who-voted_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict which voter-eligible Colorado citizens actually voted in the 2016 US presidential election.\n",
    "\n",
    "This is the Spring 2017, Harvard Statistics 149: Generalized Linear Models prediction contest/course project.\n",
    "\n",
    "The goal of this project is to use the modeling methods you learned in Statistics 149 (and possibly other related methods) to analyze a data set on whether a Colorado voting-eligible citizen ended up actually voting in the 2016 US election. These data were kindly provided by moveon.org. The competition can be found [here](inclass.kaggle.com/c/who-voted) and ended April 30, 2017, at 10pm EDT.\n",
    "\n",
    "## Model training\n",
    "\n",
    "The goal of this notebook is to build the models we will use to predict Colorado voter turnout in the 2016 election.\n",
    "\n",
    "This is the fourth notebook for this competition. See [part 1](who-voted_EDA.ipynb) and [part 2](who-voted_features.ipynb) for initial exploration of the data and [part 3](who-voted_impute.ipynb) for data imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from scipy.stats import randint as sp_randint\n",
    "from scipy import interp\n",
    "from itertools import cycle\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from mlxtend.classifier import StackingCVClassifier\n",
    "\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Local functions](https://github.com/yuwie10/who-voted/blob/master/who_voted_functions.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import who_voted_functions as wv\n",
    "import importlib as imp\n",
    "imp.reload(wv);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data and create X and y arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_imp = wv.load_data('train_imp.csv', compression = 'gzip')\n",
    "train = wv.load_data('train_renamed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>party</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "      <th>dist_ballot</th>\n",
       "      <th>dist_poll</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>U</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>25.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>27.3</td>\n",
       "      <td>420</td>\n",
       "      <td>2.658148</td>\n",
       "      <td>3.456541</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>55</td>\n",
       "      <td>U</td>\n",
       "      <td>Uncoded</td>\n",
       "      <td>7.9</td>\n",
       "      <td>97.8</td>\n",
       "      <td>59.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>36.9</td>\n",
       "      <td>307</td>\n",
       "      <td>2.240415</td>\n",
       "      <td>2.130636</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>U</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>50.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>49.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>31.7</td>\n",
       "      <td>292</td>\n",
       "      <td>2.332767</td>\n",
       "      <td>2.872147</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>D</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>47.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.4</td>\n",
       "      <td>316</td>\n",
       "      <td>2.983480</td>\n",
       "      <td>3.193841</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>R</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>30.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>392</td>\n",
       "      <td>3.160107</td>\n",
       "      <td>3.841248</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender congress_district state_house  age party       race  hs_only  \\\n",
       "0      M                 7          31   36     U   Hispanic     25.4   \n",
       "1      F                 6          38   55     U    Uncoded      7.9   \n",
       "2      F                 2          53   24     U  Caucasian     50.2   \n",
       "3      F                 7          30   25     D  Caucasian     38.0   \n",
       "4      M                 5          19   22     R  Caucasian     30.5   \n",
       "\n",
       "   married  children  cath  evang  non_chrst  other_chrst  days_reg  \\\n",
       "0     63.4      54.0  16.7   16.5       39.6         27.3       420   \n",
       "1     97.8      59.8  16.7   15.5       30.9         36.9       307   \n",
       "2      7.6      49.5  14.6   24.0       29.6         31.7       292   \n",
       "3      8.5      47.4  13.1   22.3       33.3         31.4       316   \n",
       "4     19.1      23.1  16.0   10.5       39.1         34.5       392   \n",
       "\n",
       "   dist_ballot  dist_poll voted  \n",
       "0     2.658148   3.456541     Y  \n",
       "1     2.240415   2.130636     Y  \n",
       "2     2.332767   2.872147     Y  \n",
       "3     2.983480   3.193841     Y  \n",
       "4     3.160107   3.841248     Y  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_columns', 100)\n",
    "train_imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118529,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(118529, 98)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_vector = wv.get_y_vector(train_imp, 'voted')\n",
    "\n",
    "quant, categ = wv.get_cols(train_imp)\n",
    "X_matrix = wv.design_Xmatrix(train_imp, quant, categ)\n",
    "\n",
    "y_vector.shape\n",
    "X_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to decide on an appropriate k for k-fold cross validation. Let's plot the ROC curves for different cv splits to select the best k. Code adapted from [here](http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zms0aPAUo0TBmDO2npHLliu/Z+PBpVOkmFAWynHGc2ZBBxJt26qgCwJZhxz0o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\n4nGML5cKstgR3Jlbq6aSZ38dBvQFuYmWpWuT9flKlp/0QI2eUnVDmDf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lLCsCBQoCsmQJ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Apb26DfEi+Ar/NzAPgkJIJpoZF4K+9maaXwX3Hq1EleeeVFTp48TlJSYnmHIwhCObhTkdUA4H0g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nJJJA5s3rAEDlVvHYp1iz+UYeqcYSPDw8iC08zx2vpgCE+NQur/BfWDKZDIlEQr16Ddi4cQtq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c84eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators = 500)\n",
    "cv = StratifiedKFold(n_splits = 20)\n",
    "mean_tpr = 0.0\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "colors = cycle(['c', 'indigo', 'seagreen', 'darkorange', 'b', 'k', 'r', 'm', \n",
    "               'g', 'w', 'darkred', 'darkblue', 'grey', 'y'])\n",
    "lw = 2\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10, 10);\n",
    "\n",
    "i = 0\n",
    "for (train, test), color in zip(cv.split(X_matrix, y_vector), colors):\n",
    "    probas_ = clf.fit(X_matrix[train], y_vector[train]).predict_proba(X_matrix[test])\n",
    "    # Compute ROC curve and area under the curve\n",
    "    fpr, tpr, thresholds = roc_curve(y_vector[test], probas_[:, 1])\n",
    "    mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "    mean_tpr[0] = 0.0\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    _ = plt.plot(fpr, tpr, lw=lw, color=color,\n",
    "             label='ROC fold %d (area = %0.2f)' % (i, roc_auc));\n",
    "    i += 1\n",
    "    \n",
    "_ = plt.plot([0, 1], [0, 1], linestyle = '--', lw = lw, color = 'k', label = 'Luck');\n",
    "\n",
    "mean_tpr /= cv.get_n_splits(X_matrix, y_vector)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "_ = plt.plot(mean_fpr, mean_tpr, color = 'g', linestyle = '--',\n",
    "             label = 'Mean ROC (area = {%0.2f})'.format(mean_auc), lw = lw);\n",
    "\n",
    "_ = plt.xlim([-0.05, 1.05]);\n",
    "_ = plt.ylim([-0.05, 1.05]);\n",
    "_ = plt.xlabel('False Positive Rate');\n",
    "_ = plt.ylabel('True Positive Rate');\n",
    "_ = plt.title('Receiver operating characteristic example');\n",
    "_ = plt.legend(loc = \"lower right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cv splits are similar, although the best split is cv = 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv = StratifiedKFold(n_splits = 11, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning\n",
    "\n",
    "The performance of random forests is known to increase substantially following hyperparameter tuning. We will test Bayesian optimization of hyperparameter tuning, which seeks to maximize the explore/exploit tension in searching parameter space, provided by [SigOpt](sigopt.com). Code was adapted from [here](https://github.com/sigopt/sigopt-examples/blob/master/random-forest/python/Random Forest.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sigopt import Connection\n",
    "conn = Connection(client_token = 'ZGILQJTPPAWFCOOOZPALZQHRKNBANVQQUEVEJJSYYOUVYNYI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created experiment: https://sigopt.com/experiment/17994\n"
     ]
    }
   ],
   "source": [
    "#create SigOpt experiment\n",
    "experiment = conn.experiments().create(\n",
    "  name=\"Who voted?\",\n",
    "  parameters=[\n",
    "    dict(name=\"max_features\", type=\"int\", bounds=dict(min=1, max=(X_matrix.shape[1]-1))),\n",
    "    dict(name=\"min_samples_leaf\", type=\"int\", bounds=dict(min=40, max=200)),\n",
    "    dict(name='n_estimators', type='int', bounds=dict(min=100, max=500)),\n",
    "    {\n",
    "    'categorical_values':[\n",
    "        {\n",
    "            'enum_index':1,\n",
    "            'name':'gini',\n",
    "            'object':'categorical_value'\n",
    "        },\n",
    "        {\n",
    "            'enum_index':2,\n",
    "            'name':'entropy',\n",
    "            'object':'categorical_value'\n",
    "        }\n",
    "    ],\n",
    "    'default_value':'gini',\n",
    "    'name':'criterion',\n",
    "    'type':'categorical'\n",
    "    },\n",
    "    {\n",
    "    'categorical_values':[\n",
    "        {\n",
    "            'enum_index':1,\n",
    "            'name':'balanced',\n",
    "            'object':'categorical_value'\n",
    "        },\n",
    "        {\n",
    "            'enum_index':2,\n",
    "            'name':'balanced_subsample',\n",
    "            'object':'categorical_value'\n",
    "        }\n",
    "    ],\n",
    "    'default_value':'balanced',\n",
    "    'name':'class_weight',\n",
    "    'type':'categorical'\n",
