda com : clean_text(com))", "_____no_output_____" ] ], [ [ "### Creating a 70/30 Train-Test Split", "_____no_output_____" ] ], [ [ "train, test = train_test_split(question_tag, random_state=42, test_size=0.30, shuffle=True)\n\nX_train = train.Body\nX_test = test.Body\n\nprint(\"Train data shape : {}\".format(X_train.shape))\nprint(\"Test data shape : {}\".format(X_test.shape))", "Train data shape : (736394,)\nTest data shape : (315598,)\n" ] ], [ [ "# Creating Bag of Words representation using TF - IDF\n 1. Initializing the Vectorizer object\n 2. Create a corpus from training data.\n 3. Create a document term matrix", "_____no_output_____" ] ], [ [ "#Initializing the Vectorizer object\ntfidf = TfidfVectorizer(stop_words=stop_words)\n\n#Create a corpus from training data\n#Create a document term matrix of training data based on the corpus.\nX_train_dtm = tfidf.fit_transform(X_train)\n\n#Create a document term matrix of test data based on the corpus.\n#Note that the dimensions/columns of DTM of the test data will be based on the training data corpus only.\nX_test_dtm = tfidf.transform(X_test)", "_____no_output_____" ] ], [ [ "## Pipeline\nscikit-learn provides a Pipeline utility to help automate machine learning workflows. Pipelines are very common in Machine Learning systems, since there is a lot of data to manipulate and many data transformations to apply. So we will utilize pipeline to train every classifier.\n\n## One Vs Rest Multilabel strategy\nThe Multi-label algorithm accepts a binary mask over multiple labels. The result for each prediction will be an array of 0s and 1s marking which class labels apply to each row input sample.\n\nOneVsRest strategy can be used for multilabel learning, where a classifier is used to predict multiple labels for instance. **Naive Bayes**, **SVM**, **Logistic Regression** supports multi-class, but we are in a multi-label scenario, therefore, we wrap them in the OneVsRestClassifier.\n\n### We create a Training Pipeline and a Scoring Pipeline", "_____no_output_____" ] ], [ [ "def tag_level_training_pipeline(X_train, train, X_test, test, classifier_pipeline, output_directory):\n \n #1. Create a classifier for each Tag\n for category in categories:\n print('... Processing {}'.format(category))\n \n # 1. train the model using X_dtm & y\n classifier_pipeline.fit(X_train, train[category])\n \n # 2. save the model to disk\n filename = ml_model + output_directory +str(category)+ '_model.pkl'\n joblib.dump(classifier_pipeline, filename, compress = 1)\n \n # 3. compute the testing accuracy\n prediction = classifier_pipeline.predict(X_test)\n print('Test accuracy is {}'.format(accuracy_score(test[category], prediction)))\n print(classification_report(test[category], prediction))\n\n", "_____no_output_____" ], [ "def tag_level_predict(X_train, train, X_test, test, model_directory):\n prediction_df = pd.DataFrame(columns=['dummy1'])\n \n #Score the document across classifier for each Tag\n for category in categories:\n \n # 1. load the model\n filename = ml_model + model_directory +str(category)+ '_model.pkl'\n classifier_pipeline = joblib.load(filename)\n \n # 2. predict on the test data.\n prediction = classifier_pipeline.predict(X_test)\n prediction_df[str(category)] = prediction\n\n # Remember We had encoded the labels. It time to bring them back to their original form.\n for category in categories:\n prediction_df.loc[prediction_df[str(category)] == 1, str(category)] = category\n prediction_df['predicted_labels'] = prediction_df[[str(i) for i in categories]].values.tolist()\n prediction_df['predicted_labels'] = prediction_df['predicted_labels'].apply(lambda x : list(set(x)))\n # prediction_df['predicted_labels'] = prediction_df['predicted_labels'].apply(lambda x: x.remove(0) if (0 in x) else x )\n \n # We create result having orignal labels and predicted labels for metrics Evaluation\n final_pred_df = pd.concat([test[['Id','Tag']].reset_index(), prediction_df[['predicted_labels']].reset_index()], axis=1)\n final_pred_df['original_labels'] = final_pred_df['Tag']\n # prediction_df[['Id']] = test[['Id']]\n final_pred_df_result = final_pred_df[['Id','original_labels','predicted_labels']]\n return final_pred_df_result", "_____no_output_____" ], [ "# importing os module \nimport os\ntry:\n os.rename('/content/drive/My Drive/ICDMAI_Tutorial/notebook/ml_model/SVM/_net_model.pkl', '/content/drive/My Drive/ICDMAI_Tutorial/notebook/ml_model/SVM/.net_model.pkl')\nexcept :\n print(\"Already in proper filename!\") ", "_____no_output_____" ], [ "## A Dummy example.\nX_test = [\"How to handle memory locking ?\", \"How to handle memory locking in java ?\", \"How to handle memory locking in java python ?\",\"This post is not about java\"]\nX_test_dtm = tfidf.transform(X_test)\nresult = tag_level_predict(X_train_dtm, train, X_test_dtm, test.head(1), 'SVM/')\n\nfor i in range(result.shape[0]):\n print(\"Input [\",X_test[i],\"] || Predicted classes: \",result.predicted_labels[i])", "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.svm.classes module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.svm. Anything that cannot be imported from sklearn.svm is now part of the private API.\n warnings.warn(message, FutureWarning)\n/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator LinearSVC from version 0.21.3 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n UserWarning)\n/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.preprocessing.label module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.preprocessing. Anything that cannot be imported from sklearn.preprocessing is now part of the private API.\n warnings.warn(message, FutureWarning)\n/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator LabelBinarizer from version 0.21.3 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n UserWarning)\n/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator OneVsRestClassifier from version 0.21.3 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n UserWarning)\n/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.21.3 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n UserWarning)\n" ] ], [ [ "# Evaluating our results", "_____no_output_____" ] ], [ [ "# Here we define precision, recall, f1 measure at a single document level.\ndef document_evaluation_metrics(prd_grp,grp,metric=\"precision\"):\n pred_group = prd_grp\n if 0 in pred_group: pred_group.remove(0)\n group = grp\n\n set_pred_group = set(pred_group)\n set_group = set(group)\n intrsct = set_group.intersection(set_pred_group)\n accuracy = len(intrsct) / float(len(set_pred_group) if len(set_pred_group)>1 else 1)\n recall = len(intrsct) / float(len(set_group) if len(set_group)>1 else 1)\n if metric == \"precision\":\n return accuracy\n elif metric == \"recall\":\n return recall\n elif metric == \"f1_measure\":\n if accuracy == 0 or recall == 0:\n return 0\n elif accuracy > 0 and recall >0 :\n f1_measure = 2*accuracy*recall/(float(accuracy + recall))\n return f1_measure\n \n return -1\n\n# Provide overall average stats and populate document level metrics.\ndef model_evaluation_stats(final_pred_df, model_name=\"default\"):\n final_pred_df['doc_precision'] = final_pred_df.apply(lambda x: document_evaluation_metrics(x.predicted_labels, x.original_labels, \"precision\"), axis=1)\n final_pred_df['doc_recall'] = final_pred_df.apply(lambda x: document_evaluation_metrics(x.predicted_labels, x.original_labels, \"recall\"), axis=1)\n final_pred_df['doc_f1_measure'] = final_pred_df.apply(lambda x: document_evaluation_metrics(x.predicted_labels, x.original_labels, \"f1_measure\"), axis=1)\n \n print('Avearge precision across documents is {}'.format(final_pred_df['doc_precision'].mean()))\n print('Avearge recall across documents is {}'.format(final_pred_df['doc_recall'].mean()))\n print('Avearge f1 measure across documents is {}'.format(final_pred_df['doc_f1_measure'].mean()))\n pickle.dump(final_pred_df, open(ml_model + model_name + \".pkl\", 'wb'))\n # final_pred_df.to_csv(ml_model + 'SVM_Tag_predictions.txt',sep='\\t',index=False)", "_____no_output_____" ] ], [ [ "# Let us train, score and evaluate Naive Bayes", "_____no_output_____" ] ], [ [ "#Naive Bayes Classifier\nNB_pipeline = Pipeline([\n ('clf', OneVsRestClassifier(MultinomialNB(\n fit_prior=True, class_prior=None))),\n ])\n\ntag_level_training_pipeline(X_train_dtm, train, X_test_dtm, test, NB_pipeline, 'NaiveBayes/')\nresult = tag_level_predict(X_train_dtm, train, X_test_dtm, test, 'NaiveBayes/')\nmodel_evaluation_stats(result, \"NaiveBayes\")", "_____no_output_____" ] ], [ [ "# Let us train, score and evaluate Support Vector Machines", "_____no_output_____" ] ], [ [ "#SVM Classifier\nSVC_pipeline = Pipeline([\n ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),\n ])\n\ntag_level_training_pipeline(X_train_dtm, train, X_test_dtm, test, SVC_pipeline, 'SVM/')\nresult = tag_level_predict(X_train_dtm, train, X_test_dtm, test, 'SVM/')\nmodel_evaluation_stats(result, \"SVM\")", "... Processing .net\nTest accuracy is 0.9771893358006071\n precision recall f1-score support\n\n 0 0.98 1.00 0.99 308362\n 1 0.51 0.09 0.15 7236\n\n accuracy 0.98 315598\n macro avg 0.75 0.54 0.57 315598\nweighted avg 0.97 0.98 0.97 315598\n\n... Processing agile\nTest accuracy is 0.9999429654180318\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 315573\n 1 0.89 0.32 0.47 25\n\n accuracy 1.00 315598\n macro avg 0.94 0.66 0.74 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing ajax\nTest accuracy is 0.9887356700612805\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 310952\n 1 0.70 0.41 0.52 4646\n\n accuracy 0.99 315598\n macro avg 0.84 0.71 0.76 315598\nweighted avg 0.99 0.99 0.99 315598\n\n... Processing amazon-web-services\nTest accuracy is 0.9981780619649048\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314643\n 1 0.79 0.55 0.65 955\n\n accuracy 1.00 315598\n macro avg 0.89 0.77 0.82 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing android\nTest accuracy is 0.9829815144582665\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 288322\n 1 0.96 0.84 0.90 27276\n\n accuracy 0.98 315598\n macro avg 0.97 0.92 0.94 315598\nweighted avg 0.98 0.98 0.98 315598\n\n... Processing android-studio\nTest accuracy is 0.9972401599503166\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314589\n 1 0.67 0.27 0.38 1009\n\n accuracy 1.00 315598\n macro avg 0.84 0.63 0.69 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing angular2\nTest accuracy is 0.9991064582158315\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314898\n 1 0.94 0.64 0.76 700\n\n accuracy 1.00 315598\n macro avg 0.97 0.82 0.88 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing angularjs\nTest accuracy is 0.9949904625504598\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 309420\n 1 0.93 0.80 0.86 6178\n\n accuracy 0.99 315598\n macro avg 0.96 0.90 0.93 315598\nweighted avg 0.99 0.99 0.99 315598\n\n... Processing apache\nTest accuracy is 0.9952788040481879\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 313590\n 1 0.71 0.43 0.54 2008\n\n accuracy 1.00 315598\n macro avg 0.85 0.72 0.77 315598\nweighted avg 0.99 1.00 0.99 315598\n\n... Processing apache-spark\nTest accuracy is 0.999404305477221\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314985\n 1 0.93 0.75 0.83 613\n\n accuracy 1.00 315598\n macro avg 0.97 0.87 0.91 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing api\nTest accuracy is 0.9951900835873485\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314094\n 1 0.47 0.07 0.12 1504\n\n accuracy 1.00 315598\n macro avg 0.73 0.53 0.56 315598\nweighted avg 0.99 1.00 0.99 315598\n\n... Processing asp.net\nTest accuracy is 0.9815905043758199\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 306664\n 1 0.79 0.48 0.60 8934\n\n accuracy 0.98 315598\n macro avg 0.89 0.74 0.79 315598\nweighted avg 0.98 0.98 0.98 315598\n\n... Processing asp.net-web-api\nTest accuracy is 0.9985804726265692\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314999\n 1 0.72 0.41 0.52 599\n\n accuracy 1.00 315598\n macro avg 0.86 0.71 0.76 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing azure\nTest accuracy is 0.9987769250755708\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314517\n 1 0.91 0.71 0.80 1081\n\n accuracy 1.00 315598\n macro avg 0.95 0.86 0.90 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing bash\nTest accuracy is 0.995047497132428\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 313311\n 1 0.76 0.46 0.58 2287\n\n accuracy 1.00 315598\n macro avg 0.88 0.73 0.79 315598\nweighted avg 0.99 1.00 0.99 315598\n\n... Processing c\nTest accuracy is 0.9872432651664459\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 308691\n 1 0.81 0.55 0.65 6907\n\n accuracy 0.99 315598\n macro avg 0.90 0.77 0.82 315598\nweighted avg 0.99 0.99 0.99 315598\n\n... Processing c#\nTest accuracy is 0.9414159785549971\n precision recall f1-score support\n\n 0 0.95 0.98 0.97 285147\n 1 0.77 0.56 0.65 30451\n\n accuracy 0.94 315598\n macro avg 0.86 0.77 0.81 315598\nweighted avg 0.94 0.94 0.94 315598\n\n... Processing c++\nTest accuracy is 0.9785581657678439\n precision recall f1-score support\n\n 0 0.98 0.99 0.99 301367\n 1 0.85 0.63 0.73 14231\n\n accuracy 0.98 315598\n macro avg 0.92 0.81 0.86 315598\nweighted avg 0.98 0.98 0.98 315598\n\n... Processing cloud\nTest accuracy is 0.9995722406352385\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 315459\n 1 0.75 0.04 0.08 139\n\n accuracy 1.00 315598\n macro avg 0.87 0.52 0.54 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing codeigniter\nTest accuracy is 0.9979055634066122\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 314150\n 1 0.90 0.61 0.73 1448\n\n accuracy 1.00 315598\n macro avg 0.95 0.80 0.86 315598\nweighted avg 1.00 1.00 1.00 315598\n\n... Processing css\nTest accuracy is 0.9785454914162954\n precision recall f1-score support\n\n 0 0.99 0.99 0.99 302936\n 1 0.78 0.64 0.71 12662\n\n accuracy 0.98 315598\n macro avg 0.88 0.82 0.85 315598\nweighted avg 0.98 0.98 0.98 315598\n\n... Processing devops\nTest accuracy is 0.9999524711816932\n" ] ], [ [ "# Let us train, score and evaluate Logistic Regression", "_____no_output_____" ] ], [ [ "#Logistic Regression Classifier\nLogReg_pipeline = Pipeline([\n ('clf', OneVsRestClassifier(LogisticRegression(solver='sag'), n_jobs=1)),\n ])\n\ntag_level_training_pipeline(X_train_dtm, train, X_test_dtm, test, LogReg_pipeline, 'LogisticRegression/')\nresult = tag_level_predict(X_train_dtm, train, X_test_dtm, test, 'LogisticRegression/')\nmodel_evaluation_stats(result, \"LogisticRegression\")", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Wikipedia Thanks-Receiver Study Randomization \n[J. Nathan Matias](https://twitter.com/natematias)\nOctober 29, 2019\n\nThis code takes as input data described in the [randomization data format](https://docs.google.com/document/d/1plhoDbQryYQ32vZMXu8YmlLSp30QTdup43k6uTePOT4/edit?usp=drive_web&ouid=117701977297551627494) and produces randomizations for the Thanks Recipient study.\n\nNotes:\n* We use the 99% confidence interval cutoffs from our first sample rather than relative to each subsequent sample\n * Polish Experienced: 235.380736142341\n * Polish Newcomer: 72.2118047599678\n * Arabic Newcomer: 54.7365066602131\n * German Newcomer: 63.3678642498622\n* We will be drawing only 300 Polish accounts", "_____no_output_____" ] ], [ [ "options(\"scipen\"=9, \"digits\"=4)\nlibrary(ggplot2)\nlibrary(rlang)\nlibrary(tidyverse)\nlibrary(viridis)\nlibrary(blockTools)\nlibrary(blockrand)\nlibrary(gmodels) # contains CrossTable\nlibrary(DeclareDesign)\nlibrary(DescTools) # contains Freq\nlibrary(uuid)\noptions(repr.plot.width=7, repr.plot.height=3.5)\nsessionInfo()", "_____no_output_____" ] ], [ [ "# Load Input Dataframe", "_____no_output_____" ] ], [ [ "filename <- \"all-thankees-historical-20191029.csv\"\ndata.path <- \"/home/civilservant/Tresors/CivilServant/projects/wikipedia-integration/gratitude-study/Data Drills/thankee\"\nrecipient.df <- read.csv(file.path(data.path, \"historical_output\", filename))", "_____no_output_____" ] ], [ [ "### Load Participants in the Thanker Study", "_____no_output_____" ] ], [ [ "thanker.df <- read.csv(file.path(data.path, \"..\", \"thanker_hardlaunch\", \"randomization_output\",\n \"all-thanker-randomization-final-20190729.csv\"))\nusernames.to.exclude <- thanker.df$user_name", "_____no_output_____" ] ], [ [ "### Load Liaison Usernames", "_____no_output_____" ] ], [ [ "liaison.df <- read.csv(file.path(data.path, \"..\", \"thanker_hardlaunch\", \"randomization_output\",\n \"liason-thanker-randomization-datadrill-20190718.csv\"))", "_____no_output_____" ], [ "usernames.to.exclude <- append(as.character(usernames.to.exclude), as.character(liaison.df$user_name))\nprint(paste(length(usernames.to.exclude), \"usernames to exclude\"))", "[1] \"462 usernames to exclude\"\n" ] ], [ [ "### Adjust Column Names to Match Thankee Randomization Specification", "_____no_output_____" ] ], [ [ "recipient.df$prev_experience <- factor(as.integer(gsub(\"bin_\", \"\", recipient.df$prev_experience)))\nrecipient.df$anonymized_id <- sapply( seq_along(1:nrow(recipient.df)), UUIDgenerate )\nrecipient.df$newcomer <- recipient.df$prev_experience == 0 \nrecipient.df <- subset(recipient.df, lang!=\"en\")\n#recipient.df <- subset(recipient.df, user_editcount_quality >=4 )", "_____no_output_____" ], [ "hist(recipient.df$user_editcount_quality)", "_____no_output_____" ] ], [ [ "# Confirm the number of participants", "_____no_output_____" ] ], [ [ "print(\"Newcomer Participants to Randomize\")\nsummary(subset(recipient.df, newcomer == 1)$lang)", "[1] \"Newcomer Participants to Randomize\"\n" ], [ "## Polish Experienced Accounts\nprint(\"Experienced Participants to Randomize\")\nsummary(subset(recipient.df, newcomer == 0)$lang)", "[1] \"Experienced Participants to Randomize\"\n" ] ], [ [ "# Omit Participants", "_____no_output_____" ], [ "### Omit Participants in the Thanker Study", "_____no_output_____" ] ], [ [ "print(paste(nrow(recipient.df), \"participants before removing thankers\"))\nrecipient.df <- subset(recipient.df, (user_name %in% usernames.to.exclude)!=TRUE)\nprint(paste(nrow(recipient.df), \"participants after removing thankers\")) ", "[1] \"3262 participants before removing thankers\"\n[1] \"3262 participants after removing thankers\"\n" ] ], [ [ "### Subset values outside the 99% confidence intervals\nWe are using upper confidence intervals from the first randomization, found at [generate-wikipedia-thanks-recipient-randomizations-final-07.28.3019](generate-wikipedia-thanks-recipient-randomizations-final-07.28.3019.R.ipynb)\n * Polish Experienced: 235.380736142341\n * Polish Newcomer: 72.2118047599678\n * Arabic Newcomer: 54.7365066602131\n * German Newcomer: 63.3678642498622", "_____no_output_____" ] ], [ [ "upper.conf.ints <- data.frame(lang=c(\"pl\", \"pl\", \"de\", \"ar\"),\n newcomer=c(0,1,1,1),\n conf.int = c(\n 235.380736142341,\n 72.2118047599678,\n 54.7365066602131,\n 63.3678642498622\n ))", "_____no_output_____" ], [ "upper.conf.ints\n#subset(upper.conf.ints, lang==\"pl\" & newcomer ==1)$conf.int", "_____no_output_____" ], [ "## CREATE A PLACEHOLDER WITH ZERO ROWS\n## BEFORE ITERATING\nrecipient.trimmed.df <- recipient.df[0,]\n\nfor(l in c(\"ar\", \"de\", \"fa\", \"pl\")){\n print(paste(\"Language: \", l))\n for(n in c(0,1)){\n print(paste(\" newcomer:\", n == 1))\n lang.df <- subset(recipient.df, lang==l & newcomer == n)\n print(paste( \" \", nrow(lang.df), \"rows from original dataset\"))\n prev.conf.int <- subset(upper.conf.ints, lang==l & newcomer ==n)$conf.int\n\n print( \" 99% confidence intervals:\")\n print(paste(\" upper: \", prev.conf.int ,sep=\"\"))\n \n print(paste(\" Removing\", \n nrow(subset(lang.df,\n labor_hours_84_days_pre_sample > prev.conf.int)), \"outliers\",\n \"observations because labor_hours_84_days_pre_sample is an outlier.\"))\n lang.subset.df <- subset(lang.df, labor_hours_84_days_pre_sample <= prev.conf.int)\n print(paste( \" \", nrow(lang.subset.df), \"rows in trimmed dataset\"))\n recipient.trimmed.df <- rbind(recipient.trimmed.df, lang.subset.df)\n }\n}\n\nrecipient.df.penultimate <- recipient.trimmed.df", "[1] \"Language: ar\"\n[1] \" newcomer: FALSE\"\n[1] \" 0 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: \"\n[1] \" Removing 0 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 0 rows in trimmed dataset\"\n[1] \" newcomer: TRUE\"\n[1] \" 743 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: 63.3678642498622\"\n[1] \" Removing 8 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 735 rows in trimmed dataset\"\n[1] \"Language: de\"\n[1] \" newcomer: FALSE\"\n[1] \" 0 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: \"\n[1] \" Removing 0 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 0 rows in trimmed dataset\"\n[1] \" newcomer: TRUE\"\n[1] \" 1565 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: 54.7365066602131\"\n[1] \" Removing 37 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 1528 rows in trimmed dataset\"\n[1] \"Language: fa\"\n[1] \" newcomer: FALSE\"\n[1] \" 0 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: \"\n[1] \" Removing 0 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 0 rows in trimmed dataset\"\n[1] \" newcomer: TRUE\"\n[1] \" 0 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: \"\n[1] \" Removing 0 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 0 rows in trimmed dataset\"\n[1] \"Language: pl\"\n[1] \" newcomer: FALSE\"\n[1] \" 512 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: 235.380736142341\"\n[1] \" Removing 1 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 511 rows in trimmed dataset\"\n[1] \" newcomer: TRUE\"\n[1] \" 442 rows from original dataset\"\n[1] \" 99% confidence intervals:\"\n[1] \" upper: 72.2118047599678\"\n[1] \" Removing 7 outliers observations because labor_hours_84_days_pre_sample is an outlier.\"\n[1] \" 435 rows in trimmed dataset\"\n" ] ], [ [ "# Review and Generate Variables", "_____no_output_____" ] ], [ [ "print(aggregate(recipient.df.penultimate[c(\"labor_hours_84_days_pre_sample\")],\n FUN=mean, by = list(recipient.df.penultimate$prev_experience)))", " Group.1 labor_hours_84_days_pre_sample\n1 0 5.878\n2 90 4.519\n3 180 8.063\n4 365 5.797\n5 730 5.354\n6 1460 5.866\n7 2920 8.791\n" ], [ "print(CrossTable(recipient.df.penultimate$has_email, recipient.df.penultimate$newcomer, \n prop.r = FALSE, prop.c=TRUE, prop.t = FALSE, prop.chisq = FALSE))", "\n \n Cell Contents\n|-------------------------|\n| N |\n| N / Col Total |\n|-------------------------|\n\n \nTotal Observations in Table: 3209 \n\n \n | recipient.df.penultimate$newcomer \nrecipient.df.penultimate$has_email | FALSE | TRUE | Row Total | \n-----------------------------------|-----------|-----------|-----------|\n False | 20 | 18 | 38 | \n | 0.039 | 0.007 | | \n-----------------------------------|-----------|-----------|-----------|\n True | 491 | 2680 | 3171 | \n | 0.961 | 0.993 | | \n-----------------------------------|-----------|-----------|-----------|\n Column Total | 511 | 2698 | 3209 | \n | 0.159 | 0.841 | | \n-----------------------------------|-----------|-----------|-----------|\n\n \n$t\n y\nx FALSE TRUE\n False 20 18\n True 491 2680\n\n$prop.row\n y\nx FALSE TRUE\n False 0.5263 0.4737\n True 0.1548 0.8452\n\n$prop.col\n y\nx FALSE TRUE\n False 0.039139 0.006672\n True 0.960861 0.993328\n\n$prop.tbl\n y\nx FALSE TRUE\n False 0.006232 0.005609\n True 0.153007 0.835151\n\n" ], [ "## Update the has_email field\n## recipient.df.penultimate$has_email <- recipient.df.penultimate$has_email == \"True\"\n\n## PREVIOUS EXPERIENCE\nprint(\"prev_experience\")\nprint(summary(factor(recipient.df.penultimate$prev_experience)))\ncat(\"\\n\")\n\n## SHOW LABOR HOURS BY EXPERIENCE GROUP:\nprint(\"Aggregate labor_hours_84_days_pre_sample\")\nprint(aggregate(recipient.df.penultimate[c(\"labor_hours_84_days_pre_sample\")],\n FUN=mean, by = list(recipient.df.penultimate$prev_experience)))\ncat(\"\\n\")\n\nprint(\"NEWCOMERS AND EMAILS\")\nprint(\"--------------------\")\nprint(CrossTable(recipient.df.penultimate$has_email, recipient.df.penultimate$newcomer, \n prop.r = FALSE, prop.c=TRUE, prop.t = FALSE, prop.chisq = FALSE))\n\n# VARIABLE: num_prev_thanks_pre_treatment\nprint(\"num_prev_thanks_pre_sample\")\nprint(summary(recipient.df.penultimate$num_prev_thanks_pre_sample))\ncat(\"\\n\")\n \n## SHOW PREVIOUS THANKS BY EXPERIENCE GROUP:\nprint(\"num_prev_thanks_pre_sample by prev_experience\")\nprint(aggregate(recipient.df.penultimate[c(\"num_prev_thanks_pre_sample\")],\n FUN=mean, by = list(recipient.df.penultimate$prev_experience)))\ncat(\"\\n\")", "[1] \"prev_experience\"\n 0 90 180 365 730 1460 2920 \n2698 63 52 69 81 102 144 \n\n[1] \"Aggregate labor_hours_84_days_pre_sample\"\n Group.1 labor_hours_84_days_pre_sample\n1 0 5.878\n2 90 4.519\n3 180 8.063\n4 365 5.797\n5 730 5.354\n6 1460 5.866\n7 2920 8.791\n\n[1] \"NEWCOMERS AND EMAILS\"\n[1] \"--------------------\"\n\n \n Cell Contents\n|-------------------------|\n| N |\n| N / Col Total |\n|-------------------------|\n\n \nTotal Observations in Table: 3209 \n\n \n | recipient.df.penultimate$newcomer \nrecipient.df.penultimate$has_email | FALSE | TRUE | Row Total | \n-----------------------------------|-----------|-----------|-----------|\n False | 20 | 18 | 38 | \n | 0.039 | 0.007 | | \n-----------------------------------|-----------|-----------|-----------|\n True | 491 | 2680 | 3171 | \n | 0.961 | 0.993 | | \n-----------------------------------|-----------|-----------|-----------|\n Column Total | 511 | 2698 | 3209 | \n | 0.159 | 0.841 | | \n-----------------------------------|-----------|-----------|-----------|\n\n \n$t\n y\nx FALSE TRUE\n False 20 18\n True 491 2680\n\n$prop.row\n y\nx FALSE TRUE\n False 0.5263 0.4737\n True 0.1548 0.8452\n\n$prop.col\n y\nx FALSE TRUE\n False 0.039139 0.006672\n True 0.960861 0.993328\n\n$prop.tbl\n y\nx FALSE TRUE\n False 0.006232 0.005609\n True 0.153007 0.835151\n\n[1] \"num_prev_thanks_pre_sample\"\n Min. 1st Qu. Median Mean 3rd Qu. Max. \n 0.00 0.00 0.00 0.34 0.00 112.00 \n\n[1] \"num_prev_thanks_pre_sample by prev_experience\"\n Group.1 num_prev_thanks_pre_sample\n1 0 0.1542\n2 90 0.1111\n3 180 0.1731\n4 365 0.2899\n5 730 0.9630\n6 1460 0.6176\n7 2920 3.4097\n\n" ] ], [ [ "# Subset Sample to Planned sample sizes\nSample sizes are reported in the experiment [Decisions Document](https://docs.google.com/document/d/1HryhsmWI6WthXQC7zv9Hz1a9DhpZ3FxVRLjTONuMg4I/edit)\n\n* Arabic newcomers (1750 goal) (hoping for as many as possible in first sample)\n * hoping for 1350 in the first sample and 400 later\n* German newcomers (3000 goal) (hoping for as many as possible in first sample)\n * hoping for 1600 in first sample and 1400 later\n* Persian Experienced (2400 goal)\n* Polish:\n * Newcomers: (800 goal)\n * Experienced: (2400 goal)", "_____no_output_____" ] ], [ [ "## Seed generated by Brooklyn Integers\n# https://www.brooklynintegers.com/int/1495265601/\nset.seed(1495265601)", "_____no_output_____" ], [ "print(\"Newcomers\")\nsummary(subset(recipient.df.penultimate, newcomer==1)$lang)", "[1] \"Newcomers\"\n" ], [ "print(\"Experienced\")\nsummary(subset(recipient.df.penultimate, newcomer==0)$lang)", "[1] \"Experienced\"\n" ], [ "## CREATE THE FINAL PARTICIPANT SAMPLE BEFORE RANDOMIZATION\nrecipient.df.final <- recipient.df.penultimate", "_____no_output_____" ] ], [ [ "# Generate Randomization Blocks", "_____no_output_____" ] ], [ [ "recipient.df.final$lang_prev_experience <- factor(paste(recipient.df.final$lang, recipient.df.final$prev_experience))\ncolnames(recipient.df.final)", "_____no_output_____" ], [ "## BLOCKING VARIABLES\nbv = c(\"labor_hours_84_days_pre_sample\", \"num_prev_thanks_pre_sample\")\n\nblock.size = 2\n\n## TODO: CHECK TO SEE IF I CAN DO BALANCED RANDOMIZATION\n## WITHIN BLOCKS LARGER THAN 2\nblockobj = block(data=recipient.df.final,\n n.tr = block.size,\n groups = \"lang_prev_experience\",\n id.vars=\"anonymized_id\",\n block.vars = bv,\n distance =\"mahalanobis\"\n )\n## CHECK DISTANCES\n#print(blockobj)\nrecipient.df.final$randomization_block_id <- createBlockIDs(blockobj,\n data=recipient.df.final,\n id.var = \"anonymized_id\")\nrecipient.df.final$randomization_block_size = block.size", "_____no_output_____" ] ], [ [ "### Identify Incomplete Blocks and Remove Participants in Incomplete Blocks From the Experiment", "_____no_output_____" ] ], [ [ "block.sizes <- aggregate(recipient.df.final$randomization_block_id, FUN=length, by=list(recipient.df.final$randomization_block_id))\nincomplete.blocks <- subset(block.sizes, x == 1)$Group.1\nincomplete.blocks", "_____no_output_____" ], [ "nrow(subset(recipient.df.final, randomization_block_id %in% incomplete.blocks))", "_____no_output_____" ], [ "removed.observations <- subset(recipient.df.final, (\n randomization_block_id %in% incomplete.blocks)==TRUE)\n\nrecipient.df.final <- \n subset(recipient.df.final, (\n randomization_block_id %in% incomplete.blocks)!=TRUE)\n\nprint(paste(\"Removed\", nrow(removed.observations), \"units placed in incomplete blocks.\"))", "[1] \"Removed 5 units placed in incomplete blocks.\"\n" ] ], [ [ "# Generate Randomizations", "_____no_output_____" ] ], [ [ "assignments <- block_ra(blocks=recipient.df.final$randomization_block_id, \n num_arms = 2, conditions = c(0,1))\nrecipient.df.final$randomization_arm <- assignments ", "_____no_output_____" ] ], [ [ "### Check Balance", "_____no_output_____" ] ], [ [ "print(\"Aggregating labor hours by treatment\")\nprint(aggregate(recipient.df.final[c(\"labor_hours_84_days_pre_sample\")],\n FUN=mean, by = list(recipient.df.final$randomization_arm)))\n\nprint(\"CrossTable of lang by treatment\")\nCrossTable(recipient.df.final$lang, recipient.df.final$randomization_arm, \n prop.r = TRUE, prop.c=FALSE, prop.t = FALSE, prop.chisq = FALSE)\n\nprint(\"CrossTable of lang_prev_experience by treatment\")\nCrossTable(recipient.df.final$lang_prev_experience, recipient.df.final$randomization_arm, \n prop.r = TRUE, prop.c=FALSE, prop.t = FALSE, prop.chisq = FALSE)\n", "[1] \"Aggregating labor hours by treatment\"\n Group.1 labor_hours_84_days_pre_sample\n1 0 6.008\n2 1 5.944\n[1] \"CrossTable of lang by treatment\"\n\n \n Cell Contents\n|-------------------------|\n| N |\n| N / Row Total |\n|-------------------------|\n\n \nTotal Observations in Table: 3204 \n\n \n | recipient.df.final$randomization_arm \nrecipient.df.final$lang | 0 | 1 | Row Total | \n------------------------|-----------|-----------|-----------|\n ar | 367 | 367 | 734 | \n | 0.500 | 0.500 | 0.229 | \n------------------------|-----------|-----------|-----------|\n de | 764 | 764 | 1528 | \n | 0.500 | 0.500 | 0.477 | \n------------------------|-----------|-----------|-----------|\n pl | 471 | 471 | 942 | \n | 0.500 | 0.500 | 0.294 | \n------------------------|-----------|-----------|-----------|\n Column Total | 1602 | 1602 | 3204 | \n------------------------|-----------|-----------|-----------|\n\n \n[1] \"CrossTable of lang_prev_experience by treatment\"\n\n \n Cell Contents\n|-------------------------|\n| N |\n| N / Row Total |\n|-------------------------|\n\n \nTotal Observations in Table: 3204 \n\n \n | recipient.df.final$randomization_arm \nrecipient.df.final$lang_prev_experience | 0 | 1 | Row Total | \n----------------------------------------|-----------|-----------|-----------|\n ar 0 | 367 | 367 | 734 | \n | 0.500 | 0.500 | 0.229 | \n----------------------------------------|-----------|-----------|-----------|\n de 0 | 764 | 764 | 1528 | \n | 0.500 | 0.500 | 0.477 | \n----------------------------------------|-----------|-----------|-----------|\n pl 0 | 217 | 217 | 434 | \n | 0.500 | 0.500 | 0.135 | \n----------------------------------------|-----------|-----------|-----------|\n pl 1460 | 51 | 51 | 102 | \n | 0.500 | 0.500 | 0.032 | \n----------------------------------------|-----------|-----------|-----------|\n pl 180 | 26 | 26 | 52 | \n | 0.500 | 0.500 | 0.016 | \n----------------------------------------|-----------|-----------|-----------|\n pl 2920 | 72 | 72 | 144 | \n | 0.500 | 0.500 | 0.045 | \n----------------------------------------|-----------|-----------|-----------|\n pl 365 | 34 | 34 | 68 | \n | 0.500 | 0.500 | 0.021 | \n----------------------------------------|-----------|-----------|-----------|\n pl 730 | 40 | 40 | 80 | \n | 0.500 | 0.500 | 0.025 | \n----------------------------------------|-----------|-----------|-----------|\n pl 90 | 31 | 31 | 62 | \n | 0.500 | 0.500 | 0.019 | \n----------------------------------------|-----------|-----------|-----------|\n Column Total | 1602 | 1602 | 3204 | \n----------------------------------------|-----------|-----------|-----------|\n\n \n" ] ], [ [ "# Subset Polish Experienced Accounts\nWithin Polish, identify 300 accounts (150 blocks) to include and drop all of the others.\n\nNote: since the previous randomization included a larger number of more experienced accounts, we're prioritizing accounts from experience groups 90, 180, 365, 730, and 1460 (all except 2920). ", "_____no_output_____" ] ], [ [ "## SHOW PREVIOUS THANKS BY EXPERIENCE GROUP:\nrecipient.df.final$count.var <- 1\nprint(\"Number of Accounts for each experience level among Polish Participants\")\nprint(aggregate(subset(recipient.df.final, lang=\"pl\")[c(\"count.var\")],\n FUN=sum, by = list(subset(recipient.df.final, lang=\"pl\")$prev_experience)))\ncat(\"\\n\")", "[1] \"Number of Accounts for each experience level among Polish Participants\"\n Group.1 count.var\n1 0 2696\n2 90 62\n3 180 52\n4 365 68\n5 730 80\n6 1460 102\n7 2920 144\n\n" ], [ "print(paste(\"Total number of rows: \", nrow(recipient.df.final), sep=\"\"))\n\nrecipient.df.final.a <- subset(recipient.df.final, !(lang==\"pl\" & prev_experience==2920))\nrecipient.df.final.a$initial.block.id <- recipient.df.final.a$randomization_block_id\nprint(paste(\"Total number of rows once we subset Polish:\", nrow(recipient.df.final.a)))", "[1] \"Total number of rows: 3204\"\n[1] \"Total number of rows once we subset Polish: 3060\"\n" ] ], [ [ "### Offset block IDs to be unique\nObserve the block IDs from the previous randomizations and ensure that these ones are unique and larger.", "_____no_output_____" ] ], [ [ "## LOAD PREVIOUS RANDOMIZATIONS\nprev_randomization_filename <- \"thanks-recipient-randomizations-20190729.csv\"\nprev.randomization.df <- read.csv(file.path(data.path, \"randomization_output\", prev_randomization_filename))\nprint(paste(\"Max Block ID: \", max(prev.randomization.df$randomization_block_id)))\nprev.max.block.id <- max(prev.randomization.df$randomization_block_id)\nprev.max.block.id <- 4221", "[1] \"Max Block ID: 3707\"\n" ], [ "recipient.df.final.a$randomization_block_id <- recipient.df.final.a$initial.block.id + prev.max.block.id\nsummary(recipient.df.final.a$randomization_block_id)", "_____no_output_____" ] ], [ [ "### Sort by block ID", "_____no_output_____" ] ], [ [ "recipient.df.final.a <- recipient.df.final.a[order(recipient.df.final.a$randomization_block_id),]", "_____no_output_____" ], [ "print(\"Newcomers\")\nsummary(subset(recipient.df.final.a, newcomer==1)$lang)\nprint(\"Experienced\")\nsummary(subset(recipient.df.final.a, newcomer==0)$lang)", "[1] \"Newcomers\"\n" ] ], [ [ "# Output and Archive Randomizations", "_____no_output_____" ] ], [ [ "randomization.filename <- paste(\"thanks-recipient-randomizations-\", format(Sys.Date(), format=\"%Y%m%d\"), \".csv\", sep=\"\") \nwrite.csv(recipient.df.final.a, file = file.path(data.path, \"randomization_output\", randomization.filename))", "_____no_output_____" ], [ "colnames(recipient.df.final.a)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# SELECT DISTINCT ?manVal ?wifeVal ?womanVal ?husbandVal WHERE {\n# {\n# ?man a tbio:Person .\n# ?man tbio:hasWife ?wife .\n# }\n# UNION\n# {\n# ?woman a tbio:Person .\n# ?woman tbio:hasHusband ?husband .\n# }\n# BIND(STR(?man) AS ?manStr) .\n# BIND(REPLACE(?manStr, \"http://tbio.orient.cas.cz#\", \"\") AS ?manVal) .\n# BIND(STR(?wife) AS ?wifeStr) .\n# BIND(REPLACE(?wifeStr, \"http://tbio.orient.cas.cz#\", \"\") AS ?wifeVal) .\n# BIND(STR(?woman) AS ?womanStr) .\n# BIND(REPLACE(?womanStr, \"http://tbio.orient.cas.cz#\", \"\") AS ?womanVal) .\n# BIND(STR(?husband) AS ?husbandStr) .\n# BIND(REPLACE(?husbandStr, \"http://tbio.orient.cas.cz#\", \"\") AS ?husbandVal) .\n# }", "_____no_output_____" ], [ "import pandas as pd", "_____no_output_____" ] ], [ [ "# Get marriages", "_____no_output_____" ] ], [ [ "filepath = '/Volumes/backup_128G/z_repository/TBIO_data/RequestsFromTana/20190515'\n\nfilename = 'marriages.tsv'\n\nread_filename = '{0}/{1}'.format(filepath, filename)", "_____no_output_____" ], [ "marriageDf = pd.read_csv(read_filename, delimiter='\\t')\nmarriageDf.fillna('', inplace=True)\nmarriageDf.shape, marriageDf.head()", "_____no_output_____" ] ], [ [ "# To unique spouses", "_____no_output_____" ] ], [ [ "startId = 10000\npersonIds = {}\nmarriageDic = {}\nfor idx in range(0, len(marriageDf)):\n row = marriageDf.loc[idx]\n\n man = str(row['?manVal'])\n wife = str(row['?wifeVal'])\n woman = str(row['?womanVal'])\n husband = str(row['?husbandVal'])\n \n if man != '' and man not in personIds:\n personIds[man] = startId\n startId += 1\n if wife != '' and wife not in personIds:\n personIds[wife] = startId\n startId += 1\n if woman != '' and woman not in personIds:\n personIds[woman] = startId\n startId += 1\n if husband != '' and husband not in personIds:\n personIds[husband] = startId\n startId += 1\n \n if man not in marriageDic:\n marriageDic[man] = [wife]\n elif wife not in marriageDic[man]:\n marriageDic[man].append(wife)\n# else:\n# print(\"man WRONG:\", man, wife)\n\n if wife not in marriageDic:\n marriageDic[wife] = [man]\n elif man not in marriageDic[wife]:\n marriageDic[wife].append(man)\n# else:\n# print(\"wife WRONG:\", wife, man)\n \n if woman not in marriageDic:\n marriageDic[woman] = [husband]\n elif husband not in marriageDic[woman]:\n marriageDic[woman].append(husband)\n# else:\n# print(\"woman WRONG:\", woman, husband)\n \n if husband not in marriageDic:\n marriageDic[husband] = [woman]\n elif woman not in marriageDic[husband]:\n marriageDic[husband].append(woman)\n# else:\n# print(\"husband WRONG:\", husband, woman)\n \n# marriageDic", "_____no_output_____" ], [ "len(personIds)", "_____no_output_____" ], [ "personDf = pd.DataFrame(personIds, index=['ID']).T\npersonDf.head()", "_____no_output_____" ], [ "write_nodes_to = '{0}/{1}'.format(filepath, 'nodes_person_20190516_v2.xlsx')\npersonDf.to_excel(write_nodes_to)", "_____no_output_____" ] ], [ [ "# Read person-family map table", "_____no_output_____" ] ], [ [ "familymembers = 'Familymembers.xlsx'\n\nread_familymembers = '{0}/{1}'.format(filepath, familymembers)", "_____no_output_____" ], [ "fmDf = pd.read_excel(read_familymembers)\nfmDf.shape, fmDf.head()", "_____no_output_____" ], [ "startId = 20000\nfamilyIds = {}\nfmDic = {}\nfor idx in range(0, len(fmDf)):\n row = fmDf.loc[idx]\n\n person = str(row['personStr'])\n family = str(row['familyStr'])\n \n if family not in familyIds:\n familyIds[family] = startId\n startId += 1\n \n if person not in fmDic:\n fmDic[person] = family\n elif family != fmDic[person]:\n print(\"Dup:\", person, family, fmDic[person])\n\n# fmDic", "_____no_output_____" ], [ "len(familyIds)", "_____no_output_____" ], [ "familyDf = pd.DataFrame(familyIds, index=['ID']).T\nfamilyDf.head()", "_____no_output_____" ], [ "write_nodes_to = '{0}/{1}'.format(filepath, 'nodes_family_20190516_v2.xlsx')\nfamilyDf.to_excel(write_nodes_to)", "_____no_output_____" ] ], [ [ "# results", "_____no_output_____" ], [ "### `Source (Family)` | `Target(Family)`| `Type(Undirected)` | `Person/Source` | `Person/target`", "_____no_output_____" ] ], [ [ "def getFamilyName(INperson):\n if INperson not in fmDic:\n# print(INperson, \" Not found!\")\n return ''\n return fmDic[INperson]\n\ndef getPersonId(INperson):\n if INperson not in personIds:\n return 0\n return personIds[INperson]\n\ndef getFamilyId(INfamily):\n if INfamily not in familyIds:\n return 0\n return familyIds[INfamily]\n\nresList = []\nfor sPerson in marriageDic:\n spouses = marriageDic[sPerson]\n for tPerson in spouses:\n sFamily = getFamilyName(sPerson)\n tFamily = getFamilyName(tPerson)\n \n if sFamily == '' or tFamily == '':\n# print(fPerson, fFamily, sPerson, sFamily)\n continue\n\n sPersonId = getPersonId(sPerson)\n tPersonId = getPersonId(tPerson)\n sFamilyId = getFamilyId(sFamily)\n tFamilyId = getFamilyId(tFamily)\n resList.append([sFamilyId, tFamilyId, 'Undirected', sPersonId, tPersonId])\n\nprint(len(resList))", "2573\n" ], [ "resDf = pd.DataFrame(resList, columns=['SourceFamily', 'TargetFamily', 'Type', \n 'SourcePerson', 'TargetPerson'])\nresDf.drop_duplicates(keep='first', inplace=True)\nresDf.sort_values(by=['SourceFamily', 'TargetFamily', 'SourcePerson', 'TargetPerson'], inplace=True)\nresDf.head()", "_____no_output_____" ], [ "print(len(resDf))", "2573\n" ], [ "write_file_to = '{0}/{1}'.format(filepath, 'marriages_20190516_v2.xlsx')\nresDf.to_excel(write_file_to, index=False)", "_____no_output_____" ] ] ]

