DNN_tissue_Model1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "### ROC, PR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def roc(test, pred):\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(2):\n",
    "        fpr[i], tpr[i], _ = roc_curve(test, pred)\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    " \n",
    "    print ('ROC_AUC: %.6f' %roc_auc[1])\n",
    "    plt.figure()\n",
    "    \n",
    "    plt.plot(fpr[1], tpr[1], lw=2, label='ROC curve (area = %0.4f)' % roc_auc[1])\n",
    " \n",
    "    plt.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "def roc_print(test, pred):\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(2):\n",
    "        fpr[i], tpr[i], _ = roc_curve(test, pred)\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    " \n",
    "    return roc_auc[1]    \n",
    "\n",
    "\n",
    "def pr(test, pred):\n",
    "\tlw =2\n",
    "\tfrom sklearn.metrics import precision_recall_curve\n",
    "\tfrom sklearn.metrics import average_precision_score\n",
    "\t# Compute Precision-Recall and plot curve\n",
    "\tprecision = dict()\n",
    "\trecall = dict()\n",
    "\taverage_precision = dict()\n",
    "\tfor i in range(2):\n",
    "\t    precision[i], recall[i], _ = precision_recall_curve(test,\n",
    "\t                                                        pred)\n",
    "\t    average_precision[i] = average_precision_score(test, pred)\n",
    "\n",
    "\t# Compute micro-average ROC curve and ROC area\n",
    "\tprecision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(test.ravel(),\n",
    "\t    pred.ravel())\n",
    "\taverage_precision[\"micro\"] = average_precision_score(test, pred,\n",
    "\t                                                     average=\"micro\")\n",
    "\n",
    "\n",
    "\t# Plot Precision-Recall curve\n",
    "\tplt.clf()\n",
    "\tplt.plot(recall[0], precision[0], lw=lw, color='navy',\n",
    "\t         label='Precision-Recall curve')\n",
    "\tplt.xlabel('Recall')\n",
    "\tplt.ylabel('Precision')\n",
    "\tplt.ylim([0.0, 1.0])\n",
    "\tplt.xlim([0.0, 1.0])\n",
    "\tplt.title('Precision-Recall: AUC={0:0.3f}'.format(average_precision[0]))\n",
    "\tplt.legend(loc=\"lower left\")\n",
    "\tplt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def scores(dirname, classifier):\n",
    "\n",
    "\tfor filename in os.listdir(dirname):\n",
    "\t\tif filename.endswith(\".vcf.csv\"):\n",
    "\t\t\tprint(filename)\n",
    "\t\t\tdf = pd.read_csv(dirname + filename)\n",
    "\t\t\tdf1 = df.iloc[:,1:68]\n",
    "\t\t\tout= list(classifier.predict_proba(x = df1))\n",
    "\t\t\ty_score = np.vstack(out)[:,1]\n",
    "\t\t\tfile = open('scores/'+filename[:-8], 'w+')\n",
    "\t\t\tfor item in y_score:\n",
    "\t\t\t\tfile.write(\"%s\\n\" % item)\n",
    "\n",
    "\t\t\tfile.close()\n",
    "\t\t\tcontinue\n",
    "\t\telse:\n",
    "\t\t\tcontinue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test file selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def testfile(filename, output):\n",
    "    lines = open(filename).readlines()\n",
    "    open(output, 'w+').writelines(lines[1:])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Function with 3 -layered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def main():\n",
    "    tf.logging.set_verbosity(tf.logging.INFO)\n",
    "\n",
    "    \n",
    "    training_set = tf.contrib.learn.datasets.base.load_csv_with_header (filename='tissue2_train.csv', \n",
    "        target_dtype=np.int, \n",
    "        features_dtype=np.float64, \n",
    "        target_column = 0)\n",
    "\n",
    "    test_set = tf.contrib.learn.datasets.base.load_csv_with_header (filename='tissue2_test.csv', \n",
    "        target_dtype=np.int, \n",
    "        features_dtype=np.float64, \n",
    "        target_column = 0)\n",
    "\n",
    "    feature_columns = [tf.contrib.layers.real_valued_column(\"\", dimension=80)]\n",
    "\n",
    "    validation_metrics = {\n",
    "    \"accuracy\":\n",
    "        tf.contrib.learn.MetricSpec(\n",
    "            metric_fn=tf.contrib.metrics.streaming_accuracy,\n",
    "            prediction_key=tf.contrib.learn.PredictionKey.CLASSES),\n",
    "    \"precision\":\n",
    "        tf.contrib.learn.MetricSpec(\n",
    "            metric_fn=tf.contrib.metrics.streaming_precision,\n",
    "            prediction_key=tf.contrib.learn.PredictionKey.CLASSES),\n",
    "    \"recall\":\n",
    "        tf.contrib.learn.MetricSpec(\n",
    "            metric_fn=tf.contrib.metrics.streaming_recall,\n",
    "            prediction_key=tf.contrib.learn.PredictionKey.CLASSES)}\n",
    "\n",
    "\n",
    "    validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(\n",
    "        test_set.data,\n",
    "        test_set.target,\n",
    "        eval_steps =1,\n",
    "        every_n_steps=50,\n",
    "        metrics=validation_metrics,\n",
    "        early_stopping_metric=\"loss\",\n",
    "        early_stopping_metric_minimize=True,\n",
    "        early_stopping_rounds=200)\n",
    "\n",
    "\n",
    "\n",
    "    classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,\n",
    "        hidden_units= [200,200],\n",
    "        n_classes=2,\n",
    "        dropout = 0.3,\n",
    "        model_dir=\"model2/tissue1_optimization333\",\n",
    "        config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))\n",
    "\n",
    "     # Define the test inputs\n",
    "    def get_test_inputs():\n",
    "        x = tf.constant(test_set.data)\n",
    "        y = tf.constant(test_set.target)\n",
    "        return x, y\n",
    "\n",
    "\n",
    "    # Define the training inputs\n",
    "    def get_train_inputs():\n",
    "        x = tf.constant(training_set.data)\n",
    "        y = tf.constant(training_set.target)\n",
    "        return x, y\n",
    "\n",
    "    testfile('tissue2_test.csv', 'dtest1.csv')\n",
    "\n",
    "    df = pd.read_csv('dtest1.csv')\n",
    "    df1 = df.iloc[:,1:81]\n",
    "    df2 = df.iloc[:,0]\n",
    "\n",
    "    f = open('loss.txt', 'w+')\n",
    "    \n",
    "   \n",
    "    classifier.fit(input_fn=get_train_inputs, steps=1000, monitors=[validation_monitor])\n",
    "\n",
    "    loss = classifier.evaluate(input_fn=get_test_inputs, steps = 1)[\"loss\"]\n",
    "\n",
    "#     out= list(classifier.predict_proba(x = df1.values))\n",
    "\n",
    "#     y_score = np.vstack(out)[:,1]\n",
    "\n",
    "    # Scores\n",
    "\n",
    "    # \tfile = open('scores.txt', 'w')\n",
    "    # \tfor item in y_score:\n",
    "    # \t\tfile.write(\"%s\\n\" % item)\n",
    "    # \tfile.close()\n",
    "\n",
    "\n",
    "    print(\"\\nTest Accuracy: {0:f}\\n\".format(loss))\n",
    "\n",
    "    #scores('arff/arff/', classifier)\n",
    "\n",
    "#     roc(df2, y_score)\n",
    "#     pr(df2, y_score)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main_auto(list1, filename):\n",
    "    training_set = tf.contrib.learn.datasets.base.load_csv_with_header (filename='tissue1_train.csv', \n",
    "        target_dtype=np.int, \n",
    "        features_dtype=np.float64, \n",
    "        target_column = 0)\n",
    "\n",
    "    test_set = tf.contrib.learn.datasets.base.load_csv_with_header (filename='tissue1_test.csv', \n",
    "        target_dtype=np.int, \n",
    "        features_dtype=np.float64, \n",
    "        target_column = 0)\n",
    "\n",
    "    feature_columns = [tf.contrib.layers.real_valued_column(\"\", dimension=80)]\n",
    "\n",
    "    classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,\n",
    "        hidden_units= list1,\n",
    "        n_classes=2,\n",
    "        model_dir=\"tissue1_optimization\")\n",
    "\n",
    "     # Define the test inputs\n",
    "    def get_test_inputs():\n",
    "        x = tf.constant(test_set.data)\n",
    "        y = tf.constant(test_set.target)\n",
    "        return x, y\n",
    "\n",
    "\n",
    "    # Define the training inputs\n",
    "    def get_train_inputs():\n",
    "        x = tf.constant(training_set.data)\n",
    "        y = tf.constant(training_set.target)\n",
    "        return x, y\n",
    "\n",
    "    testfile('tissue1_test.csv', 'dtest1.csv')\n",
    "\n",
    "    df = pd.read_csv('dtest1.csv')\n",
    "    df1 = df.iloc[:,1:81]\n",
    "    df2 = df.iloc[:,0]\n",
    "\n",
    "    classifier.fit(input_fn=get_train_inputs, steps=200)\n",
    "\n",
    "    accuracy_score = classifier.evaluate(input_fn=get_test_inputs, steps=100)[\"accuracy\"]\n",
    "\n",
    "    out= list(classifier.predict_proba(x = df1))\n",
