{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "# PyDataML\n",
    "## PyData Tokyo tutorial Machine Learning\n",
    "- https://github.com/PyDataTokyo/pydata-tokyo-tutorial-1\n",
    "- https://pydata.tokyo/ipynb/tutorial-1/ml.html\n",
    "- https://github.com/PyDataTokyo/pydata-tokyo-tutorial-1/blob/master/pydatatokyo_tutorial_dh.ipynb\n",
    "- https://github.com/PyDataTokyo/pydata-tokyo-tutorial-1/blob/master/pydatatokyo_tutorial_ml.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "https://www.kaggle.com/c/titanic-gettingStarted/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-03-30T03:16:41.799134",
     "start_time": "2016-03-30T03:16:41.788021"
    }
   },
   "source": [
    "## アジェンダ\n",
    "- バックグラウンド\n",
    "- ライブラリのインポートとデータの準備\n",
    "- ジェンダーモデルによる生存者推定、推定値の評価\n",
    "- ロジスティック回帰による生存者推定\n",
    "- 交差検証（クロスバリデーション）\n",
    "- 決定木（Decision Tree）による生存者推定\n",
    "- グリッドサーチ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:26.724106",
     "start_time": "2016-04-07T22:36:23.938857"
    },
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.047090",
     "start_time": "2016-04-07T22:36:26.726633"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, \n",
    "    classification_report, \n",
    "    confusion_matrix\n",
    ")\n",
    "from sklearn.cross_validation import (\n",
    "    train_test_split, \n",
    "    cross_val_score, \n",
    "    KFold\n",
    ")\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import (\n",
    "    DecisionTreeClassifier, \n",
    "    export_graphviz\n",
    ")\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "from IPython.display import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def estimated_params(estimator):\n",
    "    return [a for a in dir(estimator) if a.endswith(\"_\") and not a.startswith(\"_\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.079421",
     "start_time": "2016-04-07T22:36:27.049949"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('titanic/train.csv')\n",
    "df_test = pd.read_csv('titanic/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.114717",
     "start_time": "2016-04-07T22:36:27.081651"
    },
    "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "     PassengerId  Survived  Pclass                 Name   Sex   Age  SibSp  \\\n",
       "890          891         0       3  Dooley, Mr. Patrick  male  32.0      0   \n",
       "\n",
       "     Parch  Ticket  Fare Cabin Embarked  \n",
       "890      0  370376  7.75   NaN        Q  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.532828",
     "start_time": "2016-04-07T22:36:27.117163"
    },
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kilgannon, Mr. Thomas J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.225000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.550000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>179.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.854200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0%</th>\n",
       "      <td>268.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>357.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>535.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.679200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>624.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>713.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>802.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.958300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>846.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.079150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Kilgannon, Mr. Thomas J  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "5%        45.500000    0.000000    1.000000                      NaN   NaN   \n",
       "10%       90.000000    0.000000    1.000000                      NaN   NaN   \n",
       "20%      179.000000    0.000000    1.000000                      NaN   NaN   \n",
       "30.0%    268.000000    0.000000    2.000000                      NaN   NaN   \n",
       "40%      357.000000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "60%      535.000000    0.000000    3.000000                      NaN   NaN   \n",
       "70%      624.000000    1.000000    3.000000                      NaN   NaN   \n",
       "80%      713.000000    1.000000    3.000000                      NaN   NaN   \n",
       "90%      802.000000    1.000000    3.000000                      NaN   NaN   \n",
       "95%      846.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch    Ticket        Fare        Cabin  \\\n",
       "count   714.000000  891.000000  891.000000       891  891.000000          204   \n",
       "unique         NaN         NaN         NaN       681         NaN          147   \n",
       "top            NaN         NaN         NaN  CA. 2343         NaN  C23 C25 C27   \n",
       "freq           NaN         NaN         NaN         7         NaN            4   \n",
       "mean     29.699118    0.523008    0.381594       NaN   32.204208          NaN   \n",
       "std      14.526497    1.102743    0.806057       NaN   49.693429          NaN   \n",
       "min       0.420000    0.000000    0.000000       NaN    0.000000          NaN   \n",
       "5%        4.000000    0.000000    0.000000       NaN    7.225000          NaN   \n",
       "10%      14.000000    0.000000    0.000000       NaN    7.550000          NaN   \n",
       "20%      19.000000    0.000000    0.000000       NaN    7.854200          NaN   \n",
       "30.0%    22.000000    0.000000    0.000000       NaN    8.050000          NaN   \n",
       "40%      25.000000    0.000000    0.000000       NaN   10.500000          NaN   \n",
       "50%      28.000000    0.000000    0.000000       NaN   14.454200          NaN   \n",
       "60%      31.800000    0.000000    0.000000       NaN   21.679200          NaN   \n",
       "70%      36.000000    1.000000    0.000000       NaN   27.000000          NaN   \n",
       "80%      41.000000    1.000000    1.000000       NaN   39.687500          NaN   \n",
       "90%      50.000000    1.000000    2.000000       NaN   77.958300          NaN   \n",
       "95%      56.000000    3.000000    2.000000       NaN  112.079150          NaN   \n",
       "max      80.000000    8.000000    6.000000       NaN  512.329200          NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "5%          NaN  \n",
       "10%         NaN  \n",
       "20%         NaN  \n",
       "30.0%       NaN  \n",
       "40%         NaN  \n",
       "50%         NaN  \n",
       "60%         NaN  \n",
       "70%         NaN  \n",
       "80%         NaN  \n",
       "90%         NaN  \n",
       "95%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe(include=\"all\", percentiles=[0.05] + list(np.linspace(0.1, 0.9, 9)) + [0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 変数概要\n",
    "- PassengerId: 乗客ID\n",
    "- Survived: 1 = 生き残り 0 = 死亡\n",
    "- Pclass: 等級\n",
    "- Name: 名前\n",
    "- Sex: 性別\n",
    "- Age: 年齢\n",
    "- Parch: 子供の数\n",
    "- Ticket: チケット番号\n",
    "- Fare: 運賃\n",
    "- Cabin: 部屋番号\n",
    "- Embarked: 乗船地"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## わかったこと\n",
    "- 891レコード\n",
    "- 70%が死んだ\n",
    "- 半分が３階級\n",
    "- 年齢に欠損値多少有り\n",
    "- Cabinに欠損値が多い(そもそも部屋番号は関係なさそう)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.568680",
     "start_time": "2016-04-07T22:36:27.535633"
    },
    "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                      Name   Sex  Age  SibSp  Parch  \\\n",
       "417         1309       3  Peter, Master. Michael J  male  NaN      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "417   2668  22.3583   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:27.581505",
     "start_time": "2016-04-07T22:36:27.572552"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'female': 1, 'male': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = {val:idx for idx, val in enumerate(df_train[\"Sex\"].unique())}\n",
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:28.157463",
     "start_time": "2016-04-07T22:36:27.584231"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1179ba2e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAk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\nPIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11799de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_sex  = df_train[\"Sex\"].map(sex)\n",
    "s_sex.plot.hist(bins=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:28.404344",
     "start_time": "2016-04-07T22:36:28.159796"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104517828>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10451fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train[\"Survived\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:28.784733",
     "start_time": "2016-04-07T22:36:28.406513"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1178ff518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QbklNZFJ3F13Tdqs6RksYzZr6cuDDQ24fmpmL6p/fBxwNvBtYnJkbM3MtsAw4qKFJJUkj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\npS5JBbHUJakg/x9c8HlY3SlT/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11793e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.pivot_table(index=\"Sex\", columns=\"Survived\", values=\"PassengerId\", aggfunc=len).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.618699",
     "start_time": "2016-04-07T22:36:28.790658"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 11.,  18.,  99.,  88.,  60.,  37.,  23.,  14.,   9.,   1.]),\n",
       "  array([  23.,   23.,  110.,  117.,   73.,   46.,   29.,   16.,    9.,    2.])],\n",
       " array([  1. ,   8.9,  16.8,  24.7,  32.6,  40.5,  48.4,  56.3,  64.2,\n",
       "         72.1,  80. ]),\n",
       " <a list of 2 Lists of Patches objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B0OLkEy9iubovl9ZPL87DG1q5tC1JkqSfNHPgINXJ/QNvZ2KiTKPx48Xo4sLx\ngbdbBBajEjA2Nsb+S15G5QUX59J+/cT3GRsby6VtSZIkKQ+jfRKyJEmSJKmQLEYlSZIkSZmzGJUk\nSZIkZc5iVJIkSZKUOYtRSZIkSVLmLEYlSZIkSZmzGJUkSZIkZc5iVJIkSZKUOYtRSZIkSVLmdm/l\nSRGxC/gE8HKgAfx6SunYTgaTJEm9c2yWpHNHs9mkVqvlmmFqaopSaXvHNrdUjAK/AoynlK6MiFcC\nd3eWSToHra42OTW3mFv7p+YWWV1tbri+6PmkHeLYLEnniFqtxv1PPkBlspJL+/WFOjdffiMzMzPb\n2s9Wi9FXAZ8HSCn9XUS8YlspJA25FiefeBHL1X25tH56cR7e0Npki6Lnk3aEY7MknSNarSbLjQZj\n41st57ZnudGg1dr+F+1bTX8BsLDm8ZmIKKWU/OpfOgeNjY2x/5KXUXnBxbm0Xz/xfcbGxjZcX/R8\n0g5xbJakc0SrBY3ZiylVJnNpv1FfoHXF9vez1WL0GaC65vG2B7vmyhLN41/fzi625cye5a7bLC38\nMIMkW2/bfNtr23zba9t822v7y1/+mwySwOTk+SwsLP3E8muuuXbT52WVbyPd8gkYxNh8ZplTz/zz\n9lJtw+6JM123ObV4kvHxMsvLKxkk+sm2d2KbtZ5t7FxfBpGvH936kne+nWh72PLt5Pur37a3us1G\nttuXIv1+1+tLt7ZLpRJ7qxeyt3rhIKNtaNeuXdu+XhRgV6vV/6ljEfGfgV9OKf1aRPwCcHtK6Q3b\nTiNJkrbEsVmSNGy2emT0s8BrI+Krncdv2aE8kiRpaxybJUlDZUtHRiVJkiRJ2o7tn+grSZIkSVKf\nLEYlSZIkSZmzGJUkSZIkZc5iVJIkSZKUua3OptuTiNgFfAJ4OdAAfj2ldGyQbQ5CRLwS+HBK6ZqI\nOATcBzSBoymlW3MN16OI2A18GjgInAd8EPgGQ9aXiCgB9wBBO/fbgWWGrB9rRcQM8ARwLbDKkPYl\nIp4EFjoP/wn4EMPbl/cB1wNl2n/DHmfI+hIRvwrcArSAPbT/Dr8a+H2GqB/wo79fn6H99+sM8FaG\n+LOSN8fm4nBsLi7H5uJxbC6WnRqbB31k9FeA8ZTSlcD7gbsH3N6Oi4j30P4DO95ZdDdwW0rpMFCK\niBtyC9efm4BaSukq4PXAxxnOvrwRaKWUXgXcTvuP6jD2A/jRB/kPgaXOoqHsS0SMA6SUXtP5998Y\n3r4cBq4FfyNfAAADjElEQVTo/N26GnghQ9iXlNJnUkrXpJReAzwJvBO4gyHrR8cvAWMppV8E/gdD\n/rkvAMfm4nBsLiDH5uJxbC6kHRmbB12Mvgr4PEBK6e+AVwy4vUH4DvCmNY8vTykd6fz8CO1vzIbB\nn9EeIADGaH+Dcdmw9SWl9BDwts7DnwVOMIT9WOMu4JPAvwC7GN6+vBzYGxFfiIi/6RyxGNa+vA44\nGhF/CTwM/BXD2xci4hXAz6WU7mV4/359C9jdOaI3CawwxL+TAnBsLg7H5mJybC4ex+bi2ZGxedDF\n6AU8d2oAwJnOqRxDI6X0WdqDw1m71vy8SPvFL7yU0lJK6VREVIE/Bz7A8PalGRH3AR8D/pQh7UdE\n3AL8MKX0RZ7rw9rPx9D0hfa3xx9NKb0OeAfwJwzp7wWYAi4H/gvP9WVYfy/QPvL1O+ssH6Z+1IEX\nAd8E/i/tz/6wvr+KwLG5IBybi8exubAcm4tnR8bmQQ8+zwDVte2llJoDbnPQ1uavAifzCtKviPgZ\n4FHgMymlBxjivqSUbgFeAtxL+5z7s4apH28BXhsRX6b97eUfA9Nr1g9TX75Fe2AgpfRt4Dhw0Zr1\nw9SX48AXUkpnUkrfon1N3do/pkPTl4iYBF6SUnq8s2hYP/O/DXw+pRQ891k5b836YepLETg2F4hj\nc+E4NheTY3Px7MjYPOhi9Ku0zycmIn4B+PqA28vCP0TEVZ2frwOObLZxUUTERcAXgPemlD7TWfzU\nsPUlIm7qXMAO7T9Eq8ATnWsJYEj6AZBSOty5buAa4B+Bm4FHhu130vFrwP8GiIgDtI+8/PUw/l6A\nr9C+dutsX/YCXxrSvlwFfGnN46H7zHfM89yRvJO0J997akh/J0Xg2FwQjs3F49hcWI7NxbMjY/NA\nZ9MFPkv726Wvdh6/ZcDtZeHdwD0RUQaeBh7MOU+v3g9cCNweEXfQnsXrt4A/GLK+/AXwRxHxGO33\n7ztpnx5w75D1YyPD+v76FO3fyxHa3/DdQvtbzKH7vaSUPhcRr46Ir9E+3eQdwCxD2BfaM1uunSV1\nWN9fvw98OiIepz2L4vtoT/wwjL+TInBsLg7H5uEwrO8vx+ZicmxeY1er1RpoSkmSJEmSnm+oJiyQ\nJEmSJI0Gi1FJkiRJUuYsRiVJkiRJmbMYlSRJkiRlzmJUkiRJkpQ5i1FJkiRJUuYsRiVJkiRJmbMY\nlSRJkiRl7v8DWn6CG+odQhcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1004e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16, 4))\n",
    "axes[0].hist([df_train[(df_train[\"Sex\"] == \"female\") & (df_train[\"Survived\"] == 0)][\"Age\"],\n",
    "          df_train[(df_train[\"Sex\"] == \"female\") & (df_train[\"Survived\"] == 1)][\"Age\"]],\n",
    "         bins=10 , stacked=True, range=(1, 80))\n",
    "\n",
    "axes[1].hist([df_train[(df_train.Survived==0) & (df_train.Sex=='male')]['Age'],\n",
    "          df_train[(df_train.Survived==1) & (df_train.Sex=='male')]['Age']],\n",
    "          alpha=0.6, range=(1,80), bins=10, stacked=True,\n",
    "          label=('Died', 'Survived'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ジェンダーモデルによる生存者推定、推定値の評価"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.627433",
     "start_time": "2016-04-07T22:36:29.621498"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = df_train['Sex']\n",
    "y = df_train['Survived']\n",
    "y_pred = x.map({'female': 1, 'male': 0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.648409",
     "start_time": "2016-04-07T22:36:29.631168"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Sex\n",
       "0         0    0\n",
       "1         1    1\n",
       "2         1    1\n",
       "3         1    1\n",
       "4         0    0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([y.head(), y_pred.head()], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.660622",
     "start_time": "2016-04-07T22:36:29.651221"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40000000000000002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(\n",
    "    [1, 0, 1, 0, 0],\n",
    "    [0, 0, 1, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.677273",
     "start_time": "2016-04-07T22:36:29.665409"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78675645342312006"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.693139",
     "start_time": "2016-04-07T22:36:29.681168"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.85      0.83       549\n",
      "          1       0.74      0.68      0.71       342\n",
      "\n",
      "avg / total       0.78      0.79      0.78       891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:36:29.719949",
     "start_time": "2016-04-07T22:36:29.696682"
    },
    "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>P(0)</th>\n",
       "      <th>P(1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T(0)</th>\n",
       "      <td>468</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T(1)</th>\n",
       "      <td>109</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      P(0)  P(1)\n",
       "T(0)   468    81\n",
       "T(1)   109   233"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y, y_pred)\n",
    "# True, Predict\n",
    "cm_df = pd.DataFrame(cm, index=[\"T(0)\", \"T(1)\"], columns=[\"P(0)\", \"P(1)\"])\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:05.271366",
     "start_time": "2016-04-07T22:37:05.264231"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_df.ix[\"T(0)\"][\"P(0)\"] / cm_df.ix[\"T(0)\"][\"P(0)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:09.496974",
     "start_time": "2016-04-07T22:37:09.129339"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DOvwRMDN7k0Yj3y2HZtPDjYAtSV0NTiDVe90Y6JK0XUvTwwnZccdKGj3QiT2DNbNa6iru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\nZiVxgDUzK4kDrJlZSRxgzcxK8v+fbMNx9vqWawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a99b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm):\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    #ax.set_title('Confusion Matrix')\n",
    "    fig.colorbar(im)\n",
    "\n",
    "    target_names = ['not survived', 'survived']\n",
    "\n",
    "    tick_marks = np.arange(len(target_names))\n",
    "    ax.set_xticks(tick_marks)\n",
    "    ax.set_xticklabels(target_names, rotation=45)\n",
    "    ax.set_yticks(tick_marks)\n",
    "    ax.set_yticklabels(target_names)\n",
    "    ax.set_ylabel('True label')\n",
    "    ax.set_xlabel('Predicted label')\n",
    "    fig.tight_layout()\n",
    "\n",
    "plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作成したkaggle_gendermodel.csvをKaggleに投稿し、スコアと順位を確認してみましょう！これで皆さんもKagglerです！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:10.674484",
     "start_time": "2016-04-07T22:37:10.669072"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test = df_test['Sex']\n",
    "y_test_pred = x_test.map({'female': 1, 'male': 0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:10.866677",
     "start_time": "2016-04-07T22:37:10.850009"
    },
    "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kaggle = pd.DataFrame({'PassengerId': df_test['PassengerId'], 'Survived':np.array(y_test_pred)})\n",
    "df_kaggle.to_csv('titanic/kaggle_gendermodel.csv', index=False)\n",
    "df_kaggle.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ロジスティック回帰による生存者推定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:12.201652",
     "start_time": "2016-04-07T22:37:12.188299"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "iris2 = iris[[\"sepal_length\", \"sepal_width\"]].copy()\n",
    "iris2 = iris.loc[: , (\"sepal_length\", \"sepal_width\")]\n",
    "iris2[\"lenght_2x\"] = iris2[\"sepal_length\"] * 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:12.665261",
     "start_time": "2016-04-07T22:37:12.658622"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'warn'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:13.091309",
     "start_time": "2016-04-07T22:37:13.082436"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df_train[['Age', 'Pclass', 'Sex']].copy()\n",
    "y = df_train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:13.566145",
     "start_time": "2016-04-07T22:37:13.561307"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X['AgeFill'] = X['Age'].fillna(X['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:13.876778",
     "start_time": "2016-04-07T22:37:13.850234"
    },
    "collapsed": false,
    "scrolled": 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>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Pclass_Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Pclass     Sex    AgeFill  Gender  Pclass_Gender\n",
       "886  27.0       2    male  27.000000       1              3\n",
       "887  19.0       1  female  19.000000       0              1\n",
       "888   NaN       3  female  29.699118       0              3\n",
       "889  26.0       1    male  26.000000       1              2\n",
       "890  32.0       3    male  32.000000       1              4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['AgeFill'] = X['Age'].fillna(X['Age'].mean())\n",
    "X['Gender'] = X['Sex'].map({'female': 0, 'male': 1}).astype(int)\n",
    "X['Pclass_Gender'] = X['Pclass'] + X['Gender']\n",
    "X.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:14.051420",
     "start_time": "2016-04-07T22:37:14.034447"
    },
    "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>AgeFill</th>\n",
       "      <th>Pclass_Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AgeFill  Pclass_Gender\n",
       "886  27.000000              3\n",
       "887  19.000000              1\n",
       "888  29.699118              3\n",
       "889  26.000000              2\n",
       "890  32.000000              4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.drop(['Age'], axis=1)\n",
    "X = X.drop(['Pclass', 'Sex', 'Gender'], axis=1)\n",
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:15.279014",
     "start_time": "2016-04-07T22:37:14.907960"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11ac4d518>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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btZf+7nx51cO9TfRPnUsuZxCN+ixf7rFhQ7A/2x7aR29Hobxs3f4tRHIZ0nmb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\nt+y7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5ff358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "xmin, xmax = -5, 85\n",
    "ymin, ymax = 0.5, 4.5\n",
    "\n",
    "index_survived = y[y==0].index\n",
    "index_notsurvived = y[y==1].index\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "sc = ax.scatter(X.loc[index_survived, 'AgeFill'],\n",
    "                X.loc[index_survived, 'Pclass_Gender']+(np.random.rand(len(index_survived))-0.5)*0.1,\n",
    "                color='r', label='Not Survived', alpha=0.3)\n",
    "sc = ax.scatter(X.loc[index_notsurvived, 'AgeFill'],\n",
    "                X.loc[index_notsurvived, 'Pclass_Gender']+(np.random.rand(len(index_notsurvived))-0.5)*0.1,\n",
    "                color='b', label='Survived', alpha=0.3)\n",
    "ax.set_xlabel('AgeFill')\n",
    "ax.set_ylabel('Pclass_Gender')\n",
    "ax.set_xlim(xmin, xmax)\n",
    "ax.set_ylim(ymin, ymax)\n",
    "ax.legend(bbox_to_anchor=(1.4, 1.03))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:21.266429",
     "start_time": "2016-04-07T22:37:15.523425"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XS4QoeGba2+Vmurog+9bbmLdvYaTWyJ0+i6xv2NXcenslS0v+scPhwvZEQrC0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a62bc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_sc_test = X.copy()\n",
    "_sc_test.Pclass_Gender = _sc_test.Pclass_Gender.astype(\"category\")\n",
    "_sc_test[\"y\"] = y\n",
    "\n",
    "sns.swarmplot(x=\"AgeFill\", y=\"Pclass_Gender\", hue=\"y\", data=_sc_test, alpha=0.3, palette=[\"red\", \"blue\"])\n",
    "plt.legend(bbox_to_anchor=(1.4, 1.03))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.037638",
     "start_time": "2016-04-07T22:37:21.268997"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8kn5T2QZuftueQtlZsBDn5FMK5Z2bxmh9aAe4rt8/4+s5veU+VDrjt1kWJ/3u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\n/wdG0F/t+7jzlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e8b46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "_titanic = pd.DataFrame(\n",
    "    [\n",
    "        titanic.age.fillna(titanic.age.mean()),\n",
    "        titanic.pclass + titanic.sex.map({'female': 0, 'male': 1}).astype(int),\n",
    "        titanic.survived\n",
    "    ]).T\n",
    "#_titanic[\"Unnamed 0\"] = _titanic[\"Unnamed 0\"].astype(\"category\")\n",
    "#_titanic[\"Unnamed 0\"] = _titanic[\"Unnamed 0\"].astype(\"category\", categories=[4,3,2,1], ordered=False)\n",
    "\n",
    "#sns.swarmplot(\n",
    "sns.stripplot(jitter=0.1,\n",
    "    data=_titanic, x=\"age\", y=\"Unnamed 0\", hue=\"survived\", \n",
    "    alpha=0.3, palette=[\"red\", \"blue\"])\n",
    "plt.legend(bbox_to_anchor=(1.4, 1.03))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## トレーニングデータの分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.047928",
     "start_time": "2016-04-07T22:37:22.039763"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.068175",
     "start_time": "2016-04-07T22:37:22.050204"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>715</th>\n",
       "      <th>767</th>\n",
       "      <th>72</th>\n",
       "      <th>235</th>\n",
       "      <th>37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AgeFill</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_Gender</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                715   767   72         235   37 \n",
       "AgeFill        19.0  30.5  21.0  29.699118  21.0\n",
       "Pclass_Gender   4.0   3.0   3.0   3.000000   4.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.tail().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.076254",
     "start_time": "2016-04-07T22:37:22.070225"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of Training Samples: 712\n",
      "Num of Validation Samples: 179\n"
     ]
    }
   ],
   "source": [
    "print('Num of Training Samples: {}'.format(len(X_train)))\n",
    "print('Num of Validation Samples: {}'.format(len(X_val)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ロジスティック回帰による推定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.097840",
     "start_time": "2016-04-07T22:37:22.078914"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 学習\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classes_', 'coef_', 'intercept_', 'n_iter_']"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.03756041, -1.29748813]]), array([ 4.30808898]))"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_, clf.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([11], dtype=int32))"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.classes_, clf.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.111828",
     "start_time": "2016-04-07T22:37:22.100648"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training Set: 0.774\n",
      "Accuracy on Validation Set: 0.760\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = clf.predict(X_train)\n",
    "y_val_pred = clf.predict(X_val)\n",
    "\n",
    "print('Accuracy on Training Set: {:.3f}'.format(accuracy_score(y_train, y_train_pred)))\n",
    "print('Accuracy on Validation Set: {:.3f}'.format(accuracy_score(y_val, y_val_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.124707",
     "start_time": "2016-04-07T22:37:22.114990"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93 13]\n",
      " [30 43]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, y_val_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.137468",
     "start_time": "2016-04-07T22:37:22.128605"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      0.88      0.81       106\n",
      "          1       0.77      0.59      0.67        73\n",
      "\n",
      "avg / total       0.76      0.76      0.75       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, y_val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.147786",
     "start_time": "2016-04-07T22:37:22.140694"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.173005",
     "start_time": "2016-04-07T22:37:22.150612"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11e126ef0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LI+sEEiZpZzv7MJD4QJo6DlE+ISYjfVsQ2ajeJ6fS7JveTI0kARa9YoSrzQMY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\nQRkR3fHrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11de7b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = 0.02\n",
