diff options
| author | Christoph Helma <helma@in-silico.ch> | 2019-08-14 19:12:37 +0200 |
|---|---|---|
| committer | Christoph Helma <helma@in-silico.ch> | 2019-08-14 19:12:37 +0200 |
| commit | b6ad21e340bce9ba2a2ad09fe48c656f0c2e3905 (patch) | |
| tree | 6a248cfe29fb720491559e5973d8c1f1904bcc33 /scripts | |
| parent | 9a217185e791d6abbe46549cd4e87c1d1a643c05 (diff) | |
directory reorganization, evaluation scripts for R CVs
Diffstat (limited to 'scripts')
| -rw-r--r-- | scripts/R/Skript.docx | bin | 0 -> 16532 bytes | |||
| -rwxr-xr-x | scripts/confusion-matrix-summary.rb | 19 | ||||
| -rwxr-xr-x | scripts/cv-r-confusion-matrix.rb | 19 | ||||
| -rw-r--r-- | scripts/tensorflow/.ipynb_checkpoints/prediction-checkpoint.ipynb | 2912 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction.ipynb | 5533 | ||||
| -rw-r--r-- | scripts/tensorflow/prediction_ext.ipynb | 5493 |
6 files changed, 13976 insertions, 0 deletions
diff --git a/scripts/R/Skript.docx b/scripts/R/Skript.docx Binary files differnew file mode 100644 index 0000000..bddd8b4 --- /dev/null +++ b/scripts/R/Skript.docx diff --git a/scripts/confusion-matrix-summary.rb b/scripts/confusion-matrix-summary.rb new file mode 100755 index 0000000..a9742f7 --- /dev/null +++ b/scripts/confusion-matrix-summary.rb @@ -0,0 +1,19 @@ +#!/usr/bin/env ruby +require 'csv' +require 'json' + +csv = CSV.read(ARGV[0],headers: false,:col_sep => ",") +tp = csv[0][0].to_f +fp = csv[0][1].to_f +fn = csv[1][0].to_f +tn = csv[1][1].to_f + +result = { + :accuracy => (tp+tn)/(tp+fp+tn+fn), + :true_positive_rate => tp/(tp+fn), + :true_negative_rate => tn/(tn+fp), + :positive_predictive_value => tp/(tp+fp), + :negative_predictive_value => tn/(tn+fn), +} + +puts result.to_json diff --git a/scripts/cv-r-confusion-matrix.rb b/scripts/cv-r-confusion-matrix.rb new file mode 100755 index 0000000..11a1e1b --- /dev/null +++ b/scripts/cv-r-confusion-matrix.rb @@ -0,0 +1,19 @@ +#!/usr/bin/env ruby +require 'csv' + +tp = 0 +fp = 0 +tn = 0 +fn = 0 + +rows = CSV.read(ARGV[0],headers: true,:col_sep => ";") +rows.each do |row| + a = row.to_h["Actual Outcome"].to_i + p = row.to_h["Predicted Outcome"].to_i + tp += 1 if a == 1 and p == 1 + tn += 1 if a == 0 and p == 0 + fp += 1 if a == 0 and p == 1 + fn += 1 if a == 1 and p == 0 +end + +puts "#{tp},#{fp}\n#{fn},#{tn}" diff --git a/scripts/tensorflow/.ipynb_checkpoints/prediction-checkpoint.ipynb b/scripts/tensorflow/.ipynb_checkpoints/prediction-checkpoint.ipynb new file mode 100644 index 0000000..74d7f1c --- /dev/null +++ b/scripts/tensorflow/.ipynb_checkpoints/prediction-checkpoint.ipynb @@ -0,0 +1,2912 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "random_state = np.random.RandomState(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", + "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X = pd.read_csv(X_f).values[:,:-1]\n", + "y = pd.read_csv(y_f).values\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "y = y[ix]\n", + "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", + "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\n", + "y = y[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, ..., 0, 1, 0])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.6010 - acc: 0.6613\n", + "Epoch 2/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.6027 - acc: 0.6632\n", + "Epoch 3/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.6014 - acc: 0.6574\n", + "Epoch 4/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.6109 - acc: 0.6495\n", + "Epoch 5/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.6017 - acc: 0.6611\n", + "Epoch 6/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.6013 - acc: 0.6618\n", + "Epoch 7/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5984 - acc: 0.6619\n", + "Epoch 8/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.6002 - acc: 0.6593\n", + "Epoch 9/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5917 - acc: 0.6693\n", + "Epoch 10/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5908 - acc: 0.6681\n", + "Epoch 11/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5916 - acc: 0.6644\n", + "Epoch 12/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.6054 - acc: 0.6519\n", + "Epoch 13/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5913 - acc: 0.6680\n", + "Epoch 14/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5923 - acc: 0.6630\n", + "Epoch 15/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5960 - acc: 0.6627\n", + "Epoch 16/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5912 - acc: 0.6601\n", + "Epoch 