    "\n",
    "    y_score = np.vstack(out)[:,1]\n",
    "\n",
    "    roc_score = roc_print(df2, y_score)\n",
    "    filename.write(str(list1) + ' ' + str(accuracy_score) + ' ' + str(roc_score) + '\\n' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "f = open('test_no_norm_final', 'w')\n",
    "\n",
    "list_big = [ [200] , [600] ,[1000], [320, 160], [80, 160], [80, 20],\n",
    "            [40, 160, 40], [400,800,400] , [400, 200, 80], [160, 80, 20],\n",
    "            [80,160,320,80], [80, 320, 640, 80],\n",
    "            [80,160,320,160,80]]\n",
    "for layer in list_big:\n",
    "    main_auto(layer, f)\n",
    "    shutil.rmtree('tissue1_optimization')\n",
    "    \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "\n",
    "\n",
    "testfile('Test_OMIM.csv', 'k_DNN_test.csv')\n",
    "testfile('Train_OMIM.csv', 'k_DNN_tarin.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00000: val_loss improved from inf to 0.71551, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00001: val_loss did not improve\n",
      "Epoch 00002: val_loss did not improve\n",
      "Epoch 00003: val_loss did not improve\n",
      "Epoch 00004: val_loss did not improve\n",
      "Epoch 00005: val_loss did not improve\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 00007: val_loss did not improve\n",
      "Epoch 00008: val_loss improved from 0.71551 to 0.69129, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00009: val_loss improved from 0.69129 to 0.65493, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00010: val_loss improved from 0.65493 to 0.62335, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00011: val_loss improved from 0.62335 to 0.59036, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00012: val_loss improved from 0.59036 to 0.51673, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00013: val_loss improved from 0.51673 to 0.45743, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00014: val_loss improved from 0.45743 to 0.42450, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00015: val_loss improved from 0.42450 to 0.39015, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00016: val_loss improved from 0.39015 to 0.38268, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00017: val_loss improved from 0.38268 to 0.36863, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00018: val_loss improved from 0.36863 to 0.34377, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00019: val_loss did not improve\n",
      "Epoch 00020: val_loss did not improve\n",
      "Epoch 00021: val_loss improved from 0.34377 to 0.33528, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00022: val_loss improved from 0.33528 to 0.32401, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00023: val_loss improved from 0.32401 to 0.32264, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00024: val_loss did not improve\n",
      "Epoch 00025: val_loss did not improve\n",
      "Epoch 00026: val_loss did not improve\n",
      "Epoch 00027: val_loss improved from 0.32264 to 0.31692, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00028: val_loss did not improve\n",
      "Epoch 00029: val_loss did not improve\n",
      "Epoch 00030: val_loss did not improve\n",
      "Epoch 00031: val_loss improved from 0.31692 to 0.31605, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00032: val_loss did not improve\n",
      "Epoch 00033: val_loss did not improve\n",
      "Epoch 00034: val_loss did not improve\n",
      "Epoch 00035: val_loss did not improve\n",
      "Epoch 00036: val_loss did not improve\n",
      "Epoch 00037: val_loss did not improve\n",
      "Epoch 00038: val_loss did not improve\n",
      "Epoch 00039: val_loss did not improve\n",
      "Epoch 00040: val_loss did not improve\n",
      "Epoch 00041: val_loss did not improve\n",
      "Epoch 00042: val_loss did not improve\n",
      "Epoch 00043: val_loss did not improve\n",
      "Epoch 00044: val_loss did not improve\n",
      "Epoch 00045: val_loss did not improve\n",
      "Epoch 00046: val_loss improved from 0.31605 to 0.31441, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00047: val_loss did not improve\n",
      "Epoch 00048: val_loss did not improve\n",
      "Epoch 00049: val_loss did not improve\n",
      "Epoch 00050: val_loss did not improve\n",
      "Epoch 00051: val_loss did not improve\n",
      "Epoch 00052: val_loss did not improve\n",
      "Epoch 00053: val_loss did not improve\n",
      "Epoch 00054: val_loss improved from 0.31441 to 0.31250, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00055: val_loss did not improve\n",
      "Epoch 00056: val_loss did not improve\n",
      "Epoch 00057: val_loss did not improve\n",
      "Epoch 00058: val_loss did not improve\n",
      "Epoch 00059: val_loss did not improve\n",
      "Epoch 00060: val_loss did not improve\n",
      "Epoch 00061: val_loss did not improve\n",
      "Epoch 00062: val_loss did not improve\n",
      "Epoch 00063: val_loss did not improve\n",
      "Epoch 00064: val_loss did not improve\n",
      "Epoch 00065: val_loss did not improve\n",
      "Epoch 00066: val_loss improved from 0.31250 to 0.31169, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00067: val_loss did not improve\n",
      "Epoch 00068: val_loss did not improve\n",
      "Epoch 00069: val_loss did not improve\n",
      "Epoch 00070: val_loss did not improve\n",
      "Epoch 00071: val_loss did not improve\n",
      "Epoch 00072: val_loss did not improve\n",
      "Epoch 00073: val_loss did not improve\n",
      "Epoch 00074: val_loss did not improve\n",
      "Epoch 00075: val_loss did not improve\n",
      "Epoch 00076: val_loss did not improve\n",
      "Epoch 00077: val_loss did not improve\n",
      "Epoch 00078: val_loss improved from 0.31169 to 0.31063, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00079: val_loss did not improve\n",
      "Epoch 00080: val_loss improved from 0.31063 to 0.30830, saving model to DNN.weights.best.hdf5\n",
      "Epoch 00081: val_loss did not improve\n",
      "Epoch 00082: val_loss did not improve\n",
      "Epoch 00083: val_loss did not improve\n",
      "Epoch 00084: val_loss did not improve\n",
      "Epoch 00085: val_loss did not improve\n",
      "Epoch 00086: val_loss did not improve\n",
      "Epoch 00087: val_loss did not improve\n",
      "Epoch 00088: val_loss did not improve\n",
      "Epoch 00089: val_loss did not improve\n",
      "Epoch 00090: val_loss did not improve\n",
      "Epoch 00091: val_loss did not improve\n",
      "Epoch 00092: val_loss did not improve\n",
      "Epoch 00093: val_loss did not improve\n",
      "Epoch 00094: val_loss did not improve\n",
      "Epoch 00095: val_loss did not improve\n",
      "Epoch 00096: val_loss did not improve\n",
      "Epoch 00097: val_loss did not improve\n",
      "Epoch 00098: val_loss did not improve\n",
      "Epoch 00099: val_loss did not improve\n",
      "Epoch 00100: val_loss did not improve\n",
      "Epoch 00101: val_loss did not improve\n",
      "Epoch 00102: val_loss did not improve\n",
      "Epoch 00103: val_loss did not improve\n",
      "Epoch 00104: val_loss did not improve\n",
      "Epoch 00105: val_loss did not improve\n",
      "Epoch 00106: val_loss did not improve\n",
      "Epoch 00107: val_loss did not improve\n",
      "Epoch 00108: val_loss did not improve\n",
      "Epoch 00109: val_loss did not improve\n",
      "Epoch 00110: val_loss did not improve\n",
      "Epoch 00111: val_loss did not improve\n",
      "Epoch 00112: val_loss did not improve\n",
      "Epoch 00113: val_loss did not improve\n",
      "Epoch 00114: val_loss did not improve\n",
      "Epoch 00115: val_loss did not improve\n",
      "Epoch 00116: val_loss did not improve\n",
      "Epoch 00117: val_loss did not improve\n",
      "Epoch 00118: val_loss did not improve\n",
      "Epoch 00119: val_loss did not improve\n",
      "Epoch 00120: val_loss did not improve\n",
      "Epoch 00121: val_loss did not improve\n",
      "Epoch 00122: val_loss did not improve\n",
      "Epoch 00123: val_loss did not improve\n",
      "Epoch 00124: val_loss did not improve\n",
      "Epoch 00125: val_loss did not improve\n",
      "Epoch 00126: val_loss did not improve\n",
      "Epoch 00127: val_loss did not improve\n",
      "Epoch 00128: val_loss did not improve\n",
      "Epoch 00129: val_loss did not improve\n",
      "Epoch 00130: val_loss did not improve\n",
      "Epoch 00131: val_loss did not improve\n",
      "Epoch 00132: val_loss did not improve\n",
      "Epoch 00133: val_loss did not improve\n",
      "Epoch 00134: val_loss did not improve\n",
      "Epoch 00135: val_loss did not improve\n",
      "Epoch 00136: val_loss did not improve\n",
      "Epoch 00137: val_loss did not improve\n",
      "Epoch 00138: val_loss did not improve\n",
      "Epoch 00139: val_loss did not improve\n",
      "Epoch 00140: val_loss did not improve\n",
      "Epoch 00141: val_loss did not improve\n",
      "Epoch 00142: val_loss did not improve\n",
      "Epoch 00143: val_loss did not improve\n",
      "Epoch 00144: val_loss did not improve\n",
      "Epoch 00145: val_loss did not improve\n",
      "Epoch 00146: val_loss did not improve\n",
      "Epoch 00147: val_loss did not improve\n",
      "Epoch 00148: val_loss did not improve\n",
      "Epoch 00149: val_loss did not improve\n",
      "Epoch 00150: val_loss did not improve\n",
      "Epoch 00151: val_loss did not improve\n",
      "Epoch 00152: val_loss did not improve\n",
      "Epoch 00153: val_loss did not improve\n",
      "Epoch 00154: val_loss did not improve\n",
      "Epoch 00155: val_loss did not improve\n",
      "Epoch 00156: val_loss did not improve\n",
      "Epoch 00157: val_loss did not improve\n",
      "Epoch 00158: val_loss did not improve\n",
      "Epoch 00159: val_loss did not improve\n",
      "Epoch 00160: val_loss did not improve\n",
      "Epoch 00161: val_loss did not improve\n",
      "Epoch 00162: val_loss did not improve\n",
      "Epoch 00163: val_loss did not improve\n",
      "Epoch 00164: val_loss did not improve\n",
      "Epoch 00165: val_loss did not improve\n",
      "Epoch 00166: val_loss did not improve\n",
      "Epoch 00167: val_loss did not improve\n",
      "Epoch 00168: val_loss did not improve\n",
      "Epoch 00169: val_loss did not improve\n",
      "Epoch 00170: val_loss did not improve\n",
      "Epoch 00171: val_loss did not improve\n",
      "Epoch 00172: val_loss did not improve\n",
      "Epoch 00173: val_loss did not improve\n",
      "Epoch 00174: val_loss did not improve\n",
      "Epoch 00175: val_loss did not improve\n",
      "Epoch 00176: val_loss did not improve\n",
      "Epoch 00177: val_loss did not improve\n",
      "Epoch 00178: val_loss did not improve\n",
      "Epoch 00179: val_loss did not improve\n",
      "Epoch 00180: val_loss did not improve\n",
      "Epoch 00181: val_loss did not improve\n",
      "Epoch 00182: val_loss did not improve\n",
      "Epoch 00183: val_loss did not improve\n",
      "Epoch 00184: val_loss did not improve\n",
      "Epoch 00185: val_loss did not improve\n",
      "Epoch 00186: val_loss did not improve\n",
      "Epoch 00187: val_loss did not improve\n",
      "Epoch 00188: val_loss did not improve\n",
      "Epoch 00189: val_loss did not improve\n",
      "Epoch 00190: val_loss did not improve\n",
      "Epoch 00191: val_loss did not improve\n",
      "Epoch 00192: val_loss did not improve\n",
      "Epoch 00193: val_loss did not improve\n",
      "Epoch 00194: val_loss did not improve\n",
      "Epoch 00195: val_loss did not improve\n",
      "Epoch 00196: val_loss did not improve\n",
      "Epoch 00197: val_loss did not improve\n",
      "Epoch 00198: val_loss did not improve\n",
      "Epoch 00199: val_loss did not improve\n",
      "Epoch 00200: val_loss did not improve\n",
      "Epoch 00201: val_loss did not improve\n",
      "Epoch 00202: val_loss did not improve\n",
      "Epoch 00203: val_loss did not improve\n",
      "Epoch 00204: val_loss did not improve\n",
      "Epoch 00205: val_loss did not improve\n",
      "Epoch 00206: val_loss did not improve\n",
      "Epoch 00207: val_loss did not improve\n",
      "Epoch 00208: val_loss did not improve\n",
      "Epoch 00209: val_loss did not improve\n",
      "Epoch 00210: val_loss did not improve\n",
      "Epoch 00211: val_loss did not improve\n",
      "Epoch 00212: val_loss did not improve\n",
      "Epoch 00213: val_loss did not improve\n",
      "Epoch 00214: val_loss did not improve\n",
      "Epoch 00215: val_loss did not improve\n",
      "Epoch 00216: val_loss did not improve\n",
      "Epoch 00217: val_loss did not improve\n",
      "Epoch 00218: val_loss did not improve\n",
      "Epoch 00219: val_loss did not improve\n",
      "Epoch 00220: val_loss did not improve\n",
      "Epoch 00221: val_loss did not improve\n",
      "Epoch 00222: val_loss did not improve\n",
      "Epoch 00223: val_loss did not improve\n",
      "Epoch 00224: val_loss did not improve\n",
      "Epoch 00225: val_loss did not improve\n",
      "Epoch 00226: val_loss did not improve\n",
      "Epoch 00227: val_loss did not improve\n",
      "Epoch 00228: val_loss did not improve\n",
      "Epoch 00229: val_loss did not improve\n",
      "Epoch 00230: val_loss did not improve\n",
      "Epoch 00231: val_loss did not improve\n",
      "Epoch 00232: val_loss did not improve\n",
      "Epoch 00233: val_loss did not improve\n",
      "Epoch 00234: val_loss did not improve\n",
      "Epoch 00235: val_loss did not improve\n",
      "Epoch 00236: val_loss did not improve\n",
      "Epoch 00237: val_loss did not improve\n",
      "Epoch 00238: val_loss did not improve\n",
      "Epoch 00239: val_loss did not improve\n",
      "Epoch 00240: val_loss did not improve\n",
      "Epoch 00241: val_loss did not improve\n",
      "Epoch 00242: val_loss did not improve\n",
      "Epoch 00243: val_loss did not improve\n",
      "Epoch 00244: val_loss did not improve\n",
      "Epoch 00245: val_loss did not improve\n",
      "Epoch 00246: val_loss did not improve\n",
      "Epoch 00247: val_loss did not improve\n",
      "Epoch 00248: val_loss did not improve\n",
      "Epoch 00249: val_loss did not improve\n",
      "Epoch 00250: val_loss did not improve\n",
      "Epoch 00251: val_loss did not improve\n",
      "Epoch 00252: val_loss did not improve\n",
      "Epoch 00253: val_loss did not improve\n",
      "Epoch 00254: val_loss did not improve\n",
      "Epoch 00255: val_loss did not improve\n",
      "Epoch 00256: val_loss did not improve\n",
      "Epoch 00257: val_loss did not improve\n",
      "Epoch 00258: val_loss did not improve\n",
      "Epoch 00259: val_loss did not improve\n",
      "Epoch 00260: val_loss did not improve\n",
      "Epoch 00261: val_loss did not improve\n",
      "Epoch 00262: val_loss did not improve\n",
      "Epoch 00263: val_loss did not improve\n",
      "Epoch 00264: val_loss did not improve\n",
      "Epoch 00265: val_loss did not improve\n",
      "Epoch 00266: val_loss did not improve\n",
      "Epoch 00267: val_loss did not improve\n",
      "Epoch 00268: val_loss did not improve\n",
      "Epoch 00269: val_loss did not improve\n",
      "Epoch 00270: val_loss did not improve\n",
      "Epoch 00271: val_loss did not improve\n",
      "Epoch 00272: val_loss did not improve\n",
      "Epoch 00273: val_loss did not improve\n",
      "Epoch 00274: val_loss did not improve\n",
      "Epoch 00275: val_loss did not improve\n",
      "Epoch 00276: val_loss did not improve\n",
      "Epoch 00277: val_loss did not improve\n",
      "Epoch 00278: val_loss did not improve\n",
      "Epoch 00279: val_loss did not improve\n",
      "Epoch 00280: val_loss did not improve\n",
      "Epoch 00281: val_loss did not improve\n",
      "Epoch 00282: val_loss did not improve\n",
      "Epoch 00283: val_loss did not improve\n",
      "Epoch 00284: val_loss did not improve\n",
      "Epoch 00285: val_loss did not improve\n",
      "Epoch 00286: val_loss did not improve\n",
      "Epoch 00287: val_loss did not improve\n",
      "Epoch 00288: val_loss did not improve\n",
      "Epoch 00289: val_loss did not improve\n",
      "Epoch 00290: val_loss did not improve\n",
      "Epoch 00291: val_loss did not improve\n",
      "Epoch 00292: val_loss did not improve\n",
      "Epoch 00293: val_loss did not improve\n",
      "Epoch 00294: val_loss did not improve\n",
      "Epoch 00295: val_loss did not improve\n",
      "Epoch 00296: val_loss did not improve\n",
      "Epoch 00297: val_loss did not improve\n",
      "Epoch 00298: val_loss did not improve\n",
      "Epoch 00299: val_loss did not improve\n",
      "Epoch 00300: val_loss did not improve\n",
      "Epoch 00301: val_loss did not improve\n",
      "Epoch 00302: val_loss did not improve\n",
      "Epoch 00303: val_loss did not improve\n",
      "Epoch 00304: val_loss did not improve\n",
      "Epoch 00305: val_loss did not improve\n",
      "Epoch 00306: val_loss did not improve\n",
      "Epoch 00307: val_loss did not improve\n",
      "Epoch 00308: val_loss did not improve\n",
      "Epoch 00309: val_loss did not improve\n",
      "Epoch 00310: val_loss did not improve\n",
      "Epoch 00311: val_loss did not improve\n",
      "Epoch 00312: val_loss did not improve\n",
      "Epoch 00313: val_loss did not improve\n",
      "Epoch 00314: val_loss did not improve\n",
      "Epoch 00315: val_loss did not improve\n",
      "Epoch 00316: val_loss did not improve\n",
      "Epoch 00317: val_loss did not improve\n",
      "Epoch 00318: val_loss did not improve\n",
      "Epoch 00319: val_loss did not improve\n",
      "Epoch 00320: val_loss did not improve\n",
      "Epoch 00321: val_loss did not improve\n",
      "Epoch 00322: val_loss did not improve\n",
      "Epoch 00323: val_loss did not improve\n",
      "Epoch 00324: val_loss did not improve\n",
      "Epoch 00325: val_loss did not improve\n",
      "Epoch 00326: val_loss did not improve\n",
      "Epoch 00327: val_loss did not improve\n",
      "Epoch 00328: val_loss did not improve\n",
      "Epoch 00329: val_loss did not improve\n",
      "Epoch 00330: val_loss did not improve\n",
      "Epoch 00331: val_loss did not improve\n",
      "Epoch 00332: val_loss did not improve\n",
      "Epoch 00333: val_loss did not improve\n",
      "Epoch 00334: val_loss did not improve\n",
      "Epoch 00335: val_loss did not improve\n",
      "Epoch 00336: val_loss did not improve\n",
      "Epoch 00337: val_loss did not improve\n",
      "Epoch 00338: val_loss did not improve\n",
      "Epoch 00339: val_loss did not improve\n",
      "Epoch 00340: val_loss did not improve\n",
      "Epoch 00341: val_loss did not improve\n",
      "Epoch 00342: val_loss did not improve\n",
      "Epoch 00343: val_loss did not improve\n",
      "Epoch 00344: val_loss did not improve\n",
      "Epoch 00345: val_loss did not improve\n",
      "Epoch 00346: val_loss did not improve\n",
      "Epoch 00347: val_loss did not improve\n",
      "Epoch 00348: val_loss did not improve\n",
      "Epoch 00349: val_loss did not improve\n",
      "Epoch 00350: val_loss did not improve\n",
      "Epoch 00351: val_loss did not improve\n",
      "Epoch 00352: val_loss did not improve\n",
      "Epoch 00353: val_loss did not improve\n",
      "Epoch 00354: val_loss did not improve\n",
      "Epoch 00355: val_loss did not improve\n",
      "Epoch 00356: val_loss did not improve\n",
      "Epoch 00357: val_loss did not improve\n",
      "Epoch 00358: val_loss did not improve\n",
      "Epoch 00359: val_loss did not improve\n",
      "Epoch 00360: val_loss did not improve\n",
      "Epoch 00361: val_loss did not improve\n",
      "Epoch 00362: val_loss did not improve\n",
      "Epoch 00363: val_loss did not improve\n",
      "Epoch 00364: val_loss did not improve\n",
      "Epoch 00365: val_loss did not improve\n",
      "Epoch 00366: val_loss did not improve\n",
      "Epoch 00367: val_loss did not improve\n",
      "Epoch 00368: val_loss did not improve\n",
      "Epoch 00369: val_loss did not improve\n",
      "Epoch 00370: val_loss did not improve\n",
      "Epoch 00371: val_loss did not improve\n",
      "Epoch 00372: val_loss did not improve\n",
      "Epoch 00373: val_loss did not improve\n",
      "Epoch 00374: val_loss did not improve\n",
      "Epoch 00375: val_loss did not improve\n",
      "Epoch 00376: val_loss did not improve\n",
      "Epoch 00377: val_loss did not improve\n",
      "Epoch 00378: val_loss did not improve\n",
      "Epoch 00379: val_loss did not improve\n",
      "Epoch 00380: val_loss did not improve\n",
      "Epoch 00381: val_loss did not improve\n",
      "Epoch 00382: val_loss did not improve\n",
      "Epoch 00383: val_loss did not improve\n",
      "Epoch 00384: val_loss did not improve\n",
      "Epoch 00385: val_loss did not improve\n",
      "Epoch 00386: val_loss did not improve\n",
      "Epoch 00387: val_loss did not improve\n",
      "Epoch 00388: val_loss did not improve\n",
      "Epoch 00389: val_loss did not improve\n",
      "Epoch 00390: val_loss did not improve\n",
      "Epoch 00391: val_loss did not improve\n",
      "Epoch 00392: val_loss did not improve\n",
      "Epoch 00393: val_loss did not improve\n",
      "Epoch 00394: val_loss did not improve\n",
      "Epoch 00395: val_loss did not improve\n",
      "Epoch 00396: val_loss did not improve\n",
      "Epoch 00397: val_loss did not improve\n",
      "Epoch 00398: val_loss did not improve\n",
      "Epoch 00399: val_loss did not improve\n",
      "['acc', 'loss', 'val_acc', 'val_loss']\n"
     ]
    },
    {
     "data": {
      "image/png": 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FD8K0C1wtZ908l8yqZ8ZrRNGIK+FvXhKviU46LV5wGXesq1lOOMnVoNsb3fTE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\nWVIwxhjTyZKCMcaYTv8fDUISDTe+NWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ef37c4ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NExurj/QRlEqpIBd0SSGjn70vYdlun5JBRLx9poKOf6SUCnJBlxTS4yOIDHOxI8/nBurG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\nRJOCUkqpJv8fH/alew3KHXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e881b9cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize training history\n",
    "from keras.layers import Dropout\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "# fix random seed for reproducibility\n",
    "seed = 8\n",
    "numpy.random.seed(seed)\n",
    "\n",
    "dataset = numpy.loadtxt(\"k_DNN_train.csv\", delimiter=\",\")\n",
    "\n",
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,1:68]\n",
    "Y = dataset[:,0]\n",
    "\n",
    "X_val = dataset[:, 1:68]\n",
    "Y_val = dataset[: , 0]\n",
    "# create model\n",
    "model = Sequential()\n",
    "# model.add(input_shape=(80,))\n",
    "model.add(Dense(80, input_dim=67,  kernel_initializer='uniform', activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(160, kernel_initializer='uniform', activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(80, kernel_initializer='uniform', activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "filepath=\"DNN.weights.best.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "# Fit the model\n",
    "history = model.fit(X, Y, validation_split=0.2, epochs=400, batch_size=10000, callbacks = callbacks_list, verbose=0, initial_epoch=0)\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "filepath=\"weights.best.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "# Fit the model\n",
    "history = model.fit(X, Y, validation_split=0.2, epochs=1000, batch_size=10000, callbacks = callbacks_list, verbose=0, initial_epoch=800)\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test (With BRCA- No Tissue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created model and loaded weights from file\n",
      "19648/20000 [============================>.] - ETA: 0s \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b['loss', 'acc']\n",
      "[0.24853807984739543, 0.91705000000000003]\n",
      "ROC_AUC: 0.956915\n"
     ]
    },
    {
     "data": {
      "image/png": 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CtbsrmL9uH9npQarqIh0+fnNJYvKQQjbureTYUUUIUFFbz/iB+eyvCjNuYB6RqNI/P5NB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\nUNANKWJ9KJLYvC+7lkwZfWD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UdTLwR+C37X2z13fhUW/xImBQzLZLVXVlp0R5IM67iS/O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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1eec062ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H9gJg8OBcsrLSyMvLID8/k/z8THJzW3WL8WOCgkKkFd5/PzhzrWEXWk1N/WG71Xr0SCUt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e880f0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_test = numpy.loadtxt(\"k_DNN_test.csv\", delimiter=\",\")\n",
    "# split into input (X) and output (Y) variables\n",
    "X_test = dataset_test[:,1:68]\n",
    "Y_test = dataset_test[:,0]\n",
    "\n",
    "\n",
    "# load weights\n",
    "model.load_weights(\"DNN.weights.best.hdf5\")\n",
    "# Compile model (required to make predictions)\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "print(\"Created model and loaded weights from file\")\n",
    "\n",
    "\n",
    "# estimate accuracy on whole dataset using loaded weights\n",
    "scores = model.evaluate(X_test, Y_test, verbose=1)\n",
    "\n",
    "y_score = model.predict_proba(X_test)\n",
    "\n",
    "print(model.metrics_names)\n",
    "print(scores)\n",
    "\n",
    "roc(Y_test, y_score)\n",
    "pr(Y_test, y_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.utils import plot_model\n",
    "plot_model(model, to_file='model.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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