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[ [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from taxcrunch.cruncher import Cruncher", "_____no_output_____" ], [ "path = '../docs/example_adjustment.json'\nc = Cruncher(path)", "CTC_c was redefined in release 1.0.0\n\n" ], [ "# basic outputs\nc.basic_table()", "_____no_output_____" ], [ "# marginal tax rates\nc.mtr_table()", "_____no_output_____" ], [ "# detailed outputs with marginal tax rate analysis\nc.calc_table()", "_____no_output_____" ], [ "#detailed outputs with difference between reform and baseline\nc.calc_diff_table()", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import json\nimport pandas as pd\nimport numpy as np\nimport time\n\nimport re\n\nfrom sqlalchemy import create_engine\nimport psycopg2\n\n\n", "_____no_output_____" ], [ " def clean_movie(movie):\n movie = dict(movie) #create a non-destructive copy\n alt_titles = {}\n #combine alternate title into one list\n for key in ['Also known as','Arabic','Cantonese','Chinese','French',\n 'Hangul','Hebrew','Hepburn','Japanese','Literally',\n 'Mandarin','McCune–Reischauer','Original title','Polish',\n 'Revised Romanization','Romanized','Russian',\n 'Simplified','Traditional','Yiddish']:\n if key in movie:\n alt_titles[key] = movie[key]\n movie.pop(key)\n if len(alt_titles) > 0:\n movie['alt_titles'] = alt_titles\n def change_column_name(old_name, new_name):\n if old_name in movie:\n movie[new_name] = movie.pop(old_name)\n \n change_column_name('Adaptation by', 'Writer(s)')\n change_column_name('Country of origin', 'Country')\n change_column_name('Directed by', 'Director')\n change_column_name('Distributed by', 'Distributor')\n change_column_name('Edited by', 'Editor(s)')\n change_column_name('Length', 'Running time')\n change_column_name('Original release', 'Release date')\n change_column_name('Music by', 'Composer(s)')\n change_column_name('Produced by', 'Producer(s)')\n change_column_name('Producer', 'Producer(s)')\n change_column_name('Productioncompanies ', 'Production company(s)')\n change_column_name('Productioncompany ', 'Production company(s)')\n change_column_name('Released', 'Release Date')\n change_column_name('Release Date', 'Release date')\n change_column_name('Screen story by', 'Writer(s)')\n change_column_name('Screenplay by', 'Writer(s)')\n change_column_name('Story by', 'Writer(s)')\n change_column_name('Theme music composer', 'Composer(s)')\n change_column_name('Written by', 'Writer(s)')\n \n return movie", "_____no_output_____" ], [ "def extract_transform_load(wiki_file, kaggle_file, ratings_file):\n \n with open(wiki_file, mode='r') as file:\n wiki_movies_raw = json.load(file)\n \n kaggle_metadata = pd.read_csv(kaggle_file)\n ratings = pd.read_csv(ratings_file)\n\n wiki_movies = [movie for movie in wiki_movies_raw\n if ('Director' in movie or 'Directed by' in movie)\n and 'imdb_link' in movie]\n \n clean_movies = [clean_movie(movie) for movie in wiki_movies]\n wiki_movies_df = pd.DataFrame(clean_movies)\n\n print(wiki_movies_df)\n print(kaggle_metadata)\n print(ratings)\n#Assuming wikipedia data still contains IMDB id\n try:\n wiki_movies_df['imdb_id'] = wiki_movies_df['imdb_link'].str.extract(r'(tt\\d{7})')\n wiki_movies_df.drop_duplicates(subset='imdb_id', inplace=True)\n except Exception as e:\n print(e)\n \n wiki_columns_to_keep = [column for column in wiki_movies_df.columns if wiki_movies_df[column].isnull().sum() < len(wiki_movies_df) * 0.9]\n wiki_movies_df = wiki_movies_df[wiki_columns_to_keep] \n box_office = wiki_movies_df['Box office'].dropna() \n box_office = box_office.apply(lambda x: ' '.join(x) if type(x) == list else x)\n \n form_one = r'\\$\\d+\\.?\\d*\\s*[mb]illion'\n form_two = r'\\$\\d{1,3}(?:,\\d{3})+'\n ", "_____no_output_____" ], [ " def parse_dollars(s):\n # if s is not a string, return NaN\n if type(s) != str:\n return np.nan\n\n # if input is of the form $###.# million\n if re.match(r'\\$\\s*\\d+\\.?\\d*\\s*milli?on', s, flags=re.IGNORECASE):\n\n # remove dollar sign and \" million\"\n s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n\n # convert to float and multiply by a million\n value = float(s) * 10**6\n\n # return value\n return value\n\n # if input is of the form $###.# billion\n elif re.match(r'\\$\\s*\\d+\\.?\\d*\\s*billi?on', s, flags=re.IGNORECASE):\n\n # remove dollar sign and \" billion\"\n s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n\n # convert to float and multiply by a billion\n value = float(s) * 10**9\n\n # return value\n return value\n\n # if input is of the form $###,###,###\n elif re.match(r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)', s, flags=re.IGNORECASE):\n\n # remove dollar sign and commas\n s = re.sub('\\$|,','', s)\n\n # convert to float\n value = float(s)\n\n # return value\n return value\n\n # otherwise, return NaN\n else:\n return np.nan\n wiki_movies_df['box_office'] = box_office.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n wiki_movies_df.drop('Box office', axis=1, inplace=True)\n \n budget = wiki_movies_df['Budget'].dropna()\n budget = budget.map(lambda x: ' '.join(x) if type(x) == list else x)\n budget = budget.str.replace(r'\\$.*[-—–](?![a-z])', '$', regex=True)\n wiki_movies_df['budget'] = budget.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n \n release_date = wiki_movies_df['Release date'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)\n date_form_one = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s[123]\\d,\\s\\d{4}'\n date_form_two = r'\\d{4}.[01]\\d.[123]\\d'\n date_form_three = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s\\d{4}'\n date_form_four = r'\\d{4}'\n wiki_movies_df['release_date'] = pd.to_datetime(release_date.str.extract(f'({date_form_one}|{date_form_two}|{date_form_three}|{date_form_four})')[0], infer_datetime_format=True)\n \n running_time = wiki_movies_df['Running time'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)\n running_time_extract = running_time.str.extract(r'(\\d+)\\s*ho?u?r?s?\\s*(\\d*)|(\\d+)\\s*m')\n running_time_extract = running_time_extract.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(0)\n wiki_movies_df['running_time'] = running_time_extract.apply(lambda row: row[0]*60 + row[1] if row[2] == 0 else row[2], axis=1)\n wiki_movies_df.drop('Running time', axis=1, inplace=True)\n\n kaggle_metadata = kaggle_metadata[kaggle_metadata['adult'] == 'False'].drop('adult',axis='columns')\n kaggle_metadata['video'] = kaggle_metadata['video'] == 'True'\n kaggle_metadata['budget'] = kaggle_metadata['budget'].astype(int)\n kaggle_metadata['id'] = pd.to_numeric(kaggle_metadata['id'], errors='raise')\n kaggle_metadata['popularity'] = pd.to_numeric(kaggle_metadata['popularity'], errors='raise')\n kaggle_metadata['release_date'] = pd.to_datetime(kaggle_metadata['release_date'])\n\n movies_df = pd.merge(wiki_movies_df, kaggle_metadata, on='imdb_id', suffixes=['_wiki','_kaggle'])\n movies_df.drop(columns=['title_wiki','release_date_wiki','Language','Production company(s)'], inplace=True)\n\n def fill_missing_kaggle_data(df, kaggle_column, wiki_column):\n df[kaggle_column] = df.apply(\n lambda row: row[wiki_column] if row[kaggle_column] == 0 else row[kaggle_column]\n , axis=1)\n df.drop(columns=wiki_column, inplace=True)\n \n fill_missing_kaggle_data(movies_df, 'runtime', 'running_time')\n fill_missing_kaggle_data(movies_df, 'budget_kaggle', 'budget_wiki')\n fill_missing_kaggle_data(movies_df, 'revenue', 'box_office')\n \n movies_df = movies_df.loc[:, ['imdb_id','id','title_kaggle','original_title','tagline','belongs_to_collection','url','imdb_link',\n 'runtime','budget_kaggle','revenue','release_date_kaggle','popularity','vote_average','vote_count',\n 'genres','original_language','overview','spoken_languages','Country',\n 'production_companies','production_countries','Distributor',\n 'Producer(s)','Director','Starring','Cinematography','Editor(s)','Writer(s)','Composer(s)','Based on'\n ]]\n\n\n movies_df.rename({'id':'kaggle_id',\n 'title_kaggle':'title',\n 'url':'wikipedia_url',\n 'budget_kaggle':'budget',\n 'release_date_kaggle':'release_date',\n 'Country':'country',\n 'Distributor':'distributor',\n 'Producer(s)':'producers',\n 'Director':'director',\n 'Starring':'starring',\n 'Cinematography':'cinematography',\n 'Editor(s)':'editors',\n 'Writer(s)':'writers',\n 'Composer(s)':'composers',\n 'Based on':'based_on'\n }, axis='columns', inplace=True)\n\n rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count().rename({'userId':'count'}, axis=1).pivot(index='movieId',columns='rating', values='count')\n rating_counts.columns = ['rating_' + str(col) for col in rating_counts.columns] \n movies_with_ratings_df = pd.merge(movies_df, rating_counts, left_on='kaggle_id', right_index=True, how='left')\n movies_with_ratings_df[rating_counts.columns] = movies_with_ratings_df[rating_counts.columns].fillna(0)\n \n\n db_string = f\"postgres://postgres:{db_password}@localhost:5432/movies_data\"\n engine = create_engine(db_string)\n movies_df.to_sql(name='movies', con=engine)\n\n rows_imported = 0\n # get the start_time from time.time()\n start_time = time.time()\n for data in pd.read_csv(f'{file_dir}/ratings.csv', chunksize=1000000):\n print(f'importing rows {rows_imported} to {rows_imported + len(data)}...', end='')\n data.to_sql(name='ratings', con=engine, if_exists='append')\n rows_imported += len(data)\n\n # add elapsed time to final print out\n print(f'Done. {time.time() - start_time} total seconds elapsed')\n\n\nfile_dir = \"./Resources\" \nwiki_file = f'{file_dir}/wikipedia.movies.json' \nkaggle_file = f'{file_dir}/movies_metadata.csv' \nratings_file = f'{file_dir}/ratings.csv' \n\nextract_transform_load(wiki_file,kaggle_file,ratings_file)\n", "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3254: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n if (await self.run_code(code, result, async_=asy)):\n" ], [ " ", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# LeNet Lab Solution\n\nSource: Yan LeCun", "_____no_output_____" ], [ "## Load Data\n\nLoad the MNIST data, which comes pre-loaded with TensorFlow.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "from tensorflow.examples.tutorials.mnist import input_data\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", reshape=False)\nX_train, y_train = mnist.train.images, mnist.train.labels\nX_validation, y_validation = mnist.validation.images, mnist.validation.labels\nX_test, y_test = mnist.test.images, mnist.test.labels\n\nassert(len(X_train) == len(y_train))\nassert(len(X_validation) == len(y_validation))\nassert(len(X_test) == len(y_test))\n\nprint()\nprint(\"Image Shape: {}\".format(X_train[0].shape))\nprint()\nprint(\"Training Set: {} samples\".format(len(X_train)))\nprint(\"Validation Set: {} samples\".format(len(X_validation)))\nprint(\"Test Set: {} samples\".format(len(X_test)))", "G:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n" ] ], [ [ "The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.\n\nHowever, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.\n\nIn order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "import numpy as np\n\n# Pad images with 0s\nX_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')\nX_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')\nX_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n \nprint(\"Updated Image Shape: {}\".format(X_train[0].shape))", "Updated Image Shape: (36, 36, 1)\n" ] ], [ [ "## Visualize Data\n\nView a sample from the dataset.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "import random\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nindex = random.randint(0, len(X_train))\nimage = X_train[index].squeeze()\n\nplt.figure(figsize=(1,1))\nplt.imshow(image, cmap=\"gray\")\nprint(y_train[index])", "(32, 32)\n4\n" ] ], [ [ "## Preprocess Data\n\nShuffle the training data.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "from sklearn.utils import shuffle\n\nX_train, y_train = shuffle(X_train, y_train)", "_____no_output_____" ] ], [ [ "## Setup TensorFlow\nThe `EPOCH` and `BATCH_SIZE` values affect the training speed and model accuracy.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "import tensorflow as tf\n\nEPOCHS = 10\nBATCH_SIZE = 128", "_____no_output_____" ] ], [ [ "## SOLUTION: Implement LeNet-5\nImplement the [LeNet-5](http://yann.lecun.com/exdb/lenet/) neural network architecture.\n\nThis is the only cell you need to edit.\n### Input\nThe LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.\n\n### Architecture\n**Layer 1: Convolutional.** The output shape should be 28x28x6.\n\n**Activation.** Your choice of activation function.\n\n**Pooling.** The output shape should be 14x14x6.\n\n**Layer 2: Convolutional.** The output shape should be 10x10x16.\n\n**Activation.** Your choice of activation function.\n\n**Pooling.** The output shape should be 5x5x16.\n\n**Flatten.** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using `tf.contrib.layers.flatten`, which is already imported for you.\n\n**Layer 3: Fully Connected.** This should have 120 outputs.\n\n**Activation.** Your choice of activation function.\n\n**Layer 4: Fully Connected.** This should have 84 outputs.\n\n**Activation.** Your choice of activation function.\n\n**Layer 5: Fully Connected (Logits).** This should have 10 outputs.\n\n### Output\nReturn the result of the 2nd fully connected layer.", "_____no_output_____" ] ], [ [ "from tensorflow.contrib.layers import flatten\n\ndef LeNet(x): \n # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n mu = 0\n sigma = 0.1\n \n # SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))\n conv1_b = tf.Variable(tf.zeros(6))\n conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n\n # SOLUTION: Activation.\n conv1 = tf.nn.relu(conv1)\n\n # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.\n conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n\n # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.\n conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n conv2_b = tf.Variable(tf.zeros(16))\n conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n \n # SOLUTION: Activation.\n conv2 = tf.nn.relu(conv2)\n\n # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.\n conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n\n # SOLUTION: Flatten. Input = 5x5x16. Output = 400.\n fc0 = flatten(conv2)\n \n # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.\n fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n fc1_b = tf.Variable(tf.zeros(120))\n fc1 = tf.matmul(fc0, fc1_W) + fc1_b\n \n # SOLUTION: Activation.\n fc1 = tf.nn.relu(fc1)\n\n # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.\n fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n fc2_b = tf.Variable(tf.zeros(84))\n fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n \n # SOLUTION: Activation.\n fc2 = tf.nn.relu(fc2)\n\n # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 10.\n fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))\n fc3_b = tf.Variable(tf.zeros(10))\n logits = tf.matmul(fc2, fc3_W) + fc3_b\n \n return logits", "_____no_output_____" ] ], [ [ "## Features and Labels\nTrain LeNet to classify [MNIST](http://yann.lecun.com/exdb/mnist/) data.\n\n`x` is a placeholder for a batch of input images.\n`y` is a placeholder for a batch of output labels.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\ny = tf.placeholder(tf.int32, (None))\none_hot_y = tf.one_hot(y, 10)", "_____no_output_____" ] ], [ [ "## Training Pipeline\nCreate a training pipeline that uses the model to classify MNIST data.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "rate = 0.001\n\nlogits = LeNet(x)\ncross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\nloss_operation = tf.reduce_mean(cross_entropy)\noptimizer = tf.train.AdamOptimizer(learning_rate = rate)\ntraining_operation = optimizer.minimize(loss_operation)", "_____no_output_____" ] ], [ [ "## Model Evaluation\nEvaluate how well the loss and accuracy of the model for a given dataset.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\naccuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\nsaver = tf.train.Saver()\n\ndef evaluate(X_data, y_data):\n num_examples = len(X_data)\n total_accuracy = 0\n sess = tf.get_default_session()\n for offset in range(0, num_examples, BATCH_SIZE):\n batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\n total_accuracy += (accuracy * len(batch_x))\n return total_accuracy / num_examples", "_____no_output_____" ] ], [ [ "## Train the Model\nRun the training data through the training pipeline to train the model.\n\nBefore each epoch, shuffle the training set.\n\nAfter each epoch, measure the loss and accuracy of the validation set.\n\nSave the model after training.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n num_examples = len(X_train)\n \n print(\"Training...\")\n print()\n for i in range(EPOCHS):\n X_train, y_train = shuffle(X_train, y_train)\n for offset in range(0, num_examples, BATCH_SIZE):\n end = offset + BATCH_SIZE\n batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})\n \n validation_accuracy = evaluate(X_validation, y_validation)\n print(\"EPOCH {} ...\".format(i+1))\n print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n print()\n \n saver.save(sess, './lenet')\n print(\"Model saved\")", "_____no_output_____" ] ], [ [ "## Evaluate the Model\nOnce you are completely satisfied with your model, evaluate the performance of the model on the test set.\n\nBe sure to only do this once!\n\nIf you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model on the test set again, that would invalidate your test results. You wouldn't get a true measure of how well your model would perform against real data.\n\nYou do not need to modify this section.", "_____no_output_____" ] ], [ [ "with tf.Session() as sess:\n saver.restore(sess, tf.train.latest_checkpoint('.'))\n\n test_accuracy = evaluate(X_test, y_test)\n print(\"Test Accuracy = {:.3f}\".format(test_accuracy))", "_____no_output_____" ] ] ]

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[ "MIT" ]

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[ [ [ "import subprocess\nimport shlex\nimport pandas as pd\nimport numpy as np\nfrom astropy.table import Table\nfrom astropy.table import Column\nimport astropy\nimport os\nimport glob2\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages", "_____no_output_____" ], [ "sample_location = \"/home/xhall/Documents/NewZTF/spectra/\"\nnew_sample_location = \"/home/xhall/Documents/NewZTF/spectra_nonan/\"\nsource = \"/home/xhall/Documents/NewZTF/SNIDoutput/\"\nimage_output = \"/home/xhall/Documents/NewZTF/Images/\"\nsnid = \"/home/xhall/Documents/SNID/snid-5.0/snid\"", "_____no_output_____" ], [ "sample = Table.read(\"/home/xhall/Documents/NewZTF/ML_sample.ascii\", format = \"ascii\")", "_____no_output_____" ], [ "for i in np.unique(sample[\"col8\"]):\n try:\n spectra = Table.from_pandas(Table.read(sample_location + str(i), format = \"ascii\").to_pandas().dropna())\n astropy.io.ascii.write(spectra, new_sample_location + str(i), overwrite=True, format = \"no_header\")\n except:\n print(i)", "ZTF18abcfdzu_20181229_NOT_v1.ascii\nZTF18acbwaxk_20190110_NOT_v1.ascii\nZTF18aceqrrs_20190227_NOT_v1.ascii\nZTF18acsjrbc_20190102_NOT_v1.ascii\nZTF19aafmyow_20190816_NOT_v1.ascii\nZTF19aapfmki_20190511_NOT_v1.ascii\nZTF19abdoior_20190815_NOT_v1.ascii\nZTF19abucwzt_20200127_NOT_v1.ascii\nZTF20aaelulu_20200114_NOT_v1.ascii\nZTF20aalrqbu_20200506_NOT_v1.ascii\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pickle\n\npickle_off = open(\"clusters.dict\",\"rb\")\nclusters = pickle.load(pickle_off)", "_____no_output_____" ], [ "X = clusters[0][28]['lipid'][0][1]\nprint(len(X))", "400\n" ], [ "print(__doc__)\n\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN\nfrom sklearn import metrics\n#from sklearn.datasets.samples_generator import make_blobs\n#from sklearn.preprocessing import StandardScaler\n\n\n# #############################################################################\n#Generate sample data\n#centers = [[1, 1], [-1, -1], [1, -1]]\n#X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,random_state=0)\n\n#X = StandardScaler().fit_transform(X)\n\n# #############################################################################\n# Compute DBSCAN\ndb = DBSCAN(eps=1.1, min_samples=3).fit(X)\ncore_samples_mask = np.zeros_like(db.labels_, dtype=bool)\ncore_samples_mask[db.core_sample_indices_] = True\nlabels = db.labels_\n\n# Number of clusters in labels, ignoring noise if present.\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\nprint('Estimated number of clusters: %d' % n_clusters_)\n#print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n#print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n#print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n#print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n#print(\"Adjusted Mutual Information: %0.3f\" % metrics.adjusted_mutual_info_score(labels_true, labels))\n#print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = [0, 0, 0, 1]\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()", "Automatically created module for IPython interactive environment\nEstimated number of clusters: 26\n" ], [ "X = clusters[0][37]['lipid'][0][1]\nprint(len(X))", "369\n" ], [ "print(__doc__)\n\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN\nfrom sklearn import metrics\n#from sklearn.datasets.samples_generator import make_blobs\n#from sklearn.preprocessing import StandardScaler\n\n\n# #############################################################################\n#Generate sample data\n#centers = [[1, 1], [-1, -1], [1, -1]]\n#X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,random_state=0)\n\n#X = StandardScaler().fit_transform(X)\n\n# #############################################################################\n# Compute DBSCAN\ndb = DBSCAN(eps=1.1, min_samples=2\n ).fit(X)\ncore_samples_mask = np.zeros_like(db.labels_, dtype=bool)\ncore_samples_mask[db.core_sample_indices_] = True\nlabels = db.labels_\n\n# Number of clusters in labels, ignoring noise if present.\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\nprint('Estimated number of clusters: %d' % n_clusters_)\n#print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n#print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n#print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n#print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n#print(\"Adjusted Mutual Information: %0.3f\" % metrics.adjusted_mutual_info_score(labels_true, labels))\n#print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = [0, 0, 0, 1]\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()", "Automatically created module for IPython interactive environment\nEstimated number of clusters: 63\n" ], [ "X = clusters[0][37]['lipid'][0][2]\nprint(len(X))", "239\n" ], [ "print(__doc__)\n\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN\nfrom sklearn import metrics\n#from sklearn.datasets.samples_generator import make_blobs\n#from sklearn.preprocessing import StandardScaler\n\n\n# #############################################################################\n#Generate sample data\n#centers = [[1, 1], [-1, -1], [1, -1]]\n#X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,random_state=0)\n\n#X = StandardScaler().fit_transform(X)\n\n# #############################################################################\n# Compute DBSCAN\ndb = DBSCAN(eps=1.1, min_samples=2\n ).fit(X)\ncore_samples_mask = np.zeros_like(db.labels_, dtype=bool)\ncore_samples_mask[db.core_sample_indices_] = True\nlabels = db.labels_\n\n# Number of clusters in labels, ignoring noise if present.\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\nprint('Estimated number of clusters: %d' % n_clusters_)\n#print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n#print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n#print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n#print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n#print(\"Adjusted Mutual Information: %0.3f\" % metrics.adjusted_mutual_info_score(labels_true, labels))\n#print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = [0, 0, 0, 1]\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()", "Automatically created module for IPython interactive environment\nEstimated number of clusters: 43\n" ], [ "X = clusters[0][37]['lipid'][0][3]\nprint(len(X))", "78\n" ], [ "print(__doc__)\n\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN\nfrom sklearn import metrics\n#from sklearn.datasets.samples_generator import make_blobs\n#from sklearn.preprocessing import StandardScaler\n\n\n# #############################################################################\n#Generate sample data\n#centers = [[1, 1], [-1, -1], [1, -1]]\n#X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,random_state=0)\n\n#X = StandardScaler().fit_transform(X)\n\n# #############################################################################\n# Compute DBSCAN\ndb = DBSCAN(eps=1.1, min_samples=2\n ).fit(X)\ncore_samples_mask = np.zeros_like(db.labels_, dtype=bool)\ncore_samples_mask[db.core_sample_indices_] = True\nlabels = db.labels_\n\n# Number of clusters in labels, ignoring noise if present.\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\nprint('Estimated number of clusters: %d' % n_clusters_)\n#print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n#print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n#print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n#print(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\n#print(\"Adjusted Mutual Information: %0.3f\" % metrics.adjusted_mutual_info_score(labels_true, labels))\n#print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(X, labels))\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = [0, 0, 0, 1]\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()", "Automatically created module for IPython interactive environment\nEstimated number of clusters: 12\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import scanpy as sc\nfrom anndata import read_h5ad\nimport pandas as pd\nimport numpy as np\nimport scipy as sp\nfrom statsmodels.stats.multitest import multipletests\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nfrom os.path import join\nimport time\n\n# scTRS tools\nimport scTRS.util as util\nimport scTRS.data_loader as dl\nimport scTRS.method as md\n\n# autoreload\n%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "# File paths\nDATA_PATH = '/n/holystore01/LABS/price_lab/Users/mjzhang/scTRS_data'\nFIG_PATH = '/n/holystore01/LABS/price_lab/Users/mjzhang/scTRS_data/results/fig_hep'\nDS_LIST = ['droplet']\n\n# Score files\nDIC_SCORE_PATH = {'droplet': DATA_PATH+'/score_file/score.tms_droplet_with_cov.magma_10kb_1000'}\n\nDIC_TRAIT_LIST = {}\nDIC_TRAIT_LIST = {'droplet': ['UKB_460K.biochemistry_LDLdirect']}", "_____no_output_____" ], [ "# Load raw data \ndic_data_raw = {}\n# dic_data_raw['facs'] = dl.load_tms_ct(DATA_PATH, data_name='facs')\ndic_data_raw['droplet'] = dl.load_tms_ct(DATA_PATH, data_name='droplet')", "_____no_output_____" ], [ "# Load score \ndic_score = {x:pd.DataFrame() for x in DIC_SCORE_PATH}\nfor score in DIC_SCORE_PATH:\n for trait in DIC_TRAIT_LIST[score]:\n file_path = join(DIC_SCORE_PATH[score], '%s.score.gz'%trait)\n if os.path.exists(file_path):\n temp_df = pd.read_csv(file_path, sep='\\t', index_col=0)\n temp_df.columns = ['%s.%s'%(trait,x) for x in temp_df.columns]\n temp_df['%s.fdr'%trait] = multipletests(temp_df['%s.pval'%trait], method='fdr_bh')[1]\n dic_score[score] = pd.concat([dic_score[score], temp_df], axis=1)\n else:\n print('# missing: %s'%file_path) ", "_____no_output_____" ] ], [ [ "### Get data for only hepatocytes and rerun harmony+umap", "_____no_output_____" ] ], [ [ "# Reprocess t cell data\nfor ds in DS_LIST:\n print(ds)\n ind_select = dic_data_raw[ds].obs['cell_ontology_class']=='hepatocyte'\n adata = dic_data_raw[ds][ind_select,:].copy()\n sc.pp.filter_cells(adata, min_genes=250)\n sc.pp.filter_genes(adata, min_cells=50)\n adata.obs['batch_harmony'] = adata.obs['mouse.id']\n adata.obs['batch_harmony'] = adata.obs['batch_harmony'].astype('category')\n\n sc.pp.highly_variable_genes(adata, subset = False, min_disp=.5, \n min_mean=.0125, max_mean=10, n_bins=20, n_top_genes=None)\n sc.pp.scale(adata, max_value=10, zero_center=False)\n sc.pp.pca(adata, n_comps=50, use_highly_variable=True, svd_solver='arpack')\n sc.external.pp.harmony_integrate(adata, key='batch_harmony', max_iter_harmony=20)\n sc.pp.neighbors(adata, n_neighbors=50, n_pcs=20, use_rep=\"X_pca_harmony\")\n# sc.pp.neighbors(adata, n_neighbors=50, n_pcs=20, use_rep=\"X_pca\")\n sc.tl.leiden(adata, resolution=0.7) \n sc.tl.umap(adata)\n sc.pl.umap(adata, color='cell_ontology_class')\n sc.pl.umap(adata, color='leiden')\n sc.pl.umap(adata, color=['age', 'sex', 'mouse.id', 'n_genes', 'subtissue'])\n adata.write('/n/holystore01/LABS/price_lab/Users/mjzhang/scTRS_data/single_cell_data/tms_proc/'\n 'hep.%s.h5ad'%ds)", "droplet\n" ], [ "adata = read_h5ad('/n/holystore01/LABS/price_lab/Users/mjzhang/scTRS_data/single_cell_data/tms_proc/hep.droplet.h5ad')\n# dic_cluster = {'0':'4', '1':'3','2':'1','3':'5','4':'0','5':'2'}\n# adata.obs['leiden_old'] = adata.obs['leiden'].values\n# adata.obs['leiden'] = [dic_cluster[x] for x in adata.obs['leiden_old']]\nadata.obs = adata.obs.join(dic_score['droplet'])\nsc.pl.umap(adata, color='leiden')\nsc.pl.umap(adata, color='UKB_460K.biochemistry_LDLdirect.norm_score')\n# sc.pl.umap(adata, color=['Pecam1', 'Nrp1', 'Kdr', 'Oit3'])\n# sc.pl.umap(adata, color=['Clec4f', 'Cd68', 'Irf7'])\n# sc.pl.umap(adata, color=['Alb', 'Ttr', 'Apoa1', 'Serpina1c'])\n# adata.write('/n/holystore01/LABS/price_lab/Users/mjzhang/scTRS_data/single_cell_data/tms_proc/hep.facs_annot.h5ad')", "_____no_output_____" ], [ "temp_df = dic_data_raw['facs'][dic_data_raw['facs'].obs['cell_ontology_class']=='hepatocyte']\\\n .obs.groupby(['subtissue', 'mouse.id']).agg({'cell':len})\ntemp_df.loc[~temp_df['cell'].isna()]", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "library(tidyverse)\nlibrary(dslabs)\ndata(heights)\n\ns <- heights %>% filter(sex == \"Male\") %>% summarize(mean = mean(height), sd = sd(height))\ns", "_____no_output_____" ], [ "class(s)", "_____no_output_____" ], [ "s1 <- heights %>% filter(sex == \"Male\") %>% summarize(mean = mean(height), sd = sd(height), median = median(height), max = max(height), min = min(height))\ns1", "_____no_output_____" ], [ "quantile(heights$height, c(0.0, 0.05, 1.0))", "_____no_output_____" ], [ "# This code generates error because summarize accept only those function which return single values\nheights %>% filter(sex == \"Male\") %>% summarize(range = quantile(height, c(0.0, 0.5, 1.0)))", "_____no_output_____" ] ], [ [ "# Dot Placeholder", "_____no_output_____" ], [ "### Pull() and dot behaves in the same way", "_____no_output_____" ] ], [ [ "library(tidyverse)\nlibrary(dslabs)\ndata(murders)\n\nmurders <- murders %>% mutate(murder_rate = (total/population) * 100000) \nsummarize(murders, mean = mean(murder_rate))\n\nus_murder_rate <- murders %>% summarize(rate = sum(total)/ sum(population) *100000)\nr <- us_murder_rate %>% .$rate\nclass(r)\nr\nclass(us_murder_rate$rate)", "_____no_output_____" ], [ "us_murder_rate <- murders %>%\n summarize(rate = sum(total) / sum(population) * 100000) %>%\n .$rate\nus_murder_rate", "_____no_output_____" ], [ "heights %>% group_by(sex) %>% summarize(mean = mean(height), sd = sd(height))\nmurders %>% mutate(murder_rate = total/population * 100000) %>% group_by(region) %>% summarize(mean = mean(murder_rate), sd = sd(murder_rate))", "_____no_output_____" ], [ "murders %>% mutate(murder_rate = total/population * 100000) %>% arrange(desc(murder_rate)) %>% top_n(10, murder_rate)", "_____no_output_____" ], [ "murders %>% arrange(population) %>% head()", "_____no_output_____" ], [ "murders %>% arrange(desc(population)) %>% head()", "_____no_output_____" ], [ "murders %>% arrange(region, population) %>% head()", "_____no_output_____" ], [ "murders %>% top_n(10, murder_rate)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Gaussian Mixture Models (GMM)\n\nKDE centers each bin (or kernel rather) at each point. In a [**mixture model**](https://en.wikipedia.org/wiki/Mixture_model) we don't use a kernel for each data point, but rather we fit for the *locations of the kernels*--in addition to the width. So a mixture model is sort of a hybrid between an $N$-D histogram and KDE. Using lots of kernels (maybe even more than the BIC score suggests) may make sense if you just want to provide an accurate description of the data (as in density estimation). Using fewer kernels makes mixture models more like clustering, where the suggestion is still to use many kernels in order to divide the sample into real clusters and \"background\".", "_____no_output_____" ], [ "Gaussians are the most commonly used components for mixture models. So, the pdf is modeled by a sum of Gaussians:\n$$p(x) = \\sum_{k=1}^N \\alpha_k \\mathscr{N}(x|\\mu_k,\\Sigma_k),$$\nwhere $\\alpha_k$ is the \"mixing coefficient\" with $0\\le \\alpha_k \\le 1$ and $\\sum_{k=1}^N \\alpha_k = 1$.\n\nWe can solve for the parameters using maximum likelihood analyis as we have discussed previously.\nHowever, this can be complicated in multiple dimensions, requiring the use of [**Expectation Maximization (EM)**](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) methods.", "_____no_output_____" ], [ "### Expectation Maximization (ultra simplified version)\n\n(Note: all explanations of EM are far more complicated than seems necessary for our purposes, so here is my overly simplified explanation.)\n\nThis may make more sense in terms of our earlier Bayesian analyses if we write this as \n$$p(z=c) = \\alpha_k,$$\nand\n$$p(x|z=c) = \\mathscr{N}(x|\\mu_k,\\Sigma_k),$$\nwhere $z$ is a \"hidden\" variable related to which \"component\" each point is assigned to.\n\nIn the Expectation step, we hold $\\mu_k, \\Sigma_k$, and $\\alpha_k$ fixed and compute the probability that each $x_i$ belongs to component, $c$. \n\nIn the Maximization step, we hold the probability of the components fixed and maximize $\\mu_k, \\Sigma_k,$ and $\\alpha_k$.", "_____no_output_____" ], [ "Note that $\\alpha$ is the relative weight of each Gaussian component and not the probability of each point belonging to a specific component.", "_____no_output_____" ], [ "We can use the following animation to illustrate the process. \n\nWe start with a 2-component GMM, where the initial components can be randomly determined.\n\nThe points that are closest to the centroid of a component will be more probable under that distribution in the \"E\" step and will pull the centroid towards them in the \"M\" step. Iteration between the \"E\" and \"M\" step eventually leads to convergence.\n\nIn this particular example, 3 components better describes the data and similarly converges. Note that the process is not that sensitive to how the components are first initialized. We pretty much get the same result in the end.", "_____no_output_____" ] ], [ [ "from IPython.display import YouTubeVideo\nYouTubeVideo(\"B36fzChfyGU\")", "_____no_output_____" ] ], [ [ "A typical call to the [Gaussian Mixture Model](http://scikit-learn.org/stable/modules/mixture.html) algorithm looks like this:", "_____no_output_____" ] ], [ [ "# Execute this cell\nimport numpy as np\nfrom sklearn.mixture import GMM\n\nX = np.random.normal(size=(1000,2)) #1000 points in 2D\ngmm = GMM(3) #three components\ngmm.fit(X)\nlog_dens = gmm.score(X)\nBIC = gmm.bic(X)", "_____no_output_____" ] ], [ [ "Let's start with the 1-D example given in Ivezic, Figure 6.8, which compares a Mixture Model to KDE.\n[Note that the version at astroML.org has some bugs!]", "_____no_output_____" ] ], [ [ "# Execute this cell\n# Ivezic, Figure 6.8\n# Author: Jake VanderPlas\n# License: BSD\n# The figure produced by this code is published in the textbook\n# \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n# For more information, see http://astroML.github.com\n# To report a bug or issue, use the following forum:\n# https://groups.google.com/forum/#!forum/astroml-general\n%matplotlib inline\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import stats\n\nfrom astroML.plotting import hist\nfrom sklearn.mixture import GMM\n\nfrom sklearn.neighbors import KernelDensity\n\n#------------------------------------------------------------\n# Generate our data: a mix of several Cauchy distributions\n# this is the same data used in the Bayesian Blocks figure\nnp.random.seed(0)\nN = 10000\nmu_gamma_f = [(5, 1.0, 0.1),\n (7, 0.5, 0.5),\n (9, 0.1, 0.1),\n (12, 0.5, 0.2),\n (14, 1.0, 0.1)]\ntrue_pdf = lambda x: sum([f * stats.cauchy(mu, gamma).pdf(x)\n for (mu, gamma, f) in mu_gamma_f])\nx = np.concatenate([stats.cauchy(mu, gamma).rvs(int(f * N))\n for (mu, gamma, f) in mu_gamma_f])\nnp.random.shuffle(x)\nx = x[x > -10]\nx = x[x < 30]\n\n#------------------------------------------------------------\n# plot the results\nfig = plt.figure(figsize=(10, 10))\nfig.subplots_adjust(bottom=0.08, top=0.95, right=0.95, hspace=0.1)\nN_values = (500, 5000)\nsubplots = (211, 212)\nk_values = (10, 100)\n\nfor N, k, subplot in zip(N_values, k_values, subplots):\n ax = fig.add_subplot(subplot)\n xN = x[:N]\n t = np.linspace(-10, 30, 1000)\n\n kde = KernelDensity(0.1, kernel='gaussian')\n kde.fit(xN[:, None])\n dens_kde = np.exp(kde.score_samples(t[:, None]))\n\n # Compute density via Gaussian Mixtures\n # we'll try several numbers of clusters\n n_components = np.arange(3, 16)\n gmms = [GMM(n_components=n).fit(xN[:,None]) for n in n_components]\n BICs = [gmm.bic(xN[:,None]) for gmm in gmms]\n i_min = np.argmin(BICs)\n t = np.linspace(-10, 30, 1000)\n logprob, responsibilities = gmms[i_min].score_samples(t[:,None])\n\n # plot the results\n ax.plot(t, true_pdf(t), ':', color='black', zorder=3,\n label=\"Generating Distribution\")\n ax.plot(xN, -0.005 * np.ones(len(xN)), '|k', lw=1.5)\n ax.plot(t, np.exp(logprob), '-', color='gray',\n label=\"Mixture Model\\n(%i components)\" % n_components[i_min])\n ax.plot(t, dens_kde, '-', color='black', zorder=3,\n label=\"Kernel Density $(h=0.1)$\")\n\n # label the plot\n ax.text(0.02, 0.95, \"%i points\" % N, ha='left', va='top',\n transform=ax.transAxes)\n ax.set_ylabel('$p(x)$')\n ax.legend(loc='upper right')\n\n if subplot == 212:\n ax.set_xlabel('$x$')\n\n ax.set_xlim(0, 20)\n ax.set_ylim(-0.01, 0.4001)\n\nplt.show()", "_____no_output_____" ] ], [ [ "Hmm, that doesn't look so great for the 5000 point distribution. Plot the BIC values and see if anything looks awry.", "_____no_output_____" ], [ "What do the individual components look like? Make a plot of those. Careful with the shapes of the arrays!", "_____no_output_____" ], [ "Can you figure out something that you can do to improve the results? ", "_____no_output_____" ], [ "Ivezic, Figure 6.6 shows a 2-D example. In the first panel, we have the raw data. In the second panel we have a density plot (essentially a 2-D histogram). We then try to represent the data with a series of Gaussians. We allow up to 14 Gaussians and use the AIC/BIC to determine the best choice for this number. This is shown in the third panel. Finally, the fourth panel shows the chosen Gaussians with their centroids and 1-$\\sigma$ contours.\n\nIn this case 7 components are required for the best fit. While it looks like we could do a pretty good job with just 2 components, there does appear to be some \"background\" that is a high enough level to justify further components.", "_____no_output_____" ] ], [ [ "# Execute this cell\n# Ivezic, Figure 6.6\n# Author: Jake VanderPlas\n# License: BSD\n# The figure produced by this code is published in the textbook\n# \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n# For more information, see http://astroML.github.com\n# To report a bug or issue, use the following forum:\n# https://groups.google.com/forum/#!forum/astroml-general\n%matplotlib inline\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.stats import norm\n\nfrom sklearn.mixture import GMM\n\nfrom astroML.datasets import fetch_sdss_sspp\nfrom astroML.decorators import pickle_results\nfrom astroML.plotting.tools import draw_ellipse\n\n#------------------------------------------------------------\n# Get the Segue Stellar Parameters Pipeline data\ndata = fetch_sdss_sspp(cleaned=True)\n\n# Note how X was created from two columns of data\nX = np.vstack([data['FeH'], data['alphFe']]).T\n\n# truncate dataset for speed\nX = X[::5]\n\n#------------------------------------------------------------\n# Compute GMM models & AIC/BIC\nN = np.arange(1, 14)\n\n#@pickle_results(\"GMM_metallicity.pkl\")\ndef compute_GMM(N, covariance_type='full', n_iter=1000):\n models = [None for n in N]\n for i in range(len(N)):\n #print N[i]\n models[i] = GMM(n_components=N[i], n_iter=n_iter, covariance_type=covariance_type)\n models[i].fit(X)\n return models\n\nmodels = compute_GMM(N)\n\nAIC = [m.aic(X) for m in models]\nBIC = [m.bic(X) for m in models]\n\ni_best = np.argmin(BIC)\ngmm_best = models[i_best]\nprint \"best fit converged:\", gmm_best.converged_\nprint \"BIC: n_components = %i\" % N[i_best]\n\n#------------------------------------------------------------\n# compute 2D density\nFeH_bins = 51\nalphFe_bins = 51\nH, FeH_bins, alphFe_bins = np.histogram2d(data['FeH'], data['alphFe'], (FeH_bins, alphFe_bins))\n\nXgrid = np.array(map(np.ravel,\n np.meshgrid(0.5 * (FeH_bins[:-1]\n + FeH_bins[1:]),\n 0.5 * (alphFe_bins[:-1]\n + alphFe_bins[1:])))).T\nlog_dens = gmm_best.score(Xgrid).reshape((51, 51))\n\n#------------------------------------------------------------\n# Plot the results\nfig = plt.figure(figsize=(12, 5))\nfig.subplots_adjust(wspace=0.45, bottom=0.25, top=0.9, left=0.1, right=0.97)\n\n# plot data\nax = fig.add_subplot(141)\nax.scatter(data['FeH'][::10],data['alphFe'][::10],marker=\".\",color='k',edgecolors='None')\nax.set_xlabel(r'$\\rm [Fe/H]$')\nax.set_ylabel(r'$\\rm [\\alpha/Fe]$')\nax.xaxis.set_major_locator(plt.MultipleLocator(0.3))\nax.set_xlim(-1.101, 0.101)\nax.text(0.93, 0.93, \"Input\",\n va='top', ha='right', transform=ax.transAxes)\n\n# plot density\nax = fig.add_subplot(142)\nax.imshow(H.T, origin='lower', interpolation='nearest', aspect='auto',\n extent=[FeH_bins[0], FeH_bins[-1],\n alphFe_bins[0], alphFe_bins[-1]],\n cmap=plt.cm.binary)\nax.set_xlabel(r'$\\rm [Fe/H]$')\nax.set_ylabel(r'$\\rm [\\alpha/Fe]$')\nax.xaxis.set_major_locator(plt.MultipleLocator(0.3))\nax.set_xlim(-1.101, 0.101)\nax.text(0.93, 0.93, \"Density\",\n va='top', ha='right', transform=ax.transAxes)\n\n# plot AIC/BIC\nax = fig.add_subplot(143)\nax.plot(N, AIC, '-k', label='AIC')\nax.plot(N, BIC, ':k', label='BIC')\nax.legend(loc=1)\nax.set_xlabel('N components')\nplt.setp(ax.get_yticklabels(), fontsize=7)\n\n# plot best configurations for AIC and BIC\nax = fig.add_subplot(144)\nax.imshow(np.exp(log_dens),\n origin='lower', interpolation='nearest', aspect='auto',\n extent=[FeH_bins[0], FeH_bins[-1],\n alphFe_bins[0], alphFe_bins[-1]],\n cmap=plt.cm.binary)\n\nax.scatter(gmm_best.means_[:, 0], gmm_best.means_[:, 1], c='w')\nfor mu, C, w in zip(gmm_best.means_, gmm_best.covars_, gmm_best.weights_):\n draw_ellipse(mu, C, scales=[1], ax=ax, fc='none', ec='k')\n\nax.text(0.93, 0.93, \"Converged\",\n va='top', ha='right', transform=ax.transAxes)\n\nax.set_xlim(-1.101, 0.101)\nax.set_ylim(alphFe_bins[0], alphFe_bins[-1])\nax.xaxis.set_major_locator(plt.MultipleLocator(0.3))\nax.set_xlabel(r'$\\rm [Fe/H]$')\nax.set_ylabel(r'$\\rm [\\alpha/Fe]$')\n\nplt.show()", "_____no_output_____" ] ], [ [ "That said, I'd say that there are *too* many components here. So, I'd be inclined to explore this a bit further if it were my data.\n\nLastly, let's look at a 2-D case where we are using GMM more to characterize the data than to find clusters. ", "_____no_output_____" ] ], [ [ "# Execute this cell\n# Ivezic, Figure 6.7\n# Author: Jake VanderPlas\n# License: BSD\n# The figure produced by this code is published in the textbook\n# \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n# For more information, see http://astroML.github.com\n# To report a bug or issue, use the following forum:\n# https://groups.google.com/forum/#!forum/astroml-general\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.mixture import GMM\nfrom astroML.datasets import fetch_great_wall\nfrom astroML.decorators import pickle_results\n\n#------------------------------------------------------------\n# load great wall data\nX = fetch_great_wall()\n\n#------------------------------------------------------------\n# Create a function which will save the results to a pickle file\n# for large number of clusters, computation will take a long time!\n#@pickle_results('great_wall_GMM.pkl')\ndef compute_GMM(n_clusters, n_iter=1000, min_covar=3, covariance_type='full'):\n clf = GMM(n_clusters, covariance_type=covariance_type,\n n_iter=n_iter, min_covar=min_covar)\n clf.fit(X)\n print \"converged:\", clf.converged_\n return clf\n\n#------------------------------------------------------------\n# Compute a grid on which to evaluate the result\nNx = 100\nNy = 250\nxmin, xmax = (-375, -175)\nymin, ymax = (-300, 200)\n\nXgrid = np.vstack(map(np.ravel, np.meshgrid(np.linspace(xmin, xmax, Nx),\n np.linspace(ymin, ymax, Ny)))).T\n\n#------------------------------------------------------------\n# Compute the results\n#\n# we'll use 100 clusters. In practice, one should cross-validate\n# with AIC and BIC to settle on the correct number of clusters.\nclf = compute_GMM(n_clusters=100)\nlog_dens = clf.score(Xgrid).reshape(Ny, Nx)\n\n#------------------------------------------------------------\n# Plot the results\nfig = plt.figure(figsize=(10, 5))\nfig.subplots_adjust(hspace=0, left=0.08, right=0.95, bottom=0.13, top=0.9)\n\nax = fig.add_subplot(211, aspect='equal')\nax.scatter(X[:, 1], X[:, 0], s=1, lw=0, c='k')\n\nax.set_xlim(ymin, ymax)\nax.set_ylim(xmin, xmax)\n\nax.xaxis.set_major_formatter(plt.NullFormatter())\nplt.ylabel(r'$x\\ {\\rm (Mpc)}$')\n\nax = fig.add_subplot(212, aspect='equal')\nax.imshow(np.exp(log_dens.T), origin='lower', cmap=plt.cm.binary,\n extent=[ymin, ymax, xmin, xmax])\nax.set_xlabel(r'$y\\ {\\rm (Mpc)}$')\nax.set_ylabel(r'$x\\ {\\rm (Mpc)}$')\n\nplt.show()", "_____no_output_____" ] ], [ [ "Note that this is very different than the non-parametric density estimates that we did last time in that the GMM isn't doing that great of a job of matching the distribution. However, the advantage is that we now have a *model*. This model can be stored very compactly with just a few numbers, unlike the KDE or KNN maps which require a floating point number for each grid point. \n\nOne thing that you might imagine doing with this is subtracting the model from the data and looking for interesting things among the residuals.", "_____no_output_____" ] ] ]

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[ [ [ "%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "import xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nimport torch\nimport pandas as pd\nif torch.cuda.is_available():\n device = torch.device(\"cuda\") \nelse:\n device = torch.device(\"cpu\")", "_____no_output_____" ], [ "from src.dataloader import *\nfrom src.models import *\nfrom src.trainer import *\nfrom src.utils import *", "_____no_output_____" ], [ "DATADRIVE = '/datadrive_ssd/'", "_____no_output_____" ] ], [ [ "## Initial changes", "_____no_output_____" ] ], [ [ "ds_train = TiggeMRMSDataset(\n tigge_dir=f'{DATADRIVE}/tigge/32km/',\n tigge_vars=['total_precipitation'],\n mrms_dir=f'{DATADRIVE}/mrms/4km/RadarOnly_QPE_06H/',\n rq_fn=f'{DATADRIVE}/mrms/4km/RadarQuality.nc',\n# const_fn='/datadrive/tigge/32km/constants.nc',\n# const_vars=['orog', 'lsm'],\n data_period=('2018-01', '2019-01'),\n first_days=5,\n scale=False\n# split='train'\n)", "_____no_output_____" ], [ "mean_precip = []\nfor idx in range(len(ds_train.idxs)):\n X, y = ds_train[idx]\n mean_precip.append(y.max())", "_____no_output_____" ], [ "mean_precip = np.array(mean_precip)", "_____no_output_____" ], [ "plt.hist(mean_precip, bins=100);\n# plt.yscale('log')", "_____no_output_____" ], [ "cat_bins = np.arange(0, 102, 2, dtype='float')\ncat_bins = np.append(np.insert(cat_bins, 1, 0.01), np.inf)\nlen(cat_bins)", "_____no_output_____" ], [ "cat_bins", "_____no_output_____" ], [ "X, y = ds_train[600]\nX.shape, y.shape", "_____no_output_____" ], [ "plt.imshow(y[0])\nplt.colorbar();", "_____no_output_____" ], [ "def to_categorical(y, num_classes=None, dtype='float32'):\n \"\"\"Copied from keras source code\n \"\"\"\n y = np.array(y, dtype='int')\n input_shape = y.shape\n if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:\n input_shape = tuple(input_shape[:-1])\n y = y.ravel()\n if not num_classes:\n num_classes = np.max(y) + 1\n n = y.shape[0]\n categorical = np.zeros((n, num_classes), dtype=dtype)\n categorical[np.arange(n), y] = 1\n output_shape = input_shape + (num_classes,)\n categorical = np.reshape(categorical, output_shape)\n return categorical", "_____no_output_____" ], [ "a = pd.cut(y.reshape(-1), cat_bins, labels=False, include_lowest=True).reshape(y.shape)\na.shape", "_____no_output_____" ], [ "plt.imshow(a[0])\nplt.colorbar();", "_____no_output_____" ], [ "plt.hist(a.flat, bins=cat_bins);", "_____no_output_____" ], [ "a = to_categorical(a.squeeze(), num_classes=len(cat_bins))", "_____no_output_____" ], [ "a = np.rollaxis(a, 2)", "_____no_output_____" ], [ "a.shape", "_____no_output_____" ] ], [ [ "## Changes implemented", "_____no_output_____" ] ], [ [ "cat_bins = np.arange(0, 55, 5, dtype='float')\ncat_bins = np.append(np.insert(cat_bins, 1, 0.01), np.inf)\nlen(cat_bins)", "_____no_output_____" ], [ "plt.plot(cat_bins)", "_____no_output_____" ], [ "ds_train = TiggeMRMSDataset(\n tigge_dir=f'{DATADRIVE}/tigge/32km/',\n tigge_vars=['total_precipitation'],\n mrms_dir=f'{DATADRIVE}/mrms/4km/RadarOnly_QPE_06H/',\n rq_fn=f'{DATADRIVE}/mrms/4km/RadarQuality.nc',\n# const_fn='/datadrive/tigge/32km/constants.nc',\n# const_vars=['orog', 'lsm'],\n data_period=('2018-01', '2018-12'),\n first_days=5,\n scale=True,\n cat_bins=cat_bins\n# split='train'\n)", "/anaconda/envs/nwp-downscale/lib/python3.8/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\nchunk and silence this warning, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n ... array[indexer]\n\nTo avoid creating the large chunks, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n ... array[indexer]\n return self.array[key]\n" ], [ "ds_valid = TiggeMRMSDataset(\n tigge_dir=f'{DATADRIVE}/tigge/32km/',\n tigge_vars=['total_precipitation'],\n mrms_dir=f'{DATADRIVE}/mrms/4km/RadarOnly_QPE_06H/',\n rq_fn=f'{DATADRIVE}/mrms/4km/RadarQuality.nc',\n# const_fn='/datadrive/tigge/32km/constants.nc',\n# const_vars=['orog', 'lsm'],\n data_period=('2019-01', '2019-12'),\n first_days=2,\n scale=True,\n cat_bins=cat_bins,\n mins=ds_train.mins,\n maxs=ds_train.maxs\n# split='train'\n)", "/anaconda/envs/nwp-downscale/lib/python3.8/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\nchunk and silence this warning, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n ... array[indexer]\n\nTo avoid creating the large chunks, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n ... array[indexer]\n return self.array[key]\n" ], [ "X, y = ds_train[600]\nX.shape, y.shape", "_____no_output_____" ], [ "y", "_____no_output_____" ], [ "sampler_train = torch.utils.data.WeightedRandomSampler(ds_train.compute_weights(), len(ds_train), replacement=True)\nsampler_valid = torch.utils.data.WeightedRandomSampler(ds_valid.compute_weights(), len(ds_valid), replacement=True)", "_____no_output_____" ], [ "dl_train = torch.utils.data.DataLoader(ds_train, batch_size=32, sampler=sampler_train)\ndl_valid = torch.utils.data.DataLoader(ds_valid, batch_size=32, sampler=sampler_valid)", "_____no_output_____" ], [ "len(dl_train)", "_____no_output_____" ], [ "len(ds_train)", "_____no_output_____" ], [ "X, y = next(iter(dl_train))", "_____no_output_____" ], [ "fig, axs = plt.subplots(4, 8, figsize=(24, 12))\nfor x, ax in zip(X.numpy(), axs.flat):\n ax.imshow(x[0], cmap='gist_ncar_r', vmin=0, vmax=0.5)", "_____no_output_____" ], [ "X.shape, y.shape", "_____no_output_____" ], [ "y.type()", "_____no_output_____" ], [ "class UpsampleBlock(nn.Module):\n def __init__(self, nf, spectral_norm=False, method='PixelShuffle'):\n super().__init__()\n self.conv = nn.Conv2d(nf, nf * 4 if method=='PixelShuffle' else nf, kernel_size=3, stride=1, padding=1)\n if method == 'PixelShuffle':\n self.upsample = nn.PixelShuffle(2)\n elif method == 'bilinear':\n self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')\n else:\n raise NotImplementedError\n self.activation = nn.LeakyReLU(0.2)\n if spectral_norm: \n self.conv = nn.utils.spectral_norm(self.conv)\n \n def forward(self, x):\n out = self.conv(x)\n out = self.upsample(out)\n out = self.activation(out)\n return out", "_____no_output_____" ], [ "class Generator(nn.Module):\n \"\"\"Generator with noise vector and spectral normalization \"\"\"\n def __init__(self, nres, nf_in, nf, relu_out=False, use_noise=True, spectral_norm=True,\n nout=1, softmax_out=False, upsample_method='PixelShuffle'):\n \"\"\" General Generator with different options to use. e.g noise, Spectral normalization (SN) \"\"\"\n super().__init__()\n self.relu_out = relu_out\n self.softmax_out = softmax_out\n self.use_noise = use_noise\n self.spectral_norm = spectral_norm\n\n # First convolution\n if use_noise: \n self.conv_in = nn.Conv2d(nf_in, nf-1, kernel_size=9, stride=1, padding=4)\n else: \n self.conv_in = nn.Conv2d(nf_in, nf, kernel_size=9, stride=1, padding=4)\n self.activation_in = nn.LeakyReLU(0.2)\n\n # Resblocks keeping shape\n self.resblocks = nn.Sequential(*[\n ResidualBlock(nf, spectral_norm=spectral_norm) for i in range(nres)\n ])\n # Resblocks with upscaling\n self.upblocks = nn.Sequential(*[\n UpsampleBlock(nf, spectral_norm=spectral_norm, method=upsample_method) for i in range(3)\n ])\n self.conv_out = nn.Conv2d(nf, nout, kernel_size=9, stride=1, padding=4)\n \n if spectral_norm: \n self.conv_in = nn.utils.spectral_norm(self.conv_in)\n self.conv_out = nn.utils.spectral_norm(self.conv_out)\n \n def forward(self, x):\n out = self.conv_in(x)\n out = self.activation_in(out)\n if self.use_noise: \n bs, _, h, w = x.shape\n z = torch.normal(0, 1, size=(bs, 1, h, w), device=device, requires_grad=True)\n out = torch.cat([out, z], dim=1)\n skip = out\n out = self.resblocks(out)\n out = out + skip\n out = self.upblocks(out)\n out = self.conv_out(out)\n if self.relu_out:\n out = nn.functional.relu(out)\n if self.softmax_out:\n out = nn.functional.softmax(out, dim=1)\n return out", "_____no_output_____" ], [ "gen = Generator(\n nres=3, nf_in=1, nf=64, relu_out=False, use_noise=False, spectral_norm=False,\n nout=len(cat_bins)-1, softmax_out=False, upsample_method='bilinear'\n).to(device)", "_____no_output_____" ], [ "count_parameters(gen)", "_____no_output_____" ], [ "criterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(gen.parameters(), lr=1e-4)", "_____no_output_____" ], [ "trainer = Trainer(gen, optimizer, criterion, dl_train, dl_valid)", "_____no_output_____" ], [ "trainer.fit(10)", "_____no_output_____" ], [ "trainer.plot_losses()", "_____no_output_____" ], [ "preds = nn.functional.softmax(gen(X.to(device)), dim=1).cpu().detach().numpy()", "_____no_output_____" ], [ "preds.shape", "_____no_output_____" ], [ "target = y.cpu().detach().numpy()", "_____no_output_____" ], [ "target.shape", "_____no_output_____" ], [ "np.argmax(target.mean((1, 2)))", "_____no_output_____" ], [ "i=7\nplt.imshow(target[i])\nplt.colorbar()", "_____no_output_____" ], [ "target[i, 20, 20]", "_____no_output_____" ], [ "plt.imshow(X[i, 0] * ds_train.maxs.tp.values)\nplt.colorbar();", "_____no_output_____" ], [ "plt.imshow(np.argmax(preds[i], axis=0))\nplt.colorbar()", "_____no_output_____" ], [ "plt.plot(preds[i, :, 100, 100])\nplt.axvline(target[i, 100, 100])", "_____no_output_____" ], [ "cdf = np.cumsum(preds, axis=1)\ncdf.shape", "_____no_output_____" ], [ "len(cat_bins)", "_____no_output_____" ], [ "plt.plot(cat_bins[:-1], cdf[i, :, 20, 20])\nplt.plot(cat_bins[:-1], cdf[i, :, 100, 100])", "_____no_output_____" ], [ "from scipy.ndimage import gaussian_filter\ndef corr_random2D(size, sigma=5, inflation=0.5):\n r = np.random.uniform(size=(size, size))\n r = gaussian_filter(r, sigma)\n r = (r - 0.5) * (inflation / r.std()) + 0.5\n r = 1/(1 + np.exp(-r))\n return r", "_____no_output_____" ], [ "rand = corr_random2D(128, 3)\nplt.imshow(rand)\nplt.colorbar();", "_____no_output_____" ], [ "cat_bins", "_____no_output_____" ], [ "c = cdf[i, :, 100, 100]", "_____no_output_____" ], [ "c = np.insert(c, 0, 0)", "_____no_output_____" ], [ "c", "_____no_output_____" ], [ "len(cat_bins), len(c)", "_____no_output_____" ], [ "p = rand[100, 100]\np", "_____no_output_____" ], [ "b = np.digitize(p, c, right=True) - 1\nb", "_____no_output_____" ], [ "c[b], c[b+1]", "_____no_output_____" ], [ "w1 = (p - c[b]) / (c[b+1] - c[b])\nw2 = (c[b+1] - p) / (c[b+1] - c[b])\nw1, w2", "_____no_output_____" ], [ "v = cat_bins[b] * w1 + cat_bins[b+1] * w2\nv", "_____no_output_____" ], [ "def cat2real1D(pdf, q, cat_bins, interpolate=True):\n c = np.cumsum(pdf)\n c = np.insert(c, 0, 0)\n b = np.digitize(q, c, right=True) - 1\n if interpolate:\n w1 = (q - c[b]) / (c[b+1] - c[b])\n w2 = (c[b+1] - q) / (c[b+1] - c[b])\n else: \n w1, w2 = 0.5, 0.5\n assert w1 >0, 'Weights must be positive'\n assert w2 >0, 'Weights must be positive'\n v = cat_bins[b] * w2 + cat_bins[b+1] * w1\n return v", "_____no_output_____" ], [ "import pdb", "_____no_output_____" ], [ "def cat2real2D(pdf, q, cat_bins, interpolate=True):\n nbins, nx, ny = pdf.shape\n# pdb.set_trace()\n r = [cat2real1D(a, b, cat_bins, interpolate) for a, b in zip(pdf.reshape(nbins, -1).T, q.reshape(-1))]\n r = np.array(r).reshape(nx, ny)\n return r", "_____no_output_____" ], [ "o = cat2real2D(pdf, rand, cat_bins)", "_____no_output_____" ], [ "plt.imshow(o)\nplt.colorbar();", "_____no_output_____" ], [ "weights = ds_valid.compute_weights()", "_____no_output_____" ], [ "np.argsort(weights)[::-1][:20]", "_____no_output_____" ], [ "X_sample, y_sample = ds_valid.__getitem__(501, no_cat=True)", "_____no_output_____" ], [ "X_sample.shape, y_sample.shape", "_____no_output_____" ], [ "vmin=0\nvmax=10", "_____no_output_____" ], [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\nimg = ax1.imshow(X_sample[0]*ds_valid.maxs.tp.values, cmap='gist_ncar_r', vmin=vmin, vmax=vmax)\nplt.colorbar(img, ax=ax1, shrink=0.7)\nimg = ax2.imshow(y_sample[0], cmap='gist_ncar_r', vmin=vmin, vmax=vmax)\nplt.colorbar(img, ax=ax2, shrink=0.7)", "_____no_output_____" ], [ "pdf = nn.functional.softmax(gen(torch.from_numpy(X_sample[None]).to(device))).cpu().detach().numpy()[0]", "<ipython-input-501-b128a729a8e5>:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n pdf = nn.functional.softmax(gen(torch.from_numpy(X_sample[None]).to(device))).cpu().detach().numpy()[0]\n" ], [ "pdf.shape", "_____no_output_____" ], [ "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\nfor i in range(5):\n if i ==0:\n rand = np.ones((128, 128)) * 0.5\n else:\n rand = corr_random2D(128, 3, inflation=0.3)\n o = cat2real2D(pdf, rand, cat_bins)\n axs[i].imshow(o, cmap='gist_ncar_r', vmin=vmin, vmax=vmax)", "_____no_output_____" ] ], [ [ "## Overfitting test MSE", "_____no_output_____" ] ], [ [ "ds_train = TiggeMRMSDataset(\n tigge_dir=f'{DATADRIVE}/tigge/32km/',\n tigge_vars=['total_precipitation'],\n mrms_dir=f'{DATADRIVE}/mrms/4km/RadarOnly_QPE_06H/',\n rq_fn=f'{DATADRIVE}/mrms/4km/RadarQuality.nc',\n# const_fn='/datadrive/tigge/32km/constants.nc',\n# const_vars=['orog', 'lsm'],\n data_period=('2018-01', '2018-12'),\n first_days=5,\n scale=True,\n# cat_bins=cat_bins\n# split='train'\n)", "/anaconda/envs/nwp-downscale/lib/python3.8/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\nchunk and silence this warning, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n ... array[indexer]\n\nTo avoid creating the large chunks, set the option\n >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n ... array[indexer]\n return self.array[key]\n" ], [ "ds_valid = TiggeMRMSDataset(\n tigge_dir=f'{DATADRIVE}/tigge/32km/',\n tigge_vars=['total_precipitation'],\n mrms_dir=f'{DATADRIVE}/mrms/4km/RadarOnly_QPE_06H/',\n rq_fn=f'{DATADRIVE}/mrms/4km/RadarQuality.nc',\n# const_fn='/datadrive/tigge/32km/constants.nc',\n# const_vars=['orog', 'lsm'],\n data_period=('2019-01', '2019-12'),\n first_days=2,\n scale=True,\n# cat_bins=cat_bins,\n mins=ds_train.mins,\n maxs=ds_train.maxs\n# split='train'\n)", "_____no_output_____" ], [ "X, y = ds_train[600]\nX.shape, y.shape", "_____no_output_____" ], [ "sampler_train = torch.utils.data.WeightedRandomSampler(ds_train.compute_weights(), len(ds_train))\nsampler_valid = torch.utils.data.WeightedRandomSampler(ds_valid.compute_weights(), len(ds_valid))", "_____no_output_____" ], [ "dl_train = torch.utils.data.DataLoader(ds_train, batch_size=32, sampler=sampler_train)\ndl_valid = torch.utils.data.DataLoader(ds_valid, batch_size=32, sampler=sampler_valid)", "_____no_output_____" ], [ "len(dl_train)", "_____no_output_____" ], [ "len(ds_train)", "_____no_output_____" ], [ "X, y = next(iter(dl_train))", "_____no_output_____" ], [ "fig, axs = plt.subplots(4, 8, figsize=(24, 12))\nfor x, ax in zip(X.numpy(), axs.flat):\n ax.imshow(x[0], cmap='gist_ncar_r', vmin=0, vmax=0.5)", "_____no_output_____" ], [ "X.shape, y.shape", "_____no_output_____" ], [ "y.type()", "_____no_output_____" ], [ "class UpsampleBlock(nn.Module):\n def __init__(self, nf, spectral_norm=False, method='PixelShuffle'):\n super().__init__()\n self.conv = nn.Conv2d(nf, nf * 4 if method=='PixelShuffle' else nf, kernel_size=3, stride=1, padding=1)\n if method == 'PixelShuffle':\n self.upsample = nn.PixelShuffle(2)\n elif method == 'bilinear':\n self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')\n else:\n raise NotImplementedError\n self.activation = nn.LeakyReLU(0.2)\n if spectral_norm: \n self.conv = nn.utils.spectral_norm(self.conv)\n \n def forward(self, x):\n out = self.conv(x)\n out = self.upsample(out)\n out = self.activation(out)\n return out", "_____no_output_____" ], [ "class Generator(nn.Module):\n \"\"\"Generator with noise vector and spectral normalization \"\"\"\n def __init__(self, nres, nf_in, nf, relu_out=False, use_noise=True, spectral_norm=True,\n nout=1, softmax_out=False, upsample_method='PixelShuffle'):\n \"\"\" General Generator with different options to use. e.g noise, Spectral normalization (SN) \"\"\"\n super().__init__()\n self.relu_out = relu_out\n self.softmax_out = softmax_out\n self.use_noise = use_noise\n self.spectral_norm = spectral_norm\n\n # First convolution\n if use_noise: \n self.conv_in = nn.Conv2d(nf_in, nf-1, kernel_size=9, stride=1, padding=4)\n else: \n self.conv_in = nn.Conv2d(nf_in, nf, kernel_size=9, stride=1, padding=4)\n self.activation_in = nn.LeakyReLU(0.2)\n\n # Resblocks keeping shape\n self.resblocks = nn.Sequential(*[\n ResidualBlock(nf, spectral_norm=spectral_norm) for i in range(nres)\n ])\n # Resblocks with upscaling\n self.upblocks = nn.Sequential(*[\n UpsampleBlock(nf, spectral_norm=spectral_norm, method=upsample_method) for i in range(3)\n ])\n self.conv_out = nn.Conv2d(nf, nout, kernel_size=9, stride=1, padding=4)\n \n if spectral_norm: \n self.conv_in = nn.utils.spectral_norm(self.conv_in)\n self.conv_out = nn.utils.spectral_norm(self.conv_out)\n \n def forward(self, x):\n out = self.conv_in(x)\n out = self.activation_in(out)\n if self.use_noise: \n bs, _, h, w = x.shape\n z = torch.normal(0, 1, size=(bs, 1, h, w), device=device, requires_grad=True)\n out = torch.cat([out, z], dim=1)\n skip = out\n out = self.resblocks(out)\n out = out + skip\n out = self.upblocks(out)\n out = self.conv_out(out)\n if self.relu_out:\n out = nn.functional.relu(out)\n if self.softmax_out:\n out = nn.functional.softmax(out, dim=1)\n return out", "_____no_output_____" ], [ "gen = Generator(\n nres=3, nf_in=1, nf=64, relu_out=False, use_noise=False, spectral_norm=False,\n nout=1, softmax_out=False, upsample_method='PixelShuffle'\n).to(device)", "_____no_output_____" ], [ "count_parameters(gen)", "_____no_output_____" ], [ "criterion = nn.MSELoss()\noptimizer = torch.optim.Adam(gen.parameters(), lr=1e-5)", "_____no_output_____" ], [ "trainer = Trainer(gen, optimizer, criterion, dl_train, dl_valid)", "_____no_output_____" ], [ "trainer.fit(10)", "_____no_output_____" ], [ "trainer.plot_losses()", "_____no_output_____" ], [ "plot_sample(X, y, gen, 13)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import sys\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "HEIGHT = 96\nWIDTH = 96\nDEPTH = 3\n\nSIZE = HEIGHT*WIDTH*DEPTH", "_____no_output_____" ], [ "DATA_PATH = '../dataset/stl10_binary/train_X.bin'\nLABEL_PATH = '../dataset/stl10_binary/train_y.bin'", "_____no_output_____" ], [ "def read_labels(path_to_labels):\n with open(path_to_labels, 'rb') as f:\n labels = np.fromfile(f,dtype=np.uint8)\n return labels", "_____no_output_____" ], [ "def read_all_images(path_to_data):\n with open(path_to_data, 'rb') as f:\n all_data = np.fromfile(f,dtype=np.uint8)\n \n #Data resized to 3x64x64\n #-1 since size of the pictures depends on the input file\n images = np.reshape(all_data, (-1, 3, HEIGHT, WIDTH))\n \n #Transposing to a standard image format\n #Comment this line before training algorithms like CNNs\n images = np.transpose(images, (0,3,2,1))\n return images", "_____no_output_____" ], [ "def read_single_image(image_file):\n image = np.fromfile(image_file,dtype=np.uint8,count=SIZE)\n \n image = np.reshape(image,(3,HEIGHT,WIDTH))\n \n image = np.transpose(image, (2,1,0))\n return image", "_____no_output_____" ], [ "def plot_image(image):\n plt.imshow(image)\n plt.show()", "_____no_output_____" ], [ "def display_one_image(image):\n with open(DATA_PATH, 'rb') as f:\n image=read_single_image(f)\n plot_image(image)", "_____no_output_____" ], [ "def get_shape_of_dataset():\n images= read_all_images(DATA_PATH)\n return images.shape", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# This allows multiple outputs from a single jupyter notebook cell:\nfrom IPython.core.interactiveshell import InteractiveShell\nInteractiveShell.ast_node_interactivity = \"all\"", "_____no_output_____" ], [ "%matplotlib inline\nimport pandas as pd\nimport mplfinance as mpf\nimport io\nprint('pandas version:',pd.__version__)\nprint('mplfinance version:',mpf.__version__)", "pandas version: 1.0.3\nmplfinance version: 0.12.5a0\n" ] ], [ [ "---\n\n# Saving your plot to a file (or to an io buffer):\n\n- `mplfinance.plot()` allows you to save your plot to a file, or io-buffer, using the `savefig` keyword.\n- The value of `savefig` may be a `str`, `dict`, or `io.ByteIO` object. \n - If the value is a `str` then it is assumed to be the file name to which to save the figure/plot.\n - If the value is an `io.ByteIO` object, then the figure will be saved to the io buffer object. This avoids interaction with disk, and can also be useful when mplfinance is behind a web server (so that requests for an image file can be serviced without going to disk).\n\nIf the file extension is one of those recognized by `matplotlib.pyplot.savefig()` then the file type will be inferred from the extension, for example: `.pdf`, `.svg`, `.png`, `.jpg` ...\n", "_____no_output_____" ] ], [ [ "df = pd.read_csv('data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)", "_____no_output_____" ], [ "%%capture \n## cell magic function `%%capture` blocks jupyter notebook output,\n## which is not needed here since the plot is saved to a file anyway:\nmpf.plot(df,type='candle',volume=True,savefig='testsave.png')", "_____no_output_____" ] ], [ [ "---\n\n### We can use IPython.display.Image to display the image file here in the notebook:", "_____no_output_____" ] ], [ [ "import IPython.display as IPydisplay", "_____no_output_____" ], [ "%ls -l testsave.png\nIPydisplay.Image(filename='testsave.png')", "-rw-rw-rw- 1 dino dino 24877 Jun 7 17:52 \u001b[0m\u001b[01;35mtestsave.png\u001b[0m\r\n" ] ], [ [ "---\n\n### We can use io to save the plot as a byte buffer:", "_____no_output_____" ] ], [ [ "%%capture \n## cell magic function `%%capture` blocks jupyter notebook output, \n## which is not needed here, since the plot is saved to the io-buffer anyway:\nbuf = io.BytesIO()\nmpf.plot(df,type='candle',volume=True,savefig=buf)\nbuf.seek(0)", "_____no_output_____" ] ], [ [ "### We can use Ipython.display.Image to display the image in the ioBytes buffer:", "_____no_output_____" ] ], [ [ "IPydisplay.Image(buf.read())", "_____no_output_____" ] ], [ [ "---\n\n# Specifying image attributes with `savefig`\n\nWe can control various attributes of the saved figure/plot by passing a `dict`ionary as the value for the `savefig` keyword.\n\nThe dictionary **must** contain the keyword `fname` for the file name to be saved, **and *may* contain any of the other keywords accepted by [`matplotlib.pyplot.savefig()`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.savefig.html)** (for example: dpi, facecolor, edgecolor, orientation, format, metadata, quality)\n\nWhen creating the `dict`, I recommend using the `dict()` constructor so that that `keyword=` syntax may be used and thereby more closely resemble calling:\n**[`matplotlib.pyplot.savefig()`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.savefig.html)**\n", "_____no_output_____" ] ], [ [ "%%capture\n## %%capture blocks jupyter notebook output; plots are saved to files anyway:\nsave = dict(fname='tsave30.jpg',dpi=30,pad_inches=0.25)\nmpf.plot(df,volume=True,savefig=save)\nmpf.plot(df,volume=True,savefig=dict(fname='tsave100.jpg',dpi=100,pad_inches=0.25))", "_____no_output_____" ], [ "%ls -l tsave30.jpg\n%ls -l tsave100.jpg\nIPydisplay.Image(filename='tsave30.jpg')\nIPydisplay.Image(filename='tsave100.jpg')", "-rw-rw-rw- 1 dino dino 11016 Jun 7 17:52 \u001b[0m\u001b[01;35mtsave30.jpg\u001b[0m\n-rw-rw-rw- 1 dino dino 54172 Jun 7 17:52 \u001b[0m\u001b[01;35mtsave100.jpg\u001b[0m\n" ] ], [ [ "## Specifying image attributes (via `savefig`) dict also works with an io.BytesIO buffer:\n- Just assign the io-buffer to the `fname` key in the savefig dict", "_____no_output_____" ] ], [ [ "%%capture\nbuf30dpi = io.BytesIO()\nbuf100dpi = io.BytesIO()\nmpf.plot(df,volume=True,savefig=dict(fname=buf30dpi ,dpi=30 ,pad_inches=0.25))\nmpf.plot(df,volume=True,savefig=dict(fname=buf100dpi,dpi=100,pad_inches=0.25))", "_____no_output_____" ] ], [ [ "### Use Ipython.display.Image to display the buffer contents:", "_____no_output_____" ] ], [ [ "_ = buf30dpi.seek(0)\nIPydisplay.Image(buf30dpi.read())", "_____no_output_____" ], [ "_ = buf100dpi.seek(0)\nIPydisplay.Image(buf100dpi.read())", "_____no_output_____" ] ], [ [ "---\n\n## A note about `jpeg` files:\n\n**[`matplotlib.pyplot.savefig()`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.savefig.html)** uses the Python Image Library (PIL or pillow) to save jpeg files. \nThus you must have pillow installed (`pip install pillow`) to save jpeg images. \n\n**Version 3.1.2 of matplotlib has an incompatibility with version 7.x.x of pillow** (which was released to PyPI on On January 2, 2020. This incompatibility was **[fixed here](https://github.com/matplotlib/matplotlib/pull/16086/commits)** however (as of this writting) the fixed version of matplotlib was not yet on PyPI (to be pip installable), *but it may be by the time you read this*.\n\nIf you encounter an exception when trying to save a jpeg file, that says \"format 'jpg' is not supported\" check that you have pillow installed. If you do have pillow 7.x.x installed, and you are encountering this exception, the permanent fix for the problem is to upgrade to the new version of matplotlib `pip install --upgrade matplotlib`. (This should work if the new version of matplotlib is on Pypi at the time).\n\nIf upgrading matplotlib doesn't work, you can immediately fix this problem in one of the following temporary solutions:\n\n- install the previous version of pillow: **`pip install Pillow==6.2.2`**\n- edit your installed version of `.../site-packages/matplotlib/backend_bases.py` and apply **[this one-line fix](https://github.com/matplotlib/matplotlib/pull/16086/files).**\n\n\n---", "_____no_output_____" ] ] ]

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[ [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport pandas as pd\n\n%matplotlib inline\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "pd.options.display.max_colwidth = 200", "_____no_output_____" ] ], [ [ "### Oxford University COVID-19 forecasting project data\n\nThe data below was downloaded from the [Oxford University countermeasures database](https://www.notion.so/COVID-19-countermeasures-database-b532a58d6f944ef6982ab565627bdb08). See the website for a description of the data sources.", "_____no_output_____" ] ], [ [ "countermeasures_df = pd.read_csv(\"data/containment_measures_march23.csv\")", "_____no_output_____" ], [ "countermeasures_df.head(1)", "_____no_output_____" ], [ "countermeasures_df[[\"Country\", \"Date Start\", \"Description of measure implemented\"]].groupby(\"Country\").head(10)", "_____no_output_____" ], [ "print(countermeasures_df[\"Country\"].unique())", "['Austria' 'Germany' 'United Kingdom' 'Vietnam' 'South Korea' 'Singapore'\n 'Israel' 'Japan' 'Sweden' 'San Marino' 'Hong Kong' 'Taiwan' 'Macau'\n 'China' 'United States' nan 'Thailand' 'Italy' 'Czechia' 'Australia'\n 'Trinidad and Tobago' 'Qatar' 'North Korea' 'New Zealand' 'Colombia'\n 'France' 'Romania' 'Portugal' 'Spain' 'Belgium' 'Luxembourg' 'Albania'\n 'Andorra' 'Azerbaijan' 'Belarus' 'Bosnia and Herzegovina' 'Bulgaria'\n 'Estonia' 'Denmark' 'Cyprus' 'Croatia' 'Finland' 'Georgia' 'Hungary'\n 'Latvia' 'Lithuania' 'Moldova' 'Greece' 'Malta' 'Monaco' 'Netherlands'\n 'Iceland' 'Guernsey' 'Macedonia' 'Ireland' 'Vatican City' 'Jersey'\n 'Kosovo' 'Kazakhstan' 'US: Florida' 'Ukraine' 'Turkey' 'Poland'\n 'Slovakia' 'Serbia' 'Slovenia' 'Switzerland' 'Montenegro' 'Norway' 'Iran'\n 'Liechtenstein' 'Russia' 'US:N Carolina' 'US: Oregon' 'US: Arizona'\n 'US:California' 'US:Idaho' 'US: Nevada' 'US:Utah' 'US:Washington'\n 'US: S Carolina' 'Mexico' 'Egypt' 'Malaysia' 'US:Georgia' 'US:Maryland'\n 'US: Indiana' 'US: Illinois' 'US:Delaware' 'US: Wisconsin' 'US: Virginia'\n 'US: Michigan']\n" ], [ "print(countermeasures_df[\"Implementing City\"].unique())", "[nan 'Danang' 'Seoul' 'Wuhan' 'Beijing, Shenzhen' 'Washington DC'\n 'Huanggang' 'Huanggong' 'Wenzhou' 'Beijing' 'Shenzhen' 'Bangkok' 'Tokyo'\n 'Jingmen' 'Nanjing, Suzhou' 'Xiaogan' 'Hangzhou'\n 'Yiwu International Trade City' 'Dalian, Qingdao, Shenyang, Weihai'\n 'Yantai' 'Madrid' 'La Gomera' 'Valencia' 'Sevilla' 'Barcelona' 'Paris'\n 'Vitoria-Gasteiz' 'Banja Luka'\n 'Banja Luka, Doboj, Mrkonjić Grad, Prnjavor, Čelinac' 'Sofia' 'Budapest'\n 'Miskolc' 'Budapest, Kaposvár, Miskolc, Szeged, Székesfehérvár'\n 'Vatican City' 'San Francisco' 'Moscow'\n 'Bertonico, Casalpusterlengo, Castelgerundo, Castiglione d’Adda, Codogno, Fombio, Maleo, San Fiorano, Somaglia, Terranova, Vò'\n 'Naples, Palermo' 'Taranto' 'Messina' 'Medicina' 'Ischgl' 'Bansko']\n" ], [ "print(countermeasures_df[\"Implementing State/Province\"].unique())", "[nan 'Quang Ninh' 'Gyeongbook Province' 'Daegu, Gyeongbook Province'\n 'Busan' 'Hubei' 'Hunan' 'Tianjin' 'Zheijang' 'Meituan' 'Chongqing'\n 'Zhejian' 'Shanghai' 'Sichuan' 'Jiangsu' 'Guangdong' 'Hangzhou'\n 'Shenzhen' 'Leishenshan' 'Guangzhou' 'Kanagawa prefecture' 'Guangxi'\n 'Guizhou' 'Huanggang' 'Henan, Shandong' 'Liaoning, Shandong' 'Shandong'\n 'Madrid' 'Tyrol' 'Basque Country' 'Galicia' 'Republika Srpska' 'Bosnia'\n 'Autonomous Region of Madeira' 'California' 'Leningrad Oblast'\n 'Moscow Oblast' 'St. Petersburg Oblast' 'Santa Clara County' 'Seattle'\n 'Berkley, Countra Costa Country, Santa Clara County, los Angles'\n 'Orange County' 'Placer County, San Mateo County, Sonoma County'\n 'San Benito County, Santa Clara County'\n 'Kershan County, Lancaster County' 'Bavaria' 'Colima' 'Mexico City'\n 'South Fulton' 'Atalanta, Brookhaven, Clarkston, Dunwoody' 'Atalanta'\n 'Albany, Athens-Clark county, Bosnia, Dougherty County'\n 'Gyeonggi-Province, Paju' 'Gyeonggi-Province, Seoul'\n 'Farifax county, Loudoun county, Prince William County, Stafford County']\n" ] ], [ [ "### John Hopkins containment measures database\n\nThe data is made available as part of the John Hopkins [Containment Measures Database](http://epidemicforecasting.org/containment). See the website in the link for a description of the data sources.", "_____no_output_____" ] ], [ [ "containment_df = pd.read_csv(\"data/countermeasures_db_johnshopkins_2020_03_30.csv\")", "_____no_output_____" ], [ "containment_df.columns", "_____no_output_____" ], [ "print(containment_df[\"Country\"].unique())", "['Austria' 'Germany' 'United Kingdom' 'Vietnam' 'South Korea' 'Singapore'\n 'Israel' 'Japan' 'Sweden' 'San Marino' 'Slovenia' 'Canada' 'Taiwan'\n 'Macau' 'Hong Kong' 'China' 'Thailand' 'Italy' 'Czechia' 'Australia'\n 'Trinidad and Tobago' 'Qatar' 'New Zealand' 'Colombia' 'Romania' 'France'\n 'Portugal' 'Spain' 'Belgium' 'Luxembourg' 'Albania' 'Andorra'\n 'Azerbaijan' 'Belarus' 'Bosnia and Herzegovina' 'Bulgaria' 'Denmark'\n 'Estonia' 'Cyprus' 'Croatia' 'Finland' 'Georgia' 'Hungary' 'Latvia'\n 'Lithuania' 'Greece' 'Moldova' 'Malta' 'Monaco' 'Netherlands' 'Iceland'\n 'Ireland' 'Kosovo' 'Kazakhstan' 'Poland' 'Turkey' 'Ukraine' 'Slovakia'\n 'Serbia' 'Switzerland' 'Norway' 'Montenegro' 'Iran' 'Liechtenstein'\n 'Russia' 'Mexico' 'Egypt' 'Malaysia' 'Nepal' 'Afghanistan' 'Iraq'\n 'Philippines' 'Kuwait' 'South Africa' 'Armenia' 'Pakistan' 'Brazil'\n 'Costa Rica' 'Panama' 'India' 'Bahrain' 'United Arab Emirates'\n 'Kyrgyzstan' 'Indonesia' 'Namibia' 'Uganda']\n" ], [ "cases_df = containment_df[[\"Date\", \"Country\", \"Confirmed Cases\", \"Deaths\"]]\\\n.loc[containment_df[\"Confirmed Cases\"] > 3000]\\\n.pivot(index=\"Date\", columns=\"Country\", values=\"Confirmed Cases\")", "_____no_output_____" ], [ "cases_df.plot(figsize=(16,8), title=\"Per-country growth in confirmed cases after the first 3000\")\\\n.legend(bbox_to_anchor=(1,1))", "_____no_output_____" ], [ "deaths_df = containment_df[[\"Date\", \"Country\", \"Confirmed Cases\", \"Deaths\"]]\\\n.loc[containment_df[\"Deaths\"] > 100]\\\n.pivot(index=\"Date\", columns=\"Country\", values=\"Deaths\")", "_____no_output_____" ], [ "deaths_df.plot(figsize=(16,8), title=\"Deaths per country after the first 100 deaths recorded\")\\\n.legend(bbox_to_anchor=(1,1))", "_____no_output_____" ], [ "other_cm_cols = ['Unnamed: 0', 'Resumption', 'Diagnostic criteria loosened', 'Testing criteria', 'Date', 'Country',\n 'Confirmed Cases', 'Deaths']", "_____no_output_____" ], [ "countermeasures = list(filter(lambda m: m not in other_cm_cols, containment_df.columns))", "_____no_output_____" ], [ "cm_df = containment_df[countermeasures + ['Date', 'Country']].fillna(0)", "_____no_output_____" ], [ "cm_df[countermeasures] = cm_df[countermeasures].mask(cm_df[countermeasures] > 0, 1)", "_____no_output_____" ], [ "cm_df.groupby(\"Date\").sum().plot(figsize=(16,8), title=\"Number of countries implementing measure by date\")\\\n.legend(bbox_to_anchor=(1,1))", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

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[ "Unlicense" ]

[ "Unlicense" ]

[ "Unlicense" ]

[ [ [ "### **Rendering component declaration.**", "_____no_output_____" ] ], [ [ "# imports for setting up display for the colab server.\n!sudo apt-get update > /dev/null 2>&1\n!sudo apt-get install -y xvfb x11-utils > /dev/null 2>&1\n!pip install gym==0.17.* pyvirtualdisplay==0.2.* PyOpenGL==3.1.* PyOpenGL-accelerate==3.1.* > /dev/null 2>&1", "_____no_output_____" ], [ "# gym related import statements.\nimport gym\nfrom gym import logger as gymlogger\nfrom gym.wrappers import Monitor\ngymlogger.set_level(40) #error only\n# RL agent construction related imports.\nimport numpy as np\nnp.random.seed(0)\nimport matplotlib.pyplot as plt\nfrom scipy.special import softmax\n# virtual display related import statements.\nimport math\nimport glob\nimport io\nimport base64\nimport time\nfrom time import sleep\nfrom tqdm import tqdm\nfrom IPython.display import HTML\nfrom IPython import display as ipythondisplay", "_____no_output_____" ], [ "# This creates virtual display to send the frames for being rendered.\nfrom pyvirtualdisplay import Display\ndisplay = Display(visible=0, size=(1366, 768))\ndisplay.start()", "_____no_output_____" ], [ "def show_video():\n '''\n This function loads the data video inline into the colab notebook.\n By reading the video stored by the Monitor class.\n '''\n mp4list = glob.glob('video/*.mp4')\n if len(mp4list) > 0:\n mp4 = mp4list[0]\n video = io.open(mp4, 'r+b').read()\n encoded = base64.b64encode(video)\n ipythondisplay.display(HTML(data='''<video alt=\"test\" autoplay \n loop controls style=\"height: 400px;\">\n <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n </video>'''.format(encoded.decode('ascii'))))\n else: \n print(\"Could not find video\")\n \n\ndef wrap_env(env):\n '''\n This monitoring tool records the outputs from the output and saves it a\n mp4 file in the stated directory. If we don't change the video directory\n the videos will get stored in 'content/' directory.\n '''\n env = Monitor(env, './video', force=True)\n return env", "_____no_output_____" ] ], [ [ "### **SARSA(Lambda) Algorithm Implementation for MountainCarV0 Environment**\n\n__The implementation consists of below stated sections:__ \n* __Agent class decleration and parsing environment.__\n* __SARSA(LAMBDA) Algorithm Implementation.__\n* __Plotting results, outputting results and downloading them.__", "_____no_output_____" ], [ "### **Agent Class Decleration and Parsing Environment**", "_____no_output_____" ] ], [ [ "# This environment has two degrees of freedom: position and velocity.\n# Our agent will learn to interact with environment having these two values.\nclass State:\n def __init__(self):\n self.pos = None\n self.vel = None\n\n# Agent class defined for storing all the agent related values.\n# and getting actions from the policy. Here, target policy is same as behavior policy.\nclass Agent:\n def __init__(self, env):\n self.velocity_lim = np.array([env.observation_space.low[1], env.observation_space.high[1]])\n self.position_lim = np.array([env.observation_space.low[0], env.observation_space.high[0]])\n\n self.velocity_step, self.position_step = 0.005, 0.1\n self.velocity_space = np.arange(self.velocity_lim[0], self.velocity_lim[1] \n + self.velocity_step, self.velocity_step)\n self.position_space = np.arange(self.position_lim[0], self.position_lim[1] \n + self.position_step, self.position_step)\n self.m, self.n, self.n_action = len(self.velocity_space), len(self.position_space), 3\n self.Q_sa = np.full(shape = (self.m, self.n, 3),\n fill_value = 0.0, dtype = np.float32)\n self.collective_record = []\n self.success = []\n \n def get_action_value_index(self, state):\n pos_offset = state[0] - self.position_lim[0]\n vel_offset = state[1] - self.velocity_lim[0]\n pos_ind = pos_offset // self.position_step\n vel_ind = vel_offset // self.velocity_step\n \n return np.array([vel_ind, pos_ind], dtype= np.int)\n \n def get_action(self, state):\n ind = self.get_action_value_index(state, 0)\n p = self.Policy[ind[0], ind[1], :]\n action = np.random.choice([0, 1, 2], size = 1, p = p)\n return action[0]", "_____no_output_____" ], [ "# Wraping the environment in the Monitor class.\nenv = wrap_env(gym.make('MountainCar-v0'))\n# Fixing the randomness in the environment.\nenv.seed(0)", "_____no_output_____" ], [ "# Parsing the environment onto Agent object.\nsarsa_agent = Agent(env)\n# For some information into Q(s,a) table generated.\n# It's dimension is equal to A(Q(s,a)) = n[D(1)]*n[D(2)]*...*n[D(f)]*n(D(actions))\nprint(\"Q Shape = \",sarsa_agent.Q_sa.shape)", "Q Shape = (30, 20, 3)\n" ] ], [ [ "### **SARSA(Lambda) algorithm implementation.**", "_____no_output_____" ] ], [ [ "# SARSA(lambda) algorithm takes part from TD(0) and TD(1) algorithm.\n\neps = 0.8 # greedy epsilon exploration-vs-exploitation variable.\nchanged_eps= []\nchanges_alpha= []\nalpha = 0.2 # learning rate value\nlambda_val = 0.8 # credit assignment variable to previous states.\nalpha_decay = 0.999\neps_decay = 0.995\nsarsa_agent.e = np.zeros(shape = (sarsa_agent.m, sarsa_agent.n, 3)) # eligibility of all states.\nfinish = False\nnum_iter = 2000\nfor i_eps in tqdm(range(1, num_iter + 1)):\n state = env.reset()\n sarsa_agent.e[:, :, :] = 0\n gamma = 1.0\n ind = sarsa_agent.get_action_value_index(state)\n # greedy exploration and exploitation step.\n if np.random.random() < 1 - eps:\n action = np.argmax(sarsa_agent.Q_sa[ind[0], ind[1], :]) \n else:\n action = np.random.randint(0, 3)\n # running episodes for 200 times for this environment.\n for t in range(201):\n ind = sarsa_agent.get_action_value_index(state)\n next_state, reward, done, info = env.step(action)\n next_ind = sarsa_agent.get_action_value_index(next_state)\n \n if np.random.random() < 1 - eps:\n next_action = np.argmax(sarsa_agent.Q_sa[next_ind[0], next_ind[1], :]) \n else: \n next_action = np.random.randint(0, 3)\n \n # forward view T(lambda) SARSA equation for making updates.\n delta = reward + gamma * sarsa_agent.Q_sa[next_ind[0],next_ind[1], next_action] - sarsa_agent.Q_sa[ind[0],ind[1],action]\n sarsa_agent.e[ind[0],ind[1],action] += 1\n sarsa_agent.Q_sa = np.add(sarsa_agent.Q_sa, np.multiply(alpha * delta, sarsa_agent.e))\n sarsa_agent.e = np.multiply(gamma * lambda_val, sarsa_agent.e)\n \n if done: \n if t < 199:\n sarsa_agent.success.append((i_eps, t))\n sarsa_agent.collective_record.append(-t)\n eps = max(0.0, eps * eps_decay)\n alpha = max(0.0, alpha * alpha_decay)\n break\n state = next_state\n action = next_action", "100%|██████████| 2000/2000 [00:52<00:00, 37.97it/s] \n" ] ], [ [ "### **Plotting reward fuction resuls and displaying output.**", "_____no_output_____" ] ], [ [ "# This graph shows the saturation of rewards to a minimum under 1000 episodes.\nfig, ax = plt.subplots(figsize = (9, 5))\nplt.plot(sarsa_agent.collective_record[:],'.')\nplt.yticks(range(-110, -200, -10))\nplt.ylabel(\"reward function values\")\nplt.xlabel(\"episode number count\")\nplt.grid()\nplt.show()", "_____no_output_____" ], [ "# Calculating the mean performance of the agent.\nfor i_eps in (range(1, 100)):\n state = env.reset()\n gamma = 1.0\n ind = sarsa_agent.get_action_value_index(state)\n action = np.argmax(sarsa_agent.Q_sa[ind[0], ind[1], :]) \n \n for t in range(201):\n env.render()\n ind = sarsa_agent.get_action_value_index(state)\n next_state, reward, done, info = env.step(action)\n next_ind = sarsa_agent.get_action_value_index(next_state)\n \n next_action = np.argmax(sarsa_agent.Q_sa[next_ind[0], next_ind[1], :])\n \n if done: \n if t < 199:\n sarsa_agent.success.append((i_eps, t))\n sarsa_agent.collective_record.append(-t)\n sleep(1)\n break\n state = next_state\n action = next_action\n ", "_____no_output_____" ], [ "# Plotting the mean performance of the agent.\nfig, ax = plt.subplots(figsize = (9, 5))\nplt.plot(sarsa_agent.collective_record[-100:], '-')\nplt.yticks(range(-110, -200, -10))\nplt.title(\"Test Results\")\nplt.ylabel(\"Mean reward function value\")\nplt.xlabel(\"Episode number count\")\nplt.grid()\nplt.show()", "_____no_output_____" ], [ "# Demonstrating the output of the agent's working.\nshow_video()", "_____no_output_____" ], [ "# zipping the video folder for the given SARSA agent.\n!zip -r /content/file.zip /content/video\n# downloading the file resource.\nfrom google.colab import files\nfiles.download(\"/content/file.zip\")", " adding: content/video/ (stored 0%)\n adding: content/video/openaigym.video.0.1649.video000512.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video002000.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000001.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000729.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video001000.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000512.mp4 (deflated 6%)\n adding: content/video/openaigym.video.0.1649.video001000.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000125.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000008.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000064.mp4 (deflated 9%)\n adding: content/video/openaigym.video.0.1649.video000125.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000000.mp4 (deflated 12%)\n adding: content/video/openaigym.video.0.1649.video000343.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000027.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000027.mp4 (deflated 9%)\n adding: content/video/openaigym.video.0.1649.video000343.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000216.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000000.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000008.mp4 (deflated 9%)\n adding: content/video/openaigym.video.0.1649.video000216.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000729.mp4 (deflated 7%)\n adding: content/video/openaigym.video.0.1649.video000001.mp4 (deflated 9%)\n adding: content/video/openaigym.video.0.1649.video002000.meta.json (deflated 60%)\n adding: content/video/openaigym.video.0.1649.video000064.meta.json (deflated 60%)\n" ], [ "", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import mean_squared_error\r\n\r\ndef train_test_val_split(x, y, random_state=None, split=[0.6, 0.2, 0.2]):\r\n \"\"\"\r\n Runs the train test split twice to break data up into train, validation and test sets at ratios specified by\r\n split\r\n \"\"\"\r\n x_train, x_test_val, y_train, y_test_val = train_test_split(x, y, test_size=1-split[0], random_state=random_state)\r\n x_val, x_test, y_val, y_test = train_test_split(x_test_val, y_test_val, test_size=split[1]/(split[1]+split[2]), random_state=random_state)\r\n return x_train, x_val, x_test, y_train, y_val, y_test\r\n", "_____no_output_____" ] ], [ [ "# Data", "_____no_output_____" ] ], [ [ "df = pd.read_csv(\"HW5_data.csv\")\r\nx = df[[\"X\", \"Y\"]].values\r\ny = df.Z.values\r\ndf.head()", "_____no_output_____" ], [ "df.describe()", "_____no_output_____" ] ], [ [ "The data is split into training, validation and test sets with ratios $0.6:0.2:0.2$ ", "_____no_output_____" ] ], [ [ "x_train, x_val, x_test, y_train, y_val, y_test = train_test_val_split(x, y)\r\nprint(\"Training size\")\r\nprint(x_train.shape)\r\nprint(\"Validation size\")\r\nprint(x_val.shape)\r\nprint(\"Test size\")\r\nprint(x_test.shape)", "Training size\n(600, 2)\nValidation size\n(200, 2)\nTest size\n(200, 2)\n" ] ], [ [ "# Model\r\n\r\nWe are told to apply a polynomial transformation on the features and use linear regression to obtain the optimal coefficients of the polynomial fit. We create a simple pipeline to achieve this. The only hyperparameter is the maximal degree of the polynomial feature map. The feature map ignores the bias but the linear regressor does not which correctly accounts for the data not being centered. \r\n\r\nIn order to tune the degree hyperparameter we sweep the parameter space and compute the MSE on the unseen validation set we had created. I am assuming the question does not require anything beyond this simple approach.", "_____no_output_____" ] ], [ [ "def poly_fit_pipeline(degree):\r\n polynomial_features = PolynomialFeatures(degree=degree, include_bias=False)\r\n pipeline = Pipeline([(\"polynomial_features\", polynomial_features), (\"linear_regression\", LinearRegression())])\r\n return pipeline\r\n\r\ndegrees = [2,3,4,5,6,7]\r\nmse = []\r\n\r\nfor d in degrees:\r\n model = poly_fit_pipeline(d)\r\n model.fit(x_train, y_train)\r\n y_pred = model.predict(x_val)\r\n mse.append(mean_squared_error(y_pred, y_val))", "_____no_output_____" ], [ "print(\"Lowest validation MSE: \"+str(round(np.min(mse),3)))\r\nprint(\"Optimal degree: \"+str(degrees[np.argmin(mse)]))\r\nplt.figure()\r\nplt.plot(degrees, np.log(mse))\r\nplt.ylabel(r\"$\\log MSE$\")\r\nplt.xlabel(\"D\")", "Lowest validation MSE: 0.009\nOptimal degree: 6\n" ], [ "model_optimal = poly_fit_pipeline(6)\r\nmodel_optimal.fit(x_train, y_train)\r\nmse_train = mean_squared_error(y_train, model.predict(x_train))\r\nprint(\"Training MSE: \"+str(mse_train))\r\nmse_val = mean_squared_error(y_val, model.predict(x_val))\r\nprint(\"Validation MSE: \"+str(mse_val))\r\nmse_test = mean_squared_error(y_test, model.predict(x_test))\r\nprint(\"Test MSE: \"+str(mse_test) )", "Training MSE: 0.008621014128936854\nValidation MSE: 0.00895723100171667\nTest MSE: 0.009421933878894272\n" ] ], [ [ "The optimization over the hyperparameter space indicates that a maximal degree of $6$ is optimal. Notice that the difference between a degree 5 fit and a degree 6 fit is miniscule so one could also go with the less complex model that offers very similar accuracy. We'll stick with $D=6$ though.\r\n\r\n## Details of the optimal model", "_____no_output_____" ] ], [ [ "print(\"Model parameters\")\r\nprint(model_optimal.get_params())\r\nprint(\"regression coefficients\")\r\nprint(model_optimal['linear_regression'].coef_)\r\nprint(\"regression intercept\")\r\nprint(model_optimal['linear_regression'].intercept_)\r\n", "Model parameters\n{'memory': None, 'steps': [('polynomial_features', PolynomialFeatures(degree=6, include_bias=False, interaction_only=False,\n order='C')), ('linear_regression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False))], 'verbose': False, 'polynomial_features': PolynomialFeatures(degree=6, include_bias=False, interaction_only=False,\n order='C'), 'linear_regression': LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False), 'polynomial_features__degree': 6, 'polynomial_features__include_bias': False, 'polynomial_features__interaction_only': False, 'polynomial_features__order': 'C', 'linear_regression__copy_X': True, 'linear_regression__fit_intercept': True, 'linear_regression__n_jobs': None, 'linear_regression__normalize': False}\nregression coefficients\n[ 8.50234361e-01 9.62565178e-01 2.44544103e-01 -2.94295566e+00\n 5.06385541e-03 2.24830034e-03 4.74477640e-01 9.21766117e-04\n -1.47698485e-03 2.97942590e-01 2.64905776e-03 2.57614722e-03\n -1.42506563e-03 2.57835307e-04 2.44146426e-04 -6.50061514e-07\n -4.57242498e-04 2.01383539e-04 4.08693006e-06 1.20000278e+00\n -9.28090109e-06 -4.25226714e-06 9.58005035e-06 1.34097259e-05\n -2.02810867e-05 7.75568090e-06 -1.56872787e-06]\nregression intercept\n5.092513526440598\n" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Object Detection", "_____no_output_____" ], [ "## Setup", "_____no_output_____" ] ], [ [ "#@title\n\nimport os\n\n!pip install --quiet tensorflow_text\nos.environ[\"TFHUB_MODEL_LOAD_FORMAT\"] = \"COMPRESSED\"", "_____no_output_____" ], [ "#@title\n\nimport os\nimport math\n\nimport numpy as np\nimport requests\n\nimport tensorflow as tf\nimport tensorflow_hub as hub\nimport tensorflow_text as tf_text\nimport tensorflow_datasets as tfds\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "sns.set_style(\"whitegrid\", {'axes.grid' : False})", "_____no_output_____" ], [ "%load_ext tensorboard", "_____no_output_____" ], [ "import requests\n\ndef download_image(url, path):\n r = requests.get(url, allow_redirects=True)\n with open(path, 'wb') as f:\n f.write(r.content)\n return path\n\ndef plot(y, titles=None):\n for i, image in enumerate(y):\n if image is None:\n plt.subplot(1, len(y), i+1)\n plt.axis('off')\n continue\n \n t = titles[i] if titles else None\n plt.subplot(1, len(y), i+1, title=t)\n plt.imshow(image)\n plt.axis('off')\n plt.tight_layout()", "_____no_output_____" ] ], [ [ "## Model Definition", "_____no_output_____" ] ], [ [ "detector = hub.load(\"https://tfhub.dev/tensorflow/efficientdet/d4/1\")", "_____no_output_____" ] ], [ [ "## Application", "_____no_output_____" ] ], [ [ "INPUT_SHAPE = [299, 299, 3]\n\nDATA_DIR = 'images/'\nIMAGES = [\n 'https://raw.githubusercontent.com/keisen/tf-keras-vis/master/examples/images/goldfish.jpg',\n 'https://raw.githubusercontent.com/keisen/tf-keras-vis/master/examples/images/bear.jpg',\n 'https://raw.githubusercontent.com/keisen/tf-keras-vis/master/examples/images/soldiers.jpg',\n 'https://3.bp.blogspot.com/-W__wiaHUjwI/Vt3Grd8df0I/AAAAAAAAA78/7xqUNj8ujtY/s400/image02.png'\n]", "_____no_output_____" ], [ "#@title\n\n\nos.makedirs(os.path.join(DATA_DIR, 'unknown'), exist_ok=True)\n\nfor i in IMAGES:\n _, f = os.path.split(i)\n download_image(i, os.path.join(DATA_DIR, 'unknown', f))", "_____no_output_____" ], [ "images_set = (\n tf.keras.preprocessing.image_dataset_from_directory(\n DATA_DIR,\n image_size=INPUT_SHAPE[:2],\n batch_size=32,\n shuffle=False)\n .cache()\n .prefetch(buffer_size=tf.data.experimental.AUTOTUNE))", "_____no_output_____" ], [ "#@title\n\nplt.figure(figsize=(12, 4))\nfor images, _ in images_set.take(1):\n for i, image in enumerate(images):\n plt.subplot(math.ceil(len(images) / 4), 4, i+1)\n plt.imshow(image.numpy().astype('uint8'))\n plt.axis('off')\n\nplt.tight_layout()", "_____no_output_____" ], [ "inputs = tf.cast(images[3:4], tf.uint8)", "_____no_output_____" ], [ "y = detector(inputs)\nclass_ids = y[\"detection_classes\"]", "_____no_output_____" ], [ "print(*y.keys(), sep='\\n')", "_____no_output_____" ], [ "y['num_detections']", "_____no_output_____" ], [ "y['detection_classes']", "_____no_output_____" ], [ "y['detection_boxes']", "_____no_output_____" ], [ "import matplotlib.patches as patches\n\nfig, ax = plt.subplots(1)\n\nax.imshow(inputs[0].numpy())\n\n# for y['detection_boxes']:\nax.add_patch(patches.Rectangle((50,100),40,30,linewidth=1,edgecolor='r',facecolor='none'))\n\n;", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "CC0-1.0" ]

[ "CC0-1.0" ]

[ "CC0-1.0" ]

[ [ [ "# Laboratório Computacional 1", "_____no_output_____" ], [ "No laboratório computacional, você praticará o que aprendeu. Resolva os problemas com o auxílio do Python pesquisando apenas as informações essenciais de que precisa. Não use respostas prontas.", "_____no_output_____" ], [ "**Problema:** Quantos segundos existem em um século? Escreva a resposta usando um objeto `str` que denote o número de acordo com nosso sistema decimal (separado por milhar, milhão, etc.) e imprima o resultado com `print`. A resposta deve ser algo do tipo\n\n```python\n'1 século possui x.xxx.xxx.xxx segundos'\n```", "_____no_output_____" ], [ "**Problema:** Escreva um código para calcular as raízes da equação $ax^2 + bx + c = 0$, para $a$, $b$ e $c$ conhecidos e do tipo `int`. Em que situação sua resposta seria um objeto `complex`? Mostre um exemplo. *Obs.:* use `1j` para construir a parte imaginária.", "_____no_output_____" ], [ "**Problema:** Observe a tabela a seguir, onde **DS (UA)** é a distância do referido planeta do até o Sol em unidades astronômicas (UA), **Tm (F)** sua temperatura superficial mínima em graus Farenheit e **TM (F)** sua temperatura superficial máxima em graus Farenheit.\n\n| | DS (UA) | Tm ($^{\\circ}$C) | TM ($^{\\circ}$C) | DS (km) | TmM ($^{\\circ}$C) |\n|--|--|--|--|--|--|\nMercúrio | 0.39 | -275 | 840 | ? | ? |\nVênus | 0.723 | 870 | 870 | ? | ? |\nTerra | 1.0 | -129 | 136 | ? | ? |\nMarte | 1.524 | -195 | 70 | ? | ? |\n\n\n\n- Escreva um código para converter a temperatura dos planetas de graus Farenheit ($^{\\circ}$F) para Celsius ($^{\\circ}$C).\n\n- Escreva um código para converter unidades astronômicas em quilômetros.\n\n- Imprima os valores que deveriam ser inseridos na coluna **DS (km)** horizontalmente usando `print`.\n\n- Repita o item anterior para a coluna **TmM ($^{\\circ}$C)**, que é a média aritmética entre **Tm** e **TM**.\n \n \n*Observação:* use notação científica (exemplo: $4.2 \\times 10^8$ pode ser escrito como `4.2e8` em Python).", "_____no_output_____" ] ] ]

[ "markdown" ]

[ [ "markdown", "markdown", "markdown", "markdown", "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "#!pip install -r requirements.txt", "_____no_output_____" ], [ "#!pip3 install ffmpeg", "_____no_output_____" ], [ "#!pip3 install --upgrade tensorflow keras numpy pandas sklearn pillow", "_____no_output_____" ], [ "from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, CSVLogger\nfrom data import DataSet\nimport time\nimport os.path\nimport shutil\nimport csv\nimport glob\nimport os\nimport os.path\nfrom subprocess import call", "_____no_output_____" ], [ "def get_train_test_lists(version='01'):\n \"\"\"\n Using one of the train/test files (01, 02, or 03), get the filename\n breakdowns we'll later use to move everything.\n \"\"\"\n # Get our files based on version.\n test_file = os.path.join('/home/shared/workspace/rose_ntu_dataset/'+'ntutraintest_rgb', 'testlist' + version + '.txt')\n train_file = os.path.join('/home/shared/workspace/rose_ntu_dataset/'+'ntutraintest_rgb', 'trainlist' + version + '.txt')\n\n # Build the test list.\n with open(test_file) as fin:\n test_list = [row.strip() for row in list(fin)]\n\n # Build the train list. Extra step to remove the class index.\n with open(train_file) as fin:\n train_list = [row.strip() for row in list(fin)]\n train_list = [row.split(' ')[0] for row in train_list]\n\n # Set the groups in a dictionary.\n file_groups = {\n 'train': train_list,\n 'test': test_list\n }\n print(file_groups['train'],file_groups['test'])\n return file_groups", "_____no_output_____" ], [ "file_groups = get_train_test_lists()", "['sneezeCough/S010C003P019R002A041_rgb', 'sneezeCough/S005C001P021R001A041_rgb', 'sneezeCough/S003C002P001R001A041_rgb', 'sneezeCough/S005C001P013R002A041_rgb', 'sneezeCough/S015C001P025R001A041_rgb', 'sneezeCough/S013C002P037R002A041_rgb', 'sneezeCough/S012C003P028R001A041_rgb', 'sneezeCough/S011C003P025R002A041_rgb', 'sneezeCough/S001C003P007R002A041_rgb', 'sneezeCough/S012C003P019R002A041_rgb', 'sneezeCough/S017C001P015R002A041_rgb', 'sneezeCough/S017C002P003R002A041_rgb', 'sneezeCough/S008C001P034R001A041_rgb', 'sneezeCough/S008C002P015R002A041_rgb', 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'fanSelf/S012C001P008R002A049_rgb 49', 'fanSelf/S007C003P027R002A049_rgb 49', 'fanSelf/S002C003P010R001A049_rgb 49', 'fanSelf/S011C001P015R001A049_rgb 49', 'fanSelf/S015C003P017R001A049_rgb 49', 'fanSelf/S011C002P017R001A049_rgb 49', 'fanSelf/S012C003P017R001A049_rgb 49', 'fanSelf/S012C002P018R002A049_rgb 49', 'fanSelf/S005C003P004R002A049_rgb 49', 'fanSelf/S004C002P020R001A049_rgb 49', 'fanSelf/S016C001P008R001A049_rgb 49', 'fanSelf/S008C001P032R001A049_rgb 49', 'fanSelf/S006C003P023R002A049_rgb 49', 'fanSelf/S008C002P029R001A049_rgb 49', 'fanSelf/S016C002P040R002A049_rgb 49', 'fanSelf/S006C001P001R001A049_rgb 49', 'fanSelf/S011C002P001R002A049_rgb 49', 'fanSelf/S013C003P015R002A049_rgb 49', 'fanSelf/S012C001P025R002A049_rgb 49', 'fanSelf/S001C002P004R002A049_rgb 49', 'fanSelf/S016C003P019R002A049_rgb 49', 'fanSelf/S015C003P008R002A049_rgb 49', 'fanSelf/S001C002P006R002A049_rgb 49', 'fanSelf/S013C001P007R002A049_rgb 49', 'fanSelf/S009C003P007R002A049_rgb 49']\n" ], [ "# for group, videos in file_groups.items():\n# for video in videos:\n# # Get the parts.\n# print(video)\n# parts = video.split(os.path.sep)\n# classname = parts[0]\n# filename = parts[1].split(\" \")[0]", "_____no_output_____" ], [ "# print(classname)", "_____no_output_____" ], [ "# if not os.path.exists(os.path.join('/home/shared/workspace/rose_ntu_dataset/C3D/', group, classname)):\n# print(\"Creating folder for %s/%s\" % (group, classname))\n# os.makedirs(os.path.join('/home/shared/workspace/rose_ntu_dataset/C3D/'+group, classname))", "_____no_output_____" ], [ "# class_file = '/home/shared/workspace/rose_ntu_dataset/nturgb/'+ classname+'/'+filename+'.avi'\n# print(class_file)\n# if not os.path.exists(class_file):\n# print(\"/home/shared/workspace/rose_ntu_dataset/nturgb\"+classname+'/'+filename)\n# print(\"Can't find %s to move. Skipping.\" % (class_dir))\n \n# dest = os.path.join(\"/home/shared/workspace/rose_ntu_dataset/C3D/\",group, classname)\n# print(\"Copying %s to %s\" % (class_file, dest))\n# shutil.copy(class_file, dest)", "_____no_output_____" ], [ "def move_files(file_groups):\n \"\"\"This assumes all of our files are currently in _this_ directory.\n So move them to the appropriate spot. Only needs to happen once.\n \"\"\"\n # Do each of our groups.\n for group, videos in file_groups.items():\n \n # Do each of our videos.\n for video in videos:\n\n # Get the parts.\n parts = video.split(os.path.sep)\n classname = parts[0]\n filename = parts[1].split(\" \")[0]\n\n # Check if this class exists.\n if not os.path.exists(os.path.join('/home/shared/workspace/rose_ntu_dataset/C3D/'+ group, classname)):\n print(\"Creating folder for %s/%s\" % (group, classname))\n os.makedirs(os.path.join('/home/shared/workspace/rose_ntu_dataset/C3D/'+group, classname))\n\n # Check if we have already moved this file, or at least that it\n # exists to move.\n class_file = '/home/shared/workspace/rose_ntu_dataset/nturgb/'+ classname+'/'+filename+'.avi'\n if not os.path.exists(class_file):\n print(\"Can't find %s to move. Skipping.\" % (class_file))\n continue\n\n # Move it.\n dest = os.path.join(\"/home/shared/workspace/rose_ntu_dataset/C3D/\",group, classname)\n print(\"Moving %s to %s\" % (filename, dest))\n print(\"Copying %s to %s\" % (class_file, dest))\n shutil.copy(class_file, dest)", "_____no_output_____" ], [ "# move_files(file_groups)", "_____no_output_____" ], [ "pwd", "_____no_output_____" ], [ "#cd data", "_____no_output_____" ], [ "def extract_files():\n \"\"\"After we have all of our videos split between train and test, and\n all nested within folders representing their classes, we need to\n make a data file that we can reference when training our RNN(s).\n This will let us keep track of image sequences and other parts\n of the training process.\n\n We'll first need to extract images from each of the videos. We'll\n need to record the following data in the file:\n\n [train|test], class, filename, nb frames\n\n Extracting can be done with ffmpeg:\n `ffmpeg -i video.mpg image-%04d.jpg`\n \"\"\"\n data_file = []\n folders = ['train', 'test']\n\n for folder in folders:\n class_folders = glob.glob(os.path.join(folder, '*'))\n print(class_folders)\n for vid_class in class_folders:\n class_files = glob.glob(os.path.join(vid_class, '*.avi'))\n print(class_files)\n for video_path in class_files:\n # Get the parts of the file.\n video_parts = get_video_parts(video_path)\n\n train_or_test, classname, filename_no_ext, filename = video_parts\n print(train_or_test, classname, filename_no_ext, filename)\n\n # Only extract if we haven't done it yet. Otherwise, just get\n # the info.\n if not check_already_extracted(video_parts):\n # Now extract it.\n src = os.path.join(train_or_test, classname, filename)\n dest = os.path.join(train_or_test, classname,\n filename_no_ext + '-%04d.jpg')\n call([\"ffmpeg\", \"-i\", src, dest])\n\n # Now get how many frames it is.\n nb_frames = get_nb_frames_for_video(video_parts)\n\n data_file.append([train_or_test, classname, filename_no_ext, nb_frames])\n\n print(\"Generated %d frames for %s\" % (nb_frames, filename_no_ext))\n\n with open('data_file.csv', 'w') as fout:\n writer = csv.writer(fout)\n writer.writerows(data_file)\n\n print(\"Extracted and wrote %d video files.\" % (len(data_file)))", "_____no_output_____" ], [ "def get_video_parts(video_path):\n \"\"\"Given a full path to a video, return its parts.\"\"\"\n parts = video_path.split(os.path.sep)\n filename = parts[2]\n filename_no_ext = filename.split('.')[0]\n classname = parts[1]\n train_or_test = parts[0]\n\n return train_or_test, classname, filename_no_ext, filename", "_____no_output_____" ], [ "def check_already_extracted(video_parts):\n \"\"\"Check to see if we created the -0001 frame of this file.\"\"\"\n train_or_test, classname, filename_no_ext, _ = video_parts\n return bool(os.path.exists(os.path.join(train_or_test, classname,\n filename_no_ext + '-0001.jpg')))", "_____no_output_____" ], [ "def get_nb_frames_for_video(video_parts):\n \"\"\"Given video parts of an (assumed) already extracted video, return\n the number of frames that were extracted.\"\"\"\n train_or_test, classname, filename_no_ext, _ = video_parts\n generated_files = glob.glob(os.path.join(train_or_test, classname,\n filename_no_ext + '*.jpg'))\n return len(generated_files)", "_____no_output_____" ], [ "# extract_files()", "_____no_output_____" ], [ "pwd", "_____no_output_____" ], [ "def train(data_type, seq_length, model, saved_model=None,\n class_limit=None, image_shape=None,\n load_to_memory=False, batch_size=32, nb_epoch=100):\n # Helper: Save the model.\n checkpointer = ModelCheckpoint(\n filepath=os.path.join('data', 'checkpoints', model + '-' + data_type + \\\n '.{epoch:03d}-{val_loss:.3f}.hdf5'),\n verbose=1,\n save_best_only=True)\n\n # Helper: TensorBoard\n tb = TensorBoard(log_dir=os.path.join('data', 'logs', model))\n\n # Helper: Stop when we stop learning.\n early_stopper = EarlyStopping(patience=5)\n\n # Helper: Save results.\n timestamp = time.time()\n csv_logger = CSVLogger(os.path.join('data', 'logs', model + '-' + 'training-' + \\\n str(timestamp) + '.log'))\n\n # Get the data and process it.\n if image_shape is None:\n data = DataSet(\n seq_length=seq_length,\n class_limit=class_limit\n )\n else:\n data = DataSet(\n seq_length=seq_length,\n class_limit=class_limit,\n image_shape=image_shape\n )\n\n # Get samples per epoch.\n # Multiply by 0.7 to attempt to guess how much of data.data is the train set.\n steps_per_epoch = (len(data.data) * 0.7) // batch_size\n\n if load_to_memory:\n # Get data.\n X, y = data.get_all_sequences_in_memory('train', data_type)\n X_test, y_test = data.get_all_sequences_in_memory('test', data_type)\n else:\n # Get generators.\n generator = data.frame_generator(batch_size, 'train', data_type)\n val_generator = data.frame_generator(batch_size, 'test', data_type)\n\n # Get the model.\n rm = call_c3d(seq_length, len(data.classes))\n\n # Fit!\n if load_to_memory:\n # Use standard fit.\n rm.fit(\n X,\n y,\n batch_size=batch_size,\n validation_data=(X_test, y_test),\n verbose=1,\n callbacks=[tb, early_stopper, csv_logger],\n epochs=nb_epoch)\n else:\n # Use fit generator.\n rm.fit(\n generator,\n steps_per_epoch=steps_per_epoch,\n epochs=nb_epoch,\n verbose=1,\n callbacks=[tb, early_stopper, csv_logger, checkpointer],\n validation_data=val_generator,\n validation_steps=40,\n workers=4)", "_____no_output_____" ], [ "checkpointer = ModelCheckpoint(\n filepath=os.path.join('data', 'checkpoints', 'c3d' + '-' + 'image' + \\\n '.{epoch:03d}-{val_loss:.3f}.hdf5'),\n verbose=1,\n save_best_only=True)", "_____no_output_____" ], [ "from keras.layers import Dense, Flatten, Dropout, ZeroPadding3D\nfrom keras.layers.recurrent import LSTM\nfrom keras.models import Sequential, load_model\nfrom tensorflow.keras.optimizers import Adam,RMSprop\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras.layers.convolutional import (Conv2D, MaxPooling3D, Conv3D,\n MaxPooling2D)\nfrom collections import deque\nimport sys", "_____no_output_____" ], [ "def c3d(input_shape, num_classes):\n \"\"\"\n Build a 3D convolutional network, aka C3D.\n https://arxiv.org/pdf/1412.0767.pdf\n\n With thanks:\n https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2\n \"\"\"\n model = Sequential()\n\n # 1st layer group\n model.add(Conv3D(64, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv1a\",\n input_shape=input_shape))\n\n model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),\n padding='valid', name='pool1'))\n # 2nd layer group\n model.add(Conv3D(128, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv2a\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool2'))\n # 3rd layer group\n model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3a\"))\n\n model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3b\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool3'))\n # 4th layer group\n model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4a\"))\n\n model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4b\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool4'))\n\n # 5th layer group\n model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5a\"))\n\n model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5b\"))\n\n #model.add(ZeroPadding3D(padding=(0, 1, 1)))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool5'))\n model.add(Flatten())\n\n # FC layers group\n model.add(Dense(4096, activation='relu', name='fc6'))\n #model.add(Dropout(0.5))\n model.add(Dense(4096, activation='relu', name='fc7'))\n #model.add(Dropout(0.5))\n model.add(Dense(num_classes, activation='softmax', dtype=\"float32\"))\n\n return model", "_____no_output_____" ], [ "def shallow_c3d(input_shape, num_classes):\n \"\"\"\n Build a 3D convolutional network, aka C3D.\n https://arxiv.org/pdf/1412.0767.pdf\n\n With thanks:\n https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2\n \"\"\"\n model = Sequential()\n\n # 1st layer group\n model.add(Conv3D(32, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv1a\",\n input_shape=input_shape))\n\n model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),\n padding='valid', name='pool1'))\n # 2nd layer group\n model.add(Conv3D(64, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv2a\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool2'))\n # 3rd layer group\n model.add(Conv3D(128, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3a\"))\n\n model.add(Conv3D(128, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3b\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool3'))\n # 4th layer group\n model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4a\"))\n\n model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4b\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool4'))\n\n# # 5th layer group\n# model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5a\"))\n\n# model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5b\"))\n\n# #model.add(ZeroPadding3D(padding=(0, 1, 1)))\n# model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n# padding='valid', name='pool5'))\n model.add(Flatten())\n\n # FC layers group\n model.add(Dense(1024, activation='relu', name='fc5'))\n model.add(Dropout(0.5))\n model.add(Dense(1024, activation='relu', name='fc6'))\n model.add(Dropout(0.5))\n model.add(Dense(num_classes, activation='softmax', dtype=\"float32\"))\n\n return model", "_____no_output_____" ], [ "def more_shallow_c3d(input_shape, num_classes):\n \"\"\"\n Build a 3D convolutional network, aka C3D.\n https://arxiv.org/pdf/1412.0767.pdf\n\n With thanks:\n https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2\n \"\"\"\n model = Sequential()\n\n # 1st layer group\n model.add(Conv3D(32, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv1a\",\n input_shape=input_shape))\n\n model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),\n padding='valid', name='pool1'))\n # 2nd layer group\n model.add(Conv3D(32, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv2a\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool2'))\n model.add(Dropout(0.25))\n \n # 3rd layer group\n model.add(Conv3D(64, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3a\"))\n\n model.add(Conv3D(64, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv3b\"))\n\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n padding='valid', name='pool3'))\n model.add(Dropout(0.25))\n\n # 4th layer group\n# model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4a\"))\n\n# model.add(Conv3D(256, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv4b\"))\n\n# model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n# padding='valid', name='pool4'))\n\n# # 5th layer group\n# model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5a\"))\n\n# model.add(Conv3D(512, kernel_size=(3,3,3), strides=(1,1,1), activation='relu', padding='same', name=\"conv5b\"))\n\n# #model.add(ZeroPadding3D(padding=(0, 1, 1)))\n# model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n# padding='valid', name='pool5'))\n model.add(Flatten())\n\n # FC layers group\n# model.add(Dense(1024, activation='relu', name='fc6'))\n# model.add(Dropout(0.5))\n model.add(Dense(512, activation='relu', name='fc4'))\n model.add(Dropout(0.5))\n model.add(Dense(num_classes, activation='softmax', dtype=\"float32\"))\n\n return model", "_____no_output_____" ], [ "def call_c3d(seq_length, num_classes):\n input_shape = (seq_length, 80, 80, 3)\n# model = c3d(input_shape, num_classes)\n model = more_shallow_c3d(input_shape, num_classes)\n# model = shallow_c3d(input_shape, num_classes)\n optimizer = Adam(learning_rate =1e-3, decay=1e-6)\n model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n model.summary()\n return model", "_____no_output_____" ], [ "train('images', 16, 'c3d', saved_model=None,\n class_limit=None, image_shape=(80,80,3),\n load_to_memory=False, batch_size=64, nb_epoch=20)", "Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv1a (Conv3D) (None, 16, 80, 80, 32) 2624 \n_________________________________________________________________\npool1 (MaxPooling3D) (None, 16, 40, 40, 32) 0 \n_________________________________________________________________\nconv2a (Conv3D) (None, 16, 40, 40, 32) 27680 \n_________________________________________________________________\npool2 (MaxPooling3D) (None, 8, 20, 20, 32) 0 \n_________________________________________________________________\ndropout (Dropout) (None, 8, 20, 20, 32) 0 \n_________________________________________________________________\nconv3a (Conv3D) (None, 8, 20, 20, 64) 55360 \n_________________________________________________________________\nconv3b (Conv3D) (None, 8, 20, 20, 64) 110656 \n_________________________________________________________________\npool3 (MaxPooling3D) (None, 4, 10, 10, 64) 0 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 4, 10, 10, 64) 0 \n_________________________________________________________________\nflatten (Flatten) (None, 25600) 0 \n_________________________________________________________________\nfc4 (Dense) (None, 512) 13107712 \n_________________________________________________________________\ndropout_2 (Dropout) (None, 512) 0 \n_________________________________________________________________\ndense (Dense) (None, 9) 4617 \n=================================================================\nTotal params: 13,308,649\nTrainable params: 13,308,649\nNon-trainable params: 0\n_________________________________________________________________\nCreating train generator with 6825 samples.\nEpoch 1/20\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "### Imports", "_____no_output_____" ] ], [ [ "from IPython.display import clear_output", "_____no_output_____" ], [ "!pip install path.py\n!pip install pytorch3d\nclear_output()", "_____no_output_____" ], [ "import numpy as np\nimport math\nimport random\nimport os\nimport plotly.graph_objects as go\nimport plotly.express as px\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom torchvision import transforms, utils\n\nfrom path import Path\n\nrandom.seed = 42", "_____no_output_____" ], [ "!wget http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip\n!unzip -q ModelNet10.zip\n\npath = Path(\"ModelNet10\")\n\nfolders = [dir for dir in sorted(os.listdir(path)) if os.path.isdir(path/dir)]\n\nclear_output()\nclasses = {folder: i for i, folder in enumerate(folders)}\nclasses", "_____no_output_____" ], [ "def default_transforms():\n return transforms.Compose([\n PointSampler(1024),\n Normalize(),\n RandomNoise(),\n ToSorted(),\n ToTensor()\n ])\n\n!gdown https://drive.google.com/uc?id=1CVwVxdfUfP6TRcVUjjJvQeRcgCGcnSO_\nfrom helping import *\nclear_output()", "_____no_output_____" ] ], [ [ "### Data Preprocessing (optional)", "_____no_output_____" ] ], [ [ "with open(path/\"dresser/train/dresser_0001.off\", 'r') as f:\n verts, faces = read_off(f)\n\ni, j, k = np.array(faces).T\nx, y, z = np.array(verts).T\n\n# len(x)", "_____no_output_____" ], [ "# visualize_rotate([go.Mesh3d(x=x, y=y, z=z, color='lightpink', opacity=0.50, i=i,j=j,k=k)]).show()\n# visualize_rotate([go.Scatter3d(x=x, y=y, z=z, mode='markers')]).show()", "_____no_output_____" ], [ "# pcshow(x, y, z)", "_____no_output_____" ], [ "pointcloud = PointSampler(1024)((verts, faces))\n# pcshow(*pointcloud.T)\n\nnorm_pointcloud = Normalize()(pointcloud)\n# pcshow(*norm_pointcloud.T)\n\nnoisy_pointcloud = RandomNoise()(norm_pointcloud)\n# pcshow(*noisy_pointcloud.T)\n\nrot_pointcloud = RandomRotation_z()(noisy_pointcloud)\n# pcshow(*rot_pointcloud.T)\n\nsorted_pointcloud = ToSorted()(rot_pointcloud)\n# pcshow(*sorted_pointcloud.T)\n\ntensor_pointcloud = ToTensor()(sorted_pointcloud)", "_____no_output_____" ] ], [ [ "### Creating Loaders for Final Progress Report", "_____no_output_____" ], [ "#### Redefine classes", "_____no_output_____" ] ], [ [ "class PointCloudData(Dataset):\n def __init__(self, root_dir, valid=False, folder=\"train\", transform=default_transforms(), folders=None):\n self.root_dir = root_dir\n if not folders:\n folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir/dir)]\n self.classes = {folder: i for i, folder in enumerate(folders)}\n self.transforms = transform\n self.valid = valid\n self.pcs = []\n for category in self.classes.keys():\n new_dir = root_dir/Path(category)/folder\n for file in os.listdir(new_dir):\n if file.endswith('.off'):\n sample = {}\n with open(new_dir/file, 'r') as f:\n verts, faces = read_off(f)\n sample['pc'] = (verts, faces)\n sample['category'] = category\n self.pcs.append(sample)\n\n def __len__(self):\n return len(self.pcs)\n\n def __getitem__(self, idx):\n pointcloud = self.transforms(self.pcs[idx]['pc'])\n category = self.pcs[idx]['category']\n return pointcloud, self.classes[category]\n \nclass PointCloudDataPre(Dataset):\n def __init__(self, root_dir, valid=False, folder=\"train\", transform=default_transforms(), folders=None):\n self.root_dir = root_dir\n if not folders:\n folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir/dir)]\n self.classes = {folder: i for i, folder in enumerate(folders)}\n self.transforms = transform\n self.valid = valid\n self.pcs = []\n for category in self.classes.keys():\n new_dir = root_dir/Path(category)/folder\n for file in os.listdir(new_dir):\n if file.endswith('.off'):\n sample = {}\n with open(new_dir/file, 'r') as f:\n verts, faces = read_off(f)\n sample['pc'] = self.transforms((verts, faces))\n sample['category'] = category\n self.pcs.append(sample)\n\n def __len__(self):\n return len(self.pcs)\n\n def __getitem__(self, idx):\n pointcloud = self.pcs[idx]['pc']\n category = self.pcs[idx]['category']\n return pointcloud, self.classes[category]\n \n \nclass PointCloudDataBoth(Dataset):\n def __init__(self, root_dir, valid=False, folder=\"train\", static_transform=default_transforms(), later_transform=None, folders=None):\n self.root_dir = root_dir\n if not folders:\n folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir/dir)]\n self.classes = {folder: i for i, folder in enumerate(folders)}\n self.static_transform = static_transform\n self.later_transform = later_transform\n self.valid = valid\n self.pcs = []\n for category in self.classes.keys():\n new_dir = root_dir/Path(category)/folder\n for file in os.listdir(new_dir):\n if file.endswith('.off'):\n sample = {}\n with open(new_dir/file, 'r') as f:\n verts, faces = read_off(f)\n sample['pc'] = self.static_transform((verts, faces))\n sample['category'] = category\n self.pcs.append(sample)\n\n def __len__(self):\n return len(self.pcs)\n\n def __getitem__(self, idx):\n pointcloud = self.pcs[idx]['pc']\n if self.later_transform is not None:\n pointcloud = self.later_transform(pointcloud)\n category = self.pcs[idx]['category']\n return pointcloud, self.classes[category]", "_____no_output_____" ], [ "!mkdir drive/MyDrive/Thesis/dataloaders/final", "_____no_output_____" ] ], [ [ "#### Overfitting - all augmentations applied before training", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\ntrs = transforms.Compose([\n PointSampler(1024),\n ToSorted(),\n Normalize(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataPre(path, folders=['bed'], transform=trs)\nbeds_valid_dataset = PointCloudDataPre(path, folder='test', folders=['bed'], transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_pre\ntorch.save(beds_train_loader, 'dataloader_beds_pre/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_pre/validloader.pth')\n\n!mkdir drive/MyDrive/Thesis/dataloaders/final\n!cp -r dataloader_beds_pre drive/MyDrive/Thesis/dataloaders/final", "mkdir: cannot create directory ‘dataloader_beds_pre’: File exists\nmkdir: cannot create directory ‘drive/MyDrive/Thesis/dataloaders/final’: File exists\n" ] ], [ [ "#### Underfitting - all augmentations applied during training", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\ntrs = transforms.Compose([\n PointSampler(1024),\n ToSorted(),\n Normalize(),\n RandomNoise(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudData(path, folders=['bed'], transform=trs)\nbeds_valid_dataset = PointCloudData(path, folder='test', folders=['bed'], transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, num_workers=4, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, num_workers=4, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_dur\ntorch.save(beds_train_loader, 'dataloader_beds_dur/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_dur/validloader.pth')\n\n!cp -r dataloader_beds_dur drive/MyDrive/Thesis/dataloaders/final", "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning:\n\nThis DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n\n" ] ], [ [ "#### Both - static and dynamic transformations", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\nstatic_trs = transforms.Compose([\n PointSampler(1024),\n ToSorted(),\n Normalize(),\n])\n\ndynamic_trs = transforms.Compose([\n RandomNoise(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataBoth(path, folders=['bed'], static_transform=static_trs, later_transform=dynamic_trs)\nbeds_valid_dataset = PointCloudDataBoth(path, folder='test', folders=['bed'], static_transform=static_trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_both\ntorch.save(beds_train_loader, 'dataloader_beds_both/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_both/validloader.pth')\n\n!cp -r dataloader_beds_both drive/MyDrive/Thesis/dataloaders/final", "mkdir: cannot create directory ‘dataloader_beds_both’: File exists\n" ] ], [ [ "#### Two classes: beds and tables", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\nstatic_trs = transforms.Compose([\n PointSampler(1024),\n ToSorted(),\n Normalize(),\n])\n\ndynamic_trs = transforms.Compose([\n RandomNoise(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataBoth(path, folders=['bed', 'table'], static_transform=static_trs, later_transform=dynamic_trs)\nbeds_valid_dataset = PointCloudDataBoth(path, folder='test', folders=['bed', 'table'], static_transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_tables\ntorch.save(beds_train_loader, 'dataloader_beds_tables/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_tables/validloader.pth')\n\n!cp -r dataloader_beds_tables drive/MyDrive/Thesis/dataloaders/final", "_____no_output_____" ] ], [ [ "### For 512", "_____no_output_____" ] ], [ [ "!mkdir drive/MyDrive/Thesis/dataloaders/final512", "_____no_output_____" ] ], [ [ "#### Overfitting - all augmentations applied before training", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\ntrs = transforms.Compose([\n PointSampler(512),\n ToSorted(),\n Normalize(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataPre(path, folders=['bed'], transform=trs)\nbeds_valid_dataset = PointCloudDataPre(path, folder='test', folders=['bed'], transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_pre\ntorch.save(beds_train_loader, 'dataloader_beds_pre/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_pre/validloader.pth')\n\n!mkdir drive/MyDrive/Thesis/dataloaders/final\n!cp -r dataloader_beds_pre drive/MyDrive/Thesis/dataloaders/final512", "mkdir: cannot create directory ‘dataloader_beds_pre’: File exists\nmkdir: cannot create directory ‘drive/MyDrive/Thesis/dataloaders/final’: File exists\n" ] ], [ [ "#### Underfitting - all augmentations applied during training", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\ntrs = transforms.Compose([\n PointSampler(512),\n ToSorted(),\n Normalize(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudData(path, folders=['bed'], transform=trs)\nbeds_valid_dataset = PointCloudData(path, folder='test', folders=['bed'], transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, num_workers=4, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, num_workers=4, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_dur\ntorch.save(beds_train_loader, 'dataloader_beds_dur/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_dur/validloader.pth')\n\n!cp -r dataloader_beds_dur drive/MyDrive/Thesis/dataloaders/final512", "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning:\n\nThis DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n\n" ] ], [ [ "#### Both - static and dynamic transformations", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\nstatic_trs = transforms.Compose([\n PointSampler(512),\n ToSorted(),\n Normalize(),\n])\n\ndynamic_trs = transforms.Compose([\n RandomNoise(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataBoth(path, folders=['bed'], static_transform=static_trs, later_transform=dynamic_trs)\nbeds_valid_dataset = PointCloudDataBoth(path, folder='test', folders=['bed'], static_transform=static_trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_both\ntorch.save(beds_train_loader, 'dataloader_beds_both/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_both/validloader.pth')\n\n!cp -r dataloader_beds_both drive/MyDrive/Thesis/dataloaders/final512", "mkdir: cannot create directory ‘dataloader_beds_both’: File exists\n" ] ], [ [ "#### Two classes: beds and tables", "_____no_output_____" ] ], [ [ "BATCH_SIZE = 48\n\nstatic_trs = transforms.Compose([\n PointSampler(512),\n ToSorted(),\n Normalize(),\n])\n\ndynamic_trs = transforms.Compose([\n RandomNoise(),\n ToTensor()\n])\n\nbeds_train_dataset = PointCloudDataBoth(path, folders=['bed', 'table'], static_transform=static_trs, later_transform=dynamic_trs)\nbeds_valid_dataset = PointCloudDataBoth(path, folder='test', folders=['bed', 'table'], static_transform=trs)\n\nbeds_train_loader = DataLoader(dataset=beds_train_dataset, shuffle=True, batch_size=BATCH_SIZE, drop_last=True)\nbeds_valid_loader = DataLoader(dataset=beds_valid_dataset, batch_size=BATCH_SIZE, drop_last=True)\n\n!mkdir dataloader_beds_tables\ntorch.save(beds_train_loader, 'dataloader_beds_tables/trainloader.pth')\ntorch.save(beds_valid_loader, 'dataloader_beds_tables/validloader.pth')\n\n!cp -r dataloader_beds_tables drive/MyDrive/Thesis/dataloaders/final", "_____no_output_____" ] ] ]

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[ [ [ "# Fill in full author references for mentions of authors\n\nFor example, if we find `Calderon`, we want to produce a string\n\n```\n<author><name key=\"cald\">Pedro Calderón de la Barca</name></author>\n```", "_____no_output_____" ] ], [ [ "testTexts = [\n \"Calderón de la Barca, Pedro\",\n \"CCCCCalderón\",\n \"Caldeeeeeerón\",\n \"Pedro Barca\",\n \"Pedro Barca\",\n \"Agustin Moreto\",\n \"A. Moreto\",\n \"Agustin\",\n \"Augustine\",\n]", "_____no_output_____" ] ], [ [ "## Triggers\n\nWe are going to find trigger strings for authors in the input texts.\n\nIn order to do that successfully, we normalize the text first:\n\n* we remove all accents from accented letters\n* we make everything lowercase", "_____no_output_____" ], [ "We need a function that can strip accents from characters.\n\nFrom [stackoverflow](https://stackoverflow.com/questions/517923/what-is-the-best-way-to-remove-accents-in-a-python-unicode-string)", "_____no_output_____" ] ], [ [ "import re\nimport unicodedata\n\ndef normalize(text):\n text = unicodedata.normalize('NFD', text)\n text = text.encode('ascii', 'ignore')\n text = text.decode(\"utf-8\")\n return text.lower().strip()", "_____no_output_____" ], [ "normalize(\"Calderón de la Barca, Pedro\")", "_____no_output_____" ] ], [ [ "## Authors\n\nWe compile a list of authors that we want to detect.\n\nFor each author we have a full name, a key, and a list of triggers.\n\nWe format the specficiation as a *yaml* file (which maps to a Python dictionary).", "_____no_output_____" ] ], [ [ "authorSpec = '''\ncald:\n full: Pedro Calderón de la Barca\n triggers:\n - calderon\n - barca\nmore:\n full: Agustín Moreto\n triggers:\n - moreto\n - agustin\n - augustine\n'''", "_____no_output_____" ] ], [ [ "In order to parse this file, you need to install pyyaml first\n\n``` sh\npip install yaml\n```\n\nor\n\n```\npip3 install yaml\n```", "_____no_output_____" ] ], [ [ "import yaml", "_____no_output_____" ], [ "authors = yaml.load(authorSpec, Loader=yaml.FullLoader)", "_____no_output_____" ], [ "authors", "_____no_output_____" ] ], [ [ "We need to compile the authors specification in such a way that we can use the triggers", "_____no_output_____" ] ], [ [ "triggers = {}\nfor (key, authorInfo) in authors.items():\n for trigger in authorInfo['triggers']:\n triggers[trigger] = key", "_____no_output_____" ], [ "triggers", "_____no_output_____" ], [ "def fillInAuthorDetails(text):\n normalized = normalize(text)\n output = None\n for trigger in triggers:\n if trigger in normalized:\n authorKey = triggers[trigger]\n authorFull = authors[authorKey][\"full\"]\n output = f\"\"\"<author><name key=\"{authorKey}\">{authorFull}</name></author>\"\"\"\n break\n if output is None:\n print(f\"!!! {normalized:<36} => NO AUTHOR DETECTED\")\n return output", "_____no_output_____" ], [ "for text in (testTexts):\n result = fillInAuthorDetails(text)\n if result is not None:\n print(f\"{text:<40} => {result}\")", "Calderón de la Barca, Pedro => <author><name key=\"cald\">Pedro Calderón de la Barca</name></author>\nCCCCCalderón => <author><name key=\"cald\">Pedro Calderón de la Barca</name></author>\n!!! caldeeeeeeron => NO AUTHOR DETECTED\nPedro Barca => <author><name key=\"cald\">Pedro Calderón de la Barca</name></author>\nPedro Barca => <author><name key=\"cald\">Pedro Calderón de la Barca</name></author>\nAgustin Moreto => <author><name key=\"more\">Agustín Moreto</name></author>\nA. Moreto => <author><name key=\"more\">Agustín Moreto</name></author>\nAgustin => <author><name key=\"more\">Agustín Moreto</name></author>\nAugustine => <author><name key=\"more\">Agustín Moreto</name></author>\n" ] ] ]

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[ [ [ "## Cross-Validation\n\n1. We read the data from the npy files\n2. We combine the QUBICC and NARVAL data\n4. Set up cross validation\n\nDuring cross-validation:\n\n1. We scale the data, convert to tf data\n2. Plot training progress, model biases \n3. Write losses and epochs into file", "_____no_output_____" ] ], [ [ "# Ran with 800GB (750GB should also be fine)\n\nimport sys\nimport numpy as np\nimport time\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport copy\nimport gc\n\n#Import sklearn before tensorflow (static Thread-local storage)\nfrom sklearn.preprocessing import StandardScaler\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.regularizers import l1_l2\n\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.layers import Activation\n\n# For Leaky_ReLU:\nfrom tensorflow import nn \n\nt0 = time.time()\npath = '/pf/b/b309170'\n\n# Add path with my_classes to sys.path\nsys.path.insert(0, path + '/workspace_icon-ml/cloud_cover_parameterization/')\n\n# Reloading custom file to incorporate changes dynamically\nimport importlib\nimport my_classes\nimportlib.reload(my_classes)\n\nfrom my_classes import read_mean_and_std\nfrom my_classes import TimeOut\n\n# Minutes per fold\ntimeout = 2120 \n\n# For logging purposes\ndays = 'all_days'\n\n# Maximum amount of epochs for each model\nepochs = 30 \n\n# Set seed for reproducibility\nseed = 10\ntf.random.set_seed(seed)\n\n# For store_mean_model_biases\nVERT_LAYERS = 31\n\ngpus = tf.config.experimental.list_physical_devices('GPU')\n# tf.config.experimental.set_visible_devices(gpus[3], 'GPU')", "_____no_output_____" ], [ "# Cloud Cover or Cloud Area?\noutput_var = 'cl_area' # Set output_var to one of {'clc', 'cl_area'}\n# QUBICC only or QUBICC+NARVAL training data? Always True for the paper\nqubicc_only = True\n\npath_base = os.path.join(path, 'workspace_icon-ml/cloud_cover_parameterization/grid_cell_based_QUBICC_R02B05')\npath_data = os.path.join(path, 'my_work/icon-ml_data/cloud_cover_parameterization/grid_cell_based_QUBICC_R02B05/based_on_var_interpolated_data')\n\nif output_var == 'clc':\n full_output_var_name = 'cloud_cover'\nelif output_var == 'cl_area':\n full_output_var_name = 'cloud_area'\n \nif qubicc_only:\n output_folder = '%s_R2B5_QUBICC'%full_output_var_name\nelse:\n output_folder = '%s_R2B5_QUBICC+NARVAL'%full_output_var_name\npath_model = os.path.join(path_base, 'saved_models', output_folder)\npath_figures = os.path.join(path_base, 'figures', output_folder)\nnarval_output_file = '%s_output_narval.npy'%full_output_var_name\nqubicc_output_file = '%s_output_qubicc.npy'%full_output_var_name", "_____no_output_____" ], [ "# Prevents crashes of the code\nphysical_devices = tf.config.list_physical_devices('GPU')\ntf.config.set_visible_devices(physical_devices[0], 'GPU')", "_____no_output_____" ], [ "# Allow the growth of memory Tensorflow allocates (limits memory usage overall)\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)", "_____no_output_____" ], [ "scaler = StandardScaler()", "_____no_output_____" ] ], [ [ "### Load the data", "_____no_output_____" ] ], [ [ "# input_narval = np.load(path_data + '/cloud_cover_input_narval.npy')\n# input_qubicc = np.load(path_data + '/cloud_cover_input_qubicc.npy')\n# output_narval = np.load(path_data + '/cloud_cover_output_narval.npy')\n# output_qubicc = np.load(path_data + '/cloud_cover_output_qubicc.npy')", "_____no_output_____" ], [ "input_data = np.concatenate((np.load(path_data + '/cloud_cover_input_narval.npy'), \n np.load(path_data + '/cloud_cover_input_qubicc.npy')), axis=0)\noutput_data = np.concatenate((np.load(os.path.join(path_data, narval_output_file)), \n np.load(os.path.join(path_data, qubicc_output_file))), axis=0)", "_____no_output_____" ], [ "samples_narval = np.load(path_data + '/cloud_cover_output_narval.npy').shape[0]", "_____no_output_____" ], [ "if qubicc_only:\n input_data = input_data[samples_narval:]\n output_data = output_data[samples_narval:]", "_____no_output_____" ], [ "(samples_total, no_of_features) = input_data.shape\n(samples_total, no_of_features)", "_____no_output_____" ] ], [ [ "*Temporal cross-validation*\n\nSplit into 2-weeks increments (when working with 3 months of data). It's 25 day increments with 5 months of data. <br>\n1.: Validate on increments 1 and 4 <br>\n2.: Validate on increments 2 and 5 <br>\n3.: Validate on increments 3 and 6\n\n--> 2/3 training data, 1/3 validation data", "_____no_output_____" ] ], [ [ "training_folds = []\nvalidation_folds = []\ntwo_week_incr = samples_total//6\n\nfor i in range(3):\n # Note that this is a temporal split since time was the first dimension in the original tensor\n first_incr = np.arange(samples_total//6*i, samples_total//6*(i+1))\n second_incr = np.arange(samples_total//6*(i+3), samples_total//6*(i+4))\n\n validation_folds.append(np.append(first_incr, second_incr))\n training_folds.append(np.arange(samples_total))\n training_folds[i] = np.delete(training_folds[i], validation_folds[i])", "_____no_output_____" ] ], [ [ "### Define the model", "_____no_output_____" ], [ "Activation function for the last layer", "_____no_output_____" ] ], [ [ "def lrelu(x):\n return nn.leaky_relu(x, alpha=0.01)", "_____no_output_____" ], [ "# Create the model\nmodel = Sequential()\n\n# First hidden layer\nmodel.add(Dense(units=64, activation='tanh', input_dim=no_of_features, \n kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n\n# Second hidden layer\nmodel.add(Dense(units=64, activation=nn.leaky_relu, kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n# model.add(Dropout(0.221)) # We drop 18% of the hidden nodes\nmodel.add(BatchNormalization())\n\n# Third hidden layer\nmodel.add(Dense(units=64, activation='tanh', kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n# model.add(Dropout(0.221)) # We drop 18% of the hidden nodes\n\n# Output layer\nmodel.add(Dense(1, activation='linear', kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))", "_____no_output_____" ] ], [ [ "### 3-fold cross-validation", "_____no_output_____" ] ], [ [ "# By decreasing timeout we make sure every fold gets the same amount of time\n# After all, data-loading took some time (Have 3 folds, 60 seconds/minute)\n# timeout = timeout - 1/3*1/60*(time.time() - t0)\ntimeout = timeout - 1/60*(time.time() - t0)\nt0 = time.time()\n\n#We loop through the folds\nfor i in range(3): \n \n filename = 'cross_validation_cell_based_fold_%d'%(i+1)\n \n #Standardize according to the fold\n scaler.fit(input_data[training_folds[i]])\n\n #Load the data for the respective fold and convert it to tf data\n input_train = scaler.transform(input_data[training_folds[i]])\n input_valid = scaler.transform(input_data[validation_folds[i]]) \n output_train = output_data[training_folds[i]]\n output_valid = output_data[validation_folds[i]]\n \n # Clear memory (Reduces memory requirement to 151 GB)\n del input_data, output_data, first_incr, second_incr, validation_folds, training_folds\n gc.collect()\n \n # Column-based: batchsize of 128\n # Cell-based: batchsize of at least 512\n # Shuffle is actually very important because we start off with the uppermost layers with clc=0 basically throughout\n # This can push us into a local minimum, preferrably yielding clc=0.\n # The size of the shuffle buffer significantly impacts RAM requirements! Do not increase to above 10000.\n # Possibly better to use .apply(tf.data.experimental.copy_to_device(\"/gpu:0\")) before prefetch\n # We might want to cache before shuffling, however it seems to slow down training\n # We do not repeat after shuffle, because the validation set should be evaluated after each epoch\n train_ds = tf.data.Dataset.zip((tf.data.Dataset.from_tensor_slices(input_train), \n tf.data.Dataset.from_tensor_slices(output_train))) \\\n .shuffle(10**5, seed=seed) \\\n .batch(batch_size=1028, drop_remainder=True) \\\n .prefetch(1)\n \n # Clear memory\n del input_train, output_train\n gc.collect()\n \n # No need to add prefetch.\n # tf data with batch_size=10**5 makes the validation evaluation 10 times faster\n valid_ds = tf.data.Dataset.zip((tf.data.Dataset.from_tensor_slices(input_valid), \n tf.data.Dataset.from_tensor_slices(output_valid))) \\\n .batch(batch_size=10**5, drop_remainder=True)\n \n # Clear memory (Reduces memory requirement to 151 GB)\n del input_valid, output_valid\n gc.collect()\n \n #Feed the model. Increase the learning rate by a factor of 2 when increasing the batch size by a factor of 4\n model.compile(\n optimizer=tf.keras.optimizers.Adam(learning_rate=0.000433, epsilon=0.1),\n loss=tf.keras.losses.MeanSquaredError()\n )\n \n #Train the model\n# time_callback = TimeOut(t0, timeout*(i+1))\n time_callback = TimeOut(t0, timeout)\n history = model.fit(train_ds, validation_data=valid_ds, epochs=epochs, verbose=2, \n callbacks=[time_callback])\n# history = model.fit(train_ds, epochs=epochs, validation_data=valid_ds, callbacks=[time_callback])\n\n #Save the model \n #Serialize model to YAML\n model_yaml = model.to_yaml()\n with open(os.path.join(path_model, filename+\".yaml\"), \"w\") as yaml_file:\n yaml_file.write(model_yaml)\n #Serialize model and weights to a single HDF5-file\n model.save(os.path.join(path_model, filename+'.h5'), \"w\")\n print('Saved model to disk')\n \n #Plot the training history\n if len(history.history['loss']) > len(history.history['val_loss']):\n del history.history['loss'][-1]\n pd.DataFrame(history.history).plot(figsize=(8,5))\n plt.grid(True)\n plt.ylabel('Mean Squared Error')\n plt.xlabel('Number of epochs')\n plt.savefig(os.path.join(path_figures, filename+'.pdf'))\n \n with open(os.path.join(path_model, filename+'.txt'), 'a') as file:\n file.write('Results from the %d-th fold\\n'%(i+1))\n file.write('Training epochs: %d\\n'%(len(history.history['val_loss'])))\n file.write('Weights restored from epoch: %d\\n\\n'%(1+np.argmin(history.history['val_loss'])))", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Performance metrics of Buy & Hold Strategy", "_____no_output_____" ], [ "The purpose of this notebook is to calculate performance metrics over the benchmark and compare it with results obtained in other papers. I will compare my results with two papers: \n- Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach - Kamil Korzeń, Robert Ślepaczuk \n- Predicting prices of S&P500 index using classical methods and recurrent neural networks - Mateusz Kijewski, Robert Ślepaczuk", "_____no_output_____" ] ], [ [ "# Settings for notebook visualization\nfrom IPython.core.interactiveshell import InteractiveShell\nInteractiveShell.ast_node_interactivity = 'all'\n%matplotlib inline\nfrom IPython.core.display import HTML\nHTML(\"\"\"<style>.output_png img {display: block;margin-left: auto;margin-right: auto;text-align: center;vertical-align: middle;} </style>\"\"\")", "_____no_output_____" ], [ "# Necessary imports\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib as plt\nimport quantstats as qs\nprint(\"Libraries imported correctly\")", "Libraries imported correctly\n" ], [ "os.chdir(\"/Users/Sergio/Documents/Master_QF/Thesis/Code/Algorithmic Strategies\")\n%run Functions.ipynb", "_____no_output_____" ] ], [ [ "# Load data", "_____no_output_____" ] ], [ [ "%run Functions.ipynb\ndf = get_sp500_data(from_local_file=True, save_to_file=False)\ndf['Market_daily_ret'] = df['Close'].pct_change()\n\ndf = df.loc['1990':'2020', ['Close', 'Market_daily_ret']]\n\ndf.head()\ndf['Close'].plot(title='SP500')", "_____no_output_____" ] ], [ [ "# Paper from Kamil: Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach", "_____no_output_____" ], [ "Data from 1991-01-03 to 2018-01-03 \nUses daily returns to calculate the metrics", "_____no_output_____" ] ], [ [ "from IPython.display import Image\nImage(filename='/Users/Sergio/Documents/Master_QF/Thesis/Papers/Performance metrics/K-Formulas.png')\n# Data from 1991-01-03:2018-01-03", "_____no_output_____" ] ], [ [ "We do the backtest of buy_and_hold strategy and compare metrics with the ones from the paper:", "_____no_output_____" ] ], [ [ "%run Functions.ipynb\ndf_1 = df.loc['1991-01-03':'2018-01-03', ['Close', 'Market_daily_ret']].copy()\ndf_1 = backtest_strat(df_1, buy_and_hold(df_1), commision=0)[0]\n\ndf_1.head(4)\n#df_1.tail(2)\n#df_1['Close'].plot(title='SP500', legend=True)", "_____no_output_____" ] ], [ [ "In this paper, the return from the first day of 1991 (January 2nd) seems to be not included. \nMetrics from paper:", "_____no_output_____" ] ], [ [ "from IPython.display import Image\nImage(filename='/Users/Sergio/Documents/Master_QF/Thesis/Papers/Performance metrics/K-Table.png')", "_____no_output_____" ], [ "metrics = ['AbsRet', 'ARC', 'IR', 'aSD', 'MD']\n\npaper_data = [[742.801, 8.222, 0.466, 17.652, 56.775]]\ndf_metrics = pd.DataFrame(data=paper_data, index=['Paper metrics'], columns=metrics)\n\nmetrics_row = calculate_performance_metrics(df_1, strat_name='Buy and Hold')\ndf_metrics = pd.concat([df_metrics, metrics_row], axis=0).drop_duplicates().round(3)\n\ndf_metrics", "_____no_output_____" ] ], [ [ "# Paper: \"Predicting prices of S&P500 index using classical methods and recurrent neural networks\"", "_____no_output_____" ], [ "Data from 2000-01-01 to 2020-05-02 \nUses log returns to calculate the metrics", "_____no_output_____" ] ], [ [ "from IPython.display import Image\nImage(filename='/Users/Sergio/Documents/Master_QF/Thesis/Papers/Performance metrics/M-Formulas.png')\n# Data from 2000 : 2020-05-02", "_____no_output_____" ] ], [ [ "We do the backtest of buy_and_hold strategy and compare metrics with the ones from the paper:", "_____no_output_____" ] ], [ [ "df_2 = df.loc['2000-01-01':'2020-05-02', ['Close', 'Market_daily_ret']].copy()\n\ndf_2 = backtest_strat(df_2, buy_and_hold(df_2), commision=0)[0]\n\ndf_2.head(4)\n#df_2.tail(2)\n#df_2['Close'].plot(title='SP500', legend=True)", "_____no_output_____" ] ], [ [ "Metrics from paper:", "_____no_output_____" ] ], [ [ "from IPython.display import Image\nImage(filename='/Users/Sergio/Documents/Master_QF/Thesis/Papers/Performance metrics/M-Table.png')\n# Data from 2000 : 2020-05-02", "_____no_output_____" ], [ "metrics = ['ARC', 'IR', 'aSD', 'MD', 'AMD', 'MLD', 'All Risk', 'ARCMD', 'ARCAMD', 'Num Trades', 'No signal']\npaper_data = [[3.23, 0.16, 19.95, 64.33, 17.34, 7.155, 15.92, 0.05, 0.18, 1, 0]]\ndf_metrics = pd.DataFrame(data=paper_data, index=['Paper metrics'], columns=metrics)\n\nmetrics_row = calculate_performance_metrics(df_2, strat_name='Buy and Hold')\ndf_metrics = pd.concat([df_metrics, metrics_row], axis=0).drop_duplicates().round(3)\n\ndf_metrics", "_____no_output_____" ] ], [ [ "## Demonstration of MD, MLD and AMD using quanstats library", "_____no_output_____" ], [ "Using data from paper 1 (1991-01-03 to 2018-01-03) \nFollowing code is to check drawdowns. \n- Paper 2 gave a MD of 64.33%, which seems to be wrong", "_____no_output_____" ] ], [ [ "dd = qs.stats.drawdown_details(qs.stats.to_drawdown_series(df_1['Market_cum_ret'])).sort_values(by='max drawdown', ascending=True)\ndd.head()", "_____no_output_____" ] ], [ [ "Maximum Loss Duration (in years):", "_____no_output_____" ] ], [ [ "dd = qs.stats.drawdown_details(qs.stats.to_drawdown_series(df_1['Market_cum_ret'])).sort_values(by='days', ascending=False)\ndd.insert(4, 'years', dd['days']/365.25)\ndd.head(5)", "_____no_output_____" ] ], [ [ "For MLD in years, I believe I should divide the number of days of MLD by 365.25, but result is more similar to the one from paper if I divide the number of days by 366. Kamil, how was it calculated on the paper?", "_____no_output_____" ] ], [ [ "from datetime import datetime\nmax_loss_dur = datetime(2007, 5, 30) - datetime(2000, 3, 27)\nprint(max_loss_dur.days)\nprint(\"{:.4f}\".format(max_loss_dur.days / 365))\nprint(\"{:.4f}\".format(max_loss_dur.days / 365.25))\nprint(\"{:.4f}\".format(max_loss_dur.days / 366))", "2620\n7.1781\n7.1732\n7.1585\n" ] ], [ [ "To calculate AMD, I group returns by year and do the mean of the MD of each year:", "_____no_output_____" ] ], [ [ "print(\"AMD = {:.3f} %\".format(abs(df_2['Market_daily_ret'].groupby(by=df_2.index.year).apply(qs.stats.max_drawdown).mean()*100)))\ndf_2['Market_daily_ret'].groupby(by=df_2.index.year).apply(qs.stats.max_drawdown).mul(100).to_frame(name='MD (%)').abs().round(3).T", "AMD = 16.520 %\n" ] ] ]

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[ [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Model Prediction Verification", "_____no_output_____" ], [ "This script demonstrates how to train a single model class, embed the model, and solve the optimization problem for *regression* problems (i.e., continuous outcome prediction). We fix a sample from our generated data and solve the optimization problem with all elements of $\\mathbf{x}$ equal to our data. In general, we might have some elements of $\\mathbf{x}$ that are fixed, called our \"conceptual variables,\" and the remaining indices are our decision variables. By fixing all elements of $\\mathbf{x}$, we can verify that the model prediction matches the original sklearn model.", "_____no_output_____" ], [ "## Load the relevant packages", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport math\nfrom sklearn.utils.extmath import cartesian\nimport time\nimport sys\nimport os\nimport time\n\nfrom sklearn.metrics import roc_auc_score, r2_score, mean_squared_error\nfrom sklearn.cluster import KMeans", "_____no_output_____" ], [ "import opticl\nfrom pyomo import environ\nfrom pyomo.environ import *", "_____no_output_____" ] ], [ [ "## Initialize data\nWe will work with a basic dataset from `sklearn`.", "_____no_output_____" ] ], [ [ "from sklearn.datasets import make_regression\nfrom sklearn.model_selection import train_test_split\nX, y = make_regression(n_samples=200, n_features = 20, random_state=1)\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n random_state=1)\nX_train = pd.DataFrame(X_train).add_prefix('col')\nX_test = pd.DataFrame(X_test).add_prefix('col')\nX_train", "_____no_output_____" ] ], [ [ "## Train the chosen model type", "_____no_output_____" ] ], [ [ "# alg = 'rf' \nalg = 'gbm'\ntask_type = 'continuous'", "_____no_output_____" ] ], [ [ "The user can optionally select a manual parameter grid for the cross-validation procedure. We implement a default parameter grid; see **run_MLmodels.py** for details on the tuned parameters. If you wish to use the default, leave ```parameter_grid = None``` (or do not specify any grid).", "_____no_output_____" ] ], [ [ "parameter_grid = None\n# parameter_grid = {'hidden_layer_sizes': [(5,),(10,)]}", "_____no_output_____" ], [ "s = 1\nversion = 'test'\noutcome = 'temp'\n\nmodel_save = 'results/%s/%s_%s_model.csv' % (alg, version, outcome)\n\nalg_run = alg if alg != 'rf' else 'rf_shallow'\nm, perf = opticl.run_model(X_train, y_train, X_test, y_test, alg_run, outcome, task = task_type, \n seed = s, cv_folds = 5, \n # The user can manually specify the parameter grid for cross-validation if desired\n parameter_grid = parameter_grid,\n save_path = model_save,\n save = False)", "------------- Initialize grid ----------------\n------------- Running model ----------------\nAlgorithm = gbm, metric = None\nsaving... results/gbm_temp_trained.pkl\n------------- Model evaluation ----------------\n-------------------training evaluation-----------------------\nTrain MSE: 4314.00082576947\nTrain R2: 0.8939305706172604\n-------------------testing evaluation-----------------------\nTest MSE: 17814.940522763252\nTest R2: 0.6170544988313675\n" ] ], [ [ "After training the model, we will save the trained model in the format needed for embedding the constraints. See **constraint_learning.py** for the specific format that is extracted per method. We also save the performance of the model to use in the automated model selection pipeline (if desired).\n\nWe also create the save directory if it does not exist.\n\n", "_____no_output_____" ] ], [ [ "if not os.path.exists('results/%s/' % alg):\n os.makedirs('results/%s/' % alg)\n \nconstraintL = opticl.ConstraintLearning(X_train, y_train, m, alg)\nconstraint_add = constraintL.constraint_extrapolation(task_type)\nconstraint_add.to_csv(model_save, index = False)\n\nperf.to_csv('results/%s/%s_%s_performance.csv' % (alg, version, outcome), index= False)", "_____no_output_____" ], [ "constraint_add", "_____no_output_____" ] ], [ [ "### Check: what should the result be for our sample observation, if all x are fixed?", "_____no_output_____" ], [ "#### Choose sample to test\nThis will be the observation (\"patient\") that we feed into the optimization model.", "_____no_output_____" ] ], [ [ "sample_id = 1\nsample = X_train.loc[sample_id:sample_id,:].reset_index(drop = True)", "_____no_output_____" ] ], [ [ "Calculate model prediction directly in sklearn.", "_____no_output_____" ] ], [ [ "m.predict(sample)", "_____no_output_____" ] ], [ [ "## Optimization formulation\nWe will embed the model trained above. The model could also be selected using the model selection pipeline, which we demonstrate in the WFP example script.\n\nIf manually specifying the model, as we are here, the key elements of the ``model_master`` dataframe are:\n- model_type: algorithm name.\n- outcome: name of outcome of interest; this is relevant in the case of multiple learned outcomes.\n- save_path: file name of the extracted model.\n- objective: the weight of the objective if it should be included as an additive term in the objective. A weight of 0 omits it from the objective entirely.\n- lb/ub: the lower (or upper) bound that we wish to apply to the learned outcome. If there is no bound, it should be set to ``None``.\n\nIn this case, we set the outcome to be our only objective term, which will allow us to verify that the predictions are consistent between the embedded model and the sklearn prediction function.", "_____no_output_____" ] ], [ [ "model_master = pd.DataFrame(columns = ['model_type','outcome','save_path','lb','ub','objective'])\n\nmodel_master.loc[0,'model_type'] = alg\nmodel_master.loc[0,'save_path'] = 'results/%s/%s_%s_model.csv' % (alg, version, outcome)\nmodel_master.loc[0,'outcome'] = outcome\nmodel_master.loc[0,'objective'] = 1\nmodel_master.loc[0,'ub'] = None\nmodel_master.loc[0,'lb'] = None\nmodel_master.loc[0,'task'] = task_type\nmodel_master['SCM_counterfactuals'] = None\nmodel_master['features'] = [[col for col in X_train.columns]]", "_____no_output_____" ] ], [ [ "#### Solve with Pyomo", "_____no_output_____" ] ], [ [ "model_pyo = ConcreteModel()\n\n## We will create our x decision variables, and fix them all to our sample's values for model verification.\nN = X_train.columns\nmodel_pyo.x = Var(N, domain=Reals)\n\ndef fix_value(model_pyo, index):\n return model_pyo.x[index] == sample.loc[0,index]\n\nmodel_pyo.Constraint1 = Constraint(N, rule=fix_value)\n\n## Specify any non-learned objective components - none here \nmodel_pyo.OBJ = Objective(expr=0, sense=minimize)", "_____no_output_____" ], [ "final_model_pyo = opticl.optimization_MIP(model_pyo, model_pyo.x, model_master, X_train, tr = False)\n# final_model_pyo.pprint()\nopt = SolverFactory('gurobi')\nresults = opt.solve(final_model_pyo) ", "Embedding objective function for temp\n" ] ], [ [ "### Check for equality between sklearn and embedded models", "_____no_output_____" ] ], [ [ "print(\"True outcome: %.3f\" % m.predict(sample)[0])\nprint(\"Pyomo output: %.3f\" % final_model_pyo.OBJ())", "True outcome: 182.759\nPyomo output: 182.759\n" ] ] ]

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[ [ "markdown", "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "[View in Colaboratory](https://colab.research.google.com/github/renatopcamara/Colaboratory/blob/master/colab_drive.ipynb)", "_____no_output_____" ] ], [ [ "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n!apt-get update -qq 2>&1 > /dev/null\n!apt-get -y install -qq google-drive-ocamlfuse fuse\nfrom google.colab import auth\nauth.authenticate_user()\nfrom oauth2client.client import GoogleCredentials\ncreds = GoogleCredentials.get_application_default()\nimport getpass\n!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\nvcode = getpass.getpass()\n!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}", "gpg: keybox '/tmp/tmpdpc2ug7s/pubring.gpg' created\ngpg: /tmp/tmpdpc2ug7s/trustdb.gpg: trustdb created\ngpg: key AD5F235DF639B041: public key \"Launchpad PPA for Alessandro Strada\" imported\ngpg: Total number processed: 1\ngpg: imported: 1\nWarning: apt-key output should not be parsed (stdout is not a terminal)\nPlease, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n··········\nPlease, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\nPlease enter the verification code: Access token retrieved correctly.\n" ], [ "# Create a directory and mount Google Drive using that directory.\n!mkdir -p drive\n!google-drive-ocamlfuse drive\n\nprint ('Files in Drive:')\n!ls drive/\n\n# Create a file in Drive.\n!echo \"This newly created file will appear in your Drive file list.\" > drive/created.txt", "fuse: mountpoint is not empty\r\nfuse: if you are sure this is safe, use the 'nonempty' mount option\r\nFiles in Drive:\nAula08.zip\naula 5.pdf\naula 6.pdf\nClasse19-06.rar\nClassroom\nColab Notebooks\ncreated.txt\nDeep Learning.rar\nEfectos olvidados en el diseño de una campaña proselitista.pdf\nEnunciados.pdf\nestoque demanda fuzzy.pdf\nExercício_1.pdf\nICA\nINTEL\nLAB IBM\nPeriodos.docx\nPrograma Publicado.docx\nProjeto Data Mining.pptx\nroteirizacao.xlsx\nSample upload.txt\nScripts aulas 1 e 2.rar\nSparkExample.pdf\nteste.ipynb\n" ], [ "", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n%matplotlib inline ", "_____no_output_____" ], [ "filename = '../colliders.csv'\ndata = np.loadtxt(filename,delimiter=',',dtype='Float64',skiprows=2)\nprint(data)", "[[-310.2389 -439.2315 85.5 5. 5. 85.5 ]\n [-300.2389 -439.2315 85.5 5. 5. 85.5 ]\n [-290.2389 -439.2315 85.5 5. 5. 85.5 ]\n ...\n [ 257.8061 425.1645 1.75852 1.292725 1.292725 1.944791]\n [ 293.9967 368.3391 3.557666 1.129456 1.129456 3.667319]\n [ 281.5162 354.4156 4.999351 1.053772 1.053772 4.950246]]\n" ], [ "import os, sys\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath('planning_utils_from_seed_project.py'))))\nfrom planning_utils_from_seed_project import create_grid, a_star, heuristic, Action\nfrom udacidrone.frame_utils import global_to_local, local_to_global", "_____no_output_____" ], [ "TARGET_ALTITUDE = 5\nSAFETY_DISTANCE = 5\nglobal_home = np.array([-122.39745, 37.79248, 0.0])\nprint(f'Global Home => [lon, lat, alt] : {global_home}')\nglobal_position = np.array([-122.3974512, 37.7924799, 0.147])\nprint(f'Global Position => [lon, lat, alt] : {global_position}')\nlocal_position = global_to_local(global_position, global_home)\nprint(f'Local Position => [north, east, down] : {local_position}')", "Global Home => [lon, lat, alt] : [-122.39745 37.79248 0. ]\nGlobal Position => [lon, lat, alt] : [-122.3974512 37.7924799 0.147 ]\nLocal Position => [north, east, down] : [-0.01177589 -0.10558296 -0.147 ]\n" ], [ "grid, north_offset, east_offset = create_grid(data, TARGET_ALTITUDE, SAFETY_DISTANCE)\nprint(f'Grid offset : ({north_offset}, {east_offset})')\ngrid_start_north = int(np.ceil(local_position[0] - north_offset))\ngrid_start_east = int(np.ceil(local_position[1] - east_offset))\ngrid_start = (grid_start_north, grid_start_east)\nprint(f'Grid star : {grid_start}')", "Grid offset : (-316, -445)\nGrid star : (316, 445)\n" ], [ "grid_goal_north = grid_start_north + 400\ngrid_goal_east = grid_start_east + 400\ngrid_goal = (grid_goal_north, grid_goal_east)\nprint(f'Grid goal : {grid_goal}')\n\ngoal_local_position_north = grid_goal_north + north_offset\ngoal_local_position_east = grid_goal_east + east_offset\ngoal_local_position = np.array([goal_local_position_north, goal_local_position_east, local_position[2]])\nprint(f'Goal Local Position => [north, east, down] : {goal_local_position}')\n\ngoal_global_position = local_to_global(goal_local_position, global_home)\nprint(f'Global Global Position => [lon, lat, alt] : {goal_global_position}')\n\n", "Grid goal : (716, 845)\nGoal Local Position => [north, east, down] : [ 4.00e+02 4.00e+02 -1.47e-01]\nGlobal Global Position => [lon, lat, alt] : [-122.39287756 37.79606176 0.147 ]\n" ], [ "def visualize_path(g_start, g_goal, g, n_offset, e_offset):\n \"\"\"\n Visualize the path to get from `g_start` to `g_goal` defined by `waypoints` and the grid `g`\n \"\"\"\n path, cost = a_star(g, heuristic, grid_start, grid_goal)\n print(f'Path cost: {cost}')\n \n waypoints = np.array([[p[0] + n_offset, p[1] + e_offset, TARGET_ALTITUDE, 0] for p in path])\n print(f'Waypoint count : {waypoints.shape[0]}')\n \n fig = plt.figure(figsize=(20,10)) \n plt.imshow(g, origin='lower') \n plt.plot(g_start[1] + waypoints[:, 1], g_start[0] + waypoints[:, 0], 'g')\n plt.plot(g_start[1] + waypoints[:, 1], g_start[0] + waypoints[:, 0], 'oc')\n plt.plot(g_start[1], g_start[0], 'r+') \n plt.plot(g_goal[1], g_goal[0], 'r+')\n plt.xlabel('EAST')\n plt.ylabel('NORTH')\n plt.show()\n \n return waypoints\n \nwps = visualize_path(grid_start, grid_goal, grid, north_offset, east_offset)", "Found a path.\nPath cost: 448160.0645114182\nWaypoint count : 1283\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport pandas as pd\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ], [ "# data\ndef create_data():\n iris = load_iris()\n df = pd.DataFrame(iris.data, columns=iris.feature_names)\n df['label'] = iris.target\n df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n data = np.array(df.iloc[:100, [0, 1, -1]])\n for i in range(len(data)):\n if data[i,-1] == 0:\n data[i,-1] = -1\n # print(data)\n return data[:,:2], data[:,-1]", "_____no_output_____" ], [ "X, y = create_data()\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)", "_____no_output_____" ], [ "plt.scatter(X[:50,0],X[:50,1], label='0')\nplt.scatter(X[50:,0],X[50:,1], label='1')\nplt.legend()", "_____no_output_____" ], [ "# AdaBoost\nclass AdaBoost:\n \n def __init__(self, n_estimators=50, learning_rate=1.0):\n self.clf_num = n_estimators\n self.learning_rate = learning_rate\n \n def init_args(self, datasets, labels):\n \n self.X = datasets\n self.Y = labels\n self.M, self.N = datasets.shape\n \n # set of weak clfs\n self.clf_sets = []\n \n # initialize weights\n self.weights = [1.0 / self.M] * self.M\n \n # coeff of G(x) alpha\n self.alpha = []\n \n def _G(self, features, labels, weights):\n m = len(features)\n error = 1000000000.0 # INF\n best_v = 0.0\n # 1-dim feature\n features_min = min(features)\n features_max = max(features)\n n_step = (features_max - features_min + self.learning_rate) // self.learning_rate\n direct, compare_array = None, None\n for i in range(1, int(n_step)):\n v = features_min + self.learning_rate * i \n if v not in features:\n # calculate the wrong clf\n compare_array_positive = np.array(\n [1 if features[k] > v else -1 for k in range(m)])\n weight_error_positive = sum([\n weights[k] for k in range(m)\n if compare_array_positive[k] != labels[k]\n ])\n\n compare_array_nagetive = np.array(\n [-1 if features[k] > v else 1 for k in range(m)])\n weight_error_nagetive = sum([\n weights[k] for k in range(m)\n if compare_array_nagetive[k] != labels[k]\n ])\n\n if weight_error_positive < weight_error_nagetive:\n weight_error = weight_error_positive\n _compare_array = compare_array_positive\n direct = 'positive'\n else:\n weight_error = weight_error_nagetive\n _compare_array = compare_array_nagetive\n direct = 'nagetive'\n\n # print('v:{} error:{}'.format(v, weight_error))\n if weight_error < error:\n error = weight_error\n compare_array = _compare_array\n best_v = v\n return best_v, direct, error, compare_array\n \n def _alpha(self, error):\n return 0.5 * np.log((1 - error) / error)\n \n def _Z(self, weights, a, clf):\n return sum([\n weights[i] * np.exp(-1 * a * self.Y[i] * clf[i])\n for i in range(self.M)\n ])\n \n # update weights\n def _w(self, a, clf, Z):\n for i in range(self.M):\n self.weights[i] = self.weights[i] * np.exp(\n -1 * a * self.Y[i] * clf[i]) / Z\n \n def G(self, x, v, direct):\n if direct == 'positive':\n return 1 if x > v else -1\n else:\n return -1 if x > v else 1\n \n def fit(self, X, y):\n self.init_args(X, y)\n\n for epoch in range(self.clf_num):\n best_clf_error, best_v, clf_result = 100000, None, None\n # choose the smallest error according to features\n for j in range(self.N):\n features = self.X[:, j]\n v, direct, error, compare_array = self._G(\n features, self.Y, self.weights)\n\n if error < best_clf_error:\n best_clf_error = error\n best_v = v\n final_direct = direct\n clf_result = compare_array\n axis = j\n\n if best_clf_error == 0:\n break\n\n a = self._alpha(best_clf_error)\n self.alpha.append(a)\n self.clf_sets.append((axis, best_v, final_direct))\n Z = self._Z(self.weights, a, clf_result)\n self._w(a, clf_result, Z)\n\n def predict(self, feature):\n result = 0.0\n for i in range(len(self.clf_sets)):\n axis, clf_v, direct = self.clf_sets[i]\n f_input = feature[axis]\n result += self.alpha[i] * self.G(f_input, clf_v, direct)\n # sign\n return 1 if result > 0 else -1\n\n def score(self, X_test, y_test):\n right_count = 0\n for i in range(len(X_test)):\n feature = X_test[i]\n if self.predict(feature) == y_test[i]:\n right_count += 1\n\n return right_count / len(X_test)", "_____no_output_____" ], [ "clf = AdaBoost(n_estimators=10, learning_rate=0.2)\nclf.fit(X_train, y_train)\nclf.score(X_test, y_test)", "_____no_output_____" ], [ "# 100次结果\nresult = []\nfor i in range(1, 101):\n X, y = create_data()\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n clf = AdaBoost(n_estimators=100, learning_rate=0.2)\n clf.fit(X_train, y_train)\n r = clf.score(X_test, y_test)\n if i % 10 == 0:\n print(' * Processed {}/100 => Batch Score: {:.3f}'.format(i, r))\n result.append(r)\n\nprint('Average Score: {:.3f}%'.format(sum(result)))", " * Processed 10/100 => Batch Score: 0.667\n * Processed 20/100 => Batch Score: 0.576\n * Processed 30/100 => Batch Score: 0.939\n * Processed 40/100 => Batch Score: 0.545\n * Processed 50/100 => Batch Score: 0.939\n * Processed 60/100 => Batch Score: 0.455\n * Processed 70/100 => Batch Score: 0.697\n * Processed 80/100 => Batch Score: 0.727\n * Processed 90/100 => Batch Score: 0.636\n * Processed 100/100 => Batch Score: 0.515\nAverage Score: 66.394%\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/felipemoreia/Awari/blob/master/Exercicios_unidade_4_Manipulacao_de_Dados_Parte_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Awari - Data Science\n\n## Exercícios Unidade 4 - Parte 1", "_____no_output_____" ], [ "Neste Jupyter notebook você irá resolver uma exercícios utilizando a linguagem Python e a biblioteca Pandas.\n\nTodos os datasets utilizados nos exercícios estão salvos na pasta *datasets*.\n\nTodo o seu código deve ser executado neste Jupyter Notebook. Por fim, se desejar, revise as respostas com o seu mentor.", "_____no_output_____" ] ], [ [ "import pandas as pd", "_____no_output_____" ] ], [ [ "### Passo 1. Importando os dados\n\nCarregue os dados salvos no arquivo ***datasets/users_dataset.txt***.\n\nEsse arquivo possui um conjunto de dados de trabalhadores com 5 colunas separadas pelo símbolo \"|\" (pipe) e 943 linhas.\n\n*Dica: utilize a função read_csv com os parâmetros sep e index_col*", "_____no_output_____" ] ], [ [ "users = pd.read_csv('users_dataset.txt',sep='|', index_col='user_id')", "_____no_output_____" ] ], [ [ "### Passo 2. Mostre as 25 primeiras linhas do dataset.\n\n*Dica: use a função head do DataFrame*", "_____no_output_____" ] ], [ [ "users.head(25)", "_____no_output_____" ] ], [ [ "### Passo 3. Mostre as 10 últimas linhas", "_____no_output_____" ] ], [ [ "users.tail(10)", "_____no_output_____" ] ], [ [ "### Passo 4. Qual o número de linhas e colunas do DataFrame?", "_____no_output_____" ] ], [ [ "users.shape", "_____no_output_____" ] ], [ [ "### Passo 5. Mostre o nome de todas as colunas.", "_____no_output_____" ] ], [ [ "users.columns", "_____no_output_____" ] ], [ [ "### Passo 6. Qual o tipo de dado de cada columa?", "_____no_output_____" ] ], [ [ "users.dtypes", "_____no_output_____" ] ], [ [ "### Passo 7. Mostre os dados da coluna *occupation*.", "_____no_output_____" ] ], [ [ "users['occupation']", "_____no_output_____" ] ], [ [ "### Passo 8. Quantas ocupações diferentes existem neste dataset?", "_____no_output_____" ] ], [ [ "len(users['occupation'].unique())", "_____no_output_____" ] ], [ [ "### Passo 9. Qual a ocupação mais frequente?", "_____no_output_____" ] ], [ [ "users['occupation'].value_counts()", "_____no_output_____" ] ], [ [ "### Passo 10. Qual a idade média dos usuários?", "_____no_output_____" ] ], [ [ "users['age'].mean()", "_____no_output_____" ] ], [ [ "Referências: https://github.com/guipsamora/pandas_exercises\n\n### Awari - <a href=\"https://awari.com.br/\"> awari.com.br</a>", "_____no_output_____" ] ] ]

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