    "    }\n",
    "  ]\n",
    ")\n",
    "print(\"Created experiment: https://sigopt.com/experiment/\" + experiment.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(assignments, X, y, cv):\n",
    "    '''\n",
    "    \n",
    "    Inputs:\n",
    "    assignments = suggested parameters from SigOpt\n",
    "    X and y matrices\n",
    "    cv = cross-validation scheme\n",
    "    \n",
    "    Returns: mean and std of cv accuracies\n",
    "    '''\n",
    "    classifier = RandomForestClassifier(n_estimators = assignments['n_estimators'],\n",
    "                                        max_features = assignments['max_features'], \n",
    "                                        min_samples_leaf = assignments['min_samples_leaf'],\n",
    "                                        criterion = assignments['criterion'],\n",
    "                                        class_weight = assignments['class_weight'],\n",
    "                                        random_state = 123,\n",
    "                                       )\n",
    "    cv_accuracies = cross_val_score(classifier, X, y, cv = cv)\n",
    "    return (np.mean(cv_accuracies), np.std(cv_accuracies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for _ in range(20):\n",
    "    # Receive a Suggestion from SigOpt\n",
    "    suggestion = conn.experiments(experiment.id).suggestions().create()\n",
    "\n",
    "    # Evaluate the model locally\n",
    "    (value, std) = evaluate_model(suggestion.assignments, X_matrix, y_vector, cv)\n",
    "\n",
    "    # Report an Observation (with standard deviation) back to SigOpt\n",
    "    _ = conn.experiments(experiment.id).observations().create(\n",
    "        suggestion=suggestion.id,\n",
    "        value=value,\n",
    "        value_stddev=std,\n",
    "    );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Assignments({\n",
       "  \"class_weight\": \"balanced_subsample\",\n",
       "  \"criterion\": \"entropy\",\n",
       "  \"max_features\": 102,\n",
       "  \"min_samples_leaf\": 1,\n",
       "  \"n_estimators\": 500\n",
       "})"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-fetch the best observed value and assignments\n",
    "best_assignments = conn.experiments(experiment.id).best_assignments().fetch().data[0].assignments\n",
    "best_assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_1 = RandomForestClassifier(n_estimators = 500,\n",
    "                              #changed to None because number of features changed from initial training\n",
    "                              max_features = None, \n",
    "                              min_samples_leaf = 1,\n",
    "                              class_weight = 'balanced_subsample',\n",
    "                              criterion = 'entropy',\n",
    "                              random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High-cardinal variables\n",
    "\n",
    "As already noted, there are 65 distinct categories for the state house district variable. Combined with the categories from the other nominal variables results in 87 dummy coded variables and a much higher-dimension dataset than we originally started with. This is especially problematic for tree-based models, as the sparseness of the feature space makes it difficult to achieve a good fitting model. \n",
    "\n",
    "To incorporate these predictors without having to worry about this problem, we can transform the variables with more than 5 categories to continuous variables using either supervised ratio (SR) or weight of evidence (WOE) (see [here](http://www.kdnuggets.com/2016/08/include-high-cardinality-attributes-predictive-model.html)). Therefore race, state_house and congress_district are transformed. Although race and congress_district do not have a large number of categories (11 and 6, respectively), because there are only 16 predictors total, coding these two predictors as factors would still mean over half the predictors are dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#total number of those who did or did not vote\n",
    "voted_counts = train_imp['voted'].value_counts()\n",
    "\n",
    "card_cols = ['congress_district', 'state_house', 'race']\n",
    "ratios = []\n",
    "woes = []\n",
    "for i in card_cols:\n",
    "    sr, woe = wv.transform_to_continuous(train_imp, i, voted_counts)\n",
    "    ratios.append(sr)\n",
    "    woes.append(woe)\n",
    "\n",
    "train_ratio = wv.create_card_df(train_imp, card_cols, ratios)\n",
    "train_woe = wv.create_card_df(train_imp, card_cols, woes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check whether SR and WOE result in similar transformations, let's plot the two transformations against one another. Although they are on different scales, the transformations if done properly should result in a 1:1 relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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VYGbuBE6ZYv5F9eR9wNVd5JKkvvON0JKKZQFKKpYFKKlYFqCkYlmAkoplAUoqlgUoqVgW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115343400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afe17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154d2b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in card_cols:\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(5, 5);\n",
    "    _ = plt.plot(train_ratio[col], train_woe[col], 'o');\n",
    "    _ = plt.title(col);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also investigate the distribution of the transformed variables. Values of a cardinal variable associated with an above average likelihood of voting will have a positive WOE transformation, while those with a less than average likelihood of voting will have negative transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11739d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119daf160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OuezX8730b3oYA/4qM3es4nyL3tYbEa8CXg0cAd6ZmZ9ZrdmKc74euKz58bOZ+bbVn3J5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\nqcBoSlLB/wGZleUj7/ESQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0e4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in card_cols:\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(5, 5);\n",
    "    _ = plt.hist(train_woe[col], bins = 20);\n",
    "    _ = plt.title(col);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for all variables, the majority of categories are neither more associated with voting or not voting, although there are a few categories above or below average, especially for state_house.\n",
    "\n",
    "We are now ready to train and test our classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training random forest models\n",
    "\n",
    "Let's first train the random forest whose hyperparameters we tuned using SigOpt and test its performance on the following design matrices (4 total):\n",
    "\n",
    "1. SR vs. WOE transformation of cardinal variables + imputed data\n",
    "2. SR vs. WOE transformation without imputed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quant_card, categ_card = wv.get_cols(train_ratio)\n",
    "quant_card_nodist = quant_card.copy()\n",
    "quant_card_nodist.remove('dist_ballot')\n",
    "quant_card_nodist.remove('dist_poll')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#supervised ratio + imputed data\n",
    "X_ratio_imputed = wv.design_Xmatrix(train_ratio, quant_card, categ_card, standardize = True)\n",
    "\n",
    "#supervised ratio, no imputed data\n",
    "X_ratio = wv.design_Xmatrix(train_ratio, quant_card_nodist, categ_card, standardize = True)\n",
    "\n",
    "#woe + imputed data\n",
    "X_woe_imputed = wv.design_Xmatrix(train_woe, quant_card, categ_card, standardize = True)\n",
    "\n",
    "#woe, no imputed data\n",
    "X_woe = wv.design_Xmatrix(train_woe, quant_card_nodist, categ_card, standardize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.56585201611143254, 0.0043669852965324309)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.56475857501580007, 0.0040639826944698131)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.56716838753077814, 0.0038353064465207077)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.56475814157433202, 0.0040644929405302078)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(-0.56716748168974784, 0.0038373713667522579)"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs = [X_matrix, X_ratio_imputed, X_ratio, X_woe_imputed, X_woe]\n",
    "\n",
    "for X in Xs:\n",
    "    wv.cross_val_LL(rf_1, X, y_vector, cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log-loss scores are relatively similar, although the best scores come from the models trained on the transformed predictors (X_ratio_imputed and X_woe_imputed). The most principled method would be to tune hyperparameters for each design matrix and compare the resulting log-losses, but for time considerations we will use the random forest trained on the imputed and WOE transformed design matrix for our first submission.\n",
    "\n",
    "Let's load the test set and transform the cardinal variables using the WOE method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>party</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "      <th>dist_ballot</th>\n",
       "      <th>dist_poll</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "      <td>L</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>19.5</td>\n",
       "      <td>21.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>45.2</td>\n",
       "      <td>28.4</td>\n",
       "      <td>393</td>\n",
       "      <td>2.521099</td>\n",
       "      <td>2.770852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>39.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>29.1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>41.4</td>\n",
       "      <td>32.2</td>\n",
       "      <td>668</td>\n",
       "      <td>3.143472</td>\n",
       "      <td>3.972788</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>R</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>11.3</td>\n",
       "      <td>62.7</td>\n",
       "      <td>41.3</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>606</td>\n",
       "      <td>2.736109</td>\n",
       "      <td>2.859035</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>R</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>32.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>44.6</td>\n",
       "      <td>30.6</td>\n",
       "      <td>565</td>\n",
       "      <td>2.136312</td>\n",
       "      <td>2.329654</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>26</td>\n",
       "      <td>D</td>\n",
       "      <td>Uncoded</td>\n",
       "      <td>10.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>18.4</td>\n",
       "      <td>43.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>336</td>\n",
       "      <td>2.785245</td>\n",
       "      <td>2.904892</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender congress_district state_house  age party       race  hs_only  \\\n",
       "0      M                 2          52   30     L  Caucasian     19.5   \n",
       "1      F                 5          19   20     U  Caucasian     39.7   \n",
       "2      M                 4          44   56     R  Caucasian     11.3   \n",
       "3      F                 7          34   20     R  Caucasian     32.8   \n",
       "4      F                 6          41   26     D    Uncoded     10.2   \n",
       "\n",
       "   married  children  cath  evang  non_chrst  other_chrst  days_reg  \\\n",
       "0     21.2      25.3   9.8   16.6       45.2         28.4       393   \n",
       "1     20.2      29.1  12.0   14.4       41.4         32.2       668   \n",
       "2     62.7      41.3  14.8   14.7       36.0         34.6       606   \n",
       "3     11.6      33.1  14.5   10.3       44.6         30.6       565   \n",
       "4     14.7      22.4   8.2   18.4       43.5         29.9       336   \n",
       "\n",
       "   dist_ballot  dist_poll  Id  \n",
       "0     2.521099   2.770852   1  \n",
       "1     3.143472   3.972788   2  \n",
       "2     2.736109   2.859035   3  \n",
       "3     2.136312   2.329654   4  \n",
       "4     2.785245   2.904892   5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_imp = wv.load_data('test_imp.csv', compression = 'gzip')\n",
    "test_imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158039, 16)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>party</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "      <th>dist_ballot</th>\n",
       "      <th>dist_poll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>U</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>25.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>27.3</td>\n",
       "      <td>420</td>\n",
       "      <td>2.658148</td>\n",
       "      <td>3.456541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>55</td>\n",
       "      <td>U</td>\n",
       "      <td>Uncoded</td>\n",
       "      <td>7.9</td>\n",
       "      <td>97.8</td>\n",
       "      <td>59.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>36.9</td>\n",
       "      <td>307</td>\n",
       "      <td>2.240415</td>\n",
       "      <td>2.130636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>U</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>50.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>49.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>31.7</td>\n",
       "      <td>292</td>\n",
       "      <td>2.332767</td>\n",
       "      <td>2.872147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>D</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>47.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.4</td>\n",
       "      <td>316</td>\n",
       "      <td>2.983480</td>\n",
       "      <td>3.193841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>R</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>30.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>392</td>\n",
       "      <td>3.160107</td>\n",
       "      <td>3.841248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender congress_district state_house  age party       race  hs_only  \\\n",
       "0      M                 7          31   36     U   Hispanic     25.4   \n",
       "1      F                 6          38   55     U    Uncoded      7.9   \n",
       "2      F                 2          53   24     U  Caucasian     50.2   \n",
       "3      F                 7          30   25     D  Caucasian     38.0   \n",
       "4      M                 5          19   22     R  Caucasian     30.5   \n",
       "\n",
       "   married  children  cath  evang  non_chrst  other_chrst  days_reg  \\\n",
       "0     63.4      54.0  16.7   16.5       39.6         27.3       420   \n",
       "1     97.8      59.8  16.7   15.5       30.9         36.9       307   \n",
       "2      7.6      49.5  14.6   24.0       29.6         31.7       292   \n",
       "3      8.5      47.4  13.1   22.3       33.3         31.4       316   \n",
       "4     19.1      23.1  16.0   10.5       39.1         34.5       392   \n",
       "\n",
       "   dist_ballot  dist_poll  \n",
       "0     2.658148   3.456541  \n",
       "1     2.240415   2.130636  \n",
       "2     2.332767   2.872147  \n",
       "3     2.983480   3.193841  \n",
       "4     3.160107   3.841248  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop 'voted' column from train set and 'Id' column from test set and merge\n",
    "all_data = train_imp.drop('voted', axis = 1).append(test_imp.drop('Id', axis = 1))\n",
    "all_data.shape\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the training set we replaced each distinct value of the high-cardinal variables based on how often an instance was associated with a person who did or did not vote. We will replace the cardinal variables in the test set with those from the training dataset using WOE transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "      <th>dist_ballot</th>\n",
       "      <th>dist_poll</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>gender_U</th>\n",
       "      <th>party_G</th>\n",
       "      <th>party_L</th>\n",
       "      <th>party_O</th>\n",
       "      <th>party_R</th>\n",
       "      <th>party_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>0.072158</td>\n",
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       "      <td>-0.356047</td>\n",
       "      <td>-1.445074</td>\n",
       "      <td>-0.439574</td>\n",
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       "      <th>39506</th>\n",
       "      <td>1.242733</td>\n",
       "      <td>0.782801</td>\n",
       "      <td>-1.001241</td>\n",
       "      <td>0.335624</td>\n",
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       "      <td>-1.184228</td>\n",
       "      <td>0.415447</td>\n",
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       "      <td>1.551485</td>\n",
       "      <td>-1.108965</td>\n",
       "      <td>0.178138</td>\n",
       "      <td>-1.362826</td>\n",
       "      <td>-0.413931</td>\n",
       "      <td>-0.128204</td>\n",
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       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>1.242733</td>\n",
       "      <td>0.868673</td>\n",
       "      <td>0.529862</td>\n",
       "      <td>-2.425627</td>\n",
       "      <td>-1.080555</td>\n",
       "      <td>0.736793</td>\n",
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       "      <td>-0.698427</td>\n",
       "      <td>-1.516573</td>\n",
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       "      <td>-0.623841</td>\n",
       "      <td>-0.687215</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>-2.080612</td>\n",
       "      <td>-0.538028</td>\n",
       "      <td>-1.065037</td>\n",
       "      <td>-2.425627</td>\n",
       "      <td>1.892551</td>\n",
       "      <td>-1.155792</td>\n",
       "      <td>-0.696083</td>\n",
       "      <td>6.673957</td>\n",
       "      <td>-0.788297</td>\n",
       "      <td>-1.898467</td>\n",
       "      <td>-3.854962</td>\n",
       "      <td>-0.037715</td>\n",
       "      <td>-0.594482</td>\n",
       "      <td>-0.759875</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>-2.080612</td>\n",
       "      <td>-0.005997</td>\n",
       "      <td>-1.065037</td>\n",
       "      <td>0.335624</td>\n",
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       "      <td>-0.865111</td>\n",
       "      <td>-0.739767</td>\n",
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       "      <td>-0.245963</td>\n",
       "      <td>0.441505</td>\n",
       "      <td>-0.222501</td>\n",
       "      <td>1.159454</td>\n",
       "      <td>-0.550899</td>\n",
       "      <td>-0.673335</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       congress_district  state_house       age      race   hs_only   married  \\\n",
       "39505           0.072158     0.632641  1.295414  0.335624  0.928300  0.158591   \n",
       "39506           1.242733     0.782801 -1.001241  0.335624  2.783143 -1.184228   \n",
       "39507           1.242733     0.868673  0.529862 -2.425627 -1.080555  0.736793   \n",
       "39508          -2.080612    -0.538028 -1.065037 -2.425627  1.892551 -1.155792   \n",
       "39509          -2.080612    -0.005997 -1.065037  0.335624  1.350160 -0.865111   \n",
       "\n",
       "       children      cath     evang  non_chrst  other_chrst  days_reg  \\\n",
       "39505 -0.283506  0.521830  2.186790  -1.508472    -0.356047 -1.445074   \n",
       "39506  0.415447  0.148974  1.551485  -1.108965     0.178138 -1.362826   \n",
       "39507  1.522122 -0.698427 -1.516573   1.478321    -0.436175 -0.677424   \n",
       "39508 -0.696083  6.673957 -0.788297  -1.898467    -3.854962 -0.037715   \n",
       "39509 -0.739767 -0.376415 -0.245963   0.441505    -0.222501  1.159454   \n",
       "\n",
       "       dist_ballot  dist_poll  gender_M  gender_U  party_G  party_L  party_O  \\\n",
       "39505    -0.439574  -0.278821         0         0        0        0        0   \n",
       "39506    -0.413931  -0.128204         0         0        0        0        0   \n",
       "39507    -0.623841  -0.687215         1         0        0        0        0   \n",
       "39508    -0.594482  -0.759875         0         0        0        0        0   \n",
       "39509    -0.550899  -0.673335         1         0        0        0        0   \n",
       "\n",
       "       party_R  party_U  \n",
