Machine learning 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as pyplot\n",
    "import seaborn as sns\n",
    "import numpy as np \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import classification_report\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import recall_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "data_names = [\"webcam_test_2017-09-30-12-26-14.csv\", \n",
    "              \"webcam_test_2017-09-30-12-05-22.csv\", \n",
    "              \"webcam_test_2017-09-30-11-56-56.csv\", \n",
    "              \"webcam_test_2017-09-26-19-32-41.csv\", \n",
    "              \"webcam_test_2017-09-22-19-39-14.csv\", \n",
    "              \"webcam_test_2017-09-21-19-08-36.csv\", \n",
    "              \"webcam_test_2017-09-20-19-11-17.csv\", \n",
    "              \"webcam_test_2017-09-18-19-45-21.csv\", \n",
    "              \"webcam_test_2017-09-17-19-08-31.csv\", \n",
    "              \"webcam_test_2017-09-16-19-11-16.csv\", \n",
    "              \"webcam_test_2017-09-14-19-45-24.csv\", \n",
    "              \"webcam_test_2017-09-13-18-00-31.csv\", \n",
    "              \"webcam_test_2017-09-11-19-06-39.csv\",\n",
    "              \"webcam_test_2017-09-07-19-57-06.csv\",\n",
    "              \"webcam_test_2017-09-06-20-06-47.csv\",\n",
    "              \"webcam_test_2017-09-05-19-55-53.csv\",\n",
    "              \"webcam_test_2017-09-03-13-02-36.csv\",\n",
    "              \"webcam_test_2017-09-01-15-08-44.csv\"]\n",
    "label_names = [\"annos_s17_2017-09-30_3.csv\", \n",
    "               \"annos_s17_2017-09-30_2.csv\", \n",
    "               \"annos_s17_2017-09-30_1.csv\", \n",
    "               \"annos_s16_2017-09-26.csv\", \n",
    "               \"annos_s14_2017-09-22.csv\", \n",
    "               \"annos_s13_2017-09-21.csv\", \n",
    "               \"annos_s12_2017-09-20.csv\", \n",
    "               \"annos_s11_2017-09-18.csv\", \n",
    "               \"annos_s10_2017-09-17.csv\", \n",
    "               \"annos_s9_2017-09-16.csv\", \n",
    "               \"annos_s8_2017-09-14.csv\", \n",
    "               \"annos_s7_2017-09-13.csv\", \n",
    "               \"annos_s6_2017-09-11.csv\", \n",
    "               \"annos_s5_2017-09-07.csv\", \n",
    "               \"annos_s4_2017-09-06.csv\", \n",
    "               \"annos_s3_2017-09-05.csv\", \n",
    "               \"annos_s2_2017-09-03.csv\", \n",
    "               \"annos_s1_2017-09-01.csv\"]\n",
    "\n",
    "\n",
    "# new_data = []\n",
    "# labels = []\n",
    "# label_bools = []\n",
    "new_data = pd.DataFrame() #creates a new dataframe that's empty\n",
    "labels = pd.DataFrame()\n",
    "label_bools = pd.DataFrame()\n",
    "for data_set in range(len(data_names)):\n",
    "    curr_data = pd.read_csv(data_names[data_set])\n",
    "    curr_labels =  pd.read_csv(label_names[data_set])\n",
    "\n",
    "    diff = len(curr_data) - len(curr_labels)\n",
    "\n",
    "    if diff > 0:\n",
    "        curr_data = curr_data[:-diff]\n",
    "    if diff < 0:\n",
    "        diff = diff * -1\n",
    "        curr_labels = curr_labels[:-diff]\n",
    "    \n",
    "    print(curr_data.shape)\n",
    "    print(curr_labels.shape)\n",
    "    curr_new_data = curr_data[curr_data[' confidence'] > .70] \n",
    "    removed_data = curr_data[curr_data[' confidence'] <= .70] \n",
    "    \n",
    "    \n",
    "    #align the data\n",
    "    aligned_labels = []\n",
    "\n",
    "\n",
    "    \n",
    "    #labels.drop(removed_data[\"frame\"])\n",
    "    newFrames = []\n",
    "    for index, row in removed_data.iterrows():\n",
    "        newFrames.append(int(row[\"frame\"]) - 1)\n",
    "    curr_labels = curr_labels.drop(newFrames)\n",
    "\n",
    "    \n",
    "    curr_labels = curr_labels[\"C_Gaze\"]\n",
    "    curr_label_bools = []\n",
    "    for i in range(len(curr_labels)):\n",
    "        if curr_labels.values[i] == \"away\":\n",
    "            curr_label_bools.append(False)\n",
    "        else: \n",
    "            curr_label_bools.append(True)\n",
    "    curr_new_data = curr_new_data.drop([\"frame\", \" face_id\", \" timestamp\", \" confidence\", \" success\"], 1)\n",
    "    new_data = new_data.append(curr_new_data, ignore_index = True)\n",
    "    labels = labels.append(curr_labels, ignore_index = True)\n",
    "    label_bools = label_bools.append(curr_label_bools, ignore_index = True)\n",
    "#     new_data.append(curr_new_data)\n",
    "#     labels.append(curr_labels)\n",
    "#     label_bools.append(curr_label_bools)\n",
    "\n",
    "#label_bools\n",
    "\n",
    "#new_data is data\n",
    "#labels is annotations\n",
    "\n",
    "new_data_test = new_data[19358:]\n",
    "new_data_train = new_data[:19357]\n",
    "\n",
    "#trains vanilla log reg with unmodified data using l2 regularization\n",
    "labels_test = label_bools[19358:]\n",
    "labels_train = label_bools[:19357]\n",
    "# print(new_data.shape)\n",
    "# print(new_data_test.shape)\n",
    "# print(new_data_train.shape)\n",
    "# print(labels_test.shape)\n",
    "# print(labels_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(new_data, label_bools,\n",
    "                                                  test_size = .00001,\n",
    "                                                  random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE(random_state=12, ratio = 1.0)\n",
    "x_train_res, y_train_res = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "83792/83792 [==============================] - 29s 352us/step - loss: 8.0599 - acc: 0.4999\n",
      "Epoch 2/10\n",
      "83792/83792 [==============================] - 31s 374us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 3/10\n",
      "83792/83792 [==============================] - 33s 393us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 4/10\n",
      "83792/83792 [==============================] - 34s 412us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 5/10\n",
      "83792/83792 [==============================] - 30s 356us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 6/10\n",
      "83792/83792 [==============================] - 32s 379us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 7/10\n",
      "83792/83792 [==============================] - 33s 398us/step - loss: 8.0598 - acc: 0.5000\n",
      "Epoch 8/10\n",
      "54485/83792 [==================>...........] - ETA: 11s - loss: 8.0364 - acc: 0.5014"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    400\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 240\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sample_weight'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKerasClassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mfit_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    202\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcallback_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/callbacks.pyc\u001b[0m in \u001b[0;36mon_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mt_before_callbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delta_ts_batch_end\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt_before_callbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0mdelta_t_median\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delta_ts_batch_end\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/callbacks.pyc\u001b[0m in \u001b[0;36mon_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;31m# will be handled by on_epoch_end.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/utils/generic_utils.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m             \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m             \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/ipykernel/iostream.pyc\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    347\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m                 \u001b[0;31m# and give a timeout to avoid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m                     \u001b[0;31m# write directly to __stderr__ instead of warning because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m                     \u001b[0;31m# if this is happening sys.stderr may be the problem.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    612\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__cond\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__flag\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.pyc\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    357\u001b[0m                         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                     \u001b[0mdelay\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelay\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremaining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                     \u001b[0m_sleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelay\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgotit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def create_baseline():\n",
    "# create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(200, input_dim=709, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(20, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "estimator = KerasClassifier(build_fn=create_baseline, epochs=10, batch_size=5, verbose=1)\n",
    "kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(estimator, x_train_res, y_train_res, cv=kfold)\n",
    "print(results)\n",
    "print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1863,  1039],\n",
       "       [ 1247, 14855]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogReg = LogisticRegression(max_iter=10000, penalty='l2', tol=.0001, class_weight=\"balanced\")\n",
    "\n",
    "LogReg.fit(new_data_train, labels_train)\n",
    "#uses model to predict on test data\n",
    "y_pred = LogReg.predict(new_data_test)\n",
    "#evaluates results\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(labels_test, y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1881,  1021],\n",
       "       [ 1494, 14608]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shortens the data to only take leftmost n features\n",
    "n = 160\n",
    "new_data_train_short = new_data_train.iloc[:,0:n]\n",
    "new_data_test_short = new_data_test.iloc[:,0:n]\n",
    "#uses fewer featured data to train log reg\n",
    "LogReg = LogisticRegression(max_iter=10000, penalty='l2', tol=.0001, class_weight=\"balanced\")\n",
    "\n",
    "LogReg.fit(new_data_train_short, labels_train)\n",
    "\n",
    "y_pred = LogReg.predict(new_data_test_short)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(labels_test, y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]"
     ]
    }
   ],
   "source": [
    "#fits svm to data\n",
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma=\"scale\", C=1, verbose=True)\n",
    "clf.fit(new_data_train_pca, y_train)  \n",
    "\n",
    "#evaluates svm\n",
    "y_pred = clf.predict(new_data_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(new_data_test_pca, y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduces data dimensionality\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pca = PCA(n_components=160)\n",
    "new_data_train_pca = pca.fit_transform(StandardScaler().fit_transform(x_train))\n",
    "new_data_test_pca = pca.fit_transform(StandardScaler().fit_transform(new_data_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  500,  2402],\n",
       "       [ 2322, 13780]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#runs log reg on pca dimensionaity reduced data\n",
    "LogReg = LogisticRegression(max_iter=10000, penalty='l2', tol=.0001)\n",
    "\n",
    "LogReg.fit(new_data_train_pca, labels_train)\n",
    "\n",
    "y_pred = LogReg.predict(new_data_test_pca)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(labels_test, y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grabs data to graph\n",
    "#note: pca needs to reduce to 2 for this to work\n",
    "test_pd = pd.DataFrame(data=labels_train)\n",
    "test_pd2 = pd.DataFrame(data=new_data_train_pca)\n",
    "graph_data = pd.concat([test_pd2, test_pd], axis = 1)\n",
    "graph_data.columns = ['principal component 1', 'principal component 2', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphs pca data\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('OpenFace Visualization', fontsize = 20)\n",
    "targets = ['OpenFace']\n",
    "ax.scatter(new_data_train_pca[:,0] , new_data_train_pca[:,1] , c = 'r' , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphs pca data with colors for true and false, true on top\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [False, True]\n",
    "colors = ['r', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = graph_data['label'] == target\n",
    "    ax.scatter(graph_data.loc[indicesToKeep, 'principal component 1']\n",
    "               , graph_data.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphs pca data with colors for true and false, false on top\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [True, False]\n",
    "colors = ['r', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = graph_data['label'] == target\n",
    "    ax.scatter(graph_data.loc[indicesToKeep, 'principal component 1']\n",
    "               , graph_data.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates a kernel pca with gamma g and n components\n",
    "from sklearn.decomposition import KernelPCA\n",
    "kpca = KernelPCA(kernel=\"rbf\", gamma=1, n_components=2)\n",
    "kpca.fit(x_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data_train_kpca = kpca.fit_transform(new_data_train)\n",
    "new_data_test_kpca = kpca.fit_transform(new_data_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sets up kernel pca data to be graphed\n",
    "test_pd = pd.DataFrame(data=labels_train)\n",
    "test_pd2 = pd.DataFrame(data=new_data_train_kpca)\n",
    "graph_data2 = pd.concat([test_pd2, test_pd], axis = 1)\n",
    "graph_data2.columns = ['principal component 1', 'principal component 2', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphs kernel pca data\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [True, False]\n",
    "colors = ['r', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = graph_data2['label'] == target\n",
    "    ax.scatter(graph_data2.loc[indicesToKeep, 'principal component 1']\n",
    "               , graph_data2.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "121182/121182 [==============================] - 31s 253us/step - loss: 0.1127 - acc: 0.96274 -  - ETA: 0s - loss: 0\n",
      "Epoch 2/30\n",
      "121182/121182 [==============================] - 30s 245us/step - loss: 0.0712 - acc: 0.9758\n",
      "Epoch 3/30\n",
      " 68720/121182 [================>.............] - ETA: 12s - loss: 0.0547 - acc: 0.9813"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-b03711fb644c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKerasClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_baseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data_train_pca\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Results: %.2f%% (%.2f%%)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    400\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 240\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sample_weight'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKerasClassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mfit_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
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      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.layers import Dropout\n",
    "def create_baseline():\n",
    "# create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(200, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(160, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(20, input_dim=50, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(10, input_dim=20, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "estimator = KerasClassifier(build_fn=create_baseline, epochs=30, batch_size=10, verbose=1)\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(estimator, new_data_train_pca, y_train, cv=kfold)\n",
    "print(results)\n",
    "print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "121182/121182 [==============================] - 39s 325us/step - loss: 0.2157 - acc: 0.9295\n",
      "Epoch 2/300\n",
      "121182/121182 [==============================] - 41s 341us/step - loss: 0.1847 - acc: 0.9415\n",
