Classifying Default of Credit Card Clients.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying Default of Credit Card Clients\n",
    "<hr>\n",
    "\n",
    "The dataset can be downloaded from : https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
    "\n",
    "This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.\n",
    "\n",
    "### Data Set Information:\n",
    "\n",
    "This research that provided this dataset aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients.\n",
    "\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "The dataset contains a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: \n",
    "<pre>\n",
    "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n",
    "X2: Gender (1 = male; 2 = female). \n",
    "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n",
    "X4: Marital status (1 = married; 2 = single; 3 = others). \n",
    "X5: Age (year). \n",
    "X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n",
    "X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. \n",
    "X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.\n",
    "</pre>\n",
    "\n",
    "- [Optimization](#Optimization)\n",
    "- [Ensemble Learning](# Ensemble Learning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from time import time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_auc_score , classification_report\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read .csv from provided dataset\n",
    "xls_filename=\"default of credit card clients.xls\"\n",
    "\n",
    "# df=pd.read_csv(csv_filename,index_col=0)\n",
    "df=pd.read_excel(xls_filename, skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "              ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "0             ...                      0          0          0         0   \n",
       "1             ...                   3272       3455       3261         0   \n",
       "2             ...                  14331      14948      15549      1518   \n",
       "3             ...                  28314      28959      29547      2000   \n",
       "4             ...                  20940      19146      19131      2000   \n",
       "\n",
       "   PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0       689         0         0         0         0   \n",
       "1      1000      1000      1000         0      2000   \n",
       "2      1500      1000      1000      1000      5000   \n",
       "3      2019      1200      1100      1069      1000   \n",
       "4     36681     10000      9000       689       679   \n",
       "\n",
       "   default payment next month  \n",
       "0                           1  \n",
       "1                           1  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'ID', u'LIMIT_BAL', u'SEX', u'EDUCATION', u'MARRIAGE', u'AGE',\n",
       "       u'PAY_0', u'PAY_2', u'PAY_3', u'PAY_4', u'PAY_5', u'PAY_6',\n",
       "       u'BILL_AMT1', u'BILL_AMT2', u'BILL_AMT3', u'BILL_AMT4', u'BILL_AMT5',\n",
       "       u'BILL_AMT6', u'PAY_AMT1', u'PAY_AMT2', u'PAY_AMT3', u'PAY_AMT4',\n",
       "       u'PAY_AMT5', u'PAY_AMT6', u'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features=list(df.columns[1:-1])\n",
    "X=df[features]\n",
    "y = df['default payment next month']\n",
    "\n",
    "# split dataset to 60% training and 40% testing\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18000, 23) (18000L,)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importances with forests of trees\n",
    "\n",
    "This examples shows the use of forests of trees to evaluate the importance of features on an artificial classification task. The red bars are the feature importances of the forest, along with their inter-trees variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 5 - PAY_0 (0.096054) \n",
      "2. feature 4 - AGE (0.066378) \n",
      "3. feature 0 - LIMIT_BAL (0.065619) \n",
      "4. feature 6 - PAY_2 (0.050899) \n",
      "5. feature 11 - BILL_AMT1 (0.050399) \n",
      "6. feature 12 - BILL_AMT2 (0.046844) \n",
      "7. feature 22 - PAY_AMT6 (0.046461) \n",
      "8. feature 13 - BILL_AMT3 (0.045059) \n",
      "9. feature 14 - BILL_AMT4 (0.044771) \n",
      "10. feature 16 - BILL_AMT6 (0.044064) \n",
      "11. feature 17 - PAY_AMT1 (0.043888) \n",
      "12. feature 15 - BILL_AMT5 (0.043550) \n",
      "13. feature 21 - PAY_AMT5 (0.042619) \n",
      "14. feature 18 - PAY_AMT2 (0.042169) \n",
      "15. feature 19 - PAY_AMT3 (0.042101) \n",
      "16. feature 20 - PAY_AMT4 (0.040959) \n",
      "17. feature 7 - PAY_3 (0.033914) \n",
      "18. feature 2 - EDUCATION (0.032599) \n",
      "19. feature 8 - PAY_4 (0.030316) \n",
      "20. feature 10 - PAY_6 (0.029489) \n",
      "21. feature 9 - PAY_5 (0.028565) \n",
      "22. feature 3 - MARRIAGE (0.021776) \n",
      "23. feature 1 - SEX (0.011505) \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAAJZCAYAAACQrRt5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLanA3GajAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca851d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# Build a classification task using 3 informative features\n",
    "# Build a forest and compute the feature importances\n",
    "forest = ExtraTreesClassifier(n_estimators=250,\n",
    "                              random_state=0)\n",
    "\n",
    "forest.fit(X, y)\n",
    "importances = forest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(X.shape[1]):\n",
    "    print(\"%d. feature %d - %s (%f) \" % (f + 1, indices[f], features[indices[f]], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(num=None, figsize=(14, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X.shape[1]), indices)\n",
    "plt.xlim([-1, X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09605437,  0.06637819,  0.06561883,  0.05089928,  0.0503994 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances[indices[:5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. feature 5 - PAY_0 (0.096054)\n",
      "2. feature 4 - AGE (0.066378)\n",
      "3. feature 0 - LIMIT_BAL (0.065619)\n",
      "4. feature 6 - PAY_2 (0.050899)\n",
      "5. feature 11 - BILL_AMT1 (0.050399)\n"
     ]
    }
   ],
   "source": [
    "for f in range(5):\n",
    "    print(\"%d. feature %d - %s (%f)\" % (f + 1, indices[f], features[indices[f]] ,importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_features = []\n",
    "for i in indices[:5]:\n",
    "    best_features.append(features[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAF7CAYAAADoqUNsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90VOWB//HPTYIIBJYfkUAmMfxIQoJGkpAQf0AdbU/E\nKNBSXKKWII2y2qVK8Wz1tNuvYc8Wi1gtCOumHg4/ujYBrUtUQqCNHWpdY1wRt0ZREElIkCgVIyAQ\nknm+f1CmxITMJCSZJ8n7dU7OuXfmufc+95k7+cxz57l3HGOMEQAAsEZIsCsAAACaI5wBALAM4QwA\ngGUIZwAALEM4AwBgGcIZAADLEM5AL3Pffffp5z//ebCrAeAiOFznDJw1ZswYffrppwoLC5MxRo7j\n6MMPP9SoUaM6vM6dO3fqe9/7ng4ePNiJNe05FixYoJiYGP3bv/1bsKsC9Cj0nIG/cRxHW7du1Zdf\nfqljx47pyy+/vKhgluQL+Y5qamq6qO0Hk9frDXYVgB6LcAbOc6ETSeXl5bruuus0bNgwpaamaufO\nnb7n1q9fr4kTJ2rIkCGKi4vTr3/9a0nSV199pezsbB06dEiDBw/WkCFDdPjwYS1YsED/7//9P9/y\nO3fuVExMjG9+7NixeuyxxzRp0iSFh4fL6/Xqk08+0Zw5czRy5EiNHz9eTz311AX34fz1n1v3ihUr\nFBkZKZfLpeLiYm3btk0TJkxQRESEHn30Ud+yS5cu1W233aacnBwNGTJE6enp+r//+z/f83v27NEN\nN9ygYcOGKTk5WS+99FKz7f7gBz/QLbfcosGDB2vt2rV69tln9dhjj2nIkCGaNWuWJGn58uWKi4vT\nkCFDdOWVV2rLli2+dWzYsEHTpk3Tv/zLv2j48OEaP368SktLfc8fPXpU3//+9+VyuTRixAjNnj3b\n99zLL7+s1NRUDRs2TFOnTtVf/vIX33PLly9XdHS0hgwZoqSkJP3xj3+8YPsBVjAAjDHGjBkzxpSV\nlbV4vLa21owYMcKUlpYaY4z5wx/+YEaMGGGOHDlijDGmpKTEfPzxx8YYY/70pz+ZgQMHmrffftsY\nY4zH4zExMTHN1nfXXXeZn/3sZ775r5cZM2aMSU1NNbW1tebUqVPG6/WayZMnm3//9383jY2N5uOP\nPzbjx483O3bsaHU/zl+/x+MxYWFhvmWfeeYZc9lll5k777zTnDhxwlRWVpoBAwaYAwcOGGOMyc/P\nN5dccol54YUXTGNjo3n88cfN2LFjTWNjozlz5oyJi4szv/jFL8yZM2fMK6+8YgYPHmw+/PBD33aH\nDh1qXn/9dWOMMadOnWqxr8YY8/zzz5vDhw8bY4zZvHmzGTRokG9+/fr15pJLLjFr1641Xq/XPP30\n0yYqKsq3bHZ2tsnJyTH19fWmsbHR/OlPfzLGGLNr1y4zcuRI8+abbxqv12s2btxoxowZYxoaGswH\nH3xgYmJifNuoqqoy+/fvb7XtAFvQcwbO8+1vf1vDhw/X8OHDfb2y//qv/9Itt9yim266SZL0zW9+\nU+np6SopKZEk3XzzzRozZowkadq0acrKytKrr756UfV44IEHFBUVpf79++vNN9/UkSNH9NOf/lSh\noaEaM2aM7r77bhUVFQW0rksuuUQ/+clPFBoaqpycHB05ckSLFy/WwIEDNXHiRE2cOFHvvPOOr/zk\nyZP1ne98R6GhoVqyZIlOnz6t8vJylZeX68SJE3rooYcUFhamG264QbfeeqsKCwt9y86aNUtXX321\nJKl///6t1ue73/2uIiMjJUm33Xab4uPjVVFR4Xs+NjZW3//+9+U4jubPn69PPvlEn376qQ4fPqzt\n27eroKBAQ4YMUWhoqKZNmyZJeuaZZ3TvvfcqPT1djuNo3rx56t+/v8rLyxUaGqqGhga9++67amxs\n1OWXX66xY8e27wUBuhnhDJynuLhYn3/+uT7//HO98MILkqSqqipt3rzZF9rDhg3Ta6+9pk8++USS\ntG3bNl1zzTUaMWKEhg0bpm3btunIkSMXVY/o6GjfdFVVlWpra5tt/9FHH9Wnn34a0LpGjBjh+957\nwIABkqSRI0f6nh8wYICOHz/umz//FLvjOHK5XDp06JAOHTrU7DnpbJDW1ta2uuyFbNy40Xf6ediw\nYaqsrGzWXud/z3+uvsePH9fBgwc1fPhwDRkypMU6q6qq9Mtf/rJZG9XU1OjQoUMaP368fvWrXyk/\nP1+RkZG64447fK8dYCvCGTiPaeU755iYGOXm5vpC++jRozp27Jh+/OMfq6GhQXPmzNGPf/xjffbZ\nZzp69Khuvvlm33paGww2aNAgffXVV7751oLi/OViYmI0bty4Ztuvr69v9n1vZzp/ZLkxRjU1NYqK\nilJUVJSqq6ubla2urpbL5Wq13q3NV1dXa+HChfqP//gPHT16VEePHtUVV1xxwe/6zxcTE6PPP/9c\nX375ZavP/fSnP23WRsePH9fcuXMlSTk5OXr11VdVVVUlSXr44Yf9bg8IJsIZ8ON73/ueXnrpJe3Y\nsUNer1enTp3Szp07dejQITU0NKihoUEREREKCQnRtm3btGPHDt+ykZGR+utf/9osUFJSUlRSUqKj\nR4/q8OHDWrlyZZvbnzJligYPHqzHHntMp06dUlNTkyorK/W///u/XbK/b731lrZs2aKmpiY9+eST\nuvTSS3X11VcrMzNTgwYN0mOPPabGxkZ5PB69/PLLuv322y+4rsjISO3fv983f+LECYWEhCgiIkJe\nr1fr1q3Tu+++G1C9Ro0apZtvvlk/+MEP9MUXX6ixsdH39cE999yj//zP//SdHj9x4oRKSkp04sQJ\nffjhh/rjH/+ohoYGXXLJJRowYIBCQvjXB7txhAJ/c6FLnqKjo1VcXKxly5bpsssuU2xsrB5//HF5\nvV6Fh4dr1apVuu222zR8+HAVFRX5RiVL0oQJE3T77bdr3LhxGj58uA4fPqx58+bpqquu0pgxYzR9\n+nTl5OS0WY+QkBC9/PLL2r17t8aOHauRI0fqnnvuabUH2ZH9/Pr8rFmztGnTJg0bNkzPPvus/vu/\n/1uhoaHq16+fXnrpJZWUlCgiIkKLFi3Sb37zG8XHx1+w/fLy8lRZWen7Dj8pKUlLlizR1VdfrVGj\nRqmyslJTp04NuL6/+c1vFBYWpsTEREVGRvo+2EyePFnPPPOMFi1apOHDhyshIUEbNmyQJJ0+fVoP\nP/ywLrvsMkVFRemzzz5rNkIdsFFANyEpLS3V4sWL5fV6lZeXp4ceeqjZ8x988IEWLFigXbt2admy\nZVqyZEmz571er9LT0xUdHa0XX3yxc/cAQKdZunSpPvroI23cuDHYVQH6tDB/BbxerxYtWqSysjJF\nRUUpIyNDs2bNUmJioq/MiBEj9NRTTzW7XvF8K1eu1MSJEzv8SR8AgL7E72ntiooKxcfHKzY2Vv36\n9VNOTo6Ki4ublYmIiNDkyZMVFtYy62tqalRSUqK7776782oNAEAv5rfnXFtb2+zyiOjo6GbXJPrz\nox/9SCtWrFB9fX3Hagig2zzyyCPBrgIAdfGAsK1btyoyMlIpKSkyxgR0uQQAAH2d356zy+Vqdm1j\nTU1Ns+sa2/Laa6/pxRdfVElJiU6ePKljx44pNze31cEmF/PjAAAA9EQX6rT67TlnZGRo3759qqqq\nUkNDg4qKijRz5syANrRs2TJVV1dr//79Kioq0o033tjmKNBzvWv+Lvz3yCOPBL0OPeGPdqKtaCfa\nyva/tvjtOYeGhmr16tXKysryXUqVlJSkgoICOY6jhQsXqq6uTunp6Tp27JhCQkK0cuVKvffeewoP\nD/e3egAA8DV+w1mSpk+frg8++KDZY//0T//km46MjPT7Y/LXX3+9rr/++g5UEQCAvoU7hPUwbrc7\n2FXoEWinwNFWgaGdAkdbXbyA7hDWHRzH8XsOHgCA3qKt3KPnDACAZQhnAAAsQzgDAGAZwhkAAMsQ\nzgAAWIZwBgDAMoQzAACWIZwBALAM4QwAgGUIZwAALEM4AwBgGcIZAADLEM4AAFiGcAYAwDKEMwAA\nliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACAZcKC\nXQH45/Gc/Ts37XafnXa7/z4NAOg9HGOMCXYlJMlxHFlSFas5jkQzAUDP11bucVobAADLEM4AAFiG\ncAYAwDKEMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYJqBwLi0tVWJiohISErR8\n+fIWz3/wwQe69tprdemll+qJJ57wPV5TU6Mbb7xRV1xxhZKTk7Vq1arOqzkAAL2U33tre71eJSQk\nqKysTFFRUcrIyFBRUZESExN9ZY4cOaKqqipt2bJFw4YN05IlSyRJhw8f1uHDh5WSkqLjx49r8uTJ\nKi4ubrasryLcWzsg3FsbAHqHi7q3dkVFheLj4xUbG6t+/fopJydHxcXFzcpERERo8uTJCgtr/iNX\no0aNUkpKiiQpPDxcSUlJqq2t7eh+AADQJ/gN59raWsXExPjmo6OjOxSwBw4c0O7du5WZmdnuZQEA\n6Eu6ZUDY8ePHNWfOHK1cuVLh4eHdsUkAAHqsMH8FXC6XqqurffM1NTVyuVwBb6CxsVFz5szRvHnz\nNGvWrDbL5ufn+6bdbrfcbnfA2wEAwGYej0cejyegsn4HhDU1NWnChAkqKyvT6NGjNWXKFBUWFiop\nKalF2aVLlyo8PFwPPvig77Hc3FxFREQ0G8XdakUYEBYQBoQBQO/QVu75DWfp7KVUDzzwgLxer/Ly\n8vTwww+roKBAjuNo4cKFqqurU3p6uo4dO6aQkBCFh4frvffe0zvvvKNvfOMbSk5OluM4chxHy5Yt\n0/Tp09tVSfwd4QwAvcNFh3N3IJwDQzgDQO9wUZdSAQCA7kU4AwBgGcIZAADLEM4AAFiGcAYAwDKE\nMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACA\nZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQzAACWIZwBALAM4QwAgGUIZwAALEM4AwBgGcIZ\nAADLEM4AAFiGcAYAwDKEMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAy\nhDMAAJYhnAEAsExA4VxaWqrExEQlJCRo+fLlLZ7/4IMPdO211+rSSy/VE0880a5lAQBAc44xxrRV\nwOv1KiEhQWVlZYqKilJGRoaKioqUmJjoK3PkyBFVVVVpy5YtGjZsmJYsWRLwsr6KOI78VAWSHEei\nmQCg52sr9/z2nCsqKhQfH6/Y2Fj169dPOTk5Ki4ublYmIiJCkydPVlhYWLuXBQAAzfkN59raWsXE\nxPjmo6OjVVtbG9DKL2ZZAAD6KgaEAQBgmTB/BVwul6qrq33zNTU1crlcAa28vcvm5+f7pt1ut9xu\nd0DbAQDAdh6PRx6PJ6CyfgeENTU1acKECSorK9Po0aM1ZcoUFRYWKikpqUXZpUuXKjw8XA8++GC7\nl2VAWGAYEAYAvUNbuee35xwaGqrVq1crKytLXq9XeXl5SkpKUkFBgRzH0cKFC1VXV6f09HQdO3ZM\nISEhWrlypd577z2Fh4e3uiwAALgwvz3n7kLPOTD0nAGgd7ioS6kAAED3IpwBALAM4QwAgGUIZwAA\nLEM4AwBgGb+XUqF1Y0aNUlVdXRC2bOQ4TrdvNTYyUgcOH+727QJAX8SlVB3kOI6CUVtHRkbdH86O\n1KNeHwCwHZdSAQDQgxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQ\nzgAAWIZwBgDAMoQzAACWIZwBALAM4QwAgGUIZwAALEM4AwBgGcIZAADLEM4AAFiGcAYAwDKEMwAA\nliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACAZQhn\nAAAsQzgDAGCZgMK5tLRUiYmJSkhI0PLly1stc//99ys+Pl4pKSnavXu37/Enn3xSV155pa666ird\neeedamho6JyaA0AX83ik/Pyzf27336c9niBWCn2CY4wxbRXwer1KSEhQWVmZoqKilJGRoaKiIiUm\nJvrKbNu2TatXr9bWrVv1xhtv6IEHHlB5ebkOHTqkqVOnas+ePbrkkks0d+5c3XLLLcrNzW1ZEceR\nn6pYxXEcBaO2joyMnCBsVz3q9QE6m+NIvAXQmdrKPb8954qKCsXHxys2Nlb9+vVTTk6OiouLm5Up\nLi72BW5mZqbq6+tVV1cnSWpqatKJEyfU2Nior776SlFRURe7PwAA9Gp+w7m2tlYxMTG++ejoaNXW\n1rZZxuVyqba2VlFRUXrwwQd1+eWXy+VyaejQofrWt77VidUHAKD36dIBYV988YWKi4tVVVWlQ4cO\n6fjx4/rtb3/blZsEAKDHC/NXwOVyqbq62jdfU1Mjl8vVoszBgwdblPnDH/6gcePGafjw4ZKk2bNn\n63/+5390xx13tLqt/Px837Tb7Zbb7W7PvgAAYC2PxyNPgKMJ/Q4Ia2pq0oQJE1RWVqbRo0drypQp\nKiwsVFJSkq9MSUmJ1qxZo61bt6q8vFyLFy9WeXm5KioqlJeXpzfffFP9+/fXggULlJGRoX/+539u\nWREGhAW2XQaEAUHBgDB0trZyz2/POTQ0VKtXr1ZWVpa8Xq/y8vKUlJSkgoICOY6jhQsXKjs7WyUl\nJYqLi9OgQYO0bt06SdKUKVM0Z84cpaamql+/fkpNTdXChQs7d+8AAOhl/Pacuws95wC3S88ZCAp6\nzuhsF3UpFQAA6F6EMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMA\nAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQzAACWIZwBALBMWLArAKD7\neTxn/85Nu91np93uv08DCB7HGGOCXQlJchxHllQlII7jKBi1dWRk5ARhu+pRrw8C5zgSL61/tBM6\nW1u5R88ZvQa9QQC9BT3nDqLnbDd6OYGjrQJDO6GztZV7DAgDAMAyhDMAAJYhnAEAsAzhDACAZQhn\nAAAsw6VU6HJjRo1SVV1dN2/VyHG6f1R7bGSkDhw+3O3bBdC7cClVB3EpVTuWDUJb9cR2ChYuEQoM\n7YTOxk1IgB4gOGcYJM4yAPah59xB9JzbsSw958CW5ZiyGj1ndLa2co9w7qDu/Efq0fXyyP23abfc\n8kiS3PLIrZ3dUoeeFjo9MXAIZ7sRzuhshHMXCNY/0mDpaaHTEwOHcLYb4YzOxu07AQDoQQhnAAAs\nw2ntDuK0djuW7aa24rv5juG0dmA4rY3OxnfOXYBwbseyfaitemI7Ec6BIZzR2fjOGQCAHiSgcC4t\nLVViYqISEhK0fPnyVsvcf//9io+PV0pKinbv3u17vL6+XrfddpuSkpJ0xRVX6I033uicmgMA0Ev5\nDWev16tFixZp+/btqqysVGFhofbs2dOszLZt2/TRRx9p7969Kigo0L333ut77oEHHlB2drbef/99\nvfPOO0pKSur8vQAAoBfxe/vOiooKxcfHKzY2VpKUk5Oj4uJiJSYm+soUFxcrNzdXkpSZman6+nrV\n1dVpwIABevXVV7V+/fqzGwsL05AhQ7pgNwC0x/mD566XR/l6RFL3Dp4DcGF+w7m2tlYxMTG++ejo\naFVUVLRZxuVyqba2VqGhoYqIiNCCBQv0zjvvKD09XStXrtSAAQM6cRcAtJdbO88L4aVBrQuAlrr0\nhy8aGxu1a9curVmzRunp6Vq8eLF+8YtfaOnS1v8Z5Ofn+6bdbrfcbndXVg8AgG7j8Xjk8XgCKus3\nnF0ul6qrq33zNTU1crlcLcocPHiw1TIxMTFKT0+XJM2ZM+eCA8qk5uEMAEBv8vVO54U6qlIAA8Iy\nMjK0b98+VVVVqaGhQUVFRZo5c2azMjNnztTGjRslSeXl5Ro6dKgiIyMVGRmpmJgYffjhh5KksrIy\nTZw4sSP7BABAn+G35xwaGqrVq1crKytLXq9XeXl5SkpKUkFBgRzH0cKFC5Wdna2SkhLFxcVp0KBB\nWrdunW/5VatW6c4779SZM2c0bty4Zs8BAICWuENYB/Wlu15JPfPOV8FAOwWOO4Shr+MOYQAA9CCE\nMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACA\nZfz+KhUAAP54PGf/zk2f+9lit/vv0wgcv0rVQfyCUDuW7UNtRTsFjl+l6r1oq8Dwq1QAAPQghDMA\nAJYhnAEAsAwDwgD0KGNGjVJVXV0QtmzkOE63bzU2MlIHDh/u9u0iuBgQ1kEM3mnHsn2orWinwHW0\nrYLVTo6MjLo/nC/mmArmBxl1c1v1xA8xbeUe4dxB/CNtx7J9qK1op8ARzoFut+cdU8Foq542+l9i\ntDYAAD0K4QwAgGUIZwAALEM4AwBgGQaEdRCDd9qxbB9qK9opcAwIC3S7PeOY8uh6eeT+27Rbbnkk\nSW555NbOLt9+bxsQRjh3EP9I27FsH2or2ilwhHOg2+WYCkRvC2dOawMAYBnCGQAAyxDOAABYhnAG\nAMAyhDMAAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMgGFc2lpqRITE5WQ\nkKDly5e3Wub+++9XfHy8UlJStHv37mbPeb1epaWlaebMmRdfYwAAejm/4ez1erVo0SJt375dlZWV\nKiws1J49e5qV2bZtmz766CPt3btXBQUFuvfee5s9v3LlSk2cOLFzaw4AQC/lN5wrKioUHx+v2NhY\n9evXTzk5OSouLm5Wpri4WLm5uZKkzMxM1dfXq66uTpJUU1OjkpIS3X333V1QfQAAeh+/4VxbW6uY\nmBjffHR0tGpra9ss43K5fGV+9KMfacWKFXKc7v8dVAAAeqIuHRC2detWRUZGKiUlRcaYHvdD2AAA\nBEOYvwIul0vV1dW++ZqaGrlcrhZlDh482KLM888/rxdffFElJSU6efKkjh07ptzcXG3cuLHVbeXn\n5/um3W633G53O3cHAAA7eTweeTyewAobPxobG8348ePNgQMHzOnTp82kSZPMe++916zM1q1bTXZ2\ntjHGmNdff91kZma2WI/H4zEzZsy44HYCqIpVJBnTh/4u5vXpS21FO3V9WwWrnSTTo9qprx1TPS1D\njDFt1tlvzzk0NFSrV69WVlaWvF6v8vLylJSUpIKCAjmOo4ULFyo7O1slJSWKi4vToEGDtG7dug58\npgAAAJLk/C29g85xHFlSlYA4jqOeU9uL50gdfn36UlvRToHraFsFq50cGRl1/8BWjqnAXEw7BUtb\nuccdwgAAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQzAACWIZwBALAM4QwAgGUI\nZwAALEM4AwBgGcIZAADLEM4AAFiGcAYAwDKEMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAA\nyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQz\nAACWIZwBALAM4QwAgGUIZwAALEM4AwBgGcIZAADLEM4AAFiGcAYAwDIBhXNpaakSExOVkJCg5cuX\nt1rm/vvvV3x8vFJSUrR7925JUk1NjW688UZdccUVSk5O1qpVqzqv5gAA9FbGj6amJjN+/Hhz4MAB\n09DQYCZNmmTef//9ZmVKSkpMdna2McaY8vJyk5mZaYwx5pNPPjFvv/22McaYY8eOmYSEhBbLnhNA\nVawiyZg+9Hcxr09faivaqevbKljtJJke1U597ZjqaRlijGmzzn57zhUVFYqPj1dsbKz69eunnJwc\nFRcXNytTXFys3NxcSVJmZqbq6+tVV1enUaNGKSUlRZIUHh6upKQk1dbWdtLHCgAAeie/4VxbW6uY\nmBjffHR0dIuA/XoZl8vVosyBAwe0e/duZWZmXmydAQDo1bplQNjx48c1Z84crVy5UuHh4d2xSQAA\neqwwfwVcLpeqq6t98zU1NXK5XC3KHDx4sNUyjY2NmjNnjubNm6dZs2a1ua38/HzftNvtltvtDmQf\nAKBLeHS9PHJLkq6XR/l6RJLklkdu7QxizdATeTweeTyegMo6f/tS+oKampo0YcIElZWVafTo0Zoy\nZYoKCwuVlJTkK1NSUqI1a9Zo69atKi8v1+LFi1VeXi5Jys3NVUREhJ544om2K+I48lMVqziOo55T\n24vnSB1+ffpSW9FOgetoW9FO7Vi2D7XVxbRTsLSVe357zqGhoVq9erWysrLk9XqVl5enpKQkFRQU\nyHEcLVy4UNnZ2SopKVFcXJwGDRqk9evXS5Jee+01Pfvss0pOTlZqaqocx9GyZcs0ffr0Tt1BAAB6\nE7895+5Cz9lufHoPDO0UOHrOgeGYCkxv6zlzhzAAACxDOAMAYBnCGQAAyxDOAABYhnAGAMAyhDMA\nAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQzAACWIZwBALAM4QwAgGUI\nZwAALEM4AwBgGcIZAADLEM4AAFiGcAYAwDKEMwAAliGcAQCwDOEMAIBlCGcAACxDOAMAYBnCGQAA\nyxDOAABYhnAGAMAyhDMAAJYhnAEAsAzhDACAZQhnAAAsQzgDAGAZwhkAAMsQzgAAWIZwBgDAMoQz\nAACWIZwBALBMQOFcWlqqxMREJSQkaPny5a2Wuf/++xUfH6+UlBTt3r27XcsCAIDzGD+amprM+PHj\nzYEDB0xDQ4OZNGmSef/995uVKSkpMdnZ2cYYY8rLy01mZmbAy54TQFWsIsmYIPz9MUjbvZjXJxht\nRTv13rainWirzm6nYGmrzn57zhUVFYqPj1dsbKz69eunnJwcFRcXNytTXFys3NxcSVJmZqbq6+tV\nV1cX0LKYp7CJAAAKDElEQVRoH0+wK9BDeIJdgR7EE+wK9BCeYFegB/EEuwK9gN9wrq2tVUxMjG8+\nOjpatbW1AZUJZFkAANBclwwIO9tbBwAAHRHmr4DL5VJ1dbVvvqamRi6Xq0WZgwcPtijT0NDgd9nz\nOY7TrsoHW7BquzRI272Y1ycYbUU7Ba6ntRXt1I5lO7Ee7RGMtuppGdIWv+GckZGhffv2qaqqSqNH\nj1ZRUZEKCwublZk5c6bWrFmjuXPnqry8XEOHDlVkZKQiIiL8LnsOvW0AAM7yG86hoaFavXq1srKy\n5PV6lZeXp6SkJBUUFMhxHC1cuFDZ2dkqKSlRXFycBg0apHXr1rW5LAAAuDDH0GUFAMAq3CEMPdKW\nLVsUEhKiDz/80PfYvn37NGPGDMXHxysjI0Pf/OY39ec//1mStGHDBo0cOVJpaWlKTU1VWlqa9uzZ\nE6zqA0CbCOcgCQ0NVVpampKTkzV37lydOnXK99zXg+f06dNKSkpSZWWlr8yKFSt03333XXD9GzZs\nUEJCgiZMmKCNGzd23Y4ESVFRkaZNm+Ybw3D69Gndcsstuvfee7V37169+eabeuqpp7R//37fMjk5\nOdq1a5fefvtt7dq1S4mJicGqfrsMHjy4xWNLly7VE088IUm66667NGjQIJ04ccL3/OLFixUSEqLP\nP//ct47KykrfB5MRI0Zo3LhxSk1NVVZWVqvbraqq0sCBA5WWlqaUlBRNnTpVe/fubVZm8eLFio6O\nbvbYhg0b9MMf/vCi9rkrdeV775133tG1116r5ORkpaSkaPPmzV27M+10bt9TUlKUnp6u8vJySWdf\n6+TkZEnSzp07NWPGjBbL3nDDDdq1a1fA27rQsRESEqJXXnnF99i5Nn/hhRc0e/ZspaamKj4+XkOH\nDlVaWprS0tJUXl6uNWvWKD4+XqGhob7julfrphuh4GsGDx7sm77zzjvNk08+6ZufO3eu+cY3vmHy\n8/N9j23fvt1MmzbNGGNMTU2NiYuLM/X19a2u+/PPPzfjxo0zX3zxhTl69Khvurc4fvy4iY6ONnv3\n7jUTJkwwxhizdu1ac9ddd11wmfXr15tFixZ1VxU71fnHyjn5+fnml7/8pTHGmLvuustMmjTJPPvs\ns8YYY7xer7nqqqtMTEyM+etf/9rqOhYsWGB+97vftbndAwcOmOTkZN98QUGBmT9/vm/e6/Wa2NhY\nc8011xiPx+N7fP369eaHP/xh+3ayG3Xle2/v3r1m3759xhhjDh06ZEaPHn3BssFw/r5v377dXH/9\n9caY5q+1x+MxM2bMaLGs2+02b731VkDbaevYmDRpkrnnnnt8j82dO9ekpqY2Ox5bq8Pu3btNVVWV\nGTt2rO+47s3oOVtg2rRp2rdvnyTpxIkTeu2117R27dpmI9uzsrIUFRWljRs3asmSJVq6dKmGDBnS\n6vq2b9+urKws/cM//IOGDh2qrKwslZaWdsu+dIfi4mJNnz5dcXFxioiI0K5du1RZWam0tLQ2l9u0\naVOz09qnT5/uphp3vZycHG3atEmS5PF4dN111yks7MLjPU2AQ03OL/fll19q+PDhvnmPx6Mrr7xS\n9913n3772992sObB1dnvvbi4OI0fP16SNHr0aI0cOVKfffZZ1+9IgM5/Pevr65u9np2prWNj6tSp\nqqioUFNTk06cOKF9+/YpJSXF7zonTZqkyy+/vM9c2eN3tDa6xrkDrLGxUdu2bdPNN98sqWXwvP32\n20pNTZUkPfnkk5oyZYoSEhJ0xx13XHDdX78zm8vl6lV3ZissLNTixYslSXPnzlVhYWGL6xtnz56t\nvXv3asKECXr++eclnQ2wVatWdXt9u0N8fLxefPFFffHFFyosLNS8efM65QPZRx99pLS0NH355Zc6\nefKk3njjDd9zhYWFuuOOOzRjxgz95Cc/UVNTk0JDQy96m12tK99756uoqNCZM2d8YW2DkydPKi0t\nTSdPntThw4ebnV7uTG0dG47j6Fvf+pZKS0tVX1+vWbNm6eOPP+6SevRk9JyD5NybZMqUKYqNjVVe\nXp6kswd1Tk6OpLPBc/6nztGjR+vGG29s87vm3u7o0aN65ZVXdPfdd2vcuHF6/PHH9dxzz+mKK67Q\nW2+95Sv3wgsvaP369X3juymd/Yc3e/ZsFRUVqaKiQlOnTu2UHkZcXJx27dqlffv26Ve/+pXuuece\nSdKZM2dUUlKiWbNmafDgwZoyZYq2b99+0dvrDt3x3vvkk0+Um5ur9evXd3r9L8bAgQO1a9cuvf/+\n+9q2bZvmzZvX6dvwd2w4jqOcnBwVFRVp06ZNuv322/tMb7g96DkHybk3yfnOBc+7774rx3HU1NQk\nx3G0YsUKX5mQkBCFhLT9mcrlcsnj8fjma2pqdMMNN3Rq/YPlueeeU25urp5++mnfYzfccIPGjx+v\nRx99VC+//LJuvfVWSWo2QErq/Te6+cd//EdNnjxZCxYs6JI7Jc2YMUMLFiyQJF+vJzk5WcYYnTx5\nUgMHDlR2dnanb7ezdeV7T5KOHTumW2+9VY8++qgyMjI6vf6d5eqrr9aRI0d05MiRTl3v9u3b/R4b\n6enp+stf/qLw8HDFxcW1a/296S5gbaHnHCStBcW54Pn444+1f/9+VVVVaezYsb7LgQJ100036fe/\n/73q6+t19OhR/f73v9dNN93UWVUPqk2bNuk73/lOs8e++93vatOmTXr55Zf19NNPKy4uTtddd52W\nLVumf/3Xf/WV27x5c7PvnM+NVLVdoB8qLr/8ci1btqzV3l1HP5icv9yrr77qO0VbVFSktWvXav/+\n/b7jdceOHb6RzzZ/EOrK996ZM2f07W9/W/Pnz29xnNrg/H3fs2ePvF6vRowY0Wa5QB4/X2FhYZvH\nxjnLly/Xz3/+83buwdk62Hx8dRZ6zkHS2qe/TZs26aGHHmr22OzZs1VYWKipU6decLmvGzZsmH72\ns58pPT1djuPokUce0dChQzun4kFWVlbW4rFFixb5prdu3drqcvPnz9f8+fO7rF5d6eTJk76BMI7j\naMmSJc2Og/Onz512/vrjXz9uAu197N+/X2lpafJ6verfv7/Wrl2rkydPavv27SooKPCVGzhwoKZN\nm6aXXnpJ0tlLZoqLi311Li8vV1RUVPt2vIt05Xtv8+bN+vOf/6yjR49q3bp1chxH69ev11VXXdU5\nlb9Ip06dUlpami/cNm7c2Op+vfLKK82Oueeee06SdOutt6pfv36SpGuuucY3CPGcQI6Nc87vMATS\ntk899ZQee+wx1dXVadKkScrOztavf/3rAPe85+EOYQAAWIbT2gAAWIbT2j3Yu+++q3nz5vlOCRlj\ndOmll+r1118Pcs3Qk3ActR9tJu3YsUMPPfRQszYYN26cfve73wW5Zr0Dp7UBALAMp7UBALAM4QwA\ngGUIZwAALEM4AwBgGcIZAADL/H8xjU3OwPFaWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1392b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top 5 feature importances of the forest\n",
    "plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(5), importances[indices][:5], \n",
    "       color=\"r\",  yerr=std[indices][:5], align=\"center\")\n",
    "plt.xticks(range(5), best_features)\n",
    "plt.xlim([-1, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree accuracy and time elapsed caculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree\n",
      "Acurracy:  0.8205\n",
      "time elapsed:  0.273999929428\n"
     ]
    }
   ],
   "source": [
    "t0=time()\n",
    "print \"DecisionTree\"\n",
    "\n",
    "#dt = DecisionTreeClassifier(min_samples_split=1,random_state=99)\n",
    "dt = DecisionTreeClassifier(min_samples_split=20,max_depth=5,random_state=99)\n",
    "\n",
    "clf_dt=dt.fit(X_train,y_train)\n",
    "\n",
    "print \"Acurracy: \", clf_dt.score(X_test,y_test)\n",
    "t1=time()\n",
    "print \"time elapsed: \", t1-t0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation for DT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross result========\n",
      "[ 0.80736544  0.81233333  0.82033333  0.82616667  0.82763794]\n",
      "0.818767342417\n",
      "time elapsed:  2.09200000763\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tt0=time()\n",
    "print \"cross result========\"\n",
    "scores = cross_validation.cross_val_score(dt, X,y, cv=5)\n",
    "print scores\n",
    "print scores.mean()\n",
    "tt1=time()\n",
    "print \"time elapsed: \", tt1-tt0\n",
    "print \"\\n\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Tuning our hyperparameters using GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:  1.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n",
      "Best score: 0.468\n",
      "Best parameters set:\n",
      "\tclf__max_depth: 5\n",
      "\tclf__min_samples_leaf: 1\n",
      "\tclf__min_samples_split: 1\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.95      0.89      9408\n",
      "          1       0.67      0.33      0.44      2592\n",
      "\n",
      "avg / total       0.80      0.82      0.80     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('clf', DecisionTreeClassifier(criterion='entropy'))\n",
    "])\n",
    "\n",
    "parameters = {\n",
    "    'clf__max_depth': (5, 25 , 50),\n",
    "    'clf__min_samples_split': (1, 5, 10),\n",
    "    'clf__min_samples_leaf': (1, 2, 3)\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='f1')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print 'Best score: %0.3f' % grid_search.best_score_\n",
    "print 'Best parameters set:'\n",
    "\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print '\\t%s: %r' % (param_name, best_parameters[param_name])\n",
    "\n",
    "predictions = grid_search.predict(X_test)\n",
    "\n",
    "print classification_report(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest accuracy and time elapsed caculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest\n",
      "Acurracy:  0.820083333333\n",
      "time elapsed:  3.0\n"
     ]
    }
   ],
   "source": [
    "t2=time()\n",
    "print \"RandomForest\"\n",
    "rf = RandomForestClassifier(n_estimators=100,n_jobs=-1)\n",
    "clf_rf = rf.fit(X_train,y_train)\n",
    "print \"Acurracy: \", clf_rf.score(X_test,y_test)\n",
    "t3=time()\n",
    "print \"time elapsed: \", t3-t2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation for RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross result========\n",
      "[ 0.80619897  0.80716667  0.81816667  0.82633333  0.8198033 ]\n",
      "0.815533786811\n",
      "time elapsed:  19.9739999771\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tt2=time()\n",
    "print \"cross result========\"\n",
    "scores = cross_validation.cross_val_score(rf, X,y, cv=5)\n",
    "print scores\n",
    "print scores.mean()\n",
    "tt3=time()\n",
    "print \"time elapsed: \", tt3-tt2\n",
    "print \"\\n\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Receiver Operating Characteristic (ROC) curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6550807823129251"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,rf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x229a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "predictions = rf.predict_proba(X_test)\n",
    "\n",
    "false_positive_rate, recall, thresholds = roc_curve(y_test, predictions[:, 1])\n",
    "roc_auc = auc(false_positive_rate, recall)\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.ylabel('Recall')\n",
    "plt.xlabel('Fall-out')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning Models using GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   34.2s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 243 out of 243 | elapsed:  5.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 81 candidates, totalling 243 fits\n",
      "Best score: 0.815\n",
      "Best parameters set:\n",
      "\tclf__max_depth: 25\n",
      "\tclf__min_samples_leaf: 2\n",
      "\tclf__min_samples_split: 5\n",
      "\tclf__n_estimators: 100\n",
      "Accuracy: 0.822833333333\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.95      0.89      9408\n",
      "          1       0.66      0.36      0.47      2592\n",
      "\n",
      "avg / total       0.81      0.82      0.80     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pipeline2 = Pipeline([\n",
    "('clf', RandomForestClassifier(criterion='entropy'))\n",
    "])\n",
    "\n",
    "parameters = {\n",
    "    'clf__n_estimators': (25, 50, 100),\n",
    "    'clf__max_depth': (5, 25 , 50),\n",
    "    'clf__min_samples_split': (1, 5, 10),\n",
    "    'clf__min_samples_leaf': (1, 2, 3)\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline2, parameters, n_jobs=-1, verbose=1, scoring='accuracy', cv=3)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print 'Best score: %0.3f' % grid_search.best_score_\n",
    "\n",
    "print 'Best parameters set:'\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print '\\t%s: %r' % (param_name, best_parameters[param_name])\n",
    "\n",
    "predictions = grid_search.predict(X_test)\n",
    "print 'Accuracy:', accuracy_score(y_test, predictions)\n",
    "print classification_report(y_test, predictions)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OOB Errors for Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\ensemble\\forest.py:403: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEQCAYAAACjnUNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VFX2wL93UkhCeiONJBBqABVUmiABBMSCDQQUVNwV\newN3RVcU7Iuuuquo+FNWBBFdF6UoiqABWQREeu+BJJDQQiC9nN8fbzKZSWYmk5AK9/v5vM/Mu+2d\n+2bmnbnn3nuOEhE0Go1Go6kKU0MLoNFoNJqmgVYYGo1Go3EJrTA0Go1G4xJaYWg0Go3GJbTC0Gg0\nGo1LaIWh0Wg0Gpeoc4WhlLpWKbVLKbVHKfW0k3JXKqWKlFK3WqU9qZTappTaopT6XCnlaU4PUkot\nVUrtVkr9qJQKqOt+aDQazcVOnSoMpZQJeA8YAnQCRiulOjgo9zrwo1VaFPAo0E1ELgHcgVHm7EnA\nMhFpD/wMPFOX/dBoNBpN3Y8wugN7RSRFRIqAecBNdso9CnwNZFZIdwOaK6XcAR8gzZx+EzDL/H4W\ncHNtC67RaDQaW+paYUQDR6zOU81pFswjiZtF5ANAlaWLSDrwD+AwhqLIEpHl5uxwEckwlzsGhNdZ\nDzQajUYDNI5J73cA67kNBaCUCsQYScQBUYCvUuoOB21o/yYajUZTx7jXcftpQKzVeQzlZqUyrgDm\nKaUUEAoMVUoVAZ7AARE5BaCUmg/0BuYCGUqpFiKSoZSKoLIpC3MdrUg0Go2mBoiIqphW1yOM34E2\nSqk48wqnUcDCCkK1Nh+tMOYxHhKRhRimqJ5KKS+zMhkI7DRXWwjcY35/N7DAkQAiUuXxwgsvuFSu\noQ8tp5azMR9azgtHRkfU6QhDREqUUo8ASzGU0ycislMpdb+RLR9VrGJVd51S6mtgI1Bkfi0r/3fg\nK6XUvUAKcHtd9kOj0Wg0dW+SQkR+ANpXSJvhoOy9Fc6nAlPtlDsFXFOLYmo0Go2mChrDpHeDk5SU\n1NAiuISWs3bRctYuWs7ao7HKqJzZq5o6Sim5kPun0Wg0dYFSCrEz6V3nJqmmQHx8PCkpKQ0thkaj\n0dQrcXFxHDp0yOXyeoSBRZvWg0QajUbTeHD07HM0wtBzGBqNRqNxCa0wNBqNRuMSWmFoNBqNxiW0\nwtBYWLFiBS1btmxoMRoVJpOJAwcO1Enbc+fO5dprr7Wcr169mnbt2uHv78/ChQu57rrrmD17dp1c\nuzFQsb+aJkBDb4Gv4+3t4gqulmsI4uLixNvbW/z8/CQyMlLuueceycnJqZNrJScnS8uWLeukbet+\n+Pr6ip+fnxw9erROrmWPQ4cOiVJKSkpKbNKPHj0qf/rTnyQyMlL8/f2lY8eOMmXKFMnNzRUREZPJ\nJPv3768XGQcOHCjvvvtuvVyrMVCb/Y2Pj5fly5fXSlt1RVJSkqxYsaLW250yZYqMHTu2Rtdy9Owz\np1d6puoRRiNHKcV3331HdnY2mzZtYuPGjbz22msNLVa1se7H2bNnyc7OJiIiolptlJSU1Pj6IlJp\nRcjp06fp1asXBQUFrF27ljNnzvDTTz9x5swZ9u/fb6lXX6SkpJCYmHje7ZzPfapPaqu/tUFTuWcV\nqW+5tcJoApQ9tMLDwxkyZAibNm2y5H3//fd069aNgIAA4uLimDq13JNKSkoKJpOJzz77jLi4OMLD\nw3n11Vct+fn5+dxzzz0EBwfTuXNnfv/9d5vr7tq1i/79+xMUFESXLl1YtGiRJW/cuHE8/PDDXHfd\ndfj5+dG3b18yMjJ48sknCQ4OJjExkc2bN9vtR0UWLlxI586dCQ4OZsCAAezatcuS16pVK6ZNm8al\nl16Kr68vpaWlHD16lOHDhxMeHk5CQgLvvvuupfzvv//OlVdeSUBAAJGRkTz11FMA9OvXD4DAwED8\n/f1Zu3Yt//jHP/D392f27NkWU1x0dDRvvfUWnTt3riSns3tdUFDA2LFjCQ0NJSgoiB49enD8+HEA\nPv30UxISEvD39ychIYEvvvgCgFmzZtG3b18A2rRpw8GDB7nhhhvw9/enqKiI/v37M3PmTMs1Zs6c\nSWJiIiEhIQwdOpTDhw9b8kwmE++//z7t2rWjXbt2du+zNbNmzaJPnz5MmDCBoKAg2rRpw2+//cas\nWbOIjY0lIiKCzz77zKW+f/XVV7Ru3Zpz584BsGTJEiIjIzl58qTD69vrb3Z2Nn/+85+JioqiZcuW\nTJ482fKdOXDgAAMHDiQ0NJTw8HDGjBlDdnY2AHfddReHDx/mxhtvxN/fnzfffNOuebVVq1b8/PPP\nAEydOpURI0YwduxYAgMDmTVrFiLC66+/Tps2bQgLC2PUqFFkZWVV+fm6iuFD1cDR9xRg9uzZxMfH\nExYWxquvvupU7g8//JBXX32VL7/8Ej8/P7p27VrpWrWKvWHHhXJwAZikrIfaR44ckS5dusiTTz5p\nyV+xYoVs27ZNRES2bt0qERERsmDBAhEpN8OMHz9eCgoKZPPmzdKsWTPZtWuXiIg8/fTTcvXVV0tW\nVpakpqZK586dLSapoqIiadOmjbz++utSVFQkP//8s/j5+cmePXtEROSee+6RsLAw2bhxoxQUFMiA\nAQOkVatWMmfOHCktLZXnnntO+vfvb7cf1uzevVuaN28uy5cvl+LiYpk2bZq0adNGioqKLPW6du0q\naWlpkp+fL6WlpXL55ZfLyy+/LMXFxXLw4EFJSEiQpUuXiohIr169ZM6cOSIikpOTI2vXrrXcC5PJ\nJKWlpZZr9+zZU6ZMmeL0/iulLCYpZ/d6xowZMmzYMIuMGzZskLNnz0pOTo74+/vL3r17RUTk2LFj\nsmPHDhER+fTTT6Vv37429+jnn3+2nCclJcknn3wiIiLffvuttG3bVnbv3i0lJSXyyiuvSO/evW3k\nHDx4sGRlZUl+fr7TPpVd28PDQ2bNmmX5vGJjY+WRRx6RwsJCWbp0qfj5+VnMn876LiIyZswYGTdu\nnJw8eVKioqLk+++/r1KGiv29+eab5cEHH5S8vDw5fvy49OjRQz766CMREdm3b58sW7ZMioqK5MSJ\nE9KvXz+b30HFtuyZV62/g1OmTBFPT09ZuHChiIjk5+fLO++8I7169ZL09HQpLCyUBx54QEaPHi0i\njj9fEZGHHnpIAgMDJSgoyPJa9v7SSy+123dH39Pt27eLr6+vrFq1SgoLC2XChAni4eHhVG57JilX\ncfTsw4FJqsEf6nV51JbCgNo5akJ8fLz4+fmJn5+fKKXkmmuukTNnzjgs/8QTT8iECRNEpPwhmZ6e\nbsnv3r27fPnllyIi0rp1a8uDVkTko48+svzIVq5cKZGRkTZtjx49WqZOnSoihsIYP368Je/dd9+V\nxMREy/nWrVslKCioUj/Kfky33HKLiIi89NJLMnLkSEu50tJSiY6Otthf4+Pj5dNPP7Xkr127VuLi\n4mzkeu211+Tee+8VEZGrr75apkyZIidOnLApU3YvrOcw2rZtKzNmzLBzF8uxVhgVsb7XM2fOlKuu\nukq2bNliUyYnJ0eCgoJk/vz5kpeXZ5NnT2FYK1VrhTF06FCZOXOmJa+kpER8fHzk8OHDFjmTk5Od\n9qXitdu1a2c537p1q5hMJjl+/LglLSQkRDZv3lxl30VEsrKyJDY2Vrp06SIPPvigSzJY9zcjI0Oa\nNWtmo+y++OILmz8d1nz77bfSrVs3u22JuKYw+vXrZ5PfsWNHG6WTnp4uHh4eUlJS4vDzrSn9+vWz\n+z198cUXLUpKxPj+eHp6OpW7PhWGNkm5QG2pjJqyYMECsrOzWbFiBbt27eLEiROWvHXr1jFgwADC\nw8MJDAxkxowZNvkALVq0sLz38fGxmA7S09OJiYmx5MXFxVneHz16tNKQPi4ujrS08vhX1u16e3tX\nOi+7jnU/Tp06xalTp5g/f75FBuvrKqVo2bKlzXWsZUxJSSEtLY3g4GCCg4MJCgritddeIzPTiKE1\nc+ZMdu/eTYcOHejRowffffdd5RtqJiQkhKNHjzrMr8jatWsd3uuxY8cyZMgQRo0aRUxMDJMmTaKk\npAQfHx++/PJLPvjgAyIjI7nxxhvZvXu3y9e07vfjjz9u6XdISAhKKYf3yRUqfl4AoaGhNmlln6Gz\nvgMEBAQwYsQItm/fzoQJE2rUv6KiIiIjIy2f6wMPPGC5RmZmJqNHjyYmJobAwEDGjBlT6XteXSp+\nv1NSUrjlllss9zgxMREPDw8yMjIcfr415ZNPPrH7PU1PT7eRy8fHh5CQEKdy1ydaYTQBxKxt+vbt\ny913383EiRMteXfccQc333wzaWlpZGVlcf/991vKV0VkZCRHjpSHXLf2pxUVFWWTB3D48GGio21C\nsteoH9ZERUVV8uN15MgRm4eftT22ZcuWtG7d2qJ4Tp8+zZkzZyzzKwkJCcydO5fjx4/z17/+leHD\nh5OXl2fXpnvNNdfwzTffuCz/nXfe6fBeu7u7M3nyZLZv387q1atZtGiRZQ5g0KBBLF26lGPHjtG+\nfXvGjx/v8jWt+z1jxgybfp87d46ePXvavU+1jbO+A2zatImZM2cyevRoHn300Wq337JlS7y8vDh5\n8qSlf1lZWWzZsgWAZ599FpPJxPbt28nKymLOnDk216/Y9+bNm5Obm2s5LykpqTTnULFObGwsS5Ys\nsbnHOTk5REZGOv18H3zwQfz8/PD397c5/Pz86NKli93+OvqeVvxN5ubmVpoLqih3XX7uFdEKo4nx\nxBNP8NNPP7F161YAzp07R1BQEB4eHqxbt465c+falHemPG6//XZee+01srKySE1N5b333rPk9ejR\nAx8fH6ZNm0ZxcTHJycksXryY0aNHuyyrK4rr9ttv57vvvuOXX36huLiYN998Ey8vL3r16mW3fPfu\n3fHz82PatGnk5+dTUlLC9u3bWb9+PQCff/655Z9nQEAASilMJhNhYWGYTCbL6ieACRMmkJ2dzd13\n322ZQE5LS2PixIls27at0rWd3evk5GS2bdtGaWkpvr6+eHh4YDKZyMzMZOHCheTm5uLh4YGvry8m\nU/V/dg888ACvvvoqO3bsAODMmTN8/fXXTuv079+fF1980eVrOPu8nPU9Pz+fsWPH8vrrrzNz5kzS\n09P54IMPXL4uQEREBIMHD+bJJ5/k7NmziAgHDhxg5cqVAJw9exZfX1/8/PxIS0vjjTfeqFTfer9M\nu3btyM/PZ8mSJRQXF/Pyyy9TWFjoVIb777+fZ5991vJdOH78uGV/iKPPF+CDDz6wrPyzPs6ePWv5\nnVbE0fd0+PDhLF68mNWrV1NUVMTzzz9f5e+oRYsWHDp0yOU/iueDVhiNnIr/HkJDQ7n77rstD4Lp\n06czefJkAgICePnllxk5cqTT+tbnL7zwArGxsbRq1Yprr72Wu+66y5Ln4eHBokWL+P777wkNDeWR\nRx5h9uzZtG3b1m67VcnuqHy7du2YM2cOjzzyCGFhYXz33XcsWrQId3d3u/VMJhOLFy9m06ZNtGrV\nivDwcO677z7LipkffviBTp064e/vz5NPPsmXX35Js2bN8Pb25m9/+xtXXXUVwcHBrFu3jqCgIFav\nXo2Hhwc9evQgICCAQYMGERgYSJs2bSpd//3333d4r48dO8bw4cMJCAigU6dO9O/fn7Fjx1JaWspb\nb71FdHQ0oaGhrFy50uHD1NlndfPNNzNp0iRGjRpFYGAgl1xyCT/88IPT+3vkyBH69Olj91rVvb6z\nvj/77LPExcUxfvx4PD09mT17NpMnT7ZRzq5c77PPPqOwsJDExESCg4MZMWIEx44dA4zv6h9//EFg\nYCA33ngjt912m03dSZMm8dJLLxEcHMxbb72Fv78/06dP509/+hMxMTH4+flVabJ7/PHHuemmmxg8\neDABAQH07t2bdevWAY4/35ri6HuamJjI9OnTGT16NFFRUYSEhFQp94gRIxARQkJCuOKKK2oskyto\nb7Vob7WaC4+0tDRGjhzJqlWrGloUzXnSqlUrPvnkEwYMGFDrbWtvtRqNhujoaK0sNLWOVhgajaZO\nWLVqVaXJ4LJzjevU56R2VWiTFNokpdFoLk60SUqj0Wg0dYJWGBqNRqNxCa0wNBqNRuMSWmFoNBqN\nxiW0wtBoNBqNS2iFobGgQ7RWRodorTsu5hCtU6dOPa+d4g2FVhiNnPj4eHx8fPD39ycqKopx48bZ\nOFWrbepqzbd1P8rW4pe5fagPyoJJlZaW2qQfO3bMErQnICCAxMREpk6dSl5eHlC3a+DvuOMOG/ce\nzz//PI899hjZ2dkMGzaM77//vkk+VFylYn/PB+sgQ42V/v37W3xjQe18t9auXcvgwYMJCQmhRYsW\njBw50uZ3NW7cOJtAWOdLnSsMpdS1SqldSqk9SqmnnZS7UilVpJS61XzeTim1USm1wfx6Rin1mDnv\nUqXUb+b0dUophw5UTmXn1X6n6hEdorUcHaLVNZpKuFEdovX8OX36NPfffz8pKSmkpKTg6+vLuHHj\n6ux6daowlFIm4D1gCNAJGK2U6uCg3OvAj2VpIrJHRLqKSDfgciAHmG/Onga8ICJdgReAN3DA/qOO\nw0Q2FcoeWjpEqw7RqkO0XhghWivi7DewYcMGy72//fbbGTVqFM8//zwA1157Lbfddhu+vr54eXnx\nyCOPsHr1apeuWSPsRVWqrQPoCSyxOp8EPG2n3OPAg8BM4FY7+YOBX63OlwAjzO9HA3McXF/mJW90\nEm/KJrpUo0SHaNUhWkV0iNYLLUSrdZQ8Z7+BwsJCiYuLk3fffVeKi4tl/vz54unpKZMnT7bb7ttv\nvy29evWq8t6X4ejZR0OEaAVuAz6yOh8D/KtCmSjgF/P7fztQGJ8AD1mddwBSgMPAEaClg+vL3//z\nU41vmiV/CrVy1AQdolWHaBXRIVor0tRDtForDHu/gZiYGFmxYoWsXLlSYmJibOr26dPHrsLYvHmz\nBAcHy//+9z+X5aiuwnCv3fFKjXgHsJ7bsBlDKaU8gGEYo5MyHgQeF5FvlVLDMUYmg+w1nnrq/MI4\nAsgLDetnasGCBfTv359ff/2VO+64gxMnTlgcuK1bt45Jkyaxbds2CgsLKSwsZMSIETb1G1OI1v79\n+9uknU+IVjD+8JSWlnL11VcDhtlm8uTJdOjQgdatW/P8889z/fXXY4+ahGh95pln7N7rsWPHkpqa\nyqhRozhz5gxjxozhlVdesYRofeONN7j33nvp06cPb775Ju3bt3f5umX9fvzxxy3RFsU8J5OWlmb5\nnOo6RKujvkN5iNa3337bEn63uv0rC9Fa1j8RITY2FjBCtD7++OP8+uuvnDt3jpKSEst3oKY4CtFa\nFhhJRGxCtNr7fN3c3M5LBrD/G4iJiSEtLQ2TyVQpyqW9lYz79u3juuuu491336V3797nLZMj6nrS\nOw2ItTqPMadZcwUwTyl1EBgOTFdKWS+ZGAr8ISLWBsO7ReRbABH5GujuSIBl//mMKVOmMGXKFJKT\nk2vekwZERIdoLUOHaNUhWi+EEK3WOPoNREdHExkZSWpqaqU8a1JSUhg0aBAvvPACd9xxR5XXs0dy\ncrLlOTllyhSH5epaYfwOtFFKxSmlPIFRgM2CaxFpbT5aAV9jmJ6sy4wGvqjQbppSqh+AUmogsMeR\nAOFXdbfchKSkpPPvUQOjQ7TqEK2gQ7Q29RCt1jj6DfTu3ZtevXrh7u7O9OnTKSkpYcGCBZYogGB8\nXwcOHMijjz7Kfffd58Kdtk9SUlLDKwwRKQEeAZYC24F5IrJTKXW/Usre3yybb6xSyge4hvLVUWXc\nB/xDKbUReBlw+JftVP75m6QaEh2iVYdoBR2i9UIL0WqNs9+Ah4cH8+fP5+OPPyYoKIi5c+dy4403\n0qxZMwA++eQTDh48yJQpU+ol3sgFHw+j5ZMjOfzWvKrK6XgYmgsKHaL1wqVnz548+OCD3H333efd\nlo6HUYFzpU17hKHR1AQdovXCYeXKlWRkZFBSUsKsWbPYunWrjUuZ+qQxrJKqU/KUVhgaTUOwatUq\nhg4damN6KlvdVWZC1FTN7t27uf3228nNzaV169b897//tVnhVp9c8CYp01PRlLyRWlU5bZLSaDQX\nHdokVYFSrxOUlmploNFoNOfLBa8wKHUjMyunoaXQaDSaJs8FrzDcCkPZl67nMTQajeZ8ueAVhmdx\nKAeOaYWh0Wg058sFrzB8JJTDJ7TC0Gg0mvPlglcYvm6hpJ3WCkOj0WjOlwteYQR6hnIsWysMV9Ax\nvSujY3rXHRdzTO+mygWvMEK8Q8k4l9nQYtQYHdO7dtAxvRsfF1tM76SkJLy9vW1c9y9fvpxWrVo1\noFTV44JXGJdFd+TAucqeR5sKOqZ3OTqmt2s0lfjUF1tMb6UUvr6+vPTSS5XSmwoXvMIY3qs7xz3X\nNenNe2UPLR3TW8f01jG9m25Mb4DHHnuML774goMHD9rNr+o398gjj1juWa9evWza2bVrF4MHDyYk\nJISOHTvyn//8p9ryVYm9MHwXygFISUmpmP4SIau2HbIbitAqHKFjoHaOGqBjeuuY3iI6pveFENO7\n7POcOHGijBkzRkREli1bJq1atRIR135zoaGhsn79eikpKZE777zTIl9OTo60bNnS8nlu2rRJwsLC\nZOfOnU4/A0fPPhoipndDH2U3I+KJYfLkx19V+6Y1BnRMbx3TW0TH9K5IU4zpXfZ5Hj9+XAIDA2XH\njh02CsOV39x9991nyfv++++lY8eOIiLy5ZdfytVXX21T9/7775cXX3zRqUzVVRgXvEkKoEtwD1Ye\nWNvQYtSYBQsWkJ2dzYoVK9i1a5clohwYMb0HDBhAeHg4gYGBzJgxwyYfGldM77Lwl2Vxn88npndw\ncDBBQUG89tprZGYaCxtmzpzJ7t276dChAz169OC7776rfEPN1CSmt6N7PXbsWIYMGcKoUaOIiYlh\n0qRJlJSUWGJ6f/DBB0RGRnLjjTeye/dul69p3e/HH3/c0u+QkBBLTO8y6jqmt7PvWVlM7+3btzNh\nwoQa9a8spnfZ5/rAAw9YrpGZmcno0aOJiYkhMDCQMWPGVPqeVxdHMb3L7nFiYqJNTG97n29NKAtI\nNnnyZJt0V35z1vN+1r/llJQU1qxZY/O7mDt3bq0vLLkoFMY1HbuzN2dd1QUbKYbC1zG9Qcf01jG9\nL4yY3k899RS//PILf/zxhyXtfH5zLVu2JCkpyUb27Oxspk+fXmXd6nBRKIyRfa8gu/lG8guLG1qU\n80bH9NYxvUHH9G7qMb0DAgJ46qmnmDZtmiXtfH5zN9xwA3v27GHOnDkUFxdTVFTE+vXrbRaQ1AYX\nhcKIaxGIZ14MC9dsb2hRqo2O6a1jeoOO6X0hxPSu2N/HHnsMd3d3S/r5/OZ8fX1ZunQp8+bNIyoq\niqioKCZNmlSlkqwuF3wApbL+JTx1N1fF9OGzJ+6zV85lM45G0xTQMb01rqADKDng8oju/J6+vqHF\n0GjqBR3TW1MXXDQKo32LWE4VpTe0GBrNRcOqVasqTQaXnWuaJu4NLUB90TI0lByqvzNTo9HUjD59\n+nD27NmGFkNTi1w0I4xW4aEUmLTXWo1Go6kpF43CaBcTRrGnVhgajUZTUy4ek1RYAHjkcC6vEF9v\nT5u8uLi4JuUxUqPRaGoDay8LrnDRKAyTSWEqCGFf+kkuS4i0yTt06JBLbXR75kkCvYL4+YXnMU0K\nY9P4rVzS2tZFt8+TXXlz4Ls8dEP5+vdnP1vAJxs/JuPtRRWb1Gg0mibDRWOSAvAoCmNfes0nvnfl\nr2TkldcA0Kwgiu2Hbf0QbTlwjHyvQ9w7uKdN+mXx8ZxRh2p8XY1Go2kMXFQKw7s0lJTjNZvHyDyd\nQ57PLkZe3Q2A5kSyJ91WYXzx6xpC83vh5Wk7cOvRPo4C70NNOiaHRqPR1LnCUEpdq5TapZTao5R6\n2km5K5VSRUqpW83n7ZRSG5VSG8yvZ5RSj1mVf1QptVMptVUp9borsviawkg9VTOF8cXK9TTP7UKg\nrxcAwe5RHDhuu69jU+puYn0qRxCLaxGId24bHv3oixpdW6PRaBoDdaowlFIm4D1gCNAJGK2U6uCg\n3OvAj2VpIrJHRLqKSDfgciAHmG8unwTcCHQRkS7Am67IE+ARSlpWZZOUo3/+r321lMzTOQAs2fob\n7bzKHeKFeUeSesZ2hLHv9C4Sw9rbbWv60Bl8eGAie1MdRyHTaDSaxkxdjzC6A3tFJEVEioB5wE12\nyj0KfA1kOmjnGmC/iKSazx8EXheRYgARcWnYEOwVyvEc26J7U0/i/VQnUo9n26SXlgp/2ziKB//v\nEwA2n/yNfgnlCiPKL5JjObYjjGPFu7mytX2FMW5wdy5xG8nQdybazddoNJrGTl0rjGjA2sF7qjnN\nglIqCrhZRD4AHK1tHQlY23PaAVcrpdYopX5RSl3hijDhvmGczLMdYazYtpfCgJ2M+/Bdm/TlG/ch\nHudYfOxDSkuFDM/fGHVVucJoFRrFqcLyEUZpqZDjvYsBl1QaQFlY8peXOeC2hJ83OffiqdFoNI2R\nxrCs9h3Aem7DRmkopTyAYcAkq2R3IEhEeiqlrgS+Alrba3zKlCmW9yXZJWRhO8LYnnoYr6xLWe75\nT9JPPkZUiB8A3/y+juhzwzjOdsa/PwtTaTN6dCyPhtUmIpKzW8oVxu7UE4DQMTbMYUcjgn2JKrqa\nr9esYcBlCQ7LaTQaTX2SnJxMcnJyleXqWmGkAbFW5zHmNGuuAOYpY+dcKDBUKVUkIgvN+UOBP0TE\nemhwBPN8hoj8rpQqVUqFiEilCQJrhTHt62WsWPWbTf6ezBQ6eQ/kRMFRxn3wHj8+9wwAq1PWcWlo\nD5q592Vm6kSiSwba1OsUG0W+e7lJ6pctu/HJb4/J5HwD4GVhPfjfobXAnU7LaTQaTX2RlJREUlKS\n5Xzq1Kl2y1VpklJKtVBKfaKUWmI+T1RK/clFOX4H2iil4pRSnsAoYKF1ARFpbT5aYcxjPGSlLABG\nY2uOAvgWGGCWpx3gYU9ZVCQuLJQ8ZWuSOpKdQlxgHP8a8Rw/nX2bU9l5AOzPX8fgTt35x9i7EPdc\nLg+3jQC9ITjUAAAgAElEQVTXJT6CEu8MiktKAVi7fxcRbo7NUWUM6dSd/QVNN1ysRqO5eHFlDuNT\njNVLUebzPcATrjQuIiXAI8BSYDswT0R2KqXuV0rZC2xss1xJKeWDMeE9v0K5fwOtlVJbgbnAXbhA\n64hQCj1sTVIZBSm0j4hjWM9EQguv4OnZX3Eur5BzPlsY2fdyWkUGcXf4ezw9zDbCl3/zZqhCP3Yf\nMdrbkbmbhED7E97WjOjTjRyfrWTnFLgiskaj0TQaXDFJhYrIV0qpZwBEpFgpVeLqBUTkB6B9hbQZ\nDsreW+E8F6g0KWBecVW9+IhA2+hQSpudoLRULKajbJXCpWZ/Kvd1e4B/bXiNvv/rhFdeAhHBvgB8\n+pj9AZVnobHbu1N8OIdzd9G/zbgqZYgI9sUrtw3frN7C3YOurG4XNBqNpsFwZYSRo5QKwfzvXynV\nEzhTp1LVEYG+XlDqSfrJch/9+V4pdG9nKIzJI68jzz2V15b+H3FuPapsr3lp+W7v06bd9OlYtUkK\nIM69O99t1mYpjUbTtHBFYUzAmHdIUEr9D/gMeMx5lcaLe0Eo+9INM1JKRhYgxLUIBMDL052+vn9m\nV/OP6B7dvcq2gtyj2J+Zzrm8Qop8DpPUxbWVT92ju/PHsbU17oNGo9E0BK4ojO1AP6A3cD/Gju1d\ndSlUXdKsJIz9x4yJ77W7U2iWH2uzsumN0X+GUjdu6Fa1wgjzjuTQ6SMkb9mPe24M/s2buSTDsMt7\nkCrrOJWdx6CXXmHlloM164xGo9HUI64ojN9EpFhEtovINvP8wW9V1mqk+BDKkRPGCGNzSgr+YusP\n/op20cwfvInhfS6psq3rO/VjRf4/GfZNX4JLXTNHAdzQI5FCrzQipl7G8tx/8MFPP1jypnz+HdO+\nXuZyWxqNRlNfOJz0VkpFYOzK9lZKdaV8Q50/4FMPstUJfm5hpJ42Rhi7j6UQ7lk5gMgtV3V2qa3n\nRl3LpBEn+GH9boJ8Xb8lXp7u9HZ7jB6Jl3HwRBrbj2+35P17wxzCvWL46/BrXG5Po9Fo6gNnq6SG\nAPdgbLZ7yyr9LPBsHcpUpwR5hnLsrDHCOJSVQkv/6kWcqoi7m4kbenSsdr3/vfgKYDg4TD5avu0k\nQ7bgUeCaaUuj0WjqE4cKQ0RmAbOUUreJyH/rUaY6JcQnlOM5xggjI+8wveNcckNVZwy6rBPP/bED\ngOycAgr8dpOdHd+gMmk0Go09qtyHISL/VUpdjzHZ7WWV/mJdClZXRPiFcfDMPgBOSwqdW57fCON8\n6dYmilK3PPamnmTD/iOAkEvNowJqNBpNXVGlwlBKfYgxZ9Ef+BgYDjTZTQRRgaGcOGhEv8vzTKFb\nQmzVleoQk0nhm5fIjxt3sOXwQZpnX06BW82CPGk0Gk1d4soqqd4ichdwWkSmAr0w3Is3Sf486Gry\n1Ak6TXqAUs/TXJYQ2dAiEemeyG/7drAxfStdfAdS3EyPMDQaTePDFYWRb37NNceuKAIa/ilbQxKi\ngtn9zEqOFe7BPbcl7m4NH9a8Y0gntmfu4GDOFga1vwpMhWSdy6+6okaj0dQjrjwtFymlAoE3gA3A\nIQyHf02W2PAAUl75gf+OWFh14XrgyvhEjuTt4LTnFoZ2uwRTQSi7j+hRhkajaVw4VRjmWNvLRSTL\nvFIqDuggIs/Xi3R1iH/zZgzrmdjQYgAw8JJETvmspdQtjx4dWuJZFMaBDNfnMb7+dUsdSqfRaDQG\nThWGiJQC063OC0SkSToebMz06NASlOCfdwkmk8JHwjiU6doIY9P+o4xYfpnZL5ZGo9HUHa6YpJYr\npW4zR8TT1AEmk6J5biKxzboA4OsWypFTrimMv3/7LShh/9Eq40dpNBrNeeGKwrgf+A9QoJTKVkqd\nVUpl17FcFx1tvHrSJ96I6hfoEcbRM64pjKVHvgFRpGRqhaHRaOoWVzbu+dWHIBc7m177p+V9qE8Y\nx3OqnsM4ePQ0p3zW4JfVmyMntcLQaDR1S8OvKdVUooVvGCfzqx5h/P2bxUTkDSDYLZajWVphaDSa\nukUrjEZIZEAoZ4qqVhgL987nxja34u8RQubZU/UgmUajuZjRCqMREhsaRo44VxhZ5/I56r2cp2+5\ngSCvYE7k6hGGRqOpW6rah+GmlGqy0fWaKq1ahJFvcj6H8eMfu2iWH0tCVDChPiGcztcKQ6PR1C1V\n7cMoAXYrpRrWQ99FRkJEKEWezkcYK3fuIJxOALTwDyG7SJukNBpN3VLlKikgCNiulFoH5JQlisiw\nOpPqIqdtdAjSLIvCohI8PdzsltmcvoOEAGOnelRgCOdK9AhDo9HULa4ojMl1LkV98M03xvHZZw0t\nSZV4erihCgLZm3aSTvHhdsscOLudUZ3vACA6OJg8pRWGRqOpW6qc9BaRFcAuwM987DSnNS02bIC5\ncyE9vaElcQmPwjD2pTs2S50w7aBfR2OEERceQqGbVhgajaZuqVJhKKVuxwiYNAK4HVirlBpe14LV\nOvv2QUgIzJxpnB8+DNOnO6/TgHiVhnLouP2J7+ycAoq8DzOwa1sAEiJDKPbUcxgajaZucWVZ7d+A\nK0XkbnMgpe40RTPV/v0wZQr83/9Bfj6MHAnPPQciDS2ZXZqrMA6fsD/CWLphN565rfD19gQgOtQf\nPHLIzS+qTxE1Gs1FhisKwyQimVbnJ12s17jYtw+GD4eICBg0CIKCwN0djh1raMnsEuARRnqWfYWx\ncucOwih3ze7uZkIVBHHgqB5laDSausOVB/8PSqkflVL3KKXuAb4Dvnf1Akqpa5VSu5RSe5RSTzsp\nd6VSqkgpdav5vJ1SaqNSaoP59YxS6rEKdSYqpUqVUsFOhTh1CkpKIDQUHn4YDh40Jr87dYIdO1zt\nSr0S4hVGxjn7CmNT2g5a+9nG8vAoCuFAhp7H0Gg0dYcrk95/AWYAl5iPj0TE4YPfGnMApveAIUAn\nYLRSqoODcq8DP1pdd4+IdBWRbsDlGEt6v7GqEwMMAlKqFGTfPmjTBpSCsWNh1y5DeSQmwvbtrnSl\n3on0iyD13CG7eQfO7uCyaFuF4VkSTOoJPcLQaDR1hys7vX8RkfkiMsF8fOOsTgW6A3tFJEVEioB5\nwE12yj0KfA1k2skDuAbYLyJHrNLeBv7ikhT790NCgvFeKfD1Nd4nJjbaEcZTN9zEPrcFZJ7OqZR3\nnO30S+xkk+ajQkg9pUcYGo2m7nBlp3epUiqghu1HA9YP+VRzmgWlVBRws4h8ADgK0jQS+MKqzjDg\niIhsdUmKshFGRRqxSapHx5aEF1zFX2fPs0k/l1dIYfODDOrazibdzy3E4rE2+ImB/Hvpuhpf+1R2\nHh5PteZcXmGN29BoNBcermzcOwdsVUr9hO1O78ccV6kW7wDWJi4bpaGU8gCGAZPM597AsxjmKLt1\nrJny3HOwaBHEx5OUnExSUlJ5ZplJSsQYeTQyHrzyAab9/gKf8idL2sqtB3DPjcG/eTObsoGeIRzP\nOUV2TgGn/X/li7XLGDe4u02Z7JwCgEp1K7Lt0DGK/Q4y++ffefD6q2qpNxqNprGSnJxMcnJyleVc\nURjzzUdNSAOs/VDFmNOsuQKYZw4BGwoMVUoVichCc/5Q4A8Ri/vWBCAe2GyuEwP8oZTqXmE1FwBT\nBg2C5GSYMAH69bPNDDfvos7MhBYtatjFuuOZEUN4acNDzFn+B2MGXg7AztR0mpdEVyob5B3MydyT\nLFyzDdyK2HRqVaUyt/xjGoUlhfw69SWn192TngHA/A3JFoWRkpFFXIvA8+2SRqNphCQlJdn8mZ46\ndardclXOYQCDRWRWxcNFOX4H2iil4pRSnsAoYKF1ARFpbT5aYcxjPGSlLABGY2WOEpFtIhJhVScV\n6GpPWQCwfLljk5RSjXoew9PDjf4B9/H60k8saQcyj+FviqxUNqx5CFkFJ1myZT3hWddz3Gs1hUUl\nNmWOnktjT/amKq97IDMDCn3YcNLY0J91Lp9Wbyfw/uLKSkij0Vw8uDKHUfawrzbm+o8AS4HtwDwR\n2amUul8pNd5eFesTpZQPxoS3sxGO4MQkxYIFkJ0NkZUfsoAxj9FIV0oBDO3cm9SCcvkOnz5KaLPK\nfYkICCG7+CQbjv5Bv+iheBZGsHCNbb+yi09wyq1q5Xj4VAbReUM55bOG3Pwips5biHid5uP//ff8\nO6TRaJosrpikDgD/U0otxHYO4y1XLiAiPwDtK6TNcFD23grnuUBYFe23dirArl3Qti2YHOjGRjzC\nAOjUMpoct3Ir3tFzR4nwrawwIgODyS09xemigzzV5U9szujDV2t/ZXjfSyxlzpUep9j/ICfO5BIa\n4OPwmkezM4jzbc+ps/uZ88t6Pt/+Kd2b/4U/ir6ktPQtTKbGN9+j0WjqHlc27u0HFpvL+lkdTYPe\nve2bo8po5Aqja0I0xT6plJYag68T+UeJDaqsMGJDQ8hxSyOv+S5u6XUJfeP6sPaorQkp33QCSjz5\naeNup9c8nptBhG8L2nv14/2V8zjutZpFE5/HJB58kbyx9jqn0WiaFK5s3JsqIlOBN8rem8+bBjfd\nBN26Oc7v3Bm2bDF2gjdCwoOao4p92JtmLJk9U3KU+LCISuXiw0Mo8t+Ld057gv29GX1VH1JNv1oU\nDUChx3ECzvZk5U7nJrhThRnEBLXg2g5JbPacTpuiWwgPas5lXrfwQXJN1z9oNJqmjiveansppXZg\nuDhHKXWpUur9OpestnjiCXj+ecf5ERHGsWFD/clUTZoVxLDpQCoAuaZjtI+qPMJIiAoBINb9CgD6\nX5oApmJ+23kYgOKSUsTrFF0C+rI53fmI6mxpBnGh4dw7sC+YSnik7z0AjO97K+vPVWffpkajuZBw\nxST1DoZrj5MAIrIZuLouhap3hgyBH3+sulwD4ScxbD9iKIyiZkfpHFdZYQT7eUNxMy6PMhSGyaQI\nL+zB/DW/A8ayWFXoR/fYSzl4zrnCyHfLpE1kC9rGhPD+Fat4+Ia+ANwzqDtF7lm88mXjvVcajabu\ncMnrbAWXHACN035TU669Fn74oaGlcEiIRwz7MlM5lZ2HuOeSEFXZ16LJpHDLD2PoJVdY0lp4xbP/\nuPHR7Uk7jnthKP07d+KkybnCKPLMoGOMsS/lweuvwt3N+Jq4u5l4+Yp/8/wf99L1mSfIOpdfW13U\naDRNAFcUxhGlVG9AlFIeSqmngJ11LFf9cvXVsHkznDnT0JLYJbJ5DIezUtl26Bhu+REOVyktun0Z\nd/Qvn6+J9oshNdsYmRzKPEGzkjAGXNqGIu8jDh/22TkFiEcOrSKD7OY/c/tgdj22hb35v/HsHL3M\nVqO5mHBFYTwAPIzhAyoNuMx8fuHg7Q1XXWVs8muExAZFk5Gbxs7Uo3gVO9hPAgy9sr2NMmkdEkNm\nvqEwDp84TnMVio+XB81yW7N0g/2VUjsPZ2LKD7OMKuzRNiaELr4D2Hv8UM06pNFomiSurJI6ISJ3\nikgLEQkXkTEicuG5RR0ypNGapdq2iOFUcSr7jh3Fj8orpBzRPiqGrFJjD0d61gn83UMBCKMTv+60\nb5banZZBs6Kq3aTEB8aSevawy7JoNJqmT9OLnFdXlE18N8KQrZ1bxnDOlErKqWMEezoeYVSqFxtN\nrrsxwsg4e4KgZsYeyAT/RJbuW243pOuBjAyaU7XCaB8Ry4kirTA0mosJrTDK6NgRTp6Es2fL0778\n0va8gejaJoZC7yOkZx+lhY/rCuOyhChKfNIpLinlRO5xQn2MEcbkm8aQUbQP/8kJjPrH+xSXlFrq\npJzMwN8tvMq2u8TFclZphaHRXExohVGGUhAVBenp5WlPPw3LljWcTGZiQv0BxaFzu4gJcF1hBPp6\noQoC2ZGSyenCE0T6GyOMgV3bkPVOMjOHzOf7I3MJmZjEz5v2A3A0O5OQZlWPMK5sF0uB92GbjYEa\njebCpipvtf2UUpeY39+ulHpPKfWkUsp5QIWmirXCKC013q+reSCi2sJkUnjmxXDMtJ74UNcVBoBX\nobHp72zJcaICQ23y7rrmCk68sYJeodcx9LMbKC0VMnMyaOFbtcKIDQ8AMZGSkVUteTQaTdPFocJQ\nSk0HXgY+VkrNAe4AtgHdgJn1I149Y60wTpyAoqJGoTAAfEtjKPFLoU2E65PeAP7EsCs9jVxOEBsa\nWinf08ON7599mhK3cyxet9NwCxLoWmyQZvmx/L5Xm6U0mosFZyOM/iLSF2NX91DgNhH5ELgLuMRJ\nvaaLtcJITYWYGFi/vlH4mQp2jwEgMbZ6I4xQT2PTX4HbCVpH2Hf8azIpOrndwr+Wzie7JIO4UNcU\nhr/EsjXFvsL4bNl6Br30SrVk1Wg0jRtnCiMfQETygRRzbAtERIDKy2suBKwVRloadOkCYWGw27l3\n1/qghU80iKJzfPUiA0b5xnDkTCrFzY7TJqryCKOMcT1vZfXpb8g1ZdAmwrVrhHnGsjvDvsJ47vs3\n+d+pr6slq0ajadw4i4cRrpSagBGcqOw95nOnMSqaLFFRsGaN8T4tDaKjISgI1q413KA3ILGBMazO\nCMPL05UQJuXEBUezZP9i8C0wT57b54Hr+jBhzWHE4yztoqteJQUQ4xdLSlZlhbHtYAZHvL9DlVZP\nVo1G07hxNsL4P4y4F75W78vOP6570RqAiiOMmBjo3r1RzGO0CY+hWWH1zFEA7SJiyFSbMeWHOg18\n5OXpTpuSYeBWSLsYxyMRaxJCYzmWV1lhPPX5v2lbeDtiKtaT4hrNBYTDv4BNKuZFbVFRYfTubYRw\n/eyzhpULGD/kajzdX692vc6xMRRt3YdXducqy4669BZe3rrI5VFM55g45uyyVRjFJaUsz/qIj4d8\nyQNLfmfNrkPEtbis2nJrNJrGR1XLaocqpVYqpU6YjxVKqevqS7h6JzLSUBgi5Saprl2NMK95eQ0q\nWkyYP8+Nurba9bomRIMSvEurtiJOGj6EV6/43PW2W8eS62GrMCZ88hUeJUGMHXgFARLPhoMHqy2z\nRqNpnDhbVnsf8BIwBWhtPqYCU5RS4+tFuvqmeXPw8oLTp8sVhpeXsQt8Y9MMTRoR7IvKD8TXVLWZ\nycfLg0kjBjkucPo0TJwIZiXQtU0UJV6ZFhcjD7w/m/f2P86/hkzHZFJEeLVi1zGtMDSaCwVntocn\ngT4icsoq7Wel1FBgFfBRnUrWUJSZpVJTDYUBxihj0ybDRNUE8SyIIcAjFLKzjQR/x5PfDvnpJxg3\nDgICjE2Nb7+Nl6c7bvkt2Lg/nX8uWcz8jGl8e9svDOtpLBBoFdiKA6cP1GJPNBpNQ+LMJKUqKAsA\nLkhPtdZERcG+fVBQAMHmQEWXXAJbtzasXOeBn8QQ4h0GTz1lhKytCX/+M3z4IXz3HcyebTHR+RTF\n8tDsf/DfjNdIvnuFRVkAdIiI51hB7Y8wTmXnsXCN8yBQGo2m9nGmMLKVUpdWTDSnNbxHvroiKgp+\n/90YXSjzqqJLLoEtWxpWrvMgslkC8QFRsHgxLFwIxcXVa6C4GI4eNTz6xsdDjx7w1VcABLvFssX0\nKXNvWEyfzvE21bq2asUZZSiMwqISXvxiiY2jw5oyfsbHjJ33wHm3o9FoqoczhTERWKiUmqKUutF8\nTAUWABOc1GvaWCuMMrp0gW3bGqXrc1dY/cJb/F/vywxTVHw8/Ppr9Ro4dgxCQ8HDwzh/4AGYMQOA\nJ/qO58M+PzCyX+WVUD07xFPgfYjSUuG9xSt5Yff1No4Oa8rStHnkuqedVxsajab6OFQYIrIK6GEu\nc4/5MAE9zXkXJlFRhjsQa4URHAx+fpCS0nBynQe+3p54/LAEbrgBbrkF5s+vXgNHjkDLluXn111n\nzPFs2cITNydx/3X253ZiwwNQpZ7sTj3B3N8XczWTSYq4mWvm9ahxPPDfdhzmnPcOir3Stadcjaae\ncbqsVkSOAa8CL5iPV8xpFy5RUcZqIGuFAU3eLMWiRYbCuPVW+OYbY+LaVY4cMTYxluHmBtdfDytX\nVlnVK78V63YfYkvBIsZffRMLJk2geUFbPlm62lJmzDsfkX7SNSvnKwu+on3JCFSJFwePnXa9DxqN\n5rxxtqzWXSk1DTgCzAI+A44opaYppTzqS8B6JyrKeLV+QELTnvhOT4f9+4245R07gq+vMYpyldRU\n2xEGQPv2LvnYClatmP3bj5S45TA6qSsAl/oN5L8bjTgj+9NP8fmph/ns5zUuifJL5jz+3HMUngVR\nbDmYXnUFjUZTazgbYbwBBAOtReRyEekGJACBwJv1IVyDUKYwKo4wunRpeiOM1FRjFPDBB8aEddkc\nxK23wrffut5OxREGuKwwIrzj+eXsh7Tjeotrklu7DmTLueUA/P2bxeBWzLqDVa96+umPveR7pPHo\njf1oXhrNjlQ9j6HR1CfOFMYNwH0iYrEViEg28CDg8m5vpdS1SqldSqk9SqmnnZS7UilVpJS61Xze\nTim1USm1wfx6Rin1mDlvmlJqp1Jqk1Lqv0qpGmwscECk2V/ThWCSGjcOHnsMVqyAhx8uTx80CJKT\nXW/H3gijQwdjB3wVtA5qRalvGrd2ucGSdu+gXuT47CAlI4vF+76heVYPdp7YXmVbz/z3Q7q634Gn\nhxtB7lHsy9AKQ6OpT5wpDDG7Mq+YWAK4NNuolDIB7wFDgE7AaKVUBwflXgd+tLrOHhHpah7ZXA7k\nAGWztUuBTiJyGbAXeMYVeVyiWTNjlBEXZ5vevr0x6Z1fs8naekfE2Gz4/ffGKKNv3/K87t1h82bX\n3Z3YG2HExhpBpnJynFZNjIyHIi+eHHaNJS3Q14vgnF68+e0Sjnr/zMOXPk1aofMRxraDGWyQf/PR\nuImA4e79SJY2SWk09YkzhbFDKXVXxUSl1Big6r+WBt2BvSKSIiJFwDzgJjvlHgW+BjIdtHMNsF9E\nUgFEZJmIlM3argFiHNSrGTt2lJumyvD0hDZtjLymQEaGoTQi7Xi4bd4cOnd23QuvvRGGmxskJMCe\nPU6r3tmvJyMC3yQ0wMcmvXvYQD7c+xzBuT24K+kqznnvcLrqadzHb9JZ7qRbW+NzifaP4miOHmFo\nNPWJM4XxMPCwUipZKfUP87ECeAzDLOUK0RiT5mWkmtMsKKWigJtF5AOMWBv2GAl84SDvXmCJi/K4\nRkCA/fRLLzX+tTcFtmwx5l2Ug1vapw+scmF1dHExZGbaVzwdOlQ5j9E2JoSvnnq4UvroHgMp9jvA\n4Ja30Ck+HMSNLQftL8DbfiiTP0pm8sm4cotmQlg0pwr1CEOjqU+cuTdPA3oopQZgmJMAvheR5bUs\nwzuA9dyGzRPOvCJrGDCpYkWl1N+AIhGZ66jxKVOmWN4nJSWRlJRUc0l79YLVq+Hee2veRn2xZYsx\n7+KIvn0tm++ccvSoEXXQw87COBcnvu0xql9X7v/uEp4eczMA/vmJ/LRpB5clVFZMf507i3Ylt3Jl\n+/KBZLvIKLI36RGGRlMbJCcnk+zCvGaVgQ9E5Gfg5xrKkQbEWp3HmNOsuQKYp5RSQCgwVClVJCIL\nzflDgT9E5Lh1JaXUPRiT7wOcCWCtMM6bPn3gX/+qvfbqkq1b4eqrHedfdRXcc48Rr9zNzXG5ipv2\nrGnf3pgjqQGeHm7kvb3Zch7dLJG1B3YAAyuV3XZyA9e1ud4mrUt8NPkeeoSh0dQGFf9MT51qPxyS\n0417tcDvQBulVJxSyhMYBSy0LiAirc1HK4x5jIeslAXAaCqYo5RS1wJ/AYaJSEGd9sCaTp0MNxmZ\njqZaGhFlJilHhIVBRITh8sQZ9ia8y3DBJOUqHUMT2XHc/kqpDNlC/062fekc34JSrxPkFzrwizV5\nMuTm1opsGo3GoE4VhnlF1SMYq5q2A/NEZKdS6n4HMTVsZj2VUj4YE94VfVm8ixEq9ifzstv3a196\nO7i5GS7O//e/erlcjSkqMpa8durkvJwr8xj2JrzLKDNJVWfXuAN6JXSyu1IqO6eAAp8DXHu57eI6\nL093TPmhbD14zJjct3bbsm0bvPwy/Pbbecul0WjKqesRBiLyg4i0F5G2IvK6OW2GiFSKpyEi94rI\nfKvzXBEJs94LYk5vKyJxItLNfDxU1/2w4OpkcUOyd6/xkG/e3Hk5V/ribITh728caec/lzDoskTO\nem2vtFJqyfqdNMttg3/zZpXqeBVFsS0lHT7/HNq2LQ+v++WX4O3d+D8njaaJUecK44KjKSiMqsxR\nZfTpY3iuLdtuc+wYjBhhW8bZCANqzSzVOb4FKOHLlZt4+tNv2H3kBADLt20hQtnviz/RHN6904jz\n0bcvTJ9u9GXePHj66cb/OWk0TQytMKrLlVcaJo8qNqzVmH/+Ez7++PzaqGqFVBkJCcay2cPmuNw/\n/ghff22MKspwNsIAw+y1du35yQuYTIrAvG7c+cMg3tr6F/788T8B2JC+hQ5B9vsS7BlF0qfvwx13\nGMGdPvqoXAE++qghV3Vjf2g0GodohVFdvL2N/RiubnqrLnPmVM9thz22bnVthKFU+SgDYPlyo3/L\nrVZOO1slBXDfffDee65NMFd0HPDFF8YqLTOpry+h+NXjfDJ0HmvPzaO0VDiUs5Werez0JT+fF1Zt\npc2BPfDii4ZJ6qqrYOxYGDXKcEkfF9d09s1oNE0ArTBqwsCBxsOutjl+HP74wyUfTQ4RgTVrjJGQ\nK5SZ2ERg2TLD99Qyw5Ms27aByVR517s1l14KPXsa/+6d8dVXtqOewkIYM8amrz5eHphMijEDLkcQ\n5v6ygdOeWxjazapeaakRNbBrV1rl5nPL6CGG912AiRON0dKoUbZ902g0tYJWGDVh4kQjtnVtP4x+\n+gn69TMeojVdebRjhzER7WxUYE3ZQ3XXLsP9yX33GSMMEZg1y/jH7myfBsDzz8O0aXDypOEZ9+uv\nbfNXrIBHHjFipWdnG2kHDhh9tLOs12RS9Gg+iheW/Atxyy/fsLdqleGe/aWX4NVXWTrpFfZ7WoWY\n72i7flUAACAASURBVNMHfv7ZcHti3TeAU6dsRjMajab6aIVREwIDjbmG++6DglrcBvLjj3D77cYD\nPzW1Zm0kJxtKx1Uuu8xYkvqf/xgjp9atDQeM27YZq4/uvrvqNrp2NUY0LVsa0fwmTixXeCkpRp/m\nzTMe9mUT5GU+qBzsA5k4ZBQHms/BL6+L4RZ961a47TZ44w3DHHjLLXRrHU+2+77ySkpB//7l52Xm\ntvffN5wlDh/uusNFjUZTCa0wasptt0G7dvDOO7XTXmmpoTCGDHHZdbgF6z0IK1ZAddyfuLtDjx5G\nPwYONB66Awcaq4xatjRkcYWPPzYe/j/9ZCjUFSuM9LfeMnaUDxhg2689e4zJdAcK45arOtPsbEda\ne5qdJF53naGkhw2z+Mcacnk7StzPsn6Pg2W9sbHg5QWffWa4dPH2Nly7Z2W5fn80TYPSUsMLw9/+\nVnmuTFNraIVRU5QyHqpz5tROe1u2GCOL1q2Nf+I7d7pWr7DQ8KK7cqXxQ1mxonojDDCWpJ4+bSgK\ngGuugSVLXBtdlBEWZsgORr1Zs4wH8+zZxoolsPU9tXu3EcjJyU7zL9KvYM17s4z2Jk8un5swYzIp\nWhRcxecrHWykVMpQFKtWGfMnc+YY8zGu+NDSNB1SU40/SV99Zbiqefnlhpao3lm55aBlKXpNeeub\nX+j2zJP4PtnTYRmtMM6Hnj2NmBB791a/7m+/GQ/7MspGF2CrMJYuNUw+48cb/94rcvCgsXR06lTj\n37uPT+VYHlXRv79hmmrRwjgfMMBQABUe0C5z552wYAG8/bYR+7tsWa61wtizxxg1pKbaX2F17hy3\nLFtMs/W/G/divD3HANAtrA8/73M8l/TP9fvILjDPXZhMhmzLa9t/pqZBeeUV4w/BihXGH51//9s4\nLiLunjXZshTdFTJP5xD6xJDy/U4b9/HU2hGEN2/BK0l/d1xRRC7YA3MQqDrlgQdE/v534/0PP4iM\nH191nQMHRDw9Rb7+ujxt0CCRBQuM98uWifTrZ7wfPlzk2WdF/vlPkdBQkV27bNtatEhk4ECRVq1E\n7rxT5O67a9aP3Fzb88LCmrVTxvXXi5hMIn/8UZ62caNI587G+4gIkSNHRC65RGT9+sr133tP5JZb\nqrzMjO9Xi/cTXe3m5eQVCs/4yzOzvi1PPH1axNdX+j3zV7n7nx9Xp0eaxkhJifFd2rOnPG3rVpGw\nMJH8/Cqr//0/P0nGqXM1vvy6XUfE7/HecvpsXo3bqA2aP9FdAh9Pcrn85NmLhOdN0nriXSIiEv3k\nbTL4pVct+eZnZ6Vnqh5hnC+33mpM9BYVweOPu/bv9ZlnjPgSZXb+oiJjKWyZd9kOHYx/1WfPGiOM\nCROM5a6TJxv/tK1XUO3dC4mJ8OyzxiR1Td23e3vbnttzZ14dHnoIbroJunUrT2vbFvbvN0xV2dmG\neahz53KzVHGxYVYrKTHmVCZMqPIydyRdTp7PHlKPZ1fK+3TZWmiWzeIdy8oTAwM5HdsKUt9k0YEv\nz6+PmoZnzRoIDTW+W2V07mws955f0QWdLanHs3l60zAen/lZjS//yOx3ORuwhmdmf10pLze/yGlQ\nsNok12svWc3XcS6vsOrCwLfbfqQvfyOFFfSfOpVjbuv44rEnqqynFcb5kpRkmFdeeMEw46SmOl8S\nu2aNYVP/97/LN+itX2/sug4MNM6jooxQsLNmGSt9QkKM9IcfNlZlffJJeXt79xo/lrvuMuYgrrmG\nRsF111X+wTZvbvy4ly0zZDaZyhVGcbFhFuvRw5gbCg42NuL9f3vnHRbF1cXh31m6IPaGvRfsvSYq\nKmjsJbbYPo0tasRYYzfGEmNiEk1iTDTEWGLsHSsaE41GVLCigmLviiJ9z/fHGdhdYGFpgnrf59mH\nmTt37pwdYM7cc09JBicHW+QIrY2VB48lOLbmhDdyP2mBS5GmSvw3xzAMCGqAx45HEXrwsJjPEiOj\novkVqWLAt8uxdOc/po2bNslLW3yGDEmwVqXXMzrNX4SX4VEAgClr/oQuMhe23fRKlTx3H7/Aiehf\n0N5hAVZd+tHkOsN++B1OMwrCelJBuHh2wpGz11J1DUu4fPMRGHrYvyyDPw6fMjmm1zOevkhYVvpi\nlDc+at4Z02r9AB/MwIel5iC3s0OCfvFRCiOt2NgAbdsC8+ZJxLOzc9LpzydOlEW5Jk3Eu+nRI1Ec\nxjMDIpllzJ1ruo5gZSUuojNnGjxBYhWGra08iJNK45EVqFBBAu/KlZP9WIWxbJmkW588WaKzp041\nXy0wHpWdG2PXuYTrGKdCvDGxyURE2d6D72VJTLhw4wHsKP4CvSOjkf9pecR0f18KYu3ebXry8ePy\nlqrIEvzi/S+8bk3CcJ8uqDN5nDwEmeWlpFOnhCd06CDrZUbOIz97H8PmcE/0XyxBppsCvTC6/DcI\ns7mOXSdSng9txC8r4BLZDGtGj0SobRA2HPHH/SehKDq2K1Zcno+Vrfbi734n4GSdC2PWZFxC7UNn\nLyNbeBmUtWuCzb6m/wc9vvoeLlMbIjrG8BLrcyYQMdYh6NK4Kqb1bI2vqx3Ed4N7WXQtpTDSg6FD\nZYZRrZq4csbmZopPrOmpRw9xZ23YULybEvNsqlBBlEmHeCXQa9Qwzf8UqzBeF2KLLhkrDF9fUYJf\nfinfd98+UcIW4lGpMfyfmf6jXLrxEKEOlzCsTRMUimyKZfsOIDpGj6l/fYLqzRbA+txZ/Px3FP4u\nlF8UWJ8+EmUfy44dYj4zzqulyBRehEXiI+9B+KjUIpwd4YegUD+0nDtVYnOYE1fsNjbAwIEms4zv\nD69FkWfdsP7BLKzxOYXndhcxs1d71LDpjc+2pmyWERkVgy33FmFayzHIZm+DxtkG4ZNN81BqVjM4\nWuXA/c/+Q+/mNdGgUjHM7eAJ38g/TB7a6cl/gZeRV1cWzUo3wcn7hv+Dpy/CseH+HERaP8LU3w0l\nhpbu80aJGHdYW8njf3THpnHbyaEURnrQsKEoDMBUYbx8KX+0sQQFAYULS2wAIEpi3z5x/YxfHa9y\nZfEwcnY2bSeSUrFHj4rZ6u7dlHtFZSbly4sijFUYxYuL6ad1azFJpYL+bg3xzOEUTl+9E9e2eOde\nFAh7F04OtmhS2A37ru7HsB9XwoodMG9ob6B+fTQPCsbQBrZAo0Z4OGM2nvQ3+l3t3i0znqxe++Qt\noNOXC+DMRfHNhz1QsVg+HBjlhZMxK3D9h1/EHGVuJtq/v6S6Z0ZkVAz8Y9bh516zUUbfFh/sbIOq\nup5wcrDFpNb9cDx8JSKjLM8EsHj7YdjE5MDg1g0AAF90H4TrTn+gevZWuDj/F5N0/J0aVoZ1jBN+\n8U5oNk0Pzt+7jOJOZfHBO41w1+5I3LrJhz/8jLxRNTGu8jf45vSsuHafm97wKOOeqmsphZHeGCuM\nc+eA5cvF9RYQt9fy5Q19mzaVdYqSJcVmb8yIEaZrFcbEKozAQLmedbKVdrMOsYGAsQpDp4tL9ZFa\niuRzRiV9T4z8zTDt33l5N5oW8QAA9HvHDYG6PVhxbQoWui+UyPExYxDz628IznMRfoF3UfbiCkQE\n+aHXR8Px4uYd+V2NGqVyUWUy4ZHR2B/6Ndb0/VZ+bwCqliqIZtk+xsv1Xombo2IpXVpMtQEBWLz9\nMOyiCsG9djmsHfwZ9NYvMcG9PwCgS+MqsI3Oh4WbLHe3Xn1iO+rn7Bi3X69iUZzpdxNHZs2OkzMW\nnY7QKGcPfH94LQBg78nL2H/qCtKL4BdXUKlAWdQpXwRW0U7Y/d8lhIRGYOP9eVjw3nR83qcDGHpM\n9NqEVQd8cdfhIEa0aZmqaymFkd7EVxiATJ0BsakaR07XqiU/Ewu0c3AwLILHJ1ZhXLnyepmjAIPC\njFUYAODpKV5jaWBht9H4O3wpHj57iX8v3ECQ9Q4MbSEKw71WOYAJhWIaxL0Rok0bOHdqh1yh9VB7\nSRPktC0D6jYIdU/sxZjBbWXG17y5UhgZTEhoBH7YYX4Wt3zPMdhHFIVbjTIm7eve64CcYc/xU1JB\n+0Tyv+Xjg2VH16JpXlkPrFnWBQ8m3EHPpjXiunYo8iGWHEsmgaYRfhHbMbCJqdm0aqmCZvuPb9Md\nZ/V/YuHGA3Bf1wgt1zbC99vT52/rgf4yapeS50BRbowPVo9AgSn1kC+6Jvq2qA2djuBZYyYWXO+K\ngdv7oKndGFQsli9V11IKI70xVhjnz8tCtZ+f7MefYdjYAK1aAR4eKbtG7dqijPz9Xz+FUaSI1K6I\nP6NKI+61y6FAZAN88N23eHdZa7TJMRFNq0nkuU5H+LTqD9g09OsE5zVz6YCc+rLwn70cBTxHYNSd\nCDQOPo9zFarLetGVK69lKpGQ0Ah89OPqzBYjWaas2oThf7dGSKghJ5uxK6rXsW2o5ZxwPSvPAW+c\nrdIQM/d/ZXbsC8EP8OXzF9i3eDEu6dbj0w7vxx3LmyObSd8FfXvjtt3+OOeI3/efxM7jiafn8f4v\nADFWL0wUTnK41y4Hu6hCGHeiCxbUW4vPa63EiCOd8Pkf3haPkRh6PeOl/WU0cRWFOt1jFOrna4UF\nzb9DwOw/4/rN6dsBj8aEIvyrczgYaz5PDYkFZ7wpH7yKwL34/Psvc61asv3ee8weHswDB8p+o0bM\nPj7pc53atSUIbvHi9BnvDWDRZh/GDHCNiaM5JkZv0TkJ+lWvzlE6HXccMkD2mzZl3rkz0XMdR9dj\nr70n0iKygejo9BmHmaOiY7ioZ3fGDKRIvqjoGI6Kjkk3OSyhxJjejGlWPGPVDmZmPht0j60/KcFH\nzl5jZmY7z0r88+5jCU9s0IDv/7GRaWJOPhlwK6752QsJ1jt4+irbjCnLzQd25gdOTjzw2+XJylJh\n3GB2m/kZH794g2lCHq4w7sNE+7Wfu9DssaRYuvMf3njEP25//IqNnP3jhikex5jz1+8zTcxp8d+7\npUAF7r0i4s8wevQwzDAuXTKdYaSFBg3EHbVMmeT7viWMbPcOppbehuOzFyawI5sjQb9+/RDlUgRb\ncmzF45AwszU1/ALvIjTnv/j1751pFzwqShb/r15N+1gA6k8dh2f623iHp2GOt+V5s1rNno0G0yak\niwyWEB4Zjes2u1Ar6mOsPiUxO2NXrUCMzVN8sHwyfM4EItLmIfq1iFfb5fZt4OJF5Ov4HsrHvI/x\na5YDAOpOHo8c852QzbMm3FY1RmeX0di/bD3y5siBnz0aJyvPFI8h8Hm+DC1/6I1Cke8iKPpoov0O\n392O/kUbSyr/FDC4dQN0alQ5bn9ytzZ44XDexFkjpRw+exnZwsta/PeeVpTCSG/y55co5ocPxYOp\nfXsxHz14ILmjYvM1pZUGmi3+dTNJZSA6HWHWB20tdhFMlKFD4bB7J/JE1sGnv683qzCW7jkACssL\n3yfpkJfq0iXg1i3JxptGftp1FKcjN8B3/GZ813cYAqzXI/j+M4vOPfZ0E05G/4bwyFdT1lbWJ4ph\nUa+RuKzbipfhUdj3ZCkW1NmAYKsDGLhyJkrHtEn4+9yyRTwIbW0xrc1Q+Dz/CZO8NsM38g+c6heM\nRa2W4Ce3TVj7yXBZx2ja1KIqlr2b14R9dAHoYINzs1YhwiEowb27fu8pQuxPwHP9cnGKePEi1d/f\n2dEOxSJb4wtzgaMWcDLoCvLqXt0zQCmM9EanEzv93r3yMM+VS5TIzp2y4G1hMFqyNGwo7rnFiqXP\neArB3h5wdcWg6kOwOmCp3OeAAInVMGLv1f1o5fQJnjmexP0naYwI9/eX6/z+u2QNBmS2kYoiWl8f\n/BVuOYegtEtuVC1VEIUjWmLsb6YZlX3OBCZQCn6BdxFufx32kS74btuhVH+VlPDbse2o5dwWjSuX\ngH1kEbT4fDpsYnLCs2MzfFB4FgKz/4ZOru1MT4qIkDTmH3wAAOjZtAbsowthXkBvLGm2BtVLF8Lg\n1g0w0L2e4Zx33zWk4UmG/YM3wG/yRuR0sodzaA2sPmRaivnz9Vvx+eHCsCVI8tHY8sappEulztgT\nvCnV51+4fxklsiuF8XpTrJj48bu6yn7VqpJ6Ob3MUYCYMC5efL1cal8jpnZ/D6F2V7A34K680Q4c\nKEGXkIXGQN6PES07wDm0JpbtSaO3i5+fZCpu21Yi3leulJeL+NHnyfA4JAyXrP7E7G594tpGNx6K\nrbd/NFlIbvlbK8xcs93k3MW79sAlwg3N8vXCL8fWpu37WMjpl9vQv6EsaDfM1RlHreahc/Gh0OkI\ny4YPQK3ITzCmQyvTk+bMkWzO7oY4gskNPsOAgosxpE3DxC/UtKnEOz1/bmjz9U1UIderWBRF8kns\nU3nHBthz3tQsde7oEgw/dxdYvVocVvbtSzBGShjXyQOPsh1F0J0nKT43MioG/s8Ow7VgueQ7pxNK\nYWQEsQqjUiXZr1pVZhzpqTCA1ytg7zUjm70Nqlv1wswtXkDdutCv+BUvPNog+kUofPwCwbpItKlT\nATVzuWGLXxrNUn5+8jfi6SkxKVOmSHzBv/+maJgZa7cgV1gtQ0lbAJ4dmyFGF4r1R2Qdbf+pK4h2\nvoq9AaZKbk/gbrgVd8enHd5HgNUmi5PYpZbDfkEm6xOj3buAwvLhiz49AQC2Nlb47/MvUTC3k+Gk\nc+ckNc5335mMNen9Vlg+coD5i5UtK6bhIUMkMvzXX8WlfccO8+dcuYJOucvA/6lBYRw9H4yW1/xh\n26+/5HtzczMkG33xQrIUhCfM25QUBXM7oWBYM3yxOaEsej3jq00HEz1Pr2fUnjoaAGFuny4pumZa\nUAojIyhWTPJJGSuMqCjLq9cpsgQTPPrhWNhKRMfo8cndUPhnf4JRgwdguc9+FI9xg05H6FbLDWdf\npu0tE/7+8jdSo4a8Qf/zj9TtOH48+XONWHPBC90r9Ddp0+kI1ew6Y8kBWVReut8bNiFlcfGlwZQS\nGRWDYJu9GN7KHY1ci8MpvDwWbkrjd0qGP/45Cpeod+LWJ9rWq4in04Lhkie7+ZM+/lhSyBQunPIL\nLlok93nwYMnnNn26VIM0x6hRGHTuPzyyPxaX0mPahpXoEOQM+3bvSZ86dSR7w4MHwJIlYra0tPCZ\nER3KdsXaS78kyGz77dZD+MSvOQYt/tWk/eGzl2gyYwouRx7CmUmbTaLKMxqlMDKC2HWFWIVRpYr8\nTO8ZhiJDef+darCJzoVJXpvwbcBoRNTqjuJXDmDN7eloXlKqE/Z1q4swhyu4fPOR4URmy8uEPnki\nnxIlZH/kSHkg1q0rCsPCcc4ePY2GN/7CrJ4JI58HNuyEf0NEYRy6tRt9S36K0Gxn49Ze1vj4wjYy\nP+pVLAoAcCvUDWtPb7ZM/ljatEmRgjt3NwAls5u+QCX54PP1FeeADz9MmVyxZMsmaUL27JGEhZMn\nS4yNr2/CvnfuAN7eyBccCKuonNj93yXo9Ywzd5fD9clzSRwKSBzVO+8A27eL8qlZ0+ARmQK++l8P\nhNMTDP/RdK3pq8M/wvXlMCy/OQ5bj52HX+BdNJ42BfnnlEDg83P4a+guFC9gJrg3g1AKIyMoVkz+\nmGJdXsuUkQeAcoF97XAv0A9fBn6AStQJTUcPxfDwonCIKorh7mJbd3KwhUt4S/T5cYHhJE9PQ26x\n5PD3l7xhunj/ioUKyUPOElfbZ8+Qu3tHLN8RibzZ7RMc/tCjAaKsH2LLP+dw3+EwJnRsB6fQavjd\nRx7wXv/sgqu9YU2gecWauBmZsHTu45AwDPh2ecLrMwMHDwIzZiQva0wMEBGBa88D4FogBbb3r74S\nr6S01GmpVAm4dk0cDGxsZLzEZhmrVkmZAH9/FOYGmL3NC01nTodbcBhs3n3XkAsOELPUJ5/Iwnq3\nbqlSGNnsbbD0vZ+x7Po4XAh+AAA4G3QPN+x2Y8eYOejrMg+dNjVH9WUV8TTiMXZ1O4I7X29G7XKp\nmGnFotfL36iXl6QYspTEgjPelA8yI3CPmfnWLeaePTPn2op05WzQPc4z2p1v3H8mFdycnKRqnxHn\nr99nmzFluMeX3zM/eMCcIwdzvnwcFvKCHzwNNXQ8d4553jxmvVGQ1eLF5qs0dunCvGpV0gJGRDA3\nb85b6tbja7lzMfv6Jtqt4rghnHe0BzuOrsvMzHU+HcfNZ87iJ8/DWDfOhX/fb6iMeDboHtPEXAmC\nwcb8/CdjBuKC6uK4fZs5d27mokUlcDUpFi9mfv99zja6VuIBeYlx4wZzrlwJ7nuaefJExr1929Cm\n10tArI8Pc65cPPnLHznb6NpcZcIIvuTRXipfGuPnJ/NJPz/mHTukcmYqqTVpDOcf3ZZvPQxh98/m\nctmx/2NmCS6d5LWZLwY/SPXYCfDzYy5QgLl7d+acOZkvXDA5jMwK3CMiDyK6SEQBRGQ2KoiI6hBR\nFBF11vbLEdEpIvLVfj4jolHasVxEtIeILhGRNxHlyOjvkSJcXMSLQvHa41oiPx5+vVs8Z+zsJP4l\nnk9/RYTBu/sWrLv3GQ6PGAt07QpUq4YZvTuj2kzNY+nGDUkBs3Sp6Zu4tuB9NuhewovHmqWS4jep\nFjemYWlcrVTdbMXHPrU742HO3aiVQ9LQtKzQGGceH8GgH5YhX1Qt9G5uqIxYsVg+gAnnr5vWddlw\nfiMoLA/mbFlnOnhQkMyeJ04EZs1KWt7Nm8G7dyPS9hKaVrHQHXTxYkk/by63WmrJmVPibP4xKsp0\n6pRkT27SBKhaFbOrlkLo1yfgN/dblPM/aeKdBUBmh2fOiNm5SpVUzTBi2TNxNnJaF0CxuVWwN+Q7\nTG41FICsQ83p2wHli+Y1dF6wQEx0qeXoUfkua9cCw4YBPxnl0RptvvJehioMItIBWAzAHYArgJ5E\nlGDlV+s3D0BcYhVmDmDmGsxcE0AtAKEAYku4TQSwj5nLAzgAYFJGfg+FIg5jz5joaFmkrlABzcYN\nw7wSX6Dstt8RNnwErrTvhjanDuCO3UGc+vespG8fOVJcc1etklgCZsDPD1fyFkaVFUXwi3c8r6h6\n9fDkwCG4z56L+gNaYs8HH4oyWmvk9rptGzBwIO7gEvRNWxlke/ZMFnijJd5iZLumoLDc6FO/NQCg\nb9OGeJTtKDbdn48v25qaz3Q6glN4Rew7Y1jADQmNQLDtLgwr8S0OPojndnvtmmRcHjhQHp7Hj6PT\n/EXw2nvCtF9ICHDsGCLy5kP9W1Yo7WJhPrENG0zLBKQn1atLwa5Yfv9dlJNOJwogNnHo+fPiwl4u\nnhmNSBwWAIm/iohIuoBaEuR2dsClBT9jWq0fUIk6oo9b7cQ7Pn4sazD/+1+qYnUAiMKIDf798EN5\n8QgLk/YNG8yeltEzjLoALjPzdWaOArAWQIdE+o0EsB6AuTvdAsBVZr6p7XcAEFvxxAtAx0TPUijS\nGzc3cZFes0aSQB48KA+T6tUxdsoonMuXEwMO/oPuwWdQ8akd5hyvhCItG4nL5dixEsS5e7eU6NXe\nTmdfDABF5sSk3aZv5y8qVYHthTMo7n8Mu9b9gyD/zdBXriyVGAH5Bz94ENEtW+Fltoso37u71O+I\njBTX02XL4uIEnBxscXnUZQzyqA8AKF80L2zDiyBvdHV84FYrwdd0samIf68aFMairQfgFOaKrwd2\nR4TNbew9ednQOShIFu3t7IDp08Fjx2Lb/a+x6the00H37gUaNcLZGvXR7nIS3lDGs6qYGEm1E/9B\nnV5UqyZKLhYfH0My0KpVDTOGjRslTiapwNtY5RGrZFLJtJ6t4T9/ifl0H5s2GQqM/fhj4n2Sw1hh\nlCwps9l164AxY4DPPzd7WkYrjMIAjEuW3dTa4iAiFwAdmfkHAOZ+G90BrDHaz8/M9wCAme8CyJ9u\nEisUSVGjhng1/fijuHju2SMPy0WLQPPmIWbKYqy7PxOnrNcgZuAwjDgfhJ6tbRE+fabhYVOmjHjn\nfPstMGECNj3ZhrHll+KxtZ/JW/nwVRtwPYcjftpzGM6bt2Bki1xYUr2pzB5On5aHW/XqOPkwDLqo\n7ChaoaQ8WPfulTQjo0bJoqZG/Df6Ea6z8GuvhYl+zfJ5KuLCQ0O21tW+G9GsYGfY2ljBVdcV83f8\nYegcFCQPHQAYMAChd++j3a1gXH0WL9vr9u1A27bYW7AQ3IPNxCs8fix13UNCZP/mTSBfPtOF5vTE\neIYRGipmnpqaeS5WYTDLfezbN/nx0miWsoi1a4FeveSFYNo0SSuTEh4/lnxclQ15rTB0qDhrRETE\nRdEnRlbwkloEwHhtw0RpEJENgPYA/oR5LPRhVCjSiJWV1GI/dEhmDbFKgAgYPBjug3qgAndCXavB\nKLhwHpyCr+HfQmUxffU2vAyPMpTpJALc3PDv+//Dc/sLmNajLTrln4DxO2WW8TI8CmtuzsG14VOA\nQ4dg1bIFurh4Ys6hRWIy+fXXuAfwAf8LcI6sKOO6uYkpqlUreZjs2iUKJhEWDuyK1nUSd/WuWawC\nboTJDCMyKgYBuq34pI247A5r0gOHnv2Kuev2SK6lWJOUdn8WN/fAF942eBR5zjCgXi/pcd57D9uc\n9Cjz5LmhsJgxFzUlc1mbwQQGAqVKJftrSTUlS0r6+sePgRMnREnYae69rq4iz6FDUoipTp2kxwJM\nZyUZwb17ImebNuL11b27zFZTwrFj8l2srAxtbdrI2utXXyX02DMioxXGLQDGyY6KaG3G1AawloiC\nAHQFsISI2hsdbw3gJDM/MGq7R0QFAICICsK8KQszZsyI+/hYkIBMoUiWZN52z877Af/Mmiv/kDY2\nGFZtLL642gOOc7LBdnIBNJk+NW6Re/bmdSgX0wlODrZYNmwQHludg4tnZzT9bDKyR5dGm1kT4mzk\n3/yvL+7Z/Y0jtd4Vp4pt24C2bXHi2gUUsTNSGHfuSLR4njyyv26dOVHN0qxyRTyzEYXxzVYf2EcW\nxjtVRSl86NEAzZz6YP4/n6PE1yUQcfmKIY4EwJJstxGeuzjcb5wzBKOdOCEzhZIlERRxFdcqFh0x\nzwAAEwRJREFUVpGZUHxiF3IDAuSn8ewlI9Dp5P6eOWNqpgEAJyd5iE6dCvTrZ1keuHQwSSXJ+vWS\neDGbVs+jWzcxUaWE+N8TgM+RI5jRpQtm+PhgRlLu0Ym5TqXXB4AVgCsAigOwBXAaQMUk+q8A0Dle\n2xoA/eK1zQcwQdueAGCemfHS4nimUKQ7u09cYtfxw5gm5uKyY//Hdp6u/MX6fXHH7zx6zt0WLGb7\n0VV56c5/Epxff8pErjVpjNRWKVmSWa/niuOGcNcvvpMOkZHM+wzj8ZYt0jeFREXHMD7NxrcehnAR\nz27cbYFR3ZXHj5nz5mV+/pyLftyFI62txeVYO48m5OXgUWN5bj1HPnY+WM6ZNYt57FhmZrb1rMi+\nYz5l7tDB1MWYmXnCBGYHB+YZM2R/8mTDdkbx0UfMX33F3K4d87p1psc6dWLW6Zhv3rRsrJAQkT8d\n65uY0LAh89athv2oKPldBAVZPoabG/P27Ul2QWa41TJzDIARAPYAOAdgLTNfIKIhRDQ4sVOMd4go\nG2TBe2O8fvMBtCSiSwDcIB5WCkWWx712OZyd/z0uDA9Asewl4ah3wch2hhK9BXM7Yd3YjxD29RlD\nOVkjJr3XB6ei/kDM2LHidUWE25EXUbeUNsOwsZFZRSytW4tZ5ebNBGMlhbWVDg4vy+HHXYdwy24v\nvuxrZNc+elTMST4+6JKzDh7Z2cSZcTYc8YN1VG4UbdoQ1e/b4+BZzcR06hRQuzYio2IQ6RiIEqNH\nSvr//v0lbU4sFy9K0FzsDCOjTVKALHyfOpXomzeqVhV5LE1Hkj27mIq+1+rLx8TIIvLEiZLEMoW5\npkzw9hYPLOMKndbWkidrs4WR+TEx4lRQv37qZEhMi7wpH6gZhuINxN6zCn+39XDcPo3PzycuJfEG\n3LUrs5dXiq9TbEwPtvN05fJjB5kemDhR3mo/+ojPea3jvwpbc0SkvFG3nfMlu44fxhwQwDdyZOfO\n8yXQLbJYcfbb6cMHT19lq7HFZJwXL6QqpXGQa4UKzMuWSUVJZub69Zn/+ivFsqeI48cNgYfxefiQ\nOTg4ZeMFBjK7uEjQZdeuzO++yzxzJnPdusz9+6dOxuhoCSjcuDHhse3bmZs0sWycv/6ScZIBquKe\nQvFm0CRXDyw9IrEQV28/BluFo2YZF/MnGMeOpIDSzhURkeMcPnUfYnrgyBF5Y/b2RiV+ieDsjljj\n4wu9nnH43ha0Lu8GlCqFfC/DEXz7DPDsGSLv3EL1v1ug7a+9kCNKc5F1dJT8Tlu3iodSVJSsWbRp\nIzMM5lczw6hcWRa+488uAFkHKlo0ZeOVLCkOCUOGyLqHt7c4IOzbBxw4kKrfBZYvl9o6HROJIHBz\nk4X2e4kEf8bHyytJL6jkUApDoXjNmNiuO85hPcIjo7HtuB8cwyokXaIzVmGwGWfCS5cSDTarV6Iq\nHJ/WQd8WRgFk4eFivhk8WFJ679+Pl7kr4Leju/HxsrWI1D3FzF7tASsrPClSAs53TyHk6An452Ns\naeeHFgW6o3cloyA8R0dxY/3rL1EWhQrJQrOtrSiLkBCgYMFU3ikLcXCQxKCJKYzUUqOGJDdcu9bg\ndZU9u5iqhgyRGBpLiY6W7AALFya+8G5vL0p2eSJ5vowJC5OgvDQojEw3G2XkB8okpXhDyTa6Nleb\nOIppfD7u8/VPSXfW68XcEi9fEDMznz0ruYSmTk1wKCo6hu89fmHaeOQIc61ast23L7OdHW8b4sn2\no6uyblxBk/xQDzp25YEe2Xld9368vHJh8/LNmCEL4lu3Mnt4SFvDhsxLloiJ6lWwahXz9euv5lq9\nejE7O4tZL5H7noC//mKuVi3pPlevMufLx3zihPk+q1czt2plkYhQJimF4s2hXZEBCAg/jFWtvfHb\n6GRSfmsxHwlMITdvypupu7vMGuJhbaVD/lyOpo1Hjkj+JUAWXyMi8E7bVgh3DEAVq/dNSqPmblAH\nro/CEXp2J+wrtzQvX4sWItvFi4YSAOXKSUR8RpujYunV69WVO165UmYfGzdalnNOi7dJklKlZPbS\nvbvZuBt4eYl7cFpITIu8KR+oGYZCIaxcKS6isYSHM9esyTx3rrhkurhYNk7btsx//inbDx4wEzEH\nBvKMVTv4zqPnpn137OD9xZz5eEEdP9lzwPyYkZHyxt2xI/MPP0jbnDnMjo7i8vqmotczFyrEfPly\n0v1cXZmPHrVszOHDmd9/39RdWa9n3rlTZpKhoebPNQJqhqFQvMW4uUkqkZgY2R83ToLtJkyQUr9h\nYckvmur1kquqUSPZz5tXZgElSmB6rzam5VQBoHJlVHsQCdeHOuRskESUtI2NzFq2bTPMMMqXl4Xw\nVzXDyAyIJCLf29t8n6AgWV+yJMockHWOS5ckKzIgY1erBowfL9kBYgP+Uol1ms5WKBSvB4UKSY6m\natXElLRtm5ihYhdRa9SQfWMf//j8/rsol0KFDG2tWpnvX7QocuisEVPERaKmk8LNTVKHGJukgDdb\nYQByv1evBj76SDzD5s+XfFQtWoj31o4dYjY0TuORFPb2EtnfqBFw+LAo+MWLk0+caCFqhqFQvC3s\n3Cm1ra9dE3dW4/oSNWsmXq40losXpbKcVn/DIohgXbUK7OokzIabgJYtgRw5DMqodGl5wGVkWpCs\nQMuWMvMLD5d05XZ2MkNo2VLqUmzYALRrl7Ixy5WTGUaePJKmpF27dFEWAEBsztXuDYCI+E3+fgpF\nurF6teQk+jORHJ/h4TI7GTEi5TW1hw+XWckEs7XTDNy4YRrzMGmSxC84OKTsmq8bdeuKgrx+XZwK\ndDpJhjhqlOSOun8fcHZ+pSIREZg5gZZRCkOhUMgM4r33Eq8hvnOnpLc4ciTlb6qhoZK+IjYWQZGQ\nadOAefPEJOjqanrs0SOZKbxizCkMtYahUCiAsmXlTfbp04SlUE+dEpt4aswajo7J93nbGTRIclbF\nVxZApiiLpFBrGAqFQhZVq1aVYkJ6vcGbCpC1jZo1zZ+rSBvFiknp3dcApTAUCoVQs6asUeTObVpD\n29dXvKgUbz1qDUOhUAjXrkkSOxcXceW8e1eihosVk59JVGJTvFmoNQyFQpE0JUoYKuflySMzi5AQ\nqXutlIUCSmEoFIrE8PCQKGF7e2WOUsShXhsUCkVC3N1FYagFb4URag1DoVAk5OVLoEABKdqzfbt4\nUCneGsytYagZhkKhSEi2bEDDhhKbUbFiZkujyCKoNQyFQpE47u6SosLGJrMlUWQRlElKoVAkzpMn\nwIULMtNQvFWoXFIKhUKhsAi1hqFQKBSKNKEUhkKhUCgsQikMhUKhUFiEUhgKhUKhsAilMBQKhUJh\nEUphKBQKhcIiMlxhEJEHEV0kogAiMlvYl4jqEFEUEXU2astBRH8S0QUiOkdE9bT2akR0lIhOEdFx\nIqqd0d9DoVAo3nYyVGEQkQ7AYgDuAFwB9CSiCmb6zQPgHe/QNwB2MnNFANUAXNDavwAwnZlrAJgO\nYEFa5PTx8UnL6a8MJWf6ouRMX5Sc6UdWlTGjZxh1AVxm5uvMHAVgLYAOifQbCWA9gPuxDUTkDKAJ\nM68AAGaOZuYQ7bAeQA5tOyeAW2kRMqv+cuKj5ExflJzpi5Iz/ciqMmZ0LqnCAG4Y7d+EKJE4iMgF\nQEdmbkZExsdKAnhIRCsgs4v/AHzMzGEAPAF4E9FCAARA5S5QKBSKDCYrLHovApDY2oY1gJoAljBz\nTQAvAUzUjg2DKI9iEOWx/FUIqlAoFG8zGZpLiojqA5jBzB7a/kQAzMzzjfoExm4CyAsgFMBgAP8C\nOMrMpbR+jQFMYOZ2RPSUmXMajfGMmWNNVMbXV4mkFAqFIhVkRk3vEwDKEFFxAHcA9ADQM55QpWK3\nNfPTNmbequ3fIKJyzBwAwA3Aea3rLSJ6l5kPEZEbgIDELp7YF1YoFApF6shQhcHMMUQ0AsAeiPnr\nF2a+QERD5DD/FP+UePujAKwiIhsAgQAGaO2DAXxDRFYAwrV9hUKhUGQgb3R6c4VCoVCkH1lh0fuV\nQkRFiOiAFgjoT0SjtPZcRLSHiC4RkTcRJVgTyQRZdUTkS0SxJrqsKGOC4MosKqcnEZ0lIj8iWkVE\ntllBTiL6hYjuEZGfUZtZuYhoEhFd1u53q0yW8wtNjtNEtEFzhc9ychod+4SI9ESUO6vKSUQjNVn8\niWheZsuZAGZ+qz4ACgKorm07AbgEoAKA+QDGa+0TAMzLArJ6AvgdwFZtPyvK+CuAAdq2NSQ+JkvJ\nCcAFYtK01fb/ANAvK8gJoDGA6gD8jNoSlQtAJQCntPtcAsAVaFaCTJKzBQCdtj0PwNysKKfWXgTA\nbgBBAHJrbRWzkpwAmkLM99baft7MljP+562bYTDzXWY+rW2/gESPF4EEFHpp3bwAdMwcCQUiKgKg\nDYCfjZqzmoyJBVc+QxaTU8MKgCMRWQNwgAR7ZrqczHwEwJN4zebkag9grXafrwG4jHhxTa9STmbe\nx8x6bfcY5P8oy8mp8TWAcfHaOiBryTkM8nIQrfV5mNlyxuetUxjGEFEJiJY/BqAAM98DRKkAyJ95\nkgEw/IEbLzJlNRnjgis109lPRJQNWUxOZr4NYCGAYIiieMbM+5DF5DQivxm54gfC3tLasgL/A7BT\n285SchJRewA3mNk/3qEsJSeAcgDeIaJjRHSQiGpp7VlGzrdWYRCREyQdycfaTCP+6n+meQMQ0XsA\n7mkzoaRcgzPbYyF+cGUoJLgyy9xLACCinJC3tOIQ85QjEfVORK7Mvp/myKpyAQCIaDKAKGZek9my\nxIeIHAB8Csk5l9WxBpCLmesDGA/gz0yWJwFvpcLQzBLrAaxk5i1a8z0iKqAdLwijvFaZQCMA7bWg\nxjUAmhPRSgB3s5CMgKR6ucHM/2n7GyAKJCvdS0Bs7YHM/JiZYwBsgqSTyWpyxmJOrlsAihr1K4I0\n5lFLK0TUH2I67WXUnJXkLA2x+58hoiBNFl8iyq/JVMyob2bfzxsANgIAM58AEENEeZCF5HwrFQYk\nlch5Zv7GqG0rgP7adj8AW+Kf9Kpg5k+ZuRhLUGMPAAeYuQ+AbcgiMgKAZja5QUTltCY3AOeQhe6l\nRjCA+kRkT0QEQxBoVpGTYDqTNCfXVgA9NA+vkgDKADj+qoREPDmJyANiNm3PzBFG/bKMnMx8lpkL\nMnMpZi4Jecmpwcz3NTm7ZwU5NTYDaA4A2v+ULTM/ygJyGsiMlfbM/EDe3mMAnIZ4HvgC8ACQG8A+\niNfUHgA5M1tWTd53YfCSynIyQhJDntDu50aIl1RWlHM6xMHBD7KQbJMV5ASwGsBtABEQxTYAQC5z\ncgGYBPGSuQCgVSbLeRnAde1/yBfA91lRznjHA6F5SWU1OSEmqZUA/CHJVt/NbDnjf1TgnkKhUCgs\n4m01SSkUCoUihSiFoVAoFAqLUApDoVAoFBahFIZCoVAoLEIpDIVCoVBYhFIYCoVCobAIpTAUCoVC\nYRFKYSgU6QwRVSOi1kb77YhofDqN/TER2afHWApFSlGBewpFOkNE/QDUZuaRGTB2EIBazPw4Befo\n2JCGXKFINWqGoXhrIaLiRHReS8t+loh2E5Gdmb6liGgXEZ0gokOx+bOIqJtWHe0UEfmQ1J+fBeB9\nLeV7NyLqR0Tfaf1XENH3RHSUiK4Q0bta9bXzRLTc6HrfE9FxbezpWttISLbdg0S0X2vrSVJF0C9e\nhbbnRPQlEZ2C5NGaS1IR8TQRfZFBt1TxppNZOUnUR30y+wNJdx4JoIq2/weAXmb67gNQWtuuC2C/\ntu0HoJC27az97AfgW6Nz4/YBrACwWttuD+AZgEra/n8AqmrbObWfOgAHAVTW9gMhKbABoBAkl1Nu\nrd9+SCJAANAD6KJt5wZw0Uge58y+9+rzen7UDEPxthPEhsI6JyGpsE0gIkdIOvQ/tTf2pQAKaIf/\nBuBFRIMgyeMsYZv20x/AXWY+r+2fM7p+DyI6CUmQWUn7AKYZTusAOMiStl0PYBWAd7RjMdBSZUOU\nUhgR/UxEnQCEWSinQmGCpX/gCsWbinFa7hgAiS0o6wA8YSkSZQIzDyOiOgDaAjhJRAn6JHFNfbzr\n6wFYa5UgP4GsVYQQ0QozcgHmC2yFMTNrMsYQUV1IWvduAEZo2wpFilAzDMXbTlIVDQEAzPwcQBAR\ndY07iaiq9rMUM59g5umQQkdFATwH4JyG6zsDeAHguVZIqbXRsRCjsY9DSnrmJiIrAD0B+MQfV5sh\n5WTm3QDGAKhqoWwKhQlqhqF427HUTbA3gB+JaArk/2YtZP1iARGV1frsZ2Y/IroBYCIR+QKYm8z1\nOP62NsZpSO2DGwCOGPVZBmA3Ed1iZjcimgSDktjBzNsTGTc7gC1G7rieFn5nhcIE5VarUCgUCotQ\nJimFQqFQWIQySSkURhDRYkgZX4asAzCAb5jZK1MFUyiyAMokpVAoFAqLUCYphUKhUFiEUhgKhUKh\nsAilMBQKhUJhEUphKBQKhcIilMJQKBQKhUX8H3wIOFjkMZmRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdd7208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import OrderedDict\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "\n",
    "RANDOM_STATE = 123\n",
    "\n",
    "# NOTE: Setting the `warm_start` construction parameter to `True` disables\n",
    "# support for paralellised ensembles but is necessary for tracking the OOB\n",
    "# error trajectory during training.\n",
    "\n",
    "ensemble_clfs = [\n",
    "    (\"RandomForestClassifier, max_features='sqrt'\",\n",
    "        RandomForestClassifier(warm_start=True, oob_score=True,\n",
    "                               max_features=\"sqrt\",\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features='log2'\",\n",
    "        RandomForestClassifier(warm_start=True, max_features='log2',\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features=None\",\n",
    "        RandomForestClassifier(warm_start=True, max_features=None,\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE))\n",
    "]\n",
    "\n",
    "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n",
    "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n",
    "\n",
    "# Range of `n_estimators` values to explore.\n",
    "min_estimators = 15\n",
    "max_estimators = 175\n",
    "\n",
    "for label, clf in ensemble_clfs:\n",
    "    for i in range(min_estimators, max_estimators + 1):\n",
    "        clf.set_params(n_estimators=i)\n",
    "        clf.fit(X, y)\n",
    "\n",
    "        # Record the OOB error for each `n_estimators=i` setting.\n",
    "        oob_error = 1 - clf.oob_score_\n",
    "        error_rate[label].append((i, oob_error))\n",
    "\n",
    "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n",
    "for label, clf_err in error_rate.items():\n",
    "    xs, ys = zip(*clf_err)\n",
    "    plt.plot(xs, ys, label=label)\n",
    "\n",
    "plt.xlim(min_estimators, max_estimators)\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.ylabel(\"OOB error rate\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes accuracy and time elapsed caculation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaiveBayes\n",
      "Acurracy:  0.78775\n",
      "time elapsed:  0.0349998474121\n"
     ]
    }
   ],
   "source": [
    "t4=time()\n",
    "print \"NaiveBayes\"\n",
    "nb = BernoulliNB()\n",
    "clf_nb=nb.fit(X_train,y_train)\n",
    "print \"Acurracy: \", clf_nb.score(X_test,y_test)\n",
    "t5=time()\n",
    "print \"time elapsed: \", t5-t4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross-validation for NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross result========\n",
      "[ 0.76703883  0.75633333  0.76783333  0.7835      0.79346558]\n",
      "0.773634214225\n",
      "time elapsed:  0.482000112534\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tt4=time()\n",
    "print \"cross result========\"\n",
    "scores = cross_validation.cross_val_score(nb, X,y, cv=5)\n",
    "print scores\n",
    "print scores.mean()\n",
    "tt5=time()\n",
    "print \"time elapsed: \", tt5-tt4\n",
    "print \"\\n\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN accuracy and time elapsed caculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "Acurracy:  0.75575\n",
      "time elapsed:  2.82899999619\n"
     ]
    }
   ],
   "source": [
    "t6=time()\n",
    "print \"KNN\"\n",
    "# knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = KNeighborsClassifier()\n",
    "clf_knn=knn.fit(X_train, y_train)\n",
    "print \"Acurracy: \", clf_knn.score(X_test,y_test) \n",
    "t7=time()\n",
    "print \"time elapsed: \", t7-t6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross validation for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross result========\n",
      "[ 0.74987502  0.75016667  0.752       0.76166667  0.76312719]\n",
      "0.755367108406\n",
      "time elapsed:  8.23200011253\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tt6=time()\n",
    "print \"cross result========\"\n",
    "scores = cross_validation.cross_val_score(knn, X,y, cv=5)\n",
    "print scores\n",
    "print scores.mean()\n",
    "tt7=time()\n",
    "print \"time elapsed: \", tt7-tt6\n",
    "print \"\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n",
      "Best score: 0.770\n",
      "Best parameters set:\n",
      "\tn_neighbors: 25\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.97      0.87      9408\n",
      "          1       0.46      0.10      0.16      2592\n",
      "\n",
      "avg / total       0.72      0.78      0.72     12000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:   21.8s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import grid_search\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "parameters = {'n_neighbors': (10, 15, 25)}\n",
    "\n",
    "grid = grid_search.GridSearchCV(knn, parameters, n_jobs=-1, verbose=1, scoring='accuracy')\n",
    "\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print 'Best score: %0.3f' % grid.best_score_\n",
    "\n",
    "print 'Best parameters set:'\n",
    "best_parameters = grid.best_estimator_.get_params()\n",
    "\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print '\\t%s: %r' % (param_name, best_parameters[param_name])\n",
    "    \n",
    "predictions = grid.predict(X_test)\n",
    "print classification_report(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Ensemble Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import ClassifierMixin\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.externals import six\n",
    "from sklearn.base import clone\n",
    "from sklearn.pipeline import _name_estimators\n",
    "import numpy as np\n",
    "import operator\n",
    "\n",
    "\n",
    "class MajorityVoteClassifier(BaseEstimator, \n",
    "                             ClassifierMixin):\n",
    "    \"\"\" A majority vote ensemble classifier\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    classifiers : array-like, shape = [n_classifiers]\n",
    "      Different classifiers for the ensemble\n",
    "\n",
    "    vote : str, {'classlabel', 'probability'} (default='label')\n",
    "      If 'classlabel' the prediction is based on the argmax of\n",
    "        class labels. Else if 'probability', the argmax of\n",
    "        the sum of probabilities is used to predict the class label\n",
    "        (recommended for calibrated classifiers).\n",
    "\n",
    "    weights : array-like, shape = [n_classifiers], optional (default=None)\n",
    "      If a list of `int` or `float` values are provided, the classifiers\n",
    "      are weighted by importance; Uses uniform weights if `weights=None`.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, classifiers, vote='classlabel', weights=None):\n",
    "\n",
    "        self.classifiers = classifiers\n",
    "        self.named_classifiers = {key: value for key, value\n",
    "                                  in _name_estimators(classifiers)}\n",
    "        self.vote = vote\n",
    "        self.weights = weights\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit classifiers.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Matrix of training samples.\n",
    "\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Vector of target class labels.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        if self.vote not in ('probability', 'classlabel'):\n",
    "            raise ValueError(\"vote must be 'probability' or 'classlabel'\"\n",
    "                             \"; got (vote=%r)\"\n",
    "                             % self.vote)\n",
    "\n",
    "        if self.weights and len(self.weights) != len(self.classifiers):\n",
    "            raise ValueError('Number of classifiers and weights must be equal'\n",
    "                             '; got %d weights, %d classifiers'\n",
    "                             % (len(self.weights), len(self.classifiers)))\n",
    "\n",
    "        # Use LabelEncoder to ensure class labels start with 0, which\n",
    "        # is important for np.argmax call in self.predict\n",
    "        self.lablenc_ = LabelEncoder()\n",
    "        self.lablenc_.fit(y)\n",
    "        self.classes_ = self.lablenc_.classes_\n",
    "        self.classifiers_ = []\n",
    "        for clf in self.classifiers:\n",
    "            fitted_clf = clone(clf).fit(X, self.lablenc_.transform(y))\n",
    "            self.classifiers_.append(fitted_clf)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\" Predict class labels for X.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Matrix of training samples.\n",
    "\n",
    "        Returns\n",
    "        ----------\n",
    "        maj_vote : array-like, shape = [n_samples]\n",
    "            Predicted class labels.\n",
    "            \n",
    "        \"\"\"\n",
    "        if self.vote == 'probability':\n",
    "            maj_vote = np.argmax(self.predict_proba(X), axis=1)\n",
    "        else:  # 'classlabel' vote\n",
    "\n",
    "            #  Collect results from clf.predict calls\n",
    "            predictions = np.asarray([clf.predict(X)\n",
    "                                      for clf in self.classifiers_]).T\n",
    "\n",
    "            maj_vote = np.apply_along_axis(\n",
    "                                      lambda x:\n",
    "                                      np.argmax(np.bincount(x,\n",
    "                                                weights=self.weights)),\n",
    "                                      axis=1,\n",
    "                                      arr=predictions)\n",
    "        maj_vote = self.lablenc_.inverse_transform(maj_vote)\n",
    "        return maj_vote\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\" Predict class probabilities for X.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "\n",
    "        Returns\n",
    "        ----------\n",
    "        avg_proba : array-like, shape = [n_samples, n_classes]\n",
    "            Weighted average probability for each class per sample.\n",
    "\n",
    "        \"\"\"\n",
    "        probas = np.asarray([clf.predict_proba(X)\n",
    "                             for clf in self.classifiers_])\n",
    "        avg_proba = np.average(probas, axis=0, weights=self.weights)\n",
    "        return avg_proba\n",
    "\n",
    "    def get_params(self, deep=True):\n",
    "        \"\"\" Get classifier parameter names for GridSearch\"\"\"\n",
    "        if not deep:\n",
    "            return super(MajorityVoteClassifier, self).get_params(deep=False)\n",
    "        else:\n",
    "            out = self.named_classifiers.copy()\n",
    "            for name, step in six.iteritems(self.named_classifiers):\n",
    "                for key, value in six.iteritems(step.get_params(deep=True)):\n",
    "                    out['%s__%s' % (name, key)] = value\n",
    "            return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining different algorithms for classification with majority vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation:\n",
      "\n",
      "ROC AUC: 0.72 (+/- 0.01) [Logistic Regression]\n",
      "ROC AUC: 0.75 (+/- 0.01) [Decision Tree]\n",
      "ROC AUC: 0.60 (+/- 0.01) [KNN]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.pipeline import Pipeline\n",
    "import numpy as np\n",
    "\n",
    "clf1 = LogisticRegression(penalty='l2', \n",
    "                          C=0.001, \n",
    "                          random_state=0)\n",
    "\n",
    "clf2 = DecisionTreeClassifier(max_depth=5,\n",
    "                              min_samples_leaf=5,\n",
    "                              min_samples_split=1,\n",
    "                              criterion='entropy', \n",
    "                              random_state=0)\n",
    "\n",
    "clf3 = KNeighborsClassifier(n_neighbors=1, \n",
    "                            p=2, \n",
    "                            metric='minkowski')\n",
    "\n",
    "pipe1 = Pipeline([['sc', StandardScaler()],\n",
    "                  ['clf', clf1]])\n",
    "pipe3 = Pipeline([['sc', StandardScaler()],\n",
    "                  ['clf', clf3]])\n",
    "\n",
    "clf_labels = ['Logistic Regression', 'Decision Tree', 'KNN']\n",
    "\n",
    "print('10-fold cross validation:\\n')\n",
    "for clf, label in zip([pipe1, clf2, pipe3], clf_labels):\n",
    "    scores = cross_val_score(estimator=clf, \n",
    "                             X=X_train, \n",
    "                             y=y_train, \n",
    "                             cv=10, \n",
    "                             scoring='roc_auc')\n",
    "    print(\"ROC AUC: %0.2f (+/- %0.2f) [%s]\" \n",
    "               % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may be wondering why we trained the logistic regression and k-nearest neighbors classifier as part of a pipeline. The reason behind it is that, both the\n",
    "logistic regression and k-nearest neighbors algorithms (using the Euclidean distance metric) are not scale-invariant in contrast with decision trees.\n",
    "\n",
    "Now let's move on to the more exciting part and combine the individual classifiers for majority rule voting in our MajorityVoteClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.72 (+/- 0.01) [Logistic Regression]\n",
      "ROC AUC: 0.75 (+/- 0.01) [Decision Tree]\n",
      "ROC AUC: 0.60 (+/- 0.01) [K Nearest Neighbours]\n",
      "ROC AUC: 0.73 (+/- 0.01) [Majority Voting]\n"
     ]
    }
   ],
   "source": [
    "# Majority Rule (hard) Voting\n",
    "\n",
    "mv_clf = MajorityVoteClassifier(\n",
    "                classifiers=[pipe1, clf2,pipe3])\n",
    "\n",
    "clf_labels = ['Logistic Regression', 'Decision Tree', 'K Nearest Neighbours', 'Majority Voting'] \n",
    "all_clf = [pipe1, clf2, pipe3, mv_clf]\n",
    "\n",
    "for clf, label in zip(all_clf, clf_labels):\n",
    "    scores = cross_val_score(estimator=clf, \n",
    "                             X=X_train, \n",
    "                             y=y_train, \n",
    "                             cv=10, \n",
    "                             scoring='roc_auc')\n",
    "    print(\"ROC AUC: %0.2f (+/- %0.2f) [%s]\" \n",
    "               % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating and tuning the ensemble classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEaCAYAAACrcqiAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf5Ls3jD//WvtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd73b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "colors = ['black', 'orange', 'blue', 'green']\n",
    "linestyles = [':', '--', '-.', '-']\n",
    "for clf, label, clr, ls in zip(all_clf, clf_labels, colors, linestyles):\n",
    "\n",
    "    # assuming the label of the positive class is 1\n",
    "    y_pred = clf.fit(X_train, \n",
    "                     y_train).predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_true=y_test, \n",
    "                                     y_score=y_pred)\n",
    "    roc_auc = auc(x=fpr, y=tpr)\n",
    "    plt.plot(fpr, tpr, \n",
    "             color=clr, \n",
    "             linestyle=ls, \n",
    "             label='%s (auc = %0.2f)' % (label, roc_auc))\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], [0, 1], \n",
    "         linestyle='--', \n",
    "         color='gray', \n",
    "         linewidth=2)\n",
    "\n",
    "plt.xlim([-0.1, 1.1])\n",
    "plt.ylim([-0.1, 1.1])\n",
    "plt.grid()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/roc.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot class probabilities calculated by the VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAFdCAYAAADL4PDiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKtBxbJ+GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3e0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "clf1 = LogisticRegression(random_state=123)\n",
    "clf2 = RandomForestClassifier(random_state=123)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n",
    "                        voting='soft',\n",
    "                        weights=[1, 1, 5])\n",
    "\n",
    "# predict class probabilities for all classifiers\n",
    "probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]\n",
    "\n",
    "# get class probabilities for the first sample in the dataset\n",
    "class1_1 = [pr[0, 0] for pr in probas]\n",
    "class2_1 = [pr[0, 1] for pr in probas]\n",
    "\n",
    "\n",
    "# plotting\n",
    "\n",
    "N = 4  # number of groups\n",
    "ind = np.arange(N)  # group positions\n",
    "width = 0.35  # bar width\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# bars for classifier 1-3\n",
    "p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color='green')\n",
    "p2 = ax.bar(ind + width, np.hstack(([class2_1[:-1], [0]])), width, color='lightgreen')\n",
    "\n",
    "# bars for VotingClassifier\n",
    "p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color='blue')\n",
    "p4 = ax.bar(ind + width, [0, 0, 0, class2_1[-1]], width, color='steelblue')\n",
    "\n",
    "# plot annotations\n",
    "plt.axvline(2.8, color='k', linestyle='dashed')\n",
    "ax.set_xticks(ind + width)\n",
    "ax.set_xticklabels(['LogisticRegression\\nweight 1',\n",
    "                    'GaussianNB\\nweight 1',\n",
    "                    'RandomForestClassifier\\nweight 5',\n",
    "                    'VotingClassifier\\n(average probabilities)'],\n",
    "                   rotation=40,\n",
    "                   ha='right')\n",
    "plt.ylim([0, 1])\n",
    "plt.title('Class probabilities for sample 1 by different classifiers')\n",
    "plt.legend([p1[0], p2[0]], ['class 1', 'class 2'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging -- Building an ensemble of classifiers from bootstrap samples\n",
    "\n",
    "Bagging is an ensemble learning technique that is closely related to the MajorityVoteClassifier,however, instead of using the same training set to fit the individual classifiers in the ensemble, we draw bootstrap samples (random samples with replacement) from the initial training set, which is why bagging is also known as bootstrap aggregating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', \n",
    "                              max_depth=None)\n",
    "\n",
    "bag = BaggingClassifier(base_estimator=tree,\n",
    "                        n_estimators=500, \n",
    "                        max_samples=1.0, \n",
    "                        max_features=1.0, \n",
    "                        bootstrap=True, \n",
    "                        bootstrap_features=False, \n",
    "                        n_jobs=-1, \n",
    "                        random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree train/test accuracies 1.000/0.729\n",
      "Bagging train/test accuracies 1.000/0.821\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n",
      "C:\\Miniconda2\\lib\\site-packages\\sklearn\\externals\\joblib\\hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
      "descriptor assignment is deprecated. To maintain\n",
      "the Fortran contiguity of a multidimensional Fortran\n",
      "array, use 'a.T.view(...).T' instead\n",
      "  obj_bytes_view = obj.view(self.np.uint8)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "tree = tree.fit(X_train, y_train)\n",
    "y_train_pred = tree.predict(X_train)\n",
    "y_test_pred = tree.predict(X_test)\n",
    "\n",
    "tree_train = accuracy_score(y_train, y_train_pred)\n",
    "tree_test = accuracy_score(y_test, y_test_pred)\n",
    "print('Decision tree train/test accuracies %.3f/%.3f'\n",
    "      % (tree_train, tree_test))\n",
    "\n",
    "bag = bag.fit(X_train, y_train)\n",
    "y_train_pred = bag.predict(X_train)\n",
    "y_test_pred = bag.predict(X_test)\n",
    "\n",
    "bag_train = accuracy_score(y_train, y_train_pred) \n",
    "bag_test = accuracy_score(y_test, y_test_pred) \n",
    "print('Bagging train/test accuracies %.3f/%.3f'\n",
    "      % (bag_train, bag_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leveraging weak learners via adaptive boosting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', \n",
    "                              max_depth=1)\n",
    "\n",
    "ada = AdaBoostClassifier(base_estimator=tree,\n",
    "                         n_estimators=500, \n",
    "                         learning_rate=0.1,\n",
    "                         random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree train/test accuracies 0.596/0.597\n",
      "AdaBoost train/test accuracies 0.675/0.662\n"
     ]
    }
   ],
   "source": [
    "tree = tree.fit(X_train, y_train)\n",
    "y_train_pred = tree.predict(X_train)\n",
    "y_test_pred = tree.predict(X_test)\n",
    "\n",
    "tree_train = accuracy_score(y_train, y_train_pred)\n",
    "tree_test = accuracy_score(y_test, y_test_pred)\n",
    "print('Decision tree train/test accuracies %.3f/%.3f'\n",
    "      % (tree_train, tree_test))\n",
    "\n",
    "ada = ada.fit(X_train, y_train)\n",
    "y_train_pred = ada.predict(X_train)\n",
    "y_test_pred = ada.predict(X_test)\n",
    "\n",
    "ada_train = accuracy_score(y_train, y_train_pred) \n",
    "ada_test = accuracy_score(y_test, y_test_pred) \n",
    "print('AdaBoost train/test accuracies %.3f/%.3f'\n",
    "      % (ada_train, ada_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two-class AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:53: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFjCAYAAADPWpb4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc722b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "\n",
    "\n",
    "# Create and fit an AdaBoosted decision tree\n",
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=5),\n",
    "                         algorithm=\"SAMME\",\n",
    "                         n_estimators=200)\n",
    "\n",
    "bdt.fit(X, y)\n",
    "\n",
    "plot_colors = \"br\"\n",
    "plot_step = 0.02\n",
    "class_names = \"AB\"\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot the decision boundaries\n",
    "plt.subplot(121)\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                     np.arange(y_min, y_max, plot_step))\n",
    "\n",
    "Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.axis(\"tight\")\n",
    "\n",
    "# Plot the training points\n",
    "for i, n, c in zip(range(2), class_names, plot_colors):\n",
    "    idx = np.where(y == i)\n",
    "    plt.scatter(X[idx, 0], X[idx, 1],\n",
    "                c=c, cmap=plt.cm.Paired,\n",
    "                label=\"Class %s\" % n)\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Decision Boundary')\n",
    "\n",
    "# Plot the two-class decision scores\n",
    "twoclass_output = bdt.decision_function(X)\n",
    "plot_range = (twoclass_output.min(), twoclass_output.max())\n",
    "plt.subplot(122)\n",
    "for i, n, c in zip(range(2), class_names, plot_colors):\n",
