experiment-logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import (cross_val_score, train_test_split,\n",
    "                                    GridSearchCV, RandomizedSearchCV)\n",
    "from sklearn.preprocessing import Imputer\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train_final.csv')\n",
    "test = pd.read_csv('data/test_final.csv')\n",
    "train = train.drop(['id'], axis=1)\n",
    "test = test.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Divide dataset into X and y\n",
    "y = train.Y\n",
    "X = train.drop([\"Y\"], axis=1)\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>...</th>\n",
       "      <th>F18</th>\n",
       "      <th>F19</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F22</th>\n",
       "      <th>F23</th>\n",
       "      <th>F24</th>\n",
       "      <th>F25</th>\n",
       "      <th>F26</th>\n",
       "      <th>F27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>22902.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.028250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>144.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.021417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.253574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.492318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>4833.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.502212</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.373397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.053028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.674254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.730797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.787592</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.371157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    F1   F2        F3   F4   F5      F6   F7   F8   F9  F10    ...       F18  \\\n",
       "0  1.0  0.0  0.107576  0.0  0.0     1.0  1.0  1.0  1.0  0.0    ...     104.0   \n",
       "1  1.0  0.0  0.142357  0.0  0.0     7.0  1.0  1.0  1.0  1.0    ...     144.0   \n",
       "2  1.0  0.0  0.492318  0.0  3.0  4205.0  1.0  1.0  3.0  1.0    ...     112.0   \n",
       "3  1.0  0.0 -0.053028  0.0  2.0     2.0  1.0  1.0  5.0  2.0    ...     127.0   \n",
       "4  1.0  0.0  0.730797  0.0  0.0    11.0  1.0  1.0  1.0  1.0    ...     148.0   \n",
       "\n",
       "       F19  F20  F21   F22       F23  F24  F25   F26       F27  \n",
       "0  22902.0  1.0  0.0  18.0  0.042295  1.0  0.0  27.0  0.028250  \n",
       "1  11400.0  1.0  0.0   8.0  0.021417  1.0  0.0  67.0  0.253574  \n",
       "2   4833.0  1.0  0.0  13.0  0.502212  1.0  1.0  35.0  0.373397  \n",
       "3   3250.0  1.0  1.0   8.0  0.000000  1.0  0.0  50.0  0.674254  \n",
       "4   4000.0  1.0  1.0   5.0  0.787592  1.0  0.0  71.0  0.371157  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute missing features\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "# train = train.apply(lambda x: x.fillna(x.value_counts().index[0]))\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "train_xform = imp.fit_transform(X)\n",
    "# TODO: Impute dataframe so that F5 uses median\n",
    "# and F19 uses mean. For now, we'll impute via mean for both.\n",
    "\n",
    "X = pd.DataFrame(train_xform, columns=X.columns)\n",
    "test_xform = imp.transform(X_test)\n",
    "X_test = pd.DataFrame(test_xform, columns=X_test.columns)\n",
    "\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Split data.\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7f640534de10>,\n",
       "  <matplotlib.patches.Wedge at 0x7f6405363748>],\n",
       " [<matplotlib.text.Text at 0x7f6405359c50>,\n",
       "  <matplotlib.text.Text at 0x7f640536b550>],\n",
       " [<matplotlib.text.Text at 0x7f6405363208>,\n",
       "  <matplotlib.text.Text at 0x7f640536bac8>])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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y+iUACAGAxJCQCPZ+nbv70wbrH9M3GBY8c4vF1trskdfQ6vUIHD4V+8vLPfL8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\n+LVAk6lxTW5u0Z3Dh/fTaPg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640e5bfe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the target variable.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "amounts = y.value_counts()\n",
    "colors = ['lightcoral', 'lightskyblue']\n",
    "labels = ['no', 'yes']\n",
    "ax.axis('equal')\n",
    "# _ = ax.title(label='Ratio of resources approved vs rejected')\n",
    "ax.pie(amounts, labels=labels, colors=colors,\n",
    "         autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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-7-ecf03dfbc7c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mlog_clf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'roc_auc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\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[0;32m---> 10\u001b[0;31m \u001b[0mlog_clf\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[0m\n\u001b[0m",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    938\u001b[0m             \u001b[0mtrain\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtest\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m         \"\"\"\n\u001b[0;32m--> 940\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\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[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\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    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    560\u001b[0m                                   \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m                                   error_score=self.error_score)\n\u001b[0;32m--> 562\u001b[0;31m           \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m           for train, test in cv.split(X, y, groups))\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    766\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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    769\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m             \u001b[0melapsed_time\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    717\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0;31m# check if timeout supported in backend future implementation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    681\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m'timeout'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetfullargspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 682\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\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    683\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\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[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mself\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    603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\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[1;32m    604\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\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    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\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[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\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    548\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \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    550\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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    294\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "param_grid = {\n",
    "              'penalty': ['l1', 'l2'],\n",
    "              'C': np.power(10.0, np.arange(-3, 3)), \n",
    "             }\n",
    "\n",
    "log_clf = GridSearchCV(LogisticRegression(), param_grid, cv=10, scoring='roc_auc', n_jobs=-1)\n",
    "log_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_results(outfile, predictions):\n",
    "    ID = range(49999, 99998 + 1)\n",
    "    with open(outfile, 'w') as f:\n",
    "        f.write('id,Y\\n')\n",
    "        for instance, prediction in zip(ID, predictions):\n",
    "            f.write('{},{}\\n'.format(instance, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARN: OMP_NUM_THREADS=None =>\n",
      "... If you are using openblas if you are using openblas set OMP_NUM_THREADS=1 or risk subprocess calls hanging indefinitely\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import hpsklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter set found on development set with cv=10:\n",
      "\n"
     ]
    },
    {
     "ename": "NotFittedError",
     "evalue": "This GridSearchCV instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-1ec8643b1c1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m print(\"Best parameter set found on development set with cv={}:\\n\"\n\u001b[1;32m      2\u001b[0m       .format(10))\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m print(\"Best holdout score found on development set with cv={}:\\n\"\n\u001b[1;32m      5\u001b[0m       .format(10))\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mbest_params_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    646\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbest_params_\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--> 648\u001b[0;31m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cv_results_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_index_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/aetherzephyr/anaconda3/envs/datasci/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m    688\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall_or_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mattributes\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[1;32m    689\u001b[0m         \u001b[0;31m# FIXME NotFittedError_ --> NotFittedError in 0.19\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 690\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0m_NotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\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    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: This GridSearchCV instance is not fitted yet. Call 'fit' with appropriate arguments before using this method."
     ]
    }
   ],
   "source": [
    "print(\"Best parameter set found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(log_clf.best_params_)\n",
    "print(\"Best holdout score found on development set with cv={}:\\n\"\n",
    "      .format(10))\n",
    "print(log_clf.best_score_)\n",
    "print()\n",
    "score = log_clf.score(X_val, y_val)\n",
    "\n",
    "print(log_clf.cv_results_['mean_train_score'])\n",
    "print(log_clf.cv_results_['mean_test_score'])\n",
    "print(\"Validation score:\\n\")\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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