      "Epoch 3/300\n",
      "121182/121182 [==============================] - 35s 288us/step - loss: 0.1728 - acc: 0.9446\n",
      "Epoch 4/300\n",
      "121182/121182 [==============================] - 33s 272us/step - loss: 0.1690 - acc: 0.9458\n",
      "Epoch 5/300\n",
      "121182/121182 [==============================] - 34s 283us/step - loss: 0.1663 - acc: 0.9466\n",
      "Epoch 6/300\n",
      "121182/121182 [==============================] - 34s 283us/step - loss: 0.1611 - acc: 0.9473\n",
      "Epoch 7/300\n",
      "121182/121182 [==============================] - 33s 272us/step - loss: 0.1581 - acc: 0.9479\n",
      "Epoch 8/300\n",
      "121182/121182 [==============================] - 33s 274us/step - loss: 0.1570 - acc: 0.9490\n",
      "Epoch 9/300\n",
      "121182/121182 [==============================] - 34s 279us/step - loss: 0.1532 - acc: 0.9500\n",
      "Epoch 10/300\n",
      "121182/121182 [==============================] - 34s 277us/step - loss: 0.1527 - acc: 0.9503\n",
      "Epoch 11/300\n",
      "121182/121182 [==============================] - 33s 275us/step - loss: 0.1515 - acc: 0.9512\n",
      "Epoch 12/300\n",
      "121182/121182 [==============================] - 34s 279us/step - loss: 0.1539 - acc: 0.9512\n",
      "Epoch 13/300\n",
      "121182/121182 [==============================] - 35s 286us/step - loss: 0.1532 - acc: 0.9506\n",
      "Epoch 14/300\n",
      "121182/121182 [==============================] - 33s 276us/step - loss: 0.1582 - acc: 0.9511\n",
      "Epoch 15/300\n",
      "121182/121182 [==============================] - 33s 276us/step - loss: 0.1554 - acc: 0.9512\n",
      "Epoch 16/300\n",
      "121182/121182 [==============================] - 34s 285us/step - loss: 0.1506 - acc: 0.9518\n",
      "Epoch 17/300\n",
      "121182/121182 [==============================] - 34s 280us/step - loss: 0.1509 - acc: 0.9507\n",
      "Epoch 18/300\n",
      "121182/121182 [==============================] - 34s 278us/step - loss: 0.1571 - acc: 0.95020s - loss: 0.1\n",
      "Epoch 19/300\n",
      "121182/121182 [==============================] - 34s 278us/step - loss: 0.1529 - acc: 0.9511\n",
      "Epoch 20/300\n",
      "121182/121182 [==============================] - 35s 286us/step - loss: 0.1468 - acc: 0.9518\n",
      "Epoch 21/300\n",
      "121182/121182 [==============================] - 34s 282us/step - loss: 0.1512 - acc: 0.9517\n",
      "Epoch 22/300\n",
      "121182/121182 [==============================] - 35s 286us/step - loss: 0.1519 - acc: 0.9528\n",
      "Epoch 23/300\n",
      "  4800/121182 [>.............................] - ETA: 35s - loss: 0.1289 - acc: 0.9594"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-d74ba5350277>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKerasClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_baseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data_train_pca\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Results: %.2f%% (%.2f%%)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    400\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 240\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sample_weight'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKerasClassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mfit_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2653\u001b[0m                 array_vals.append(\n\u001b[1;32m   2654\u001b[0m                     np.asarray(value,\n\u001b[0;32m-> 2655\u001b[0;31m                                dtype=tf.as_dtype(tensor.dtype).as_numpy_dtype))\n\u001b[0m\u001b[1;32m   2656\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2657\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \"\"\"\n\u001b[0;32m--> 501\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.layers import Dropout\n",
    "def create_baseline():\n",
    "# create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(200, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(160, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(20, input_dim=50, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(10, input_dim=20, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "estimator = KerasClassifier(build_fn=create_baseline, epochs=300, batch_size=10, verbose=1)\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(estimator, new_data_train_pca, y_train, cv=kfold)\n",
    "print(results)\n",
    "print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "121182/121182 [==============================] - 30s 247us/step - loss: 0.1110 - acc: 0.9632\n",
      "Epoch 2/30\n",
      "121182/121182 [==============================] - 29s 238us/step - loss: 0.0680 - acc: 0.97682s - loss:  - ETA: 1s - loss: - ETA: 0s - loss: 0.0\n",
      "Epoch 3/30\n",
      "121182/121182 [==============================] - 28s 232us/step - loss: 0.0498 - acc: 0.9829\n",
      "Epoch 4/30\n",
      "121182/121182 [==============================] - 28s 235us/step - loss: 0.0402 - acc: 0.9866\n",
      "Epoch 5/30\n",
      "121182/121182 [==============================] - 30s 250us/step - loss: 0.0353 - acc: 0.9881\n",
      "Epoch 6/30\n",
      "121182/121182 [==============================] - 33s 268us/step - loss: 0.0291 - acc: 0.9898\n",
      "Epoch 7/30\n",
      "121182/121182 [==============================] - 30s 244us/step - loss: 0.0260 - acc: 0.99133s - lo\n",
      "Epoch 8/30\n",
      "121182/121182 [==============================] - 28s 231us/step - loss: 0.0223 - acc: 0.9920\n",
      "Epoch 9/30\n",
      "121182/121182 [==============================] - 28s 233us/step - loss: 0.0218 - acc: 0.9926\n",
      "Epoch 10/30\n",
      "121182/121182 [==============================] - 28s 235us/step - loss: 0.0207 - acc: 0.9934\n",
      "Epoch 11/30\n",
      "121182/121182 [==============================] - 29s 240us/step - loss: 0.0191 - acc: 0.9938\n",
      "Epoch 12/30\n",
      "121182/121182 [==============================] - 28s 234us/step - loss: 0.0168 - acc: 0.9949\n",
      "Epoch 13/30\n",
      "121182/121182 [==============================] - 28s 234us/step - loss: 0.0167 - acc: 0.9948\n",
      "Epoch 14/30\n",
      "121182/121182 [==============================] - 28s 234us/step - loss: 0.0153 - acc: 0.9954\n",
      "Epoch 15/30\n",
      "121182/121182 [==============================] - 29s 240us/step - loss: 0.0158 - acc: 0.9956\n",
      "Epoch 16/30\n",
      "121182/121182 [==============================] - 28s 233us/step - loss: 0.0142 - acc: 0.9955\n",
      "Epoch 17/30\n",
      "121182/121182 [==============================] - 29s 237us/step - loss: 0.0137 - acc: 0.99594s  - ETA: 2s - loss: 0.0 \n",
      "Epoch 18/30\n",
      "121182/121182 [==============================] - 29s 237us/step - loss: 0.0149 - acc: 0.9962\n",
      "Epoch 19/30\n",
      "121182/121182 [==============================] - 30s 246us/step - loss: 0.0219 - acc: 0.9954\n",
      "Epoch 20/30\n",
      "121182/121182 [==============================] - 29s 235us/step - loss: 0.0154 - acc: 0.9960\n",
      "Epoch 21/30\n",
      "121182/121182 [==============================] - 28s 235us/step - loss: 0.0130 - acc: 0.9964\n",
      "Epoch 22/30\n",
      "121182/121182 [==============================] - 30s 248us/step - loss: 0.0121 - acc: 0.9967\n",
      "Epoch 23/30\n",
      "121182/121182 [==============================] - 30s 248us/step - loss: 0.0130 - acc: 0.9965\n",
      "Epoch 24/30\n",
      "121182/121182 [==============================] - 30s 247us/step - loss: 0.0104 - acc: 0.99690s - loss: 0.0102 - acc\n",
      "Epoch 25/30\n",
      "121182/121182 [==============================] - 30s 246us/step - loss: 0.0099 - acc: 0.99720s - loss: 0.010\n",
      "Epoch 26/30\n",
      "121182/121182 [==============================] - 29s 238us/step - loss: 0.0095 - acc: 0.99732s - l - ETA: 1s\n",
      "Epoch 27/30\n",
      "121182/121182 [==============================] - 30s 245us/step - loss: 0.0140 - acc: 0.997110s - loss: 0.0133 - acc: 0.99 -  - ETA: 2s - loss: 0.0138 - ac \n",
      "Epoch 28/30\n",
      "121182/121182 [==============================] - 29s 236us/step - loss: 0.0132 - acc: 0.99711s - lo\n",
      "Epoch 29/30\n",
      "121182/121182 [==============================] - 29s 236us/step - loss: 0.0121 - acc: 0.99710s - loss: 0.0121 - acc: 0.99\n",
      "Epoch 30/30\n",
      "121182/121182 [==============================] - 28s 234us/step - loss: 0.0127 - acc: 0.9970\n",
      "24238/24238 [==============================] - 3s 123us/step\n",
      "Epoch 1/30\n",
      "121183/121183 [==============================] - 31s 253us/step - loss: 0.1100 - acc: 0.9634\n",
      "Epoch 2/30\n",
      "121183/121183 [==============================] - 29s 237us/step - loss: 0.0675 - acc: 0.9769TA: 0s - loss: 0.0678 - acc: \n",
      "Epoch 3/30\n",
      "121183/121183 [==============================] - 29s 236us/step - loss: 0.0506 - acc: 0.9827\n",
      "Epoch 4/30\n",
      "121183/121183 [==============================] - 28s 235us/step - loss: 0.0406 - acc: 0.98580s - loss: 0.0407 - ac\n",
      "Epoch 5/30\n",
      "121183/121183 [==============================] - 30s 245us/step - loss: 0.0324 - acc: 0.98850s - loss: 0.0325 \n",
      "Epoch 6/30\n",
      "121183/121183 [==============================] - 29s 236us/step - loss: 0.0284 - acc: 0.99000s - loss: 0.0284 - acc: 0.99\n",
      "Epoch 7/30\n",
      "121183/121183 [==============================] - 29s 236us/step - loss: 0.0245 - acc: 0.9916\n",
      "Epoch 8/30\n",
      "121183/121183 [==============================] - 30s 244us/step - loss: 0.0227 - acc: 0.99261s - loss: 0.0229 - acc: - ETA: 0s - loss: 0\n",
      "Epoch 9/30\n",
      "121183/121183 [==============================] - 30s 249us/step - loss: 0.0215 - acc: 0.9931\n",
      "Epoch 10/30\n",
      "121183/121183 [==============================] - 29s 237us/step - loss: 0.0189 - acc: 0.9936\n",
      "Epoch 11/30\n",
      "121183/121183 [==============================] - 32s 265us/step - loss: 0.0181 - acc: 0.99390s - loss: 0.0181 - acc: 0\n",
      "Epoch 12/30\n",
      "121183/121183 [==============================] - 30s 248us/step - loss: 0.0189 - acc: 0.9940\n",
      "Epoch 13/30\n",
      "121183/121183 [==============================] - 34s 282us/step - loss: 0.0165 - acc: 0.99503\n",
      "Epoch 14/30\n",
      "121183/121183 [==============================] - 34s 278us/step - loss: 0.0152 - acc: 0.99562s - loss: 0.015\n",
      "Epoch 15/30\n",
      "121183/121183 [==============================] - 31s 257us/step - loss: 0.0150 - acc: 0.9955\n",
      "Epoch 16/30\n",
      "121183/121183 [==============================] - 38s 311us/step - loss: 0.0140 - acc: 0.9959\n",
      "Epoch 17/30\n",
      "121183/121183 [==============================] - 36s 296us/step - loss: 0.0135 - acc: 0.9958\n",
      "Epoch 18/30\n",
      "121183/121183 [==============================] - 29s 242us/step - loss: 0.0143 - acc: 0.9962\n",
      "Epoch 19/30\n",
      "121183/121183 [==============================] - 29s 241us/step - loss: 0.0160 - acc: 0.9957\n",
      "Epoch 20/30\n",
      "121183/121183 [==============================] - 30s 244us/step - loss: 0.0149 - acc: 0.9958\n",
      "Epoch 21/30\n",
      "121183/121183 [==============================] - 30s 250us/step - loss: 0.0127 - acc: 0.9965\n",
      "Epoch 22/30\n",
      "121183/121183 [==============================] - 30s 246us/step - loss: 0.0128 - acc: 0.9964\n",
      "Epoch 23/30\n",
      "121183/121183 [==============================] - 33s 271us/step - loss: 0.0119 - acc: 0.9970\n",
      "Epoch 24/30\n",
      "121183/121183 [==============================] - 36s 299us/step - loss: 0.0131 - acc: 0.9966\n",
      "Epoch 25/30\n",
      "121183/121183 [==============================] - 32s 262us/step - loss: 0.0128 - acc: 0.9965\n",
      "Epoch 26/30\n",
      "121183/121183 [==============================] - 33s 273us/step - loss: 0.0109 - acc: 0.99701s - loss: 0. - ETA: 0s - loss: 0.0109 -\n",
      "Epoch 27/30\n",
      "121183/121183 [==============================] - 33s 273us/step - loss: 0.0127 - acc: 0.9966\n",
      "Epoch 28/30\n",
      "121183/121183 [==============================] - 35s 286us/step - loss: 0.0142 - acc: 0.9968\n",
      "Epoch 29/30\n",
      "121183/121183 [==============================] - 31s 255us/step - loss: 0.0119 - acc: 0.9968\n",
      "Epoch 30/30\n",
      "121183/121183 [==============================] - 28s 232us/step - loss: 0.0204 - acc: 0.9956\n",
      "24237/24237 [==============================] - 4s 162us/step\n",
      "Epoch 1/30\n",
      "121183/121183 [==============================] - 30s 250us/step - loss: 0.1096 - acc: 0.9639\n",
      "Epoch 2/30\n",
      "121183/121183 [==============================] - 33s 273us/step - loss: 0.0659 - acc: 0.9771\n",
      "Epoch 3/30\n",
      "121183/121183 [==============================] - 33s 270us/step - loss: 0.0506 - acc: 0.9823\n",
      "Epoch 4/30\n",
      "121183/121183 [==============================] - 35s 290us/step - loss: 0.0413 - acc: 0.98590s - loss: 0.0414 - ac\n",
      "Epoch 5/30\n",
      "121183/121183 [==============================] - 34s 278us/step - loss: 0.0345 - acc: 0.9879\n",
      "Epoch 6/30\n",
      "121183/121183 [==============================] - 27s 224us/step - loss: 0.0293 - acc: 0.9901\n",
      "Epoch 7/30\n",
      "121183/121183 [==============================] - 30s 247us/step - loss: 0.0255 - acc: 0.9911\n",
      "Epoch 8/30\n",
      "121183/121183 [==============================] - 30s 244us/step - loss: 0.0219 - acc: 0.9924\n",
      "Epoch 9/30\n",
      "121183/121183 [==============================] - 31s 252us/step - loss: 0.0190 - acc: 0.99331s - loss: \n",
      "Epoch 10/30\n",
      "121183/121183 [==============================] - 30s 251us/step - loss: 0.0194 - acc: 0.9930\n",
      "Epoch 11/30\n",
      "121183/121183 [==============================] - 29s 239us/step - loss: 0.0168 - acc: 0.9943\n",
      "Epoch 12/30\n",
      "121183/121183 [==============================] - 31s 252us/step - loss: 0.0165 - acc: 0.9945\n",
      "Epoch 13/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121183/121183 [==============================] - 31s 254us/step - loss: 0.0168 - acc: 0.9948\n",
      "Epoch 14/30\n",
      "121183/121183 [==============================] - 31s 252us/step - loss: 0.0146 - acc: 0.9953\n",
      "Epoch 15/30\n",
      "121183/121183 [==============================] - 31s 255us/step - loss: 0.0177 - acc: 0.9953\n",
      "Epoch 16/30\n",
      "121183/121183 [==============================] - 30s 246us/step - loss: 0.0141 - acc: 0.9958\n",
      "Epoch 17/30\n",
      "121183/121183 [==============================] - 30s 245us/step - loss: 0.0149 - acc: 0.9955\n",
      "Epoch 18/30\n",
      "121183/121183 [==============================] - 28s 230us/step - loss: 0.0117 - acc: 0.9963\n",
      "Epoch 19/30\n",
      " 60850/121183 [==============>...............] - ETA: 14s - loss: 0.0204 - acc: 0.9947"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-257cb7973caf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKerasClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_baseline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data_train_pca\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Results: %.2f%% (%.2f%%)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    400\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 240\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 986\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    987\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sample_weight'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKerasClassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mfit_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#final neural network. Achieves 99.7% accuracy\n",
    "from keras.layers import Dropout\n",
    "def create_baseline():\n",
    "# create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(200, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(160, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "estimator = KerasClassifier(build_fn=create_baseline, epochs=30, batch_size=10, verbose=1)\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(estimator, new_data_train_pca, y_train, cv=kfold)\n",
    "print(results)\n",
    "print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "121193/121193 [==============================] - 6s 47us/step - loss: 0.1134 - acc: 0.9617\n",
      "Epoch 2/50\n",
      "121193/121193 [==============================] - 5s 41us/step - loss: 0.0622 - acc: 0.9786: 2s - loss: 0 - ETA: 1s - loss: 0.0652 - acc: 0. - ETA: 1s - \n",
      "Epoch 3/50\n",
      "121193/121193 [==============================] - 5s 41us/step - loss: 0.0433 - acc: 0.9853: 1s - lo\n",
      "Epoch 4/50\n",
      "121193/121193 [==============================] - 5s 41us/step - loss: 0.0337 - acc: 0.9883\n",
      "Epoch 5/50\n",
      "121193/121193 [==============================] - 5s 41us/step - loss: 0.0278 - acc: 0.9902\n",
      "Epoch 6/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0232 - acc: 0.9918: 4s  - ETA: 1s - loss: 0.0229 - a - ETA: 0s - loss: 0\n",
      "Epoch 7/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0192 - acc: 0.9933: 0s - loss: 0.0193 - a\n",
      "Epoch 8/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0177 - acc: 0.9940: 3s - loss: 0.0164 - - ETA: 3s - loss\n",
      "Epoch 9/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0160 - acc: 0.9945\n",
      "Epoch 10/50\n",
      "121193/121193 [==============================] - 6s 47us/step - loss: 0.0140 - acc: 0.9952\n",
      "Epoch 11/50\n",
      "121193/121193 [==============================] - 6s 51us/step - loss: 0.0121 - acc: 0.9959\n",
      "Epoch 12/50\n",
      "121193/121193 [==============================] - 6s 47us/step - loss: 0.0118 - acc: 0.9960\n",
      "Epoch 13/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0110 - acc: 0.9966\n",
      "Epoch 14/50\n",
      "121193/121193 [==============================] - 6s 47us/step - loss: 0.0108 - acc: 0.9964ETA: 1s - lo\n",
      "Epoch 15/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0099 - acc: 0.9970\n",
      "Epoch 16/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0094 - acc: 0.9970\n",
      "Epoch 17/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0085 - acc: 0.9974\n",
      "Epoch 18/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0080 - acc: 0.9972: 3s - loss: 0.0072 - acc: - ETA: 2s - loss: 0 - ET\n",
      "Epoch 19/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0080 - acc: 0.9975\n",
      "Epoch 20/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0077 - acc: 0.9976: 3s - loss: 0.0071 - acc: - ETA: 3s - loss: 0.0069 - acc: 0.998 - ETA: 3s - \n",
      "Epoch 21/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0076 - acc: 0.9976\n",
      "Epoch 22/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0064 - acc: 0.9980: 3s - loss: 0.0058 - acc: 0.9 - ETA: 3s - \n",
      "Epoch 23/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0066 - acc: 0.9978\n",
      "Epoch 24/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0063 - acc: 0.9980: 2s - l - ETA\n",
      "Epoch 25/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0065 - acc: 0.9981\n",
      "Epoch 26/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0067 - acc: 0.9982\n",
      "Epoch 27/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0060 - acc: 0.9982: 0s - loss: 0.0\n",
      "Epoch 28/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0062 - acc: 0.9981\n",
      "Epoch 29/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0058 - acc: 0.9983: 3s - loss: \n",
      "Epoch 30/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0060 - acc: 0.9982\n",
      "Epoch 31/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0052 - acc: 0.9985\n",
      "Epoch 32/50\n",
      "121193/121193 [==============================] - 6s 50us/step - loss: 0.0051 - acc: 0.9985\n",
      "Epoch 33/50\n",
      "121193/121193 [==============================] - 6s 49us/step - loss: 0.0053 - acc: 0.9985\n",
      "Epoch 34/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0052 - acc: 0.9986\n",
      "Epoch 35/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0050 - acc: 0.9986: 0s - loss: 0\n",
      "Epoch 36/50\n",
      "121193/121193 [==============================] - 6s 46us/step - loss: 0.0049 - acc: 0.9985\n",
      "Epoch 37/50\n",
      "121193/121193 [==============================] - 6s 50us/step - loss: 0.0046 - acc: 0.9987\n",
      "Epoch 38/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0046 - acc: 0.9986\n",
      "Epoch 39/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0041 - acc: 0.9988: 2s - loss: 0\n",
      "Epoch 40/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0041 - acc: 0.9989: 1s\n",
      "Epoch 41/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0045 - acc: 0.9989\n",
      "Epoch 42/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0045 - acc: 0.9987: 0s - loss: 0.0048 -\n",
      "Epoch 43/50\n",
      "121193/121193 [==============================] - 5s 43us/step - loss: 0.0047 - acc: 0.9987\n",
      "Epoch 44/50\n",
      "121193/121193 [==============================] - 5s 42us/step - loss: 0.0043 - acc: 0.9989\n",
      "Epoch 45/50\n",
      "121193/121193 [==============================] - 5s 43us/step - loss: 0.0034 - acc: 0.9990\n",
      "Epoch 46/50\n",
      "121193/121193 [==============================] - 5s 43us/step - loss: 0.0037 - acc: 0.9990\n",
      "Epoch 47/50\n",
      "121193/121193 [==============================] - 5s 44us/step - loss: 0.0053 - acc: 0.9986\n",
      "Epoch 48/50\n",
      "121193/121193 [==============================] - 5s 45us/step - loss: 0.0039 - acc: 0.9990\n",
      "Epoch 49/50\n",
      "121193/121193 [==============================] - 5s 42us/step - loss: 0.0039 - acc: 0.9988: 3s - loss: 0. - ETA: 1s - loss:\n",
      "Epoch 50/50\n",
      "121193/121193 [==============================] - 5s 43us/step - loss: 0.0040 - acc: 0.9988\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Classification metrics can't handle a mix of binary and continuous targets",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-bf410f829648>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0my_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.pyc\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \"\"\"\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sklearn/metrics/classification.pyc\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 81\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
     ]
    }
   ],
   "source": [
    "#final neural network. Achieves 99.7% accuracy\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(200, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(160, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "\n",
    "for train_index, test_index in kfold.split(new_data_train_pca, y_train):\n",
    "    X_train_new, X_test_new = new_data_train_pca[train_index], new_data_train_pca[test_index]\n",
    "    y_train_new, y_test_new = y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    model.fit(X_train_new, y_train_new, batch_size=50, verbose=True, epochs=50)\n",
    "   `predictions = model.predict(X_test_new)\n",
    "    binary_preds = []\n",
    "    for prediction in predictions:\n",
    "        if prediction > .4:\n",
    "             binary_preds.append(True)\n",
    "        else:\n",
    "            binary_preds.append(False)\n",
    "    print(binary_preds)\n",
    "    print confusion_matrix(y_test_new, binary_preds)\n",
    "\n",
    "    \n",
    "# results = cross_val_score(estimator, new_data_train_pca, y_train, cv=kfold)\n",
    "# print(results)\n",
    "# print(\"Results: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, False, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, 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True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, False, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, False, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, 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True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, False, True, False, True, 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False, True, True, True, True, True, True, False, True, True, True, True, False, False, True, True, True, True, False, True, True, True, False, True, True, False, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]\n",
      "[[ 2597   143]\n",
      " [   66 21434]]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test_new)\n",
    "binary_preds = []\n",
    "for prediction in predictions:\n",
    "    if prediction > .5:\n",
    "         binary_preds.append(True)\n",
    "    else:\n",
    "        binary_preds.append(False)\n",
    "print(binary_preds)\n",
    "print confusion_matrix(y_test_new, binary_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduces data dimensionality with smote data\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pca = PCA(n_components=200)\n",
    "new_data_train_pca = pca.fit_transform(StandardScaler().fit_transform(x_train))\n",
    "new_data_test_pca = pca.fit_transform(StandardScaler().fit_transform(new_data_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.1182 - acc: 0.9574\n",
      "Epoch 2/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0426 - acc: 0.9860\n",
      "Epoch 3/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0285 - acc: 0.9906\n",
      "Epoch 4/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0214 - acc: 0.9931\n",
      "Epoch 5/50\n",
      "214996/214996 [==============================] - 6s 26us/step - loss: 0.0177 - acc: 0.9942\n",
      "Epoch 6/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0142 - acc: 0.9953\n",
      "Epoch 7/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0133 - acc: 0.9959\n",
      "Epoch 8/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0111 - acc: 0.9965\n",
      "Epoch 9/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0096 - acc: 0.9968\n",
      "Epoch 10/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0087 - acc: 0.9970\n",
      "Epoch 11/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0078 - acc: 0.9976\n",
      "Epoch 12/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0077 - acc: 0.9977\n",
      "Epoch 13/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0067 - acc: 0.9979\n",
      "Epoch 14/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0059 - acc: 0.9982\n",
      "Epoch 15/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0059 - acc: 0.9984\n",
      "Epoch 16/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0059 - acc: 0.9981\n",
      "Epoch 17/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0048 - acc: 0.9986\n",
      "Epoch 18/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0053 - acc: 0.9984\n",
      "Epoch 19/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0042 - acc: 0.9987\n",
      "Epoch 20/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0044 - acc: 0.9986: 0s - loss: 0.0\n",
      "Epoch 21/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0038 - acc: 0.9989\n",
      "Epoch 22/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0045 - acc: 0.9987\n",
      "Epoch 23/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0036 - acc: 0.9989\n",
      "Epoch 24/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0037 - acc: 0.9989\n",
      "Epoch 25/50\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0043 - acc: 0.9987\n",
      "Epoch 26/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0031 - acc: 0.9991\n",
      "Epoch 27/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0035 - acc: 0.9990: 1s - loss:\n",
      "Epoch 28/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0032 - acc: 0.9991\n",
      "Epoch 29/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0038 - acc: 0.9990\n",
      "Epoch 30/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0032 - acc: 0.9991\n",
      "Epoch 31/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0030 - acc: 0.9992\n",
      "Epoch 32/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0037 - acc: 0.9990: 0s - loss: 0.0\n",
      "Epoch 33/50\n",
      "214996/214996 [==============================] - 7s 30us/step - loss: 0.0026 - acc: 0.9993\n",
      "Epoch 34/50\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0025 - acc: 0.9993\n",
      "Epoch 35/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0024 - acc: 0.9993\n",
      "Epoch 36/50\n",
      "214996/214996 [==============================] - 7s 30us/step - loss: 0.0028 - acc: 0.9992\n",
      "Epoch 37/50\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0027 - acc: 0.9992\n",
      "Epoch 38/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0029 - acc: 0.9991\n",
      "Epoch 39/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0021 - acc: 0.9994\n",
      "Epoch 40/50\n",
      "214996/214996 [==============================] - 6s 26us/step - loss: 0.0027 - acc: 0.9992\n",
      "Epoch 41/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0021 - acc: 0.9994: 3s - l\n",
      "Epoch 42/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0028 - acc: 0.9992: 0s - loss: 0.0029 - acc: 0\n",
      "Epoch 43/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0021 - acc: 0.9994\n",
      "Epoch 44/50\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0023 - acc: 0.9994\n",
      "Epoch 45/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0021 - acc: 0.9995\n",
      "Epoch 46/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0020 - acc: 0.9995\n",
      "Epoch 47/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0026 - acc: 0.9993\n",
      "Epoch 48/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0021 - acc: 0.9995\n",
      "Epoch 49/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0023 - acc: 0.9994\n",
      "Epoch 50/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0018 - acc: 0.9994\n",
      "[[21487    13]\n",
      " [  102 21398]]\n",
      "Epoch 1/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0045 - acc: 0.9989\n",
      "Epoch 2/50\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0025 - acc: 0.9994\n",
      "Epoch 3/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0028 - acc: 0.9993\n",
      "Epoch 4/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0016 - acc: 0.9996\n",
      "Epoch 5/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0022 - acc: 0.9993\n",
      "Epoch 6/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0021 - acc: 0.9995\n",
      "Epoch 7/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0023 - acc: 0.9994\n",
      "Epoch 8/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0022 - acc: 0.9994\n",
      "Epoch 9/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0024 - acc: 0.9994\n",
      "Epoch 10/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0019 - acc: 0.9995\n",
      "Epoch 11/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0019 - acc: 0.9994\n",
      "Epoch 12/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0016 - acc: 0.9997\n",
      "Epoch 13/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0017 - acc: 0.9995\n",
      "Epoch 14/50\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0018 - acc: 0.9995\n",
      "Epoch 15/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0018 - acc: 0.9995: 1\n",
      "Epoch 16/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0019 - acc: 0.9996\n",
      "Epoch 17/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0021 - acc: 0.9995\n",
      "Epoch 18/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0023 - acc: 0.9994\n",
      "Epoch 19/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0018 - acc: 0.9996\n",
      "Epoch 20/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0017 - acc: 0.9996\n",
      "Epoch 21/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0016 - acc: 0.9996\n",
      "Epoch 22/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0022 - acc: 0.9995\n",
      "Epoch 23/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0016 - acc: 0.9997\n",
      "Epoch 24/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0020 - acc: 0.9995\n",
      "Epoch 25/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0019 - acc: 0.9996\n",
      "Epoch 26/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0016 - acc: 0.9996\n",
      "Epoch 27/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0019 - acc: 0.9995\n",
      "Epoch 28/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0018 - acc: 0.9996: 0s - loss: 0.0016 - acc\n",
      "Epoch 29/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0016 - acc: 0.9996\n",
      "Epoch 30/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0020 - acc: 0.9996\n",
      "Epoch 31/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0016 - acc: 0.9997\n",
      "Epoch 32/50\n",
      "214996/214996 [==============================] - 6s 27us/step - loss: 0.0017 - acc: 0.9996\n",
      "Epoch 33/50\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0017 - acc: 0.9995\n",
      "Epoch 34/50\n",
      "214996/214996 [==============================] - 6s 29us/step - loss: 0.0016 - acc: 0.9997: 1s - loss: 0.0015 - acc: 0.999 - ETA: 1\n",
      "Epoch 35/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0017 - acc: 0.9994\n",
      "Epoch 36/50\n",
      "214996/214996 [==============================] - 6s 26us/step - loss: 0.0012 - acc: 0.9997\n",
      "Epoch 37/50\n",
      "214996/214996 [==============================] - 6s 30us/step - loss: 0.0019 - acc: 0.9995\n",
      "Epoch 38/50\n",
      "214996/214996 [==============================] - 6s 28us/step - loss: 0.0019 - acc: 0.9996\n",
      "Epoch 39/50\n",
      "133100/214996 [=================>............] - ETA: 2s - loss: 0.0015 - acc: 0.9998"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-8c228718778b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0mX_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_data_train_pca\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data_train_pca\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0my_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train_res\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_res\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mbinary_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#uses smote data for nn training\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(200, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(160, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "\n",
    "for train_index, test_index in kfold.split(new_data_train_pca, y_train_res):\n",
    "    X_train_new, X_test_new = new_data_train_pca[train_index], new_data_train_pca[test_index]\n",
    "    y_train_new, y_test_new = y_train_res[train_index], y_train_res[test_index]\n",
    "    model.fit(X_train_new, y_train_new, batch_size=100, verbose=True, epochs=50)\n",
    "    predictions = model.predict(X_test_new)\n",
    "    binary_preds = []\n",
    "    for prediction in predictions:\n",
    "        if prediction > .4:\n",
    "             binary_preds.append(True)\n",
    "        else:\n",
    "            binary_preds.append(False)\n",
    "    print confusion_matrix(y_test_new, binary_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "214996/214996 [==============================] - 4s 19us/step - loss: 0.3481 - acc: 0.8423\n",
      "Epoch 2/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.1758 - acc: 0.9375\n",
      "Epoch 3/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.1328 - acc: 0.9530\n",
      "Epoch 4/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.1096 - acc: 0.9622\n",
      "Epoch 5/200\n",
      "214996/214996 [==============================] - 4s 19us/step - loss: 0.0944 - acc: 0.9678\n",
      "Epoch 6/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0846 - acc: 0.9715\n",
      "Epoch 7/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0753 - acc: 0.9749\n",
      "Epoch 8/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0668 - acc: 0.9777\n",
      "Epoch 9/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0630 - acc: 0.9795\n",
      "Epoch 10/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0578 - acc: 0.9813\n",
      "Epoch 11/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0549 - acc: 0.9821\n",
      "Epoch 12/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0523 - acc: 0.9834\n",
      "Epoch 13/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0500 - acc: 0.9843\n",
      "Epoch 14/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0462 - acc: 0.9853\n",
      "Epoch 15/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0442 - acc: 0.9860\n",
      "Epoch 16/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0427 - acc: 0.9864\n",
      "Epoch 17/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0415 - acc: 0.9868\n",
      "Epoch 18/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0403 - acc: 0.9875\n",
      "Epoch 19/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0385 - acc: 0.9878\n",
      "Epoch 20/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0376 - acc: 0.9881\n",
      "Epoch 21/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0367 - acc: 0.9885\n",
      "Epoch 22/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0353 - acc: 0.9890\n",
      "Epoch 23/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0350 - acc: 0.9891\n",
      "Epoch 24/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0343 - acc: 0.9892\n",
      "Epoch 25/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0337 - acc: 0.9894\n",
      "Epoch 26/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0324 - acc: 0.9899\n",
      "Epoch 27/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0317 - acc: 0.9899\n",
      "Epoch 28/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0301 - acc: 0.9905\n",
      "Epoch 29/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0298 - acc: 0.9907\n",
      "Epoch 30/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0298 - acc: 0.9909\n",
      "Epoch 31/200\n",
      "214996/214996 [==============================] - 4s 19us/step - loss: 0.0288 - acc: 0.9912\n",
      "Epoch 32/200\n",
      "214996/214996 [==============================] - 4s 19us/step - loss: 0.0287 - acc: 0.9909\n",
      "Epoch 33/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0282 - acc: 0.9915\n",
      "Epoch 34/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0280 - acc: 0.9911\n",
      "Epoch 35/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0275 - acc: 0.9916\n",
      "Epoch 36/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0275 - acc: 0.9915\n",
      "Epoch 37/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0268 - acc: 0.9916\n",
      "Epoch 38/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0260 - acc: 0.9919\n",
      "Epoch 39/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0265 - acc: 0.9918\n",
      "Epoch 40/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0258 - acc: 0.9920\n",
      "Epoch 41/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0260 - acc: 0.9919\n",
      "Epoch 42/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0254 - acc: 0.9921\n",
      "Epoch 43/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0248 - acc: 0.9922\n",
      "Epoch 44/200\n",
      "214996/214996 [==============================] - 4s 17us/step - loss: 0.0245 - acc: 0.9923\n",
      "Epoch 45/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0246 - acc: 0.9923\n",
      "Epoch 46/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0240 - acc: 0.9923\n",
      "Epoch 47/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0234 - acc: 0.9926\n",
      "Epoch 48/200\n",
      "214996/214996 [==============================] - 4s 18us/step - loss: 0.0233 - acc: 0.9925\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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      "[[ 2597   143]\n",
      " [   76 21424]]\n",
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      "151500/214996 [====================>.........] - ETA: 1s - loss: 0.0752 - acc: 0.9752"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-a14db185a0f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mX_train_smote\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_smote\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_smote\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_smote\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mbinary_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training_arrays.pyc\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#runs smote AFTER cross validation split, to avoid repeat data in both train and test set\n",
    "#Current Best Model\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# create model\n",
    "def create_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(300, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(160, input_dim=300, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(100, input_dim=160, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(75, input_dim=100, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(50, input_dim=75, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "\n",
    "for train_index, test_index in kfold.split(new_data_train_pca, y_train):\n",
    "    X_train_new, X_test_new = new_data_train_pca[train_index], new_data_train_pca[test_index]\n",
    "    y_train_new, y_test_new = y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    \n",
    "    sm = SMOTE(random_state=None, ratio = 1.0)\n",
    "    X_train_smote, y_train_smote = sm.fit_sample(X_train_new, y_train_new)\n",
    "    model = create_model()\n",
    "    model.fit(X_train_smote, y_train_smote, batch_size=1500, verbose=True, epochs=50)\n",
    "    predictions = model.predict(X_test_new)\n",
    "    binary_preds = []\n",
    "    for prediction in predictions:\n",
    "        if prediction > .5:\n",
    "             binary_preds.append(True)\n",
    "        else:\n",
    "            binary_preds.append(False)\n",
    "    print confusion_matrix(y_test_new, binary_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "214996/214996 [==============================] - 11s 53us/step - loss: 0.1591 - acc: 0.9371\n",
      "Epoch 2/200\n",
      "214996/214996 [==============================] - 7s 34us/step - loss: 0.0423 - acc: 0.9864\n",
      "Epoch 3/200\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0241 - acc: 0.9927\n",
      "Epoch 4/200\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0166 - acc: 0.9948\n",
      "Epoch 5/200\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0143 - acc: 0.9957\n",
      "Epoch 6/200\n",
      "214996/214996 [==============================] - 7s 32us/step - loss: 0.0111 - acc: 0.9967\n",
      "Epoch 7/200\n",
      "214996/214996 [==============================] - 7s 32us/step - loss: 0.0126 - acc: 0.9968\n",
      "Epoch 8/200\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0082 - acc: 0.9976\n",
      "Epoch 9/200\n",
      "214996/214996 [==============================] - 7s 32us/step - loss: 0.0074 - acc: 0.9978\n",
      "Epoch 10/200\n",
      "214996/214996 [==============================] - 7s 31us/step - loss: 0.0073 - acc: 0.9978\n",
      "Epoch 11/200\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0071 - acc: 0.9979\n",
      "Epoch 12/200\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0046 - acc: 0.9986\n",
      "Epoch 13/200\n",
      "214996/214996 [==============================] - 7s 33us/step - loss: 0.0058 - acc: 0.9983\n",
      "Epoch 14/200\n",
      "214996/214996 [==============================] - 7s 32us/step - loss: 0.0052 - acc: 0.9987\n",
      "Epoch 15/200\n",
      "214996/214996 [==============================] - 8s 38us/step - loss: 0.0043 - acc: 0.9988\n",
      "Epoch 16/200\n",
      "214996/214996 [==============================] - 8s 38us/step - loss: 0.0051 - acc: 0.9986\n",
      "Epoch 17/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0045 - acc: 0.9988\n",
      "Epoch 18/200\n",
      "214996/214996 [==============================] - 8s 35us/step - loss: 0.0046 - acc: 0.9987\n",
      "Epoch 19/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0041 - acc: 0.9989\n",
      "Epoch 20/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0039 - acc: 0.9989\n",
      "Epoch 21/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0048 - acc: 0.9987\n",
      "Epoch 22/200\n",
      "214996/214996 [==============================] - 8s 35us/step - loss: 0.0035 - acc: 0.9991\n",
      "Epoch 23/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0035 - acc: 0.9991\n",
      "Epoch 24/200\n",
      "214996/214996 [==============================] - 8s 38us/step - loss: 0.0028 - acc: 0.9992\n",
      "Epoch 25/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0025 - acc: 0.9994\n",
      "Epoch 26/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0034 - acc: 0.9992\n",
      "Epoch 27/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0038 - acc: 0.9991\n",
      "Epoch 28/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0036 - acc: 0.9991\n",
      "Epoch 29/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0030 - acc: 0.9994\n",
      "Epoch 30/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0029 - acc: 0.9993\n",
      "Epoch 31/200\n",
      "214996/214996 [==============================] - 9s 43us/step - loss: 0.0024 - acc: 0.9994\n",
      "Epoch 32/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0027 - acc: 0.9993\n",
      "Epoch 33/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0024 - acc: 0.9994\n",
      "Epoch 34/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0029 - acc: 0.9993\n",
      "Epoch 35/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0052 - acc: 0.9986\n",
      "Epoch 36/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0030 - acc: 0.9992\n",
      "Epoch 37/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0022 - acc: 0.9995\n",
      "Epoch 38/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0022 - acc: 0.9995\n",
      "Epoch 39/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0020 - acc: 0.9996\n",
      "Epoch 40/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0026 - acc: 0.9994\n",
      "Epoch 41/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0036 - acc: 0.9992\n",
      "Epoch 42/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0029 - acc: 0.9994\n",
      "Epoch 43/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0026 - acc: 0.9994\n",
      "Epoch 44/200\n",
      "214996/214996 [==============================] - 8s 36us/step - loss: 0.0019 - acc: 0.9996\n",
      "Epoch 45/200\n",
      "214996/214996 [==============================] - 8s 37us/step - loss: 0.0018 - acc: 0.9996\n",
      "Epoch 46/200\n",
      "214996/214996 [==============================] - 9s 41us/step - loss: 0.0025 - acc: 0.9993\n",
      "Epoch 47/200\n",
      "214996/214996 [==============================] - 8s 38us/step - loss: 0.0016 - acc: 0.9997\n",
      "Epoch 48/200\n",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 156/200\n",
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      "[[ 2642    98]\n",
      " [   71 21429]]\n",
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      " 69000/214996 [========>.....................] - ETA: 5s - loss: 0.0242 - acc: 0.9923"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#runs smote AFTER cross validation split, to avoid repeat data in both train and test set\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# create model\n",
    "def create_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(400, input_dim=200, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(400, input_dim=400, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(350, input_dim=350, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(300, input_dim=350, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(300, input_dim=300, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 10\n",
    "np.random.seed(seed)\n",
    "\n",
    "# evaluate model with standardized dataset\n",
    "kfold = StratifiedKFold(n_splits=6, shuffle=True, random_state=seed)\n",
    "\n",
    "for train_index, test_index in kfold.split(new_data_train_pca, y_train):\n",
    "    X_train_new, X_test_new = new_data_train_pca[train_index], new_data_train_pca[test_index]\n",
    "    y_train_new, y_test_new = y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    \n",
    "    sm = SMOTE(random_state=None, ratio = 1.0)\n",
    "    X_train_smote, y_train_smote = sm.fit_sample(X_train_new, y_train_new)\n",
    "    model = create_model()\n",
    "    model.fit(X_train_smote, y_train_smote, batch_size=1500, verbose=True, epochs=200)\n",
    "    predictions = model.predict(X_test_new)\n",
    "    binary_preds = []\n",
    "    for prediction in predictions:\n",
    "        if prediction > .5:\n",
    "             binary_preds.append(True)\n",
    "        else:\n",
    "            binary_preds.append(False)\n",
    "    print confusion_matrix(y_test_new, binary_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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