       "39505        0        0  \n",
       "39506        1        0  \n",
       "39507        0        0  \n",
       "39508        0        1  \n",
       "39509        0        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_woe = wv.create_card_df(all_data, card_cols, woes)\n",
    "\n",
    "#create a df \n",
    "X_df = wv.design_Xmatrix(all_woe, quant_card, categ_card, standardize = True, matrix = False)\n",
    "X_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118529, 17)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(39510, 17)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imp.shape\n",
    "test_imp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_full = X_df.iloc[:118529, ].as_matrix()\n",
    "X_test_full = X_df.iloc[118529:, ].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Although all submissions were trained and tested on the set where standardization was performed using both the test and train set, in retrospect, the test set should have been standardized *separately* from the training set to assess the full predictive performance of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  voted\n",
       "0   1  0.626\n",
       "1   2  0.558\n",
       "2   3  0.764\n",
       "3   4  0.526\n",
       "4   5  0.864"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_1_fit = rf_1.fit(X_train_full, y_vector)\n",
    "series_id = test_imp['Id']\n",
    "to_submit = wv.for_submission(rf_1_fit, X_test_full, series_id, 1)\n",
    "to_submit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This first submission resulted in a log-loss score of 0.56252 on the public leaderboard and a score of 0.56227 on the private leaderboard. For brevity and clarity submissions that did not result in improved log-loss scores relative to previous submissions will be omitted from this notebook, as they mainly involved tuning of hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training logistic regression\n",
    "\n",
    "For binary response predictions, logistic regression also often performs well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#re-define cv splits with shuffle = True\n",
    "kfold = StratifiedKFold(n_splits = 11, shuffle = True, random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=11, random_state=7, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=7, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "{'C': 0.1}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "clf = GridSearchCV(LogisticRegression(random_state = 7), \n",
    "                   param_grid, scoring = 'neg_log_loss', cv = kfold)\n",
    "clf.fit(X_woe_imputed, y_vector)\n",
    "\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.56823770024757259, 0.0035349868155999968)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit = LogisticRegression(random_state = 7, C = 0.1)\n",
    "wv.cross_val_LL(logit, X_woe_imputed, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of logistic regression can suffer more from inclusion of extraneous or highly correlated variables. Let's examine the correlation matrix again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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smZlVSktr9oEm7gB2Ba6StAVpjm8AIuLYjteSTgJm9Ce5ghOsmZlVTf4Eew2wnaQ7gRbg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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120ccb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(7, 6);\n",
    "sns.heatmap(train_woe.corr());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test whether removing certain correlated predictors improves performance of logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_remove = ['congress_district', 'age', 'dist_poll', 'non_chrst', 'other_chrst']\n",
    "train_no_corr = train_woe.drop(cols_remove, axis = 1)\n",
    "\n",
    "quant_corr, categ_corr = wv.get_cols(train_no_corr)\n",
    "X_no_corr = wv.design_Xmatrix(train_no_corr, quant_corr, categ_corr,\n",
    "                             standardize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.56864956169628467, 0.00354391352181419)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.cross_val_LL(logit, X_no_corr, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log-loss score when the 5 most correlated features are eliminated is higher than when all the features are included. Although we could potentially get a lower log-loss by testing different combinations of predictors, it does not seem like logistic regression on this dataset is likely to perform better than random forest. This is probably due to the non-linear relationships we saw in the pairplot in [part 1](who_voted_EDA.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient boosted trees\n",
    "\n",
    "Although random forests are generally good out-of-the-box classifiers, it is known that gradient boosted trees often outperform random forests. As opposed to random forests, which grow full decision trees trained on bootstrapped (and ideally highly uncorrelated) subsamples of the data, gradient boosted trees are an ensemble of weak learners, each of which is trained on the errors of the previous tree. Hyperparameter tuning is particularly important for these models. We start by tuning the maximum depth of the individual trees, total number of trees, the learning rate and gamma (both of which control model complexity and therefore can help prevent overfitting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created experiment: https://sigopt.com/experiment/18408\n"
     ]
    }
   ],
   "source": [
    "#create SigOpt experiment\n",
    "experiment = conn.experiments().create(\n",
    "  name='Who voted?',\n",
    "  parameters=[\n",
    "    dict(name='max_depth', type='int', bounds=dict(min=1, max=9)),\n",
    "    dict(name='n_estimators', type='int', bounds=dict(min=50, max=500)),\n",
    "    dict(name='learning_rate', type='double', bounds=dict(min=0.001, max=0.3)),\n",
    "    dict(name='gamma', type='double', bounds=dict(min=0, max=10))\n",
    "  ]\n",
    ")\n",
    "print(\"Created experiment: https://sigopt.com/experiment/\" + experiment.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(assignments, X, y, cv):\n",
    "    '''\n",
    "    Evaluates different Random Forest models with different hyperparameters.\n",
    "    \n",
    "    Inputs:\n",
    "    assignments = suggested parameters from SigOpt\n",
    "    X and y matrices\n",
    "    cv = cross-validation scheme\n",
    "    \n",
    "    Returns: mean and std of cv log loss\n",
    "    '''\n",
    "    classifier = XGBClassifier(max_depth = assignments['max_depth'],\n",
    "                               n_estimators = assignments['n_estimators'],\n",
    "                               learning_rate = assignments['learning_rate'],\n",
    "                               gamma = assignments['gamma'],\n",
    "                               seed = 7)\n",
    "    cv_LL = cross_val_score(classifier, X, y, cv = kfold, scoring = 'neg_log_loss')\n",
    "    return (np.mean(cv_LL), np.std(cv_LL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Assignments({\n",
       "  \"gamma\": 3.3285290509838568,\n",
       "  \"learning_rate\": 0.08654237617740236,\n",
       "  \"max_depth\": 7,\n",
       "  \"n_estimators\": 314\n",
       "})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for _ in range(50):\n",
    "    # Receive a Suggestion from SigOpt\n",
    "    suggestion = conn.experiments(experiment.id).suggestions().create()\n",
    "\n",
    "    # Evaluate the model locally\n",
    "    (value, std) = evaluate_model(suggestion.assignments, X_woe_imputed, y_vector, kfold)\n",
    "\n",
    "    # Report an Observation (with standard deviation) back to SigOpt\n",
    "    _ = conn.experiments(experiment.id).observations().create(\n",
    "        suggestion=suggestion.id,\n",
    "        value=value,\n",
    "        value_stddev=std,\n",
    "    );\n",
    "\n",
    "# Re-fetch the best observed value and assignments\n",
    "best_assignments = conn.experiments(experiment.id).best_assignments().fetch().data[0].assignments\n",
    "best_assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BestAssignments({\n",
       "  \"assignments\": {\n",
       "    \"gamma\": 3.3285290509838568,\n",
       "    \"learning_rate\": 0.08654237617740236,\n",
       "    \"max_depth\": 7,\n",
       "    \"n_estimators\": 314\n",
       "  },\n",
       "  \"metadata\": null,\n",
       "  \"value\": -0.5566347742985939,\n",
       "  \"value_stddev\": 0.0034365479004968536,\n",
       "  \"values\": [\n",
       "    {\n",
       "      \"name\": null,\n",
       "      \"object\": \"metric_evaluation\",\n",
       "      \"value\": -0.5566347742985939,\n",
       "      \"value_stddev\": 0.0034365479004968536\n",
       "    }\n",
       "  ]\n",
       "})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.experiments(experiment.id).best_assignments().fetch().data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_1 = XGBClassifier(max_depth = 7,\n",
    "                      n_estimators = 314,\n",
    "                      learning_rate = 0.08654237617740236,\n",
    "                      gamma = 3.3285290509838568,\n",
    "                      seed = 7)\n",
    "\n",
    "xgb_1_fit = xgb_1.fit(X_train_full, y_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.829266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.853076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.849594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.445434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.505540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.829266\n",
       "39506  39507  0.853076\n",
       "39507  39508  0.849594\n",
       "39508  39509  0.445434\n",
       "39509  39510  0.505540"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_submit3 = wv.for_submission(xgb_1_fit, X_test_full, series_id, 3)\n",
    "to_submit3.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This submission resulted in an improved public log-loss of 0.55157 and a private one of 0.55402.\n",
    "\n",
    "Let's add in an L2 regularization term to further reduce variance in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created experiment: https://sigopt.com/experiment/18468\n"
