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Diffstat (limited to '10-fold-crossvalidations/cdk/tensorflow/prediction-v5.ipynb')
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diff --git a/10-fold-crossvalidations/cdk/tensorflow/prediction-v5.ipynb b/10-fold-crossvalidations/cdk/tensorflow/prediction-v5.ipynb new file mode 100644 index 0000000..a860500 --- /dev/null +++ b/10-fold-crossvalidations/cdk/tensorflow/prediction-v5.ipynb @@ -0,0 +1,11615 @@ +{ + "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", + "from sklearn.svm import SVC\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": [ + { + "data": { + "text/plain": [ + "\"X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\\ny_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\\n\\nX = pd.read_csv(X_f).values[:,:-1]\\ny = pd.read_csv(y_f).values\\n\\n\\nix = [i for i in range(y.shape[0])]\\nshuffle(ix)\\nX = X[ix, :]\\ny = y[ix]\\nnames = pd.read_csv(X_f)['Unnamed: 0'][ix].values\\nX = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\\ny = y[: ,0]\\n\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": [], + "source": [ + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-fingerprints.csv'\n", + "\n", + "X = pd.read_csv(X_f_ext,sep=',')\n", + "X['Mutagenicity_bin'] = np.int32(X['Mutagenicity'] == 'mutagenic')\n", + "del X['Mutagenicity']\n", + "\n", + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", + "\n", + "X_ext = pd.read_csv(X_f_ext,sep=';')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.merge(X[['Canonical SMILES','Mutagenicity_bin']], X_ext, left_on='Canonical SMILES', right_on='Name')\n", + "y = X['Mutagenicity_bin'].values\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#X.to_csv('/home/drewe/notebooks/genotox/mutagenicity-mod-2.new.csv')\n", + "#X.set_index('Canonical SMILES').to_csv('/home/drewe/notebooks/genotox/mutagenicity-mod-2.new.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "del X['Mutagenicity_bin']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "names = X['Name'].values\n", + "del X['Name']\n", + "\n", + "X = np.float64(X.values[:,1:])\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "names = names[ix]\n", + "y = y[ix]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8083, 1442)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 42us/step - loss: 0.4451 - acc: 0.7986\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4496 - acc: 0.7961\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4410 - acc: 0.8000\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4555 - acc: 0.7903\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4735 - acc: 0.7792\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4449 - acc: 0.7994\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.4403 - acc: 0.8016\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4437 - acc: 0.8008\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4498 - acc: 0.7906\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4442 - acc: 0.7993\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4411 - acc: 0.7996\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.4447 - acc: 0.7942\n", + "Epoch 13/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4432 - acc: 0.7979\n", + "Epoch 14/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4381 - acc: 0.7978\n", + "Epoch 15/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4449 - acc: 0.8005\n", + "Epoch 16/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4401 - acc: 0.8007\n", + "Epoch 17/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4312 - acc: 0.8090\n", + "Epoch 18/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4465 - acc: 0.7939\n", + "Epoch 19/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4406 - acc: 0.8020\n", + "Epoch 20/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4291 - acc: 0.8060\n", + "Epoch 21/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4317 - acc: 0.8077\n", + "Epoch 22/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4325 - acc: 0.8038\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4388 - acc: 0.8033\n", + "Epoch 24/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4309 - acc: 0.8078\n", + "Epoch 25/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.4226 - acc: 0.8133\n", + "Epoch 26/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4190 - acc: 0.8125\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4258 - acc: 0.8101\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4464 - acc: 0.8041\n", + "Epoch 29/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4410 - acc: 0.8073\n", + "Epoch 30/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4440 - acc: 0.8020\n", + "Epoch 31/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4373 - acc: 0.8056\n", + "Epoch 32/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4334 - acc: 0.8031\n", + "Epoch 33/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4221 - acc: 0.8126\n", + "Epoch 34/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4454 - acc: 0.8049\n", + "Epoch 35/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4297 - acc: 0.8096\n", + "Epoch 36/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4212 - acc: 0.8180\n", + "Epoch 37/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4311 - acc: 0.8121\n", + "Epoch 38/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4330 - acc: 0.8108\n", + "Epoch 39/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4241 - acc: 0.8165\n", + "Epoch 40/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4278 - acc: 0.8119\n", + "Epoch 41/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4235 - acc: 0.8145\n", + "Epoch 42/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4334 - acc: 0.8093\n", + "Epoch 43/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4379 - acc: 0.8082\n", + "Epoch 44/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4398 - acc: 0.8048\n", + "Epoch 45/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4321 - acc: 0.8074\n", + "Epoch 46/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4219 - acc: 0.8167\n", + "Epoch 47/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4217 - acc: 0.8166\n", + "Epoch 48/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.4245 - acc: 0.8143\n", + "Epoch 49/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4171 - acc: 0.8189\n", + "Epoch 50/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4250 - acc: 0.8129\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4386 - acc: 0.7979\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4482 - acc: 0.7978\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4321 - acc: 0.8022\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4291 - acc: 0.8055\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4332 - acc: 0.8048\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4257 - acc: 0.8059\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4309 - acc: 0.8089\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4319 - acc: 0.8016\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4232 - acc: 0.8064\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4394 - acc: 0.7982\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4193 - acc: 0.8128\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4195 - acc: 0.8088\n", + "Epoch 13/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4184 - acc: 0.8108\n", + "Epoch 14/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4235 - acc: 0.8067\n", + "Epoch 15/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4124 - acc: 0.8159\n", + "Epoch 16/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4126 - acc: 0.8141\n", + "Epoch 17/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4116 - acc: 0.8173\n", + "Epoch 18/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4243 - acc: 0.8077\n", + "Epoch 19/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4138 - acc: 0.8110\n", + "Epoch 20/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4460 - acc: 0.7960\n", + "Epoch 21/50\n", + "7274/7274 [==============================] - 0s 42us/step - loss: 0.4108 - acc: 0.8155\n", + "Epoch 22/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4008 - acc: 0.8221\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4013 - acc: 0.8176\n", + "Epoch 24/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4022 - acc: 0.8162\n", + "Epoch 25/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4148 - acc: 0.8172\n", + "Epoch 26/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4012 - acc: 0.8194\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 41us/step - loss: 0.4045 - acc: 0.8187\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4100 - acc: 0.8162\n", + "Epoch 29/50\n", + "7274/7274 [==============================] - 0s 44us/step - loss: 0.4003 - acc: 0.8228\n", + "Epoch 30/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4024 - acc: 0.8189\n", + "Epoch 31/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4098 - acc: 0.8129\n", + "Epoch 32/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4008 - acc: 0.8240\n", + "Epoch 33/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.3954 - acc: 0.8250\n", + "Epoch 34/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.3955 - acc: 0.8217\n", + "Epoch 35/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3972 - acc: 0.8240\n", + "Epoch 36/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.3964 - acc: 0.8269\n", + "Epoch 37/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3944 - acc: 0.8254\n", + "Epoch 38/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3843 - acc: 0.8317\n", + "Epoch 39/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3907 - acc: 0.8276\n", + "Epoch 40/50\n", + "7274/7274 [==============================] - 0s 43us/step - loss: 0.3759 - acc: 0.8323\n", + "Epoch 41/50\n", + "7274/7274 [==============================] - 0s 43us/step - loss: 0.3847 - acc: 0.8275\n", + "Epoch 42/50\n", + "7274/7274 [==============================] - 0s 41us/step - loss: 0.4160 - acc: 0.8123\n", + "Epoch 43/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.3959 - acc: 0.8224\n", + "Epoch 44/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.3908 - acc: 0.8238\n", + "Epoch 45/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3979 - acc: 0.8231\n", + "Epoch 46/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4004 - acc: 0.8228\n", + "Epoch 47/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.3798 - acc: 0.8353\n", + "Epoch 48/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.3894 - acc: 0.8240\n", + "Epoch 49/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.3934 - acc: 0.8216\n", + "Epoch 50/50\n", + "7274/7274 [==============================] - 0s 36us/step - loss: 0.3826 - acc: 0.8299\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4435 - acc: 0.7963\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 63us/step - loss: 0.4440 - acc: 0.7982\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 52us/step - loss: 0.4525 - acc: 0.7934\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 49us/step - loss: 0.4433 - acc: 0.7964\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 43us/step - loss: 0.4321 - acc: 0.8067\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4395 - acc: 0.7986\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4360 - acc: 0.8066\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4355 - acc: 0.8068\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4525 - acc: 0.7901\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4285 - acc: 0.8093\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4351 - acc: 0.8089\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4323 - acc: 0.8022\n", + "Epoch 13/50\n", + 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"Epoch 22/50\n", + "7274/7274 [==============================] - 0s 40us/step - loss: 0.4250 - acc: 0.8112\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4219 - acc: 0.8107\n", + "Epoch 24/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4274 - acc: 0.8059\n", + "Epoch 25/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4127 - acc: 0.8145\n", + "Epoch 26/50\n", + "7274/7274 [==============================] - 0s 41us/step - loss: 0.4215 - acc: 0.8085\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 38us/step - loss: 0.4301 - acc: 0.8031\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 39us/step - loss: 0.4062 - acc: 0.8216\n", + "Epoch 29/50\n", + "7274/7274 [==============================] - 0s 37us/step - loss: 0.4113 - acc: 0.8188\n", + "Epoch 30/50\n", + "7274/7274 [==============================] - 0s 41us/step - loss: 0.4170 - 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- loss: 0.3791 - acc: 0.8326\n", + "Epoch 37/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3924 - acc: 0.8256\n", + "Epoch 38/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3877 - acc: 0.8297\n", + "Epoch 39/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3971 - acc: 0.8230\n", + "Epoch 40/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3947 - acc: 0.8195\n", + "Epoch 41/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3782 - acc: 0.8330\n", + "Epoch 42/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3968 - acc: 0.8214\n", + "Epoch 43/50\n", + "7275/7275 [==============================] - 0s 40us/step - loss: 0.3823 - acc: 0.8297\n", + "Epoch 44/50\n", + "7275/7275 [==============================] - 0s 36us/step - loss: 0.3902 - acc: 0.8293\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 41us/step - loss: 0.3761 - acc: 0.8326\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3752 - acc: 0.8362\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3739 - acc: 0.8334\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 37us/step - loss: 0.3840 - acc: 0.8296\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3778 - acc: 0.8351\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 38us/step - loss: 0.3810 - acc: 0.8329\n", + " 0.8730056613484303\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([0.52050287, 0.69786465, 0.89777595, ..., 0.16324709, 0.93710172,\n", + " 0.68392801])" + ] + }, + "execution_count": 7, + "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": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v4.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": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f21bb64eba8>]" + ] + }, + "execution_count": 10, + "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": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4980 - acc: 0.7615\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.5028 - acc: 0.7563\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4927 - acc: 0.7708\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4966 - acc: 0.7615\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4958 - acc: 0.7656\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4982 - acc: 0.7641\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4993 - acc: 0.7633\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4955 - acc: 0.7649\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4935 - acc: 0.7677\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 15us/step - loss: 0.4981 - acc: 0.7612\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4948 - acc: 0.7612\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4928 - acc: 0.7692\n", + "Epoch 13/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4886 - acc: 0.7699\n", + "Epoch 14/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4902 - acc: 0.7706\n", + "Epoch 15/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4902 - acc: 0.7695\n", + "Epoch 16/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4957 - acc: 0.7684\n", + "Epoch 17/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4889 - acc: 0.7707\n", + "Epoch 18/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4906 - acc: 0.7685\n", + "Epoch 19/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4858 - acc: 0.7739\n", + "Epoch 20/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4889 - acc: 0.7707\n", + "Epoch 21/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4870 - acc: 0.7715\n", + "Epoch 22/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4907 - acc: 0.7681\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4879 - acc: 0.7700\n", + "Epoch 24/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4878 - acc: 0.7706\n", + "Epoch 25/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4882 - acc: 0.7693\n", + "Epoch 26/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4888 - acc: 0.7732\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4872 - acc: 0.7718\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4900 - acc: 0.7714\n", + "Epoch 29/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4922 - acc: 0.7667\n", + "Epoch 30/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4889 - acc: 0.7666\n", + "Epoch 31/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4836 - acc: 0.7788\n", + "Epoch 32/50\n", + "7274/7274 [==============================] - 0s 12us/step 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[==============================] - 0s 11us/step - loss: 0.4875 - acc: 0.7715\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 13us/step - loss: 0.4905 - acc: 0.7674\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4890 - acc: 0.7695\n", + "Epoch 29/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4836 - acc: 0.7785\n", + "Epoch 30/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4902 - acc: 0.7652\n", + "Epoch 31/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4902 - acc: 0.7699\n", + "Epoch 32/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4862 - acc: 0.7733\n", + "Epoch 33/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4845 - acc: 0.7704\n", + "Epoch 34/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4873 - acc: 0.7692\n", + "Epoch 35/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4843 - acc: 0.7777\n", + "Epoch 36/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4878 - acc: 0.7690\n", + "Epoch 37/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4839 - acc: 0.7752\n", + "Epoch 38/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4837 - acc: 0.7770\n", + "Epoch 39/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4846 - acc: 0.7750\n", + "Epoch 40/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4833 - acc: 0.7741\n", + "Epoch 41/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4832 - acc: 0.7763\n", + "Epoch 42/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4819 - acc: 0.7774\n", + "Epoch 43/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4817 - acc: 0.7747\n", + "Epoch 44/50\n", + "7274/7274 [==============================] - 0s 10us/step - loss: 0.4909 - acc: 0.7682\n", + "Epoch 45/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4827 - acc: 0.7737\n", + "Epoch 46/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4816 - acc: 0.7788\n", + "Epoch 47/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4807 - acc: 0.7748\n", + "Epoch 48/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4822 - acc: 0.7751\n", + "Epoch 49/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4828 - acc: 0.7728\n", + "Epoch 50/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4820 - acc: 0.7740\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.5006 - acc: 0.7604\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 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20/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4874 - acc: 0.7711\n", + "Epoch 21/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4912 - acc: 0.7682\n", + "Epoch 22/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4971 - acc: 0.7618\n", + "Epoch 23/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4845 - acc: 0.7769\n", + "Epoch 24/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4910 - acc: 0.7714\n", + "Epoch 25/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4920 - acc: 0.7717\n", + "Epoch 26/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4849 - acc: 0.7719\n", + "Epoch 27/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4838 - acc: 0.7750\n", + "Epoch 28/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4891 - acc: 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loss: 0.4872 - acc: 0.7715\n", + "Epoch 38/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4830 - acc: 0.7756\n", + "Epoch 39/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4816 - acc: 0.7796\n", + "Epoch 40/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4893 - acc: 0.7673\n", + "Epoch 41/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4859 - acc: 0.7743\n", + "Epoch 42/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4799 - acc: 0.7783\n", + "Epoch 43/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4841 - acc: 0.7759\n", + "Epoch 44/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4800 - acc: 0.7761\n", + "Epoch 45/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4829 - acc: 0.7761\n", + "Epoch 46/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4864 - acc: 0.7729\n", + "Epoch 47/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4837 - acc: 0.7741\n", + "Epoch 48/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4869 - acc: 0.7763\n", + "Epoch 49/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4822 - acc: 0.7747\n", + "Epoch 50/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4819 - acc: 0.7756\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4968 - acc: 0.7631\n", + "Epoch 2/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4992 - acc: 0.7640\n", + "Epoch 3/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4957 - acc: 0.7659\n", + "Epoch 4/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4941 - acc: 0.7677\n", + "Epoch 5/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4970 - acc: 0.7629\n", + "Epoch 6/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4940 - acc: 0.7693\n", + "Epoch 7/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4963 - acc: 0.7692\n", + "Epoch 8/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4964 - acc: 0.7663\n", + "Epoch 9/50\n", + "7274/7274 [==============================] - 0s 12us/step - loss: 0.4931 - acc: 0.7673\n", + "Epoch 10/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4986 - acc: 0.7615\n", + "Epoch 11/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4955 - acc: 0.7678\n", + "Epoch 12/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4913 - acc: 0.7678\n", + "Epoch 13/50\n", + "7274/7274 [==============================] - 0s 11us/step - loss: 0.4968 - acc: 0.7626\n", + "Epoch 14/50\n", 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loss: 0.4807 - acc: 0.7769\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4820 - acc: 0.7731\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4808 - acc: 0.7757\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4964 - acc: 0.7651\n", + "Epoch 2/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.5010 - acc: 0.7608\n", + "Epoch 3/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4945 - acc: 0.7692\n", + "Epoch 4/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4963 - acc: 0.7665\n", + "Epoch 5/50\n", + "7275/7275 [==============================] - 0s 12us/step - loss: 0.4941 - acc: 0.7667\n", + "Epoch 6/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4929 - acc: 0.7693\n", + "Epoch 7/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4943 - acc: 0.7667\n", + "Epoch 8/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4989 - acc: 0.7622\n", + "Epoch 9/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4958 - acc: 0.7629\n", + "Epoch 10/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4933 - acc: 0.7662\n", + "Epoch 11/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4963 - acc: 0.7629\n", + "Epoch 12/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4929 - acc: 0.7721\n", + "Epoch 13/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4932 - acc: 0.7676\n", + "Epoch 14/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4895 - acc: 0.7717\n", + "Epoch 15/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4881 - acc: 0.7724\n", + "Epoch 16/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4899 - acc: 0.7696\n", + "Epoch 17/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4945 - acc: 0.7660\n", + "Epoch 18/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4890 - acc: 0.7726\n", + "Epoch 19/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4891 - acc: 0.7707\n", + "Epoch 20/50\n", + "7275/7275 [==============================] - 0s 12us/step - loss: 0.4882 - acc: 0.7692\n", + "Epoch 21/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4890 - acc: 0.7722\n", + "Epoch 22/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4905 - acc: 0.7659\n", + "Epoch 23/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4924 - acc: 0.7693\n", + "Epoch 24/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4876 - acc: 0.7707\n", + "Epoch 25/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4897 - acc: 0.7725\n", + "Epoch 26/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4893 - acc: 0.7695\n", + "Epoch 27/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4877 - acc: 0.7722\n", + "Epoch 28/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4847 - acc: 0.7801\n", + "Epoch 29/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4896 - acc: 0.7714\n", + "Epoch 30/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4859 - acc: 0.7717\n", + "Epoch 31/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4879 - acc: 0.7750\n", + "Epoch 32/50\n", + "7275/7275 [==============================] - 0s 12us/step - loss: 0.4894 - acc: 0.7695\n", + "Epoch 33/50\n", + "7275/7275 [==============================] - 0s 12us/step - loss: 0.4881 - acc: 0.7710\n", + "Epoch 34/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4883 - acc: 0.7663\n", + "Epoch 35/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4870 - acc: 0.7754\n", + "Epoch 36/50\n", + "7275/7275 [==============================] - 0s 12us/step - loss: 0.4858 - acc: 0.7724\n", + "Epoch 37/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4843 - acc: 0.7744\n", + "Epoch 38/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4854 - acc: 0.7748\n", + "Epoch 39/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4906 - acc: 0.7684\n", + "Epoch 40/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4837 - acc: 0.7742\n", + "Epoch 41/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4824 - acc: 0.7764\n", + "Epoch 42/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4857 - acc: 0.7711\n", + "Epoch 43/50\n", + "7275/7275 [==============================] - 0s 10us/step - loss: 0.4851 - acc: 0.7743\n", + "Epoch 44/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4820 - acc: 0.7748\n", + "Epoch 45/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4840 - acc: 0.7769\n", + "Epoch 46/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4797 - acc: 0.7786\n", + "Epoch 47/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4829 - acc: 0.7759\n", + "Epoch 48/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4823 - acc: 0.7802\n", + "Epoch 49/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4825 - acc: 0.7761\n", + "Epoch 50/50\n", + "7275/7275 [==============================] - 0s 11us/step - loss: 0.4786 - acc: 0.7802\n", + " 0.82888513099527\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([0.27145401, 0.72216815, 0.89101219, ..., 0.1970748 , 0.64862049,\n", + " 0.88528073])" + ] + }, + "execution_count": 11, + "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": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 14, + "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": [ + " 0.8370533804349155\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": [ + " 0.8573200992555832\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": [ + " 0.8643731129826793\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": [ + " 0.8609691689336569\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": [ + " 0.8549095654731269\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": [ + " 0.862216013528417\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": [ + " 0.8659841188147931\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": [ + " 0.8646361787123495\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": [ + " 0.8428761365585865\n", + " 0.8554946940175967\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([0.29569575, 0.79363506, 0.87995268, ..., 0.2425908 , 0.71409916,\n", + " 0.89744282])" + ] + }, + "execution_count": 14, + "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": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.8877751836594996\n", + ">>\n", + " 0.8965822831228838\n", + ">>\n", + " 0.8960688921756774\n", + ">>\n", + " 0.9045934122490994\n", + ">>\n", + " 0.9076875474842537\n", + ">>\n", + " 0.8974830282087102\n", + ">>\n", + " 0.9089221380780825\n", + ">>\n", + " 0.9136766904394286\n", + ">>\n", + " 0.8947411955003308\n", + ">>\n", + " 0.9068267039188295\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([0.52053967, 0.55151724, 0.89673963, ..., 0.33650874, 0.7162114 ,\n", + " 0.73551716])" + ] + }, + "execution_count": 17, + "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": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.840451539561662\n", + ">>\n", + " 0.855633243286191\n", + ">>\n", + " 0.8512266376560037\n", + ">>\n", + " 0.8707080361738109\n", + ">>\n", + " 0.8386454427370537\n", + ">>\n", + " 0.8562666960762688\n", + ">>\n", + " 0.8301411660907286\n", + ">>\n", + " 0.8652703232605446\n", + ">>\n", + " 0.8698380021076881\n", + ">>\n", + " 0.8635394456289978\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([0.61071404, 0.07386691, 0.89655324, ..., 0.19502204, 0.27621208,\n", + " 0.96733729])" + ] + }, + "execution_count": 10, + "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 = SVC(kernel='rbf', gamma='scale', probability=True)\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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.svm.v4.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": 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": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |