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Diffstat (limited to 'scripts/tensorflow/prediction_ext.ipynb')
| -rw-r--r-- | scripts/tensorflow/prediction_ext.ipynb | 5493 |
1 files changed, 0 insertions, 5493 deletions
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", - "Epoch 23/50\n", - "7271/7271 [==============================] - 0s 36us/step - loss: 0.6199 - acc: 0.6486\n", - "Epoch 24/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6201 - acc: 0.6461\n", - "Epoch 25/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6246 - acc: 0.6384\n", - "Epoch 26/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6205 - acc: 0.6508\n", - "Epoch 27/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6187 - acc: 0.6485\n", - "Epoch 28/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6245 - acc: 0.6376\n", - "Epoch 29/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6231 - acc: 0.6437\n", - "Epoch 30/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6222 - acc: 0.6483\n", - "Epoch 31/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6216 - 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 - loss: 0.6159 - acc: 0.6522\n", - "Epoch 41/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6305 - acc: 0.6382\n", - "Epoch 42/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6220 - acc: 0.6463\n", - "Epoch 43/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6166 - acc: 0.6512\n", - "Epoch 44/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6190 - acc: 0.6487\n", - "Epoch 45/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6206 - acc: 0.6487\n", - "Epoch 46/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6198 - acc: 0.6441\n", - "Epoch 47/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6213 - acc: 0.6463\n", - "Epoch 48/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6154 - acc: 0.6487\n", - "Epoch 49/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6179 - acc: 0.6485\n", - "Epoch 50/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6247 - acc: 0.6399\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6218 - acc: 0.6456\n", - "Epoch 2/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6243 - acc: 0.6430\n", - "Epoch 3/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6310 - acc: 0.6380\n", - "Epoch 4/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6273 - acc: 0.6395\n", - "Epoch 5/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6217 - acc: 0.6481\n", - "Epoch 6/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6201 - acc: 0.6463\n", - "Epoch 7/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6225 - acc: 0.6454\n", - "Epoch 8/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6247 - acc: 0.6382\n", - "Epoch 9/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6205 - acc: 0.6432\n", - "Epoch 10/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6218 - acc: 0.6430\n", - "Epoch 11/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6191 - acc: 0.6399\n", - "Epoch 12/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6287 - acc: 0.6336\n", - "Epoch 13/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6182 - acc: 0.6452\n", - "Epoch 14/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6218 - acc: 0.6442\n", - "Epoch 15/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6199 - acc: 0.6454\n", - "Epoch 16/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6291 - acc: 0.6394\n", - "Epoch 17/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6236 - acc: 0.6397\n", - "Epoch 18/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6215 - acc: 0.6467\n", - "Epoch 19/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6216 - acc: 0.6452\n", - "Epoch 20/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6214 - acc: 0.6438\n", - "Epoch 21/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6331 - acc: 0.6428\n", - "Epoch 22/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6217 - acc: 0.6470\n", - "Epoch 23/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6205 - acc: 0.6456\n", - "Epoch 24/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6219 - acc: 0.6468\n", - "Epoch 25/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6210 - acc: 0.6423\n", - "Epoch 26/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6113 - acc: 0.6544\n", - "Epoch 27/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6172 - acc: 0.6530\n", - "Epoch 28/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6137 - acc: 0.6485\n", - "Epoch 29/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6129 - acc: 0.6518\n", - "Epoch 30/50\n", - "7271/7271 [==============================] - 0s 39us/step - loss: 0.6123 - acc: 0.6536\n", - "Epoch 31/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.6143 - acc: 0.6534\n", - "Epoch 32/50\n", - "7271/7271 [==============================] - 0s 40us/step - loss: 0.6154 - acc: 0.6504\n", - "Epoch 33/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6136 - acc: 0.6512\n", - "Epoch 34/50\n", - "7271/7271 [==============================] - 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 43/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6111 - acc: 0.6580\n", - "Epoch 44/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6083 - acc: 0.6552\n", - "Epoch 45/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6144 - acc: 0.6509\n", - "Epoch 46/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6081 - acc: 0.6537\n", - "Epoch 47/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6134 - acc: 0.6498\n", - "Epoch 48/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6123 - acc: 0.6518\n", - "Epoch 49/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6153 - acc: 0.6432\n", - "Epoch 50/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6085 - acc: 0.6564\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6335 - acc: 0.6357\n", - "Epoch 2/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6316 - acc: 0.6360\n", - "Epoch 3/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6249 - acc: 0.6439\n", - "Epoch 4/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6316 - acc: 0.6369\n", - "Epoch 5/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6243 - acc: 0.6443\n", - "Epoch 6/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6286 - acc: 0.6394\n", - "Epoch 7/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6271 - acc: 0.6384\n", - "Epoch 8/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6215 - acc: 0.6397\n", - "Epoch 9/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6200 - acc: 0.6479\n", - "Epoch 10/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6226 - acc: 0.6415\n", - "Epoch 11/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6247 - acc: 0.6372\n", - "Epoch 12/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6244 - acc: 0.6412\n", - "Epoch 13/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6202 - acc: 0.6476\n", - "Epoch 14/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6223 - acc: 0.6439\n", - "Epoch 15/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6319 - acc: 0.6320\n", - "Epoch 16/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6263 - acc: 0.6419\n", - "Epoch 17/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6223 - acc: 0.6415\n", - "Epoch 18/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6243 - acc: 0.6405\n", - "Epoch 19/50\n", - "7271/7271 [==============================] - 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 28/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6211 - acc: 0.6456\n", - "Epoch 29/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6167 - acc: 0.6470\n", - "Epoch 30/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6142 - acc: 0.6520\n", - "Epoch 31/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6205 - acc: 0.6432\n", - "Epoch 32/50\n", - "7271/7271 [==============================] - 0s 37us/step - loss: 0.6181 - acc: 0.6508\n", - "Epoch 33/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6116 - acc: 0.6507\n", - "Epoch 34/50\n", - "7271/7271 [==============================] - 0s 38us/step - loss: 0.6151 - acc: 0.6515\n", - "Epoch 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[==============================] - 0s 38us/step - loss: 0.6234 - acc: 0.6387\n", - "Epoch 45/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6355 - acc: 0.6295\n", - "Epoch 46/50\n", - "7273/7273 [==============================] - 0s 37us/step - loss: 0.6204 - acc: 0.6429\n", - "Epoch 47/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6293 - acc: 0.6312\n", - "Epoch 48/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6257 - acc: 0.6406\n", - "Epoch 49/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6212 - acc: 0.6385\n", - "Epoch 50/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6189 - acc: 0.6442\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6436 - acc: 0.6270\n", - "Epoch 2/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6360 - acc: 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[==============================] - 0s 38us/step - loss: 0.6238 - acc: 0.6373\n", - "Epoch 30/50\n", - "7273/7273 [==============================] - 0s 40us/step - loss: 0.6289 - acc: 0.6343\n", - "Epoch 31/50\n", - "7273/7273 [==============================] - 0s 38us/step - loss: 0.6267 - acc: 0.6297\n", - "Epoch 32/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6248 - acc: 0.6354\n", - "Epoch 33/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6275 - acc: 0.6293\n", - "Epoch 34/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6288 - acc: 0.6369\n", - "Epoch 35/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6221 - acc: 0.6381\n", - "Epoch 36/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6251 - acc: 0.6257\n", - "Epoch 37/50\n", - "7273/7273 [==============================] - 0s 39us/step - loss: 0.6229 - acc: 0.6339\n", - "Epoch 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[==============================] - 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 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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": { - "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([0.48989287, 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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vcl6/y0T/6tIImXkUOL40wnybgO3V7fuBjRERHbaxC0v2Q2Y+mJmvVHcfZnAtQknqfBYAfgP4HeC/u2xch+r0w88Bn8zMFwEy83DHbWxbnT5I4I3V7e9lgetyJl1mPgR88xS7bAI+nQMPA2dGxJq6r99loa+zNMKr+2TmMeBl4E2dtK47y10iYguD3+QlWbIPIuLtwPmZ+ZddNqxjdT4LbwXeGhH/GBEPVyvDlqROH2wF3hcRBxnM5ruRlafR0jJd/uGRhZL5yVN+6uwz6Wr/GyPifcAM8KOttqh7p+yDiHgNcBtwfVcNGpM6n4VVDIZvZhkc2f19RFyYmS+13Lau1OmDa4FPZebvR8QPA39a9cG3229ebzSqjV0m+jpLI7y6T0SsYnCYdqrDmUlUa4mIiHgX8GvAlZn5Px21rStL9cEbGKx/tCcinmUwJrmrwBOydX8mdmbm/2bm14CnGRT+UtTpgy3AfQCZ+U/AdzNY/2UlqVU3FtNloa+zNMIuYHN1+yrg77I6E1GQJfuhGrb4IwZFvrQxWViiDzLz5cxcnZnrMnMdg/MUV2bmvvE0tzV1fib+gsHJeSJiNYOhnGc6bWW76vTBAWAjQES8jUGhP9JpK8dvF/Az1eybS4CXM/NQ3Sd3NnSTiyyNEBG/DuzLzF3A3QwOy/YzSPLXdNW+rtTsh98FXg98rjoXfSAzrxxbo0esZh8Ur2Y/fAH48Yh4Evg/4Jcz8z/G1+rRqtkHHwP+OCI+wmC44vrSAmBEfJbB8Nzq6lzELcBrATLzTgbnJq4A9gOvADcs6/UL6y9J0km8MlaSCmehl6TCWeglqXAWekkqnIVekgpnoZekwlnoJalwFnpJKtz/A9U1g5FlATpwAAAAAElFTkSuQmCC\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 -} |