    "    plt.hist(twoclass_output[y == i],\n",
    "             bins=10,\n",
    "             range=plot_range,\n",
    "             facecolor=c,\n",
    "             label='Class %s' % n,\n",
    "             alpha=.5)\n",
    "x1, x2, y1, y2 = plt.axis()\n",
    "plt.axis((x1, x2, y1, y2 * 1.2))\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Samples')\n",
    "plt.xlabel('Score')\n",
    "plt.title('Decision Scores')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.35)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete versus Real AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEQCAYAAABFtIg2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5eb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import zero_one_loss\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "n_estimators = 400\n",
    "# A learning rate of 1. may not be optimal for both SAMME and SAMME.R\n",
    "learning_rate = 1.\n",
    "\n",
    "\n",
    "dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)\n",
    "dt_stump.fit(X_train, y_train)\n",
    "dt_stump_err = 1.0 - dt_stump.score(X_test, y_test)\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=9, min_samples_leaf=1)\n",
    "dt.fit(X_train, y_train)\n",
    "dt_err = 1.0 - dt.score(X_test, y_test)\n",
    "\n",
    "ada_discrete = AdaBoostClassifier(\n",
    "    base_estimator=dt_stump,\n",
    "    learning_rate=learning_rate,\n",
    "    n_estimators=n_estimators,\n",
    "    algorithm=\"SAMME\")\n",
    "ada_discrete.fit(X_train, y_train)\n",
    "\n",
    "ada_real = AdaBoostClassifier(\n",
    "    base_estimator=dt_stump,\n",
    "    learning_rate=learning_rate,\n",
    "    n_estimators=n_estimators,\n",
    "    algorithm=\"SAMME.R\")\n",
    "ada_real.fit(X_train, y_train)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot([1, n_estimators], [dt_stump_err] * 2, 'k-',\n",
    "        label='Decision Stump Error')\n",
    "ax.plot([1, n_estimators], [dt_err] * 2, 'k--',\n",
    "        label='Decision Tree Error')\n",
    "\n",
    "ada_discrete_err = np.zeros((n_estimators,))\n",
    "for i, y_pred in enumerate(ada_discrete.staged_predict(X_test)):\n",
    "    ada_discrete_err[i] = zero_one_loss(y_pred, y_test)\n",
    "\n",
    "ada_discrete_err_train = np.zeros((n_estimators,))\n",
    "for i, y_pred in enumerate(ada_discrete.staged_predict(X_train)):\n",
    "    ada_discrete_err_train[i] = zero_one_loss(y_pred, y_train)\n",
    "\n",
    "ada_real_err = np.zeros((n_estimators,))\n",
    "for i, y_pred in enumerate(ada_real.staged_predict(X_test)):\n",
    "    ada_real_err[i] = zero_one_loss(y_pred, y_test)\n",
    "\n",
    "ada_real_err_train = np.zeros((n_estimators,))\n",
    "for i, y_pred in enumerate(ada_real.staged_predict(X_train)):\n",
    "    ada_real_err_train[i] = zero_one_loss(y_pred, y_train)\n",
    "\n",
    "ax.plot(np.arange(n_estimators) + 1, ada_discrete_err,\n",
    "        label='Discrete AdaBoost Test Error',\n",
    "        color='red')\n",
    "ax.plot(np.arange(n_estimators) + 1, ada_discrete_err_train,\n",
    "        label='Discrete AdaBoost Train Error',\n",
    "        color='blue')\n",
    "ax.plot(np.arange(n_estimators) + 1, ada_real_err,\n",
    "        label='Real AdaBoost Test Error',\n",
    "        color='orange')\n",
    "ax.plot(np.arange(n_estimators) + 1, ada_real_err_train,\n",
    "        label='Real AdaBoost Train Error',\n",
    "        color='green')\n",
    "\n",
    "ax.set_ylim((0.0, 0.5))\n",
    "ax.set_xlabel('n_estimators')\n",
    "ax.set_ylabel('error rate')\n",
    "\n",
    "leg = ax.legend(loc='upper right', fancybox=True)\n",
    "leg.get_frame().set_alpha(0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformations with ensembles of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb70438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEZCAYAAABWwhjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8727b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(10)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,\n",
    "                              GradientBoostingClassifier)\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "n_estimator = 10\n",
    "# It is important to train the ensemble of trees on a different subset\n",
    "# of the training data than the linear regression model to avoid\n",
    "# overfitting, in particular if the total number of leaves is\n",
    "# similar to the number of training samples\n",
    "X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,\n",
    "                                                            y_train,\n",
    "                                                            test_size=0.5)\n",
    "\n",
    "# Unsupervised transformation based on totally random trees\n",
    "rt = RandomTreesEmbedding(max_depth=3, n_estimators=n_estimator,\n",
    "\trandom_state=0)\n",
    "\n",
    "rt_lm = LogisticRegression()\n",
    "pipeline = make_pipeline(rt, rt_lm)\n",
    "pipeline.fit(X_train, y_train)\n",
    "y_pred_rt = pipeline.predict_proba(X_test)[:, 1]\n",
    "fpr_rt_lm, tpr_rt_lm, _ = roc_curve(y_test, y_pred_rt)\n",
    "\n",
    "# Supervised transformation based on random forests\n",
    "rf = RandomForestClassifier(max_depth=3, n_estimators=n_estimator)\n",
    "rf_enc = OneHotEncoder()\n",
    "rf_lm = LogisticRegression()\n",
    "rf.fit(X_train, y_train)\n",
    "rf_enc.fit(rf.apply(X_train))\n",
    "rf_lm.fit(rf_enc.transform(rf.apply(X_train_lr)), y_train_lr)\n",
    "\n",
    "y_pred_rf_lm = rf_lm.predict_proba(rf_enc.transform(rf.apply(X_test)))[:, 1]\n",
    "fpr_rf_lm, tpr_rf_lm, _ = roc_curve(y_test, y_pred_rf_lm)\n",
    "\n",
    "grd = GradientBoostingClassifier(n_estimators=n_estimator)\n",
    "grd_enc = OneHotEncoder()\n",
    "grd_lm = LogisticRegression()\n",
    "grd.fit(X_train, y_train)\n",
    "grd_enc.fit(grd.apply(X_train)[:, :, 0])\n",
    "grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)[:, :, 0]), y_train_lr)\n",
    "\n",
    "y_pred_grd_lm = grd_lm.predict_proba(\n",
    "    grd_enc.transform(grd.apply(X_test)[:, :, 0]))[:, 1]\n",
    "fpr_grd_lm, tpr_grd_lm, _ = roc_curve(y_test, y_pred_grd_lm)\n",
    "\n",
    "\n",
    "# The gradient boosted model by itself\n",
    "y_pred_grd = grd.predict_proba(X_test)[:, 1]\n",
    "fpr_grd, tpr_grd, _ = roc_curve(y_test, y_pred_grd)\n",
    "\n",
    "\n",
    "# The random forest model by itself\n",
    "y_pred_rf = rf.predict_proba(X_test)[:, 1]\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_rf)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')\n",
    "plt.plot(fpr_grd, tpr_grd, label='GBT')\n",
    "plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "plt.xlim(0, 0.2)\n",
    "plt.ylim(0.8, 1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')\n",
    "plt.plot(fpr_grd, tpr_grd, label='GBT')\n",
    "plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve (zoomed in at top left)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target_names = ['Shares > 1400' , 'Shares < 1400']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.20000000e+01,   2.19000000e+02,   6.63594467e-01, ...,\n",
       "         -1.87500000e-01,   0.00000000e+00,   1.87500000e-01],\n",
       "       [  9.00000000e+00,   2.55000000e+02,   6.04743081e-01, ...,\n",
       "          0.00000000e+00,   5.00000000e-01,   0.00000000e+00],\n",
       "       [  9.00000000e+00,   2.11000000e+02,   5.75129531e-01, ...,\n",
       "          0.00000000e+00,   5.00000000e-01,   0.00000000e+00],\n",
       "       ..., \n",
       "       [  1.00000000e+01,   4.42000000e+02,   5.16355139e-01, ...,\n",
       "          1.36363636e-01,   4.54545450e-02,   1.36363636e-01],\n",
       "       [  6.00000000e+00,   6.82000000e+02,   5.39493293e-01, ...,\n",
       "          0.00000000e+00,   5.00000000e-01,   0.00000000e+00],\n",
       "       [  1.00000000e+01,   1.57000000e+02,   7.01986750e-01, ...,\n",
       "          2.50000000e-01,   1.66666667e-01,   2.50000000e-01]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "reduced_X = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18490\n",
      "18490\n",
      "21154\n",
      "21154\n"
     ]
    }
   ],
   "source": [
    "for a in [red_x, red_y,blue_x,blue_y]:\n",
    "    print len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEACAYAAAD4NNLwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npiAIgsAVEZiCIAgCV0RgCoIgCFzxLxJjc0w/4CA3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203e78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "red_x, red_y = [], []\n",
    "blue_x, blue_y = [], []\n",
    "\n",
    "for i in range(len(reduced_X)):\n",
    "    if y[i] == 0:\n",
    "        red_x.append(reduced_X[i][0])\n",
    "        red_y.append(reduced_X[i][1])\n",
    "    elif y[i] == 1:\n",
    "        blue_x.append(reduced_X[i][0])\n",
    "        blue_y.append(reduced_X[i][1])\n",
    "        \n",
    "plt.scatter(red_x, red_y, c='r', marker='x')\n",
    "plt.scatter(blue_x, blue_y, c='b', marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:28: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n",
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:29: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n",
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:40: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio (first two components): [ 0.7628241   0.16383047]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:41: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e39dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmZll5CRhZmYZOUmYmVlG/x/uWh+PUtfkvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15d71908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\nfor c, i, target_name in zip(\"rb\", [0, 1], target_names):\\n    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)\\nplt.legend()\\nplt.title(\\'PCA\\')\\n\\nplt.figure()\\nfor c, i, target_name in zip(\"rb\", [0, 1], target_names):\\n    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], c=c, label=target_name)\\nplt.legend()\\nplt.title(\\'LDA\\')\\n\\nplt.show()\\n'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\"\"\"\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X.values).transform(X.values)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r2 = lda.fit(X.values, y.values).transform(X.values)\n",
    "\"\"\"\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r2 = lda.fit(X, y).transform(X)\n",
    "\n",
    "# Percentage of variance explained for each components\n",
    "print('explained variance ratio (first two components): %s'\n",
    "      % str(pca.explained_variance_ratio_))\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "colors = ['blue','red']\n",
    "for i in xrange(len(colors)):\n",
    "    px = X_r[:, 0][y == i]\n",
    "    py = X_r[:, 1][y == i]\n",
    "    plt.scatter(px, py, c=colors[i])\n",
    "plt.legend(target_names)\n",
    "plt.title('PCA')\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "colors = ['blue','red']\n",
    "for i in xrange(len(colors)):\n",
    "    px = X_r2[:, 0][y == i]\n",
    "    py = X_pca[:, 1][y == i]\n",
    "    plt.scatter(px, py, c=colors[i])\n",
    "plt.legend(target_names)\n",
    "plt.title('LDA')\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "for c, i, target_name in zip(\"rb\", [0, 1], target_names):\n",
    "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)\n",
    "plt.legend()\n",
    "plt.title('PCA')\n",
    "\n",
    "plt.figure()\n",
    "for c, i, target_name in zip(\"rb\", [0, 1], target_names):\n",
    "    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], c=c, label=target_name)\n",
    "plt.legend()\n",
    "plt.title('LDA')\n",
    "\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "def plot_pca_scatter():\n",
    "    colors = ['blue','red']\n",
    "    for i in xrange(len(colors)):\n",
    "        px = X_pca[:, 0][y == i]\n",
    "        py = X_pca[:, 1][y == i]\n",
    "        plt.scatter(px, py, c=colors[i])\n",
    "    plt.legend(target_names)\n",
    "    plt.xlabel('First Principal Component')\n",
    "    plt.ylabel('Second Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:5: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n",
      "C:\\Miniconda2\\lib\\site-packages\\ipykernel\\__main__.py:6: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "estimator = PCA(n_components=2)\n",
    "X_pca = estimator.fit_transform(X.values)\n",
    "plot_pca_scatter() # Note that we only plot the first and second principal component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
    "for i, comp in enumerate(images):\n",
    "    plt.subplot(n_row, n_col, i + 1)\n",
    "    plt.imshow(comp.reshape((8, 8)), interpolation='nearest')\n",
    "    plt.text(0, -1, str(i + 1) + '-component')\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Optimization\n",
    "\n",
    "## Concatenating multiple feature extraction methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1, score=0.555539 -   0.3s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1, score=0.595807 -   0.3s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=1, score=0.572953 -   0.3s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2, score=0.565829 -   0.3s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 tasks       | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks       | elapsed:    1.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2, score=0.594899 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=3, features__univ_select__k=2, score=0.595807 -   0.3s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1, score=0.554782 -   0.3s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1, score=0.595807 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=1, score=0.573331 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2, score=0.565829 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2, score=0.594899 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=3, features__univ_select__k=2, score=0.595807 -   0.4s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   7 tasks       | elapsed:    3.1s\n",
      "[Parallel(n_jobs=1)]: Done  12 tasks       | elapsed:    5.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1, score=0.563711 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1, score=0.595807 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=1, score=0.571364 -   0.3s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2, score=0.600257 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2, score=0.594899 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=3, features__univ_select__k=2, score=0.595807 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1, score=0.541616 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1, score=0.596186 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=1, score=0.571666 -   0.3s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2, score=0.597079 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2, score=0.595656 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=5, features__univ_select__k=2, score=0.589299 -   0.4s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  17 tasks       | elapsed:    8.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 tasks       | elapsed:   11.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1, score=0.532309 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1, score=0.596035 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=1, score=0.572120 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2, score=0.594658 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2, score=0.594899 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=5, features__univ_select__k=2, score=0.588997 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1, score=0.569234 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1, score=0.595959 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=1, score=0.567126 -   0.4s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2, score=0.590118 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2, score=0.593613 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=5, features__univ_select__k=2, score=0.608370 -   0.5s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1, score=0.552815 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1, score=0.598759 -   0.4s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=1, score=0.570531 -   0.3s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2, score=0.597306 -   0.4s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  31 tasks       | elapsed:   15.2s\n",
      "[Parallel(n_jobs=1)]: Done  40 tasks       | elapsed:   20.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2, score=0.591267 -   0.5s\n",
      "[CV] features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=1, dt__max_depth=7, features__univ_select__k=2, score=0.608597 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1, score=0.548956 -   0.5s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1, score=0.598002 -   0.4s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=1, score=0.571666 -   0.5s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2, score=0.598214 -   0.5s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2, score=0.591494 -   0.6s\n",
      "[CV] features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=2, dt__max_depth=7, features__univ_select__k=2, score=0.595278 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1, score=0.559095 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1, score=0.594899 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=1, score=0.582715 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2, score=0.596323 -   0.5s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2, score=0.599743 -   0.6s\n",
      "[CV] features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2 \n",
      "[CV]  features__pca__n_components=3, dt__max_depth=7, features__univ_select__k=2, score=0.619949 -   0.5s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:   25.7s\n",
      "[Parallel(n_jobs=1)]: Done  54 out of  54 | elapsed:   28.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pipeline(steps=[('features', FeatureUnion(n_jobs=1,\n",
      "       transformer_list=[('pca', PCA(copy=True, n_components=3, whiten=False)), ('univ_select', SelectKBest(k=2, score_func=<function f_classif at 0x0000000006D019E8>))],\n",
      "       transformer_weights=None)), ('dt', DecisionTreeClassifier(class_weight=None, cr...plit=1, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=99, splitter='best'))])\n",
      "0.605337503784\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "# This dataset is way to high-dimensional. Better do PCA:\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "# Maybe some original features where good, too?\n",
    "selection = SelectKBest(k=1)\n",
    "\n",
    "# Build estimator from PCA and Univariate selection:\n",
    "\n",
    "combined_features = FeatureUnion([(\"pca\", pca), (\"univ_select\", selection)])\n",
    "\n",
    "# Use combined features to transform dataset:\n",
    "X_features = combined_features.fit(X, y).transform(X)\n",
    "\n",
    "dt = DecisionTreeClassifier(min_samples_split=1,max_depth=5,min_samples_leaf=5,random_state=99)\n",
    "\n",
    "# Do grid search over k, n_components and max_depth:\n",
    "\n",
    "pipeline = Pipeline([(\"features\", combined_features), (\"dt\", dt)])\n",
    "\n",
    "param_grid = dict(features__pca__n_components=[1, 2, 3],\n",
    "                  features__univ_select__k=[1, 2],\n",
    "                  dt__max_depth=[3, 5, 7])\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)\n",
    "grid_search.fit(X, y)\n",
    "print(grid_search.best_estimator_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of Calibration of Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAALLCAYAAAAL/R/mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncbd9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1bec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "# Create classifiers\n",
    "lr = LogisticRegression()\n",
    "gnb = GaussianNB()\n",
    "knn = KNeighborsClassifier(n_neighbors=25)\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "\n",
    "###############################################################################\n",
    "# Plot calibration plots\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "for clf, name in [(lr, 'Logistic'),\n",
    "                  (gnb, 'Naive Bayes'),\n",
    "                  (knn, 'K Neighbors Classifier'),\n",
    "                  (rfc, 'Random Forest')]:\n",
    "    clf.fit(X_train, y_train)\n",
    "    if hasattr(clf, \"predict_proba\"):\n",
    "        prob_pos = clf.predict_proba(X_test)[:, 1]\n",
    "    else:  # use decision function\n",
    "        prob_pos = clf.decision_function(X_test)\n",
    "        prob_pos = \\\n",
    "            (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n",
    "    fraction_of_positives, mean_predicted_value = \\\n",
    "        calibration_curve(y_test, prob_pos, n_bins=10)\n",
    "\n",
    "    ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
    "             label=\"%s\" % (name, ))\n",
    "\n",
    "    ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,\n",
    "             histtype=\"step\", lw=2)\n",
    "\n",
    "ax1.set_ylabel(\"Fraction of positives\")\n",
    "ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax1.set_title('Calibration plots  (reliability curve)')\n",
    "\n",
    "ax2.set_xlabel(\"Mean predicted value\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "ax2.legend(loc=\"upper center\", ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability Calibration curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic:\n",
      "\tBrier: 0.238\n",
      "\tPrecision: 0.598\n",
      "\tRecall: 0.690\n",
      "\tF1: 0.641\n",
      "\n",
      "Naive Bayes:\n",
      "\tBrier: 0.324\n",
      "\tPrecision: 0.676\n",
      "\tRecall: 0.223\n",
      "\tF1: 0.335\n",
      "\n",
      "Naive Bayes + Isotonic:\n",
      "\tBrier: 0.236\n",
      "\tPrecision: 0.610\n",
      "\tRecall: 0.714\n",
      "\tF1: 0.658\n",
      "\n",
      "Naive Bayes + Sigmoid:\n",
      "\tBrier: 0.240\n",
      "\tPrecision: 0.629\n",
      "\tRecall: 0.603\n",
      "\tF1: 0.615\n",
      "\n",
      "Logistic:\n",
      "\tBrier: 0.238\n",
      "\tPrecision: 0.598\n",
      "\tRecall: 0.690\n",
      "\tF1: 0.641\n",
      "\n",
      "Decision Tree:\n",
      "\tBrier: 0.422\n",
      "\tPrecision: 0.605\n",
      "\tRecall: 0.593\n",
      "\tF1: 0.599\n",
      "\n",
      "Decision Tree + Isotonic:\n",
      "\tBrier: 0.241\n",
      "\tPrecision: 0.581\n",
      "\tRecall: 0.819\n",
      "\tF1: 0.679\n",
      "\n",
      "Decision Tree + Sigmoid:\n",
      "\tBrier: 0.241\n",
      "\tPrecision: 0.579\n",
      "\tRecall: 0.811\n",
      "\tF1: 0.675\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAALLCAYAAAAL/R/mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMyvy3+6epDSCde2bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19186320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAALLCAYAAAAL/R/mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a69d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (brier_score_loss, precision_score, recall_score,\n",
    "                             f1_score)\n",
    "from sklearn.calibration import CalibratedClassifierCV, calibration_curve\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "\n",
    "\n",
    "def plot_calibration_curve(est, name, fig_index):\n",
    "    \"\"\"Plot calibration curve for est w/o and with calibration. \"\"\"\n",
    "    # Calibrated with isotonic calibration\n",
    "    isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic')\n",
    "\n",
    "    # Calibrated with sigmoid calibration\n",
    "    sigmoid = CalibratedClassifierCV(est, cv=2, method='sigmoid')\n",
    "\n",
    "    # Logistic regression with no calibration as baseline\n",
    "    lr = LogisticRegression(C=1., solver='lbfgs')\n",
    "\n",
    "    fig = plt.figure(fig_index, figsize=(10, 10))\n",
    "    ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "    ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "    ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "    for clf, name in [(lr, 'Logistic'),\n",
    "                      (est, name),\n",
    "                      (isotonic, name + ' + Isotonic'),\n",
    "                      (sigmoid, name + ' + Sigmoid')]:\n",
    "        clf.fit(X_train, y_train)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        if hasattr(clf, \"predict_proba\"):\n",
    "            prob_pos = clf.predict_proba(X_test)[:, 1]\n",
    "        else:  # use decision function\n",
    "            prob_pos = clf.decision_function(X_test)\n",
    "            prob_pos = \\\n",
    "                (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n",
    "\n",
    "        clf_score = brier_score_loss(y_test, prob_pos, pos_label=y.max())\n",
    "        print(\"%s:\" % name)\n",
    "        print(\"\\tBrier: %1.3f\" % (clf_score))\n",
    "        print(\"\\tPrecision: %1.3f\" % precision_score(y_test, y_pred))\n",
    "        print(\"\\tRecall: %1.3f\" % recall_score(y_test, y_pred))\n",
    "        print(\"\\tF1: %1.3f\\n\" % f1_score(y_test, y_pred))\n",
    "\n",
    "        fraction_of_positives, mean_predicted_value = \\\n",
    "            calibration_curve(y_test, prob_pos, n_bins=10)\n",
    "\n",
    "        ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
    "                 label=\"%s (%1.3f)\" % (name, clf_score))\n",
    "\n",
    "        ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,\n",
    "                 histtype=\"step\", lw=2)\n",
    "\n",
    "    ax1.set_ylabel(\"Fraction of positives\")\n",
    "    ax1.set_ylim([-0.05, 1.05])\n",
    "    ax1.legend(loc=\"lower right\")\n",
    "    ax1.set_title('Calibration plots  (reliability curve)')\n",
    "\n",
    "    ax2.set_xlabel(\"Mean predicted value\")\n",
    "    ax2.set_ylabel(\"Count\")\n",
    "    ax2.legend(loc=\"upper center\", ncol=2)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "# Plot calibration cuve for Gaussian Naive Bayes\n",
    "plot_calibration_curve(GaussianNB(), \"Naive Bayes\", 1)\n",
    "\n",
    "# Plot calibration cuve for Linear SVC\n",
    "plot_calibration_curve(DecisionTreeClassifier(), \"Decision Tree\", 2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot classification probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classif_rate for K Nearest Neighbors : 76.077086 \n",
      "classif_rate for L1 logistic : 53.309454 \n",
      "classif_rate for L2 logistic : 52.103723 \n",
      "classif_rate for Random Forest : 82.065382 \n",
      "classif_rate for Decision Tree : 55.047422 \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAKzCAYAAAAa82gjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd204780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#PCA\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "\n",
    "n_features = X_r.shape[1]\n",
    "\n",
    "C = 1.0\n",
    "\n",
    "# Create different classifiers. The logistic regression cannot do\n",
    "# multiclass out of the box.\n",
    "classifiers = {'L1 logistic': LogisticRegression(C=C, penalty='l1'),\n",
    "               'L2 logistic': LogisticRegression(C=C, penalty='l2'),\n",
    "               'Decision Tree': DecisionTreeClassifier(max_depth=5,min_samples_leaf=5,min_samples_split=1,random_state=99),\n",
    "               'K Nearest Neighbors': KNeighborsClassifier(n_neighbors=3),\n",
    "               'Random Forest' : RandomForestClassifier(max_depth=25,min_samples_leaf=2,\n",
    "                                                        min_samples_split=10,n_estimators=100,n_jobs=-1)\n",
    "              }\n",
    "\n",
    "n_classifiers = len(classifiers)\n",
    "\n",
    "plt.figure(figsize=(2 * 2, n_classifiers * 2))\n",
    "plt.subplots_adjust(bottom=.2, top=.95)\n",
    "\n",
    "xx = np.linspace(3, 9, 100)\n",
    "yy = np.linspace(1, 5, 100).T\n",
    "xx, yy = np.meshgrid(xx, yy)\n",
    "Xfull = np.c_[xx.ravel(), yy.ravel()]\n",
    "\n",
    "\n",
    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
    "    classifier.fit(X_r, y)\n",
    "\n",
    "    y_pred = classifier.predict(X_r)\n",
    "    classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100\n",
    "    print(\"classif_rate for %s : %f \" % (name, classif_rate))\n",
    "\n",
    "    # View probabilities=\n",
    "    probas = classifier.predict_proba(Xfull)\n",
    "    n_classes = np.unique(y_pred).size\n",
    "    for k in range(n_classes):\n",
    "        plt.subplot(n_classifiers, n_classes, index * n_classes + k + 1)\n",
    "        plt.title(\"Class %d\" % k)\n",
    "        if k == 0:\n",
    "            plt.ylabel(name)\n",
    "        imshow_handle = plt.imshow(probas[:, k].reshape((100, 100)),\n",
    "                                   extent=(3, 9, 1, 5), origin='lower')\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "        idx = (y_pred == k)\n",
    "        if idx.any():\n",
    "            plt.scatter(X_r[idx, 0], X_r[idx, 1], marker='o', c='k')\n",
    "\n",
    "ax = plt.axes([0.15, 0.04, 0.7, 0.05])\n",
    "plt.title(\"Probability\")\n",
    "plt.colorbar(imshow_handle, cax=ax, orientation='horizontal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive feature elimination with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEcCAYAAADgJkIVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd21d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Create the RFE object and compute a cross-validated score.\n",
    "\n",
    "rf = RandomForestClassifier(max_depth=25,min_samples_leaf=2,min_samples_split=10,n_estimators=100,n_jobs=-1)\n",
    "\n",
    "# The \"accuracy\" scoring is proportional to the number of correct classifications\n",
    "rfecv = RFECV(estimator=rf, step=1, cv=StratifiedKFold(y, 2),\n",
    "              scoring='accuracy')\n",
    "rfecv.fit(X, y)\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)\n",
    "\n",
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}