17/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5827 - acc: 0.6693\n", + "Epoch 18/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5874 - acc: 0.6672\n", + "Epoch 19/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5900 - acc: 0.6726\n", + "Epoch 20/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5827 - acc: 0.6718\n", + "Epoch 21/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5836 - acc: 0.6673\n", + "Epoch 22/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5843 - acc: 0.6743\n", + "Epoch 23/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5838 - acc: 0.6728\n", + "Epoch 24/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5759 - acc: 0.6785\n", + "Epoch 25/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5803 - acc: 0.6726\n", + "Epoch 26/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5761 - acc: 0.6803\n", + "Epoch 27/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5762 - acc: 0.6755\n", + "Epoch 28/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5693 - acc: 0.6814\n", + "Epoch 29/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5833 - acc: 0.6693\n", + "Epoch 30/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5686 - acc: 0.6825\n", + "Epoch 31/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5725 - acc: 0.6803\n", + "Epoch 32/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5849 - acc: 0.6684\n", + "Epoch 33/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5709 - acc: 0.6817\n", + "Epoch 34/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5726 - acc: 0.6786\n", + "Epoch 35/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5746 - acc: 0.6779\n", + "Epoch 36/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5687 - acc: 0.6831\n", + "Epoch 37/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5725 - acc: 0.6806\n", + "Epoch 38/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5656 - acc: 0.6810\n", + "Epoch 39/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5627 - acc: 0.6847\n", + "Epoch 40/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5594 - acc: 0.6940\n", + "Epoch 41/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5645 - acc: 0.6899\n", + "Epoch 42/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5653 - acc: 0.6853\n", + "Epoch 43/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5629 - acc: 0.6845\n", + "Epoch 44/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5590 - acc: 0.6887\n", + "Epoch 45/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5603 - acc: 0.6888\n", + "Epoch 46/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5570 - acc: 0.6896\n", + "Epoch 47/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5599 - acc: 0.6924\n", + "Epoch 48/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5522 - acc: 0.6997\n", + "Epoch 49/50\n", + "6463/6463 [==============================] - 0s 41us/step - loss: 0.5519 - acc: 0.6960\n", + "Epoch 50/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5532 - acc: 0.6930\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5846 - acc: 0.6738\n", + "Epoch 2/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5867 - acc: 0.6690\n", + "Epoch 3/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5851 - acc: 0.6724\n", + "Epoch 4/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5877 - acc: 0.6706\n", + "Epoch 5/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5739 - acc: 0.6797\n", + "Epoch 6/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5722 - acc: 0.6905\n", + "Epoch 7/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5713 - acc: 0.6827\n", + "Epoch 8/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5723 - acc: 0.6808\n", + "Epoch 9/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5703 - acc: 0.6822\n", + "Epoch 10/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5714 - acc: 0.6837\n", + "Epoch 11/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5698 - acc: 0.6859\n", + "Epoch 12/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5620 - acc: 0.6933\n", + "Epoch 13/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5586 - acc: 0.6967\n", + "Epoch 14/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5590 - acc: 0.6935\n", + "Epoch 15/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5647 - acc: 0.6876\n", + "Epoch 16/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5504 - acc: 0.7039\n", + "Epoch 17/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5454 - acc: 0.7086\n", + "Epoch 18/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5502 - acc: 0.6998\n", + "Epoch 19/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5457 - acc: 0.7052\n", + "Epoch 20/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5463 - acc: 0.7073\n", + "Epoch 21/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5425 - acc: 0.7076\n", + "Epoch 22/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5451 - acc: 0.7076\n", + "Epoch 23/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5327 - acc: 0.7189\n", + "Epoch 24/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5344 - acc: 0.7125\n", + "Epoch 25/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5282 - acc: 0.7244\n", + "Epoch 26/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5288 - acc: 0.7136\n", + "Epoch 27/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5182 - acc: 0.7303\n", + "Epoch 28/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.5299 - acc: 0.7189\n", + "Epoch 29/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5199 - acc: 0.7300\n", + "Epoch 30/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5107 - acc: 0.7337\n", + "Epoch 31/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5085 - acc: 0.7384\n", + "Epoch 32/50\n", + "6463/6463 [==============================] - 0s 42us/step - loss: 0.5081 - acc: 0.7337\n", + "Epoch 33/50\n", + "6463/6463 [==============================] - 0s 39us/step - loss: 0.5228 - acc: 0.7238\n", + "Epoch 34/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5041 - acc: 0.7421\n", + "Epoch 35/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4986 - acc: 0.7407\n", + "Epoch 36/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4940 - acc: 0.7459\n", + "Epoch 37/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4924 - acc: 0.7486\n", + "Epoch 38/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4888 - acc: 0.7497\n", + "Epoch 39/50\n", + "6463/6463 [==============================] - 0s 40us/step - loss: 0.4865 - acc: 0.7483\n", + "Epoch 40/50\n", + "6463/6463 [==============================] - 0s 44us/step - loss: 0.4948 - acc: 0.7484\n", + "Epoch 41/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4825 - acc: 0.7543\n", + "Epoch 42/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4748 - acc: 0.7534\n", + "Epoch 43/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4822 - acc: 0.7487\n", + "Epoch 44/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4692 - acc: 0.7597\n", + "Epoch 45/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4676 - acc: 0.7653\n", + "Epoch 46/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4696 - acc: 0.7565\n", + "Epoch 47/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4624 - acc: 0.7693\n", + "Epoch 48/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.4753 - acc: 0.7571\n", + "Epoch 49/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4582 - acc: 0.7721\n", + "Epoch 50/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.4452 - acc: 0.7767\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5930 - acc: 0.6656\n", + "Epoch 2/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5933 - acc: 0.6678\n", + "Epoch 3/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5953 - acc: 0.6666\n", + "Epoch 4/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5909 - acc: 0.6714\n", + "Epoch 5/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5947 - acc: 0.6663\n", + "Epoch 6/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5930 - acc: 0.6703\n", + "Epoch 7/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5901 - acc: 0.6760\n", + "Epoch 8/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5870 - acc: 0.6754\n", + "Epoch 9/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5948 - acc: 0.6737\n", + "Epoch 10/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5802 - acc: 0.6791\n", + "Epoch 11/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5919 - acc: 0.6794\n", + "Epoch 12/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5813 - acc: 0.6777\n", + "Epoch 13/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5866 - acc: 0.6741\n", + "Epoch 14/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5889 - acc: 0.6743\n", + "Epoch 15/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5782 - acc: 0.6833\n", + "Epoch 16/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5758 - acc: 0.6853\n", + "Epoch 17/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5751 - acc: 0.6819\n", + "Epoch 18/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5819 - acc: 0.6793\n", + "Epoch 19/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5733 - acc: 0.6890\n", + "Epoch 20/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5789 - acc: 0.6830\n", + "Epoch 21/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5756 - acc: 0.6868\n", + "Epoch 22/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5755 - acc: 0.6867\n", + "Epoch 23/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5658 - acc: 0.6929\n", + "Epoch 24/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5653 - acc: 0.6950\n", + "Epoch 25/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5731 - acc: 0.6921\n", + "Epoch 26/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5665 - acc: 0.6932\n", + "Epoch 27/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5659 - acc: 0.6927\n", + "Epoch 28/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5645 - acc: 0.6950\n", + "Epoch 29/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5560 - acc: 0.7003\n", + "Epoch 30/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5623 - acc: 0.6947\n", + "Epoch 31/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5618 - acc: 0.6952\n", + "Epoch 32/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5551 - acc: 0.7045\n", + "Epoch 33/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5562 - acc: 0.7001\n", + "Epoch 34/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5630 - acc: 0.6926\n", + "Epoch 35/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5563 - acc: 0.7062\n", + "Epoch 36/50\n", + "6463/6463 [==============================] - 0s 36us/step - loss: 0.5520 - acc: 0.7028\n", + "Epoch 37/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5482 - acc: 0.7035\n", + "Epoch 38/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5423 - acc: 0.7156\n", + "Epoch 39/50\n", + "6463/6463 [==============================] - 0s 38us/step - loss: 0.5612 - acc: 0.6940\n", + "Epoch 40/50\n", + "6463/6463 [==============================] - 0s 37us/step - loss: 0.5531 - acc: 0.7035\n", + "Epoch 41/50\n", + "6463/6463 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41/50\n", + "6465/6465 [==============================] - 0s 39us/step - loss: 0.6063 - acc: 0.6637\n", + "Epoch 42/50\n", + "6465/6465 [==============================] - 0s 38us/step - loss: 0.6049 - acc: 0.6668\n", + "Epoch 43/50\n", + "6465/6465 [==============================] - 0s 39us/step - loss: 0.6032 - acc: 0.6636\n", + "Epoch 44/50\n", + "6465/6465 [==============================] - 0s 39us/step - loss: 0.6026 - acc: 0.6645\n", + "Epoch 45/50\n", + "6465/6465 [==============================] - 0s 38us/step - loss: 0.6073 - acc: 0.6647\n", + "Epoch 46/50\n", + "6465/6465 [==============================] - 0s 37us/step - loss: 0.6103 - acc: 0.6596\n", + "Epoch 47/50\n", + "6465/6465 [==============================] - 0s 38us/step - loss: 0.6050 - acc: 0.6671\n", + "Epoch 48/50\n", + "6465/6465 [==============================] - 0s 38us/step - loss: 0.6068 - acc: 0.6645\n", + "Epoch 49/50\n", + "6465/6465 [==============================] - 0s 39us/step - loss: 0.6024 - acc: 0.6704\n", + "Epoch 50/50\n", + "6465/6465 [==============================] - 0s 38us/step - loss: 0.6025 - acc: 0.6656\n", + " 0.6633972392638037\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([0.58053386, 0.84379703, 0.25155956, ..., 0.17350598, 0.7701112 ,\n", + " 0.32959276])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=5)\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": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "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": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8080,)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/tensorflow/prediction.ipynb b/scripts/tensorflow/prediction.ipynb new file mode 100644 index 0000000..b63bcb4 --- /dev/null +++ b/scripts/tensorflow/prediction.ipynb @@ -0,0 +1,5533 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "random_state = np.random.RandomState(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", + "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X = pd.read_csv(X_f).values[:,:-1]\n", + "y = pd.read_csv(y_f).values\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "y = y[ix]\n", + "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", + "X = sklearn.preprocessing.quantile_transform(X, axis=1, output_distribution='uniform', copy=True)\n", + "y = y[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 1, ..., 1, 1, 0])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.5932 - acc: 0.6739\n", + "Epoch 2/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5952 - acc: 0.6707\n", + "Epoch 3/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5905 - acc: 0.6805\n", + "Epoch 4/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5894 - acc: 0.6732\n", + "Epoch 5/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5918 - acc: 0.6767\n", + "Epoch 6/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5923 - acc: 0.6768\n", + "Epoch 7/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5880 - acc: 0.6749\n", + "Epoch 8/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5861 - acc: 0.6779\n", + "Epoch 9/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5802 - acc: 0.6871\n", + "Epoch 10/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5892 - acc: 0.6734\n", + "Epoch 11/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5797 - acc: 0.6851\n", + "Epoch 12/50\n", + "7271/7271 [==============================] - 0s 36us/step - loss: 0.5894 - acc: 0.6773\n", + "Epoch 13/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5809 - acc: 0.6813\n", + "Epoch 14/50\n", + "7271/7271 [==============================] - 0s 40us/step - loss: 0.5792 - acc: 0.6852\n", + "Epoch 15/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5752 - acc: 0.6867\n", + "Epoch 16/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5812 - acc: 0.6819\n", + "Epoch 17/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5730 - acc: 0.6914\n", + "Epoch 18/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5740 - acc: 0.6889\n", + "Epoch 19/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5751 - acc: 0.6885\n", + "Epoch 20/50\n", + "7271/7271 [==============================] - 0s 35us/step - loss: 0.5686 - acc: 0.6967\n", + "Epoch 21/50\n", + "7271/7271 [==============================] - 0s 45us/step - loss: 0.5720 - acc: 0.6899\n", + "Epoch 22/50\n", + "7271/7271 [==============================] - 0s 36us/step - loss: 0.5727 - acc: 0.6884\n", + "Epoch 23/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5760 - acc: 0.6906\n", + "Epoch 24/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5652 - acc: 0.7025\n", + "Epoch 25/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5689 - acc: 0.6962\n", + "Epoch 26/50\n", + "7271/7271 [==============================] - 0s 42us/step - loss: 0.5766 - acc: 0.6873\n", + "Epoch 27/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5616 - acc: 0.6973\n", + "Epoch 28/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5585 - acc: 0.7032\n", + "Epoch 29/50\n", + "7271/7271 [==============================] - 0s 35us/step - loss: 0.5635 - acc: 0.6981\n", + "Epoch 30/50\n", + "7271/7271 [==============================] - 0s 40us/step - loss: 0.5659 - acc: 0.6966\n", + "Epoch 31/50\n", + "7271/7271 [==============================] - 0s 35us/step - loss: 0.5624 - acc: 0.6947\n", + "Epoch 32/50\n", + "7271/7271 [==============================] - 0s 35us/step - loss: 0.5555 - acc: 0.7062\n", + "Epoch 33/50\n", + "7271/7271 [==============================] - 0s 38us/step - loss: 0.5583 - acc: 0.7043\n", + "Epoch 34/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5589 - acc: 0.7033\n", + "Epoch 35/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5483 - acc: 0.7091\n", + "Epoch 36/50\n", + "7271/7271 [==============================] - 0s 33us/step - loss: 0.5483 - acc: 0.7104\n", + "Epoch 37/50\n", + "7271/7271 [==============================] - 0s 34us/step - loss: 0.5605 - acc: 0.7006\n", + "Epoch 38/50\n", + "7271/7271 [==============================] - 0s 35us/step - loss: 0.5459 - acc: 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38/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.5568 - acc: 0.6990\n", + "Epoch 39/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5492 - acc: 0.7019\n", + "Epoch 40/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5514 - acc: 0.7034\n", + "Epoch 41/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5536 - acc: 0.6982\n", + "Epoch 42/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5474 - acc: 0.7071\n", + "Epoch 43/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5563 - acc: 0.6992\n", + "Epoch 44/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5502 - acc: 0.7001\n", + "Epoch 45/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5464 - acc: 0.7019\n", + "Epoch 46/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5477 - acc: 0.7060\n", + "Epoch 47/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5403 - acc: 0.7129\n", + "Epoch 48/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5475 - acc: 0.7026\n", + "Epoch 49/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.5446 - acc: 0.7018\n", + "Epoch 50/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5366 - acc: 0.7180\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5954 - acc: 0.6640\n", + "Epoch 2/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.6007 - acc: 0.6623\n", + "Epoch 3/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5960 - acc: 0.6629\n", + "Epoch 4/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5924 - acc: 0.6714\n", + "Epoch 5/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5921 - acc: 0.6620\n", + "Epoch 6/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5954 - acc: 0.6601\n", + "Epoch 7/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5921 - acc: 0.6704\n", + "Epoch 8/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5917 - acc: 0.6625\n", + "Epoch 9/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5901 - acc: 0.6719\n", + "Epoch 10/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5894 - acc: 0.6754\n", + "Epoch 11/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5879 - acc: 0.6659\n", + "Epoch 12/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5907 - acc: 0.6670\n", + "Epoch 13/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5876 - acc: 0.6707\n", + "Epoch 14/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5877 - acc: 0.6699\n", + "Epoch 15/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5824 - acc: 0.6730\n", + "Epoch 16/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5869 - acc: 0.6755\n", + "Epoch 17/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5777 - acc: 0.6767\n", + "Epoch 18/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5822 - acc: 0.6721\n", + "Epoch 19/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5775 - acc: 0.6814\n", + "Epoch 20/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.5790 - acc: 0.6740\n", + "Epoch 21/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5760 - acc: 0.6792\n", + "Epoch 22/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5755 - acc: 0.6833\n", + "Epoch 23/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5761 - acc: 0.6781\n", + "Epoch 24/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5825 - acc: 0.6736\n", + "Epoch 25/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5772 - acc: 0.6799\n", + "Epoch 26/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5727 - acc: 0.6796\n", + "Epoch 27/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5688 - acc: 0.6879\n", + "Epoch 28/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5677 - acc: 0.6884\n", + "Epoch 29/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5746 - acc: 0.6754\n", + "Epoch 30/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5751 - acc: 0.6774\n", + "Epoch 31/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5705 - acc: 0.6846\n", + "Epoch 32/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5679 - acc: 0.6851\n", + "Epoch 33/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5640 - acc: 0.6884\n", + "Epoch 34/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5683 - acc: 0.6872\n", + "Epoch 35/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5671 - acc: 0.6851\n", + "Epoch 36/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5569 - acc: 0.6997\n", + "Epoch 37/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5636 - acc: 0.6941\n", + "Epoch 38/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5600 - acc: 0.6937\n", + "Epoch 39/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5526 - acc: 0.7001\n", + "Epoch 40/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5640 - acc: 0.6924\n", + "Epoch 41/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5585 - acc: 0.6912\n", + "Epoch 42/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5570 - acc: 0.6921\n", + "Epoch 43/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.5471 - acc: 0.7067\n", + "Epoch 44/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5511 - acc: 0.7060\n", + "Epoch 45/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5508 - acc: 0.6997\n", + "Epoch 46/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5526 - acc: 0.6981\n", + "Epoch 47/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5480 - acc: 0.7051\n", + "Epoch 48/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5590 - acc: 0.6912\n", + "Epoch 49/50\n", + "7273/7273 [==============================] - 0s 37us/step - loss: 0.5463 - acc: 0.7014\n", + "Epoch 50/50\n", + "7273/7273 [==============================] - 0s 36us/step - loss: 0.5408 - acc: 0.7095\n", + " 0.6812592137592137\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.44792596, 0.69121736, 0.4685978 , ..., 0.83220756, 0.48728314,\n", + " 0.46735853])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SakXtO+E2R8RrgBuA93ZVUAfqvM/bGI1dVhl9CvubiHhTZj7fcm1tqrPd7wb+KDN/LyLeAvxJtd3fbr+8uWi1h/Upodc5lcArj4mIbYw+op3o403f1Tp9QkS8A/gwcFlm/l9HtbVl0jafCrwJWI+IJxnNGQ8MfGG07vf2HZn5YmZ+DXiUUYMfsjrbvRu4FSAz/w54LaNznpSq1s/8VvWpodc5lcAB4Krq8hXAX2e10jBQE7e5Gj/8AaNmXsJc9YTbnJkvZOb2zNyRmTsYrRtclpmH5lNuI+p8b/85owVwImI7oxHME51W2bw62/0UsBMgIn6IUUN/rtMqu3UA+Llqb5dLgBcy82hjf/u8V4U3WQH+J0Yr4x+ubvtNRj/QMHqzbwMeB/4euGDeNXewzX8FPAPcX/05MO+a297m4x67zsD3cqn5PgfwMeBh4AHgynnX3NF2Xwh8mdEeMPcDPz7vmmfc3s8CR4EXGaXx3cD7gPdteJ9vrP49Hmj6e9sjRSWpEH0auUiSZmBDl6RC2NAlqRA2dEkqhA1dkgphQ5ekQtjQJakQNnRJKsT/A5/HZXaMeD1qAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f6aec4c5128>]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/tensorflow/prediction_ext.ipynb b/scripts/tensorflow/prediction_ext.ipynb new file mode 100644 index 0000000..ffb457d --- /dev/null +++ b/scripts/tensorflow/prediction_ext.ipynb @@ -0,0 +1,5493 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "random_state = np.random.RandomState(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8080, 1442)\n", + "(8080, 1)\n" + ] + } + ], + "source": [ + "X_f_ext = '/home/drewe/notebooks/genotox/mutagenicity-mod-2.csv'\n", + "y_f_ext = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X_ext = np.float64(pd.read_csv(X_f_ext,sep=';').values[:,1:])\n", + "y_ext = np.float64(pd.read_csv(y_f_ext).values)\n", + "\n", + "print(X_ext.shape)\n", + "print(y_ext.shape)\n", + "\n", + "\n", + "ix = [i for i in range(y_ext.shape[0])]\n", + "shuffle(ix)\n", + "X_ext = X_ext[ix, :]\n", + "y_ext = y_ext[ix]\n", + "names = pd.read_csv(X_f_ext,sep=';')['Name'][ix].values\n", + "#X_norm = QuantileTransformer(output_distribution='uniform').fit_transform(X.values.T).T\n", + "X = sklearn.preprocessing.quantile_transform(X_ext, axis=1, output_distribution='uniform', copy=True)\n", + "y = y_ext[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6294 - acc: 0.6390\n", + "Epoch 2/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6295 - acc: 0.6432\n", + "Epoch 3/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6239 - acc: 0.6494\n", + "Epoch 4/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6283 - acc: 0.6398\n", + "Epoch 5/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6267 - acc: 0.6416\n", + "Epoch 6/50\n", + "7271/7271 [==============================] - 0s 38us/step - loss: 0.6258 - acc: 0.6399\n", + "Epoch 7/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6254 - acc: 0.6481\n", + "Epoch 8/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6211 - acc: 0.6467\n", + "Epoch 9/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6289 - acc: 0.6419\n", + "Epoch 10/50\n", + "7271/7271 [==============================] - 0s 38us/step - loss: 0.6246 - acc: 0.6465\n", + "Epoch 11/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6232 - acc: 0.6481\n", + "Epoch 12/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6239 - acc: 0.6430\n", + "Epoch 13/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6207 - acc: 0.6437\n", + "Epoch 14/50\n", + "7271/7271 [==============================] - 0s 38us/step - loss: 0.6229 - acc: 0.6479\n", + "Epoch 15/50\n", + "7271/7271 [==============================] - 0s 36us/step - loss: 0.6241 - acc: 0.6478\n", + "Epoch 16/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6218 - acc: 0.6437\n", + "Epoch 17/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6223 - acc: 0.6434\n", + "Epoch 18/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6240 - acc: 0.6454\n", + "Epoch 19/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6265 - acc: 0.6410\n", + "Epoch 20/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6274 - acc: 0.6371\n", + "Epoch 21/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6202 - acc: 0.6457\n", + "Epoch 22/50\n", + "7271/7271 [==============================] - 0s 37us/step - loss: 0.6222 - acc: 0.6443\n", + 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loss: 0.6237 - acc: 0.6374\n", + "Epoch 49/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6247 - acc: 0.6330\n", + "Epoch 50/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6260 - acc: 0.6319\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6395 - acc: 0.6256\n", + "Epoch 2/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6355 - acc: 0.6307\n", + "Epoch 3/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6374 - acc: 0.6292\n", + "Epoch 4/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6376 - acc: 0.6330\n", + "Epoch 5/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6337 - acc: 0.6358\n", + "Epoch 6/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6395 - acc: 0.6317\n", + "Epoch 7/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6335 - acc: 0.6344\n", + "Epoch 8/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6337 - acc: 0.6307\n", + "Epoch 9/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6345 - acc: 0.6325\n", + "Epoch 10/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6308 - acc: 0.6365\n", + "Epoch 11/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6430 - acc: 0.6172\n", + "Epoch 12/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6357 - acc: 0.6315\n", + "Epoch 13/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6327 - acc: 0.6339\n", + "Epoch 14/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6315 - acc: 0.6373\n", + "Epoch 15/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6368 - acc: 0.6222\n", + "Epoch 16/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6324 - acc: 0.6372\n", + "Epoch 17/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6351 - acc: 0.6304\n", + "Epoch 18/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6292 - acc: 0.6350\n", + "Epoch 19/50\n", + "7273/7273 [==============================] - 0s 40us/step - loss: 0.6307 - acc: 0.6332\n", + "Epoch 20/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6290 - acc: 0.6348\n", + "Epoch 21/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6337 - acc: 0.6312\n", + "Epoch 22/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6312 - acc: 0.6334\n", + "Epoch 23/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6294 - acc: 0.6340\n", + "Epoch 24/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6271 - acc: 0.6345\n", + "Epoch 25/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6288 - acc: 0.6358\n", + "Epoch 26/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6271 - acc: 0.6354\n", + "Epoch 27/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6333 - acc: 0.6296\n", + "Epoch 28/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6294 - acc: 0.6270\n", + "Epoch 29/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6355 - acc: 0.6231\n", + "Epoch 30/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6265 - acc: 0.6363\n", + "Epoch 31/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6268 - acc: 0.6292\n", + "Epoch 32/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6314 - acc: 0.6348\n", + "Epoch 33/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6283 - acc: 0.6318\n", + "Epoch 34/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6288 - acc: 0.6317\n", + "Epoch 35/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6255 - acc: 0.6352\n", + "Epoch 36/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6282 - acc: 0.6332\n", + "Epoch 37/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6239 - acc: 0.6369\n", + "Epoch 38/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6287 - acc: 0.6376\n", + "Epoch 39/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6280 - acc: 0.6297\n", + "Epoch 40/50\n", + "7273/7273 [==============================] - 0s 41us/step - loss: 0.6278 - acc: 0.6354\n", + "Epoch 41/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6253 - acc: 0.6365\n", + "Epoch 42/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6232 - acc: 0.6319\n", + "Epoch 43/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6229 - acc: 0.6389\n", + "Epoch 44/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6245 - acc: 0.6380\n", + "Epoch 45/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6264 - acc: 0.6332\n", + "Epoch 46/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6233 - acc: 0.6333\n", + "Epoch 47/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6244 - acc: 0.6344\n", + "Epoch 48/50\n", + "7273/7273 [==============================] - 0s 38us/step - loss: 0.6272 - acc: 0.6402\n", + "Epoch 49/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6273 - acc: 0.6314\n", + "Epoch 50/50\n", + "7273/7273 [==============================] - 0s 39us/step - loss: 0.6272 - acc: 0.6361\n", + " 0.7056603194103194\n" + ] + }, + { + "data": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.48989287, 0.59242761, 0.59383744, ..., 0.50447261, 0.47094053,\n", + " 0.76454699])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred_ext.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |