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Diffstat (limited to '10-fold-crossvalidations/mp2d/tensorflow/prediction-v5-ext.ipynb')
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diff --git a/10-fold-crossvalidations/mp2d/tensorflow/prediction-v5-ext.ipynb b/10-fold-crossvalidations/mp2d/tensorflow/prediction-v5-ext.ipynb new file mode 100644 index 0000000..d00bb7e --- /dev/null +++ b/10-fold-crossvalidations/mp2d/tensorflow/prediction-v5-ext.ipynb @@ -0,0 +1,11388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.svm import SVC\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#X = pd.merge(X_ext,X[['Canonical SMILES','Mutagenicity_bin']], right_on='Canonical SMILES', left_on='Name')\n", + "y = X['Mutagenicity_bin'].values\n", + "del X['Mutagenicity_bin']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "names = X['Canonical SMILES'].values\n", + "X = np.float64(X.values[:,1:])\n", + "\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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8309, 9638)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0163 - acc: 0.9900\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0161 - acc: 0.9910\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0145 - acc: 0.9909\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0138 - acc: 0.9912\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0134 - acc: 0.9905\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0134 - acc: 0.9906\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0132 - acc: 0.9912\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9910\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9914\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0131 - acc: 0.9918\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9920\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 78us/step - loss: 0.0132 - acc: 0.9905\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0130 - acc: 0.9912\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0129 - acc: 0.9909\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0130 - acc: 0.9910\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0128 - acc: 0.9918\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9918\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0130 - acc: 0.9905\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0129 - acc: 0.9920\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0128 - acc: 0.9921\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 76us/step - loss: 0.0129 - acc: 0.9914\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 77us/step - loss: 0.0127 - acc: 0.9912\n", + 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[==============================] - 1s 71us/step - loss: 0.0114 - acc: 0.9932\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0111 - acc: 0.9925\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0110 - acc: 0.9930\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0111 - acc: 0.9928\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9922\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0110 - acc: 0.9929\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9916\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9930\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9922\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9924\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9925\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9929\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9922\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9928\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9929\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9932\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9925\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9920\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9925\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9925\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0141 - acc: 0.9922\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9938\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9928\n", + "Epoch 30/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9932\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9929\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9928\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9925\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9928\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9934\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9928\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9932\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9936\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9928\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9930\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0105 - acc: 0.9929\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9928\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9932\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9933\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9928\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9932\n", + "Epoch 50/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9930\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9928\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9926\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9930\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0109 - acc: 0.9926\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0108 - acc: 0.9921\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9926\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0107 - acc: 0.9932\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9924\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9924\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9921\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0106 - acc: 0.9925\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9932\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9924\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9933\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9921\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9932\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9925\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9924\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9921\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9930\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9924\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9922\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0104 - acc: 0.9921\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0103 - acc: 