     ]
    }
   ],
   "source": [
    "#create SigOpt experiment\n",
    "experiment = conn.experiments().create(\n",
    "  name='Who voted?',\n",
    "  parameters=[\n",
    "    dict(name='max_depth', type='int', bounds=dict(min=1, max=9)),\n",
    "    dict(name='n_estimators', type='int', bounds=dict(min=50, max=500)),\n",
    "    dict(name='learning_rate', type='double', bounds=dict(min=0.001, max=0.3)),\n",
    "    dict(name='gamma', type='double', bounds=dict(min=0, max=10)),\n",
    "    dict(name='reg_lambda', type='double', bounds=dict(min=1, max=10))\n",
    "  ]\n",
    ")\n",
    "print(\"Created experiment: https://sigopt.com/experiment/\" + experiment.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(assignments, X, y, cv):\n",
    "    '''\n",
    "    Evaluates different Random Forest models with different hyperparameters.\n",
    "    \n",
    "    Inputs:\n",
    "    assignments = suggested parameters from SigOpt\n",
    "    X and y matrices\n",
    "    cv = cross-validation scheme\n",
    "    \n",
    "    Returns: mean and std of cv log loss\n",
    "    '''\n",
    "    classifier = XGBClassifier(max_depth = assignments['max_depth'],\n",
    "                               n_estimators = assignments['n_estimators'],\n",
    "                               learning_rate = assignments['learning_rate'],\n",
    "                               gamma = assignments['gamma'],\n",
    "                               reg_lambda = assignments['reg_lambda'],\n",
    "                               seed = 7)\n",
    "    cv_LL = cross_val_score(classifier, X, y, cv = kfold, scoring = 'neg_log_loss')\n",
    "    return (np.mean(cv_LL), np.std(cv_LL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for _ in range(60):\n",
    "    # Receive a Suggestion from SigOpt\n",
    "    suggestion = conn.experiments(experiment.id).suggestions().create()\n",
    "\n",
    "    # Evaluate the model locally\n",
    "    (value, std) = evaluate_model(suggestion.assignments, X_woe_imputed, y_vector, kfold)\n",
    "\n",
    "    # Report an Observation (with standard deviation) back to SigOpt\n",
    "    _ = conn.experiments(experiment.id).observations().create(\n",
    "        suggestion=suggestion.id,\n",
    "        value=value,\n",
    "        value_stddev=std,\n",
    "    );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BestAssignments({\n",
       "  \"assignments\": {\n",
       "    \"gamma\": 2.8869657909958493,\n",
       "    \"learning_rate\": 0.03311755173249714,\n",
       "    \"max_depth\": 9,\n",
       "    \"n_estimators\": 358,\n",
       "    \"reg_lambda\": 9.925559973077503\n",
       "  },\n",
       "  \"metadata\": null,\n",
       "  \"value\": -0.5555487877236724,\n",
       "  \"value_stddev\": 0.0034731854503106843,\n",
       "  \"values\": [\n",
       "    {\n",
       "      \"name\": null,\n",
       "      \"object\": \"metric_evaluation\",\n",
       "      \"value\": -0.5555487877236724,\n",
       "      \"value_stddev\": 0.0034731854503106843\n",
       "    }\n",
       "  ]\n",
       "})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Assignments({\n",
       "  \"gamma\": 2.8869657909958493,\n",
       "  \"learning_rate\": 0.03311755173249714,\n",
       "  \"max_depth\": 9,\n",
       "  \"n_estimators\": 358,\n",
       "  \"reg_lambda\": 9.925559973077503\n",
       "})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-fetch the best observed value and assignments\n",
    "conn.experiments(experiment.id).best_assignments().fetch().data[0]\n",
    "best_assignments2 = conn.experiments(experiment.id).best_assignments().fetch().data[0].assignments\n",
    "best_assignments2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_2 = XGBClassifier(max_depth = 9, \n",
    "                      n_estimators = 358,\n",
    "                      learning_rate = 0.03311755173249714,\n",
    "                      gamma = 2.8869657909958493,\n",
    "                      reg_lambda = 9.925559973077503,\n",
    "                      seed = 7)\n",
    "\n",
    "xgb_2_fit = xgb_2.fit(X_train_full, y_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.837693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.858705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.835815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.439149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.507000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.837693\n",
       "39506  39507  0.858705\n",
       "39507  39508  0.835815\n",
       "39508  39509  0.439149\n",
       "39509  39510  0.507000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_submit4 = wv.for_submission(xgb_2_fit, X_test_full, series_id, 4)\n",
    "to_submit4.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding L2 regularization decreased the public log-loss score to 0.55104 and the private score to 0.55302.\n",
    "\n",
    "Let's tune min_child_weight (number of weights required for a split) and add max_delta_step, which helps fit imbalanced classes, and subsample/colsample_bytree to control the fraction of samples to be sampled for each individual tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created experiment: https://sigopt.com/experiment/18564\n"
     ]
    }
   ],
   "source": [
    "#create SigOpt experiment\n",
    "experiment = conn.experiments().create(\n",
    "  name='Who voted?',\n",
    "  parameters=[\n",
    "    dict(name='max_depth', type='int', bounds=dict(min=3, max=10)),\n",
    "    dict(name='min_child_weight', type='int', bounds=dict(min=1, max=6)),\n",
    "    dict(name='learning_rate', type='double', bounds=dict(min=0.01, max=0.1)),\n",
    "    dict(name='gamma', type='double', bounds=dict(min=1, max=5)),\n",
    "    dict(name='reg_lambda', type='double', bounds=dict(min=3, max=10))\n",
    "  ]\n",
    ")\n",
    "print(\"Created experiment: https://sigopt.com/experiment/\" + experiment.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(assignments, X, y, cv):\n",
    "    '''\n",
    "    Evaluates different Random Forest models with different hyperparameters.\n",
    "    \n",
    "    Inputs:\n",
    "    assignments = suggested parameters from SigOpt\n",
    "    X and y matrices\n",
    "    cv = cross-validation scheme\n",
    "    \n",
    "    Returns: mean and std of cv log loss\n",
    "    '''\n",
    "    classifier = XGBClassifier(max_depth = assignments['max_depth'],\n",
    "                               n_estimators = 400,\n",
    "                               learning_rate = assignments['learning_rate'],\n",
    "                               min_child_weight = assignments['min_child_weight'],\n",
    "                               gamma = assignments['gamma'],\n",
    "                               max_delta_step = 1,\n",
    "                               subsample = 0.8,\n",
    "                               colsample_bytree = 0.8,\n",
    "                               reg_lambda = assignments['reg_lambda'],\n",
    "                               seed = 7)\n",
    "    cv_LL = cross_val_score(classifier, X, y, cv = kfold, scoring = 'neg_log_loss')\n",
    "    return (np.mean(cv_LL), np.std(cv_LL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BestAssignments({\n",
       "  \"assignments\": {\n",
       "    \"gamma\": 1.0,\n",
       "    \"learning_rate\": 0.04753131124690685,\n",
       "    \"max_depth\": 7,\n",
       "    \"min_child_weight\": 1,\n",
       "    \"reg_lambda\": 10.0\n",
       "  },\n",
       "  \"metadata\": null,\n",
       "  \"value\": -0.5545851729767545,\n",
       "  \"value_stddev\": 0.003877853689564293,\n",
       "  \"values\": [\n",
       "    {\n",
       "      \"name\": null,\n",
       "      \"object\": \"metric_evaluation\",\n",
       "      \"value\": -0.5545851729767545,\n",
       "      \"value_stddev\": 0.003877853689564293\n",
       "    }\n",
       "  ]\n",
       "})"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for _ in range(60):\n",
    "    # Receive a Suggestion from SigOpt\n",
    "    suggestion = conn.experiments(experiment.id).suggestions().create()\n",
    "\n",
    "    # Evaluate the model locally\n",
    "    (value, std) = evaluate_model(suggestion.assignments, X_woe_imputed, y_vector, kfold)\n",
    "\n",
    "    # Report an Observation (with standard deviation) back to SigOpt\n",
    "    _ = conn.experiments(experiment.id).observations().create(\n",
    "        suggestion=suggestion.id,\n",
    "        value=value,\n",
    "        value_stddev=std,\n",
    "    );\n",
    "\n",
    "# Re-fetch the best observed value and assignments\n",
    "conn.experiments(experiment.id).best_assignments().fetch().data[0]\n",
    "best_assignments3 = conn.experiments(experiment.id).best_assignments().fetch().data[0].assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_3 = XGBClassifier(max_depth = 7,\n",
    "                      n_estimators = 400,\n",
    "                      learning_rate = 0.04753131124690685,\n",
    "                      min_child_weight = 1,\n",
    "                      gamma = 1,\n",
    "                      max_delta_step = 1,\n",
    "                      subsample = 0.8,\n",
    "                      colsample_bytree = 0.8,\n",
    "                      reg_lambda = 10,\n",
    "                      seed = 7)\n",
    "\n",
    "xgb_3_fit = xgb_3.fit(X_train_full, y_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.836096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.877321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.865151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.468858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.494811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.836096\n",
       "39506  39507  0.877321\n",
       "39507  39508  0.865151\n",
       "39508  39509  0.468858\n",
       "39509  39510  0.494811"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_submit5 = for_submission(xgb_3_fit, X_test_full, series_id, 5)\n",
    "to_submit5.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Including more hyperparameters to control overfitting in this model resulted in a public log-loss of 0.55038 and a private log-loss of 0.5526."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing classifiers\n",
    "\n",
    "As the first random forest model was trained on the original dataset prior to cardinal variable transformation, let's tune and train a second random forest on the X_woe_imputed data. We can then compare the behavior of all four classifiers.\n",
    "\n",
    "SigOpt limits the number of experiments that can be run, so we can use GridSearch from sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=11, random_state=7, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=500, n_jobs=1, oob_score=False, random_state=123,\n",
       "            verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'min_samples_leaf': [1, 5, 10, 50, 100, 200, 500], 'max_features': [1, 'auto', 'log2', None]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "{'max_features': 'auto', 'min_samples_leaf': 10}"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([-0.56197653, -0.56278573, -0.56600765, -0.57653708, -0.58268952,\n",
       "       -0.59041719, -0.59845059, -0.56035626, -0.55749748, -0.5572556 ,\n",
       "       -0.55996088, -0.56239156, -0.56519416, -0.56975085, -0.56035626,\n",
       "       -0.55749748, -0.5572556 , -0.55996088, -0.56239156, -0.56519416,\n",
       "       -0.56975085, -0.56877893, -0.56256707, -0.56048994, -0.55947255,\n",
       "       -0.5607711 , -0.5630915 , -0.56758455])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.00359379,  0.00285189,  0.00263128,  0.00215329,  0.00162593,\n",
       "        0.00149089,  0.00096314,  0.00393833,  0.00368591,  0.00353362,\n",
       "        0.00324806,  0.00306728,  0.00297814,  0.00270851,  0.00393833,\n",
       "        0.00368591,  0.00353362,  0.00324806,  0.00306728,  0.00297814,\n",
       "        0.00270851,  0.00456538,  0.00419051,  0.00395729,  0.00376091,\n",
       "        0.0035548 ,  0.00341228,  0.00317142])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid_rf = {'min_samples_leaf': [1, 5, 10, 50, 100, 200, 500],\n",
    "                'max_features': [1, 'auto', 'log2', None]}\n",
    "gs = GridSearchCV(RandomForestClassifier(n_estimators = 500, random_state = 7), \n",
    "                  param_grid_rf, scoring = 'neg_log_loss', cv = kfold)\n",
    "\n",
    "gs.fit(X_woe_imputed, y_vector)\n",
    "\n",
    "gs.best_params_\n",
    "gs.cv_results_['mean_test_score']\n",
    "gs.cv_results_['std_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_2 = RandomForestClassifier(n_estimators = 500, \n",
    "                              max_features = 'auto', \n",
    "                              min_samples_leaf = 10, \n",
    "                              random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the distribution of each model's predicted probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#split into train/test\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_woe_imputed, y_vector, stratify = y_vector)\n",
    "\n",
    "models = [logit, rf_1, rf_2, xgb_3]\n",
    "predictions = []\n",
    "for model in models:\n",
    "    pred = model.fit(x_train, y_train).predict_proba(x_test)\n",
    "    predictions.append(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>logit</th>\n",
       "      <th>obs</th>\n",
       "      <th>rf_1</th>\n",
       "      <th>rf_2</th>\n",
       "      <th>xgb_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.647253</td>\n",
       "      <td>1</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.688660</td>\n",
       "      <td>0.640573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.789965</td>\n",
       "      <td>0</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.748904</td>\n",
       "      <td>0.812762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810083</td>\n",
       "      <td>1</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.839133</td>\n",
       "      <td>0.875785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.473929</td>\n",
       "      <td>1</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.571596</td>\n",
       "      <td>0.613146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.615095</td>\n",
       "      <td>1</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.600761</td>\n",
       "      <td>0.566924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      logit  obs   rf_1      rf_2     xgb_3\n",
       "0  0.647253    1  0.690  0.688660  0.640573\n",
       "1  0.789965    0  0.786  0.748904  0.812762\n",
       "2  0.810083    1  0.714  0.839133  0.875785\n",
       "3  0.473929    1  0.622  0.571596  0.613146\n",
       "4  0.615095    1  0.628  0.600761  0.566924"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predict = pd.DataFrame({'obs':y_test.ravel(), #include observed values for comparison \n",
    "                           'logit':predictions[0][:, 1], \n",
    "                           'rf_1':predictions[1][:, 1],\n",
    "                           'rf_2':predictions[2][:, 1],\n",
    "                           'xgb_3':predictions[3][:, 1]\n",
    "                          })\n",
    "df_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MJ/lnSV6UZCHJu5KcS3JPkvNJvprkzlrrYinlg0neuvz4e2qtD2+8bABgpxu0E/KWJGO1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\n/P/7wtr0OuwKvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161515c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['blue', 'gray', 'green', 'red']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(9, 9);\n",
    "for col, color in zip(df_predict.drop('obs', axis = 1).columns, colors):\n",
    "    _ = sns.distplot(df_predict[col], kde = False, color = color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the predictions of all four models are similar overall, there are probability ranges where the models perform differently. For example, the first random forest (gray) makes the most extreme predictions. \n",
    "\n",
    "## Ensemble voting \n",
    "\n",
    "To overcome the weaknesses of any individual model, it is common to combine multiple models to obtain final predictions. In ensemble models, the predictions of individually calibrated models can be averaged to achieve final predictions that can give better predictions than any individual classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eclf = VotingClassifier(estimators = [\n",
    "    ('lr', logit), ('rf', rf_1), ('xgb', xgb_3)],\n",
    "    voting = 'soft', weights = [1, 1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the gradient boosted tree classifier performed the best individually, let's weight this model more heavily than the other two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55448830852481124, 0.0036930911647835465)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.cross_val_LL(eclf, X_woe_imputed, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.845237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.846536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.813295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.477712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.491258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.845237\n",
       "39506  39507  0.846536\n",
       "39507  39508  0.813295\n",
       "39508  39509  0.477712\n",
       "39509  39510  0.491258"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf_fit = eclf.fit(X_train_full, y_vector)\n",
    "to_submit6 = wv.for_submission(eclf_fit, X_test_full, series_id, 6)\n",
    "to_submit6.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The public and private leaderboard scores from this submission were 0.55036 and 0.55183, respectively.\n",
    "\n",
    "Viewing the prediction distributions in the distplot above, we can see the predictions given by the second random forest (green) are most different from the XGBoost model. Therefore let's train an ensemble model with all four models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eclf2 = VotingClassifier(estimators = [\n",
    "    ('lr', logit), ('rf', rf_1), ('rf', rf_2), ('xgb', xgb_3)],\n",
    "    voting = 'soft', weights = [1, 1, 1, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid search (not shown) suggests giving the XGBoost model a weight of 3 results in the lowest log-loss score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55421127565951434, 0.0037041608773769547)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.cross_val_LL(eclf2, X_woe_imputed, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.840061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.850196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.824467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.475766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.490968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.840061\n",
       "39506  39507  0.850196\n",
       "39507  39508  0.824467\n",
       "39508  39509  0.475766\n",
       "39509  39510  0.490968"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf2_fit = eclf2.fit(X_train_full, y_vector)\n",
    "to_submit7 = wv.for_submission(eclf2_fit, X_test_full, series_id, 7)\n",
    "to_submit7.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This classifier resulted in log-loss scores of 0.55004 (public) and 0.55158 (private)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking models\n",
    "\n",
    "As opposed to ensemble methods where the final prediction is a weighted average or vote, stacking involves taking the probability predictions from individually tuned models and using these probabilities as a training set for a meta-classifier to produce final predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5545367030834748, 0.0038581139531508172)"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_clf = XGBClassifier()\n",
    "sclf = StackingCVClassifier(classifiers = [logit, rf_1, rf_2, xgb_3],\n",
    "                            use_probas = True,\n",
    "                            meta_classifier = meta_clf,\n",
    "                            random_state = 42)\n",
    "\n",
    "wv.cross_val_LL(sclf, X_woe_imputed, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39505</th>\n",
       "      <td>39506</td>\n",
       "      <td>0.845467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39506</th>\n",
       "      <td>39507</td>\n",
       "      <td>0.841269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39507</th>\n",
       "      <td>39508</td>\n",
       "      <td>0.836503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39508</th>\n",
       "      <td>39509</td>\n",
       "      <td>0.485742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39509</th>\n",
       "      <td>39510</td>\n",
       "      <td>0.495457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id     voted\n",
       "39505  39506  0.845467\n",
       "39506  39507  0.841269\n",
       "39507  39508  0.836503\n",
       "39508  39509  0.485742\n",
       "39509  39510  0.495457"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclf_fit = sclf.fit(X_train_full, y_vector)\n",
    "to_submit10 = wv.for_submission(sclf, X_test_full, series_id, 10)\n",
    "to_submit10.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This submission resulted in public and private log-loss scores of 0.54996 and 0.55156, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importances\n",
    "\n",
    "Although it is difficult to investigate feature importances of the entire ensemble or stacked model, we can examine the feature importance scores of the individual models. Let's first look at the tree-based models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train the second random forest\n",
    "rf_2_fit = rf_2.fit(X_train_full, y_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitted = [rf_1_fit, rf_2_fit, xgb_3_fit]\n",
    "names = ['rf1', 'rf2', 'xgb3']\n",
    "list_importances = []\n",
    "for fit, name in zip(fitted, names):\n",
    "    feat = wv.find_important_features(train_imp, fit, \n",
    "                                      categ_card, quant_card, \n",
    "                                      col2 = name)\n",
    "    list_importances.append(feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>rf1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>state_house</td>\n",
       "      <td>0.049366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.018311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.017131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dist_ballot</td>\n",
       "      <td>0.007234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dist_poll</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       features       rf1\n",
       "2   state_house  0.049366\n",
       "0        gender  0.018311\n",
       "3           age  0.017131\n",
       "14  dist_ballot  0.007234\n",
       "15    dist_poll  0.000115"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>rf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>children</td>\n",
       "      <td>0.057868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.029696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dist_ballot</td>\n",
       "      <td>0.015317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.013257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dist_poll</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       features       rf2\n",
       "8      children  0.057868\n",
       "0        gender  0.029696\n",
       "14  dist_ballot  0.015317\n",
       "3           age  0.013257\n",
       "15    dist_poll  0.000004"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>xgb3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>state_house</td>\n",
       "      <td>0.053414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.020218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.020153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dist_ballot</td>\n",
       "      <td>0.006881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dist_poll</td>\n",
       "      <td>0.000163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       features      xgb3\n",
       "2   state_house  0.053414\n",
       "3           age  0.020218\n",
       "0        gender  0.020153\n",
       "14  dist_ballot  0.006881\n",
       "15    dist_poll  0.000163"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in list_importances:\n",
    "    i.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in all three models the dist variables are ranked very low. Let's try training the stacked classifier without the presence of these two variables.\n",
    "\n",
    "### Removing distance columns (imputed data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55465009977429791, 0.0037332539486871459)"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.cross_val_LL(sclf, X_woe, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log-loss is lower compared to the classifier trained on the matrix containing the imputed data. Let's also see if using fewer individual models for the stacking classifier results in better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55479833372607268, 0.0037889816080180008)"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclf2 = StackingCVClassifier(classifiers = [logit, rf_2, xgb_3],\n",
    "                            use_probas = True,\n",
    "                            meta_classifier = meta_clf,\n",
    "                            random_state = 42)\n",
    "\n",
    "wv.cross_val_LL(sclf2, X_woe, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55522346867976935, 0.0036879204991137485)"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclf3 = StackingCVClassifier(classifiers = [logit, xgb_3],\n",
    "                            use_probas = True,\n",
    "                            meta_classifier = meta_clf,\n",
    "                            random_state = 42)\n",
    "\n",
    "wv.cross_val_LL(sclf3, X_woe, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55468384387783121, 0.0036940756573150446)"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_clf = XGBClassifier()\n",
    "sclf4 = StackingCVClassifier(classifiers = [logit, rf_1, xgb_3],\n",
    "                            use_probas = True,\n",
    "                            meta_classifier = meta_clf,\n",
    "                            random_state = 42)\n",
    "\n",
    "wv.cross_val_LL(sclf4, X_woe, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using 4 individual classifiers still leads to the best performance as evaluated by cross-validation.\n",
    "\n",
    "Let's create the new test matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_nodist = X_df.iloc[118529:, ].drop(['dist_poll', 'dist_ballot'], axis = 1).as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.643778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.480491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.706415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.622526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.807769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id     voted\n",
       "0   1  0.643778\n",
       "1   2  0.480491\n",
       "2   3  0.706415\n",
       "3   4  0.622526\n",
       "4   5  0.807769"
      ]
     },
     "execution_count": 575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclf_fit_nodist = sclf.fit(X_woe, y_vector)\n",
    "submit_12 = wv.for_submission(sclf_fit_nodist, X_test_nodist, series_id, 12)\n",
    "submit_12.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This submission resulted in public and private log-loss scores of 0.54943 and 0.55155, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming all categorical variables\n",
    "\n",
    "Originally party and gender were one-hot-encoded rather than transformed using WOE. However, party still has a larger number of categories relative to the total number of features. Let's test whether transforming all the categorical variables (and dropping the distance variables) results in better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>party</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "      <th>dist_ballot</th>\n",
       "      <th>dist_poll</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.165044</td>\n",
       "      <td>0.044297</td>\n",
       "      <td>-0.075085</td>\n",
       "      <td>36</td>\n",
       "      <td>-0.381198</td>\n",
       "      <td>-0.434060</td>\n",
       "      <td>25.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>27.3</td>\n",
       "      <td>420</td>\n",
       "      <td>2.658148</td>\n",
       "      <td>3.456541</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.178863</td>\n",
       "      <td>0.056369</td>\n",
       "      <td>0.378928</td>\n",
       "      <td>55</td>\n",
       "      <td>-0.381198</td>\n",
       "      <td>0.080037</td>\n",
       "      <td>7.9</td>\n",
       "      <td>97.8</td>\n",
       "      <td>59.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>36.9</td>\n",
       "      <td>307</td>\n",
       "      <td>2.240415</td>\n",
       "      <td>2.130636</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.178863</td>\n",
       "      <td>0.285996</td>\n",
       "      <td>0.375024</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.381198</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>50.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>49.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>31.7</td>\n",
       "      <td>292</td>\n",
       "      <td>2.332767</td>\n",
       "      <td>2.872147</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.178863</td>\n",
       "      <td>0.044297</td>\n",
       "      <td>-0.167379</td>\n",
       "      <td>25</td>\n",
       "      <td>0.396916</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>47.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.4</td>\n",
       "      <td>316</td>\n",
       "      <td>2.983480</td>\n",
       "      <td>3.193841</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.165044</td>\n",
       "      <td>-0.172422</td>\n",
       "      <td>0.401082</td>\n",
       "      <td>22</td>\n",
       "      <td>0.339505</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>30.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>392</td>\n",
       "      <td>3.160107</td>\n",
       "      <td>3.841248</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender  congress_district  state_house  age     party      race  hs_only  \\\n",
       "0 -0.165044           0.044297    -0.075085   36 -0.381198 -0.434060     25.4   \n",
       "1  0.178863           0.056369     0.378928   55 -0.381198  0.080037      7.9   \n",
       "2  0.178863           0.285996     0.375024   24 -0.381198  0.066171     50.2   \n",
       "3  0.178863           0.044297    -0.167379   25  0.396916  0.066171     38.0   \n",
       "4 -0.165044          -0.172422     0.401082   22  0.339505  0.066171     30.5   \n",
       "\n",
       "   married  children  cath  evang  non_chrst  other_chrst  days_reg  \\\n",
       "0     63.4      54.0  16.7   16.5       39.6         27.3       420   \n",
       "1     97.8      59.8  16.7   15.5       30.9         36.9       307   \n",
       "2      7.6      49.5  14.6   24.0       29.6         31.7       292   \n",
       "3      8.5      47.4  13.1   22.3       33.3         31.4       316   \n",
       "4     19.1      23.1  16.0   10.5       39.1         34.5       392   \n",
       "\n",
       "   dist_ballot  dist_poll voted  \n",
       "0     2.658148   3.456541     Y  \n",
       "1     2.240415   2.130636     Y  \n",
       "2     2.332767   2.872147     Y  \n",
       "3     2.983480   3.193841     Y  \n",
       "4     3.160107   3.841248     Y  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_woes = []\n",
    "#transform all categorical variables\n",
    "for i in categ:\n",
    "    _, woe = wv.transform_to_continuous(train_imp, i, voted_counts)\n",
    "    all_woes.append(woe)\n",
    "\n",
    "train_all_woe = wv.create_card_df(train_imp, categ, all_woes)\n",
    "train_all_woe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>congress_district</th>\n",
       "      <th>state_house</th>\n",
       "      <th>age</th>\n",
       "      <th>party</th>\n",
       "      <th>race</th>\n",
       "      <th>hs_only</th>\n",
       "      <th>married</th>\n",
       "      <th>children</th>\n",
       "      <th>cath</th>\n",
       "      <th>evang</th>\n",
       "      <th>non_chrst</th>\n",
       "      <th>other_chrst</th>\n",
       "      <th>days_reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.988270</td>\n",
       "      <td>0.160564</td>\n",
       "      <td>-0.205271</td>\n",
       "      <td>0.021008</td>\n",
       "      <td>-1.085858</td>\n",
       "      <td>-2.428058</td>\n",
       "      <td>0.163349</td>\n",
       "      <td>0.598340</td>\n",
       "      <td>1.006435</td>\n",
       "      <td>0.743003</td>\n",
       "      <td>0.109214</td>\n",
       "      <td>-0.109643</td>\n",
       "      <td>-1.023448</td>\n",
       "      <td>-0.359789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.006917</td>\n",
       "      <td>0.214521</td>\n",
       "      <td>0.792340</td>\n",
       "      <td>1.233787</td>\n",
       "      <td>-1.085858</td>\n",
       "      <td>0.414434</td>\n",
       "      <td>-1.007899</td>\n",
       "      <td>1.684611</td>\n",
       "      <td>1.287718</td>\n",
       "      <td>0.743003</td>\n",
       "      <td>-0.045930</td>\n",
       "      <td>-0.937032</td>\n",
       "      <td>1.540124</td>\n",
       "      <td>-1.392784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.006917</td>\n",
       "      <td>1.240874</td>\n",
       "      <td>0.783762</td>\n",
       "      <td>-0.744957</td>\n",
       "      <td>-1.085858</td>\n",
       "      <td>0.337771</td>\n",
       "      <td>1.823175</td>\n",
       "      <td>-1.163692</td>\n",
       "      <td>0.788199</td>\n",
       "      <td>0.386895</td>\n",
       "      <td>1.272794</td>\n",
       "      <td>-1.060665</td>\n",
       "      <td>0.151523</td>\n",
       "      <td>-1.529907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.006917</td>\n",
       "      <td>0.160564</td>\n",
       "      <td>-0.408070</td>\n",
       "      <td>-0.681127</td>\n",
       "      <td>0.996590</td>\n",
       "      <td>0.337771</td>\n",
       "      <td>1.006647</td>\n",
       "      <td>-1.135273</td>\n",
       "      <td>0.686355</td>\n",
       "      <td>0.132531</td>\n",
       "      <td>1.009049</td>\n",
       "      <td>-0.708787</td>\n",
       "      <td>0.071411</td>\n",
       "      <td>-1.310510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.988270</td>\n",
       "      <td>-0.808095</td>\n",
       "      <td>0.841021</td>\n",
       "      <td>-0.872618</td>\n",
       "      <td>0.842944</td>\n",
       "      <td>0.337771</td>\n",
       "      <td>0.504684</td>\n",
       "      <td>-0.800550</td>\n",
       "      <td>-0.492123</td>\n",
       "      <td>0.624300</td>\n",
       "      <td>-0.821650</td>\n",
       "      <td>-0.157195</td>\n",
       "      <td>0.899231</td>\n",
       "      <td>-0.615752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender  congress_district  state_house       age     party      race  \\\n",
       "0 -0.988270           0.160564    -0.205271  0.021008 -1.085858 -2.428058   \n",
       "1  1.006917           0.214521     0.792340  1.233787 -1.085858  0.414434   \n",
       "2  1.006917           1.240874     0.783762 -0.744957 -1.085858  0.337771   \n",
       "3  1.006917           0.160564    -0.408070 -0.681127  0.996590  0.337771   \n",
       "4 -0.988270          -0.808095     0.841021 -0.872618  0.842944  0.337771   \n",
       "\n",
       "    hs_only   married  children      cath     evang  non_chrst  other_chrst  \\\n",
       "0  0.163349  0.598340  1.006435  0.743003  0.109214  -0.109643    -1.023448   \n",
       "1 -1.007899  1.684611  1.287718  0.743003 -0.045930  -0.937032     1.540124   \n",
       "2  1.823175 -1.163692  0.788199  0.386895  1.272794  -1.060665     0.151523   \n",
       "3  1.006647 -1.135273  0.686355  0.132531  1.009049  -0.708787     0.071411   \n",
       "4  0.504684 -0.800550 -0.492123  0.624300 -0.821650  -0.157195     0.899231   \n",
       "\n",
       "   days_reg  \n",
       "0 -0.359789  \n",
       "1 -1.392784  \n",
       "2 -1.529907  \n",
       "3 -1.310510  \n",
       "4 -0.615752  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop response and dist_\n",
    "train_all_woe.drop(['voted', 'dist_ballot', 'dist_poll'],\n",
    "                  axis = 1, inplace = True)\n",
    "\n",
    "#normalize all variables\n",
    "train_all_woe = (train_all_woe - train_all_woe.mean()) / train_all_woe.std()\n",
    "train_all_woe.head()\n",
    "\n",
    "X_all_woe = train_all_woe.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118529, 14)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(118529,)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all_woe.shape\n",
    "y_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.55456212066322985, 0.0038112458042181135)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.cross_val_LL(sclf, X_all_woe, y_vector, kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create new train and test sets\n",
    "train_test = wv.create_card_df(all_data, categ, all_woes)\n",
    "train_test = train_test.drop(['dist_ballot', 'dist_poll'], axis = 1)\n",
    "train_test = (train_test - train_test.mean()) / train_test.std()\n",
    "\n",
    "X_train_card = train_test.iloc[:118529, ].as_matrix()\n",
    "X_test_card = train_test.iloc[118529:, ].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "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>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.636529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.491942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.694913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.612622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.816451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id     voted\n",
       "0   1  0.636529\n",
       "1   2  0.491942\n",
       "2   3  0.694913\n",
       "3   4  0.612622\n",
       "4   5  0.816451"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclf_fit_final = sclf(X_train_card, y_vector)\n",
    "to_submit14 = wv.for_submission(sclf_fit_final, X_test_card, series_id, 14)\n",
    "to_submit14.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The public log-loss score from this submission was 0.5491. The final score is 0.55154, and my final rank is 5 out of 40 in this [competition](https://inclass.kaggle.com/c/who-voted/leaderboard).\n",
    "\n",
    "Let's save predictions from the final stacked classifier, the individually fitted models and the test and train matrices for further analysis and future reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get predictions from final model\n",
    "predictions = sclf_fit_final.predict_proba(X_test_card)\n",
    "\n",
    "#save probabilities with predictor information\n",
    "pred_test = test_imp.copy()\n",
    "pred_test['voted_proba'] = predictions[:, 1]\n",
    "pred_test.to_csv('pred_test.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train individual models on final predictors\n",
    "logit_fit_final = logit.fit(X_train_card, y_vector)\n",
    "xgb_fit_final = xgb_3.fit(X_train_card, y_vector)\n",
    "rf1_fit_final = rf_1.fit(X_train_card, y_vector)\n",
    "rf2_fit_final = rf_2.fit(X_train_card, y_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['final_sclf.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['final_logit.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['final_xgb.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['final_rf1.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['final_rf2.pkl']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save trained models\n",
    "final_models = [sclf_fit_final, logit_fit_final, \n",
    "                xgb_fit_final, rf1_fit_final, rf2_fit_final]\n",
    "file_names = ['final_sclf', 'final_logit', 'final_xgb', 'final_rf1', 'final_rf2']\n",
    "\n",
    "for model, name in zip(final_models, file_names):\n",
    "    joblib.dump(model, '{}.pkl'.format(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matrices = [X_train_card, y_vector, X_test_card]\n",
    "matrix_names = ['final_train', 'final_y', 'final_test']\n",
    "\n",
    "for m, n in zip(matrices, matrix_names):\n",
    "    np.savetxt('{}.csv'.format(n), m, delimiter = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [part 5](who-voted_final.ipynb) for model interpretation and final analyses."
   ]
  }
 ],
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