    "xmin, xmax = -5, 85\n",
    "ymin, ymax = 0.5, 4.5\n",
    "xx, yy = np.meshgrid(np.arange(xmin, xmax, h), np.arange(ymin, ymax, h))\n",
    "Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "levels = np.linspace(0, 1.0, 5)\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "ax = sns.stripplot(jitter=0.1,\n",
    "    data=_titanic, \n",
    "    x=\"age\", y=\"Unnamed 0\", hue=\"survived\", \n",
    "    alpha=0.5, palette=[\"red\", \"blue\"])\n",
    "\n",
    "contour = ax.contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)\n",
    "fig.colorbar(contour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-04-07T22:37:22.173827",
     "start_time": "2016-04-07T13:37:18.784Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123109b70>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/+bT5sxawUbGSMexmMCWFuVXzCIcuXAqU09IcOwqbBOj1dYSnmI+9PyOLgd4e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O5+g8Gw7ZTNqcBAvV7vCzSilN683NEx7X1osafWs9/IKh2u3tlYGpq3WtF51jm39584lIaKjynL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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1230e84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = 0.02\n",
    "xmin, xmax = -5, 85\n",
    "ymin, ymax = 0.5, 4.5\n",
    "xx, yy = np.meshgrid(np.arange(xmin, xmax, h), np.arange(ymin, ymax, h))\n",
    "Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "levels = np.linspace(0, 1.0, 5)\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "contour = ax.contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)\n",
    "ax.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1]+(np.random.rand(len(X_train))-0.5)*0.1, c=y_train, cmap=cm_bright)\n",
    "ax.scatter(X_val.iloc[:, 0], X_val.iloc[:, 1]+(np.random.rand(len(X_val))-0.5)*0.1, c=y_val, cmap=cm_bright, alpha=0.5)\n",
    "ax.set_xlabel('AgeFill')\n",
    "ax.set_ylabel('Pclass_Gender')\n",
    "ax.set_xlim(xmin, xmax)\n",
    "ax.set_ylim(ymin, ymax)\n",
    "fig.colorbar(contour)\n",
    "\n",
    "x1 = xmin\n",
    "x2 = xmax\n",
    "y1 = -1*(clf.intercept_[0]+clf.coef_[0][0]*xmin)/clf.coef_[0][1]\n",
    "y2 = -1*(clf.intercept_[0]+clf.coef_[0][0]*xmax)/clf.coef_[0][1]\n",
    "ax.plot([xmin, xmax] ,[y1, y2], 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rqIk0dVimhFAsyuSqShJ9VONtG39F6RHTDagsJxH2gsCw6iO3oa/r6Zjzbom/\nOcECSCGfb7C5+e9LqGEaL2EzAXMkgUMetfY5rCob5iZYABar+Byv0tIQALiJUsaynMtY1dw7NhLz\nIw7wRQ4ygucBH1ur5lO4e2dzggXwaUyidAnmGNO0JFgAl2M6OubTsvPm0FKPPwn8AxObZmFiTtvY\n9HNMAydhAaZjwwJuZTUmouWzEdMoPx8TY/Ka95mJDV5gCClMJAqYepxosIzD1F8Hk2CBibeJOlqA\nidMT3eV+CFxG69hUhjnrlTgA0t1/uOU54E5/rlv+AcAiN86XczXvMZlZboIFJjYl6uCFmAaiB1CY\nnvKrk/bJ1cCuQk1HikoOclm45cTIpxrqKCw91Gqa8sZGFlRXNG/TzFiMSFkJeZVlJM55pQODO6jn\n+yoruKomKTZFmjhcUtRhmZJ5ykuYaNuA2ebp1VU0RKOtptlfcogrQy3b8MlQPbuLW29DX9fT8cZx\nHA4fGI35FQMYyvlsaY7zqcCFfEQ2G6nlsub5DvIfFL34R2oOXdE8rIZLeYlpze0OMHXnfN5nDq+2\najfNAIqBoTicx7sAhGsvpWRbETPcBAtgOOYYHIKJUw2Yetn8W4XpTHbcacDU48TxOxnzuw2m8+N+\nTIxKrsclmFiUaDelYzqBAD5OHdP4B2DRwPW8jI9sd73JcfXTmI6qrWTyNVpONF4MbMXU49mY9shl\nSfMltzHOwsTOK91xA9psUw6wFrgoaf4rMGfzktt9ibgKcBNRBvIzIJ0PWcQgN8GClti0011Xot2X\naDdlucsF08EzJdzqRGsrccdmTHVly2+K45BaWXbEdBkVZQzDAcwRN7K6iuKig80JFsCn6mth58p2\n1zOwqqL5u00DBldXdFimZHvKS7gmqR21MNJERTuxakRVJYnz6UOBjIoTP2nclY+g784HX3wWuFgp\n9Qbm2HnCvXYZoJaW4wogk5Y2e4e8Pg8+v7fT/7KCVa3mLyOF5Kb0VvKIoAiwvXmYhzJSgyGygslf\nkMMASgiDe4gZB8nlMCMpTdqN+2n54S9uTqniZKfUctjX+sRhLSajTxz+4TbbG8acfYokDTsEzUG0\n1v2/DhOs2u7AMFDuLiOhEtNIcoDS5kO+hAlucGnCVNLkZZRgAuYOWi6piyeVaxcmoCQf9lF3GjA9\nz+OTlhumJXgCHMT0CO1KGlZPy2nWEloaUIl9VA2UuGEsjx3kYPZ9QiktB1gdJtA57nrb7ucqIJgS\n6PA4CgUCcosrAAAgAElEQVRbP22mMRg8Ypp4amqrbdobCDA0J49Cb8t33gBYaantriMcTGn1PRUH\nAmSnpRzzGE9PCVAZTGmez+mgfA1ttqGhnWk68w844flOgR6NOUMzonhoSXDDeNlAS899I1DOIHxU\nt1qHjxKyU2ogKToNZAtjMD29CWV4OMxIDtJy2VXimAY4gI8aRgOQHSzDl5lJ8s/YXswleYllBmjd\nCtyLSUySm8Jt6/EE93Oluz1tY5OidT2uo6UebyaPRE1OcxOWaqDtT2EiLagm3ire1mN+3B13mrZd\nEIm/45j6n9j2ce62td2m5G1Pnj85PhygpZuupHlL6hlEBdVt2tXJsSlRIyswsfNA0nRlgCc9o8Pj\nKJCRTnJqtNvjITc7u9U0OWkp7E+q9zEgmhqkIdC6noc6qOd52VlHxCbS0zp1nIdTgq1iVVkwQEZq\n6/iZlplJSfJ+tCyysjJPWcw5RU55vAHw+Tz4fB78fi+BtLqkuR2KaX3ZezE57nHd0hBOZyPBCQX4\nUpJ/6RvJp+KI38YScigjt1W7qRSSWg6JX+ZK0gYEqUpqnDruBle6n1Pb7AAHEz+OVo8TW1eIiU17\nkqZL1OO2TfXkelzh1l6L3UwmBu70yTFnvzvPKKJspeWy1ypaEs4iTD1Ojo3JbYwyzBecWG7rCN9y\nK0Xy9u/FJAPJZY6CW0qzzYcxV8wMoJgqjmw35beZvwGTxLb9Hiusjo+rgN9HbdLlvg7QEDiyPdS2\nDVEfCOBJTWu1rYUeL07e8OZj1Oczx0O7bZBA59ogednZ7EqKVbWAJ+3IWNV2+Y0px9fGAVrKm1Tu\nk4053Xa5oNb6/MRnNwjdprVOpMfbgAlKqcSZwwWYM6xHFY/Zx5qklS+ovVQ03s32qnk47GE95/N5\n3udGqijEx89ZQJgpDOW7hLgKh1RGWL/hewtn8lHFGn69+ZdUhRU5zt+4IP4KMeB7Xi8XWB7W2x7+\nz55NLdl8jgl8ybeHiAUfxWJc6jg8QIC/+eaTwT+YmruCz6k69uTO5+er32VWPM5WLCp8XhbG43zb\ncbgCU0F/jOkVWYmp4GMxlwBejqlUO7GwcFiJRQSHFcDzwCUE+QCLXxNmCuayuiGYnqM7Mb2rhZge\nqBxgOVm8zALgBQbxOJdRyGOYax4exQSiHMylQ+diTvOvxCJGLhMIswKbhYR5Fngfk1imuPPlAa8A\nC4H/w/Tw3OLz87Qdp9y2mZy0TcWYHvWLMKft92O6+/7ujn8C02hLxZwKH4Q5Tf9HstB8icH8lE9Y\n/0ts9AR+U13F2TVVeIA3vF4ux+IRn48KHN6I27zn8zEpGqURh7vjcc5zl108YgyxaMdnsvILZvLb\nUIiC+jp2paWRXjDjiOmnjS/gV1VVzKoo5bDPS8WEKcweOIg1aipFhTvItGOsHzSM+WMmtruugknT\neKDmMDOrKykJBGicPAMrDrGjnGFLiE2ezpNbNjA00sTGAbkUtFO+IZNn8ptQA1Pqatmdmkbq5COn\n6Qyf33tC850KPR1zFg0NsG7Md1ix/zKabAsPu/k+c6niTYYAv2Ic25lJOisYzB3U8THyeJXFEzWL\nJ42n8PA32FJ1KX5rN4uiPycCPGRZzPf5CDjwYGw0NeTwLHPI8+xgjifO247NuHicF4D/9U6mkXRG\npv6KLxSs5Owhc/lJeRkL62uJAG94vFzj8fCnWJQ45gf+u5hLDeuANe7ndzD1chjwAhYX4/A08CYe\nrsfml8AOvCwkyDdp5AocSt0dfDHwO0wil4mpq5cDj+Lhfs4GVuLnXT7Lw7yIuUfh75iezXmYHuU3\nMZe8jKCILzKYK6ljOx7qaWAgDq9iGnmlwC8xieM2TAPjdcxlgOOBGsvDfY7NPHd4jbvNL2LiYQPm\nzNtsd9t9mMtuXsSc7XvSLZ8NrMLHE1xFOs8yn9/iz66iIr+ABwu3M9lx+MCyGOj18bJl9vPHYzGe\n8PkoAs6Kx3kgHuM8x8EPrPD7uWHegg7r0bRhY3h8dDmTi/YT81jsGTWeM3IGtZo+iJfdajpP79jC\nwFiU9bkDmT1xKrXhEL9rbGRSfR2F6elkFkxvdz2DUtJZmxSbNgwaxrwOYlNbE9V0HqipYebhKooD\nQaIFM3FiDrGkpvjkwSN4auwk1KF92EDhyLHMzR/Wr2JOT8WbWNI0BZdYbPn7Y4QqhgBb+ScL+W+2\ncAmNvEMaf2Ihh5nPUG6mniX4KWV02pOMuvge/Cmb2PVOOf5QKiPiDzLb2cZW4AGfn1nAiliA15hN\nPRP5V/K42VfLfgv2RaOkAa9amfwjeD5+zz8YOm0Tw2fPpHbLFH6/YyvjHJv3LIs8y4Nlx7kHc5Zl\nO/BbzGXGr2DqXghz+fL5wHosoji8BryMxUwc/oo5g341qbxIjBqiDHGHLcQkPr9wl7UK0w55FXiQ\n4WxlJhZ/YgYPUoKJLQswt1lchUloXgC+DWwmzPcYwjWESCXOa0T4F6I8gjljVOEuP3Fl0JuYM1eP\nYM6W32R5WObYXO6W4R7MVTpb3G2cjWmLXY7pMPoIczbrl5hk85/uNl0API3Fo4zASx6j+BYX+v5K\n6sTp3LV7J4uawpRj7j11LHjK6yPNjvMKDm94vSyMxdlv2/zKjjMD05ZMnzH3qHXIM3kmf9iyjpHh\nMJuycxjbThtiVMEM/l99HdNqa9ifkkpgykymDB/BI+VlTC0+SJPHYv+YiQRGTWl1jALEonEGFsxo\nFZuyOtkGGZqexUeTpnBg9w7S7Tgbhwxn/ugJR8zrnzyDxzetZXS4kS1Z2YzqIPYdTSxm4/N5msvf\nFXHHchzn2FOdJKXU68AXMfcJpmutH1ZKXY65BckCfq+1fvBYy3nvEzced2G9Pg9VoUYaY1HCsTgj\n0zPYVVfDkLQ0Un0+SkMhhqdlcKChlqZYnEk5ec3zxmybhliULH+AqibTN5AbTKE2GiHd56c+EqGw\ntpqZuflEHAePZRHweDgUqmdwWhoePIRiMTL9/uZ7a2K2TbE7HseiKR4nMxDg/3ZuZWxmNgW5+bxV\ntJ/zh42i8HAVu2pruHaC4oH1q5mTP4jxWTk8tH0jS8YXsKWqnKJQAzeNmcgvtm/kgmGjOHPoEHbX\n1DAsNYO15SXUR6OcmTuQp/fu4uLhY0j1wF8P7ObLk6azv6mRuO0wLi2dh3dtYcGgoQQ9PtZVV/K/\n2zYAcM8ZZzNr4CCKQg2MG5BNYeVh3ig5wOfGF/BhVTn5KalMycljbWUZo7OyCMfiHKiv49yhI1hV\nWsSglHRGZmVRG2kiKxDkcDhMVVOYCQNy2FBexpC0NLKDKWytqSTHFyTV68PyWOQFgmysrmRUZiZZ\ngUCr76khGqNgQC7FoQbSfX4CXi9pfnPJRFU4jINDbjCFumiENJ8ZHopFyfQHaIzHsLAIWBa6pprR\nGVmdesCE7TjURSNk+P0d3kgOUBeJEPR6CXi9zY2DpnicqB0nw3/09TiOQ100SprP1+kbQhNitt28\njR3dx+U4DrWd2IajOdEGzzl/e/L47kA9ST0Zc5qwCTdFOdRQx4TsHOoiEWoiTYzLHkBxQz2Z/gB4\nHLZWVjIjbxAp7hnuxPef6vPi4DQf8xHbxnYcApbFhqpyxmflkBEIHDU2JR8/FY0hfB4P2YFg8/e/\np/ow75Ud4l9GT2BtdSV5KSmMScvgmb07+fiYceysrGRteRlfmzWXB9avZFJOHuMzB/Dkzi3cNmU2\na8tL2FhZzn+feTa7DleT4fcTicd5+cBuLh05jm1V5RxsqOf6MRN5pHA75wwZzvicAawtLeXcwcN4\ns+gAVU1hzsjL58OyUg7qjfwdk+g8s+gKwoDHY5FjeXho5xauHDGGjEAK75cXs3jEGPaF6gGLvECA\nDVUVnDFkCNGozfaaKuYPHEwE8HksfJh6PjY7i4ZojP11dczJH8zmynIyAgG8WGDB0LQMKhtDlIRC\nzMwf1Pw9+TweNlaWMSNvEFHbpi4aId3vJ9MfwGNZROxYq+8p7jik+XytYk/ieypuaMDGYURGZqeO\no1A0iseymo+P9kTtOI2xePNvjM/vJRKJUReNNJfxqMdqJ2NTW44bD9PaHGtHbEMsioVF6lG24VhO\nJOb013hz79ylBH3+VsM8lk24vo7aw2XcvuMlsgMp7HKP+XgcfjD5KnLyR1CyeyO3736d31/wleZl\nhBob+Maqx8kMBJqP+VS/j7pIhKxAkKKGOmojESbn5LUbm+6ZtQS/14s/mEZTLModqx8nHItRHjbj\nw7b53qxojD/s3s5lQ0fjDwRYX1nKoqEjebP4AJmBINMzB/DA9g3cUDCN6toG/lmyjxtGT+S5g3sZ\nlZnJBQOH8qMta/js1Fnk+VOoaGpkcFo6L+8tZFRGJoPTMnh2j2bppOlsr65gS3UVX5o+mw+KDzEy\nI4MUr58/7drCoqFjONzUSFUkzE82rgbgwfMuZkh6GmWhRlR2Div27+ZwUxPXjp3ISwf3cmb+ULIC\nAbYdrmRi1gAOhRqwsFDZA/igrJjpOQPJTElp/v09WF+L1/IyKCWFtZVlTMnLIxZz2NtQQ7bXT8Dn\nIzMQIM3jax7vwaK6KcLw9Aw2V5aTF0wlPy2N4lA9Gf4AaT4/QffBFYnvKc3vb67nUdsmasdJ9/mb\nv6eYE2dPTS0qO+eoD71ISG7vHqsNkfiNSdTNhmgUrxur2h6jieMCWtpRnYlNbTXFY8Rsh3S/v8Np\nOrMN7Zl662KWrM8n6PM3J1nJ5e6MjmLOKUmyusqJNHh6ay/YyZBt6htkm1qc6kZPV5GYY8g29Q2y\nTUZ/jTftJVnHahQm5kmMT17G8TYkj1aek11WMjmO+4b2tuloSVZvlVzmrk6y5GXEQgghhBD9jP+e\nu4/7MdYnzONl6q2Ljz2dEKcRSbKEEEIIIfqZZcsLCXg7vlQz6PNz79ylXba+HaPPOvZEQpxGJMkS\nQgghhBAnLOjxsGx54bEnFOI0IkmWEEIIIUQ/J5fzCdFJHm+XnOWVJEsIITogjRIhRH9gWRZL1uef\nunu0hOjDuuqFxJJkCSFEB5aszz/2REII0csFvD4syzrqPVpCiK4lSZYQQnTAsizunbu0S28OF0KI\nniAJlhCnliRZQgjRgYDX1/z+DEm0hBBCCNFZkmQJIcQxJCdakmwJIYQQ4lgkyRJCiE4I+vxyVksI\nIYQQnSJJlhBCHIdEouW/527899zdw6URQgghxIno7icIS5IlhBDHy+Nl2Qt7Wba8UBItIYQQog9a\nsj6/ueO0O0iSJYQQxyno8Zj3aHi8LFteyNRbF8s7tYQQPUpikBC9iyRZQghxghKJ1pKNQ1iyPl8a\nOUKIHhH0+eW9fkL0MpJkCSHESUic1bIsiyXr8ym45RJJtoQQQojTnLyZTgghukDA6yMSj7Fk/WAc\nx+aOni6QEEIIIXqMnMkSQoguEvD6SAvKY96FEEKI050kWUII0cXk5cVCCCFE33ayTw+WJEsIIbqB\nvLxYCCGE6JuCPj/Llhee1DK69Z4spZQHeAhQgA18UWu9NWn814FbgTJ30G1a653dWSYhRP/UW+NN\n0OenKRZtTrTuWP14d69SCNHNemu8EUL0Ht394IsrAUdrfa5S6nzgB8A1SePPAG7SWq/r5nIIIfq/\nXhtvEme0mmJR/PfcTfTO757qIgghulavjTdgLnOynt/dE6sWQri69XJBrfVy4Avun2OA6jaTnAF8\nRyn1jlLq291ZFiFE/9Yn4o378mL/PXef9LXeQoie09vjzbLlhQS88gBpIXpSt9+TpbW2lVKPAQ8A\nT7YZ/RTwRWAhcK5SSl4uI4Q4Yb093iReXrzshb0sW14o79MSog/r7fFGCNGzTsmDL7TWtwCTgIeV\nUqlJox7QWldprWPAi8DsU1EeIUT/1dvjTduXF0+9dbEkW0L0Ub093gghek53P/jiM8AIrfWPgDAQ\nx9wgilIqC9islCoAGoFFwO+PtjyvzzRMjpfP7z3ueXo72aa+Qbbp1OnqeANuzDmBsvh8x+6/8vkC\nhJqi3LDBvLz4z59fzPbHVpzA2k6N3vq9nwzZpr6hN25Tt8WbY7Rx2ostiWFNsdb7qjNxqO08xyt5\nHZG4B/+9d+N873snvLzm5fbC7/xknS7blHxMnOzxdSq0rSddWf7uvmD3r8CjSqm33HV9HfikUipd\na/2wUuo7wJuYAPWa1vrloy0sHrOPuwA+v5dYNH7c8/Vmsk19g2zTKdel8QZOMOb4PMQ6OV/Aa4J3\nJO5w/bp87uil+7aXf+8nRLapb+jF29Qj8aZtbGkbb5L3VWfj0Mns3+R1BLxelj1XeNJxrBd/5yfs\ndNqmtsddb9/u5PK29/t9MuXv1iRLax0CPn2U8U9y5HXMHbp37lJ5/LEQol1dHW9OpYDXJ495F6IP\n6cvxRghxavS5lxHfO3epvNhTCNHvyMuLhRBCiP6jTyVZbRsh0hARQvQ3EuOEEEKIvq9PJVkJycmW\nvG9GCNHftI1xQghxTB6vxAshepE+mWQlBH1+fvDifnm5pxCiXwr6/BLfhBCdEvR4WLa8sKeLIUSf\nMPXWxSf0xPLj0aeTrGZJL/f033O3vHNGCNF/tIlvQgjRF0jnkOjNlqzPJ+Dt3oes94skK/Fyz0Rj\nJPGCTyGE6OtaxbflhfLyYiFEn7BseaGcWROntX6RZCUkGiOWZTUnWgW3XNLTxRJCiJOWiG03bBgk\nHUlCiF4tOT5JrBKnq36VZCUEvL7mxsj16/J7ujhCCNElAl5fc3xbsl5imxDiSPfOXdrt95p0xLIs\n/PfczZL1+ViWJbFKnNb6ZZIFyY0RjzwKWQjRrySuI7937lK550EIcYTuvtfkaOtNXCKYaIcJcbrq\nt0lWQlpQ3q0lhOh/Eo95X7a8UOKaEEII0cv0+yQrQV5kLIToj5LjmhDi9JaIBz1dht5QDiF62mmT\nZCVIsiWE6G+CPj94vBLThBCS4AjRS5x2SVZCcrIl73IQQvR1QY+nVUwTQojOkKf/CdE9Ttskq1mb\nF31K40QI0ae579OSeCaEOJagzy9P/xOim5z2SVbbFxlL40QI0ZclzmglxzMhhBBCnFqnfZKV0F6y\nNfXWxXIaXQjRJzXHM4llQogeZFmWxB/RJwV9/pO6z7lTSZZSasYJr6GPSSRbiZcZL1mfLw0UIU6h\n0ynedLf2YpkQojWJOd0r4PXJJYnitNTZM1lPd2speqGWlxm3NFCEEKfEaRdvulNyLJM4JkS7JOYI\nIbpcZ1/FvVUp9V3gI6AxMVBr/Xa3lKoXSbytvCkWbT5leMfqx3uySEL0d6dtvOlOAa+PSDwmcUyI\nI0nMEaILPDWrnCXr8+U1Aq7OJlm5wEL3X4IDLOryEvVSiQNGki0hut1pH2+6S9tOI4lhQgASc4To\nElsefgnkXY3NOpVkaa0XHnuqIymlPMBDgAJs4Ita661J468E7gSiwKNa64dPZD2nkiRbQnQviTfd\nL+jzSwwTwnUiMUfijRDiWDr74IvRSqlXlVI7lVJDlFKvK6XGdGLWKwFHa30uJtj8IGmZPuB+4CLg\nAuALSqk+c8NA8suM7527VG4oF6KLSLw5NdrGMCFOVycYcyTeCCGOqrOXC/4WuA/4MVAKPAU8ASw4\n2kxa6+VKqRfcP8cA1UmjJwM7tda1AEqpd93lPdvZwnfG1ldeY9srIWAaUEE+L/JVdlGKhxcpIMQi\nmtjAYc7B7I71wFmksZHP5fyNiUGLTUPGUXLt7cSjEXb+6q801E0mO76CW5zXGQysweKXg0YzLC+d\nQ8XFTIrbHE5JZfD0OQzOyuYx7eGtork4TiNNDRsYGy8lSAVvcRMR8hjO/3Enr9KAl7+Ty2UeDw/b\nmdRzDQ7ZBHmFG9hMNUG8VDHP08QK2+YcwA+8RgYzyKSKOhzqmQ48Sx4zCRAE3ifOpymjAlgDLMLH\nX8jjY1hY2OylgvOxKQb2AztI42pCZAKrgHoyKSCDXaRQTzUXU8+7+JlBlB2kkk0dU4BNgJcscknj\nXWzW80UsyrmaRziDJvb6gvxfyucIxSdwYePtnItDHfChx8uVGZl8EItQEG4iFYen7BFs41ZSWcuN\n1guMtRy2e70MCqaRZcHa+jrmOjZ7AH9qGuN8AT6KRpgaCRN3HLZZHs7AocLjJTjzDGaNHMfGXdvI\nPLCXOKCxmG7brIvHGdbUSK5jsy0tnYsuuIySulq2vPMKBbbNAcsiY/Z85o4a13xM1TY1smv9KgbW\n11OZnsaYGfMYkJrK6i3rGFxWQrllEbIsxtg25WnpTJg9j4xACg9sCrK+4gx8nmosp5SIPZnRmdv5\n1qxSMgIt1y+X1h6mbNNassNhSrOymT57Pim+9qvqxl3byTywBwcIjZnAtLETu7D29Jg+G28i4RDL\n7/oLcCYQxOJDbuNVhhLnZdIp5GaglCp8xJgJvI+JTalcEPgFn8gLURXMYO3FS/GMn82BZ56hbNNQ\nYnGby+0fcS5NlAMb0gcwLzOXdT4/U20bC/jgjEuInfNJqvYXse3leppCWXhDb5NXF+NC52We5gpq\nORMfO/kCv2EWDbxOOlPI4ANqWMIVRJhFgJ0M5m9cSSrraeAqq4atjkMAmAi8j48c8sjEopASzgO2\n4aGcIRQQZxNeJlHECOBt4Fx8rMNPjFzGEWMntUyjkUzgHUzMmePuhUPAerxMJR8bH68R5LPsowgP\nu/AxCz9rqWEh5saZzXgYSz4HCbGCywkzhYk8wg3sJebx8PfgZLb6vsTEhru5zC4j293j81PTqfZ5\nqWtsZIIdZ70T5FnnM8QZxgJ+xsc9IeotD4UpKZzp8fFOU4hpkQhBYLXXy4L0TA44DpFQA6Mdmw+x\nmI5DCrAzbxCLz1lESe1hyt16rL0eRtg2tuOwI9zIjHisVWx6+p8vMrO+BhvYkp3LdQsvbXVcrdNb\nyC3aT8zyEBk7kcmjx7GnrISm7ZtIi0VZb3mY6dhEfT7scYpJI0bzXkmcP+6YQTiWgdeznlh8Fin+\nBj4zcQPnDvU2L9t2HNZsWsPgijIafT4CBTMYO2hIu8d3aW0NZZvWkB0OU5KVzYyjxKY+5rhjTm+I\nNwBv/PopqvYMcYuwh5m8wHWUofHzTxZgU0A9G2jgUqAIqAWmMNR6kc/nryLX6+H9iXNxcjxsrzzM\nL1YOoTY2kTH2w1zPblKB9yyLCzOzKbIsLMtiaNymNCuL2LTr8Kems+G5jVQfGIbHLiKlWjMvupY9\nZLGdTxHDyywe4its4FV8vE82F3m8/M4eRYjLsLBJ43luoYxt2IynnPEem9dtmwVAFbCFARQQ5BDV\nDCLCMOAvDOZcLOqw2EEjV3GYQqAcmIGH5xnMAmxqiNFIJXOAbZhW3g78XEwUB1gJpDCQ4fhZRSqT\n2c0ofLxGCguI8zYwh0aGYmLHUPLxYPEaOezmM6Sxhht5nlGWwzpfBi+lfIeIXcanGn7BDHeP7/AF\nuCQ9jXfDTcyNNtEIPGbPoohPMJy/cr1nA7nAGp+fuYEghxyb+oYGJuGwHhiXkUWuZfFBUxNzYxEO\nOQ5hj4eJjsMef4CCcxYxNDOb1RvXMKSyjBqPh3LHYYLt8EEsyrSmMAGc5tj03q7t2FvWMcxx2Ozx\nMv+iyxmSltF8TBVVV1K9ZT2ZTU2UDMhh1qx52I7DxnUfMaS2ht0eDzkW5Do2xQPymDPzTJpicX60\nfjD76gqoXLUBHA+2M5Sc0QexBzt4LKt5+YUlh4jt2EJqLPb/2TvvOCuqu/+/b9/eK52lDLJUKRbA\nFhFFY8eAMWqiMdXEJ3nMk/xEsWAsSTSJNcYSK7GLWFCRoqL03mZhge29795+Z35/nLl37y4LXJZd\ndhfO+/Xa1947c0+bOedzvt9zZs6hIiOLybkTMYWdD6LpOhu2byTL0CaHMpYhmdld2Xy6nUjVMU1V\n1c8VRXlEVVUd+LeiKL+KJKCqqpqiKP8BrgSuDTuVADSEfW8CEiPMT0QEfB52f+4FHkK4I1BFMsO5\nm/kUk0aAe3kY8dj1FOA94HEgih9zNk/UHQDAV76fW2PiWXcggX11/wYs/A/3cqeRzlx07q48SHwl\nzAcsAE31PLttPTuHnMPLex7Cpw8HwMwXPMZlzOM5/IjR41KuwMk0fodKDhW8qOlU8Tx13AKAhRt5\nm9ncxzbmAZ9ocCNwEfAR8C+aSaAZEApuw8Iv8HIJNQBsx8Fy4vk9TTwBWLFxN81MowUQwuMCbgLu\nAb6Hk2uMss0G3qCJO2gC4FcM5lbqmYef/yWWBJp4CHADScAVNAKN3AacTTyj2M0/8WAC8HtoaH6f\n/bh4GJ00I41MLUBNYz2zgVnGsXMo5xyGci1/5x49ADpomsYjvgZcwKNANKInnOdysgMno4HxxjX4\niR7ADqBp/H3LevY5ojl393ZGB/yAcKVNiO7mZ0aazuYmHlizgkBdLQ9rmpjm1XUe3LwWwpysvVs3\ncntZsShTUz1Psh5LRjY37NtDGvAGcL3xW72xnie3bmR38nTe3f8YkGyc+QCYxf4mBw7Lr5k/qbUp\nVGzdwC9qKgEINNXzpM3G5AlTac++yjLO2b2NXKNMm3ZuYXdiEkNS+vyAaZ/UG4Blf3kXuAWYDoDO\nuWykknV8yC1YmEYNBZyBqCHFCMPoHHJ5kCXeHcSViXgeX/xP3jr/VvZuuoImLuZSpvEkHqKMdJ5o\nqWdaSz0XIJwTgA3VJdyTMYRtn8bTUBqs1VehcA1fcR4NPAlY8AE7qeZJnmceDfwEM/u5iCLeQLQK\n0DExnef5KZCtwwLgXoTBk4GfS6gAoBLYBmwlk5coNcLC7fTjHkqZCTyMn2gy+CslgHiu6i1gLmKI\n/3FEew4+VvEIAf6PcgAuI4b3SOBhankEjTKczAdGGXE8hYbFyMttlPIhc3iKMkYBaBojXLuYCSyg\nkhuN+K8E/sfVgmKUyQT8CBdFVFJEA/+gmTRN5PTtlmbigCsQ2vQR8HAgQGJjvdAeYD8wHDjLiH9/\nVT8iZw0AACAASURBVDmv7dhMRm01P6+tAsCP6F3yEdMdQW36x5b1vF9ZwS+aGzgteB8banlv5xYu\nzZ0AwJ7SIi5RtzNM0wD4amczBYlJmLet55bmJlYirPf+RvjPWpopiE/k8a0/oNwVLHU1sAm4iMe3\nvcK41CUk2O0AbN23m5v355Fk/HLR1vW4z7+kQ+epcut6fh6BNvVBOqU5Pa03FXtVag/kAH8wjuhs\nJ473+BM5BLiBRF7nRuDHQBaix5wL1PCA/v+4pVK0yTnlB/hw/GSe2DWGXd4XsPI1/+auUH98oa7z\nfGM9Cq19m9ZUz9ZPn2V1wiwK1/8S0SODl79xAR/yP3wKTAQgn/EMYxa30shDBHhRy6KUp3EZ5+OY\nzufM4dfUMwdYoIm2OQT4L/B76gma4IuAPFJ4lhoGIfq+V0liIq02TAEpvEI56egAfIxQ5BuBm4H7\n8QmNMHJYRTWzEdp0HcN5kH18j2aeJ4opuLkTMYg8ERiHaNOX4GQmZzGHxdyDBjrM8zVR4HuP/mzk\nUQxbEHjE78XZ4OX3wFDjWDaFzOFC7uN+fmK07cu9Hp73egB4AKgHMoCLmxv5HKHDacY1mBcIiDvu\ncXP/dyuozFH4yYG8UCV7H4gH5gAzjGP5VeW8vmMzUft280fjmF8L8Ifln3LNZXNEfLpO3db13FZf\nK+5nUz3P2h3ogQC3lxRgAt4BfmCEdzbU80JUNEtd01lR+g/ALGq7oZBNZQ0syv0lPzTGf5u8XuK2\nbuBql7A/qxvreTsmjnE5I2nPtn27+XG4Nm3bcFht6q1EuoS7S1GUAYj+E0VRpgOeSBNRVfVmYCTw\nvKIo0cbhRoQQBYlH1KnDYrGasR7Dn99ZD/Qj6GAJsthqdEcXUA18gxhHBeFzRgEBxlAQCmEDBjZU\nojWnGd/KyW1dgIgohHxF09qoALIsOgda4kMOFoDG2azHgZ8poWM6qeQZTe9MdDYTQ11I3iBAf3QG\nEWt8XwVMDcXX9iLmAEuJZqbhFAGMxcNBQwBzgeVEhRwsEENuxcbneAjLmWjgetj3kUa4WOPcUEQl\nKkUYGkGSgaHsZRSFhI9PjKKBkTSFHCyAyYj1c8PTzcHLQHYyIqwcZiPNeMS11oHg2Mt+ILjRid34\nCzI0EKC6ribkYIFwxtYCo8N+FwOkOZ0MDQTaNIxBuo7VZgn9pbqdbcqU6nJidjaHyhQXds4EpLid\nlLrSaXWwQFytMsBMlXtQKG6L1Uyaq/XeWIAkt6tN+sG/pqaGkIMFMNHvo6ahrsPfdtUf0Olwx0Cv\n0Bs4ds3xuWJoNbcBhnOAwQD0J8Ag8mmtIXsBYUgPI79NvTmjtoy6nVtpYiYAuRSHHCwQTkZwDizI\nZK8TS8lunPWDwo5GYSOdKkYTrk4F5AKiHQ8igM5ICKvVXkbSDATHDAcZZwvapZkBOI2yBTEBWUZc\nAxFtcSDe0Hkzoq2JcgvNCm9v/cM+T8XJbuPKDMdPjVF2UbK2eptLAf3ZyqiwqnIWOvE8H1J4EINB\nAxGaHd6OR1PIKPa00aZJCMcqqE0arZZyUI9V43dBcgBPfQ2pYe042LMk01abhgQCVFdXhhysYJql\nlSWhtuNtbgg5WABneD0cqKkgt0UMrDXS9ppN9rhRa6spd80IO5qGGAqDCte5lLt9ofgdruaQEQOQ\n29JMfcB7SBu2WM1tymQBkt3ObtWbzmpOJ+i05nS53hztWoTpTXXePoSNE8SExjA2GLV0NLVAC6K2\ng9AeE1Hs4mJj0AOgn65hammkyS968Ww+5Kywnn8g4KVt32YGBtVX4a6LI+hgAQQYxTIGEnSwAJo4\nk01kAKIdu8kOOVgAzVzAfjINpRQ1dYjxOYa27TQWaCAq5GABXEQ9q3AAMBjwEhVysEBoRpERTyat\nGgLC/tgUXibj83CgFhMDje8HgLFh4abQTCZrmBRmKzqAXMoYid5GmwYC5bQ6WAAXU00Ci5mCL3Qs\nCaEPQW0qgJA2uCGkTbFh8ZiAAV4fDmdzGy9+GLAMOKPdMU99TahMILRpSMAfqmOaxUS/sHZuBxLc\nLSR5nFgRA23h4WOAWFcLle5BtFXyYG1JpMSZEYq/xutkYlj8aYDF2dRhfXe4Wtpo05jDaNNxaUu7\nPvywxzqpOZE6Wb9D9DUjFEXZghis/+3RAimKcoOiKEGH2Q0EEP0UiAmU4YqiJCmKYkcMxn13pPgC\nfg3/MfxZY5MR1dQZFkshZ1MIwEdkIR6Z/jbsfANgYR2tXrUL2J86EGtSlRFXFhvC5KYRYS57aFVl\nHVgVP4hZc0YRG7U99Fszy5mEHzsrQ8csFDIBFYAVmLkIJ2m8FzpvJw8r+TQb8V4KodAOxGhyEBW4\nFifvhlXNb4kl13CONgNX0MInYc1xI6LxBUsfvmZtAa1GgQ5sM8pdg5iWz0OM0g4AdoWFK8JCHhPY\nzIjQDdeBLaSxk7SQUwfikaHbEM5jkK1Ec4CpbCMpJJU+xHV2Ip7LCM5EgRCitcbnABjzeoI8i5XM\n1Ew2hC0pugZR4baE/a4WqIlLIM9qC8meDuwzmfD7AqG/qtj4kEmpA1WxcZgSkig0prubaHVMA0BV\nbDw5cRWYjZF+wW6EaeRjQGx+KO6AX6MitrVueYD62Pg26Qf/kpNSWW9rNdlWOxxkJad3+Nuu+gM6\nHe4Y6BV6A8euOVEJTuDLsBi2M5q9AOzBwX7G01pDRiNqIuxhLOVhcrwyfRBpk84kydCBTYxoMyy+\nFbEM2vqwYytiEggMnUBc2v6wo434qGQAmwhXp5FsAEQ7LsCCmZ0QMlp07GwjFTHzArDPuJDDEBoS\npABhGBRgaVPnS41veYhOZj+20HkvrWtkb0PcmNaUhTET5AsSmGq08l3YGIjQq2A84Xq7gZEUcBbr\nwkyQLzDTyJ2sDouzHDGoVGrkNRh+EyPYxmSjdxCsRozYBrUpXG+Dd3Gs8bsg24H49Cwq4uJDx9xG\nfus5VJuyswcYd0PwNTCs/9BQ24lJTGF72MjtyqgoRqb3Y3O80PB0MGqY4JuYGMamZzIwdmnY0UKC\nNv/A2KUMiLa3ts+4JGPeULA5IYEUq+OQNhzwa5THtpbJA9QdRpt6WnM6wTFrTrfpzdGuRZjeZOWO\nBg7S2uP4sbKTadQZdToDsXBiUBOaAA0343g71OuDarVjSUgmybYD0ClhHsvC9GgPop03hqXkBfal\nDSA2o4Vw/9HCdi5nPya+CR1L4XOmI6bpd2AnnmLiwiyNJD5gEiUhvUml1Z5w0lYfmoBsnOw0nCqA\n90nlYjxoCM1JxMn+sMH1bQinLYB4JHkjrawCzjY++2jVn23AIPRQ2xqFeLQwyDKSKeUCvqb1Ef1m\nYBM57MDSxkPfZ6S/M+zYW/Snnnkh5xAItcMSI685tNooCQhHMZhOuN4ecDjwxycZc/qCPYiXBpeH\nHdsBxKVlkh/2aJ4byLe16oFFg8LY1vGBZqAxLoH6mHjciHtzMCzOOsAVl0j/mP3QxnIyrDNTOcPi\ny0PxZ0TFsiZMRwpNJohP7rjOxyUatUawKSGB5A606bi0pV0ffthjndQck67rhz0ZjqIoNsRojQXY\no6qq9yhBUBQlBngJ4ZhbgYcR7m2sqqrPK4pyKWIG1AS8oKrqs0eK78pHVkSW2TBKtm5gw6JChEFT\nRRafcgOVVAEryEEznYmTndQyHXQbmDeDfgZ28rgh6zOGO8zs7jec+kt/iRYIkP/MG7hqh2DTvuV6\n35dko7PebKX/wNPQrBaKzFZG+TyUxqegXvIzrIlpHFyzi9SD0YCLqryVpPmaSNQKWcE8PKTTj/f4\nLd/SgpkvSeQCq4NFfvBwKRoJWFnB1VRRhYaZcs62+vnC7+cMxPjRZ8STSxJVtOCgljHAEhIZQQIO\nYBMurqFavMsBTMfBEhI4HQegU0oZ56BRjGjcO4nmKlwkIgyqBlIYShz7sWOllDPw8x02xhHgAClE\nU0ouQpQ00kglmg14yGceOrWcz5tMMvs5aItiWcxPCJj7M67mLs5EoxHYbLVzWVIiG7x+hrhaiNY1\n3tayKdSuw0weV1k+I8eksddiY2BcHDG6zrrGek73+ykEbAmJDLPa2ejzMsLlRNd1dpnNTNQ1qixW\nsk4/CyUzm10F+7EV7kczQb7JzKhAgB1agKSWZtJ0jfy4BC495yLqPB6+WraEEQE/RWYzQ884l1Fh\nzwG7/H52bttASnMjdTFxKOMmEWd3sCVvF/EVpdQBTrOZgXqAmpg4csdOJspq5SXVysaqXOzmenRq\n8AaGMzg+nzvGNeCwtI6E1DlbKNixiQS3m5rEJCaOnYTV3PF4yO7C/VgLRbegDRmOMmBwh7/rKqw2\nS6eMmGnvv37oA9dHoDfoDRy75mgBP0vueRkCE4AoTKzhR3xLOiZWYqfCdA2aXkmlyXgnS18LptGg\nx3BG7Etcke6hNiqW3TN/jLX/CMo+WULVeht+v5lz/P9guu6iAtie2p9J8Slst0UxUvNjRmfT5Etg\n4kyaKqvYtbQarzMOq/Mb4us0pvlW8DHTqGMqFvZyKy8yAh8riSPHmsxufzN7mILGGMzsJ4vPmE4K\nO2jgmugWDvh8uPwBYwbNQizZJGKikBKmI96NPEA/RmJmJxoTKSUDYcCcQRQqJhpJIQfYRz2TaSEa\n4VD4EaPLYxDOzw7MDKM/Aax8h84PKaUUM2XAcHMc27RazkPDA2zGxlAyKaSJdZyPnxyyeZu5FOG3\n2lgWM4YC61ySm55mlq+AZMRgzPSEZBptFiqbm8kJ+Nmm21gauAYTGYw3Pc9Mi5Mmk5mS6DgmWi18\n63IxzN1CjK6zxWZnRkIiZZpOo7OJwYEAGzExGjEgVZbVj4smT6PW2Uzhjs0kuN3kWywM0DTQdXa6\nWhjt81JtsZJpaNM7X3/B8JoqNOBgRhZXn9125fAdB/YSVVKA32TCmqMwPHsAJXU11BnvZO02Wxil\nafitFuzDRjE0I5ttNV5eUUfhDsRiN+/Eq+USZWnhRyP3MD6tdXBG13U2qdtJrqrAabWRpIxlQEpq\nh/W7zumkYMdGoU0JiUwYNwmbuVMzRxHTGc05Vr2BY9ec7tCb1Vf98Ih6s3DyTYfsP7TxzcUUb7Yj\nTPL9jGcpF9HCXmAdZ2I2DaXRtItGfSboVWCqAUaTYl7JLQN2kmg2s2HkFP432klJczOPro6myTeU\nZP+7zGEvMYj3pi9MTqHSbCKAmexAgJqERD69+XGi7NHsWrqV2sI0TJQRVbGL0Z48ajWdbVyNDwcT\neIGbyKMIKzuI50xrFG/4E/AyC/BjZylX4WEvPkaZy8kx6yz3+5mOMNU3kcpIYiiimiHG+1Hvks4U\nomgESqjjEprZCzRg4jQcfEwSZ2ClHh8BKjgd4eTEYGUbFi7Gg45wuExkkE0Um7AzlYMkY+Zr7Jxh\nMrFWtzCGegYghsTSyMaCiW+IpZLLMbOTa/iMwWYTWx2JfBd9Bx6tnvPrH2ccwjGqiI7l3NhY1rhc\njPW4cQOv+XOp50IS+JLrrDtIQWebw8EkezRVuk55Yx0jNY0dmBiRkkqSycx6l5OxXjcVOjSZTYzQ\ndQodUUydcRHJDgeb92wnqbqCRpOZGpN4imeD38cwpws7ekibtpQUULXxO7I1jd02G5fMvII4e6sm\nVDY3UbZzM/FeD7VJqUwcMwFdh83bN5LaUE+R2Uy8yUSyrlGTnMqk0RPwahr/2JbAwabhlKT4MeFA\nD6SRNKSa/yRsbPPOVWFNFU3qDqICARozspmo5HZY349FmzpDR+3JajWHHKsgHr/vqCvwHk5zjuhk\nKYryEm2fFmuDqqo/OWKqXUxnnKyOLtiJxms80qXrepcsldxZQ7c3I8vUN+hOJ6u36Q30Xc3pKrpa\nu3oTsn32DbrTyeptmtMZJ6sr9MYb8HeqjXeUn65IQ9bjvsHhyhReLyJxUHqKE+FkHe1xwZWIwch4\nxIO/y4HPEY+WR/qo4SmP3WINbQK6cPJNcrlkiaRjViL1plfRXrskkpOMlUjNCbXxvp6GRNLbOGKt\nV1X1ZQBFUX4JnKWqqmZ8f4vgywSSiJEbGUskh0fqTe8lNspBi9sjdUtyUiE1p/PYHrgflhzs6WxI\nJL2aSEdqEhFvTwbJpO1CM5JjoP0moHKEWCJpg9SbXojcvFhyEiM15xhZsDgfx2HeFZZIJIJI528f\nBLYpirIa8VLoGcBvui1XpwhyZksi6RCpN70Yh9UmNUtysiE1RyLpYrxhW8ycqkQ0DKGq6quI7Tv+\nC7wGTFRVtct3Lj9VCR8htj1wv5iGl0hOUaTe9H7aa5ZE0peRmiOQbVnSlZyMCyYdKxE5WYqiJAFX\nI9ZBHwP8XFGUe7ozY6ckZgsLlhxkweJ86WxJTlmk3vQhzBapV5I+j9QcMXCyYHH+0X8okUgiJtIH\nat9G7H1pQez5EPyTdCEOs1k84xzmbOXeOpvcW2f3dNYkkhOJ1Js+Qkd6JZH0QaTmGMg2LJF0HZG+\nk5WlqurMbs2JJETwZVKvrnH91gx0XWeRIXw7n/+kJ7MmkZwIpN70IcL1at6WdKlVkr6I1BzAZDIx\nb0s683s6IxLJSUKkM1mbFUUZ1605kRxCcI8ak8nE9VszmLclvaezJJGcCKTe9EGkVkn6MFJzkHtZ\nSSRdTaQtagxChCoAN2IaXVdVNafbciYJERQ+b8DPg1Nuli8TSk52pN70UcK1auHkm6ROSfoKUnNO\nACaTSerCKcB9VwyT7/cZROpkXdWtuZBERNCAkcsnS05ypN70cewWq9QpSV9Cas4JIKgLkpMb3933\nQC/fSzH31tmYtnb/a5eRLuFeAEwDbgOqgHONY5IeIDbKITcFlZy0SL05OZCbF0v6Cn1Fc+67YtgJ\nSUe2V8nJzrwt6Sfk8dhIl3B/GJiNWOLUCvxYUZS/dWfGJEfHYbWB2cLCyTdJUZScNEi9ObkId7Sk\nTkl6I1JzWgm2V4lEcvxEuvDFLOBHgFtV1UZgJnBJt+VKEjEOs1luZCw52ZB6c5IhNy+W9HKk5kgk\nki4nUidLM/7rxn9H2DFJL8BhtcmNjCUnC1JvTlbk5sWS3kmf0RyP1iuz1e1IzZD0RSJ1st4C3gRS\nFEW5A/gKeKPbciXpFO03BpWiJOmjSL05SelIoySSXkCf0Bzf3feAFujpbBwfxisOx8qCxflyxbo+\nxKIJVSyaUNXT2ehxInrrS1XVRxRFmQUUAIOABaqqftStOZN0iuDGoB5gwZKDoAXk5qCSPoXUm5Ob\nNhq1OF/qk6THkZpz4nCYzXiO0VHMvXU2bOmmDJ0E5PZCDe1NeelJIl34wqaq6mfAu8AHQF235kpy\n3DjMZhxmc5vNQXNvnR1qjBJJb0XqzalBR/okkfQEUnN6N/O2pIPZAiB1oh25t85m3pZ0qaG9lCM6\nWYqi9FcUZS1wu3HoTeA+4HVFUa7u7sxJjh+7xYrdYm1jzEgkvRGpN6ce4foktUlyopGa03cIDspI\nnWhL0AGV16Z3crSZrMeBl1VVfcz4Xquq6vnA5bSKkqQPEDRmQC6lLOm1SL05RZHaJOkhpOb0IU7E\nvkZ9EYfZHLo2cjard3G0GjtBVdXr2h9UVXWboiipRwqoKIoVeBEYAtiBB1VVXRJ2/g7gVqDSOPQz\nVVX3HkPeJZ0guIyyx+8LGTPzN7zck1mSSIJ0Wm9Aak5fp702SV2SnACkjXMYcm+d3a3v1Zzqbbw7\n7C+H1ca8LenM77IYJcfL0Zys9m8nTg37fLR1RG8AqlVVvVFRlGTEa4tLws5PAn6kqurmiHIq6VKk\nsyXphRyP3oDUnJMCh9UmHS3JiULaOB3Q3cZ6sI13J+1nxLtSS7rSZpI6d3JztMcFKxRFmRL8oqqq\nD8A4VnnYUIK3gLvD0mnfoiYBf1IU5WtFUf4YeZYlXUn4JqHyUR1JD3M8egNSc04aHFZbaKlnqUmS\nbkTaOH2cI2lEuH3Tlel19LkzcRxv3g63BYbUzN7D0Way7gc+UBTlfuBrxEZ90xHC8oMjBVRV1Qmg\nKEo88DZwV7ufLAKeAhqNNGarqirXfOwhwme2gg3Xd/c9PZklyalHp/UGpOacbIg9tcwhTZJ6JOkG\npI3TBwnqge2B+8HYOytcI8KPd0VabVic38Ze6lR8YXEcDws6iOdEzBJKIueITpaqqssVRZkLzAce\nNQ6vA+apqvrd0SJXFGUg8B7wpKqqb7Y7/Q9VVRuN330MTASOKEAWqxnT0RLtAKs10j2X+w7dVSav\nbuXejwrQA37uWyjERb/vvm5Jqz1Wm+WEpHMikWWKnOPVG5Ca0530VJm8upUFi/O7RY9k++wb9FbN\n6Ra9MR1dcdq3xe5omx7/0a/78aR7tPjbnwumZbU6WLA4n3ttFhYszsdkLPgQ1Aj9vvtCx4NhvLqV\nhZNv4t6tr0WUN9OCBaHPCz5oTQNoE28k1ygc28L7Q/F1No72dHQPjjfOY0q/D+vN4epv++PHcz2P\nulSLqqpfA7MOd15RlNtUVX2ug+OZwGfAr1RVXdHuXAKwQ1GUUYALuAB44Wh5CfgjeS2jLVarGX8n\nwvVmurNMdkPgPWYLCz48CFqA++5ZwMiCNd36EqzVZsHv6+M72bdDlunY6azeGOek5nQTPVkmu8kE\nVltIjxbdNKtLtEi2z75Bb9WcntSb8LbYnW3zaNf9eNNVDtOWO7rn7dMy3bMAFue3tVk+yOe+sOPB\nMHaTCQ9HL0+QhR/kh/blwmwJpdFRXiKNc8xPZzPXiDc8b8cSR0cc7h6cCB3o63rT0bU7XHvqbDm7\nYvjj54c5/icgCbhbUZQViqIsVxRlnqIotxqjO38CVgKrgB2qqi7tgrxIuojgZsaYLSxYclBuZizp\nLRxOb0BqzklNUI+kFklOMNLG6Q6MttzZsAuWHGx1hKCNvRJ+/FgJ6krQBnKYj2AmG++NRsLczWI/\nqyPG14VIfewddMWmAx3Obauqegdwx+ECqar6OvB6F6Qv6UaCguDVNa7fmoGu6yzq5qVdJZIjcNhn\naaTmnPw4zGapRZITjbRx2rFw8k0RPdZ4JBxmMx6tc7MDh3NUusKBmbclPeKyHWsZTpSDFdyYWC7l\n3vN0xR3XuyAOSS8nuJmx3FVc0sNIvTnFkVokOcFIzemAE7UxcFc4dMfKidz02GQydflqgHLT5t7D\nyfd2tqRbsVuscmlliUTS4wQNCalFEsnJTVc4Dd3hzHQF0iE6uZFOluSYcZjNcm8tiUTS47Tf508i\nkUg6oiedGTEbJ83tU5GuuOv1XRCHpA8iNzKW9ABSbySHIHVI0o1IzekujmHhiBNBd+YlxtG1myJL\n+gYRufaKogwDzgTeAP6F2O/hf1RV/UZV1Qu6MX+SPkD4xnxBkZq/4eWezJKkDyP1RtIZ2uuQ1CBJ\npEjN6RmOZ/GL7qIrNgmWSIJEOpP1EuAFrgBGAr8D/tpdmZL0TcJntmwP3H/oTukSSWRIvZF0GqlB\nkk4gNUdy0iH1r+eJ1MmKUlX1beAy4HVj8z7p7ks6xtirYsHifGnoSDqD1BvJ8dFOgySSoyA1pweR\nbfTYOeo+WGYLCxbnn5jMSA5LpE5WQFGUaxAC9JGiKFcCvWuOV9JraL+R8YLF+XLzUMmxIPVGcly0\n0SCpP5KjIzWnp5DOQKeYtyX9iI82nqg9uSRHJtK7cBtwKfArVVXLgLnArd2WK8lJQdDQMZlMXL81\ng3lb0qWxI4kEqTeSLqEj/ZFIOkBqTntO0KIUfd4Z6GWLd0h6FxHVblVVtwN3qar6rqIoM4CvATn0\nIImI8M1DpbMlORpSbyRdSfvNi6X2SNojNedQ+rzzc4KQ10lyJCKqHYqiPAPMVxRlNGL1ndOBV7oz\nY5KTj46cLYmkPVJvJN2B1B7J4ZCa07P01o2CJZLjJVIXfCrwa+A64AVVVW8BBndbriQnNUFnC+S+\nNpIOkXoj6RbCtefe8Tf0cG4kvQipOT1IT24ULJF0J5E6WRbjt1cAnyqKEgPEdFuuJKcE4Uu+3zv+\nBulsSYJIvZF0K3LzYkk7pOZEyMLJN4HZ0tPZ6BQmk+mEr2TY0ysn9nT6pzqROlmvAGXAQVVV1wIb\ngee6LVeSUwqH1UZslAOQRo8EkHojOQHERjnaOFuSUxqpOcdAd72H1N3t0G6xHnYlw4WTb8JkMnV5\nmgsW5/fYBscOq02u3NjDRLrwxWNAtqqqVxmHZqiq+vfuy5bkVCR8Zmvh5JvkCMwpitQbyYlEao5E\nak7P05EjsnDyTSfUQZGPLUq6mkgXvpgOvKcoypeKoiwH3lUU5WB3Zkxy6hJ0tuRmxqcmUm8kJ5r2\nmiM5tZCaI5FIuoNI53yfBz4ArMBTwF7g/e7KlEQCtNnMWBo+pxRSbyQ9g7ExqhzcOeWQmiPpUeS2\nEicnkTpZLlVVXwJWAnXAT4FzuytTEgm0bmYcdLYWTr5JCtGpgdQbSY8QrjcLFudLvTl1kJoj6TEc\nVlu3bishdazniNTJciuKkgKowJmqqupAbPdlSyJpJehsyc1ETxmk3kh6DKk3pyRSc3oJsq11LUEd\nk/QMkTpZjwFvAkuAGxVF2YlYfUciOWHIzURPGaTeSHocqTenFFJzegNmi2xrXYxczKNniejqq6r6\ntqIo76iqqiuKMgkYCWw5UhhFUazAi8AQwA48qKrqkrDz3wfuBnzAS6qqPt+5IkhOJYKC4Q34Q8u9\nzt/wck9mSdLFdEZvQGqOpOsJ6o3H72Ph5Juk1pykSBund+Awm/FogZ7OhkTSZRzRyVIU5SVAD/ve\n/ic/OULwG4BqVVVvVBQlGSFYS4x4rIiRo0mAC1itKMpiVVWrjrkEEaBrGs3VpVgdDhxxyTRXleKI\ni8dsteGqqyY2LZuy3evwe1z0H3cOrrpKHPGJmExm3I21xKRk4WmuQ9d1ouJTcNaWE5WQjD0m4ZC0\nAj4PLTXlRCemYouOa02/pgyr3Y4tOh5nTTnRyenYog59GqG5uoTyXRvIGj2Z5poS3LVVpJ82Ug3N\nPAAAIABJREFUlZJ1S4nJGsiAceeFfltXrOJrbiCh/0jKt31NXL/BxCRnUl+UT9qwMaxd9Cia2824\nOXdQsmE5aTmjiE8fSFPZfmIHjGL3py+gawEGnH4huz9+mX7jp5CYOZhqdRMHt32Jyecma/JsRp83\nl+J1n5I2+gwc0Qk0lhdhj0+kqWwfUQnpxKRkkv/NxwycdC4+ZxMNpUVkjz2Dre/+k5i0TEZfdDPV\nu9aQetpUGkr34qkoZND0q/A212OJjqe+dB+F333G0HMuJy61H36PB7PNzt7lb5M8ZDiDJ80Klbnm\n4HbwerAlZZC39GWi+g3md4PGoTlbWFC7neXFBwnoGtOzB/B+YSlnZiQTbbWxq7aOGZkZvKzuJcqs\n8+Mx49lSXcnwxCQ8gQAlzS1MzEgjxmoHoNzZzDclJZyekYmOToXTxZiUVFaVFpMWHcWEtEzKW5rI\njImjuKWZencLEzKysJtFk6p2OVEr6jktMYVKVwuNHg+5KWnsqKkgLSaOYYnJoTLtrKnE5fMzKiWF\n7dVVZMclMCQhMbK6reuUtTRjMkGs1c6OmgoGJ6bQPzYuovDHgtPno9LZTIbdQUw3LKt7nHoDvUhz\nPC0NeJoaiEvLxuNswO92EZOcibO2HFtMHF5nE5XqFrLGTMViseL3eolJSqelpvwQbXI316P7/cSm\n9TtkHxdd13HWVaDrOrEpWaHz3pYG3E0NxKZm4WqoxmQ2E5uSdUg+A