for_investigation.ipynb
4.5 KB
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.models import Model\n",
"from keras.layers import Input, Dense, Dropout, Activation, BatchNormalization\n",
"from keras.optimizers import SGD, Adam\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filename = 'test_set.csv'\n",
"raw_data = open(filename, 'rt')\n",
"test_data = np.loadtxt(raw_data, delimiter= '\\t')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"number_of_features = 1126\n",
"test_set = test_data[:,0:1126]\n",
"test_labels = test_data[:,1126] #last column consists of labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural network configuration"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inputs = Input(shape=(number_of_features,))\n",
"output_from_1st_layer = Dense(1000, activation='relu')(inputs)\n",
"output_from_1st_layer = Dropout(0.5)(output_from_1st_layer)\n",
"output_from_1st_layer = BatchNormalization()(output_from_1st_layer)\n",
"output_from_2nd_layer = Dense(500, activation='relu')(output_from_1st_layer)\n",
"output_from_2nd_layer = Dropout(0.5)(output_from_2nd_layer)\n",
"output_from_2nd_layer = BatchNormalization()(output_from_2nd_layer)\n",
"output = Dense(1, activation='sigmoid')(output_from_2nd_layer)\n",
"\n",
"model = Model(inputs, output)\n",
"model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load weights learnt earlier"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.load_weights(\"weights_2017_05_10.h5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, calculate predictions for test set"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predictions = model.predict(test_set)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can calculate basic metrics"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy:0.7316259444607988\n",
"Precision: 0.7378337531486147\n",
"Recall: 0.7185752134236091\n",
"F1: 0.7280771525154107\n"
]
}
],
"source": [
"true_positives = 0.0\n",
" false_positives = 0.0\n",
" true_negatives = 0.0\n",
" false_negatives = 0.0\n",
"\n",
" for i in range(len(test_set)):\n",
" if (predictions[i]<0.5 and test_labels[i]==0): true_negatives += 1 \n",
" if (predictions[i]<0.5 and test_labels[i]==1): false_negatives += 1\n",
" if (predictions[i]>=0.5 and test_labels[i]==1): true_positives += 1\n",
" if (predictions[i]>=0.5 and test_labels[i]==0): false_positives += 1 \n",
" \n",
" accuracy = (true_positives+true_negatives)/len(test_set)\n",
" precision = true_positives/(true_positives+false_positives)\n",
" recall = true_positives/(true_positives+false_negatives)\n",
" f1 = 2*(precision*recall)/(precision+recall)\n",
"\n",
" print ('Accuracy:' + repr(accuracy))\n",
" print ('Precision: ' + repr(precision))\n",
" print ('Recall: ' + repr(recall))\n",
" print ('F1: ' + repr(f1))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}