0.9924\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9929\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9934\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9932\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0104 - acc: 0.9934\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9928\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9926\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9925\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0103 - acc: 0.9929\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0103 - acc: 0.9926\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0103 - acc: 0.9934\n", + "Epoch 48/50\n", + "7478/7478 [==============================] 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[==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9932\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9932\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9926\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9928\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9925\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9921\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9929\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9925\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9930\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9929\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9933\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9933\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9926\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9928\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 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loss: 0.0106 - acc: 0.9929\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9930\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9928\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0106 - acc: 0.9926\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9934\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0106 - acc: 0.9930\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0105 - acc: 0.9936\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0105 - acc: 0.9930\n", + "Epoch 42/50\n", + "7478/7478 [==============================] 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acc: 0.9912\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0126 - acc: 0.9906\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0134 - acc: 0.9918\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0125 - acc: 0.9917\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0122 - acc: 0.9920\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0122 - acc: 0.9906\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0122 - acc: 0.9910\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0126 - acc: 0.9914\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0119 - acc: 0.9921\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 71us/step 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".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0214 - acc: 0.9898\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0223 - acc: 0.9892\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0183 - acc: 0.9909\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0142 - acc: 0.9913\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0143 - acc: 0.9910\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0127 - acc: 0.9917\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9914\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0122 - acc: 0.9918\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0119 - acc: 0.9916\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0118 - acc: 0.9917\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0116 - acc: 0.9912\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0116 - acc: 0.9921\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0113 - acc: 0.9917\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0119 - acc: 0.9917\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 1s 71us/step - loss: 0.0115 - acc: 0.9916\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0116 - acc: 0.9922\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0122 - acc: 0.9914\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 1s 72us/step 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0.9922\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 27/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0122 - acc: 0.9928\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9918\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0122 - acc: 0.9922\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9925\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0122 - acc: 0.9925\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0127 - acc: 0.9921\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0132 - acc: 0.9921\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0126 - acc: 0.9912\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0124 - acc: 0.9917\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9921\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0124 - acc: 0.9913\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0123 - acc: 0.9925\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0123 - acc: 0.9921\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0123 - acc: 0.9926\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0122 - acc: 0.9922\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0122 - acc: 0.9924\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0122 - acc: 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".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 1s 73us/step - loss: 0.0199 - acc: 0.9898\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0196 - acc: 0.9904\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0192 - acc: 0.9910\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0194 - acc: 0.9912\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0193 - acc: 0.9908\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0190 - acc: 0.9906\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0190 - acc: 0.9904\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 1s 72us/step - loss: 0.0189 - acc: 0.9910\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 1s 74us/step - loss: 0.0190 - 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24/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9926\n", + "Epoch 25/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9917\n", + "Epoch 26/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9924\n", + "Epoch 27/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 28/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 29/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9921\n", + "Epoch 30/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0117 - acc: 0.9921\n", + "Epoch 31/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9922\n", + "Epoch 32/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 33/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9924\n", + "Epoch 34/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9925\n", + "Epoch 35/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9917\n", + "Epoch 36/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9925\n", + "Epoch 37/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9922\n", + "Epoch 38/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9924\n", + "Epoch 39/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9921\n", + "Epoch 40/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9925\n", + "Epoch 41/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9926\n", + "Epoch 42/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0109 - acc: 0.9920\n", + "Epoch 43/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 44/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9922\n", + "Epoch 45/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9909\n", + "Epoch 46/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9924\n", + "Epoch 47/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + "Epoch 48/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0108 - acc: 0.9917\n", + "Epoch 49/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0107 - acc: 0.9922\n", + "Epoch 50/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0108 - acc: 0.9928\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0117 - acc: 0.9912\n", + "Epoch 2/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9925\n", + "Epoch 3/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9925\n", + "Epoch 4/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9925\n", + "Epoch 5/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0118 - acc: 0.9922\n", + "Epoch 6/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9925\n", + "Epoch 7/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0115 - acc: 0.9924\n", + "Epoch 8/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9929\n", + "Epoch 9/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9922\n", + "Epoch 10/50\n", + "7479/7479 [==============================] - 1s 74us/step - loss: 0.0113 - acc: 0.9925\n", + "Epoch 11/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9917\n", + "Epoch 12/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9916\n", + "Epoch 13/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9921\n", + "Epoch 14/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0113 - acc: 0.9925\n", + "Epoch 15/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9925\n", + "Epoch 16/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9926\n", + "Epoch 17/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9918\n", + "Epoch 18/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0112 - acc: 0.9921\n", + "Epoch 19/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9924\n", + "Epoch 20/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 21/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0111 - acc: 0.9918\n", + "Epoch 22/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0114 - acc: 0.9913\n", + "Epoch 23/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0112 - acc: 0.9920\n", + "Epoch 24/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0113 - acc: 0.9922\n", + "Epoch 25/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9930\n", + "Epoch 26/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9922\n", + "Epoch 27/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 28/50\n", + "7479/7479 [==============================] - 1s 74us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 29/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9913\n", + "Epoch 30/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 31/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 32/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9925\n", + "Epoch 33/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9930\n", + "Epoch 34/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9924\n", + "Epoch 35/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 36/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9918\n", + "Epoch 37/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9920\n", + "Epoch 38/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9926\n", + "Epoch 39/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9921\n", + "Epoch 40/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9917\n", + "Epoch 41/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0109 - acc: 0.9918\n", + "Epoch 42/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0116 - acc: 0.9924\n", + "Epoch 43/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9926\n", + "Epoch 44/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9920\n", + "Epoch 45/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 46/50\n", + "7479/7479 [==============================] - 1s 75us/step - loss: 0.0110 - acc: 0.9916\n", + "Epoch 47/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0111 - acc: 0.9918\n", + "Epoch 48/50\n", + "7479/7479 [==============================] - 1s 72us/step - loss: 0.0110 - acc: 0.9917\n", + "Epoch 49/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9922\n", + "Epoch 50/50\n", + "7479/7479 [==============================] - 1s 73us/step - loss: 0.0110 - acc: 0.9918\n", + " 0.9073547090696851\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([1.31914479e-08, 2.52192222e-05, 9.99993205e-01, ...,\n", + " 1.42529135e-08, 1.00000000e+00, 1.18783440e-07])" + ] + }, + "execution_count": 6, + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fb2b11d67f0>]" + ] + }, + "execution_count": 9, + "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": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2395 - acc: 0.9192\n", + "Epoch 2/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2380 - acc: 0.9207\n", + "Epoch 3/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2366 - acc: 0.9206\n", + "Epoch 4/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2352 - acc: 0.9215\n", + "Epoch 5/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2340 - acc: 0.9212\n", + "Epoch 6/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2326 - acc: 0.9222\n", + "Epoch 7/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2314 - acc: 0.9230\n", + "Epoch 8/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2301 - acc: 0.9230\n", + "Epoch 9/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2289 - acc: 0.9239\n", + "Epoch 10/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2277 - acc: 0.9240\n", + "Epoch 11/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2265 - acc: 0.9252\n", + "Epoch 12/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2254 - acc: 0.9244\n", + "Epoch 13/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2243 - acc: 0.9246\n", + "Epoch 14/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2233 - acc: 0.9252\n", + "Epoch 15/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2221 - acc: 0.9260\n", + "Epoch 16/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2210 - acc: 0.9258\n", + "Epoch 17/50\n", + "7478/7478 [==============================] - 0s 45us/step - loss: 0.2201 - acc: 0.9260\n", + "Epoch 18/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2191 - acc: 0.9267\n", + "Epoch 19/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2181 - acc: 0.9274\n", + "Epoch 20/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2171 - acc: 0.9279\n", + "Epoch 21/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2162 - acc: 0.9278\n", + "Epoch 22/50\n", + "7478/7478 [==============================] - 0s 42us/step - loss: 0.2153 - acc: 0.9279\n", + "Epoch 23/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2143 - acc: 0.9282\n", + "Epoch 24/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2134 - acc: 0.9281\n", + "Epoch 25/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2126 - acc: 0.9291\n", + "Epoch 26/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2117 - acc: 0.9298\n", + "Epoch 27/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2109 - acc: 0.9298\n", + "Epoch 28/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2100 - acc: 0.9294\n", + "Epoch 29/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2092 - acc: 0.9302\n", + "Epoch 30/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2084 - acc: 0.9305\n", + "Epoch 31/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2076 - acc: 0.9305\n", + "Epoch 32/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2068 - acc: 0.9306\n", + "Epoch 33/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2061 - acc: 0.9303\n", + "Epoch 34/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2054 - acc: 0.9307\n", + "Epoch 35/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2046 - acc: 0.9310\n", + "Epoch 36/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2039 - acc: 0.9313\n", + "Epoch 37/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.2031 - acc: 0.9314\n", + "Epoch 38/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2025 - acc: 0.9310\n", + "Epoch 39/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2018 - acc: 0.9322\n", + "Epoch 40/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2011 - acc: 0.9319\n", + "Epoch 41/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.2004 - acc: 0.9315\n", + "Epoch 42/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1998 - acc: 0.9323\n", + "Epoch 43/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1991 - acc: 0.9327\n", + "Epoch 44/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1985 - acc: 0.9323\n", + "Epoch 45/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1979 - acc: 0.9329\n", + "Epoch 46/50\n", + "7478/7478 [==============================] - 0s 44us/step - loss: 0.1972 - acc: 0.9331\n", + "Epoch 47/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1966 - acc: 0.9335\n", + "Epoch 48/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1960 - acc: 0.9334\n", + "Epoch 49/50\n", + "7478/7478 [==============================] - 0s 43us/step - loss: 0.1954 - acc: 0.9343\n", + "Epoch 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- 0s 44us/step - loss: 0.1995 - acc: 0.9315\n", + "Epoch 43/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1988 - acc: 0.9317\n", + "Epoch 44/50\n", + "7479/7479 [==============================] - 0s 43us/step - loss: 0.1981 - acc: 0.9318\n", + "Epoch 45/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1975 - acc: 0.9322\n", + "Epoch 46/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1969 - acc: 0.9325\n", + "Epoch 47/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1963 - acc: 0.9327\n", + "Epoch 48/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1957 - acc: 0.9325\n", + "Epoch 49/50\n", + "7479/7479 [==============================] - 0s 43us/step - loss: 0.1951 - acc: 0.9329\n", + "Epoch 50/50\n", + "7479/7479 [==============================] - 0s 44us/step - loss: 0.1945 - acc: 0.9337\n", + " 0.9092741115866867\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([0.191219 , 0.27326334, 0.97852647, ..., 0.04910662, 0.94311446,\n", + " 0.1081358 ])" + ] + }, + "execution_count": 10, + "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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v4_ext.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": 13, + "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": [ + " 0.9098222459124671\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": [ + " 0.9059896406679103\n", + " 0.9144572938272749\n", + " 0.9007954900983789\n", + " 0.9133072225634132\n", + " 0.895995318601606\n", + " 0.9145124847923064\n", + " 0.8979781009211516\n", + " 0.9030154683969642\n", + " 0.9073663242135096\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([0.3525606 , 0.2494471 , 0.98007672, ..., 0.06643755, 0.86553187,\n", + " 0.11520544])" + ] + }, + "execution_count": 13, + "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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.8903898075296354\n", + ">>\n", + " 0.8864558106119422\n", + ">>\n", + " 0.8847234614537828\n", + ">>\n", + " 0.8610557480387954\n", + ">>\n", + " 0.8935995782106398\n", + ">>\n", + " 0.8672348462902236\n", + ">>\n", + " 0.8833903018365101\n", + ">>\n", + " 0.8821331324952205\n", + ">>\n", + " 0.8771073518336134\n", + ">>\n", + " 0.8798936052825674\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([0.2541524 , 0.23359182, 0.80675604, ..., 0.18775895, 0.49521864,\n", + " 0.27560522])" + ] + }, + "execution_count": 16, + "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": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v4_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.8961111947994763\n", + ">>\n", + " 0.9161432925062862\n", + ">>\n", + " 0.9201786810971159\n", + ">>\n", + " 0.9135331811492602\n", + ">>\n", + " 0.9060939292459936\n", + ">>\n", + " 0.9149584583830634\n", + ">>\n", + " 0.906963675337466\n", + ">>\n", + " 0.9028126991483691\n", + ">>\n", + " 0.9167921904872255\n", + ">>\n", + " 0.895454994221466\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([0.17113368, 0.09954229, 0.3102958 , ..., 0.11191282, 0.89457673,\n", + " 0.22076209])" + ] + }, + "execution_count": 8, + "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_ext.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 +} |