diff options
Diffstat (limited to 'scripts/tensorflow')
| -rw-r--r-- | scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb | 1007 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v2-norm.ipynb | 11720 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction-v2.ipynb | 11361 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction.ipynb | 5533 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction_ext.ipynb | 5493 |
5 files changed, 12727 insertions, 22387 deletions
diff --git a/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb b/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb new file mode 100644 index 0000000..a66b942 --- /dev/null +++ b/scripts/tensorflow/prediction-v2-ext-Padel-2D.ipynb @@ -0,0 +1,1007 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8080, 1173)\n", + "(8080, 1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (517,531,534,542,545,547,573,576,599,601,604,607,620,627,629,636,650,653,661,664,666,692,695,718,720,723,726,739,746,748,755,769,772,780,783,785,805,811,814,819,837,845,901,1005) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + } + ], + "source": [ + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", + "y_f_ext = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "x_pad = pd.read_csv('/home/drewe/notebooks/genotox/PA_Padel_2D_m2.csv', sep=';', decimal=',')\n", + "\n", + "X_ext = pd.read_csv(X_f_ext,sep=';')\n", + "del X_ext['Name']\n", + "selected_cols = list(set(x_pad.columns.tolist()).intersection(set(X_ext.columns.tolist())))\n", + "\n", + "X_ext = np.float64(X_ext[selected_cols].values)\n", + "y_ext = np.float64(pd.read_csv(y_f_ext).values)\n", + "x_pad = np.float64(x_pad[selected_cols].values)\n", + "print(X_ext.shape)\n", + "print(y_ext.shape)\n", + "\n", + "names = pd.read_csv('/home/drewe/notebooks/genotox/PA_Padel_2D_m2.csv', sep=';')['Name'].values\n", + "#X_norm = QuantileTransformer(output_distribution='uniform').fit_transform(X.values.T).T\n", + "\n", + "\n", + "trnsf = sklearn.preprocessing.QuantileTransformer( output_distribution='uniform')\n", + "trnsf.fit(X_ext)\n", + "\n", + "X = trnsf.transform(X_ext)\n", + "x_pad = trnsf.transform(x_pad)\n", + "y = y_ext[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + ">>\n", + ".\n", + ">>\n", + ".\n", + ">>\n", + ".\n", + ">>\n", + ".\n" + ] + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "\n", + "keras.backend.clear_session()\n", + "prbs=[]\n", + "for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + "\n", + " # Fit the model\n", + " history = model.fit(X, y, epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " probas_ = model.predict(x_pad)\n", + " prbs.append(probas_)\n", + "# Average the predictions\n", + "probas_ = np.mean(np.hstack(prbs), axis=1)\n", + "results = probas_\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3-ext-Padel-2D.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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izzkWGDoM1BD7wD4A+wDsA+jRB2O/uStJ2jc5ZYMkNWbVBb/TQIzUB3+e5BtJbkvy+SRHr0Sd07RUHyxod26SSrLffcJjlD5I8srutXB7kk8sd43TNsLvwmySLyT5Wvf7sH4l6pyWJFuS7Eqyfcj+JHl/1z+3JTlppANX1ap5MP8m8beBXwUOBm4FjlvU5k+AS7rlDcAVK133CvTBqcCTu+ULWuyDrt0hwPXAjcDcSte9Aq+DY4CvAU/v1p+10nWvQB9sBi7olo8D7lnpuifcBy8BTgK2D9m/HrgaCPAi4MujHHe1XfE7DcQIfVBVX6iqx7rVG5m/h2J/MsrrAODdwN8BP1nO4pbJKH3wx8AHquphgKratcw1TtsofVDAr3TLT+OJ9xLt06rqeobc+9Q5B/hYzbsRODTJ4Usdd7UFv9NAjNYHC53P/F/8/cmSfZDkBcBRVfVvy1nYMhrldfA84HlJ/jPJjUnOWLbqlscofXAR8KokO5j/hOFbaMve5gXQ8+OcUzDoyn3xx45GabMvG/n7S/IqYA747alWtPz22AdJnsT8bK+vXa6CVsAor4MDmR/uOYX5f/V9McnxVfWDKde2XEbpg43AZVX13iQvZv6+oeOr6ufTL29VGCsPV9sV/95MA8FS00Dso0bpA5K8FHgHcHZV/c8y1bZcluqDQ4Djgf9Icg/zY5tX7Wdv8I76u/CZqvrfqvpv5ue3OmaZ6lsOo/TB+cA/AVTVDcAvMz+HTStGyovFVlvwOw3ECH3QDXN8iPnQ39/GdWGJPqiqR6pqTVWtraq1zL/PcXZVbVuZcqdilN+FTzP/Rj9J1jA/9HP3slY5XaP0wXeB0wCS/Abzwf/Asla5sq4CXtN9uudFwCNVdd9ST1pVQz3lNBCj9sF7gKcCn+re1/5uVZ29YkVP2Ih9sF8bsQ+uAU5P8g3gZ8Bf1ICZb/dVI/bB24APJ/kz5oc4Xrs/XQgmuZz5obw13fsYFwIHAVTVJcy/r7EeuAt4DHjdSMfdj/pIkjSC1TbUI0maMoNfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TG/B9x4xPmUSVEdwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results,100, color='red', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6169 - acc: 0.6601\n", + "Epoch 2/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6184 - acc: 0.6558\n", + "Epoch 3/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6161 - acc: 0.6590\n", + "Epoch 4/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6197 - acc: 0.6550\n", + "Epoch 5/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6150 - acc: 0.6579\n", + "Epoch 6/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6167 - acc: 0.6572\n", + "Epoch 7/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6193 - acc: 0.6522\n", + "Epoch 8/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6150 - acc: 0.6620\n", + "Epoch 9/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6164 - acc: 0.6589\n", + "Epoch 10/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6147 - acc: 0.6610\n", + "Epoch 11/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6145 - acc: 0.6562\n", + "Epoch 12/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6162 - acc: 0.6575\n", + "Epoch 13/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6146 - acc: 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0.6642\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6171 - acc: 0.6542\n", + "Epoch 2/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6166 - acc: 0.6593\n", + "Epoch 3/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6170 - acc: 0.6568\n", + "Epoch 4/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6180 - acc: 0.6577\n", + "Epoch 5/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6155 - acc: 0.6580\n", + "Epoch 6/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6177 - acc: 0.6583\n", + "Epoch 7/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6147 - acc: 0.6584\n", + "Epoch 8/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6169 - acc: 0.6561\n", + "Epoch 9/50\n", + "8080/8080 [==============================] - 0s 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[==============================] - 0s 35us/step - loss: 0.6129 - acc: 0.6619\n", + "Epoch 21/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6172 - acc: 0.6563\n", + "Epoch 22/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6136 - acc: 0.6636\n", + "Epoch 23/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6138 - acc: 0.6652\n", + "Epoch 24/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6140 - acc: 0.6603\n", + "Epoch 25/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6128 - acc: 0.6604\n", + "Epoch 26/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6113 - acc: 0.6636\n", + "Epoch 27/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6131 - acc: 0.6640\n", + "Epoch 28/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6125 - acc: 0.6592\n", + "Epoch 29/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6124 - acc: 0.6592\n", + "Epoch 30/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6137 - acc: 0.6609\n", + "Epoch 31/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6147 - acc: 0.6571\n", + "Epoch 32/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6145 - acc: 0.6587\n", + "Epoch 33/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6117 - acc: 0.6637\n", + "Epoch 34/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6093 - acc: 0.6646\n", + "Epoch 35/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6118 - acc: 0.6613\n", + "Epoch 36/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6105 - acc: 0.6605\n", + "Epoch 37/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6113 - acc: 0.6694\n", + "Epoch 38/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6102 - acc: 0.6613\n", + "Epoch 39/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6120 - acc: 0.6587\n", + "Epoch 40/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6142 - acc: 0.6579\n", + "Epoch 41/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6108 - acc: 0.6603\n", + "Epoch 42/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6090 - acc: 0.6627\n", + "Epoch 43/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6089 - acc: 0.6642\n", + "Epoch 44/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6101 - acc: 0.6635\n", + "Epoch 45/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6106 - acc: 0.6615\n", + "Epoch 46/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6108 - acc: 0.6595\n", + "Epoch 47/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6106 - acc: 0.6595\n", + "Epoch 48/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6118 - acc: 0.6605\n", + "Epoch 49/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6105 - acc: 0.6639\n", + "Epoch 50/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6099 - acc: 0.6632\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6169 - acc: 0.6575\n", + "Epoch 2/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6150 - acc: 0.6625\n", + "Epoch 3/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6166 - acc: 0.6583\n", + "Epoch 4/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6130 - acc: 0.6609\n", + "Epoch 5/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6135 - acc: 0.6609\n", + "Epoch 6/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6166 - acc: 0.6599\n", + "Epoch 7/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6159 - acc: 0.6601\n", + "Epoch 8/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6176 - acc: 0.6538\n", + "Epoch 9/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6129 - acc: 0.6620\n", + "Epoch 10/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6139 - acc: 0.6574\n", + "Epoch 11/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6157 - acc: 0.6621\n", + "Epoch 12/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6133 - acc: 0.6645\n", + "Epoch 13/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6139 - acc: 0.6623\n", + "Epoch 14/50\n", + "8080/8080 [==============================] - 0s 40us/step - loss: 0.6141 - acc: 0.6592\n", + "Epoch 15/50\n", + "8080/8080 [==============================] - 0s 50us/step - loss: 0.6149 - acc: 0.6613\n", + "Epoch 16/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6122 - acc: 0.6613\n", + "Epoch 17/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6137 - acc: 0.6604\n", + "Epoch 18/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6147 - acc: 0.6592\n", + "Epoch 19/50\n", + "8080/8080 [==============================] - 0s 39us/step - loss: 0.6145 - acc: 0.6618\n", + "Epoch 20/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6138 - acc: 0.6594\n", + "Epoch 21/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6126 - acc: 0.6573\n", + "Epoch 22/50\n", + "8080/8080 [==============================] - 0s 35us/step - loss: 0.6120 - acc: 0.6624\n", + "Epoch 23/50\n", + "8080/8080 [==============================] - 0s 61us/step - loss: 0.6112 - acc: 0.6636\n", + "Epoch 24/50\n", + "8080/8080 [==============================] - 0s 44us/step - loss: 0.6115 - acc: 0.6653\n", + "Epoch 25/50\n", + "8080/8080 [==============================] - 0s 36us/step - loss: 0.6153 - acc: 0.6599\n", + "Epoch 26/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6142 - acc: 0.6589\n", + "Epoch 27/50\n", + "8080/8080 [==============================] - 0s 40us/step - loss: 0.6139 - acc: 0.6605\n", + "Epoch 28/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6146 - acc: 0.6573\n", + "Epoch 29/50\n", + "8080/8080 [==============================] - 0s 39us/step - loss: 0.6104 - acc: 0.6656\n", + "Epoch 30/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6112 - acc: 0.6662\n", + "Epoch 31/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6109 - acc: 0.6640\n", + "Epoch 32/50\n", + "8080/8080 [==============================] - 0s 37us/step - loss: 0.6139 - acc: 0.6624\n", + "Epoch 33/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6146 - acc: 0.6631\n", + "Epoch 34/50\n", + "8080/8080 [==============================] - 0s 46us/step - loss: 0.6145 - acc: 0.6625\n", + "Epoch 35/50\n", + "8080/8080 [==============================] - 0s 47us/step - loss: 0.6099 - acc: 0.6631\n", + "Epoch 36/50\n", + "8080/8080 [==============================] - 0s 51us/step - loss: 0.6102 - acc: 0.6640\n", + "Epoch 37/50\n", + "8080/8080 [==============================] - 0s 50us/step - loss: 0.6119 - acc: 0.6592\n", + "Epoch 38/50\n", + "8080/8080 [==============================] - 0s 39us/step - loss: 0.6109 - acc: 0.6623\n", + "Epoch 39/50\n", + "8080/8080 [==============================] - 0s 48us/step - loss: 0.6128 - acc: 0.6577\n", + "Epoch 40/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6111 - acc: 0.6636\n", + "Epoch 41/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6096 - acc: 0.6634\n", + "Epoch 42/50\n", + "8080/8080 [==============================] - 0s 39us/step - loss: 0.6104 - acc: 0.6606\n", + "Epoch 43/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6090 - acc: 0.6645\n", + "Epoch 44/50\n", + "8080/8080 [==============================] - 0s 42us/step - loss: 0.6103 - acc: 0.6641\n", + "Epoch 45/50\n", + "8080/8080 [==============================] - 0s 45us/step - loss: 0.6112 - acc: 0.6630\n", + "Epoch 46/50\n", + "8080/8080 [==============================] - 0s 43us/step - loss: 0.6098 - acc: 0.6649\n", + "Epoch 47/50\n", + "8080/8080 [==============================] - 0s 45us/step - loss: 0.6103 - acc: 0.6624\n", + "Epoch 48/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8080/8080 [==============================] - 0s 39us/step - loss: 0.6118 - acc: 0.6597\n", + "Epoch 49/50\n", + "8080/8080 [==============================] - 0s 38us/step - loss: 0.6102 - acc: 0.6611\n", + "Epoch 50/50\n", + "8080/8080 [==============================] - 0s 43us/step - loss: 0.6083 - acc: 0.6649\n" + ] + } + ], + "source": [ + "#Logistic regression (SGD)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "keras.backend.clear_session()\n", + "prbs=[]\n", + "for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + "\n", + " # Fit the model\n", + " history = model.fit(X, y, epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X, y, epochs=50, batch_size=64, verbose=1)\n", + "\n", + " # evaluate the model\n", + " probas_ = model.predict(x_pad)\n", + " prbs.append(probas_)\n", + "# Average the predictions\n", + "probas_ = np.mean(np.hstack(prbs), axis=1)\n", + "results = probas_" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3-ext-Padel-2D.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results,100, color='red', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + } + ], + "source": [ + "#Logistic regression (scikit)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "keras.backend.clear_session()\n", + "prbs=[]\n", + "model = LogisticRegression(random_state=0)\n", + "model.fit(X, y)\n", + "probas_ = model.predict_proba(x_pad)[:, 1]\n", + "results = probas_\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3-ext-Padel-2D.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results,100, color='red', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "keras.backend.clear_session()\n", + "prbs=[]\n", + "model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", + "# Fit the model\n", + "model.fit(X, y)\n", + "\n", + "\n", + "probas_ = model.predict_proba(x_pad)[:, 1]\n", + "results = probas_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3-ext-Padel-2D.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results,100, color='red', alpha=0.5)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/tensorflow/prediction-v2-norm.ipynb b/scripts/tensorflow/prediction-v2-norm.ipynb new file mode 100644 index 0000000..a013912 --- /dev/null +++ b/scripts/tensorflow/prediction-v2-norm.ipynb @@ -0,0 +1,11720 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", + "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X = pd.read_csv(X_f).values[:,:-1]\n", + "y = pd.read_csv(y_f).values\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "y = y[ix]\n", + "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", + "X = sklearn.preprocessing.quantile_transform(X, axis=0, output_distribution='uniform', copy=True)\n", + "y = y[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.4109 - acc: 0.8058\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3981 - acc: 0.8133\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.4038 - acc: 0.8046\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3869 - acc: 0.8196\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3724 - acc: 0.8258\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3895 - acc: 0.8194\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3761 - acc: 0.8219\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 39us/step - loss: 0.3636 - acc: 0.8332\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3596 - acc: 0.8288\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3411 - acc: 0.8392\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3469 - acc: 0.8391\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3447 - acc: 0.8441\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3425 - acc: 0.8409\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 39us/step - loss: 0.3313 - acc: 0.8436\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3159 - acc: 0.8545\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3230 - acc: 0.8584\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3212 - acc: 0.8553\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2947 - acc: 0.8688\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3099 - acc: 0.8623\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3064 - acc: 0.8628\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2890 - acc: 0.8711\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3001 - acc: 0.8674\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3063 - acc: 0.8599\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2808 - acc: 0.8740\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2679 - acc: 0.8782\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2628 - acc: 0.8859\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2655 - acc: 0.8805\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2608 - acc: 0.8827\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2845 - acc: 0.8700\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2455 - acc: 0.8903\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2503 - acc: 0.8893\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2449 - acc: 0.8860\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2559 - acc: 0.8886\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2434 - acc: 0.8908\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2328 - acc: 0.8958\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2326 - acc: 0.8981\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2380 - acc: 0.8944\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2162 - acc: 0.9064\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2256 - acc: 0.9006\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2275 - acc: 0.8959\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2220 - acc: 0.9025\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2103 - acc: 0.9076\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2416 - acc: 0.8926\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2079 - acc: 0.9051\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2303 - acc: 0.8951\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2055 - acc: 0.9103\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2086 - acc: 0.9109\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2075 - acc: 0.9102\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.1959 - acc: 0.9116\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2170 - acc: 0.9040\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 44us/step - loss: 0.3747 - acc: 0.8229\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3632 - acc: 0.8311\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3711 - acc: 0.8265\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3593 - acc: 0.8317\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3483 - acc: 0.8355\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 41us/step - loss: 0.3388 - acc: 0.8432\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3455 - acc: 0.8390\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3354 - acc: 0.8478\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 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[==============================] - 0s 38us/step - loss: 0.2671 - acc: 0.8800\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2686 - acc: 0.8797\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2633 - acc: 0.8839\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2598 - acc: 0.8823\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2733 - acc: 0.8761\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2393 - acc: 0.8914\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2345 - acc: 0.8926\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2338 - acc: 0.8967\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2364 - acc: 0.8938\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2410 - acc: 0.8940\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2403 - acc: 0.8915\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2193 - acc: 0.9026\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2267 - acc: 0.8992\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2047 - acc: 0.9097\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2197 - acc: 0.9042\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2153 - acc: 0.9028\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2000 - acc: 0.9098\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2106 - acc: 0.9044\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2156 - acc: 0.9039\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2004 - acc: 0.9110\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1857 - acc: 0.9186\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1993 - acc: 0.9097\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2016 - acc: 0.9131\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2030 - acc: 0.9058\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1882 - acc: 0.9152\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1992 - acc: 0.9079\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1865 - acc: 0.9213\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1716 - acc: 0.9241\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1760 - acc: 0.9216\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1768 - acc: 0.9183\n", + " 0.6616299019607843\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.40160879, 0.23865981, 0.0192348 , ..., 0.20569094, 0.98110449,\n", + " 0.58497733])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fe47ca5b470>]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6205 - acc: 0.6522\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6506\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6555\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6197 - acc: 0.6536\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6562\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6194 - acc: 0.6528\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6550\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6526\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6568\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6200 - acc: 0.6535\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6602\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6176 - acc: 0.6591\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6201 - acc: 0.6528\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6199 - acc: 0.6536\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6160 - acc: 0.6562\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6169 - acc: 0.6577\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6164 - acc: 0.6561\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6156 - acc: 0.6555\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6155 - acc: 0.6568\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6168 - acc: 0.6558\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6587\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 27us/step - loss: 0.6151 - acc: 0.6586\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6167 - acc: 0.6568\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6581\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6575\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6624\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6536\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6531\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6149 - acc: 0.6581\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6155 - acc: 0.6594\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6565\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6614\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6612\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6598\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6565\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6152 - acc: 0.6631\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6555\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6144 - acc: 0.6632\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6588\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6137 - acc: 0.6586\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6130 - acc: 0.6624\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6134 - acc: 0.6630\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6151 - acc: 0.6572\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6594\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6159 - acc: 0.6612\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6597\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6632\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6581\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6137 - acc: 0.6580\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6510\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6200 - acc: 0.6528\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6526\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6517\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6207 - acc: 0.6522\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6202 - acc: 0.6533\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6575\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6192 - acc: 0.6546\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6558\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6542\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6189 - acc: 0.6590\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6187 - acc: 0.6573\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6188 - acc: 0.6531\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6507\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6198 - acc: 0.6528\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6190 - acc: 0.6587\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6575\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6587\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6166 - acc: 0.6564\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6576\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6584\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6518\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6565\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6515\n", + "Epoch 26/50\n", + "7272/7272 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0.6561\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 24us/step - loss: 0.6136 - acc: 0.6609\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6137 - acc: 0.6620\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6118 - acc: 0.6588\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6114 - acc: 0.6610\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6614\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6562\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6553\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6186 - acc: 0.6575\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 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[==============================] - 0s 22us/step - loss: 0.6163 - acc: 0.6558\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6592\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6165 - acc: 0.6554\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6161 - acc: 0.6565\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6143 - acc: 0.6566\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6147 - acc: 0.6616\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6166 - acc: 0.6551\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6544\n", + "Epoch 21/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6150 - acc: 0.6535\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6564\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6587\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6146 - acc: 0.6558\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6150 - acc: 0.6595\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6181 - acc: 0.6553\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6136 - acc: 0.6532\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6526\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6141 - acc: 0.6597\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6126 - acc: 0.6588\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6591\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6586\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6144 - acc: 0.6612\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6566\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6130 - acc: 0.6612\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6132 - acc: 0.6581\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6175 - acc: 0.6586\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6577\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6125 - acc: 0.6608\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6148 - acc: 0.6564\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6126 - acc: 0.6588\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6118 - acc: 0.6638\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6562\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6612\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6138 - acc: 0.6581\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6605\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6113 - acc: 0.6609\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6128 - acc: 0.6584\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6122 - acc: 0.6642\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6123 - acc: 0.6586\n", + " 0.6677879901960784\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.70463479, 0.57834029, 0.35220766, ..., 0.52625859, 0.25496623,\n", + " 0.43516079])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (SGD)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6685537828100875\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.7202411587383281\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.7168627450980394\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6643872549019608\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.671936274509804\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6846323529411765\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6668872549019609\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6213235294117647\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6297058823529411\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6967401960784314\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6637254901960785\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.713872549019608\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6824999999999999\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6579166666666668\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.640735294117647\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6849264705882354\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6687990196078433\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6941176470588236\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6903676470588236\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.73577705, 0.50272183, 0.19845793, ..., 0.3937505 , 0.3072859 ,\n", + " 0.41628284])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (scikit)\n", + "cv = StratifiedKFold(n_splits=20)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = LogisticRegression(random_state=0)\n", + " model.fit(X[train,:], y[train])\n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.6996023454876323\n", + ">>\n", + " 0.6807397966998965\n", + ">>\n", + " 0.706390931372549\n", + ">>\n", + " 0.6830269607843136\n", + ">>\n", + " 0.6797181372549019\n", + ">>\n", + " 0.6988848039215687\n", + ">>\n", + " 0.7022120098039215\n", + ">>\n", + " 0.6979289215686274\n", + ">>\n", + " 0.7029473039215686\n", + ">>\n", + " 0.7025183823529413\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.54703285, 0.55016726, 0.34399391, ..., 0.4632062 , 0.38298533,\n", + " 0.47006439])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " print('>>')\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train])\n", + "\n", + " \n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + "\n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/tensorflow/prediction-v2.ipynb b/scripts/tensorflow/prediction-v2.ipynb deleted file mode 100644 index e8a809d..0000000 --- a/scripts/tensorflow/prediction-v2.ipynb +++ /dev/null @@ -1,11361 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from keras import optimizers, regularizers\n", - "from keras.layers import Dense, Dropout, Input\n", - "from keras.models import Model, Sequential\n", - "from random import shuffle\n", - "from scipy import interp\n", - "from sklearn.linear_model import LogisticRegression\n", - "from scipy.stats.mstats import gmean\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.model_selection import StratifiedKFold, train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer\n", - "import contextlib\n", - "import glob\n", - "import gzip\n", - "import h5py\n", - "import keras\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import pylab as plt\n", - "import random\n", - "import scipy\n", - "import sklearn\n", - "import tensorflow as tf\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.datasets import make_classification\n", - "\n", - "\n", - "\n", - "random_state = np.random.RandomState(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/preprocessing/_data.py:2357: UserWarning: n_quantiles (1000) is greater than the total number of samples (517). n_quantiles is set to n_samples.\n", - " % (self.n_quantiles, n_samples))\n" - ] - } - ], - "source": [ - "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", - "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", - "\n", - "X = pd.read_csv(X_f).values[:,:-1]\n", - "y = pd.read_csv(y_f).values\n", - "\n", - "\n", - "ix = [i for i in range(y.shape[0])]\n", - "shuffle(ix)\n", - "X = X[ix, :]\n", - "y = y[ix]\n", - "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", - "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\n", - "y = y[: ,0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6014 - acc: 0.6687\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5998 - acc: 0.6709\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.6020 - acc: 0.6608\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6017 - acc: 0.6603\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5958 - acc: 0.6696\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5955 - acc: 0.6694\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5917 - acc: 0.6742\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5897 - acc: 0.6785\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5990 - acc: 0.6642\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5970 - acc: 0.6683\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5895 - acc: 0.6760\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5870 - acc: 0.6784\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5849 - acc: 0.6797\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5849 - acc: 0.6821\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5788 - acc: 0.6867\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5958 - acc: 0.6685\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5857 - acc: 0.6777\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5829 - acc: 0.6779\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5847 - acc: 0.6786\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5830 - acc: 0.6799\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5799 - acc: 0.6830\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 47us/step - loss: 0.5717 - acc: 0.6913\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5741 - acc: 0.6883\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5704 - acc: 0.6942\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5741 - acc: 0.6845\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5686 - acc: 0.6938\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5645 - acc: 0.6988\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5689 - acc: 0.6928\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5630 - acc: 0.6983\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5635 - acc: 0.6975\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5664 - acc: 0.6876\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5717 - acc: 0.6863\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5586 - acc: 0.6977\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5570 - acc: 0.6988\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5565 - acc: 0.6960\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5493 - acc: 0.7130\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5520 - acc: 0.7027\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5499 - acc: 0.7082\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5515 - acc: 0.7043\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5508 - acc: 0.7057\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5442 - acc: 0.7114\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5430 - acc: 0.7092\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5443 - acc: 0.7046\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5479 - acc: 0.7072\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5442 - acc: 0.7096\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5308 - acc: 0.7204\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5374 - acc: 0.7158\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5446 - acc: 0.7093\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5398 - acc: 0.7119\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5358 - acc: 0.7144\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.6079 - acc: 0.6537\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.6001 - acc: 0.6654\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5989 - acc: 0.6658\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5953 - acc: 0.6661\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5958 - acc: 0.6669\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 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36us/step - loss: 0.5859 - acc: 0.6819\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5881 - acc: 0.6751\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5812 - acc: 0.6778\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5753 - acc: 0.6804\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5823 - acc: 0.6784\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 54us/step - loss: 0.5767 - acc: 0.6789\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5751 - acc: 0.6894\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5758 - acc: 0.6811\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5781 - acc: 0.6847\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 50us/step - loss: 0.5717 - acc: 0.6913\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 64us/step - loss: 0.5698 - acc: 0.6865\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 59us/step - loss: 0.5705 - acc: 0.6867\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 51us/step - loss: 0.5650 - acc: 0.6909\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 52us/step - loss: 0.5652 - acc: 0.6906\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 47us/step - loss: 0.5636 - acc: 0.6935\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5670 - acc: 0.6870\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 54us/step - loss: 0.5667 - acc: 0.6917\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5657 - acc: 0.6929\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5597 - acc: 0.6957\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5567 - acc: 0.7010\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5557 - acc: 0.7023\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5542 - acc: 0.7035\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.5525 - acc: 0.7017\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5480 - acc: 0.7064\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5417 - acc: 0.7093\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.5424 - acc: 0.7098\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.5518 - acc: 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loss: 0.5401 - acc: 0.7164\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5890 - acc: 0.6746\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5873 - acc: 0.6789\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5912 - acc: 0.6734\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5947 - acc: 0.6672\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5807 - acc: 0.6815\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5772 - acc: 0.6854\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5912 - acc: 0.6696\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5780 - acc: 0.6867\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5729 - acc: 0.6899\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5684 - acc: 0.6949\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5672 - acc: 0.6899\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5804 - acc: 0.6760\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 55us/step - loss: 0.5712 - acc: 0.6955\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5657 - acc: 0.6986\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5634 - acc: 0.6913\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5764 - acc: 0.6874\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5671 - acc: 0.6942\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5633 - acc: 0.7016\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5651 - acc: 0.6951\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5516 - acc: 0.7130\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5518 - acc: 0.7072\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5469 - acc: 0.7075\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5480 - acc: 0.7122\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5480 - acc: 0.7074\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5572 - acc: 0.7048\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5449 - acc: 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loss: 0.5218 - acc: 0.7291\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5240 - acc: 0.7274\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5206 - acc: 0.7336\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5128 - acc: 0.7360\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5117 - acc: 0.7356\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 55us/step - loss: 0.5132 - acc: 0.7324\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 1s 71us/step - loss: 0.5079 - acc: 0.7386\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 53us/step - loss: 0.5108 - acc: 0.7339\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 50us/step - loss: 0.5116 - acc: 0.7321\n", - "Epoch 44/50\n", - "7272/7272 [==============================] 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[==============================] - 0s 44us/step - loss: 0.6127 - acc: 0.6517\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6108 - acc: 0.6558\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.6122 - acc: 0.6487\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.6113 - acc: 0.6547\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6117 - acc: 0.6586\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6080 - acc: 0.6524\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6080 - acc: 0.6555\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.6091 - acc: 0.6537\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.6076 - acc: 0.6559\n", - "Epoch 12/50\n", - 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[==============================] - 0s 42us/step - loss: 0.6026 - acc: 0.6660\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.6016 - acc: 0.6595\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6016 - acc: 0.6606\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6021 - acc: 0.6617\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6000 - acc: 0.6641\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 51us/step - loss: 0.6006 - acc: 0.6643\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5980 - acc: 0.6625\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.6008 - acc: 0.6621\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 60us/step - loss: 0.5995 - acc: 0.6668\n", - "Epoch 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loss: 0.5736 - acc: 0.6895\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5876 - acc: 0.6766\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5899 - acc: 0.6760\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5886 - acc: 0.6777\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5891 - acc: 0.6751\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5856 - acc: 0.6819\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5826 - acc: 0.6821\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 61us/step - loss: 0.5789 - acc: 0.6845\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 53us/step - loss: 0.5790 - acc: 0.6862\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 52us/step - loss: 0.5786 - acc: 0.6817\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 47us/step - loss: 0.5754 - acc: 0.6847\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5759 - acc: 0.6870\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5724 - acc: 0.6867\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5723 - acc: 0.6896\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5670 - acc: 0.6962\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5679 - acc: 0.6899\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 59us/step - loss: 0.5764 - acc: 0.6861\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5613 - acc: 0.6909\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5640 - acc: 0.7024\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5685 - acc: 0.6933\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5524 - acc: 0.7043\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5575 - acc: 0.6990\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5556 - acc: 0.7026\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5564 - acc: 0.6990\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5497 - acc: 0.7046\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5519 - acc: 0.7035\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5563 - acc: 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[==============================] - 0s 48us/step - loss: 0.6086 - acc: 0.6536\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 61us/step - loss: 0.6025 - acc: 0.6624\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6064 - acc: 0.6562\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5987 - acc: 0.6602\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6125 - acc: 0.6469\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.6051 - acc: 0.6642\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.6003 - acc: 0.6613\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5950 - acc: 0.6709\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5980 - acc: 0.6667\n", - "Epoch 12/50\n", - 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"Epoch 21/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5847 - acc: 0.6737\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5859 - acc: 0.6755\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5855 - acc: 0.6766\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5770 - acc: 0.6804\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5914 - acc: 0.6722\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5852 - acc: 0.6785\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5788 - acc: 0.6839\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5914 - acc: 0.6718\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5773 - acc: 0.6870\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5898 - acc: 0.6774\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5768 - acc: 0.6855\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5839 - acc: 0.6762\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5783 - acc: 0.6779\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5830 - acc: 0.6768\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5801 - acc: 0.6806\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5746 - acc: 0.6900\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5792 - acc: 0.6797\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 39us/step 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loss: 0.5565 - acc: 0.7038\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5499 - acc: 0.7164\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5416 - acc: 0.7202\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5383 - acc: 0.7261\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5483 - acc: 0.7152\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5388 - acc: 0.7221\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5424 - acc: 0.7255\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5367 - acc: 0.7250\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5376 - acc: 0.7272\n", - "Epoch 32/50\n", - "7272/7272 [==============================] 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"text": [ - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5721 - acc: 0.6825\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5781 - acc: 0.6841\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5713 - acc: 0.6833\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5785 - acc: 0.6782\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5691 - acc: 0.6867\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5783 - acc: 0.6803\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5688 - acc: 0.6850\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5714 - acc: 0.6834\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 39us/step - 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[==============================] - 0s 38us/step - loss: 0.6067 - acc: 0.6610\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6024 - acc: 0.6613\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5979 - acc: 0.6691\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6000 - acc: 0.6654\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6041 - acc: 0.6628\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6025 - acc: 0.6669\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6024 - acc: 0.6627\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5933 - acc: 0.6749\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5952 - acc: 0.6700\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6002 - acc: 0.6656\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5959 - acc: 0.6719\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5951 - acc: 0.6669\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 61us/step - loss: 0.5983 - acc: 0.6653\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5958 - acc: 0.6652\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5999 - acc: 0.6774\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5978 - acc: 0.6704\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5981 - acc: 0.6646\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5957 - acc: 0.6741\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5928 - acc: 0.6719\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5944 - acc: 0.6698\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5913 - acc: 0.6748\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5898 - acc: 0.6707\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5915 - acc: 0.6722\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5850 - acc: 0.6731\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5995 - acc: 0.6573\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5893 - acc: 0.6735\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5860 - acc: 0.6771\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5964 - acc: 0.6639\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5913 - acc: 0.6715\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6020 - acc: 0.6587\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5996 - acc: 0.6672\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.6016 - acc: 0.6650\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5979 - acc: 0.6680\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5937 - acc: 0.6738\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5939 - acc: 0.6693\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5946 - acc: 0.6664\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5895 - acc: 0.6727\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5960 - acc: 0.6636\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5861 - acc: 0.6707\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5827 - acc: 0.6767\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5838 - acc: 0.6727\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5900 - acc: 0.6668\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5882 - acc: 0.6735\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5790 - acc: 0.6785\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 62us/step - loss: 0.5868 - acc: 0.6701\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.5807 - acc: 0.6785\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5829 - acc: 0.6705\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5824 - acc: 0.6746\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5741 - acc: 0.6792\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5733 - acc: 0.6803\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5752 - acc: 0.6804\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5709 - acc: 0.6865\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 56us/step - loss: 0.5699 - acc: 0.6889\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 59us/step - loss: 0.5697 - acc: 0.6823\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 58us/step - loss: 0.5732 - acc: 0.6775\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 69us/step - loss: 0.5625 - acc: 0.6935\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 48us/step - loss: 0.5633 - acc: 0.6858\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 1s 75us/step - loss: 0.5615 - acc: 0.6902\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5641 - acc: 0.6911\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5559 - acc: 0.6936\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5573 - acc: 0.6962\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5532 - acc: 0.6958\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5506 - acc: 0.6987\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5488 - acc: 0.7028\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5573 - acc: 0.6920\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5486 - acc: 0.7001\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5526 - acc: 0.6991\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5482 - acc: 0.6997\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5460 - acc: 0.7015\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5412 - acc: 0.7061\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5358 - acc: 0.7122\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5340 - acc: 0.7114\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5449 - acc: 0.7064\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5313 - acc: 0.7206\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5514 - acc: 0.6954\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5431 - acc: 0.7104\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5316 - acc: 0.7170\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5386 - acc: 0.7109\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5257 - acc: 0.7230\n", - " 0.7064249695172389\n" - ] - }, - { - "data": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.43307701, 0.66932142, 0.69341809, ..., 0.47402373, 0.34038126,\n", - " 0.57002372])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7fa445a36ac8>]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "results[test] = probas_\n", - "plt.plot(results)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6410\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6339 - acc: 0.6419\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6427\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6340 - acc: 0.6348\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 30us/step - loss: 0.6302 - acc: 0.6462\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6294 - acc: 0.6448\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6339 - acc: 0.6377\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6309 - acc: 0.6418\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6338 - acc: 0.6352\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6415\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6317 - acc: 0.6408\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6305 - acc: 0.6445\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6302 - acc: 0.6434\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6415\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6415\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6299 - acc: 0.6443\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6285 - acc: 0.6488\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6297 - acc: 0.6471\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6298 - acc: 0.6441\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6283 - acc: 0.6438\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6291 - acc: 0.6448\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6437\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6279 - acc: 0.6434\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6282 - acc: 0.6396\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6476\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6312 - acc: 0.6405\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6425\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6283 - acc: 0.6471\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6281 - acc: 0.6444\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6281 - acc: 0.6477\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6273 - acc: 0.6463\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6285 - acc: 0.6440\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6259 - acc: 0.6495\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6279 - acc: 0.6493\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6296 - acc: 0.6440\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6286 - acc: 0.6403\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6263 - acc: 0.6481\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6265 - acc: 0.6496\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6276 - acc: 0.6474\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6518\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6455\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6285 - acc: 0.6448\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6287 - acc: 0.6434\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6262 - acc: 0.6459\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6251 - acc: 0.6495\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6274 - acc: 0.6466\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.6270 - acc: 0.6469\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6294 - acc: 0.6476\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6266 - acc: 0.6462\n", - "Epoch 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[==============================] - 0s 29us/step - loss: 0.6284 - acc: 0.6484\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 31us/step - loss: 0.6259 - acc: 0.6449\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6275 - acc: 0.6448\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6269 - acc: 0.6470\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6270 - acc: 0.6470\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 28us/step - loss: 