14vhVtWEh0fT0JWDuXbvyVN\nOZ2migKaS/czeMa1RBl1yd1YS33BLhIHj6Kp4iDO6gqyx06nriiP6KQUaov2kP/Vx4y88FoI+Gmp\nq2boGbNpKNpDdGoWdSV7KVq3nJHfuw71q3cJuN2MvepXFK/+gNqK/TQX78Icm860n/2V6rx16JpG\n/9MvpO7AbmzR0fjczXhqq0gfO50Dqz/CYrMyeNIFHFy3jKzRp1O6/Wtq83aQe/XtuKoOYItOIC5z\nEKXrPiF93HnEJGcQcDZiiUlm50f/Ij4jk5zp1+GsrSA6JYPizV/QWFbK6FnXY4sR7c/VWE1TwS4S\nho5l77fvc21FGQOmzuSa7R8zICGRFp+PNaVFzBySw+aqahq9AS4d1J+VpWUoyUmUOptZVVzGjSNG\n4tID+HUYmZDI1tpqchIS6R8XL+5DIMAXxYU4TCYmZ/VjS3UlOYlJ1Lic5Nc3cP7AAbT4fFjNZhwW\nKzurKxmenEpmjOhD/JrGxspyUmJjSLVFsbumEiUljVqPp2Ntqq3mtNSMkDadnpGFNcK9j5q8Xmrc\nLWTHxpNXW4NP15iQlom5i/dO0nSdouZm4qPtpFgdXRp3kJPFxgn4vbRUlxEVn4TZ5gjZGAGfB5+z\nmaiEFIq2fE1MSippQ8birK0gJiUTn6flEG2y2By46qqISc0E86Emos/dgqepnti0bCw2cV80v4/m\n6rI2dlNsajYWm/2Q8HWFu6kp2Ef2uLOo2bsZk9VG8oARIRsjfsAoQNTp6rwNxMQl4ohPDWmTrgWE\nNqZmseG/jxOflcnwGXMo3vQV/SdOw2wy42moxZ42gN0fPkfiwMFEJ2WEtMnZWM2Crz9nVVM1fuCh\nqTNIdkSx95MXGHzmbDzOJjzNDdii42kuziNxyGl4Wxoo3bmeIdMuo2b/NnQtQGLWUHZ/+G8yx59J\n6vBJvLdPZXpmP1aUFmI3W5idM5yyliZ8rmZKtn9Fbf5OlFk3gMmCxWrD62xg/zcf0X/iDDJHTg6V\neVNlOQkOB26/j7fy9nFWdgaZsXGgawxPSeXjfSr9E5Kpz95L1fa1DJx8Hq76StwNNSQPHcPez18j\naego+uVOo7FoH8lDc6mtKOSOxLE8Vrc11E7V+lp2VlczI7s/Jc5mArrOkLh4VpWWMColhQFx8dS6\nnWRFx7GrrhqLycT49NY+5GBjA+VuJ2OSUlDraoiyWukfl9ChNgXLlBEdG9KmtOjI9vr2axqlzY3E\n2R34NZ29ddXkpmWQ5IiKvIFEiKelAaerkaikLMxdZOOYdF0/7ElFUY64M5yqqocd1jN2TDepqtqi\nKEoqsFZV1eHGubHAI6qqzja+PwasVlX13SOld+UjKw6f2cNgJsC3/9lERd4lmEzFOGI2426+Covt\nICbTTvyeS4EngZsAB/AScDMW+2pMpmz8nlxs0Z/i84zEZMrEan8fn+sGouL3ctrFpQyaNDKUVmNZ\nBRvfdNFYPo2oxF2MubSa7NzBrH99E+V7ZmM2NWG2f4HfNY/opG2Mu7KRrFFDQ+G3fvAFB9eMAs7C\nzCtYmYaPJH7IdfwfJRwgmkeiFLIWPkbT3+/lzuK1VOLm9/yMSn6NnTy87EBjDvA08EPACawG5gDL\nMJGMziTgQ2A0YAM2A1diZhu3chcqcaxnIR4GMIg/Mo/N3Eg5X5HCw0xgP//gUuZwP/towMxdzOA7\nngb+CVwJZHA1V3MPByjHwcNk8xz7eQwTc9FJA14DrgXKsXI3l7OZhzDxDTp1QBZicae5QBF22xtc\n8sCNpD5+C7dWHCAaeIBhvMcHjORf3MW/OBMfHwKnAWVE8zPuReMKQGUGj/IvvuU50nmW5/ATAPLw\ncxVRrAGq8HApQyxP88C5B9lWWsNre2ZRylwS+ZJf8ghzKGIOV3KABaSznD9xF7NxsdpsBk1jKvCW\nzcaUC7/PmoPVpO/ZzSV6FZ8STTIuJgKvGnekEvgqI5tZZ5/PkuUfc0VjA7HA68CNQDHwbb+BXDR1\nxhHrtqbrrNn4Hd8rKSSAzge6zo3AbmDH0BGcP37KMbWVI7G+0s9ft15AScsFDIxdxv9NXMGEtMhF\naNr7rx91l8fj0RsjfK/QnIL1KruWDsbbkkNU/Kv4PDMJ+NKwRr2J33UVmN4EfQJwOvANUAjMxBa9\nAp/7mjBtuhxb9DJ8nixMDCM7dymT503CZLYAwsHa8s5GirdegK7DgHHLmHjdFArW5bH780F4W4Zh\ni/4Qn3sCZksCA0//ivFXTWp1xFzNLF24Cj1wE7CDKMpwcykT+SP38BnD8fA3BrDvF3/EWlLM7I9e\n5EqtnMs5jwLuxMMALLyMjxuAj4BhwGRETb4Q8TT6Z5j4ATr5wB6EPrwCzAZiyeE+5vI5L3EnZcwl\njS+Ywp+ZTzUW4B4G8znvMIxnWcDrTKaZf5HOU/wHPzuBGOBCFB7lft5gDG6eIZNa6hiOlwHonAts\nABpMZs7Vdf5KNi/xLuDHxFvo3A48irCZ04FXOff2ocTnreMHX7zEVE1jCdE8wp+pYRK3cTW/oZr9\nwHbgauBc5lDO74AYYniSb/g3KrE8yFXs4C6ieBYvczBjxc7rOPk5SWzgykGvcuP4VH79SQN7A/cC\nfsZzD6+ynIcYzbvcTQu5XMsVzOcALSYTS3WdG4CtQP6I05ik5LLo8/X81ltMFRZWEOBmAnwDmKCN\nNu0uyKff7m1M0XW+Qky/nA68YbdzzoWXE2c/1CgOJ6+4gOQdmxntdvKJyUSOrjMIWBQdw+yZl0fs\nqB0NnxbgrrVprK24DbulliuH/odfjfFFHD4SvYHeZ+OsvuqHR9Wb9ntHeZvr+e4/5dQXX4A16mss\nVhOe5hlYozeg+V1ovuHAp4jepQZM/wX9DqxRb6EHzibgzwhpk8Veg8m8Hr/7GuLS15F7lY+/VS4L\npbWyNMBC9TrcDWOIz/qW03/gwBETxfrXSqkruhCLYx+wl4BnNon9VjF5XiJx6a2P/618cgkNxRcA\nw7DyHHA90ezkN9zJj6hmFak8mzWBx8/K4KVPvuM3nmL+TSrv8huquIZoVuFCByYA7xll2oywc84H\n3sTMFCAbjUXAdbROSE4jiqX8mbt5njPI40HMeBnNL/kpJXyPGhaRzT+4nEau4WfcxO2Us4VoFnAD\ne7kDsfDkzdgo43Zu4adUsY5EHiObl9nD88AtgBd4x/j8DVHcy28p5mZMvI3OOIRNpgAzgC0kpX3I\n5J9fw5mPzmOu10M58ADnsYrnmMbt3M3n9EfnDeAa4GlyeJH7gSnAN/yaB7iVIh5gCO/xDna+wk8s\nAaYTxRIC9ENnFKOiHuLvF5r515qDfFn9U2qZQTov8zhPEIufX/BzKriNXP7KfNOrTNB9LDaZmKDr\nJAPvxsZx6fcuY9GGfZxXmsdYmngdOzPxYAM+Me5IuDat/HwJ87we6oBlCBtoAybKThvLmcqYI9b1\nFq+X3WtXMaumigKzmc2axtXA1yYz7olTmTAo52jN5bC0b0f71+xB/WIo3pahJA9axpQb+hNtDHZ7\n/L6jPsVwOM054kxWUGAURYkHblRV9SlFUfoDPwMePkpYZ1jYt4G7wk4nAA1h35uAyIbuj5G93+yg\nYs9vAQc6ebib/gSYCHhPQ3RBrwJ/AgYYIRYC8wl4pyO6T/C5FOAddCbhc+UAG3E3fZ+9K15m4Ol6\nyGhRv6yhsfxXALgbRpK34iVcDTso3/VbIAoN0FxpgAtX/bXkLX+BrFGteT24LhVRRQvQuBwvU5jE\n/+M/FGEBxtACbpU733+a14u/ZRJeJjKLcp4GTLjJRbyruwr4X2AQ8C7i0puAHehcY6R2J0IG3MD/\nAaCh8BF51DIFNxcBMIlYHqQcAIVaCtnDG3zJ2+wh2ojp33zNVFbg5PvAhYzmIRaRjx0Yj4sESrgM\nGy/iY5oR5gGEWXYlfsx8wWyeQOcnCBFbjxgENAOj8Po8FLzzV/5ecYAhRvhXyWcjq5jLSm5EdL7/\na5T2YW5E4w/GLxXWUIHCah6nEpXFfMps4I/iPqEY1+E0DgT+ybPrr6a2ZTilLACgAYVlHCSBJeTz\nX8DOZfyB3+ICYISm8Q7CXZ3v83H32q/QG2P5jS4GLEfg4l1gjFHmpcDlgLeyjPd3beUXjQ0MM3I6\nHLG01WygobSIJq+X+CMYPduKDvDT4oMkGd+HAjsQZmxVQT50oZP1sppLScvPAChqGcHLajkT0vZ1\nWfxwfHpjhO9xzdECftTlSXhbLgEO4m66AtERgt91N/AJ6P0RAwggnkr6K1COzyXaqdAmM5CNz/VL\nhPaMp3T7cA4Oe46hZ04AoGTrTgo3/gRIBaBo81DShr/C3pUZeFvEy+M+l2gVmn8mBesHkDVqMVmj\nTwPg62c+RA88AUQBKm5+B1RzF6u40qjfL1DIBc8/wxirjzu1ct4CDjIfN98T5eURREtOA64HdgE/\nQLSId4H70DEBuUY6xcBtCG2C/bzKi/yYcqO9wTZeooxM49si9jGeO7iNIn5EEwB/pxKVt1nKcESr\nd3EHS7nOyPM/Kef75BDDfn4adpXf0TVGA89Tym7eZQ1/QScGeAa4GcLUafVTt/Cw7R1u0DQAfo+L\nXSxlD0U8RTVmxICOA3ibFMp5GBAdvpO/82c28TYbqWEVv+ZG3NwFpKMBfk4D1lLPjXxaVEZZ4xvs\nCqwCoyVv4lW2MJbl/JIW5jKUJ3iZA8QA6DrZQC1i2OyZ/Xm83mDjOW8BQikCZAIa8GPjDgS1af7a\nr+jfWM/1xsDqCITy5QILvF7uXv8Nl0+7gCNhztvJlW6nuKa6zjsIc3eoy8mfN6/j8klnHjF8pLy1\nz8S3FX8DHLgC8M7+RGYOuI+RSV27FsXJYOPs+qyY+uLbAfC7t+PnWvHZNRJxhz9EFMUwKHUH8Cl+\n9yzEoEhQmz4m4L0SUasTaa66gfyVz4gKZPBo8UW4G4Rd1FQ+krxlz2CP9VFXdDtgIuBRgMXAUBpK\nR6J++SST5gonK+B10VA8AbgM+Aw/9wEJXMxfWEg1AAo1FJXv4ZW1tTzjOUg08DmXU8X/A8AV6q+X\nI2w1G2JY8QagALgIMeQJIuMfAfFgXBM3I3mU3UZ7FbdjFjq/pAaAeyljHyspB56iDBNwGk6qWMpv\niQcWAOlcxE38DdHHj6KBUmz8G/gz4sYDDAbygFtwc5BPWcjD6MwHHgGSES4YwEjqqytJ+u9C7vZ6\nsINhya1hOoUsYDUzjcnW+xFTpe9wG8JdEeEXUcAT3M/r5DOBz9jDsLAyB6/Z6exwv8yzmy7hu+qL\nqeVWAKr4M0tQicFNOY8DGj/kK36gC7vqD0Y7vwgY1NLMI9s3Mra0hutoBOABPLyHcP6ygRqENj2b\nn8eyhnoeMMqEUWoNuB6dJ/bugqM4WXvUHdxeUyUsQU3DhuhpbtE1/rJzCxyHkxWO5vexb0UK3paL\nAagrHIn65RNMuGrscccd6RtxrwPbjM9NCAvgVQhZ7B2iKMpAxHDDk6qqvhl2qpHWugiiFdQfLRMW\nq5mIhqfC8LvNCNEA0SDDY0gBqoGMsGNxxm/aT0UGq0kSYtQE/N54LGYds0WMLAf8bac/A94YAt6m\ndnFlAZuM89FYrWEjf1rwktSH8pRIM5aw0Dm4cBYXMASviIOkDsr0LYTMlGCZtbAytC9TK81k4DYM\nIIBkw3Bpjd1HPPUhBwtgMF6i2Y2TMwBIoLFNzEPx0EwC2bSORFohVK6hNGOmBo1+CCcxmbZrsmQR\nqDxI+MNOMUACtaTS3CZ/NsBjGJ1BAqTiRNzZZFoOcx10wERAS0DT2vaFzSTTSEIoXAoth4QOlik2\nEMDc7plyW7vfgRCjyuaGQ8rkNz6nAzW6j2Rb+JVui+b3tum1U8AwQyFW0zBbTF32CI9ba/torCuQ\ngNVmOcyvj5tO6Q30vOb4/D78nuCjoMFZ2SDBthjfLlQ8HWtTs3EuWHNiCbhNIc3wubzG74Ik423x\n4PcmhR0ztYbXM/A6PaHwAW8MrdoUTKOenFAtEhc+QTORFBBtdzdRuEPDAiBqvZnWhdhqEe5HeHmD\npCJms84OOxaLn9b8xlBO+GvvyUAMTWSF5QkgCSet17GFgWHnTUASZto/zB0soRmhq4J+wEHa3icr\nmp5CQsBPOCk0k0hzG2VKBVTiES26tRQ1xn0ZQBOiHoRrUgJicAu8egYtPhuEXQONTIqIoYmBAMTT\nQHjPkgkEhzfidB2bv622ZAL7jc9B7bECcYEA8VrbiRJ72P/ogP+IbVrXdaIDbWeTguETAXyeLtME\npxZFa78NPm0g9X5zr9OcbtEb09EVJ9xuCHjD7YuO+rZoWmsCtNb5K8OO2Wjtc9MR/mEmAU90m2vu\n97S1iwK+aDS/lbbtPFi/bQR8rTaOr8WJaNEAHoKXJ7ldf5qEj7J+g4gu3QyARjJtsRt/7XvWVrtJ\nENSmttfETRrh/m4ynjbnU9Bx42xTItGOGwi242TDBgySjo882t7wVDDcEEiliaCNISyR9v1yAglu\nZ5ucDsFNLPsYGmbjBO0mH0ltQnuNa+QAEmg4pMyt32NweuyH2EhOktFCtp6PjHZ2VTB0GtDiamFA\n2DUL62HaahM6UX7fIdqUb3yO1/SjtucYXWtzpdIQjTMFSNC049aDUN30+Qh4w/tSE5ovKnTe46fT\naUVqgQ1WVXU+gKqqjcbnYUcKoChKJvAZ8IcOptx3A8MVRUlSFMUOnAN8d7RMBPwa/mP8GzAug+ik\nj4wYssG02vjsQ0wz3w78i9bHst9APOpSBaGGtAvRMEAsQHQm4CQ1Zx+abg6llT6iBbM1WMUaScsp\nIntMBjHJi41jOmJEYQpQS2pOaZu8RiVuBSoQRspngMYmruE9owEFgEcZwMi5v+Y+22B04Fy+I57P\nwsq0EfEY+b+N9NIQD8uYEWMMQUHbhTCMEmntW1xcyDsM5RGEUwZryWCHIWbNwCeksIfreNowKnTg\nbsZSw11G2fxs51peNkwlHXiQLJ6mkVdDsYpp42FGmf7MWWicBpQC5YjJ9m+NX/qBd0i/9k6estpD\nd+kpssnjB3zAOaES7UY4KpfwKeJBHlGm/iwhDlhLFF9zGaLT2WOcb0aIs4k4ljM5u5h+ceuws9c4\nX8d5fMQcdpLBSwB8xoUUGhJcRdBcgi+BuMHDUBMSqDCaVklYmT8x7qwfeN9m54rRE3jObAmV6QNg\nvFH6ZfYoMm3R+H2Bw/4NzRrEorhWg30RwnxtAbbGxKEF9COGP5a/SambMFMBgIUSJqVtOabwx8gx\n6w30Ds0xWaNJz9mKMCJywbSY1hrwBWJOZSvCGQFRQ/YiDJ9gnQ9qUxaiSxLtLyb1bbLG9A+llZU7\nnLiMV0N5jUt/jf7jR5I6dAettXIzokvSic96hazThofC58zIoHW/VRPC8BrKX5hG8I59SCJFwwaz\npl8OJVi4HTf9eChUJhNLEXOwBxH6cjpiJFsH+iMeiwFR6zcAlwL/Jai3/XiC0azGbnTNxczkoTBT\n5WGyyOf3LGJGqETfEcU3fB8oQ3TpqTzPNGPYCVYQx2aEo1FkHKulVfmWkMAGrjLy8CriUcFwdfqC\nzJw6vskcYoyvQyEmPmcm67mapUZf4AfWAQspwMEToTKZ+S//xxq8wItMQ/Qnr4XKZOK/iD7Axf9v\n787jq7rr/I+/znKX7AlZCYEkbIe1UKBQoLtdbK1Wp1qly7Qd61QdnRl/M27zU2lLdXQWf446rZ2h\nasdWHG1tsXa1O3YFChQCHCCQEEISkpA9d7/n98c5NwkhLAm5N7k3n+fj0UfJvcm93++557zv93O2\n7zTvH7lxXjYl/KDv+Vncw+3UcxEPA0FMbuIBpwi06P8GageqMrKYU1nEI86gyXKW/iKGyKZp09mT\nndPXp6PQ9zk/AxRWzj7tNhwJR6nLL+4bfpnQt7PtUUVh3qy5o5Y3l5V0UpwWq1ks5uX9mEV5rnjl\nDYynMc5Z9G1g5kyeH0V3x06JC2Mf0QF7e+zF3j6fcx6LAr8E/oHYGMP2AnY2RYA3gOko6hEKZh0/\n4b0nTa8jdoBO1Q9SOLuL4jkWuvd953UC2JmVheraQ5ER6GunKyMfVX8Ve61cBvwWgFf5MFVOxnUA\nL6h57DFW8jPFLpjKeA1X39A8tiUvGNCnEPbaPBd4kRPzdib2JXH2Wq9Qy/U8TQn/2rfMXyGzr/rd\nh5vXyectPsYbztodBH7Jauyp0+x7l7zB9Wx2dgL4gKfJ4mbsFIn5Nfb3cTewkUuxM3Yzdim2i/50\nakNhE4dXfoxnnUcs4F6W0MnNfJeVJ42b5vNsX5+ggSXOsvg5RVTxKedd25znDxLbpV2gPsC1szRK\ntD8SS0Q3H3Atr/FRNuHlXcDDb7moLzt20L8LaL2qcvmc8/i9p7Avb9/FLssHZ9OujCyKKw1io2+L\n/jFOC7A3O+eM67qnZAp/dtvLOQy8A0x1llxtXv45ZQz0b0eKK51JldvAKR5171aK5/Q/D4w4c057\nTVaMYRjbgdtM09zp/DwH+JVpmqc8H8kwjB9hnxC7F3vtsrBH/hmmaa43DOMj2MdeFeBh0zR/dqZ2\njOT6CF1Xaa6po+79dlQtTG6ZzvEaFd3jR/dE8Xekg1ZL/fYIluWiZF4vusvA5e1F1VUCXV4yCnvp\nPe4GFNIn+elpzsCTHWTWpQv6jmLF1G0zaauNkpYXYubFC1BUlfajDRzechxVC+PNCdHbkkFGQYjp\nqxeesNcqEomw6T+fpKctH1faQYqCfrRIOqp3P0anj04tivr5v6Vg2iw66mvQf/tL0qIWO125RJtz\nieqddBdOIdBTTMT/Jr0dFUAailYFLEFRm5g1xYfuy6YxXENr6yzsjW8XdujVcTkvo3CMRpajk8tx\njpKV6WaGL0yjW2Vf/iUEu7JJj3zAkkA9YbfGplAJkeA8UPeiaoUQzSc78hIX00EPEfa7LFbpaex3\nK8zvrWVS1GJH2VxmTplKp+bi+T+3EbVmoemHKFh5NVbERcu+N+humYaitnDxly8mZ3IlvUf2Uf77\nf8cTjfJSezoh/2wiNHFhZhWF7nTMrDxm1+9DAx5Ju4bO3gWoegPnuasoCSs0ZUJLcxkQIT+rnmwq\nSPe0EI5oBMN5LCk9zo1z7cHMv7y+j6auYtyeJipcvaRHNBrdEZo6pxGll2vLW5gcCdHh9tDWeJQM\nK0JG+QyWz5hDNBrlV5v3ofWE8HkVSn2HSVcsmtOzKOzpokd3cdGFl5Dr8VLb1sru7e/htqK0ZGRT\n3N1Jp9vNFSsuPeP1EQDHujpoqDlA1IIjQT/ZHe10edO4euUlfRe6jwbLsvj9IYvDPYVUZjZzQ4Vy\nVntcY872GgkYWd44vzcuMkclwu4/7STY6yG3PELn0TDRsJvM/B66WzJQtF6O7TuAv6uY9EnHKJw+\nh2hEJyPfT09L+gnZlFHoo7dNx4pqVKwoILu4+IT36m5t4dBbDYBC5coSMgsKiEbC7H9tF4EuDxlF\nvfS2ekCJMuOiqaTnnrhX2Hzldar/HEBR/RSnHcTVnUco7RjTOuvItTTqZ1ZQfuffoarQ+uh/Udh4\nlOpMN23HctEiaQRKQnT65hG12uhpqsU+Ce0DVNd0sDKYVHSI4mgeIa2b/fV+LGZg79SZDniZzDN8\nmDa2kUEUgyBN1OtprKAbFYWq0mm09y4j5D/CvOA2JqsqezxuajqXA2F0bxOR4AwU7RAXBt+lEJ2d\nipvz0ywCCoS0dub1tHPAm0na0svJCQV5fV8DDa2LsOgkd3EOunch4cheGrb2YkVzKaxo4sLP/xXR\naBT3r++jrLmOXa50DhwpQ8dLof4eS9NDtOcU4mutZ2agl02eQt4N3oiupjFLewUjFKZTC7MnkIfO\nFFAOUZldgoJCbtoxjvdMIcPTzv+5cBJeXeflQ4d5dnc6KFHKshsoCCh0u6Ic7M0hGp5ERd4hLskK\nE1FUjvh6yenqoCsjgw8vvwRdVXmjpoGagy0E1SjZ7jaKfT20eNNwB4NkDsqm5zf/mYzuLlrSMvD6\neklXLLKnGyytmHnGdTtiRdmxfw9uXy+tbjf+xqNolsXkeYuYXzJluJvKae1pC/BCXRFuPcjNMzrI\n9Zw5D2OGkzcwfsY4I7kmS9dV6rbvp/lAEE9WAHemTmeDRnpeL8EehUjIi79rLy0Hs1HULsqWZhIN\nlJGW10OwRyMa9vRlk+YO4EqL4GvPIHcKlJw/84RrUdYtuY0j75j0HneRO01l2hL7RiFykaZ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6mJhl2NuNPBne6mqzlIfkUOhTMrErX6jarY3DnBSJibdxRhWdaYH9kab5kTjYRR3t6IJ9hLz+Ir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\nIYQQYhRJkSWEEEIIIYQQo+j/AyOcHhCmCX+EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c99c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_log = LogisticRegression()\n",
    "clf_svc_lin = SVC(kernel='linear', probability=True)\n",
    "clf_svc_rbf = SVC(kernel='rbf', probability=True)\n",
    "titles = ['Logistic Regression', 'SVC with Linear Kernel', 'SVC with RBF Kernel',]\n",
    "\n",
    "h = 0.02\n",
    "xmin, xmax = -5, 85\n",
    "ymin, ymax = 0.5, 4.5\n",
    "xx, yy = np.meshgrid(np.arange(xmin, xmax, h), np.arange(ymin, ymax, h))\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12,4))\n",
    "levels = np.linspace(0, 1.0, 5)\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "for i, clf in enumerate((clf_log, clf_svc_lin, clf_svc_rbf)):\n",
    "    clf.fit(X, y)\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    axes[i].contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)\n",
    "    axes[i].scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, cmap=cm_bright)\n",
    "    axes[i].set_title(titles[i])\n",
    "    axes[i].set_xlabel('AgeFill')\n",
    "    axes[i].set_ylabel('Pclass_Gender')\n",
    "    axes[i].set_xlim(xmin, xmax)\n",
    "    axes[i].set_ylim(ymin, ymax)\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## over fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.80758426966292129, 0.7988826815642458)"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(kernel='rbf', probability=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = clf.predict(X_train)\n",
    "y_val_pred = clf.predict(X_val)\n",
    "\n",
    "accuracy_score(y_train, y_train_pred), accuracy_score(y_val, y_val_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['class_weight_',\n",
       " 'classes_',\n",
       " 'coef_',\n",
       " 'dual_coef_',\n",
       " 'fit_status_',\n",
       " 'intercept_',\n",
       " 'n_support_',\n",
       " 'probA_',\n",
       " 'probB_',\n",
       " 'shape_fit_',\n",
       " 'support_',\n",
       " 'support_vectors_']"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7837078651685393, 0.74301675977653636)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=33)\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = clf.predict(X_train)\n",
    "y_val_pred = clf.predict(X_val)\n",
    "\n",
    "accuracy_score(y_train, y_train_pred), accuracy_score(y_val, y_val_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_val(clf, X, y, K, random_state=0):\n",
    "    cv = KFold(len(y), K, shuffle=True, random_state=random_state)\n",
    "    scores = cross_val_score(clf, X, y, cv=cv)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 0.80446927  0.74719101  0.80337079  0.74719101  0.76966292]\n",
      "Mean Score: 0.774 (+/-0.051)\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "scores = cross_val(clf, X, y, 5)\n",
    "print('Scores:', scores)\n",
    "print('Mean Score: {0:.3f} (+/-{1:.3f})'.format(scores.mean(), scores.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 2], [4, 1], [7, 6], [8, 5]]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_test_split([1,2,3,4], [5,6,7,8], train_size=0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4, 1], [2, 3], [8, 5], [6, 7]]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_test_split([1,2,3,4], [5,6,7,8], train_size=0.5, random_state=33))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 3, 1, 5, 4, 8, 2, 0, 6])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled = np.array(range(9))\n",
    "np.random.shuffle(shuffled)\n",
    "shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3, 4, 6, 2, 5, 7, 8])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_state = np.random.RandomState(seed=0)\n",
    "random_state.shuffle(shuffled)\n",
    "shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html\n",
    "KFold(len(y), 5, shuffle=True, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.7150838 ,  0.75418994,  0.82022472,  0.79213483,  0.79661017])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://scikit-learn.org/stable/modules/cross_validation.html\n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html\n",
    "cross_val_score(LogisticRegression(), X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>) \n",
      "\n",
      "0\n",
      "[  0   1   2   3   5   6   8   9  10  11  12  13  14  15  17  19  20  21\n",
      "  23  25  26  27  28  29  32  34  35  36  39  40  41  42  43  44  47  48\n",
      "  49  51  52  53  54  55  56  57  58  59  60  62  65  68  69  71  73  74\n",
      "  75  77  78  79  80  81  82  84  86  87  88  89  90  92  93  94  97  98\n",
      "  99 100 101 102 103 106 107 108 109 110 113 114 115 116 117 119 120 122\n",
      " 123 124 125 127 128 129 130 131 132 133 135 136 137 138 139 140 141 142\n",
      " 143 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 174 175 176 180 182 183 185 186\n",
      " 187 188 189 190 191 192 193 194 196 197 198 199 200 202 203 205 206 207\n",
      " 211 212 213 214 215 216 217 218 219 220 222 223 224 225 226 228 229 230\n",
      " 231 232 233 234 235 236 238 239 240 241 243 244 245 246 247 248 249 251\n",
      " 252 253 254 255 256 257 258 259 260 262 263 264 265 267 268 269 270 271\n",
      " 273 274 276 277 278 279 280 281 282 283 284 285 287 288 289 290 291 293\n",
      " 294 296 297 298 299 300 301 302 303 304 306 308 309 310 311 312 313 314\n",
      " 315 317 318 319 320 321 322 323 324 326 327 328 329 330 331 332 333 334\n",
      " 336 337 338 339 340 341 344 345 346 347 348 351 352 354 355 356 357 358\n",
      " 360 361 362 363 364 366 367 368 370 371 373 374 375 376 377 378 379 380\n",
      " 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 400\n",
      " 401 402 404 406 407 409 410 411 412 413 415 416 418 419 420 421 422 423\n",
      " 424 425 428 429 430 431 432 433 434 435 436 437 438 439 440 441 444 446\n",
      " 447 448 450 451 452 453 454 455 456 457 459 460 461 462 465 466 467 468\n",
      " 469 470 471 472 473 474 475 476 478 479 480 481 482 483 484 485 487 490\n",
      " 491 492 493 495 496 497 498 499 500 501 502 503 505 506 513 514 515 516\n",
      " 517 518 520 521 522 523 524 525 531 532 533 534 535 537 538 539 540 541\n",
      " 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 559 560 561\n",
      " 563 564 565 568 570 571 576 577 578 579 580 581 582 584 585 586 590 592\n",
      " 594 595 596 598 599 600 601 603 604 605 606 609 611 613 614 615 616 617\n",
      " 618 620 621 622 625 626 627 628 629 630 631 632 633 634 635 636 637 638\n",
      " 639 643 644 645 646 647 648 649 650 651 654 655 656 657 659 660 661 662\n",
      " 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680\n",
      " 681 683 684 685 686 687 688 689 690 693 694 695 696 697 700 701 702 703\n",
      " 705 706 708 709 710 711 713 714 715 716 717 718 719 720 721 722 724 725\n",
      " 726 727 728 729 730 731 733 734 736 737 738 739 740 741 742 743 744 745\n",
      " 747 748 749 750 751 754 755 756 757 759 760 764 765 766 767 768 769 770\n",
      " 771 772 774 775 777 778 779 780 781 782 783 784 785 786 787 788 789 792\n",
      " 793 794 795 796 797 798 799 800 804 805 806 807 808 810 811 812 813 814\n",
      " 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833\n",
      " 834 835 837 839 840 841 843 844 847 848 849 850 851 852 853 854 856 857\n",
      " 858 859 860 861 864 865 866 867 868 870 871 872 873 874 875 876 877 878\n",
      " 879 880 881 882 883 886 887 888 889 890]\n",
      "[  4   7  16  18  22  24  30  31  33  37  38  45  46  50  61  63  64  66\n",
      "  67  70  72  76  83  85  91  95  96 104 105 111 112 118 121 126 134 154\n",
      " 172 173 177 178 179 181 184 195 201 204 208 209 210 221 227 237 242 250\n",
      " 261 266 272 275 286 292 295 305 307 316 325 335 342 343 349 350 353 359\n",
      " 365 369 372 398 399 403 405 408 414 417 426 427 442 443 445 449 458 463\n",
      " 464 477 486 488 489 494 504 507 508 509 510 511 512 519 526 527 528 529\n",
      " 530 536 542 558 562 566 567 569 572 573 574 575 583 587 588 589 591 593\n",
      " 597 602 607 608 610 612 619 623 624 640 641 642 652 653 658 682 691 692\n",
      " 698 699 704 707 712 723 732 735 746 752 753 758 761 762 763 773 776 790\n",
      " 791 801 802 803 809 815 836 838 842 845 846 855 862 863 869 884 885]\n",
      "1\n",
      "[  0   1   2   4   5   6   7   8  10  11  12  13  14  15  16  18  20  21\n",
      "  22  24  25  28  29  30  31  33  34  35  36  37  38  39  40  42  43  44\n",
      "  45  46  47  48  49  50  51  52  53  56  57  58  59  60  61  62  63  64\n",
      "  65  66  67  69  70  71  72  73  74  76  77  78  79  80  81  82  83  84\n",
      "  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102\n",
      " 103 104 105 106 107 109 111 112 113 116 117 118 119 120 121 122 124 126\n",
      " 127 129 130 131 132 133 134 135 136 138 139 141 142 143 144 145 146 147\n",
      " 151 152 154 155 156 157 158 159 160 162 164 165 168 170 171 172 173 175\n",
      " 176 177 178 179 180 181 182 184 185 186 187 188 189 190 194 195 197 199\n",
      " 200 201 202 203 204 205 206 207 208 209 210 212 213 214 215 216 217 219\n",
      " 221 222 223 224 225 227 228 229 230 231 232 234 235 236 237 239 241 242\n",
      " 243 244 245 248 250 251 252 254 255 256 258 259 260 261 263 264 265 266\n",
      " 267 269 270 271 272 273 274 275 278 279 280 282 283 284 285 286 287 290\n",
      " 292 293 294 295 296 297 299 301 302 303 305 306 307 309 312 313 314 316\n",
      " 319 320 321 322 323 324 325 327 328 329 330 331 332 333 335 338 339 340\n",
      " 341 342 343 344 345 346 347 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 369 370 371 372 373 374 375 376 377 378\n",
      " 379 380 381 382 383 386 387 388 392 393 394 395 396 398 399 400 402 403\n",
      " 404 405 406 407 408 411 412 413 414 415 417 418 419 420 421 422 423 424\n",
      " 425 426 427 428 430 431 432 433 434 435 438 439 440 441 442 443 444 445\n",
      " 446 447 448 449 450 451 452 453 455 456 457 458 460 461 462 463 464 466\n",
      " 467 468 469 470 471 472 473 476 477 479 480 481 482 483 484 486 487 488\n",
      " 489 490 491 492 493 494 496 497 498 499 500 501 502 503 504 507 508 509\n",
      " 510 511 512 514 515 516 517 518 519 521 522 523 524 525 526 527 528 529\n",
      " 530 531 532 533 534 535 536 538 539 540 542 543 544 545 546 547 549 550\n",
      " 551 552 553 554 555 556 557 558 559 560 561 562 563 565 566 567 568 569\n",
      " 571 572 573 574 575 576 577 578 581 583 585 586 587 588 589 591 593 594\n",
      " 595 597 598 599 600 602 603 604 605 606 607 608 609 610 611 612 613 615\n",
      " 616 617 618 619 621 623 624 625 626 627 628 629 630 631 632 633 634 636\n",
      " 637 639 640 641 642 643 644 645 646 647 649 650 651 652 653 654 655 657\n",
      " 658 659 660 661 662 665 667 672 673 674 678 680 682 683 687 689 690 691\n",
      " 692 693 695 696 697 698 699 700 704 705 706 707 708 709 711 712 714 715\n",
      " 716 717 722 723 724 725 727 731 732 733 734 735 737 738 739 740 741 742\n",
      " 743 744 746 747 748 749 750 751 752 753 754 755 758 759 760 761 762 763\n",
      " 765 766 767 769 770 771 772 773 774 776 778 779 780 781 782 783 784 787\n",
      " 788 789 790 791 792 793 794 795 799 801 802 803 805 807 808 809 810 811\n",
      " 813 814 815 816 818 819 821 822 823 824 825 827 829 830 831 832 833 834\n",
      " 835 836 837 838 840 841 842 843 844 845 846 847 848 851 853 854 855 856\n",
      " 857 858 859 860 861 862 863 864 865 866 867 869 870 871 872 873 874 875\n",
      " 877 878 879 880 881 884 885 886 888 889 890]\n",
      "[  3   9  17  19  23  26  27  32  41  54  55  68  75 108 110 114 115 123\n",
      " 125 128 137 140 148 149 150 153 161 163 166 167 169 174 183 191 192 193\n",
      " 196 198 211 218 220 226 233 238 240 246 247 249 253 257 262 268 276 277\n",
      " 281 288 289 291 298 300 304 308 310 311 315 317 318 326 334 336 337 348\n",
      " 368 384 385 389 390 391 397 401 409 410 416 429 436 437 454 459 465 474\n",
      " 475 478 485 495 505 506 513 520 537 541 548 564 570 579 580 582 584 590\n",
      " 592 596 601 614 620 622 635 638 648 656 663 664 666 668 669 670 671 675\n",
      " 676 677 679 681 684 685 686 688 694 701 702 703 710 713 718 719 720 721\n",
      " 726 728 729 730 736 745 756 757 764 768 775 777 785 786 796 797 798 800\n",
      " 804 806 812 817 820 826 828 839 849 850 852 868 876 882 883 887]\n",
      "2\n",
      "[  0   1   2   3   4   5   6   7   9  10  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  30  31  32  33  34  36  37  38\n",
      "  39  40  41  43  44  45  46  47  48  49  50  52  54  55  56  57  61  62\n",
      "  63  64  66  67  68  69  70  72  73  74  75  76  77  78  79  80  81  82\n",
      "  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  99 101 103\n",
      " 104 105 108 109 110 111 112 113 114 115 116 117 118 119 120 121 123 124\n",
      " 125 126 128 129 130 131 132 133 134 135 136 137 139 140 141 144 145 147\n",
      " 148 149 150 152 153 154 155 156 157 159 160 161 162 163 165 166 167 169\n",
      " 170 171 172 173 174 175 177 178 179 180 181 182 183 184 186 189 191 192\n",
      " 193 195 196 198 199 200 201 202 203 204 205 206 208 209 210 211 213 215\n",
      " 216 217 218 219 220 221 223 225 226 227 229 230 231 233 236 237 238 239\n",
      " 240 242 243 244 246 247 248 249 250 252 253 254 256 257 258 261 262 264\n",
      " 265 266 267 268 269 272 274 275 276 277 278 279 280 281 283 284 286 287\n",
      " 288 289 290 291 292 293 294 295 297 298 300 301 302 304 305 307 308 310\n",
      " 311 312 313 315 316 317 318 321 322 323 325 326 329 330 331 333 334 335\n",
      " 336 337 338 340 341 342 343 344 346 347 348 349 350 351 352 353 355 356\n",
      " 358 359 364 365 366 367 368 369 370 371 372 373 375 377 378 380 382 384\n",
      " 385 388 389 390 391 392 393 394 395 397 398 399 400 401 402 403 405 407\n",
      " 408 409 410 411 412 414 415 416 417 418 420 421 422 423 426 427 429 432\n",
      " 433 434 436 437 438 439 440 442 443 445 446 447 448 449 451 452 453 454\n",
      " 455 456 458 459 460 463 464 465 467 468 470 471 472 473 474 475 476 477\n",
      " 478 480 481 482 483 484 485 486 488 489 490 491 492 493 494 495 496 497\n",
      " 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 517\n",
      " 519 520 522 523 525 526 527 528 529 530 533 534 535 536 537 538 539 540\n",
      " 541 542 543 544 545 546 547 548 550 551 552 553 554 555 558 559 562 564\n",
      " 565 566 567 569 570 571 572 573 574 575 578 579 580 582 583 584 587 588\n",
      " 589 590 591 592 593 595 596 597 599 600 601 602 605 606 607 608 609 610\n",
      " 611 612 613 614 616 617 619 620 621 622 623 624 628 629 630 631 633 634\n",
      " 635 637 638 640 641 642 643 644 646 647 648 649 651 652 653 656 657 658\n",
      " 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676\n",
      " 677 678 679 681 682 683 684 685 686 687 688 690 691 692 693 694 695 696\n",
      " 698 699 701 702 703 704 707 708 710 711 712 713 715 718 719 720 721 722\n",
      " 723 725 726 727 728 729 730 732 733 734 735 736 738 742 743 745 746 747\n",
      " 748 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 767\n",
      " 768 769 770 772 773 774 775 776 777 778 779 780 781 782 783 785 786 787\n",
      " 788 790 791 792 793 794 796 797 798 799 800 801 802 803 804 806 807 808\n",
      " 809 812 813 814 815 816 817 818 819 820 821 823 825 826 827 828 829 830\n",
      " 831 832 833 836 838 839 840 841 842 844 845 846 847 848 849 850 852 853\n",
      " 855 856 857 859 860 861 862 863 864 866 867 868 869 871 872 873 874 875\n",
      " 876 878 880 882 883 884 885 886 887 889 890]\n",
      "[  8  29  35  42  51  53  58  59  60  65  71  98 100 102 106 107 122 127\n",
      " 138 142 143 146 151 158 164 168 176 185 187 188 190 194 197 207 212 214\n",
      " 222 224 228 232 234 235 241 245 251 255 259 260 263 270 271 273 282 285\n",
      " 296 299 303 306 309 314 319 320 324 327 328 332 339 345 354 357 360 361\n",
      " 362 363 374 376 379 381 383 386 387 396 404 406 413 419 424 425 428 430\n",
      " 431 435 441 444 450 457 461 462 466 469 479 487 515 516 518 521 524 531\n",
      " 532 549 556 557 560 561 563 568 576 577 581 585 586 594 598 603 604 615\n",
      " 618 625 626 627 632 636 639 645 650 654 655 680 689 697 700 705 706 709\n",
      " 714 716 717 724 731 737 739 740 741 744 749 766 771 784 789 795 805 810\n",
      " 811 822 824 834 835 837 843 851 854 858 865 870 877 879 881 888]\n",
      "3\n",
      "[  3   4   7   8   9  10  13  14  15  16  17  18  19  20  21  22  23  24\n",
      "  25  26  27  29  30  31  32  33  34  35  37  38  39  40  41  42  45  46\n",
      "  47  48  50  51  53  54  55  56  57  58  59  60  61  62  63  64  65  66\n",
      "  67  68  70  71  72  73  75  76  77  78  79  80  82  83  84  85  86  87\n",
      "  88  89  90  91  95  96  98 100 101 102 103 104 105 106 107 108 109 110\n",
      " 111 112 113 114 115 117 118 120 121 122 123 125 126 127 128 130 132 134\n",
      " 135 137 138 139 140 141 142 143 145 146 147 148 149 150 151 152 153 154\n",
      " 155 156 158 159 160 161 162 163 164 165 166 167 168 169 171 172 173 174\n",
      " 175 176 177 178 179 181 182 183 184 185 186 187 188 189 190 191 192 193\n",
      " 194 195 196 197 198 201 202 204 207 208 209 210 211 212 214 215 216 217\n",
      " 218 220 221 222 224 226 227 228 230 232 233 234 235 237 238 239 240 241\n",
      " 242 243 244 245 246 247 249 250 251 253 255 257 259 260 261 262 263 264\n",
      " 265 266 268 269 270 271 272 273 274 275 276 277 281 282 283 285 286 288\n",
      " 289 290 291 292 293 295 296 297 298 299 300 303 304 305 306 307 308 309\n",
      " 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327\n",
      " 328 331 332 334 335 336 337 339 340 342 343 344 345 346 348 349 350 352\n",
      " 353 354 357 359 360 361 362 363 364 365 366 367 368 369 370 371 372 374\n",
      " 375 376 378 379 380 381 383 384 385 386 387 389 390 391 392 395 396 397\n",
      " 398 399 401 402 403 404 405 406 407 408 409 410 413 414 416 417 418 419\n",
      " 420 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 439\n",
      " 441 442 443 444 445 446 448 449 450 452 453 454 457 458 459 460 461 462\n",
      " 463 464 465 466 467 469 470 473 474 475 477 478 479 480 481 484 485 486\n",
      " 487 488 489 492 494 495 497 499 500 501 503 504 505 506 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 526 527 528 529\n",
      " 530 531 532 534 535 536 537 540 541 542 546 548 549 550 551 556 557 558\n",
      " 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577\n",
      " 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 596\n",
      " 597 598 601 602 603 604 605 606 607 608 609 610 611 612 614 615 616 618\n",
      " 619 620 621 622 623 624 625 626 627 628 631 632 633 635 636 638 639 640\n",
      " 641 642 644 645 647 648 650 651 652 653 654 655 656 657 658 661 663 664\n",
      " 666 667 668 669 670 671 673 675 676 677 678 679 680 681 682 684 685 686\n",
      " 687 688 689 691 692 693 694 697 698 699 700 701 702 703 704 705 706 707\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 735 736 737 738 739 740 741 744 745 746 748 749\n",
      " 751 752 753 756 757 758 759 760 761 762 763 764 766 767 768 769 771 772\n",
      " 773 774 775 776 777 778 784 785 786 789 790 791 794 795 796 797 798 800\n",
      " 801 802 803 804 805 806 808 809 810 811 812 813 815 816 817 820 822 823\n",
      " 824 826 828 830 832 833 834 835 836 837 838 839 842 843 845 846 849 850\n",
      " 851 852 854 855 856 858 859 860 861 862 863 865 867 868 869 870 876 877\n",
      " 878 879 881 882 883 884 885 886 887 888 889]\n",
      "[  0   1   2   5   6  11  12  28  36  43  44  49  52  69  74  81  92  93\n",
      "  94  97  99 116 119 124 129 131 133 136 144 157 170 180 199 200 203 205\n",
      " 206 213 219 223 225 229 231 236 248 252 254 256 258 267 278 279 280 284\n",
      " 287 294 301 302 329 330 333 338 341 347 351 355 356 358 373 377 382 388\n",
      " 393 394 400 411 412 415 421 438 440 447 451 455 456 468 471 472 476 482\n",
      " 483 490 491 493 496 498 502 525 533 538 539 543 544 545 547 552 553 554\n",
      " 555 559 595 599 600 613 617 629 630 634 637 643 646 649 659 660 662 665\n",
      " 672 674 683 690 695 696 708 733 734 742 743 747 750 754 755 765 770 779\n",
      " 780 781 782 783 787 788 792 793 799 807 814 818 819 821 825 827 829 831\n",
      " 840 841 844 847 848 853 857 864 866 871 872 873 874 875 880 890]\n",
      "4\n",
      "[  0   1   2   3   4   5   6   7   8   9  11  12  16  17  18  19  22  23\n",
      "  24  26  27  28  29  30  31  32  33  35  36  37  38  41  42  43  44  45\n",
      "  46  49  50  51  52  53  54  55  58  59  60  61  63  64  65  66  67  68\n",
      "  69  70  71  72  74  75  76  81  83  85  91  92  93  94  95  96  97  98\n",
      "  99 100 102 104 105 106 107 108 110 111 112 114 115 116 118 119 121 122\n",
      " 123 124 125 126 127 128 129 131 133 134 136 137 138 140 142 143 144 146\n",
      " 148 149 150 151 153 154 157 158 161 163 164 166 167 168 169 170 172 173\n",
      " 174 176 177 178 179 180 181 183 184 185 187 188 190 191 192 193 194 195\n",
      " 196 197 198 199 200 201 203 204 205 206 207 208 209 210 211 212 213 214\n",
      " 218 219 220 221 222 223 224 225 226 227 228 229 231 232 233 234 235 236\n",
      " 237 238 240 241 242 245 246 247 248 249 250 251 252 253 254 255 256 257\n",
      " 258 259 260 261 262 263 266 267 268 270 271 272 273 275 276 277 278 279\n",
      " 280 281 282 284 285 286 287 288 289 291 292 294 295 296 298 299 300 301\n",
      " 302 303 304 305 306 307 308 309 310 311 314 315 316 317 318 319 320 324\n",
      " 325 326 327 328 329 330 332 333 334 335 336 337 338 339 341 342 343 345\n",
      " 347 348 349 350 351 353 354 355 356 357 358 359 360 361 362 363 365 368\n",
      " 369 372 373 374 376 377 379 381 382 383 384 385 386 387 388 389 390 391\n",
      " 393 394 396 397 398 399 400 401 403 404 405 406 408 409 410 411 412 413\n",
      " 414 415 416 417 419 421 424 425 426 427 428 429 430 431 435 436 437 438\n",
      " 440 441 442 443 444 445 447 449 450 451 454 455 456 457 458 459 461 462\n",
      " 463 464 465 466 468 469 471 472 474 475 476 477 478 479 482 483 485 486\n",
      " 487 488 489 490 491 493 494 495 496 498 502 504 505 506 507 508 509 510\n",
      " 511 512 513 515 516 518 519 520 521 524 525 526 527 528 529 530 531 532\n",
      " 533 536 537 538 539 541 542 543 544 545 547 548 549 552 553 554 555 556\n",
      " 557 558 559 560 561 562 563 564 566 567 568 569 570 572 573 574 575 576\n",
      " 577 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595\n",
      " 596 597 598 599 600 601 602 603 604 607 608 610 612 613 614 615 617 618\n",
      " 619 620 622 623 624 625 626 627 629 630 632 634 635 636 637 638 639 640\n",
      " 641 642 643 645 646 648 649 650 652 653 654 655 656 658 659 660 662 663\n",
      " 664 665 666 668 669 670 671 672 674 675 676 677 679 680 681 682 683 684\n",
      " 685 686 688 689 690 691 692 694 695 696 697 698 699 700 701 702 703 704\n",
      " 705 706 707 708 709 710 712 713 714 716 717 718 719 720 721 723 724 726\n",
      " 728 729 730 731 732 733 734 735 736 737 739 740 741 742 743 744 745 746\n",
      " 747 749 750 752 753 754 755 756 757 758 761 762 763 764 765 766 768 770\n",
      " 771 773 775 776 777 779 780 781 782 783 784 785 786 787 788 789 790 791\n",
      " 792 793 795 796 797 798 799 800 801 802 803 804 805 806 807 809 810 811\n",
      " 812 814 815 817 818 819 820 821 822 824 825 826 827 828 829 831 834 835\n",
      " 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853\n",
      " 854 855 857 858 862 863 864 865 866 868 869 870 871 872 873 874 875 876\n",
      " 877 879 880 881 882 883 884 885 887 888 890]\n",
      "[ 10  13  14  15  20  21  25  34  39  40  47  48  56  57  62  73  77  78\n",
      "  79  80  82  84  86  87  88  89  90 101 103 109 113 117 120 130 132 135\n",
      " 139 141 145 147 152 155 156 159 160 162 165 171 175 182 186 189 202 215\n",
      " 216 217 230 239 243 244 264 265 269 274 283 290 293 297 312 313 321 322\n",
      " 323 331 340 344 346 352 364 366 367 370 371 375 378 380 392 395 402 407\n",
      " 418 420 422 423 432 433 434 439 446 448 452 453 460 467 470 473 480 481\n",
      " 484 492 497 499 500 501 503 514 517 522 523 534 535 540 546 550 551 565\n",
      " 571 578 605 606 609 611 616 621 628 631 633 644 647 651 657 661 667 673\n",
      " 678 687 693 711 715 722 725 727 738 748 751 759 760 767 769 772 774 778\n",
      " 794 808 813 816 823 830 832 833 856 859 860 861 867 878 886 889]\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(len(y), 5, shuffle=True, random_state=random_state)\n",
    "print(kf, \"\\n\")\n",
    "for i, (kf_train, kf_test) in enumerate(kf):\n",
    "    print(i)\n",
    "    print(kf_train)\n",
    "    print(kf_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.cross_validation.StratifiedKFold(labels=[0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 1\n",
       " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0\n",
       " 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0\n",
       " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0\n",
       " 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1\n",
       " 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0\n",
       " 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1\n",
       " 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0\n",
       " 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0\n",
       " 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1\n",
       " 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0\n",
       " 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 0 0 1\n",
       " 1 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0\n",
       " 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0\n",
       " 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 1\n",
       " 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1\n",
       " 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0\n",
       " 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0\n",
       " 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0\n",
       " 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0\n",
       " 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0\n",
       " 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0\n",
       " 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0\n",
       " 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1\n",
       " 0 1 0], n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import StratifiedKFold\n",
    "StratifiedKFold(y, 5, shuffle=True, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2, shuffle=False, random_state=None)\n",
      "TRAIN: [1 3] TEST: [0 2]\n",
      "TRAIN: [0 2] TEST: [1 3]\n"
     ]
    }
   ],
   "source": [
    "skf_x = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])\n",
    "skf_y = np.array([0, 0, 1, 1])\n",
    "skf = StratifiedKFold(skf_y, n_folds=2)\n",
    "len(skf)\n",
    "\n",
    "print(skf)  \n",
    "\n",
    "\n",
    "for train_index, test_index in skf:\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    skf_X_train, skf_X_test = skf_x[train_index], skf_x[test_index]\n",
    "    skf_y_train, skf_y_test = skf_y[train_index], skf_y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_val_score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# http://scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html#cross-validated-estimators\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "#help(LogisticRegressionCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1]),\n",
       " array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1]),\n",
       " array(['a', 'b', 'c'], dtype=object))"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html\n",
    "le = LabelEncoder()\n",
    "labels = pd.Series(list(\"acb\") * 5)\n",
    "#le.fit(labels)\n",
    "le.fit_transform(labels), le.transform(labels), le.inverse_transform([0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit_transform(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['classes_'], array(['female', 'male'], dtype=object))"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(le), le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['a', 'b', 'c'], dtype=object), numpy.ndarray, {0: 'a', 1: 'b', 2: 'c'})"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = {k: v for k, v in enumerate(le.classes_)}\n",
    "le.classes_, le.classes_.__class__, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  SibSp  Parch    AgeFill  Gender  Embarked_C  Embarked_Q  \\\n",
       "886       2      0      0  27.000000       1         0.0         0.0   \n",
       "887       1      0      0  19.000000       0         0.0         0.0   \n",
       "888       3      1      2  29.699118       0         0.0         0.0   \n",
       "889       1      0      0  26.000000       1         1.0         0.0   \n",
       "890       3      0      0  32.000000       1         0.0         1.0   \n",
       "\n",
       "     Embarked_S  \n",
       "886         1.0  \n",
       "887         1.0  \n",
       "888         1.0  \n",
       "889         0.0  \n",
       "890         0.0  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train[['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']].copy()\n",