0.6271 - acc: 0.6467\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6276 - acc: 0.6460\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6286 - acc: 0.6433\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6265 - acc: 0.6469\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6263 - acc: 0.6466\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6276 - acc: 0.6465\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6503\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6270 - acc: 0.6476\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6415\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6253 - acc: 0.6517\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6270 - acc: 0.6449\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6259 - acc: 0.6482\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6249 - acc: 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[==============================] - 0s 24us/step - loss: 0.6303 - acc: 0.6436\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6308 - acc: 0.6415\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6296 - acc: 0.6393\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6415\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6429\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6297 - acc: 0.6438\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6316 - acc: 0.6385\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6416\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6414\n", - "Epoch 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- "7272/7272 [==============================] - 0s 25us/step - loss: 0.6298 - acc: 0.6430\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6315 - acc: 0.6396\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6309 - acc: 0.6415\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6281 - acc: 0.6441\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6444\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6320 - acc: 0.6392\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6433\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6313 - acc: 0.6427\n", - "Epoch 22/50\n", - "7272/7272 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[==============================] - 0s 24us/step - loss: 0.6314 - acc: 0.6429\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6407\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6297 - acc: 0.6429\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6298 - acc: 0.6393\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6327 - acc: 0.6378\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 29us/step - loss: 0.6303 - acc: 0.6469\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6451\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6425\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6312 - acc: 0.6419\n", - "Epoch 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[==============================] - 0s 25us/step - loss: 0.6314 - acc: 0.6427\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6290 - acc: 0.6458\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6282 - acc: 0.6449\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6281 - acc: 0.6434\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6312 - acc: 0.6445\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6270 - acc: 0.6462\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6296 - acc: 0.6459\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6289 - acc: 0.6433\n", - "Epoch 42/50\n", - "7272/7272 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[==============================] - 0s 59us/step - loss: 0.6316 - acc: 0.6397\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.6330 - acc: 0.6382\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6299 - acc: 0.6397\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6307 - acc: 0.6462\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6305 - acc: 0.6390\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6305 - acc: 0.6408\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 29us/step - loss: 0.6320 - acc: 0.6388\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6294 - acc: 0.6392\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 36/50\n", - "7272/7272 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[==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6407\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6355\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6398 - acc: 0.6331\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6317 - acc: 0.6425\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6334 - acc: 0.6385\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6339 - acc: 0.6386\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6327 - acc: 0.6371\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6458\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6416\n", - "Epoch 12/50\n", - 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"Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6322 - acc: 0.6396\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6306 - acc: 0.6458\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6309 - acc: 0.6434\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6329 - acc: 0.6443\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6303 - acc: 0.6427\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6296 - acc: 0.6418\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6309 - acc: 0.6423\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6300 - acc: 0.6459\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6314 - 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[==============================] - 0s 22us/step - loss: 0.6283 - acc: 0.6441\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6292 - acc: 0.6399\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6285 - acc: 0.6425\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6302 - acc: 0.6421\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6341 - acc: 0.6405\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6326 - acc: 0.6381\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6317 - acc: 0.6403\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6344 - acc: 0.6359\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6331 - acc: 0.6360\n", - 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- 0s 21us/step - loss: 0.6293 - acc: 0.6426\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6296 - acc: 0.6415\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6465\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6471\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6288 - acc: 0.6465\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6283 - acc: 0.6422\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6456\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6280 - acc: 0.6452\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6301 - acc: 0.6449\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6275 - acc: 0.6482\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6274 - acc: 0.6444\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6273 - acc: 0.6434\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6272 - acc: 0.6471\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6432\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6291 - acc: 0.6471\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6280 - acc: 0.6425\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6268 - acc: 0.6474\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6273 - acc: 0.6467\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6271 - acc: 0.6491\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6331 - acc: 0.6361\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6322 - acc: 0.6394\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6333 - acc: 0.6407\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6342 - acc: 0.6385\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6345 - acc: 0.6374\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6336 - acc: 0.6419\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6327 - acc: 0.6370\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6315 - acc: 0.6422\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6335 - acc: 0.6386\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6318 - acc: 0.6418\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6322 - acc: 0.6403\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6329 - acc: 0.6379\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6330 - acc: 0.6415\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6316 - acc: 0.6429\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6316 - acc: 0.6401\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6322 - acc: 0.6383\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 22us/step 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[==============================] - 0s 23us/step - loss: 0.6308 - acc: 0.6455\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6287 - acc: 0.6437\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6301 - acc: 0.6421\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6447\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6291 - acc: 0.6415\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6316 - acc: 0.6449\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6292 - acc: 0.6419\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6460\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6436\n", - "Epoch 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[==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6429\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6326 - acc: 0.6407\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6357 - acc: 0.6381\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6324 - acc: 0.6401\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6418\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6319 - acc: 0.6427\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6307 - acc: 0.6379\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6411\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6421\n", - "Epoch 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0.6427\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6348 - acc: 0.6429\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6469\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6306 - acc: 0.6465\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6415\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6304 - acc: 0.6447\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6463\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6314 - acc: 0.6436\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6298 - acc: 0.6484\n", - "Epoch 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[==============================] - 0s 25us/step - loss: 0.6342 - acc: 0.6346\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6334 - acc: 0.6410\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6322 - acc: 0.6366\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6336 - acc: 0.6377\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6317 - acc: 0.6422\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6418\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6318 - acc: 0.6411\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6374\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6324 - acc: 0.6393\n", - "Epoch 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loss: 0.6297 - acc: 0.6440\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6407\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6304 - acc: 0.6361\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6352 - acc: 0.6366\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6350 - acc: 0.6381\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6340 - acc: 0.6381\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6360\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6351 - acc: 0.6327\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6350 - acc: 0.6399\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6351 - acc: 0.6364\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6336 - acc: 0.6370\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6324 - acc: 0.6390\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6338 - acc: 0.6416\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6363 - acc: 0.6375\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6329 - acc: 0.6363\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6346 - acc: 0.6381\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6361\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6329 - acc: 0.6374\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6381\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6366\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6321 - acc: 0.6404\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6456\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6315 - acc: 0.6447\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6419\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6343 - acc: 0.6356\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6301 - acc: 0.6463\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6367\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6302 - acc: 0.6437\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6337 - acc: 0.6411\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6331 - acc: 0.6361\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6317 - acc: 0.6410\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6381\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6306 - acc: 0.6443\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6295 - acc: 0.6454\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6449\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6301 - acc: 0.6388\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6309 - acc: 0.6394\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6318 - acc: 0.6466\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6414\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6329 - acc: 0.6360\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6288 - acc: 0.6485\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6438\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6306 - acc: 0.6448\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6288 - acc: 0.6436\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6308 - acc: 0.6389\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6289 - acc: 0.6465\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6288 - acc: 0.6419\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6295 - acc: 0.6485\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6294 - acc: 0.6470\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6408\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6432\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6289 - acc: 0.6410\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6302 - acc: 0.6397\n", - " 0.6936436549902885\n" - ] - }, - { - "data": 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dO9O/f38WLFhQY35qQ3oeunbtyvz580kkEiSTSWzbxnVdXNelvLyc6667jo4dO9KpUyfeeOMNXNdl0qRJNG/enB49etCje3cm/vnPiFJs/2Y9w4adT+Eu2LolzDcbsli7oQEbt9Rn2/Z6lBSHUY5giktmJEGT+rtp0Xw7uY120TwnTv0sIbt+DpHshlixepjRbIxIFhLJAiviJb6oCFav9gyCYUCzZtC6NYTD3+qeHCxHnXR2QMCRSiwWY/HixQBcddVVPPfcc9x7772Ul5dz/vnnM2HCBIYMGUJZWRkXX3wxf/zjH7nxxhtZunQpN910E++88w4dO3bEcRxeeOGFKvEvWrQI27ZT1zhUzJw5k5UrV7Jy5UoWLFjAz372s2oL6ZdeeomLLroIM632mp+fT+/evZk2bRpXX311ra734x//mDZt2rBy5UoMw2DNmjV88cUXtTq3ppr/jBkzWLFiBV9++SXz5s1j7NixzJ8/H8CvnXv1+N/85jfk5uaycuVKXNdlx44dmKagRXPxJRfz+O9+i2Nrym2Dbza7xJMtyMppwXuzF5HX/TQEzytkGS6hkEs05JIRVYTDoJWNGTGxQtlYoRCmaXmtANlH3XvzZti1y/udmQm5uXVmDCoIZC4CAg4D/fr1Y8OGDQC8/vrrnHbaaQwZMgSAjIwMnn32WX73u98B8Nhjj3HvvffSsWNHwPNT33DDDZXi27p1K1deeSWLFy8mLy+P1atXM2vWLLp3707Xrl0ZM2YMiUSiSjomTZpEhw4dGDBgAHPnzq02rdOnT2f06NGICH379qWwsJBNm6p277322mtccMEFqe3Vq1dTUlLCuHHjyM/Pr9V9Wb16NQsWLGDcuHEYhlf8tG3blmHDhu33XNd1cRwn5d9P/5s+fTpXXHEFSin69OlDUVERW7ZswTAMHK1JKkXCdXnxxRe56ZYb2FlWyK54MRILs6O0mJJym/JyxaZtYdZvyWLb9kxKSyMoZTBk8Hm8+8/XaFy/jJZNd9Pm+F20Oa6Q45vspkGDBNEssKJCLCeTjHo5RDKyMEMRMMx9GwSA7GzPRXTccdCqVZ0bBAhaCgHHKO99vuWQxzm4U7NahXNdl1mzZnHttdcCnuuoZ8+elcK0a9eOkpISdu/ezdKlS7n99tv3GWfTpk2ZOHEijz/+OG+//TbxeJyBAwcya9YsOnTowOjRo5kwYQK33HJL6pxNmzbx4IMP8sknn5CTk8OgQYPo3r17lbg3bNhAy5Z7BgK2aNGCDRs2kJubm9qXTCZZs2YN6ase5ufnc/nll3PGGWewfPlytm7dStOmTfeZj2XLlpGXl1eptVETl112GcuXLweo1Dq49dZbq7RKNm/eTJs2bQj7hWqLFi1Y9/VaGjRuSJmrMLTLrl270GgefPA3zP1gLq1atuG+e8eTk3M8pbsizHjn78z77zxan9CBX/7yMVq3ySUj5jBoYEee++MD1KunCFkhRCJe/4EIhmlhWF4xmt4iqZFEAsrKoEEDbzsrC9q3r5O+g5oIjELAMUltC/BDSXl5OXl5eaxdu5aePXty9tlnA16BVlNhcbAjRZYvX06bNm3o0KEDsMddlW4UFixYwMCBA2nSxFuB8bLLLmPFihVV4qrOHbN3urZv3079+vUr7SsoKGDatGkYhsFFF13E1KlTufHGGw9ZXqdMmZJqGQCEw+FU66JqHhRJxybuJNBa4yqXuJOgJBlHaRtTaYp3Jtm4YSMd2w3g5uufYvLkJ/jt7+7nd7/9f5x15jBGXHQJOQ0yKJjyJx781bX87e2ZWJiErVy2bNlCZna9Gq+/X7SGHTtg2zbvdyQCGRnesSPIIEDgPgoIOGRU9CmsW7eOZDLJc889B0Dnzp2rdPyuWbOGrKwssrOz6dy5M5988skBXau2I2pqUxC3aNGCb77ZIy6wfv16jjvuuEphYrFYpdniS5YsYeXKlZx99tm0bt2agoKClAupUaNG7Krwk/vs3LmTxo0b07lzZz777LMaJ32B16Eej8e55JJL6NmzJ3369KFv37706NGDvLw8Jk+enAqrtabcLqNJbjNWrfuKeDJBwk6yYcMGGjVvimtD6fYsNn/dEMNtRSyaweAzhxMNKS6+8CJWrviEE5rvpnuXMC3aRshs4HLFNVexaNEicG1M0Wg//wdtEOJx+Oor2LrVMwg5OUeEm6gmAqMQEHCIycnJ4emnn+bxxx/Htm2uuOIKPvzwQ9577z3Aa1GMHTuWO++8E4A77riDRx55JFWLV0oxfvz4fV6jY8eOrF27llWrVgHwyiuvMGDAgEph+vTpw5w5c9ixYwe2bTN16tRq4zr//POZPHkyWmvmz59PTk5OJdcRQIMGDXBdN2UY8vPzeeihh1i7di1r165l48aNbNiwgXXr1tG7d2/mzp3L5s2bAVi4cCGJRIKWLVvSrl07evXqxYMPPpgybCtXrmTatGkkk0ni8Thaa8LhMH/5y19YvHgxn332GYsXL079XXnllbiuSzJZSnmikNJkkh+ecy5vvFpAzA3xv/mLqJeVQ7bRlu3rsygtDoHSxKKKIUOGserrWTRr4bB48Uw6nXwiKhph3bZd2K5GKXj/3X9w8kknEQ1ZhKMx1qxdS5cuXWr9/FMo5RmCr77yDEMo5PUbHH88WEeuk+bITVlAwFFM9+7dOeWUUygoKGDUqFFMnz6dm2++mRtvvBHXdRk1alRq2Ga3bt148sknufzyyykrK0NE9tvxGo1GmTRpEpdeeimO49C7d2+uv/76SmFyc3N56KGH6NevH7m5ufTo0aPScNIKzj33XGbMmEH79u3JyMhg0qRJ1V5zyJAhfPjhhwwePJiCggJmzpxZ6fiFF15IQUEBd911F0899RTnnnsuSimysrLIz89P1bQnTpzI7bffTvv27YnFYjRs2JBHHnkEgFAo5M/i3dPC0VqjtSbuOLhuEq0VWtmAIBLGVAbDh53H7FmzOKlbZyLhDH718ERKy7za+IjLujP3/X+jM8L86pFfc8N1Y3jgl7+gceNGvPDCRGKREC/9+QXefvttLMuiQf36vPD8BKxwGMM0mT17dq06wquwdSvs3On9btgQmjQ54lxF1SEHM7GjLunVq5eubgx2wPebL774gpNPPrmuk3FMs2jRIsaPH88rr7xy0HG4ruv5/P3/4M3vME2zknumwhC4rouyXUq1xtU2YeUiYgKCGCE/TkVxiVC828J1vJnBCGRnanIykxhGApwkVlYGBhpDJTGssDdfwO8cdh0nlR7XThKK7nEX9e/fn+nTp9OgonO4tjgOfPONN++gov/gO6K670FEPtFa99rfuUFLISAgoFZ0796dQYMG4bpurUYPga8I6g8brcAwDAzDwDTNSiN2tNY4roNt22hH4SjHE4kQF0eEsE6AEcZBSCZMkglFIm6QKA8DAhpCpiYjI0lWlo3lxhHXBQVWOISJ8oxPOBNthFGui1Yurt+RbVoWIBiWlUrTtm3buO2222pnEEpKoLDQcw+JeC6i1q2930cRgVEICAioNWPGjKlVuPQWgYhgWVbKkOztGqqQlHCcJAmnDMMVkobGMQVLDMDATYQpLs8kGbeIJ6SSnrKgyYy5RKM2sYhDJOxiIOgEqEgGhEKEwuHU9ZPlZWhd7rmpDJNQJIJhWtV2yjdp0oThw4fvL7OwZYtnEMCbhFZhRI4ygwCBUQgICDgEVBTs6ZpB6S2CSq4hpVCOwk4kcZWNxgEclBg4WrBMCzFNwnaIeKnJ7t0Gtg1ohScyrQmHFNGwQyTiEg27KEPQQDQqGGYUFU+iw1HMaNQTrXNdXNv2WwVCJCPj0Ago7t7tzUp2HM8ANGkCew3dPdoIjEJAQMABkd4fUDGHALwWgGEYVTqLtda4CQftKGzXxlEOCoU2NGaoosofIe5YxMtM7ESIRLmgXMETnIaQ4VAvyyaaHSYWVXg2xsRRBnHHK8ZihgatcG0X7boYIQtlJ0naSQCsUAgrEsYwzG9vEPYWsMvI8CQqIpFvF+8RQGAUAgICao1SXg3fEMOb0YuBld6/4GhAY6sESTeZ6jDWWuNa3pj/kBVCiwU6iaOEZDxGaYlFaakgumK5AY1luGRGHbIzbTIyFIQzQDxNag3EbYWyHUKiCZuCTiTRloUIaNPEME0MK4RRjdvqW1NcvEfArmlTz110FLqKqiMwCgEBAftFuwqtNE7S9jpuI1ZqsRhXuyT8mcQajRKNqyskIUIgXmGPo3HRuBqS5S7Jsijx0jAV89gETUbUITtLkZGhvRXGDIsqxZTrohwXEkkiaIyQBZhYsRgSCmEnEoSjMcyDXI6yRpSCCjdY/fqQTHrG4AieiHYwBJPXAgIOEUerdPaXX35Jv379iEQiPP7444C3uLyy3dSfthXKVQwZNpQyuxwjbCKWwdS/vkE4EuHL5cvRRhhthPnwPx9x+aVX4hhhkiJoV3PdmDFMef1NiraZbFoND939awb178YFF5zC//1fXz5b/DZtT0jQoqUmp6FFKBryBOXS0MkkKpHALivHth0wTULZWYSzswlnZvLo+D/Q4aST6Na9O++9/36190Zrzb333kuHDh04+eSTefrppysd//jjjzFNkzfeeAPwRh8NHTrUm2+wapVnCMBrFTRrdswZBAiMQkDAIaNC5mLp0qU0bNgwJXNRIZ199913s2LFCj777DP++9//8sc//hEgJZ396quv8sUXX7B06VLatm1bJf506ezLLrvskKW7YcOGPP300ylDpF2FTrig0ob4mMLb/3iHrt26kpOTQ3G8mJ1lO8nPz6ffD/rx1zen44hgGgamgCmQZQiUOmzfDPEyi3hZmGQ8xDPPPsTOnZv49+yFLFq4gHfenooYpViREIiJBpKu9v5sl2RxCWW7iigvjRN3wDFDSDhMJOYtWqMchyWLPmVKwRSWLF7MP//5T2644YZqJ+q9/PLLfPPNN3z55Zd88cUXjBw5MnXMdV3uuusuzjnnnNS+JvXqkZudzdwZM7x+hIo+hGOYwCgEBBwGjibp7KZNm9K7d29CoRBaaXRSeaV6yMDRLgknSaldziuvvcqZPxzCtvLdlDoO5WWKBfM/5snnX2DK1L9gAGERDC04trBmjbBpU5Tycq82HQs71M/YyrS3JvLn5x/luBYxwrEQzZofxyWXXAJ4NflE0sEuL0cn4lBWigDhrCwy62eTlRkhMyNCSBRW2oimt2fMZOTll5OZnU2bNm1o3749H330UZW8TpgwgQceeCA1Gipd1fWZZ57h4osv9vZpDdu3w5o1DB8wgNdmzIAWLaBx4/0++6OdoE8h4Nhk+cz9hzlQTvphrYIdbdLZsGcGMa7GxvFGfybBFSEuBglls2DBR/zhuQlkhjIwEAqmvs6QIUPIO6kDjRo0ZOGHn9GmdQ82bjRIJr2KddjS1M+xyc5yqN/IYPPODbRs1YrsBn7hmkyiNWjlomyHpOspysbCJnfdfx///uADEEErlerIvWzECO66885KfQabNm+mb9++qe0K+e+9Wb16NVOmTGHatGk0adKEp59+mhNPPJENGzYwbdo03n//fT6eP9+TqNi6FYBe/fpx35/+BPXq7fMZHSsERiHg2KSWBfih5EiXzh4xYgQrVqyoIjOBBsd1cGyH8pBDmVKIKSRdB1u7mGiiAkW7dnFc/Rw/TzAl/y+MGTOWdeuEQYNG8NprU7j99h7eZDVT06JZGRlZprda2V56RgDadbHjCZJiYRiCGc0gGgkTNr1Zzk8+/bQ/0UxjGCahaLTG+1Ub+W/w+mWi0SgLFy7krbfeYsyYMfznP//hlltu4dFHH90zU9u2PQG73FyaRiJs3LjxgJ7P0UxgFAICDhEVfQpFRUX86Ec/4rnnnmPs2LF07tyZDz74oFLY6qSzTznllFpf60Cks7VSaEfj2g6u4xIvK0drjYlXULvaxdaQ1BpTFA4JtKMxDYsMM0zY8IoJy7IoLdWUlJisX7+LDz6YzdKlyzy/vvJmLv/2kUfo0qUB5eU7yMy20EYInUyyc/sOGmZm0qZZM775+msKN24iIysLbYaIZWYQtQwcx0Yl4yTxjOgdd97Fvz/4ANlLsnrkyJHcfffdlfbVRv67ItzFF18MeAJ+11xzDQALWIC1AAAgAElEQVQLP/441b+wfft2ZvzjH1itWjH8xBOJFxcTi8Vq+WSOfoI+hYCAQ0xdSWf379/f0w3SGsdx6NGjB7Nnz2b9N+spLinmzbfe8mYZWwbKVNimQ9yyKTMdyk2NGBA1DHJCmdQPZ5FtRQkbFkrBrl0GJ5xwEvPnr2X3bvjHP6YyfPgVLJj/Of9bvIzVXyyjXZtWLFsyh5PatGLTxk18vvRzSCZZu3oNS5Yt46TuvZD6jbnyqmu47f4HiIeihLIy2LppAy9PegnlOJjhMJGMTCIZmTz97LN8tmRJJdnsxYsXVzEI4Ml/FxQUkEgk+Oqrr1i5ciWnnnpqlXDDhw/nfX9k0r///W+vpbV5M1/NmMHaJUtYu3Ytl1xyCX+cMIHhF10EwIoVKw5OOvsoJTAKAQGHgXTp7FgsxvTp0xk3bhwnnXQSXbt2pXfv3tVKZ5988sl06dKl2jWS04lGo7z00ktceumlqQLr6quvrjTipnnjZtx7zy8ZdNaZDLvgR3Tr0Q0Hh7hOYIiJJSFcFWLLpp30OPEknn/mWR579DHatm1LUdFuSkuFzZtN1qyx2L7d4IzTf8gnC9+nQVac99/NZ+SFQ2mSZZNpJjBFc+EF5zHlLwVEQsLLf/oj1/3sBnr/4AeMvOZqnnp2Ajk5OcQseOThh2jWtDF9enSj5ynduOSSS2jarDmRjEysUOigXGqdO3dmxIgRdOrUiaFDh/Lcc8+lXEHnnntuyv1z99138+abb9K1a1fuufNOJt5/vzfcVMRzGVXDQUtnH6UE0tkBxwTfB+lsrTVo0HgtARzt6QhpjWWYIGD6Y/u11iTcBI6lsMTC0F79T/t/IuIpDmmIGZKqHSaTQmGhQXGxkL44WiyWpLxsHWNv/ikz//kvXFsBghnyIhTtIE4cbUYAQWmFKxau0riOImJ65a5phSq5g8QQTPM79mK7ridRUVTkbUejcNxx3v9qOGjp7DokkM4OCDjGUUkXO2mDsEd51DQxwhaW6YnOKa1Iut7kKqUVjihChBDtaRJpoEx5UhOmXxeM+LONS0qFoiKDsrI9tfRw2CYrGiczGidkaYx6Dbhm1GgKt24lK6seVgiM5J7atTIslFigNaYVQiM4TpJIyCQcDvnS1HVMPA5ff11ZwK5RoxolKg5IOvsY4Qh4SgEBAemkLzBT0ZLXSRdteOsCWBKqsp5BxWL1CSeBheV3JPvqpCIoIK40BhATwXUF24bCUmH3boMKr5MhiuzMBPUyigkZDioZ8hezEZQJF108AjHAML2FbFwtaLFQylu3QJQnkOcoRdx2CZkGkUg4pT9U54TDnlRFLQXsaiWdfYwRGIWAgCOECmOQSCQQ5UlNmIaJiDek04hYlYxBRXjbsUk4CZT2/D2WZSGGZ0yUdil1HJQW4qUmiWKLZMJA67SasVaEQw7ZGeXUyyj1h5CGUGRjZocwwpYfTOE6NkprT8FUAKURw8EQwQyFcBFvroFhEAkJGeE6LmK09mYhZ2V5S2EaBpxwgrcAzjEiYHeoCYxCQEAdkZowBt5cgaSN67oYYhCyLMQ0QPD+p+EtSOPguDaOtlHaxRAhZPhDUHU5WivAJOEIu3eFiZeEUKqiENSYpiZkaSJhl3oZZURIok0TbWShdAgxvT4KI2SkrmnHyzEti3A4ghZP1tpWGu33Yzh4yhghC2J1bQzA0ynatAlKSz3hutxcb/+hFso7xjgCnlxAwPcHr7NYozUo5Xodxgq062lGh8K+a8gQxJAq59q2je0mSKoyBBcwCBlhDBEE01MlBUAoLDLZtsMA1xM0jUUd6mU7ZMfiGKLRCCrhoJXgmDEwI15ntSUYpniromkFGkzDgFCISEYmrtKUJ10MAaWFiOGviwyYgGnUcQ1ca9i1y5uRrJTXQviO10g+mgmMQkDAd4TjOCjHxU06aAFDBMMwsUwTiRpVWgSwpzWhlCJplxJ3SxExMBAiVg6GVK71KgW7dxsUFRnEE95Yo8yIQ6OGinBYoxW4RHC1gNJoTIyMMKZpIMaeayZshRbBNEy0ckkm4ohhkIx7fQaWeQS4hqojkfBaB2Vl3na9etC8uecuCqgVwTyFgIBDxL6ks5f+bymDBw+mc9cudOtxCo8/8QcimTHCGRGMqMU//vXPStLZt912G7adJJEoJ5EoJ2mXUlJeyEXnXc7AH5zD2399L2UQtIbSUmHTZpNVayyGDRvMJ58uxLQ0zRonadYMIhkmr+a/zu133IqJi+nEMXUSK2pS8MYUevXuRY+ePek/YAALFy9B+YvnmKIQ1yYcDhOJxrjg3HNQybKUQZg2bRoiwpdffpnK65w5c/jRj35U6d5cffXVKTlq27a5++67OfHEE+nSpQunnnoqM2d+e62q344bR/sTT+SkQYP457x50LKlJ2KXZhBqks7etWsXF154Id26dePUU09l6dKlACSTSfr3719phbljncAoBAQcImqSzi4tLeX8C87ntp/fxpf/+5xFnyxm3rx5VaSzJ0+ezJIlS1i4cCGtTmhFuV1M0i3G1mU4OsFnSz7HdRQff/wxl156KYnEnsllGzaaFO0WtALD0DTISdCqWQnZMRcrYmAoB5KJPe6USARiMZJi0vz4lrw9Yybz5s3n7jvu4NabbyQiLpayMZRLJBIhGovy7r/+QV7eKdTPyUnlOT8/n9NPP52CgoJa36f777+fTZs2sXTpUpYuXcrf//53iouLv9W9//zzzymYOpVlc+bwj/x8bnjkEdxqXEY1SWc/8sgj5OXlsWTJEiZPnszPf/5zAMLhMGedddY+16841jisRkFEhorIchFZJSJV5qaLSCsRmS0ii0RkiYicezjTExDwXZEunf3aq6/Rr28/fvSjYZjREFk5WVWks++6+y5atG5BmV2GMhVX/WQUrnYwjQghM4tdO8q5/tqbWLJkCT179mbuf9cy5S9zOOecXpx/wSk88MC1xKKltD7BIRp2yclIEImFeXVKPl26dOasIUOYt3Ah2rJQpoW2Qmh/Sc0z+vYht3EDoiGLM/r3Z+PGjUQzMgjHvD/LX0jmtdde44ILLkjlsaSkhLlz5/Liiy/W2iiUlZXx5z//mWeeeYaIPxy0WbNmjBgx4sBvslJev0F5OdOnT2fkyJFE2rShTd++Byyd/fnnn3PWWWcBeyREtmzZAnjSGK+99tqBp+8o5bA52kTEBJ4DzgbWAx+LyN+01p+nBbsP+IvWeoKIdAJmAK0PV5oCvj/M+WbOIY9zYMuBtQqXLp2ttWbp0qX06tETsfbUwdq2bUtJSQk7duzgsyWfce0N16JQWIaFKSamSmIaUURCiJjkNsvliWee54nHn+TpZ/5OIhHnvvvO4vWCf9C5aztuuelapv/1T4wdOxYEtBFh/bpvePjhh/nPu++S06A+Pxx+IV26dvNWLcMbYorWhEMWoWgUwzB4+dln+eEPq1eYnTt3Ln/6059S23/9618ZOnQoHTp0oGHDhnz66af06NFjn/dm1apVtGrVinq1kKG+9dZbmT17dpX9I0eO5O6xY2HjRm+EUUkJG9avp2+/fqlhpgcqnX3KKafw1ltvcfrpp/PRRx+xbt061q9fT7NmzejSpQsff/zxftN7rHA4e19OBVZprdcAiEgBcAGQbhQ0UPF25ADfH33agMNKbQvwQ8ne0tlnnnkmSilf+nmPdHTFkFIgNQM5akbJsGIoFQftYIqBYcS8SWc27NhpsH2LiXK99W92bf+cdm1PoE+Pdlghg6tGj+L555/n5ptuRCuNG08wf/48zjj9dBofl0soEuWiSy5l9apVZMb2TNgSw0jVmmfPns2LL77Ihx9+WG3+du7cSXZ2dmo7Pz8/JdU9cuRI8vPz6dGjxyGTCX/iiSeq7nRdr3Wwdq23HYlA8+ZUJ9ZzINLZd999Nz//+c/Jy8uja9eudO/eHcvvizBNk3A4THFxcaX8H6scTqNwPPBN2vZ6oM9eYR4C/iUiNwOZwODqIhKR64DrAFq1anXIExoQcCio6FMoLCxk2LBhPPvss9x4/Q106ngy8+bPS6mXuq7Lmq/WkJmZSSwrRqdOnfhs0Wec0rUjoNES9SQpEp46aVmx4U0ow+szbdXSpqRYIQJWyB81pDXadXEKt4NSaAEJhTFDITKy62G7GsTwJDGqGYmzZMkSfvzjHzNz5kwaNWpUbf4sy0IphWEY7Nixg/fff5+lS5d68tu+9MZjjz1Go0aN2LVrV6Vzd+7cSePGjWnfvj1ff/11rQrYKi0FpcC2GTl0KHdfd523ClqjRmAY31o6u169ekyaNAnwOqPbtGlDmzZtUudVGJPvBRVD3g71H3ApMDFtexTwzF5hbgNu93/3w2tFGPuKt2fPnjogYG8+//zzuk6CzszM1FprrZTS8+bN0y1bttTxojJdWlyi27Rpo9955x1dXl6udxXu0kOGDtGP/uFRvbtst/7oo//qtm3b6MVLFuid5eV6w45yfc+9j+n/LXX1/5a6eulSV69d6+i3//4vPfScH+p4PKGLiop0ixYt9JJPP9WlO7bpK0aM0I8+/KAu27pJn3H66frDDz/U33zzjW7VqpXetm2bLiop1z847TR94403Vkn3unXrdLt27fTcuXP3mb8+ffrolStXaq21fv755/V1111X6Xj//v31Bx98oOPxuG7dunXqmaxdu1a3atVKFxYWaq21vuOOO/TVV1+tE4mE1lrrjRs36ldeeWXfN9dxtP7yS62XLdN6zRqty8srHV66dKnu1q2bjsfjes2aNbpNmzbacZwq0dx11136xRdf1FprPXv2bN2rVy+ttda7du1KpeeFF17Qo0aNSp2zfft23bFjx32n7wijuu8BWKhrUXYfzo7m9UDLtO0WVHUPXQv8BUBrPQ+IAsf+IqgBxyyu65JMJsnrdgrdunSj4M2/EM2IMXXqVH73u9/RrVs3evboSY+ePbjpZz8hJDZdu57EY79/jFGjrqdXl+6c0bsHmzZswRDIqado1cqhWTMH0/AmuIl40tl/eu4Zrhw9ilNPPwPTMrj+Zz9FsusjhkE4HKZFixY89NBD9PvBDzh36JAa/f0PP/wwO3bs4IYbbiAvL49evaoX0hw2bBhz5swBPNfRhRdeWOn4xRdfzOuvv04kEuHVV1/lmmuuIS8vj0suuYSJEyeS449aGjduHE2aNKFTp0506dKF4cOHp1aHq4Q/0Q/wRkw1bw7NmkHr1lUUTQ9KOvuee5g4cSLgqYp27tyZjh07MnPmTJ566qlU3LNnz+bcc78/Y2AOm3S2iFjACuAsYAPwMfB/WutlaWFmAlO01i+LyMnALOB4vY9EBdLZAdVxJEhnK6VIJpOYponh+ktQhgySySSu9twrSZVEaxsLjYiFlijlZSY7d5okE56EtYimXraiXpaLlTY3TQRMw0GUk9IhwgHJysYwPV2kir4LrTWu0iRdheNqwpZBNPTtROk2bdrE6NGjeffdd7/djaoNtu3JW2dkeC6iOuSiiy7it7/9LSeddFKdpuNAOCKls7XWjojcBPwTb/b7S1rrZSLyMF4z5m/A7cCfReRWvE7nq/dlEAICjmSUUp6ktWGiHQWmUBIvwVY2IoIpJoImJOA6MXYWRygtNnAdQWuwTE29bJeceopQ2AAxvME0WiPJUk9YyFUoCWEnXFCCZVmEI9FUZ7HSmvKkg6O0L08hxMImoWpmSx8oubm5/OQnP2H37t21Gj10UGjtrXOwZYvXqVxe7ukWGXUzpSqZTDJ8+PCjyiB8W4JFdgKOCeqypaC1RjmKRHmckGV50hACSW0Td+NErAgmJrYWSoodindbJOKWt9gNnoBcvSyHBg3BnzqQFrmCsmJP/z8UQSO4SqNcl3BGJhIKg1/ga62J255SajRkYhmCUdc6RAdCuoAdeMqmubmBgN1BcES2FAICvg9orXGTDk7SxjRNrGgYV7kk7TgJVY7CoEwpSgpNSgoNtBtG/BZARqaifrYiGvZE54y9avPilEM8jnZdyKiHKwauY2MYQiQzE1tMlAbDVX5a8NYvCBkYR5MstNbekpjbtu2Zcd28uadbdDTl4xghMAoBAd8C13VJ2AnEMHAtm0Qy6Q3PxMYUwdARtm0OkygzESAS0WRnOWRlKgQwTK9MNMNGeqTesNKyMrQRwg1HUK6LaWhCoTBJTJQWlNbEQofGNVTnFBd7BiEnx+tMDgTs6ozgzgcEHASu61KWKMNRDiYGliFoN4mJxjIEMClOZrBjcxjlCKZomjR2iUUVpiWI6c09YK+KsHZdVFkpol1vxbRwBFtpMCy0aaG1t65xNOwZmaOqRZCOUt5fxWI3ubme++h7MDnsSCcwCgEBB0HSTaJQZIYzMJSB48YJGQaGEUEpi+07DAqLDAwgEtI0a+oQiQmCUcUQeGicpI1OliF2EsIWjhlFI2gRMiLh1BBLQw58dvARRXm5J1ERCnlKpiLezOT9LI0Z8N1wDLQ7AwK+W5Tj4tgOljIxHMF2dqMlSVZWY3r27Ee3bj248srhFO8uJKeeIrdpnDWrlzD0nLPp3KUznTt14jfjfo1KxtF2Ah0vZub0aZx++mn07Hs6eWcM4o6HfocKZWCGo2RmZhAOWTh2knOGnE337t33qdo5cOBAqhuM8fLLL3PTTTdV2T99+nS6deuWmqNQk8xFeXk5AwYMwK1Y0BlPiiIajVJUVLTP6wwcOJCFH30EW7ZQsmwZP73nHtqdfjqdu3Shf//+LFiwYL/3fV9orRk7dizt27enW7dufPrpp9WGSyaTXHfddXTo0IGOHTvy5ptvAt7s6by8PPLy8ujQoQP169cHYNu2bQwdOvRbpe1oI2gpBATUEu2P+rHjSVzlELIiOOKCaeA4mUSjMaZMWYQG7rv/Kt55+xl+ededJEsLufjSETzz5HjOHnwWZWVlXDryCiZMmMD1117Dss8/59Y77ubN/Nc5+cQOlGLyWv5rZEdDlUYPLVq0CNu2Wbx48SHN11lnncX555+PiLBkyRJGjBhRaX2ECl566SUuuuiiSutE5+fn07t3b6ZNm8bVV19d80VcFzZsgKwsfvzAA7Rp356Va9ZgWBZr1qzhiy+++FZ5mDlzJitXrmTlypUsWLCAn/3sZ9Uamt/85jc0bdqUFStWoJRi586dQGWdpWeeeYZFixYB0KRJE3Jzc5k7dy6nnXbat0rj0ULQUggIqAVaa1zbwU4k0QZIyADTQOOwY0eYr7/2/P2GqWnYOEn//r3Yvm09YSPO1LfeoF/ffgwefA5KGVhmhMcffZQ/PPk0ok2eePZ57r7zTjr16IXUq084K4uxN99UySBs3bqVK6+8ksWLF5OXl8fq1auZNWsW3bt3p2vXrowZM4ZEIlEl3ZMmTaJDhw4MGDCAuXPnVpu3rKyslDuqtLS0RtfU3tLZq1evpqSkhHHjxpGfn1/TjfMmoSUSYNus3ryZBZ9/zrgnnsDwO5Pbtm3LsGHDavUcamL69OmMHj0aEaFv374UFhayadOmKuFeeukl7rnnHgAMw6Bx46oCCvn5+Vx++eWp7UA6OyDgGKD4/aqSyweLq12UUoT7/QBESIo/Wzih2LY1imN7dSsRaNasBNE2H/57NmOuGg2GybIvVpLXtQvJsrKUYmq7tu0pLS1lW1KxbPlybrj1FySUEDIhXM1ooqZNmzJx4kQef/xx3n77beLxOAMHDmTWrFl06NCB0aNHM2HChJRqKXgzkB988EE++eQTcnJyGDRoEN27d682j9OmTeOee+5h69atvPPOO1WOJ5NJ1qxZQ+vWrVP7KgrPM844g+XLl7N169bU+gQpRPZMPGvQgGW7d5PXvXul1kZNXHbZZSxfvrzK/ttuu43Ro0dX2rdhwwZattyjqlMhnZ2bm5vaV7ES3v3338+cOXNo164dzz77LM2aNUuFWbduHV999RVnnnlmal+vXr2477779pveY4XAKAQck2SfOeiQxVVcVozhKrRSOIZgK4eywhAlRd7iM1HLpXmjUuLxcgb1P511X39N97w8zjxjAG7cxrGTKA06HEGsEA7i1cZFyIiGMMRb7zg7atW6A3n58uW0adOGDh06AHDVVVfx3HPPVTIKCxYsYODAgSldocsuu4wVK1ZUG9+FF17IhRdeyAcffMD999/Pe++9V+n49u3bU372CgoKCpg2bRqGYXDRRRcxdepUbrzxRkQpb7JdBY0bQzSKNGzodTLXkgNZ7ay6Sbh730vHcVi/fj2nnXYa48ePZ/z48fziF7/glVdeqZSnSy65pJLRatq0aUo76ftArdxHIhIWkfaHOzEBAUcS8bJydhQWUR53cLVJwrBwdIjdWzIoLYphiEHjeglOOL6cqOESi8X4+L//ZeXy5SQdhwkvv0yZNjixU1c+XboMCUcxrRCxsMXWjd+QlZVFowb16dy5M4sWfXpAI4pqq0RwoKOU+vfvz+rVq9m+fXul/bFYjHg8ntpesmQJK1eu5Oyzz6Z169YUFBR4LqSiIholk+zasMHrRwAwDHYWFtK4cWM6d+7MZ599hlJqv2m57LLLUp2/6X+TJ0+uErY20tmNGjUiIyMjJeR36aWXVumQLigoqOQ6AojH48Risf2m91hhv0ZBRIYB/wPe9bfzRGTa4U5YQEBdkHRtdifK2bG7mJ2JBAlDY0VChMIhLEx2bjRJlBtYhkvLJkU0rlcKWiDsDadUoRAZ9XIY9+jveeLJJ7FdlzFjruaj+fOY9585xMImrp3klp//nDvvvBOAO+64g0ceeSRVi1dKMX78+H2ms2LJyFWrVgHwyiuvMGDAgEph+vTpw5w5c9ixYwe2bTN16tRq41q1alXKyHz66ackk8kqayo0aNAA13VThiE/P5+HHnqItWvXsnbtWjauW8eGr79m3ccf07tTJ+YuWsRmv3a9cOFCEokELVu2pF27dvTq1YsHH3wwdc2VK1cyffr0KumaMmUKixcvrvK3t+sI4Pzzz2fy5MlorZk/fz45OTmVXEfgGcjzzjsvpfQ6a9YsOnXqlDq+fPlydu3aRb9+/Sqdt2LFCrp06VLtvTsWqY376GG8xXFmA2itFwethoBjCa01juuSdByKE0nQYGlFxDSIGBaJMmFHqaak1EQriFguxzcuxgobaCMKCMp3N5SWJxANPbt15ZSuXZjx9t+4ZswYpk+fzs0338xNN92E67qMGjUqNWyzW7duPPnkk1x++eWUlZUhIvvteI1Go0yaNIlLL70Ux3Ho3bs3119/faUwubm5nnR2v37k5ubSo0ePSsNJK3jzzTeZPHkyoVCIWCzGlClTqm1hDBkyhA8//JDBgwdTUFDAzJkzvY7kwkLYsoULBw2iYOZM7rrvPp569lnOveAClFJkZWWRn5+fEu2bOHEit99+O+3btycjI4NGjRrx+9///mAeXYpzzz2XGTNmpOKsWDAHIC8vLzVi69FHH2XUqFHccsstNGnSpFK4/Px8Ro4cWSXvs2fP/tYd4UcT+xXEE5H5Wuu+IrJIa93d37dEa93tO0nhXgSCeAHVcTCCeEorHNdGOYrdtoNyFSaaDMtC6wS7d0coLg5jOxVqpZAVSdK8WdKTpwhloJRCuQ6OEhzHIRaNEA5ZGL6ROKonme3FokWLGD9+fCUfPBs3ekYBvNnIzZsfcwJ2/fv3Z/r06TRo0KCuk1JrDrcg3hciMgIwRKQN8HNg/kGlNCCgjlFaYSsbEhrHsXGUSwJwEbJMT0hu506T4uJ6KO1LXRuK7AybetFyIlEFVgQlBspxQGuUAo2QmRElcgzPyu3evTuDBg3Cdd09HbE5OVBS4ukVHYMCdtu2beO22247qgzCt6U2RuEm4AFAAW/hrY9wz+FMVEDAocRVnmid7dqgNOIKooVy8RQ5DcMkI5GkZDfs3B1CKa9lEAvbNMhOkllPkHAYcSOA5v+zd+bxUZX34n7e2Zckk30lIZCFHSIClSLautWLSxEr0lq81KWtS/Wq7ZVba6+/llZrrbZuXBWq1y3hakW0RW2LSxUXVNZAgBDIvu8zmf2c9/fHIUNCQjIYlizzfD7ng2fmzMl7xuR8z7t8n68ijIBAUbS6BUJvwGwyYjSM/rSf6773Pa1n0D3nYLdDbu5pq3dwsklKSmLx4sWnuxmnlHCCwreklHcDd3e/IIRYghYgIkQYlqhSxR1wa0NEgSACgUHRI6QeVQcevUCnM2LR6/C5FGoarQQUHSCw2RSS4jxYzNr4uwCk30fQH0BBj48gOqFVOLPYrJiN4S8lHbGoKrS0QHOzNo9gtWpV0WDUBoSxSjhB4Rf0DQD39PNahAinle5AINHmySQCfdBAMKhD6HT4BWDSIaTE5PNhUiXNHQY6u8yAwGQSpMS7sUUJpM6MFHqUYICg3w+A0Fsw22woQYixaOPmoz4YwBGBXXfGdFxcRF43ijlmUBBCfAu4GMgQQvRcHxeDNpQUIcKwQEqJROJTfAghMAsziipxBRRUfxC90YDBqMME6IVA+Hy4/EYq20wEgwIBJMT6iHd40elA1UdrK5J8PqSqYLJYMZpMBFWJJ6DSbZ8Y9QFBVaGxUSuAA2AyaYpru/30tivCSWWgnkIjUAx4gd09XncCK09moyJEGIwOXwct3hY6fZ20+9pxBB34ggEsmHH5fQSkVhg82qBHbzqSnaoEVBobDTjd2q++xRwkLcmL0W4DjKgcqbWsE2Cy29EbjCiqijegYjbqMBsGVzSMCroDghAQHw9JSZGhojHAMf8PSym3SSnXApOklGt7bP8npWw+1uciRDhZSFUiVYk34KW0ZT9dXhdxplimW6YgFAOKz4DLFyAgJRaDgWizEb3ZgFQVFF8nHa0eyssFTrcBoRMkJyqMHxc4HBAO11pWVQwGA6qiokgISIE3oOAPquh1YsCAoNfrKSgoYPr06Vx22WUh1w7A7t27Oe+888jPzycvL49f//rXvbKS33rrLebMmVZzc30AACAASURBVMOUKVOYPHkyP/3pT/uc3+fzccEFF1BQUHBC1dndfP755+j1el599VXthcRErVeQnQ0pKXh8vqGpsw+3yeVy8aMf/YicnBymTZs2LNTZlZWVITfUzJkz2bhxIwC7du0a2P46Cgkn7GcIIYqEEDuFEPu7t5PesggRDiOlxF/lxLu3Fc++Fiq2lmCqUMhoiMewJ8D+Tw/iVxV0OhW7SU+0yYDFqAOdJOBz4+roorrWTkOTHUU1YLMLsrMlcbFKqOBNd+8ARaGry43XH0AYzQhEqMKZaZDVRVarle3bt1NcXEx8fDxPPPEEoNUhuPzyy1m5ciX79+9nx44dfPzxxzz55JMAFBcXc+utt/Liiy9SUlJCcXExEydO7HP+nursq6+++oR+x4qicPdPf8q3zjlHm0gGrSra+PHapDKDq7PD5YYbbiA+Pp7S0lJ2797Nc88910ercbz0VGc//fTT3HTTTf0e11OdvWfPnlAW+KpVq1i6dCnbtm2jqKiIm2++GYAZM2ZQXV1NZWXlkNo3kggnKDwHPIv25/NvwP8BRSexTREi9EYFpSuALw22RBVTk9qGOT2OMl8dH/jqKEnQodODxSDQ60GnU1BVD4rio6VJT32jA3/QjMGoIyVVMm6citEoQQaRqnpk3b1Oj19R0ZmsxMREYbeYMBv1oe14aiHPnz+fmpoaAF5++WUWLFjARRddBIDNZuPxxx/ngQceAODBBx/knnvuYfLkyQAYDIbQTambk6nOJhjksV//misXLiTZ4YCurn4P+0rq7KMoKyvjs88+Y9WqVaEM5+GgzhZC0NnZCUBHR0cvb9Jll11GUdHYueWF81tuk1K+AyClLJNS/gI4cQrKCBEGQEqJEgyiyCANshmbtJHozaC6oRO3zoBITCI3rgWjUBAiiKr6kTJI2VYnX77r5mCxm5aKNryNTehcDbRXNlGxq4WKXc0c2tFM2Y5WKnY2U7a1nn1baqje007DvjYqi1s5tLO51xYuiqKwadMmLr/8ckAbOjrzzDN7HZOTk4PL5aKzs5Pi4uI+7x9Ntzp74cKFbN++nYyMDFasWMG6devYtWsXwWCQ1atX9/pMtzp78+bN/OMf/2DPnj1Hf7nQ0UHNxx+z/m9/48ff/a7WK+hnIjlcdfZg7N69m4KCgrDV2eEK8Y6lzu5JT3X27Nmzueqqq2hoaADgvvvu48UXX2TcuHEsWrSIxx57LPS5OXPm8OGHHw7a3tFCOEtSfUJbZlEmhPgxUAMkD/KZCBGGhJSSYJMHX1sX5S2HcNFFjauVoNdBHSpWvY4kuw27rpU44UVgBL8Ot0eP06VHRkURG6V56tLSJWaz7HlyAl4PBHWYouLwSu3ZyGQY2iSyx+OhoKCA8vJyzjzzTC688MLQtRxrpdJXXcE0ZHV2IAB1deBy8R+/+Q2/+/nP0eflaUtN+2nTcamzT9C1nkp1dmFhIStWrOCuu+7ik08+Yfny5RQXF6PT6cacOjucoHAHEAXcBvwGcADXncxGRYiAhPa6JkqNFXTFeolyRRPrT6PSamOKzcpEYyVKcymKt5W2hlx06ZKDlRbUwzd4oYO4eElCgtrrHqcqCorXDVLBarOhM5vxegPYzQZ0Q1xi2j2n0NHRwaWXXsoTTzzBbbfdxrRp0/jXv/7V69iDBw8SFRVFdHQ006ZN48svv2TWrFnhfz1DVWe7XNqm1/PF3r0su+MOuOMOmpub2bhxIwaDoVcm70DqbNB6EhMnTuSWW24hISGBtra2Xj+utbWVxMREYmNjQ+ps3SArmY6nyM5XVWevXbsWgLVr1/L2228D2tCf1+ulubmZ5OTkiDr7aKSUn0kpnVLKSinlcinl5UDFKWhbhDGKDKj4vC7avNtxde3E2FpPi+8APlsLdlMT440HcTY10LjTRuEr53H7qgV4Agak0GO2CBKTJOOzVRITewYECaqC6nWiI4jVbkdnshJQVMK8v4aNw+Hg0Ucf5aGHHiIQCHDNNdfw0UcfhQrXeDwebrvtttOrzo6N1ZaYTpzIoYqKkAL7O9/5Dk8++WQftcOg6uzaWmpqaqioqGDu3Lls3ryZ+vp6YGSos7Oysti0aROgyeS8Xm+ohxVRZ/dACDEXyAA+klI2CyGmoekuzgPGnYL2RRhDBNu8KO0+mtuqONDxDs1tLXjt+XTG5mGzWki2WsjUg/tQG689n8i727LpIgb0ApMJMjMVLJa+d3ipqigeJ1IJghCYY+IRBiOegEpQVTAZdEPuJRzNGWecwaxZsygqKmL58uUhdfYtt9xyetTZiYnMzs09spxUCC0oHAf9qrN7cMUVV1BUVMTdd9/Nn/70JxYtWjRi1Nl/+MMfuPHGG3nkkUcQQvDcc8+FelkRdXb3G0LcD1wJ7AAmAOvRDKm/A1ZLKd2nqpE9iaizRydSqrjKyumUTra3bcHX2UqMdQG2jCxagyrZnQqdbYK33rLy1ptGPKoFg83E5MlBVqzoIjOzITS+3ougl6DHA0gMthgMZgs6nQ5Flbj9QcwGPQa9OOFBYdjg9WqKiu6hn5SUIzK746RfdfYox3c4N+Ojjz7CYBg51YtPljr728AsKaVHCBEP1B7e7zvIFyHCV0RRfASDHfid7bS07eZARyNOtQMZVUB9bArRbi+BQ/DUazFs/sSK3ytBp6NgHnzvmg4KCgIIccTE0BMR9ILiR9XpMdujMRi1msq+oIKiSASD5x6MWFRVk9e1tGirjIxGTVERFfWVT9mvOnuUU1lZyQMPPDCiAsJQGehKvVJKD4CUslUIsTcSECKcaDzuKlqaDtBY0YazU9LpUtE5xiF9drLKDvHBm/EUbcpBRYdOJ5l/dpCrr1WZOjU44HlFoAupBAlgRG80YDxcLtMfVPEFVEwGHSbdKO4d1NQcEdh1KypOwI38uuvG1hqTvLw88vLyTnczTikDBYWJQohuE6oAsnvsI6VcMtjJhRAXA39C09CskVI+0M8xS4H7AAnskFJ+L/zmRxhJBAIBVFWls7MTv9+PqrppLPuItmYBhmRkAtBaRVfSmVironng6WkcrLQgzDouuMjH1Vd7GT++bznJo5GqQsDrQ+pN6A0GTBbLkfekxKjXYTGO4iddIcDv1wR26elHFNcRIoTBQEHhyqP2Hz+eEwsh9MATwIVANfC5EOINKeWeHsfkoRXsWSClbBNCRPIfRhHBYJBgUHuidzqdtLa2YjQaUVWVaFs0FYcO0uFspyMpDWOcglSjaLNMZsvreXz2lh2/RyUlOchtt3fwta/3L+aVUuJqbtI8RT4fIJFKEJ0QWKId6PV6LQFO1XoIQVVqCozRhscDFosWEMxmyMrSEtEiArsIx8kxg4KUctMQzz0POCClPAgghChCm6fomVZ5I/CElLLt8M8cPCUywoihoaEBr9eLTqdDVVUSYxOIc8Ti8Xv5pOTv+Lp240/IIuCMxdtkZ/fHRv65YTpujw0dKosu7OTmO4P0eNDvg7ezg5aaKmxp4zAYjeh1WtU0neJFHA4ILl8QKbX7pc2kx3Acuophj6JAQ4NWDS0jQyuPCRG9dYSvzMmcPckAqnrsVwNfO+qYfAAhxGa0Iab7pJRvH30iIcQPgR+Ctp44wvDH7Xbj8XhITU3FZtDu6u37GyjevZOKuip0xgqCsdNpq4uhdZ+ZDz6czIEDVoROz8QJPm7+UQdTZoDZcuxhnkAggLOzA3tcHEazGbPFAooflEDoGG9Q62FEmQ3oRtscQmcn1NdDUFtqizL40FqECINxMh+Z+vsLPHr9qwHIA74BfBdYI4SI7fMhKZ+WUs6RUs5JOs611RFOPaqq0tzcSKDTg7O8keIvvmD71i18UvE5u1qrMY2zEps3nkOuVN55biL/+9IcyquiiU/Sc9sdbv5njYuCr+kx23oHBE9HO83lB6k7sI/qfXtoqTiIIeAnK1szigaDQZSAH29Q4saM2x8kEFSxGvWnJCCcMnX2rFmse/JJqK7WAoLNBhMnahPKHL86+/3338fhcITcQr/61a/6vT4pJeedd15IHAewfv16hBDs3bu31/kuvfTSXp9dsWJFSMkdCARYuXIleXl5TJ8+nXnz5vXJefgq3H///eTm5jJp0iTeeeedY17DPffcQ35+PlOmTOHRRx/t1e6CggKmTZsWSgT0+/2cc845oWHQsUDYPQUhhFlK2VfDeGyqgcwe++PQlrUefcynUsoAcEgIsQ8tSHx+HD8nwjBCBlRc7e0cLNlLUFWptKsoZkiOTiXVPoEMt4DY/bzxRT7r/5iF4jZgdwiWLPGwZIkHm62f5DMp8Xo8tDU2oNPriU9NJyYmBrPZTFtzDQ1Vu/EbEwi4O0GnR9GbMRmMCAEmPadsuKhbcwFHXET33HNPSJ29evVqLrroItxuN1deeSVPPvkkt9xyS0id/be//Y3JkycTDAZ5+umn+5x/27ZtBHw+tv/f/2m9Ap0OkpO18phDzLNYuHAhf/3rXwc8ZuPGjcyaNYuYmJjQa4WFhZx99tkUFRVx3333hfWz7r33Xurq6iguLsZsNtPQ0MAHH3wwlOazZ88eioqK2L17N7W1tVxwwQXs37+/z9LZ5557jqqqKvbu3YtOpwtJ/Nrb27n55pt5++23ycrKCr1uMpk4//zzWbduHddcc82Q2jhSGPSvRQgxTwixCyg9vD9LCPHYIB8D7caeJ4SYIIQwAcuAN4465nUOG1eFEIlow0kHj6P9EYYRgUCAtvJ6Sg+UENRLouMSyUubwdTEWSSaUxBtVjpkGb97toDn788j2CGZXeBjzZpWvv99d5+A4Pe46Whppr25CeH3EW0xkz99BhPyJ5GQmIBo209LRTEGsxWj0YglKgZrlIMoqwWTQYdRrztt8wcnTZ29cycFixdT1tLCpvJyzjj/fGbMnDk0dXaYHK3OdrlcbN68mbVr14atlna73TzzzDM89thjmA/XeU5JSWHp0qVDatuGDRtYtmwZZrOZCRMmkJuby5YtW/oct3r1an75y1+GsquTk7W1LS+//DJLliwJDU93vw6wePFiXnrppSG1byQRTk/hUeBStBs4UsodQohB1dlSyqAQ4lbgHbT5gj9LKXcLIX4FfCGlfOPwexcJIfYACvAzKWXLV7yWCKeRNlcr+7ftxO/10R7lJS0uA3+HwKn4EToVVTayt9zN6hfOpL4ukaigj2uvqGHp7fG9FshIVcXZ1ICzrRVvl4vYxCSiLUaMAS9xyXFEmSS4W6GtnMqaWozj55Gbl0NJSQlCd+TXuezLoVXy6o+cM4+eEuufbnX29ddfD4Snzr7rrrv6P5mU0N5OckICa9as4aGHHuKvr7+ONxDgG/n5bNq0ifz8fK699lpWr17dy5Larc7+8ssvcTgcocpi/fHJJ58wa9Ys0tPTeeihh5g2bVqfYzZv3sxTTz0V2n/99de5+OKLyc/PJz4+nq1btzJ79uwBv5sDBw6QlZXVq7dxLO644w7ee++9Pq8vW7aMlSt7VwSuqanhrLPOCu33p84GrZ7DunXrWL9+PUlJSTz66KPk5eWxf/9+AoEA3/jGN3A6ndx+++0hx9L06dP5/POxM3gRTlDQSSkrjrIthjWjJaXcCGw86rVf9vhvCdx5eIswgtlRsx2jVyUhYxyG1i5aqpqx2WsxREdR6rXy4rOZfPnPaSB1TEh1cff39jP9W2koAR/NVeUIvfar6HN34fN6SMzIYtKUPGL0HjyNZRzqUHH6nDQ0VYd+ZnPMNGZnje+3PeHewE8kJ1yd7fNpemu3W1ty2o3BwL7du4emzu7B7NmzqaioICoqio0bN7J48WJKS0v7HNfa2kp0dHRov7CwMPTzli1bRmFhIbNnzz5h6uxHHnkk7GPDUWeDNi9jsVj44osveO2117juuuv48MMPCQaDfPnll2zatAmPx8P8+fM566yzyM/PR6/XYzKZcDqdva5/tBJOUKgSQswD5OHcg58AkXKcEVBVlbq2Olr3N9BR10SiIZnaBhdVbR7ik4wQlc7bW2by5rMOAo1BHGY9S5d6+N61QYyGbDqbGnA1NSL0euJS0vB6vZhjHKRZPMQY3IjORvzGGEr8KfhTJzAptffTZerhJabDhROmzpZS01M0NWn/bTD00VMMWZ3dg55P7YsWLeLmm2+mubk5VJWsG4PBEFJet7S08O6771JcXIwQAkVREELw4IMPDqjOzs3NpbKyMqwb7PH0FMJRZ3cfd+WVWgrWFVdcwQ9+8IPQ64mJidjtdux2O+eccw47duwIBd3uYDIWCGfA9Sa0J/ksoAE46/BrEcYYMqCguPz4OtwcOnSAjZvfZMs//sXBQwfAbMJnsFCrWrFmpeBWAqx+aA5/+VMCshVmZHfw2J9aWXFDAG97A/X7S3A1NRKVlELs+IlgMJKeNZ787FQcsgOPOYmqmDP5lzeXDvt4zhgfR5zd1GuLtZm+cpGak8mQ1NleL2pZGQ8/+KAWEGJjIScHjhpuGbI6uwf19fWhILNlyxZUVSWhH2nepEmTOHhQm/J79dVXufbaa6k4rN2uqqpiwoQJfPTRR+Tl5VFbW0tJSQkAFRUV7Nixg4KCAmw2G9dffz233XYbfr8f0Ia5XnzxxT4/75FHHulXnX10QABNnV1UVITP5+PQoUOUlpYyb968PsctXryYd999F4APPvggdNP/9re/HeoxuN1uPvvss5BQrqWlhaSkJIxGY7/f32gjnJ5CUEq57KS3JMKwRnH5CbZ48Xt9HGo+RI2zFpM0YLMkkpyZQcBjoFINond0Ig9aeez3Z9DVFUNCjOS6S8q54NxOZEoiHXWNuFqbcaSlozOZkUKHw+EgLtqGIdiF6umg3GPiQDAOq0lHRpyJvOSoEZdw9pXU2cuW4e7oQACXfPObWlbyMQR2x63OTktj9uzZR9TZPXj11VdZvXo1BoMBq9VKUVFRv8H2kksu4f333yc3N5fCwsI+N+crr7ySl19+mYULF/Liiy/ygx/8AK/Xi9FoZM2aNTgOJ9atWrWKX/ziF0ydOhWLxYLdbj/mMthwmTZtGkuXLmXq1KkYDAaeeOKJ0MqjRYsWsWbNGtLT01m5ciXXXHMNjzzyCFFRUaxZswaAKVOmcPHFFzNz5kx0Oh033HBDqIbCe++9x6JFi4bUvpHEMdXZoQOEKAP2AeuA16SUzlPRsGMRUWefehSXH3+Vky7VS727nYqGRjzBLuyqHbPJhpoYT1kwiCmqjcpNFl57Ph8pBXPnSu66qR571S70eXnU1VVjttvRm60YoqKJjo4mIcqMkQAdjZU0NTfRETTiN8WRmTONzHhr2D2B/lTBI5LaWm2p6QkS2J1I6urquPbaa/nHP/5xuptySlmyZAn3338/kyZNOt1NCZuTpc4GQEqZI4T4OtqS0v8nhNgOFEkpw1uDFmFEIAMKqrfvU6TS4SPY6aNV7WK30kVnp6RJ9aNzGLDFpVBnMqDIFqJrnLz1VDY7dmQghIGrr3bzvWWtBIqL8QnwOttBqjgcZkAlKdaE1aZDNO9hZ20XXlWHOXkS49PH4bAah1QrecSgKNq8QUzMEWldWtqQcw5OFmlpadx44410dnaGtXpoNOD3+1m8ePGICghDJazkNSnlx8DHQoj7gD8CLwGRoDCKCDR7kZ4A4ih7qKqqtBrc1Aa9VLVKLL52zDEqMUkxxNujkI07+Wj9eN56azpKQIdZ5+a6q/Zz1qwmGrf7EI2NWKZOQ9dZTbzaQVRnG46MPAzBVugEabLTHDOeBVMyxkYg6Mbl0lYWBQLa6qIJE7RgMEwDQjdDzScYaZhMpn7Lf45mBg0KQogoNJHdMmAKsAH4+kluV4TTgD7OgiFOW2EhpcTj8dDc3MannmYaXBJ3Sz2p1lYcUSayVMnHGxIofOlMnO1W8Ls5d8YBvndhKemTUgAzGC0YE1MQEyciq1qJSZ+PIyMfYeyxikNK1I7GsRMQFEXzFXV0aPsWi6a3HubBIMLYIZyeQjHwJvCglPLDk9yeCKcB1R1A7fShs2pDGFowaKaxs5Eafw2VXXrimnWkWLzkZE+hYm8M9zxuo7YmCsXjYVpWHT++qoK8b2agjzoXvckUOreiKHg8HjLiE7AnpuHFSHuHN/R+ddtpqep6ejhaYJeUpJXGjASECMOIcILCRCll/zL7CCMeqUr8tS50NiO6aBNtbW00NTXRRRcVwQocungcXVHY/bXE64wUrQ7w17cdoEJqVCvXXrmf86/NQW+f0WdSWFEUutxuzDoFn9eJ0+Wjsd1Jl08h2qL96pkMOuZOiD8dl35qURRtuEhRtPmDtDSt7kGECMOMYwYFIcQfpJR3AX8RQvRZohRO5bUIwx+1KwCKRJ9qo7mlmebWZmqVWlo7nbS5HRx0GfAE2zB7jKwumse+/XpMZgPLzt7P0ovqsBRMQ/ToGXRT3+GhtNFFtCMOh6+eoDUJnxKNTsCk1Gji7X0/M+roXtknhLaSKDVVCwonQGAXIcLJYqDF3+sO//s4WgW1o7cII5xgiwd/lRO/RWVX5S4ONB5gm6eZrZUu9jbG0hmwkh9vJaM1iqf+Zw77SlTizK38evkHXHNlG5a5Bf0GBJ/fT0OHh/ysNL6ZF8fX4jrIS09geoaD6RmOURsQeqmzL7mE9l27tMxkDquzr7iC/LPOIi8/f2jq7IIC1q1b1+f9bo5XnQ39a6OPZjSosxcuXBhShKenp7N48eLQtd12223k5uYyc+ZMtm7dCkBTUxMXX3zxkNs2khio8lq3YnCKlLJXKc7DoruhVmaLcIqRioq/xoXqDiL0AhlU8dlVSrvKqfHWECPjKa9TSfLbyE5NIictis0fRvHHPxpQlQAFE9u5++ZG4qdMBrO513BRqzuALyjx+32UtXiJj48nK8mBVecBkxXic07jlZ8arFYr27dtg7Y2/v2663hi7VruufVWPFbriVNnBwIhPfeJ4lja6KMZDersDz88Mi165ZVXhqyvb731FqWlpZSWlvLZZ59x00038dlnn5GUlERaWhqbN29mwYIFQ2rjSCGcNNHr+nnt+hPdkAgnF8Xlx7uvDdUVwJQRhUi30mLt4pOWL6gJ1GD3x+HoTCHREc8ZExxMHmeh8CU7Dz0URcDn58K52/nVz2pJmD0BYbXSFZDUdfpCW3FNJ41tTnyqnhk547hwegYJUYfHzIVu7NQKrqiA+nrmz5pFTVsbTJjAy0VFJ06dvX07BQUFlJWVsWnTJs444wxmzJgxJHX2QNronowGdXY3TqeTd999N9RT2LBhA9deey1CCM466yza29upq6sDIursEEKIq9GWoU4QQrzW461ooL3/T0UYjqg+hUBdF/pYM/pkK411LTQ2tGDQ63HXC5JN47EarFgTdQh3Fe3tLTz+x4Vs3WpE4OZ7F7/D0qtMmHOOPClVd/gIKBKzXhvWSHdYmJOfga07Ces049lz4g3s1ql9fUDAEYGdqoLbjSIEm3bu5Pof/QgMhqGpsw+TnJx8RJ3917/i9Xr5xje+cULU2QNpo3syGtTZ3axfv57zzz8/1I6amhoyM4/UBOv+fFpaGnPmzOEXv/jFoO0dLQy0+mgL0IJWMa3nHIIT2HYyGxVh6Kg+BbUrgOoNorT7EGY9IsFEZXk1DYc6iYq14lN8YAuSNM6O3WTjo5ptNFYGeWXN5dRVGbDqOvnxv73PrLxO7Jn/hiug0tLlp7rdhxCQE28k2iBJyh6Hw+HovfrI74aaL8DfBdZTv7romDfwk4XLhcfno+CqqyivqTkx6uwB2Ldv3wlTZw+kje7JaFBnd1NYWMgNN9wQ1ueTk5OprT26aOToZaA5hUPAIeCfp645EU4USocP1R1AZzFgTLejd5gpL6ui/qATe5SVlngj5Z5mfMZoyjGgCyiUlNn566Nn4m2HjJgali0tJnHyOCrjZlFZ7yeo+rCb9WTHW3AYFcxGA6mpqf0rhV312rDR+AVg6l/qNqJRVW0zGLSVRGlp2pzC7t1DV2eHwYlUZw+mje5mNKizQbOebtmyhfXr14f1ea/Xi9VqHbCto4ljDvQKIT44/G+bEKK1x9YmhGg9dU2M8FXRRZkwptoxxFqoLq+n9kA7MfE29Gk+driqCfgbyBRmon0+6j7r4i+rcumq76Jg3AHuu30PCWdP4YwzcpiTHcuczGjOGh9DQbqdOGOQaLuNzMzM/gOC3w3NByAqBSwxo28+oasLDh6Empojy0575BwMSZ2NphZ5+OGHB2zCiVRnD6SN7sloUGcDvPLKK1x66aW9fncvv/xynn/+eaSUfPrppzgcDtLS0gBteK3bmDoWGOivtbvkZiKQ1GPr3o8wTJGKiuoJhvZbmzuoPtBETJKFKkspxR378B6qJXVrDVFb91L+WhmP/HcsQZ+eBed6+O2aJFryp5IWa8GgF6FNJyRdXV3ExcWRlpbWZ2VHiJovtGCQkHuKrvgU0Z2AVlEBfr+WmdyPihp6q7OtVisbNmxg1apVTJo0iRkzZjB37ty+6uzvfpcpU6Ywffr00CTnseipzp4xYwY6nW5AdfYFF1xwzPH+ntroefPm9dJG96RbnQ3a8MsVV1zR6/1udbbZbA6pswsKCvjOd77TR52dlJTE1KlTmT59OosXLw4NcX1VeqqzL7744j7q7J7DP0VFRXz3u9/t9flFixYxceJEcnNzufHGG3nyySdD77333ntccsklQ2rfSCIcdXY2UCul9AshzgZmAi9KKTsH/OBJIqLOHhgpJd6SVtAJzNkxBHUqe7aWIdDj9BxiX2MFQb8dqTcyP3MCb743gcKXVVRVZcGCWm75qZED7dqo4pzMaMwG7bkhEAjg9/tJSUkZeJKwq0ULCjnngf7UFSU56ersngI7ISAxUVNUjLZe0ACMVXX2Oeecw4YNG4iLizvdTQmbk6rOBl4H5gohcoDngb8BLwOXDvipCKeFQLULYdBhzotFSkllaRVep0qnfUG6ewAAIABJREFUqOGT6kPEJiQyIT2D6m2S25/IoqHJCMLLkqtUfvSjeNq8kGAPkp9kRXd4PNrr9SKEOPZwUU/ayyE6DXRhCXiHP1JqwaD98II7q1VTVIyR0ow9GYvq7KamJu68884RFRCGSjh/uaqUMiCEWAL8UUr5qBAisvpoGKJ6g6jeIMaMKIQQNDU14Wz1EOWwUOVqJsnuYGHe1/nfNTY2vikwWqzkTgpy441fkDdtPJUdgtoOP2kOEzohkFLidrux2+0kJydjMAzy6+Lv0rbESaNH4yAEGI3av8nJEB8/eq7tKzDW1NlJSUmhXIaxQljlOIUQVwHLge5vZ2wUKx1hKJ1+hFmPzqLH6XTS1tqG4tWhS/bA3jpqaufyk9XxNNUGMBhUvr+8mUsv2YVO30mdM52gaiAvyUpKtKahcLvdxMTEkJycHN5yQlcj6IxgjT3JV3qSCQS0rTvnIiEBHA7oR+kRIcJoI5ygcB1wM5o6+6AQYgJQeHKbFeF4CbZ4tCWodiO+gJ+GhgacONnZcACdz8uO3bls+OtsbAJysjv52coA48bXoKoGupRpNLpgcoqJRLsW77t7CElJSccOCKoCrQe1IZagBzprITEfDCPU/imlVuegoUHrDUycqC051ekiASHCmCGccpzFQojbgFwhxGTggJTyNye/aRGOh0CDG0OyDZ8+QH11I02BJkprD2BVbOgbJvO3N7IxqEEuuaiCq5dWEDcuhUCgno5AFofaBGmOIwHB6/VisVhITU1Fd6yJVFeTlovgaoC4CVouQvoZEJ16Cq/6BOL3a3MHXV3a/iBr6CNEGK2EU3ltIfACUAMIIFUIsVxK2b9IJcJpw2MKUF9fT4vaQlN7K9HuOILuNP7fE1mg6jj/64f4/g07MFmtBJUOdIZ06ttimJBgIsOhPd37fD50Ot3AAQGgsxoQkDIDolNOzQWeDKSE1latVrKqHlFcx8SM6bmDCGOXcNbTPQIsklIukFJ+HbgE+NPJbVaE40Hp8OHqclFXV0cnnRxsPERHu4F2Xxx/fDoNj8fI9CntLP7+FtqUFOo9M9lWn8GO+lgCigz1EPx+P1JKMjIyBp5UVoIgVYhKHtkBAaC2VhsuUlVt3iAnR/v3KwSEXursyy6jvf2IImz37t2cd9555Ofnk5eXN6zU2b///e9DOunp06ej1+tpbe2bnxpRZ48NwgkKJinlnu4dKWUJEBlgHSaoniAtZfXUKy3s9+2npKYEkxKN05bIupdSqG3Wk5rexmVL/oXJbsFiycZmMjEp2cb8bAdnZcdgNugIBoMoikJGRgZG4wDrCPxdcOCf4G4D/QidO+hJbKy2uigzEzIytDmEr4jVamX79u0UFxcTHx/PE09oyjCPx8Pll1/OypUr2b9/Pzt27ODjjz8OJUh1q7NffPFFSkpKKC4uZuLEiX3O31OdffXVV3/ldh7Nz372s1C28P3338+5555LfHxfX9Vg6uxw6anOLi4u5s0338TpdA7pGnqqs99++21uvvlmlH4SCz/88MPQtc6fP58lS7RaYT3V2U8//TQ33XQTQC919lghnKCwVQjxlBDi7MPbaiJCvGFDS1Ujxc172KsvQ1EFDnMOFms8n7+pUF4cRazZx4/v2E/WlAQyk7LIcNjJcJhJijpy4/f5fPj9ftLT00M642NS/QXYEiDvArCfYuncicDjgebmI/t2u9Y7OMFzCPPnzw9ZOl9++eVhrc7uSWFhYZ9s324i6uyxQTiPRT8GbgP+E21O4V/AYyezURHCo62+hfIDpbSbXSR6DXT66tnbWIOnwsonb34LRVhY+q0dTM3PJ95mDGUndxMMBvF6vaFVRqZwVthIBVJnnKQrOnHs27ev9wuqCp2d0P1Empx83DWSJ02aFNZxiqKwadMmrr9eKzsy3NXZ3bjdbt5++20ef/zxft+PqLPHBgMGBSHEDCAHWC+lfPDUNClCOLi73JSVHqBdujDprfhMLqrcE7F3BnnnjbMwWSzMWdjG4vmdOGJ63/yklKEs5bS0NKKiogbOQ1ACoAa7P3wSr+rE0esG3tWlrSyKjtYmkOPjISnphCsqPB4PBQUFlJeXjyh1djdvvvkmCxYs6HfoCCLq7LHCQEV2fo5WYW0rmubiV1LKP5+ylkU4JoFAgAPby9lX10yrqRyPNwFTwEBUu4lXX5hMU7OZ9PE+vnVlC4am3n8YiqLg8XiIiYkhKSnp2FK7nlR8rAUFITR9xUhRWCgKNDZCt8bZbIb0dE1VcRLonlMYaersbvoTxfUkos4eGwz0qHQNMFNKeRUwF7jpeE8uhLhYCLFPCHFACNHXd3vkuO8IIaQQYlBZ01jGGVQ45PLw99JtbGoqpyK4H9XvYpLioCDgYMPaidQ0xJGeofKTm8qZ6KrG1GPIyO/3h+YOUlNTBw8IUkJbOQS9MP7rmuRu4rmgHyFBoalJCwhCaD2DCRNOWkDoyUhTZwN0dHTwwQcf9JozOJqIOntsMFBQ8EkpuwCklE2DHNsHIYQerWLbvwFTge8KIab2c1w02pzFZ8dz/rGGVCUHOzy899EWDu58H1vDXibQwRkz5jFtUgHPvPR1KusSSU0TPPBABwn6FpAS/eFhFL/fj6qqZGZmEhUVZtGblgPQWAKOTDCMEAFczyfoxERtyGjixJMyXDQQI0mdDdoY+0UXXYTdbj/mMRF19tjgmOpsIUQ78G73Llp9he59pJRLBjyxEPOB+6SU3zq8/1+HP3f/Ucf9Ea2620+Bn0opB/Rij0V1tpSSXZUdfF5SSlf5LiYmNaBTJpM8LQNXUzb33x9Nfb2exESVBx9sJy1NpXLnfvQGPRlTc0IBYdy4ceFNJitBaCgGZ51WOc0y/I2YJSUlTElP12ymmZljSml9qoios0eOKfVkqbOvPGq//yUJxyYDqOqxXw18recBQogzgEwp5V+FEH2zdY4c90PghwBZWVnH2YyRSYMvgFtRqfMFaHP7KS+tIsbTSPL4eKxRHcSYU/ns/TzWrrWjKJCTE+TnP+8kLU1FKStD196GTEoiGAyiqioZGRnhBQSfU5tDkCqkzx4RAYHGRi0rufsBp7NTyz+IcEKJqLPHBgPVaN40xHP3N8MV6pYIIXRo2dIrBjuRlPJp4GnQegpDbNeI4MvyCuxqkKArgLWxjfEdJSRmRlPX2ozOC++8k8Mrb2pd/csv93D99V2YTKD6fFSXVtGaPoGE+Dh8Ph/jxo0bPP+gG58TLA7IOPOUFsn5SqgqvP46/PGPcN99mqIiJUXLSI5wUoios0c/J3PGsBrI7LE/Dui5risamA68f3iFRCrwhhDi8sGGkEY77YEgHZ0dnDE+kw53O1V6J/6UGpxkg1dyqPE8XnkjFp0eVq7sZOFCf+izSmkprToLmTnp6BUviYmJ4a+caNoPXU1gsg//gFBVBatWwZdfavsWizZ3MFA2doQIEQblZAaFz4G8w6rtGmAZ8L3uN6WUHWj1ngEQQrxPGHMKo51mf5CtnV3YBdiNUWxr3YvHXk+yI4PEjjPZ6QnwP89MBgE33NAVCghSStTaOqTLhS9tHNEGFbM9mtjjGUZx1kFcNtgTBz30tLNtmxYQ4uPhP/8T4uIiASFChBNA2EFBCGGWUvbNoz8GUsqgEOJW4B1AD/xZSrlbCPEr4Asp5RvH39zRjSolNT4/ySYjDgN4nG48Sj3jHXqizZOoaujiqWen4/MJvnmum2+f34g8nKAb3LcP/H5EWhr+gAUpZfjFcXpiS9B6CsMRp/OIjuKyy7RJ5W9/WxsuOrz8MUKECEMjHHX2PGAt4ACyhBCzgBuklD8Z7LNSyo3AxqNe++Uxjv1GOA0ejXgVlUqvn3KPFnPnOOy0BVVKDpYh9U1YDOPpLLfw+NNZNLeYyEtr4eb5n6Ds1SEOr7UWOj1yZgFlXSr+1i7S0tIGL585UvD74dln4eWX4YUXICtLyz249trT3bIIEUYd4azbexS4FGgBkFLuQFueGuEE0RFUaPIHmGS3cEFCDPFGAz5fgA5nG0lJsegCcTz4eCoHaqJJinJxzw92Y5s1GcOcORhmztS22WdQ4xfUtnaxYErm8WVgetq1fISAZ/jVENi1C77/fXjmGU1X8emnp7tFx2SkqrM7Ojq47LLLmDVrFtOmTePZZ5/t97wej4dzzz23l330kUcewWKx0NHRMeDP6dkml8vFj370I3Jycpg2bRrnnHMOn302tDSlY6mvj8bv9/PDH/6Q/Px8Jk+ezF/+8hcAKisrQ26omTNnsnGj9iy7a9cuVqxYMaS2jTTCCQo6KWXFUa/1ddJGCBtVSpxBhRKXhxKXhxqvH5tez3irGZ0QqKpKeWUFPvE5Vl2AR/6Uzu59scTF6/jVis9JmZeNLjYW0WMtvsevUNXiYvr4ZHLSj2NOwNsBlZ+CuxUS84bP0JHHAw8/DNddBwcPar2DZ56BYbz6ZaSqs5944gmmTp3Kjh07eP/997nrrrtC2cY9+fOf/8ySJUt6ZcIXFhYyd+7cXsqIwbjhhhuIj4+ntLSU3bt389xzz9Hc01z7FTiW+vpofvOb35CcnMz+/fvZs2dPKAt81apVLF26lG3btlFUVBSy1M6YMYPq6moqKyuH1L6RRDhBoerwEJIUQuiFEP8BHNuqFWFAWvxB/tnSySftLloDQWx6HQkmAxOsWg6B1+OjbH8Fjc4DJCXH8/c3LuLDj5Ow23T8+tcdjEv2aCtteuD3+2lzduGIsjM1e5BymEEf1Hyp5SJUfqr9a0+E7AWQkHOyLvv4KC6GZcu04SIh4N//HQoLYQDD53BjJKmzhRA4nU6klLhcLuLj4/sdejxanV1WVobL5WLVqlUUFoZXtr2srIzPPvuMVatWhSr7TZw4ccgZwwOpr3vy5z//mf/6r/8CQKfTkZioPUAJIULFgzo6Onp5ky677LLjqhcx0gln0PkmtCGkLKABLfv4uD1IETSaAgFSzEZmRFnR9RiqCfoVDu6qp7amnmZ/M+YoF1b1Yl59NQaDTs8vftJCXqaPYMuRIYdgMIjL7UU1mGkX0SQn2TAZBvAZqSo07tGGi1Kmgd6k3XQtwyzRKzpa8xbl58O998JRmZnh0NQ81DSbviQlnh/WcSNNnX3rrbdy+eWXk56ejtPpZN26dX1Ksfr9fg4ePEh2dnbote7aCwsXLmTfvn00NjaSnJw84HXs3r2bgoKCsESMV199dV8FOnDnnXdy7VHzSQOpr7vpHs679957ef/998nJyeHxxx8nJSWF++67j4suuojHHnuMrq6ukKcKYM6cOTzwwAMhV9VoZ9CgIKVsRFtOGmEISCnZ5/bS4g8yzmIKBQQpJW31XVSVNdLV5YIUF0K2k9wYx7qi8fj9ggUzncz5WhClvBwRrdUOVhQFj9eLx+TAq+qJjdIxNW2QLNP2CnC3aJnKtv71yKeN7dth1iwtSI0fD//zPzB16leuhBbuDfxEMlLV2e+88w4FBQW8++67lJWVceGFF7Jw4cJeWcvNzc19ljcXFRWxfv16dDodS5Ys4ZVXXuGWW245Ydc60LzJ0YSjzg4Gg1RXV7NgwQIefvhhHn74YX7605/ywgsvUFhYyIoVK7jrrrv45JNPWL58OcXFxeh0ujGnzh50+EgI8YwQ4umjt1PRuNFEpddPlcfPeKuZFJMRRVFxd/ppbXBSVlKFNHhJmWzBqWwm3tBKXXUyb79jQadTue47HUjViezsRDcuA4DWzi72O020eWFSajRzsuOxGPt5+urWVpR/BE17ISF3eAWE1lb4+c/hhhtgY4+FajNnDqk05umge06hoqICv98fmlOYNm1an4nf/tTZx8OJVGc/++yzLFmyBCEEubm5TJgwoVfNZdCuzev1hvZ37txJaWkpF154IdnZ2RQVFYWGkAZSZ0+bNo0dO3agquqg7br66qtD9ZR7bs8//3yfY8NRZyckJGCz2UIiv6uuuio0Ib127dpQtvb8+fPxer2heY6IOrsv/wQ2Hd42A8lA2PkKY5Vmf5CdTndoK3P7mGS3MM5iwq+4KCurpmRPOcV7S/BbXbTF1LOn5T0CnW4szRN4qWghQtFxxVnNZHjL8ZcdpNMeS5vBRnVzJ5UeI7Exds7OSyQxagCFRcALCEiZDhPO0ZLThgNSakHgO9+Bv/9dmycJBE53q04II02dnZWVxaZN2nBbQ0MD+/bt6zPRHRcXh6IoocBQWFjIfffdR3l5OeXl5dTW1lJTU0NFRQVz585l8+bN1NfXA/DFF1/g8/nIzMwkJyeHOXPm8N///d+hwFZaWsqGDRv6tGvdunX9qrOPHjqCgdXX3QghuOyyy0Km102bNjF16tQ+30FJSQlerzfUwxpr6uxwho969eGEEC8AY0uT+BVoCQSRQJJR+4qTTAZSTFrG7bbyXQTr9QSiOtDZBKrRiBqoIaq9BZM/l237JnCoPJkEh45l/1YJ6Q6qSaZdGtG1OLFarMzITmF8gv3YT4IdNdB2SCuOY44G6zCaN6ivh9/+Fj7+WNv/2tfgnnu0AjijhJ7q7OXLl7NhwwZ+8pOfcMstt6AoCsuXL+9Xne12uxFCDDrx2lOdHQwGmTt37oDq7LS0NGbPnt1vMft7772XFStWMGPGDKSU/O53vwtNwPbkoosu4qOPPuKCCy6gqKiIt956q9f7V1xxBUVFRdx999386U9/YtGiRaiqSlRUFIWFhaF5ijVr1nDXXXeRm5uLzWYjISGB3//+98f1/R7NokWL2LhxY+icPZfVFhQUsH37dgB+97vfsXz5cv7jP/6DpKSk0HF/+MMfuPHGG3nkkUcQQvDcc8+F/rYi6uzBPiBEDvCOlDL35DRpYIa7OtunquxxeelSFMZZTGRbez/FB3wK//jkI1KsCSSkRGE0GnC799PZUEbtwXg2vPMNPv3CiqIq3HJdK5dN3oonO4c9HhNZMTpSHFbS09MHn6hr2geqAo5xWi0EQxiG1FNBcTHcfDO43dqE8p13wqWXDjk/oj9VcIQTy7Zt23j44Yd54YUXTndTThk+n49zzz2Xjz76aEQlg54sdXb3ido4YjfVAa3AMauojTWCqqTU7cWjqggETX5tCGRmtI24o8b4PS4/B0vqaXe2MzEjBZPJRDDgobFsK+/+7Wxe++c0PEGwWAIsu7qTs8+qYW+TBa/XiEmnEm8zkZaWNnhAaD0ErQchadLwU1/n52sm0+xsuPturRBOhBHBGWecwTe/+U0URQmvjOsooLKykgceeGBEBYShMuCVCq3/NAtNaAegyuPtWoxyyr0+qr1+8uwWrDodGWYjiSZDr+Wm3XS5PJR2luAYZyTKFoWUQVqq9vPa63P5x/sFSAMsPLuFW28VtJVsp6HURdS4NLKTrajBABkZGYP/cqqqNqEcnwOx2Sfnoo8HRYF167TeQEwMmEywdq323xFGHNddd93pbsIpJS8vj7y8vNPdjFPKgHcYKaUUQqyXUp450HFjFSklPlWSY7P0GSY6mi/KtrO/uBJp8fO1hAIE4PEcZPWaWD76ZBJGg8p1y/fx9TNbiGuQNLS2M/6cOVijbPj8PjIz0geviRDwQM3h9P6k/BNzkUNh/3741a9g717tv++7T3s9EhAiRBi2hNMn2iKEmC2l7F8mMoZpDSjU+wLMiOq9XC2gBmjxtHCo4xCBYABVUanf28n4+HQys5PxO51UVm1l9XPj+fSTiZgtOv7j2l3MTN9PvEjHI3S4ciZhc8QQDPiIiYk5dl3l7kAQ8IAaAJ1RK6F5OvH7Yc0a+N//1XoKqanwrW+d3jZFiBAhLI4ZFIQQBillEDgbuFEIUQZ0oVVUk1LKY1cBH+UEVElnUGGPy0OsQU+y+YjHP6gG2VyzGalK6IIEXQLeNpVxhliyxiei1+torK+jcP0ZfL41HrvdwKq7q5mR1EanYzLltjiaXAFiLHqQClLKfleC4GkDnwu6GrXSmePOBL1Zm1Q+nfWJd+7Uegfl5drk8dKlcOutYLOdvjZFiBAhbAbqKWwBZgNjqxZdGFR4fdR6Axh1gilH9RIUqaAXejJFJq1tHlTVgEGRxGeZ0Ru0m/X/vZ7Ke/9Kxaj3ctcNB4hVDrCjIxqvyUCCDfKTrCRHm+jq6iIlJaX/eYTmUhA6MJghZQJYh0EN2aoqLQlNVbWs5F/+UstSjhAhwohhoEdKASClLOtvO0XtG3aoUuJRVDIsRubHRmHTa1+hs9VLZ7OHjkY3tVud1O93IgN64jIsJE20YXNovYlXXrHy5jvJCBng9ptKibHuQUlLI/OMqczLT2Jyso3kaFMoizK6u6hMT/xubbgoLhtSZwyfDOXMTLjiCs1sWlg45gLCSFVnt7W1ccUVVzBz5kzmzZtHcXFxv+eVUnLeeeeFxHEA69evRwjRKwP6/fff59JLL+312RUrVvDqq68CEAgEWLlyJXl5eUyfPp158+b1yXn4Ktx///3k5uYyadIk3nnnnWNewz333EN+fj5Tpkzh0UcfBY79Hfj9fs455xyCweCQ2zdSGCgoJAkh7jzWdspaOMxoDyo0+YPEHBbP+T1Bync101ztwu8N0tLYhmr1kJEfR0qOHaNFj96gQ1UV1j5Vy9OrVVAkd17fxpSZKigqmbnjSI42oRdatmtXVxdGo7H/ymmdtVrtA71RS0o7nXR2akNF27YdeW3lSi0PwTRM8iJOISNVnf3b3/6WgoICdu7cyfPPP8/tt9/e73EbN25k1qxZvZxIhYWFnH322cdlEb333nupq6ujuLiY4uJi3nzzTZxO55CuYc+ePRQVFbF7927efvttbr755n4T9Z577jmqqqrYu3cvJSUlLFumad2O9R2YTCbOP//84/IwjXQGCgp6IAqIPsY25nAFFdoDCjEGPUmHs5OdrV7MVgPjJsWimjwoZjf2FCN6gw6dXuD31+FyfcmaZ1p5uWgcQhr5wfdrmTB+Fw07SrA67BjMRk1w5/EQFxfH+PHjyczMxNR9Y1UVaCvX8g/qdoLRAkmTtaGj08W778JVV8Ebb8CDD2raChh+RXpOEyNJnb1nzx7OP18TCHbrMxoaGvocd7Q62+VysXnzZtauXRt2UHC73TzzzDM89thjodV0KSkpIe/QV2XDhg0sW7YMs9nMhAkTyM3NZcuWLX2OW716Nb/85S9D2dXdVteBvoPFixfz0ksvDal9I4mB5hTqpJS/OmUtGQGUdHlRpST18MSytyuAs9WLI8VCfWMdrV2tlPnKMAjta/V6D+B2N/LSy2fy5oYkdNLHzxbvJjpmN+0NKrbcXMZPmogQAo/HQ3p6OnZ7P0Vuyt7TVhbFZWs1DxJP47rplhb43e+0oABQUKDprYdZMPh7c8fgBx0nFyU6wjpupKmzZ82axWuvvcbZZ5/Nli1bqKiooLq6mpSUlF7Hbd68maeeeiq0//rrr3PxxReTn59PfHw8W7duZfbsgdefHDhwgKysrF69jWNxxx138N577/V5fdmyZaxc2Tt/tqamhrPOOiu0363OPpqysjLWrVvH+vXrSUpK4tFHHyUvL2/A72D69Ol8/vnng7Z3tDBQUBhef+XDhDy7hfj/3965h0VV7f//tRgYhvtd5SJKqIiAoqlUx8zUTM2S1FP6tWNq2im72PHY1+xysnM83TuWZRezfn5NRYsyrcxOeSkzLa8poqYiiuKVOwy3mVm/P/YwIgIOwoDAej3PPM/sPWv2+qwR92ev2/vt4ozFIjmbno/e3YnsgrOcKz1HgSzAx8WHzt6dKS8/y9G0It55ezDpRw2IskIeHHqADhFFZAR24MZekbi7GZBSUlRURLt27apPCKCtLuo0WBsyaiqkhG++0dzQ8vO11USPPQajRzftaqcasPcG3pA0V+nsp556iunTpxMfH09cXBw9e/asdnFDdnb2JXNcSUlJtvrGjh1LUlISvXr1arC2zps3z+6y9khngzYvYzAY2LFjB1988QWTJ09m8+bNtf4GOp0OvV5PQUFB9XN8LYzakkLjC9Jfw5wuLSPPZLaNt0kpsZgtlDoVYpEWzpSfoZ1bO9oY2qATgs9XO7NoYW/Ki8rx98jifxK3E93Fj8CewRjMJehdnJFSYjQaCQgIqPnJqShLSwqiiW+8BQUwb56WEG66CWbPhioqlK2dijmFvLw8RowYwYIFC3j88ceJiYnhp59+uqRsddLZPeowMd+Q0tne3t42YTgpJREREURERFxWztnZGYvFgpOTE1lZWWzYsIGUlBSE1d9DCMGrr75aq3R2p06dOHHihF032Lr0FOyRzq4oN3r0aEAT8Js0aZJdv0FFMmkN1HinkVJmN2Yg1zJFJjMHC0tob9DjhRO5Z43knTOSm5uDxWJBr9ejEzo6eHbAoDOwdm0GCxeGYipzZljCaZ5/KZuYobG4hfpTLMuQFgtCCIxGIz4+Pvj717J6KCcdvEPAqQm0ZiwWbfMZaLuQn3lGm1h+6y2VEGqhuUln5+bm2jyZFy1aRP/+/at9SImKiiItLQ2A5ORkJkyYwPHjx0lPTycjI4OIiAh+/vlnOnfuTGZmJgcOHADg+PHj/P7778THx+Pu7s4DDzzA448/bqvz9OnTLF269LL65s2bV610dtWEAJp09ooVKygtLeXYsWMcPnyYvn37XlYuMTGRDdahzx9//NHW06rtN8jKyiIoKAgXlybsqTci116//xqkVErcdE50dnelxFhOYU4Jubm5OOlLMJ05wb593+GSeR5zRgYHk/ey4I32mIskoxJ+4Z4Rf6B3MuDjLBBuZXh4eBAaGkppaSmenp4EBQVV/zRnMUPWUSgrBK8r+C47gvR0mDoVFi++eG7gQBg+/JqbP7gWqSyd7ebmxurVq5k7dy5RUVHExcXRp0+faqWzo6OjiY2NrdZfuDKVpbPj4uJwcnKqVTp78ODBNY73HzhwgJiYGLp27cq3337LW2+9VW25O+64w+ZFkJSUZDOrqWAOml6TAAAgAElEQVT06NEsX74cV1dXli5dyqRJk4iPj2fMmDEsWrQIHx9tSG/u3LkEBQXRrVs3YmNjSUxMtA1xXS0xMTHcc889dOvWjaFDh7JgwQKbaN/w4cNtzmlPPfUUn3/+OXFxccyePZtFixZd8TfYuHEjw4cPr1d8zYk6S2c3NU0hnZ1dbuKosZQ+Ph4U5ZWSdTqPU2VH0GWexJh7DhEYQIihPUcySnn25Whyi5zo1TOLKaPS8G/TEZ1wxivMldy8LEJDQykpKUGv1xMSEnKZF66Nkjw4uQN8O4Bv+8ZbaWQywZIl8OGHmulNSAgkJ1/zS0yVdLbjOX36NBMmTOD771uXncqoUaN46aWXiIqKaupQ7Mah0tmKi0gpsVgs/HH6EAWlBwjOKscjqisBba8jO0/HvH87U1isJ6ZrNnNn6/Ao7gpeOvBw5kzOOZycnDAajbi6uhIcHFxzQqjA2QCBjWhbcegQvPCCJl4HMHIkTJ9+zScEReMQHBzM1KlTyc/Pt2v1UEugrKyMxMTEZpUQ6otKCnXgzNFczv+8HUthGhF+OgI69UYX0p6iIsFL//Lk2FEI8i/i+X+YKcsxklVehHByQRi14RYfHx88PT1p06bN5Qmh8DwUV5rGMTWi46nJBB98oAnYWSxa7+DZZ6GaMVlF66a++wmaG3q9vlr7z5aMSgp2IqXEVFpOlvkguTe2o51fV3SGQLKzBU8+5cXRozq8vQp57tFf8SwIIstYRlDnEDy8PbFYLLZVRv7+/tXPIeRlaCuMKnYp653Bs+3l5RyBTqc5okkJ48bBww8rATuFopWikoId5J0r5lxWIS77D5GPke4B3fHWe5Od7cTjf/PmRIaF4LZ5zJi2lYiQEiy+LnhKTzy8PW3LToOCgvDzu4JonWdb8G6kVT1GIxQVQVCQNnH83HNw4QJ079449SsUimsStfroCkiLJP+8EXcvZwyuOcio9njrvSkuhtnPenI8Q9ChQxbPPraOTsESZwLJzim2rXwwGo34+fnh6+tbcyWlBdqrsdi6VZO0fvbZi/IUISEqISgUCtVTqA2LxcLh0wWcKS5GnD8GxkKc9AYsFnjpZW9SDzrRrk0Oz05aS6CIwN8pEpObwNmUhZ+fH0ajEU9PTwIDA6sfMio4C8YLmi+Csyu4Bzi2QXl52o7kb77Rjv38tHO1JSyFQtGqcGhSEEIMBd5CE9dbJKV8ucrnM4ApgAk4D0yWUh53ZEz2klNcxrqD59FlnKKdNKKXBRj69EGWHmLhQg+2bbXgpc9l1v98i79zGL7xXRFueoTJhDgjKC0txdXVlbZt215MCOZybXdy7gltItmYpfkgeLUFN39wdtAqHyk1raJXXoHsbG010UMPwfjx2nyCosE4c+YMTzzxBNu3b8fV1ZWOHTuSmJjImjVr+Prrr5s6PIXiijgsKQghdMAC4DbgJLBdCLFGSplaqdhuoLeU0iiEeBh4FWg4TeB6UFxiwkvnRP+2TpQEdSDfYiZH5LN7YxBffukKFDJ17AkioqMJ6KzJHEspOXdOW3pqsVho167dxVVG5w9Bdhro9GAug8Au4B8Bnu0clwy0oLRhogp9+V69tOPwcMfV2UqRUnL33Xdz//3321RD9+zZw1dffdXEkSkU9uPInkJf4IiUMg1ACLECGAnYkoKUsrKwyTbgPgfGUyfOlpoQZSVYigvJzdNhCAzgyIksvv0kgbLyMv581yG6Rmbh7B1q+052djYmkwkfHx/atm17cVu8xQL5pyC4hyZZ0ZgIAdddp60mmj5dM8G5BgXsGpzetezRefppGDVKe//FF/DiizWXrcNGyY0bN+Li4nLJzuL4+Hhyc3NZv349Y8aMISUlheuvv56lS5cihOCf//wnX331FcXFxdx000188MEHCCEYMGAACQkJbNy4kdzcXD766CNuvvlmzGYzs2bN4rvvvkMIwdSpU3nsscfYuXMnM2bMoLCwkMDAQBYvXkywkiJRXAWOvDuEAhmVjk9az9XEA0C19ktCiAeFEDuEEDvOnz/fgCFWz9HTBew7mUe4qYRCIdB5eZFZksln/68DBXk6Ijud4s4BOfgEGPBpG4zFYqGwsJCioiI8PDwIDAzE09NTu5iUcHaf9t6jflv57SYzEypryd9/v7Yr+RpVNG0pVNzwq2P37t28+eabpKamkpaWZvM2ePTRR9m+fTspKSkUFxdfMsRkMpn47bffePPNN3nhhRcAWLhwIceOHWP37t3s3buX8ePHU15ezmOPPUZycjI7d+5k8uTJPPPMM45vsKJF4sieQnUCOdVqaggh7gN6A7dU97mUciGwEDSZi4YKsCYOZxcR7edK2OnjnAc83NzYtcGTlK1tMehKmTLuKAbhgVu7QADOnj2L2WzG1dUVX19fAgIqTRgXnNEmlEOvd7z0tcUCK1fCggXg6gqffQb+/uDsDFYzkVaDvU/4o0Zd7DU4kL59+xIWFgZgk9fu168fGzdu5NVXX8VoNJKdnU1MTAx33nmnNTQtruuvv5709HQAfvjhBx566CGbrLO/v7/NwaxCqttsNqteguKqcWRSOAm0r3QcBmRWLSSEGAw8A9wipWzEbbzVIy0SU3YOAU5GigoL0XXsSEGBYMmCQDCVMnLECTyD3CmjHB93d4xGI+Xl5fj5+WEwGC6Xr5AWbSLZw8Eri9LSYO5c2LtXO+7fX/UKGpmYmBibD3FVKlzGQNPnN5lMlJSUMG3aNHbs2EH79u2ZM2cOJSUll32nojxU780gpSQmJoatW7c2dJMUrRBH3jW2A52FEBFCCD0wFlhTuYAQoifwAXCXlPKcA2Oxm9yzhZTn5OPk40lxcDAGLy/e+zCP7BxJeGQpUbe74OdmwM3XB2e9KyUlJbi4uKDX6wkNDb3cnMRc5tiATSb46CNtJdHevdpmtP/8RxsnV0tNG5WBAwdSWlrKhx9+aDu3fft2fvzxx2rLVySAwMBACgsLa0wolRkyZAjvv/++LUlkZ2cTFRXF+fPnbUmhvLyc/fv317c5ilaKw5KClNIEPAp8BxwAPpVS7hdC/FMIcZe12GtoPtCfCSH2CCHW1HC5RkFKSdmJDAx6E9LfG+HhQWpqPmvXBmCSekaNKSKw8AQ6cwkGbx+EkxPl5eW4uLgQFhZ2eUIwlWqrjlwd6Nb0zDPw3nuaoundd2tDRv37O64+RY0IIVi1ahXff/89kZGRxMTEMGfOnGrNXgB8fX2ZOnUqcXFxJCYm0qdPnyvWMWXKFMLDw+nevTs9evRg+fLl6PV6kpOTmTVrFj169CA+Pp5ffvmloZunaCUo6exKmHNzObN5D6ntw2nnq8NTwvS/ObE7Vc/I2wqZMVWSnXWUkOg4TCYThYWFZGdn06VLF5sB+CWcO6DNKUTe6pB4AdizR1M2ffppsOOm0lJR0tkKxUWUdHYDIaXEycMTPDwoKclj/UYDqYdc8Hc388S9Ar2bDpPZTF5eHllZWZjNZsLDwy+dWK4gPxNyM7RlqA3Jrl3aJOqDD2rH8fHayiK1CU2hUDQAKilUQ0lJCYUlFj5Z7IpOCO65MxNdez9KTOVk5eaDdx4Gg4GYmBjc3NyquUC+NmzkGw6eDbTqp6gI5s+Hzz/Xjnv31jaigUoICoWiwVBJoRKytBSJJK+gkK8+1ZOXL4iMLKBT5BmysyXSYkHv7ExAQAAhISGXJoSSfJBWP+OiLHBxh4BODWNduWUL/PvfcO6ctrx08mSIi6v/dRUKhaIKKilUImdXKuelL7uOSTb81w9pdmLEgMOEhvsRHByM2WSi+Gwmbdu2xcPD4+IXz+7X9IwMlVb7eLUDXT1/3txceOMN+Na6py8mBv7xD4iMrN91FQqFogZUUqhEZjZsCjGw7es2OFskN/Y5RWxPHR27tEdKSVFREd7eXpdaEZrKoOg8hPTS9iM0JB9+qCUEV1eYNk0zwFF7DxQKhQNRSaESBWYLqanOnD/ght7lLEPvPE5QREcEkJ+dhcVYhKeH58UvWCxwNkVzTHO7goGOvUh5ccjpr3/VVE0feQSsu2EVCoXCkajHzkpkZhezLbkNzsLCkDv38KdO0fgb/DHm55F78gSeHh4Ed65k4F1k9VVuG1t/pVMpYdUqbb6gzLrhzdsbXnpJJQSFQtFoqJ4CYDZZOHgil827fMnOhJDA09x8WzpCxCOlpLS0lLahYXSI7Q7GbCi0ivKV5Gk9BHf/+gVw8qQmUVGx/+L77+GOO+p3TYVCobgKWn1PwWKR7P0ji4MHLrAltQNSmLj3vjTi3AaAgJLiYjzzDuOWdxjSf4aMXyEnXXuV5IF7YH0qh2XL4N57tYTg56fJUwwf3kCtUzQ2Qgj+8pe/2I5NJhNBQUGMGDHCofXqdDri4+OJjY3lzjvvJDc31/bZyZMnGTlyJJ07dyYyMpLp06dTVnZRfuXMmTOMHTuWyMhIunXrxvDhw/njjz8uq6O4uJhbbrkFs9lsO7dq1SqEEBw8eNB2Lj09ndjY2Eu+O2fOHF5//fU61VdX1q1bR1RUFJ06deLll1+utkxubi5jxoyha9euREdHX6IXNW/ePGJiYoiNjWXcuHGX6FA5MqaaymRkZHDrrbcSHR1NTEwMb731FgBlZWX079/fJnXS0LT6pJBfUs7ZvBLyDhZxIceDAG8jI3p74G72otCpgNyUzZTnZeEW3h3axkBEf2jf5+LLr8PVVZyWBpMmwbx5UFoKw4ZpEhVDhjTMMlZFk+Dh4WGTwQb4/vvvCQ2tTTG+YXBzc2PPnj2kpKTg7+/PggULAG1D5qhRo0hMTOTw4cP88ccfFBYW2qS1K4yBBgwYwNGjR0lNTeXFF1/k7Nmzl9Xx8ccfM2rUKJv/OEBSUhL9+vWzmQpdibrUVxfMZjOPPPII3377LampqSQlJZGamnpZuenTpzN06FAOHjzI77//btv1e+rUKebPn8+OHTtISUnBbDbX2qZNmzYxceLEesdUWxlnZ2feeOMNDhw4wLZt21iwYAGpqano9XoGDRrEypUr6/gr2UerHz4qMJkxOQtW/+SLRQeDbjiIodwHc4AzJaeO085L0Cb+TlwC2l/5YnXh4EHYv1+TtH76aejXr2Gv34qpzV+nPtirrjJs2DC++eYbxowZQ1JSEuPGjWPz5s0ALF26lPnz51NWVkZCQgLvvvsuOp2OxMREMjIyKCkpYfr06Tz44IOkp6czbNgw+vXrxy+//EJoaCirV6+ufsNkJW688Ub2WtVyN2zYgMFgYNKkSYDWo5g3bx4RERG88MILbNu2rVpjoOpYtmwZy5cvtx0XFhayZcsWNm7cyF133cWcOXOu+NvUZERUX3777Tc6derEdddpLohjx45l9erVdOvWzVYmPz+fn376icWLFwOg1+vR6y/OBZpMJoqLi3FxccFoNNaoWdWQMdVWJjg42CaB7uXlRXR0NKdOnaJbt24kJiYye/Zsxo8fX68Yq6PV9xS+PHqelFOlpOz3x01aGDowBxkUhMnFQls/D9zbXoeLfwNN9ObkXHw/bBj87//Cp5+qhNDCGDt2LCtWrKCkpIS9e/eSkJAAaHo0K1euZMuWLezZswedTseyZcsA7Sl8586d7Nixg/nz55OVlQXA4cOHeeSRR9i/fz++vr58XrGjvQbMZjPr16/nrrs0zcn9+/dfZvzj7e1NeHg4R44cqdUYqDJlZWWkpaXRsWNH27kvv/ySoUOH0qVLF/z9/dm1a9cVr2NvfQA333wz8fHxl71++OGHy8qeOnWK9u0vPriFhYVx6tSpS8qkpaURFBTEpEmT6NmzJ1OmTKGoqAiA0NBQZs6cSXh4OMHBwfj4+DBkyJDL6klISCA+Pp4pU6awZs0aW0zfVdjd1jEme8qANiS3e/du299SbGws27dvr/Z3qy+tvqeQd6aIwm90uJVbiOpxiDbtCnDN+R1/b3dy8i9Au8j6D+eUlMD772saRZ98AhER2jXvuadhGqG4BAfpJdpN9+7dSU9PJykpieGV5ofWr1/Pzp07bWqoxcXFNiHF+fPns2rVKkAbSz58+DDt2rUjIiLC9iRd2WynKsXFxTbznuuvv95muFOd/0Jt52viwoUL+FaRYk9KSuKJJ54AtESYlJREr169arxuXeoDbL0re6hO2LNqfSaTiV27dvH222+TkJDA9OnTefnll/nXv/5FTk4Oq1ev5tixY/j6+vLnP/+ZpUuXct99lzoE//rrr4A2fLR48WJbr+NqY7KnTGFhIaNHj+bNN9+07ZHS6XTo9XoKCgrw8mpYFeZWnRRKi02UF5axeWsb0JvpO3A7gaXeeLbx42R2ORhC8QnuWr9KduzQVhadPKltPNu9W0sKihbNXXfdxcyZM9m0aZPtqV9Kyf33389LL710SdlNmzbxww8/sHXrVtzd3RkwYIBtkrOqOU/FXEVVKuYU8vLyGDFiBAsWLODxxx8nJibmst5Ffn4+GRkZREZGcu7cObt8HNzc3C6ZeM3KymLDhg2kpKQghMBsNiOE4NVXXyUgIICcyr1iNN+HiIgIwsLC7KoPtJ5CQUHBZedff/11Bg8efMm5sLAwMjIuuv+ePHnysuGfsLAwwsLCbE/bY8aMsU3s/vDDD0RERBAUpFnmjho1il9++eWypFAX7I2ptjLl5eWMHj2a8ePH25z4KigtLcVgMFx1fDXRqoePTh7PI22fgZwiHW1DcwgJO4fFyYscix9uQe3p2Lsfbj5XaVRTWKitJHroIS0hdOoEixc3ivWjoumZPHky//jHP4irpFE1aNAgkpOTOXdO85PKzs7m+PHj5OXl4efnh7u7OwcPHmTbtm1XXa+Pjw/z58/n9ddfp7y8nEGDBmE0GlmyZAmgDS/9/e9/Z+LEibi7u9ttDOTn54fZbLYlhuTkZCZMmMDx48dJT08nIyODiIgIfv75Zzw9PQkODmb9+vW2dq5bt45+/frVyYho8+bN7Nmz57JX1YQA0KdPHw4fPsyxY8coKytjxYoVtiG0Ctq1a0f79u05dOgQoPXcKsb3w8PD2bZtG0ajESkl69evr1WKfcCAAbX2EuyNqbYyUkoeeOABoqOjmTFjxiXfy8rKIigoCBeXhrf4bbVJwVRu5vi5IlI2e2IRJu4Ymo1BWCjRh2Dw9KJNRCS6qqY59rJnjzY09MUXmoDdQw9pw0aVJpgULZuwsDCmT59+yblu3boxd+5chgwZQvfu3bnttts4ffo0Q4cOxWQy0b17d5577jluuOGGetXds2dPevTowYoVK2zGP5999hmdO3emS5cuGAwGXnzxRaBuxkBDhgzh559/BrSho7vvvvuSz0ePHm2biF6yZAlz584lPj6egQMH8vzzzxMZGVlnIyJ7cXZ25p133uH2228nOjqae+65h5iYGACGDx9OZqbmBPz2228zfvx4unfvzp49e3j66acBba5gzJgx9OrVi7i4OCwWCw9WyNNXomJOoeqrujkFe2KqrcyWLVv45JNP2LBhg62etWvXAtqE/XAHLV1vtSY7Z08V8MGKDD54L4hgfSFPvfI5wtWJG8KHEtq1njfvjAxt70FUFDz3HFhXFigchzLZcTy7d+/mP//5D5988klTh9LqGTVqFC+99BJRUVHVfq5Mdq6CAzm5rN2sx0VaGN7vDEH+beh63UBM+ZePYV4RKeHXXyEhQZtAbt9e802OilICdooWQ8+ePbn11lsxm82X7FVQNC5lZWUkJibWmBDqS6u8Y1mkZNeh82Sm+ODjZOFPAzMx5RRRdOYMTnW9iZ89C3/7Gzz6KHz11cXz0dEqIShaHJMnT1YJoYnR6/VMmDDBYddvdT2Fg0XFnCgu47/fBOCEEwldd+AnzuLqHkibjtfh7uNj34UsFvjyS3jzTTAawdMTHDDpo1AoFI1Jq0sKRpMF77PlHNvkgrOpiJHdD+Drdws6b3B20ePkZMdT0IkT2jLTis06AwbArFlgXc6mUCgUzZVWlRSKzRayi47z36RizOY2xHU5Q5u468ClHL+QCAyenle+yN692mqisjLw99d2JQ8apPSKFApFi6BVJYVTxQVYzAXs/LkNpnJn+vdKI6BTCIEdu+AVYOdTfnQ0hIdrk8gzZoC9w00KhULRDGhVSaGs7ALGM0aOH3PDVWdicIKJjtGxOHnU4odQVgZLl2qbznx9tXmDjz8Gd/fGC1yhUCgaiVa3PCZlpx7KLUSG5RAQc1PtCWHfPrjvPnj3XXjjjYvnVUJQKBQtlFbVUygxlbJ7qwkkdI+XuHrXMGRUXAzvvQdJSdoehPBwJU+hUChaBa0qKeQX5ZK+vy046ejZ1xtPv2p06X/7TVtZlJmp7TO4/3548EHQ19ODWdGoZGRkUFpa2mDXc3V1vUTiuCGYPHkyX3/9NW3atCElJcXu7+Xm5rJ8+XKmTZtW7edz5szB09OTmTNn2nW9upZXtGxazfCRsdzIwcNZZF3wxdXNTI/uelzdquTEEyfgkUe0hNClCyxZom1KUwmh2VFaWoq7u3uDveqaYOxx5po4cSLr1q2rc9tyc3N599136/w9hcIeWk1SMEkTaVv1YLFwXftsPNyrudGHh8O4cTBtmpYQutZTNluhqIX+/fvj71/LnBZQVFTEHXfcQY8ePYiNjWXlypU89dRTHD16lPj4eJ588kkA/v3vfxMVFcXgwYNtKqC1UVv5pUuX0rdvX+Lj4/nrX/+K2Wxm1qxZlySiOXPm8EbleTZFi6HVDB9JsyRtvw8IQVycHv8QL8jOhtdeg9GjL3o4VpGoVSjqQkJCAqWlpRQWFpKdnW0zyHnllVe4/fbb63y9devWERISwjfffANAXl4eCQkJpKSksGfPHgB27tzJihUr2L17NyaTiV69etXqblZb+crucC4uLkybNo1ly5YxduxYnnjiCduQ1aeffnpVvRzFtU+rSQrm4kLS0vyQQHycBffNP8B/3oD8fDh+HJYtUxvQFPWmLs5c9hAXF8fMmTOZNWsWI0aM4Oabb77MwGbz5s3cfffduFtXxVXV7K9KbeVrcoebMGEC586dIzMzk/Pnz+Pn50d4eHi92qa4NnFoUhBCDAXeAnTAIinly1U+dwWWANcDWcC9Usp0R8Ry8I9Czl7wwUuUMXj9a3Dkd+2DG26Ap59WCUFxTdKlSxd27tzJ2rVrmT17NkOGDKlWDK2uVpc1la/JHQ40p7Lk5GTOnDnD2LFj61SfovngsDkFIYQOWAAMA7oB44QQVY0KHgBypJSdgHnAK46K55fNFgxFRgbnr8f/yO8Ib2+YMwfefhvqafChUFTFHmcue8jMzMTd3Z377ruPmTNnsmvXLry8vC6xqezfvz+rVq2iuLiYgoICvqqs1lsNtZWvyR0ONB/mFStWkJyczJgxY+rdNsW1iSN7Cn2BI1LKNAAhxApgJJBaqcxIYI71fTLwjhBCSAc4/6TuErgXFnFDwD4YOFATsAsIaOhqFNcIrq6uGI3GBr2ePVTMKVSlujmFcePGsWnTJi5cuEBYWBgvvPACDzzwwCVl9u3bx5NPPomTkxMuLi689957BAQE8Kc//YnY2FiGDRvGa6+9xr333kt8fDwdOnTg5ptvtn1/+PDhLFq06BJns169etVYvrI7nMViwcXFhQULFtChQwdiYmIoKCggNDSU4ODgWutQNF8c5rwmhBgDDJVSTrEe/wVIkFI+WqlMirXMSevxUWuZC1Wu9SDwIEB4ePj1FU8udeGRaRfY9FUhnz5/jJgpt15tsxTXKMp5TaG4yLXqvFbdoGXVDGRPGaSUC4GFoNlxXk0wC94NpHReIHp9x6v5ukKhULQKHLlP4SRQeQtoGJBZUxkhhDPgA2Q7KiBXVzWfrFAoFLXhyKSwHegshIgQQuiBscCaKmXWAPdb348BNjhiPkHROlB/OgpF/f8fOCwpSClNwKPAd8AB4FMp5X4hxD+FEBULoz8CAoQQR4AZwFOOikfRsjEYDGRlZanEoGjVSCnJysrCYDBc9TUcNtHsKHr37i137NjR1GEorjHKy8s5efIkJSUlTR2KQtGkGAwGwsLCcKniGX8tTDQrFI2Gi4sLERERTR2GQtHsaTWCeAqFQqG4MiopKBQKhcKGSgoKhUKhsNHsJpqFEOeBum9p1ggELlyxVMtCtbl1oNrcOqhPmztIKWvwIL5Is0sK9UEIscOe2feWhGpz60C1uXXQGG1Ww0cKhUKhsKGSgkKhUChstLaksLCpA2gCVJtbB6rNrQOHt7lVzSkoFAqFonZaW09BoVAoFLWgkoJCoVAobLTIpCCEGCqEOCSEOCKEuEx5VQjhKoRYaf38VyFEx8aPsmGxo80zhBCpQoi9Qoj1QogOTRFnQ3KlNlcqN0YIIYUQzX75oj1tFkLcY/233i+EWN7YMTY0dvxthwshNgohdlv/voc3RZwNhRDiYyHEOaszZXWfCyHEfOvvsVcI0atBA5BStqgXoAOOAtcBeuB3oFuVMtOA963vxwIrmzruRmjzrYC79f3DraHN1nJewE/ANqB3U8fdCP/OnYHdgJ/1uE1Tx90IbV4IPGx93w1Ib+q469nm/kAvIKWGz4cD36I5V94A/NqQ9bfEnkJf4IiUMk1KWQasAEZWKTMS+D/r+2RgkBDN2pPtim2WUm6UUlY42W9Dc8Jrztjz7wzwL+BVoCVoatvT5qnAAillDoCU8lwjx9jQ2NNmCXhb3/twucNjs0JK+RO1O1COBJZIjW2ArxAiuKHqb4lJIRTIqHR80nqu2jJSMwPKAwIaJTrHYE+bK/MA2pNGc+aKbRZC9ATaSym/bszAHIg9/85dgC5CiC1CiG1CiKGNFp1jsKfNc4D7hBAngbXAY40TWpNR1//vdaIl+ilU98Rfdd2tPWWaE3a3RwhxH9AbuMWhETmeWtsshHAC5gETGyugRsCef2dntCGkAWi9wc1CiFgpZa6DY3MU9rR5HLBYSvmGEOJG4BNrmy2OD69JcM08jAcAAAV0SURBVOj9qyX2FE4C7Ssdh3F5d9JWRgjhjNblrK27dq1jT5sRQgwGngHuklKWNlJsjuJKbfYCYoFNQoh0tLHXNc18stnev+3VUspyKeUx4BBakmiu2NPmB4BPAaSUWwEDmnBcS8Wu/+9XS0tMCtuBzkKICCGEHm0ieU2VMmuA+63vxwAbpHUGp5lyxTZbh1I+QEsIzX2cGa7QZillnpQyUErZUUrZEW0e5S4pZXP2crXnb/tLtEUFCCEC0YaT0ho1yobFnjafAAYBCCGi0ZLC+UaNsnFZA0ywrkK6AciTUp5uqIu3uOEjKaVJCPEo8B3ayoWPpZT7hRD/BHZIKdcAH6F1MY+g9RDGNl3E9cfONr8GeAKfWefUT0gp72qyoOuJnW1uUdjZ5u+AIUKIVMAMPCmlzGq6qOuHnW3+O/ChEOJvaMMoE5vzQ54QIglt+C/QOk/yPOACIKV8H23eZDhwBDACkxq0/mb82ykUCoWigWmJw0cKhUKhuEpUUlAoFAqFDZUUFAqFQmFDJQWFQqFQ2FBJQaFQKBQ2VFJQXHMIIcxCiD2VXh1rKduxJjXJOta5yarE+btVIiLqKq7xkBBigvX9RCFESKXPFgkhujVwnNuFEPF2fOcJIYR7fetWtA5UUlBcixRLKeMrvdIbqd7xUsoeaGKJr9X1y1LK96WUS6yHE4GQSp9NkVKmNkiUF+N8F/vifAJQSUFhFyopKJoF1h7BZiHELuvrpmrKxAghfrP2LvYKITpbz99X6fwHQgjdFar7Cehk/e4gq07/PqvOvav1/Mvioj/F69Zzc4QQM4UQY9D0pZZZ63SzPuH3FkI8LIR4tVLME4UQb19lnFupJIQmhHhPCLFDaD4KL1jPPY6WnDYKITZazw0RQmy1/o6fCSE8r1CPohWhkoLiWsSt0tDRKuu5c8BtUspewL3A/Gq+9xDwlpQyHu2mfNIqe3Av8CfreTMw/gr13wnsE0IYgMXAvVLKODQFgIeFEP7A3UCMlLI7MLfyl6WUycAOtCf6eCllcaWPk4FRlY7vBVZeZZxD0WQtKnhGStkb6A7cIoToLqWcj6aLc6uU8lar9MWzwGDrb7kDmHGFehStiBYnc6FoERRbb4yVcQHesY6hm9E0faqyFXhGCBEGfCGlPCyEGARcD2y3ynu4oSWY6lgmhCgG0tHkl6OAY1LKP6yf/x/wCPAOmj/DIiHEN4Dd0txSyvNCiDSrZs1hax1brNetS5weaLIPlV237hFCPIj2/zoYzXBmb5Xv3mA9v8Vajx7td1MoAJUUFM2HvwFngR5oPdzLTHOklMuFEL8CdwDfCSGmoMkM/5+UcrYddYyvLJgnhKjWY8Oqx9MXTYRtLPAoMLAObVkJ3AMcBFZJKaXQ7tB2x4nmQPYysAAYJYSIAGYCfaSUOUKIxWjCcFURwPdSynF1iFfRilDDR4rmgg9w2qqR/xe0p+RLEEJcB6RZh0zWoA2jrAfGCCHaWMv4C/v9qQ8CHYUQnazHfwF+tI7B+0gp16JN4la3AqgATb67Or4AEtF8AFZaz9UpTillOdow0A3WoSdvoAjIE0K0BYbVEMs24E8VbRJCuAshqut1KVopKikomgvvAvcLIbahDR0VVVPmXiBFCLEH6IpmWZiKdvP8rxBiL/A92tDKFZFSlqApUH4mhNgHWID30W6wX1uv9yNaL6Yqi4H3Kyaaq1w3B0gFOkgpf7Oeq3Oc1rmKN4CZUsrf0byZ9wMfow1JVbAQ+FYIsVFKeR5tZVSStZ5taL+VQgEolVSFQqFQVEL1FBQKhUJhQyUFhUKhUNhQSUGhUCgUNlRSUCgUCoUNlRQUCoVCYUMlBYVCoVDYUElBoVAoFDb+P0YQ4wflcvcGAAAAAElFTkSuQmCC\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.32885039, 0.69790697, 0.65291864, ..., 0.55279464, 0.39190483,\n", - " 0.36502624])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Logistic regression (SGD)\n", - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6600275735294119\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6410049019607843\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.693688725490196\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6756525735294118\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6942279411764706\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6694975490196078\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6883976715686274\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.7092647058823529\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.6774096699283731\n", - " 0.7044642693021745\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.24498057, 0.76407456, 0.60673071, ..., 0.66584657, 0.42254475,\n", - " 0.40462401])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Logistic regression (scikit)\n", - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " model = LogisticRegression(random_state=0)\n", - " model.fit(X[train,:], y[train])\n", - " probas_ = model.predict_proba(X[test,:])[:, 1]\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - " 0.6918474264705883\n", - ">>\n", - " 0.6687438725490196\n", - ">>\n", - " 0.7036397058823529\n", - ">>\n", - " 0.6899785539215686\n", - ">>\n", - " 0.7006372549019608\n", - ">>\n", - " 0.6927267156862745\n", - ">>\n", - " 0.7107383578431373\n", - ">>\n", - " 0.7054718137254903\n", - ">>\n", - " 0.696354935756432\n", - ">>\n", - " 0.7062564718425067\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.40430639, 0.58317981, 0.54512806, ..., 0.45729833, 0.39574646,\n", - " 0.39546193])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " print('>>')\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train])\n", - "\n", - " \n", - " probas_ = model.predict_proba(X[test,:])[:, 1]\n", - " results[test] = probas_\n", - "\n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/tensorflow/prediction.ipynb b/scripts/tensorflow/prediction.ipynb deleted file mode 100644 index b63bcb4..0000000 --- a/scripts/tensorflow/prediction.ipynb +++ /dev/null @@ -1,5533 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from keras import optimizers, regularizers\n", - "from keras.layers import Dense, Dropout, Input\n", - "from keras.models import Model, Sequential\n", - "from random import shuffle\n", - "from scipy import interp\n", - "from scipy.stats.mstats import gmean\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.model_selection import StratifiedKFold, train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer\n", - "import contextlib\n", - "import glob\n", - "import gzip\n", - "import h5py\n", - "import keras\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import pylab as plt\n", - "import random\n", - "import scipy\n", - "import sklearn\n", - "import tensorflow as tf\n", - "random_state = np.random.RandomState(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", - "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", - "\n", - "X = pd.read_csv(X_f).values[:,:-1]\n", - "y = pd.read_csv(y_f).values\n", - "\n", - "\n", - "ix = [i for i in range(y.shape[0])]\n", - "shuffle(ix)\n", - "X = X[ix, :]\n", - "y = y[ix]\n", - "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", - "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\n", - "y = y[: ,0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 1, ..., 1, 1, 0])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.5932 - acc: 0.6739\n", - "Epoch 2/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5952 - acc: 0.6707\n", - "Epoch 3/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5905 - acc: 0.6805\n", - "Epoch 4/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5894 - acc: 0.6732\n", - "Epoch 5/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5918 - acc: 0.6767\n", - "Epoch 6/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5923 - acc: 0.6768\n", - "Epoch 7/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5880 - acc: 0.6749\n", - "Epoch 8/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5861 - acc: 0.6779\n", - "Epoch 9/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5802 - acc: 0.6871\n", - "Epoch 10/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5892 - acc: 0.6734\n", - "Epoch 11/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5797 - acc: 0.6851\n", - "Epoch 12/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5894 - acc: 0.6773\n", - "Epoch 13/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5809 - acc: 0.6813\n", - "Epoch 14/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.5792 - acc: 0.6852\n", - "Epoch 15/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5752 - acc: 0.6867\n", - "Epoch 16/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5812 - acc: 0.6819\n", - "Epoch 17/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5730 - acc: 0.6914\n", - "Epoch 18/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5740 - acc: 0.6889\n", - "Epoch 19/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5751 - acc: 0.6885\n", - "Epoch 20/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5686 - acc: 0.6967\n", - "Epoch 21/50\n", - "7271/7271 [==============================] - 0s 45us/step - loss: 0.5720 - acc: 0.6899\n", - "Epoch 22/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5727 - acc: 0.6884\n", - "Epoch 23/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5760 - acc: 0.6906\n", - "Epoch 24/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5652 - acc: 0.7025\n", - "Epoch 25/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5689 - acc: 0.6962\n", - "Epoch 26/50\n", - "7271/7271 [==============================] - 0s 42us/step - loss: 0.5766 - acc: 0.6873\n", - "Epoch 27/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5616 - acc: 0.6973\n", - "Epoch 28/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5585 - acc: 0.7032\n", - "Epoch 29/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5635 - acc: 0.6981\n", - "Epoch 30/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.5659 - acc: 0.6966\n", - "Epoch 31/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5624 - acc: 0.6947\n", - "Epoch 32/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5555 - acc: 0.7062\n", - "Epoch 33/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.5583 - acc: 0.7043\n", - "Epoch 34/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5589 - acc: 0.7033\n", - "Epoch 35/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5483 - acc: 0.7091\n", - "Epoch 36/50\n", - "7271/7271 [==============================] - 0s 33us/step - loss: 0.5483 - acc: 0.7104\n", - "Epoch 37/50\n", - "7271/7271 [==============================] - 0s 34us/step - loss: 0.5605 - acc: 0.7006\n", - "Epoch 38/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5459 - acc: 0.7126\n", - "Epoch 39/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5522 - acc: 0.7064\n", - "Epoch 40/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5499 - acc: 0.7080\n", - "Epoch 41/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5507 - acc: 0.7127\n", - "Epoch 42/50\n", - "7271/7271 [==============================] 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- 0s 35us/step - loss: 0.5450 - acc: 0.7150\n", - "Epoch 37/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5478 - acc: 0.7164\n", - "Epoch 38/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5490 - acc: 0.7110\n", - "Epoch 39/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5446 - acc: 0.7185\n", - "Epoch 40/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5453 - acc: 0.7178\n", - "Epoch 41/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5479 - acc: 0.7099\n", - "Epoch 42/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5399 - acc: 0.7196\n", - "Epoch 43/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5316 - acc: 0.7248\n", - "Epoch 44/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5343 - acc: 0.7209\n", - "Epoch 45/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5311 - acc: 0.7215\n", - "Epoch 46/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5446 - acc: 0.7161\n", - "Epoch 47/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5300 - acc: 0.7258\n", - "Epoch 48/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5256 - acc: 0.7293\n", - "Epoch 49/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5232 - acc: 0.7310\n", - "Epoch 50/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.5162 - acc: 0.7332\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.6050 - acc: 0.6619\n", - "Epoch 2/50\n", - "7271/7271 [==============================] - 0s 35us/step - loss: 0.6005 - acc: 0.6661\n", - "Epoch 3/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5969 - acc: 0.6734\n", 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38us/step - loss: 0.5883 - acc: 0.6729\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 16/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.5859 - acc: 0.6754\n", - "Epoch 17/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5806 - acc: 0.6813\n", - "Epoch 18/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5859 - acc: 0.6771\n", - "Epoch 19/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.5817 - acc: 0.6747\n", - "Epoch 20/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5789 - acc: 0.6775\n", - "Epoch 21/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5793 - acc: 0.6764\n", - "Epoch 22/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.5759 - acc: 0.6841\n", - "Epoch 23/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.5716 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[==============================] - 0s 37us/step - loss: 0.5593 - acc: 0.6982\n", - "Epoch 30/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5633 - acc: 0.6968\n", - "Epoch 31/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5533 - acc: 0.7027\n", - "Epoch 32/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5564 - acc: 0.6950\n", - "Epoch 33/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5571 - acc: 0.7001\n", - "Epoch 34/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5589 - acc: 0.6941\n", - "Epoch 35/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5569 - acc: 0.7033\n", - "Epoch 36/50\n", - "7273/7273 [==============================] - 0s 35us/step - loss: 0.5598 - acc: 0.6957\n", - "Epoch 37/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5556 - acc: 0.6961\n", - "Epoch 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[==============================] - 0s 36us/step - loss: 0.5908 - acc: 0.6722\n", - "Epoch 15/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5860 - acc: 0.6783\n", - "Epoch 16/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5874 - acc: 0.6789\n", - "Epoch 17/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5781 - acc: 0.6816\n", - "Epoch 18/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5873 - acc: 0.6747\n", - "Epoch 19/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5826 - acc: 0.6784\n", - "Epoch 20/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5806 - acc: 0.6869\n", - "Epoch 21/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5757 - acc: 0.6853\n", - "Epoch 22/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5764 - acc: 0.6854\n", - "Epoch 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[==============================] - 0s 37us/step - loss: 0.5954 - acc: 0.6640\n", - "Epoch 2/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.6007 - acc: 0.6623\n", - "Epoch 3/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5960 - acc: 0.6629\n", - "Epoch 4/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5924 - acc: 0.6714\n", - "Epoch 5/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5921 - acc: 0.6620\n", - "Epoch 6/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5954 - acc: 0.6601\n", - "Epoch 7/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5921 - acc: 0.6704\n", - "Epoch 8/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5917 - acc: 0.6625\n", - "Epoch 9/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5901 - acc: 0.6719\n", - "Epoch 10/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5894 - acc: 0.6754\n", - "Epoch 11/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5879 - acc: 0.6659\n", - "Epoch 12/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5907 - acc: 0.6670\n", - "Epoch 13/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5876 - acc: 0.6707\n", - "Epoch 14/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5877 - acc: 0.6699\n", - "Epoch 15/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5824 - acc: 0.6730\n", - "Epoch 16/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5869 - acc: 0.6755\n", - "Epoch 17/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5777 - acc: 0.6767\n", - "Epoch 18/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5822 - acc: 0.6721\n", - "Epoch 19/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5775 - acc: 0.6814\n", - "Epoch 20/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.5790 - acc: 0.6740\n", - "Epoch 21/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5760 - acc: 0.6792\n", - "Epoch 22/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5755 - acc: 0.6833\n", - "Epoch 23/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5761 - acc: 0.6781\n", - "Epoch 24/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5825 - acc: 0.6736\n", - "Epoch 25/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5772 - acc: 0.6799\n", - "Epoch 26/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5727 - acc: 0.6796\n", - "Epoch 27/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5688 - acc: 0.6879\n", - "Epoch 28/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5677 - acc: 0.6884\n", - "Epoch 29/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5746 - acc: 0.6754\n", - "Epoch 30/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5751 - acc: 0.6774\n", - "Epoch 31/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5705 - acc: 0.6846\n", - "Epoch 32/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5679 - acc: 0.6851\n", - "Epoch 33/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5640 - acc: 0.6884\n", - "Epoch 34/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5683 - acc: 0.6872\n", - "Epoch 35/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5671 - acc: 0.6851\n", - "Epoch 36/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5569 - acc: 0.6997\n", - "Epoch 37/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5636 - acc: 0.6941\n", - "Epoch 38/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5600 - acc: 0.6937\n", - "Epoch 39/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5526 - acc: 0.7001\n", - "Epoch 40/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5640 - acc: 0.6924\n", - "Epoch 41/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5585 - acc: 0.6912\n", - "Epoch 42/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5570 - acc: 0.6921\n", - "Epoch 43/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.5471 - acc: 0.7067\n", - "Epoch 44/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5511 - acc: 0.7060\n", - "Epoch 45/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5508 - acc: 0.6997\n", - "Epoch 46/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5526 - acc: 0.6981\n", - "Epoch 47/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5480 - acc: 0.7051\n", - "Epoch 48/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5590 - acc: 0.6912\n", - "Epoch 49/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.5463 - acc: 0.7014\n", - "Epoch 50/50\n", - "7273/7273 [==============================] - 0s 36us/step - loss: 0.5408 - acc: 0.7095\n", - " 0.6812592137592137\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.44792596, 0.69121736, 0.4685978 , ..., 0.83220756, 0.48728314,\n", - " 0.46735853])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y, dtype=float)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7f6aec4c5128>]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "results[test] = probas_\n", - "plt.plot(results)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/tensorflow/prediction_ext.ipynb b/scripts/tensorflow/prediction_ext.ipynb deleted file mode 100644 index ffb457d..0000000 --- a/scripts/tensorflow/prediction_ext.ipynb +++ /dev/null @@ -1,5493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from keras import optimizers, regularizers\n", - "from keras.layers import Dense, Dropout, Input\n", - "from keras.models import Model, Sequential\n", - "from random import shuffle\n", - "from scipy import interp\n", - "from scipy.stats.mstats import gmean\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.model_selection import StratifiedKFold, train_test_split\n", - "from sklearn.preprocessing import QuantileTransformer\n", - "import contextlib\n", - "import glob\n", - "import gzip\n", - "import h5py\n", - "import keras\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import pylab as plt\n", - "import random\n", - "import scipy\n", - "import sklearn\n", - "import tensorflow as tf\n", - "random_state = np.random.RandomState(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(8080, 1442)\n", - "(8080, 1)\n" - ] - } - ], - "source": [ - "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", - "y_f_ext = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", - "\n", - "X_ext = np.float64(pd.read_csv(X_f_ext,sep=';').values[:,1:])\n", - "y_ext = np.float64(pd.read_csv(y_f_ext).values)\n", - "\n", - "print(X_ext.shape)\n", - "print(y_ext.shape)\n", - "\n", - "\n", - "ix = [i for i in range(y_ext.shape[0])]\n", - "shuffle(ix)\n", - "X_ext = X_ext[ix, :]\n", - "y_ext = y_ext[ix]\n", - "names = pd.read_csv(X_f_ext,sep=';')['Name'][ix].values\n", - "#X_norm = QuantileTransformer(output_distribution='uniform').fit_transform(X.values.T).T\n", - "X = sklearn.preprocessing.quantile_transform(X_ext, axis=1, output_distribution='uniform', copy=True)\n", - "y = y_ext[: ,0]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6294 - acc: 0.6390\n", - "Epoch 2/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6295 - acc: 0.6432\n", - "Epoch 3/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6239 - acc: 0.6494\n", - "Epoch 4/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6283 - acc: 0.6398\n", - "Epoch 5/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6267 - acc: 0.6416\n", - "Epoch 6/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6258 - acc: 0.6399\n", - "Epoch 7/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6254 - acc: 0.6481\n", - "Epoch 8/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6211 - acc: 0.6467\n", - "Epoch 9/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6289 - acc: 0.6419\n", - "Epoch 10/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6246 - acc: 0.6465\n", - "Epoch 11/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6232 - acc: 0.6481\n", - "Epoch 12/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6239 - acc: 0.6430\n", - "Epoch 13/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6207 - acc: 0.6437\n", - "Epoch 14/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6229 - acc: 0.6479\n", - "Epoch 15/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.6241 - acc: 0.6478\n", - "Epoch 16/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6218 - acc: 0.6437\n", - "Epoch 17/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6223 - acc: 0.6434\n", - "Epoch 18/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6240 - acc: 0.6454\n", - "Epoch 19/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6265 - acc: 0.6410\n", - "Epoch 20/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6274 - acc: 0.6371\n", - "Epoch 21/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6202 - acc: 0.6457\n", - "Epoch 22/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6222 - acc: 0.6443\n", - 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acc: 0.6410\n", - "Epoch 32/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6170 - acc: 0.6486\n", - "Epoch 33/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6170 - acc: 0.6500\n", - "Epoch 34/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6191 - acc: 0.6463\n", - "Epoch 35/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6208 - acc: 0.6413\n", - "Epoch 36/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6198 - acc: 0.6514\n", - "Epoch 37/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6206 - acc: 0.6456\n", - "Epoch 38/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6185 - acc: 0.6443\n", - "Epoch 39/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6170 - acc: 0.6459\n", - "Epoch 40/50\n", - "7271/7271 [==============================] - 0s 37us/step 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[==============================] - 0s 37us/step - loss: 0.6094 - acc: 0.6571\n", - "Epoch 35/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6240 - acc: 0.6471\n", - "Epoch 36/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6140 - acc: 0.6452\n", - "Epoch 37/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.6193 - acc: 0.6479\n", - "Epoch 38/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6100 - acc: 0.6536\n", - "Epoch 39/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6109 - acc: 0.6500\n", - "Epoch 40/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6100 - acc: 0.6494\n", - "Epoch 41/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6208 - acc: 0.6468\n", - "Epoch 42/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6075 - acc: 0.6591\n", - "Epoch 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[==============================] - 0s 37us/step - loss: 0.6222 - acc: 0.6410\n", - "Epoch 20/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6184 - acc: 0.6486\n", - "Epoch 21/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6267 - acc: 0.6379\n", - "Epoch 22/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6168 - acc: 0.6492\n", - "Epoch 23/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6181 - acc: 0.6420\n", - "Epoch 24/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6189 - acc: 0.6449\n", - "Epoch 25/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6210 - acc: 0.6457\n", - "Epoch 26/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6181 - acc: 0.6482\n", - "Epoch 27/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6185 - acc: 0.6461\n", - "Epoch 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[==============================] - 0s 39us/step - loss: 0.6314 - acc: 0.6313\n", - "Epoch 5/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.6337 - acc: 0.6321\n", - "Epoch 6/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6261 - acc: 0.6405\n", - "Epoch 7/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.6285 - acc: 0.6343\n", - "Epoch 8/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6289 - acc: 0.6371\n", - "Epoch 9/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6277 - acc: 0.6382\n", - "Epoch 10/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6219 - acc: 0.6408\n", - "Epoch 11/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6281 - acc: 0.6371\n", - "Epoch 12/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.6260 - acc: 0.6362\n", - "Epoch 13/50\n", - 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[==============================] - 0s 37us/step - loss: 0.6314 - acc: 0.6344\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6321 - acc: 0.6370\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6251 - acc: 0.6375\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6237 - acc: 0.6403\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6257 - acc: 0.6368\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6299 - acc: 0.6315\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6249 - acc: 0.6392\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6255 - acc: 0.6404\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6291 - acc: 0.6264\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6243 - acc: 0.6360\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6217 - acc: 0.6335\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6239 - acc: 0.6401\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.6202 - acc: 0.6430\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6273 - acc: 0.6349\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6211 - acc: 0.6400\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6205 - acc: 0.6447\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6200 - acc: 0.6388\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6213 - acc: 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- 0s 38us/step - loss: 0.6316 - acc: 0.6359\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6460 - acc: 0.6297\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6506 - acc: 0.6239\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6409 - acc: 0.6410\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6421 - acc: 0.6366\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6409 - acc: 0.6386\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6269 - acc: 0.6350\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6267 - acc: 0.6436\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6315 - acc: 0.6313\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6281 - acc: 0.6372\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6275 - acc: 0.6390\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6213 - acc: 0.6397\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6333 - acc: 0.6379\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6243 - acc: 0.6418\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6313 - acc: 0.6302\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6253 - acc: 0.6427\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6222 - acc: 0.6418\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6219 - acc: 0.6411\n", - "Epoch 13/50\n", - 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loss: 0.6256 - acc: 0.6381\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6297 - acc: 0.6421\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.6309 - acc: 0.6379\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6276 - acc: 0.6403\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6288 - acc: 0.6382\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6248 - acc: 0.6416\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6276 - acc: 0.6399\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6296 - acc: 0.6357\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6265 - acc: 0.6405\n", - "Epoch 33/50\n", - "7272/7272 [==============================] 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"7272/7272 [==============================] - 0s 39us/step - loss: 0.6235 - acc: 0.6385\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6216 - acc: 0.6401\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6267 - acc: 0.6403\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6196 - acc: 0.6467\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6176 - acc: 0.6465\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.6196 - acc: 0.6400\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6177 - acc: 0.6432\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6253 - acc: 0.6315\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6196 - acc: 0.6426\n", - 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[==============================] - 0s 39us/step - loss: 0.6271 - acc: 0.6345\n", - "Epoch 21/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6273 - acc: 0.6332\n", - "Epoch 22/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6216 - acc: 0.6370\n", - "Epoch 23/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6237 - acc: 0.6336\n", - "Epoch 24/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6260 - acc: 0.6366\n", - "Epoch 25/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6249 - acc: 0.6370\n", - "Epoch 26/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6224 - acc: 0.6362\n", - "Epoch 27/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6233 - acc: 0.6410\n", - "Epoch 28/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6336 - acc: 0.6292\n", - "Epoch 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loss: 0.6237 - acc: 0.6374\n", - "Epoch 49/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6247 - acc: 0.6330\n", - "Epoch 50/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6260 - acc: 0.6319\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6395 - acc: 0.6256\n", - "Epoch 2/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6355 - acc: 0.6307\n", - "Epoch 3/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6374 - acc: 0.6292\n", - "Epoch 4/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6376 - acc: 0.6330\n", - "Epoch 5/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6337 - acc: 0.6358\n", - "Epoch 6/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6395 - acc: 0.6317\n", - "Epoch 7/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6335 - acc: 0.6344\n", - "Epoch 8/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6337 - acc: 0.6307\n", - "Epoch 9/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6345 - acc: 0.6325\n", - "Epoch 10/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6308 - acc: 0.6365\n", - "Epoch 11/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6430 - acc: 0.6172\n", - "Epoch 12/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6357 - acc: 0.6315\n", - "Epoch 13/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6327 - acc: 0.6339\n", - "Epoch 14/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6315 - acc: 0.6373\n", - "Epoch 15/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6368 - acc: 0.6222\n", - "Epoch 16/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6324 - acc: 0.6372\n", - "Epoch 17/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6351 - acc: 0.6304\n", - "Epoch 18/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6292 - acc: 0.6350\n", - "Epoch 19/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6307 - acc: 0.6332\n", - "Epoch 20/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6290 - acc: 0.6348\n", - "Epoch 21/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6337 - acc: 0.6312\n", - "Epoch 22/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6312 - acc: 0.6334\n", - "Epoch 23/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6294 - acc: 0.6340\n", - "Epoch 24/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6271 - acc: 0.6345\n", - "Epoch 25/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6288 - acc: 0.6358\n", - "Epoch 26/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6271 - acc: 0.6354\n", - "Epoch 27/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6333 - acc: 0.6296\n", - "Epoch 28/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6294 - acc: 0.6270\n", - "Epoch 29/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6355 - acc: 0.6231\n", - "Epoch 30/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6265 - acc: 0.6363\n", - "Epoch 31/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6268 - acc: 0.6292\n", - "Epoch 32/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6314 - acc: 0.6348\n", - "Epoch 33/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6283 - acc: 0.6318\n", - "Epoch 34/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6288 - acc: 0.6317\n", - "Epoch 35/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6255 - acc: 0.6352\n", - "Epoch 36/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6282 - acc: 0.6332\n", - "Epoch 37/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6239 - acc: 0.6369\n", - "Epoch 38/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6287 - acc: 0.6376\n", - "Epoch 39/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6280 - acc: 0.6297\n", - "Epoch 40/50\n", - "7273/7273 [==============================] - 0s 41us/step - loss: 0.6278 - acc: 0.6354\n", - "Epoch 41/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6253 - acc: 0.6365\n", - "Epoch 42/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6232 - acc: 0.6319\n", - "Epoch 43/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6229 - acc: 0.6389\n", - "Epoch 44/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6245 - acc: 0.6380\n", - "Epoch 45/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6264 - acc: 0.6332\n", - "Epoch 46/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6233 - acc: 0.6333\n", - "Epoch 47/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6244 - acc: 0.6344\n", - "Epoch 48/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6272 - acc: 0.6402\n", - "Epoch 49/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6273 - acc: 0.6314\n", - "Epoch 50/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6272 - acc: 0.6361\n", - " 0.7056603194103194\n" - ] - }, - { - "data": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.48989287, 0.59242761, 0.59383744, ..., 0.50447261, 0.47094053,\n", - " 0.76454699])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv = StratifiedKFold(n_splits=10)\n", - "results = np.zeros_like(y)\n", - "\n", - "tprs = []\n", - "aucs = []\n", - "mean_fpr = np.linspace(0, 1, 100)\n", - "\n", - "i = 0\n", - "for train, test in cv.split(X, y):\n", - " keras.backend.clear_session()\n", - " prbs=[]\n", - " for mod in range(5):\n", - " print('>>')\n", - " curr_try = 0\n", - " while curr_try <10:\n", - " print('.')\n", - "\n", - " model = Sequential()\n", - " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(64, activation='relu'))\n", - " model.add(Dense(1, activation='sigmoid'))\n", - " # Compile model\n", - " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", - " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", - " \n", - " # Fit the model\n", - " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", - " if history.history['acc'][-1] > 0.53:\n", - " break\n", - " else:\n", - " curr_try += 1\n", - "\n", - " # Fit the model\n", - " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", - " \n", - " # evaluate the model\n", - " probas_ = model.predict(X[test,:])\n", - " prbs.append(probas_)\n", - " # Average the predictions\n", - " probas_ = np.mean(np.hstack(prbs), axis=1)\n", - " results[test] = probas_\n", - " \n", - " # Compute ROC curve and area the curve\n", - " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", - " print(' ' + str(auc(fpr, tpr)))\n", - " tprs.append(interp(mean_fpr, fpr, tpr))\n", - " tprs[-1][0] = 0.0\n", - " roc_auc = auc(fpr, tpr)\n", - " aucs.append(roc_auc)\n", - " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", - " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", - "\n", - " i += 1\n", - "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", - " label='Chance', alpha=.8)\n", - "\n", - "mean_tpr = np.mean(tprs, axis=0)\n", - "mean_tpr[-1] = 1.0\n", - "mean_auc = auc(mean_fpr, mean_tpr)\n", - "std_auc = np.std(aucs)\n", - "plt.plot(mean_fpr, mean_tpr, color='b',\n", - " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", - " lw=2, alpha=.8)\n", - "\n", - "std_tpr = np.std(tprs, axis=0)\n", - "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", - "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", - "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", - " label=r'$\\pm$ 1 std. dev.')\n", - "\n", - "plt.xlim([-0.05, 1.05])\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('Receiver operating characteristic example')\n", - "plt.legend(loc=\"lower right\")\n", - "plt.show()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred_ext.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", - "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} |