    "y = df_train['Survived'].copy()\n",
    "X_test = df_test[['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']].copy()\n",
    "\n",
    "X['AgeFill'] = X['Age'].fillna(X['Age'].mean())\n",
    "X_test['AgeFill'] = X_test['Age'].fillna(X['Age'].mean())\n",
    "X = X.drop(['Age'], axis=1)\n",
    "X_test = X_test.drop(['Age'], axis=1)\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(X['Sex'])\n",
    "X['Gender'] = le.transform(X['Sex'])\n",
    "X_test['Gender'] = le.transform(X_test['Sex'])\n",
    "\n",
    "X = X.join(pd.get_dummies(X['Embarked'], prefix='Embarked'))\n",
    "X_test = X_test.join(pd.get_dummies(X['Embarked'], prefix='Embarked'))\n",
    "X = X.drop(['Sex', 'Embarked'], axis=1)\n",
    "X_test = X_test.drop(['Sex', 'Embarked'], axis=1)\n",
    "\n",
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.79329609,  0.78089888,  0.80337079,  0.81460674,  0.80337079]),\n",
       " 0.79910865607934212,\n",
       " 0.022661053339677807)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "scores = cross_val(clf, X, y, 5)\n",
    "scores, scores.mean(), scores.std()*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 0.78212291  0.76404494  0.79213483  0.78089888  0.7247191 ]\n",
      "Mean Score: 0.769 (+/-0.048)\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)\n",
    "scores = cross_val(clf, X, y, 5)\n",
    "print('Scores:', scores)\n",
    "print('Mean Score: {0:.3f} (+/-{1:.3f})'.format(scores.mean(), scores.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classes_',\n",
       " 'feature_importances_',\n",
       " 'max_features_',\n",
       " 'n_classes_',\n",
       " 'n_features_',\n",
       " 'n_outputs_',\n",
       " 'tree_']"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=2,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)\n",
    "#tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=3)\n",
    "\n",
    "tree_clf.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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egLQtdYSAEBACpiIgirSpyEu/QkAIWCUBjprBB28yZB/sBpVLWeU8ZVJCQAgI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\nWp3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5eM33QiPtAVMLrmJITgMiYZbTTjEyG/DinhA8Ac2cPPoc+PKxSOIDu0dMDJjsRxvG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\noK6AugLqCiSoFfg/eHDO3AJrSUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 351,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO as SkStringIO\n",
    "from IPython.display import Image\n",
    "import graphviz\n",
    "\n",
    "def tree_plot(decision_tree, width=500, height=500, max_depth=None,\n",
    "         feature_names=None, class_names=None, label='all',\n",
    "         filled=False, leaves_parallel=False, impurity=True,\n",
    "         node_ids=False, proportion=False, rotate=False,\n",
    "         rounded=False, special_characters=False):\n",
    "    in_memory_dot_file = SkStringIO()\n",
    "    export_graphviz(\n",
    "            decision_tree, out_file=in_memory_dot_file, max_depth=max_depth,\n",
    "            feature_names=feature_names, class_names=class_names, label=label,\n",
    "            filled=filled, leaves_parallel=leaves_parallel, impurity=impurity,\n",
    "            node_ids=node_ids, proportion=proportion, rotate=rotate,\n",
    "            rounded=rounded, special_characters=special_characters)\n",
    "    src = graphviz.Source(in_memory_dot_file.getvalue())\n",
    "    return Image(src.pipe(format='png'), height=height, width=width)\n",
    "\n",
    "tree_plot(tree_clf,\n",
    "#tree_plot(clf,\n",
    "    feature_names=X.columns,  \n",
    "#    class_names=[\"0\", \"1\"],\n",
    "    class_names=[\"d\", \"s\"],\n",
    "#    class_names={1: \"s\", 0: \"d\"},\n",
    "#    class_names=list(y.apply(str)),\n",
    "    filled=True,\n",
    "    rounded=True,\n",
    "    special_characters=True\n",
    ") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    161\n",
       "0      9\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_y = pd.concat([X, y], axis=1)\n",
    "t1 = X_y[X_y.Gender <= 0.5]\n",
    "t1[t1.Pclass <= 2.5].Survived.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    72\n",
       "0    72\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1[t1.Pclass > 2.5].Survived.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    77\n",
       "1    45\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1 = X_y[X_y.Gender > 0.5]\n",
    "f1[f1.Pclass <= 1.5].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    391\n",
       "1     64\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1[f1.Pclass > 1.5].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classes_',\n",
       " 'feature_importances_',\n",
       " 'max_features_',\n",
       " 'n_classes_',\n",
       " 'n_features_',\n",
       " 'n_outputs_',\n",
       " 'tree_']"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(tree_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]),\n",
       " array([ 0.30417882,  0.        ,  0.        ,  0.        ,  0.69582118,\n",
       "         0.        ,  0.        ,  0.        ]))"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.classes_, tree_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 2)"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.max_features_, tree_clf.n_classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 1)"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.n_features_, tree_clf.n_outputs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.tree._tree.Tree at 0x11e626030>"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.tree_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "startswith first arg must be str or a tuple of str, not bytes",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-311-7387233abd8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mfilled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrounded\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     11\u001b[0m     special_characters=True) \n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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[0m\u001b[1;32m     13\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_png\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     14\u001b[0m \u001b[0;31m#print(dot_data.getvalue())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/knt/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/pydot.py\u001b[0m in \u001b[0;36mgraph_from_dot_data\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    218\u001b[0m     \"\"\"\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mdot_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_dot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/knt/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/dot_parser.py\u001b[0m in \u001b[0;36mparse_dot_data\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0mtop_graphs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBOM_UTF8\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    511\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'utf-8'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: startswith first arg must be str or a tuple of str, not bytes"
     ]
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO as SkStringIO\n",
    "from io import StringIO as SkStringIO\n",
    "import pydot \n",
    "from IPython.display import Image\n",
    "dot_data = SkStringIO()\n",
    "export_graphviz(\n",
    "    tree_clf, out_file=dot_data,  \n",
    "    feature_names=X.columns,  \n",
    "    class_names=y.unique(),\n",
    "    filled=True, rounded=True,  \n",
    "    special_characters=True) \n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())\n",
    "#print(dot_data.getvalue())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## グリッドサーチ\n",
    "グリッドサーチは、分類器のパラメータを指定した範囲で変化させ、最もスコアの高いパラメータの組合せを探してくれる便利な機能です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=0),\n",
       "       error_score='raise',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=2,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best'),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [2, 3, 4, 5], 'min_samples_leaf': [2, 3, 4, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)\n",
    "\n",
    "param_grid = {'max_depth': [2, 3, 4, 5], 'min_samples_leaf': [2, 3, 4, 5]}\n",
    "cv = KFold(len(y), 5, shuffle=True, random_state=0)\n",
    "\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=cv, n_jobs=-1, verbose=1)\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_estimator_', 'best_params_', 'best_score_', 'grid_scores_', 'scorer_']"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_params(grid_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=3,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.81705948372615034, {'max_depth': 4, 'min_samples_leaf': 3}]"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.metrics.scorer._passthrough_scorer>"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.scorer_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 2},\n",
       " mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 3},\n",
       " mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 4},\n",
       " mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 5},\n",
       " mean: 0.80471, std: 0.01474, params: {'max_depth': 3, 'min_samples_leaf': 2},\n",
       " mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 3},\n",
       " mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 4},\n",
       " mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 5},\n",
       " mean: 0.81594, std: 0.01180, params: {'max_depth': 4, 'min_samples_leaf': 2},\n",
       " mean: 0.81706, std: 0.01391, params: {'max_depth': 4, 'min_samples_leaf': 3},\n",
       " mean: 0.81257, std: 0.01345, params: {'max_depth': 4, 'min_samples_leaf': 4},\n",
       " mean: 0.80808, std: 0.01750, params: {'max_depth': 4, 'min_samples_leaf': 5},\n",
       " mean: 0.80247, std: 0.01421, params: {'max_depth': 5, 'min_samples_leaf': 2},\n",
       " mean: 0.80471, std: 0.01552, params: {'max_depth': 5, 'min_samples_leaf': 3},\n",
       " mean: 0.79686, std: 0.02005, params: {'max_depth': 5, 'min_samples_leaf': 4},\n",
       " mean: 0.79686, std: 0.01660, params: {'max_depth': 5, 'min_samples_leaf': 5}]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([1, 2])"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\"a\": 1, \"b\": 2}.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>mean_validation_score</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.768799</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.768799</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_validation_score  max_depth  min_samples_leaf\n",
       "0               0.768799          2                 2\n",
       "1               0.768799          2                 3"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_df = pd.DataFrame(grid_search.grid_scores_)\n",
    "params_df = gs_df.parameters.apply(\n",
    "    lambda p: pd.Series(list(p.values()), p.keys()))\n",
    "pd.concat([\n",
    "    gs_df.drop([\"parameters\", \"cv_validation_scores\"], axis=1),\n",
    "    params_df\n",
    "], axis=1).head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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     },
     "execution_count": 371,
     "metadata": {},
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    "s = pd.Series([\"k:v\", \"k1:v1\"])\n",
    "s.str.split(\":\", expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  col1 col2         0\n",
       "0    k    v    [k, v]\n",
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     "execution_count": 418,
     "metadata": {},
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    "pd.concat([\n",
    "    s.apply(lambda x: pd.Series(x.split(\":\"), index=['col1', 'col2'])),\n",
    "    s.apply(lambda x: (x.split(\":\")))\n",
    "], axis=1)"
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  {
   "cell_type": "code",
   "execution_count": 415,
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    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>London</th>\n",
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      "text/plain": [
       "   London  New York  Helsinki\n",
       "0      20        21        12\n",
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     "execution_count": 415,
     "metadata": {},
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   "source": [
    "series = pd.Series([20, 21, 12], index=['London', 'New York','Helsinki'])\n",
    "pd.DataFrame([series, series])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method StringMethods.split of <pandas.core.strings.StringMethods object at 0x11e82b438>>"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
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    "pd.Series(list(\"abc\")).str.split"
   ]
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   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "<function sklearn.grid_search.BaseSearchCV.predict>"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = grid_search.predict(X_test)\n",
    "grid_search.predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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