Final_Model_Random_Forest_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest Classifier (RFC) is a model made up of many decision trees. Rather than simply averaging the prediction of trees (which we could call a “forest”), the RFC uses two key concepts: \n",
    "\n",
    "(1) _Random sampling of training data when building trees._ When training, each tree learns from a random sample of the data points. The samples are drawn with replacement, known as bootstrapping, meaning that some samples will be used multiple times in a single tree. By training each tree on different samples, although each tree might have high variance with respect to a particular set of the training data, overall, the _entire forest_ will have lower variance but not at the cost of increasing bias. With the test set, predictions are made by averaging the predictions of each decision tree. This procedure of training each individual learner on different bootstrapped subsets of the data and then averaging the predictions is known as _bagging_.\n",
    "\n",
    "(2) _Random subsets of features when splitting nodes_. Only a subset of all the features are considered for splitting each node in each decision tree. Generally this is set to 'sqrt'(n_features) for classification. So, with the current data with 15 features, this would be set to just under 4. \n",
    "\n",
    "#### The Random Forest Classifier performed the best out of all the models which were run. Specifically, the RFC model utilizing under-sampling, due to imbalanced data. \n",
    "\n",
    "#### _Below are three models_: \n",
    "\n",
    "#### 1. Baseline \n",
    "\n",
    "#### 2. Under-Sampled\n",
    "\n",
    "#### 3. Under-Sampled with hyper-parameters tuned (FINAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from matplotlib import pyplot\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.auto_scroll_threshold = 9999;\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.auto_scroll_threshold = 9999;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEX</th>\n",
       "      <th>_AGE_G</th>\n",
       "      <th>_BMI5CAT</th>\n",
       "      <th>_EDUCAG</th>\n",
       "      <th>_INCOMG</th>\n",
       "      <th>_RFDRHV5</th>\n",
       "      <th>_PACAT1</th>\n",
       "      <th>_RFHLTH</th>\n",
       "      <th>_HCVU651</th>\n",
       "      <th>EMPLOY1</th>\n",
       "      <th>VETERAN3</th>\n",
       "      <th>MARITAL</th>\n",
       "      <th>ADDEPEV2</th>\n",
       "      <th>POORHLTH</th>\n",
       "      <th>PHYSHLTH</th>\n",
       "      <th>MENTHLTH</th>\n",
       "      <th>MENTHLTH2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "      <td>450016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.441595</td>\n",
       "      <td>4.416081</td>\n",
       "      <td>0.713664</td>\n",
       "      <td>0.374187</td>\n",
       "      <td>3.790430</td>\n",
       "      <td>0.054351</td>\n",
       "      <td>2.404368</td>\n",
       "      <td>0.807718</td>\n",
       "      <td>0.926998</td>\n",
       "      <td>0.532363</td>\n",
       "      <td>0.128591</td>\n",
       "      <td>0.524508</td>\n",
       "      <td>0.198235</td>\n",
       "      <td>2.765302</td>\n",
       "      <td>4.321413</td>\n",
       "      <td>3.537850</td>\n",
       "      <td>0.317053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.496578</td>\n",
       "      <td>1.583006</td>\n",
       "      <td>0.452049</td>\n",
       "      <td>0.483913</td>\n",
       "      <td>1.324389</td>\n",
       "      <td>0.226710</td>\n",
       "      <td>1.168220</td>\n",
       "      <td>0.394094</td>\n",
       "      <td>0.260140</td>\n",
       "      <td>0.498952</td>\n",
       "      <td>0.334747</td>\n",
       "      <td>0.499400</td>\n",
       "      <td>0.398671</td>\n",
       "      <td>7.255185</td>\n",
       "      <td>8.825211</td>\n",
       "      <td>7.859638</td>\n",
       "      <td>0.465329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SEX         _AGE_G       _BMI5CAT        _EDUCAG  \\\n",
       "count  450016.000000  450016.000000  450016.000000  450016.000000   \n",
       "mean        0.441595       4.416081       0.713664       0.374187   \n",
       "std         0.496578       1.583006       0.452049       0.483913   \n",
       "min         0.000000       1.000000       0.000000       0.000000   \n",
       "25%         0.000000       3.000000       0.000000       0.000000   \n",
       "50%         0.000000       5.000000       1.000000       0.000000   \n",
       "75%         1.000000       6.000000       1.000000       1.000000   \n",
       "max         1.000000       6.000000       1.000000       1.000000   \n",
       "\n",
       "             _INCOMG       _RFDRHV5        _PACAT1        _RFHLTH  \\\n",
       "count  450016.000000  450016.000000  450016.000000  450016.000000   \n",
       "mean        3.790430       0.054351       2.404368       0.807718   \n",
       "std         1.324389       0.226710       1.168220       0.394094   \n",
       "min         1.000000       0.000000       1.000000       0.000000   \n",
       "25%         3.000000       0.000000       1.000000       1.000000   \n",
       "50%         4.000000       0.000000       2.000000       1.000000   \n",
       "75%         5.000000       0.000000       4.000000       1.000000   \n",
       "max         5.000000       1.000000       4.000000       1.000000   \n",
       "\n",
       "            _HCVU651        EMPLOY1       VETERAN3        MARITAL  \\\n",
       "count  450016.000000  450016.000000  450016.000000  450016.000000   \n",
       "mean        0.926998       0.532363       0.128591       0.524508   \n",
       "std         0.260140       0.498952       0.334747       0.499400   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         1.000000       0.000000       0.000000       0.000000   \n",
       "50%         1.000000       1.000000       0.000000       1.000000   \n",
       "75%         1.000000       1.000000       0.000000       1.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "            ADDEPEV2       POORHLTH       PHYSHLTH       MENTHLTH  \\\n",
       "count  450016.000000  450016.000000  450016.000000  450016.000000   \n",
       "mean        0.198235       2.765302       4.321413       3.537850   \n",
       "std         0.398671       7.255185       8.825211       7.859638   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       3.000000       2.000000   \n",
       "max         1.000000      30.000000      30.000000      30.000000   \n",
       "\n",
       "           MENTHLTH2  \n",
       "count  450016.000000  \n",
       "mean        0.317053  \n",
       "std         0.465329  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "LLCP2 = pd.read_csv(r'C:\\Users\\Nick\\Desktop\\GitProjects\\LLCP_Project\\LLCP2.csv')\n",
    "LLCP2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Baseline RFC Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data by features and target\n",
    "X = LLCP2[['SEX','_AGE_G','_BMI5CAT','_EDUCAG','_INCOMG','_RFDRHV5','_PACAT1','_RFHLTH','_HCVU651','EMPLOY1',\n",
    "           'VETERAN3','MARITAL','ADDEPEV2','POORHLTH','PHYSHLTH']].values\n",
    "\n",
    "y = LLCP2['MENTHLTH2'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows/columns in X_test dataset:  (135005, 15)\n",
      "Number of rows/columns in y_test dataset:  (135005,)\n",
      "Number of rows/columns in X_train dataset:  (315011, 15)\n",
      "Number of rows/columns in y_train dataset:  (315011,)\n"
     ]
    }
   ],
   "source": [
    "# Complete a 70/30 train-test-split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "# describes info about train and test set \n",
    "print(\"Number of rows/columns in X_test dataset: \", X_test.shape) \n",
    "print(\"Number of rows/columns in y_test dataset: \", y_test.shape) \n",
    "print(\"Number of rows/columns in X_train dataset: \", X_train.shape) \n",
    "print(\"Number of rows/columns in y_train dataset: \", y_train.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Default options for RFC\n",
    "# class sklearn.ensemble.RandomForestClassifier(n_estimators=’warn’, criterion=’gini’, max_depth=None, \n",
    "    # min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, \n",
    "    # max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, \n",
    "    # oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None\n",
    "\n",
    "    \n",
    "# ---BASELINE MODEL---\n",
    "\n",
    "RFC_baseline = RandomForestClassifier()\n",
    "RFC_baseline.fit(X_train, y_train)\n",
    "y_pred = RFC_baseline.predict(X_test)\n",
    "probs = RFC_baseline.predict_proba(X_test)\n",
    "probs = probs[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=== CLASSIFICATION REPORT ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.79      0.89      0.83     92290\n",
      "         1.0       0.67      0.48      0.56     42715\n",
      "\n",
      "   micro avg       0.76      0.76      0.76    135005\n",
      "   macro avg       0.73      0.68      0.70    135005\n",
      "weighted avg       0.75      0.76      0.75    135005\n",
      "\n",
      "Accuracy: 0.7589348542646569\n",
      "ROC AUC score: 0.7606114555233177\n",
      "Sensitivity: 0.47767763080884934\n",
      "Specificity: 0.8891104128291256\n",
      "Precision: 0.6659703636007572\n",
      "\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[82056 10234]\n",
      " [22311 20404]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WREREIkE5S1GlYElERCQqNLIUSQphRURERJLQyJKIiEhUaBoukhQsiYiIRIGZpuEiSsGSiIhIVGhkKZIULImIiESFRpYiScGSiIhIJGjpgKhSsCQiIhIVGlmKJAVLIiIiUaDHnUSWvisiIiIiSWhkSUREJBKUsxRVCpZERESiQjlLkaRgSUREJCo0shRJCpZERESiQiNLkaRgSUREJApMOUtRpWBJREQkKjSyFEkKlkRERCLCFCxFksb7RERERJLQyJKIiEgEGBpZiioFSyIiIlFg4UsiR8GSiIhIJJhGliJKwZKIiEhEKFiKJgVLIiIiEaFgKZoULImIiESEgqVo0tIBIiIiUWD55JXTZTA70sx+jnmtNrMuZlbOzEaY2fTwa9mwvplZbzObYWaTzey4mLY6hvWnm1nHmPLjzeyX8JjelkOUqmBJREREIsPd/3D3eu5eDzgeWA8MBO4GvnD3msAX4TZAC6Bm+LoOeAnAzMoB3YEGwIlA94wAK6xzXcxxzZP1SdNwIiIiEWC6Gy6epsCf7j7HzFoBTcLyfsAooCvQCujv7g6MNbMDzKxSWHeEuy8HMLMRQHMzGwWUdvcxYXl/oDXwWaJOKFgSERGJiHwSLJU3swkx233cvU+Cuu2Bd8L3Fdx9IYC7LzSzg8PyysC8mGPmh2XJyufHKU9IwZKIiEhE5JNgaam718+pkpkVBs4HuuVUNU6Z70J5QgqWREREIiKfBEupagH86O6Lwu1FZlYpHFWqBCwOy+cDh8YcVwVYEJY3yVI+KiyvEqd+QkrwFhERiYK8vkstInfDxbiEHVNwAIOBjDvaOgKDYso7hHfFNQRWhdN1w4GzzaxsmNh9NjA83LfGzBqGd8F1iGkrLo0siYiIRIRGlgJmVhw4C7g+pvhRYICZdQLmAm3D8qFAS2AGwZ1zVwG4+3Iz+w8wPqz3UEayN3Aj0BcoRpDYnTC5GxQsiYiIRILuhtvB3dcDB2YpW0Zwd1zWug50TtDO68DrcconAEen2h8FSyIpaN64Lp0vbUKdwytSrkwJ/l66mp9+m0vvt75i3ORZ2+sdcdhBtD7jGM48qQ41DjuYgw8sxYrV6/nhl9k8/7+v+GbC9ITnuOy8BtzQ7hRqH16JrVu3MemP+TzT/ws+G/1rtrr3Xt+S+25ombCt8zu/wIjvf4u7r26NQ7itY1NOq1+Lg8qVZNXajfwx62/6fjyGtz/5YSeuiuwJH334AaO/+ZrJk37ml8mTWLNmDe0vuYw3+r+V8Jgx33/PY4/04IdxY9m4cSNHHFGDDldezU3/vJmCBQtmqjvp558ZMvhjvhg5gtmzZrJs2TLKH3QQjU85ldtuv4tjjzsuW/uDB33MgPfe4ZfJk1i8aBEbN26kcpUqHHd8fW7tcgfH188xV5dvR39DszNPZ9u2bXTtdi8PPtRj5y9OPqBgKZoULInkoMctrbjjqrNYumItQ0ZNZtnKtRxx6EGc2+QftG5aj073v8m7Q4NR3u43nUvbZscz9c+FDPt2CitWr6dW1YM557T/47wm/+COx9/nxXe+znaOR267gC4dmjL/7xW88dF3FCqURttmx/NR7xu47dEBvPzeN3H79ubgscxZsDxb+Z/zlsStf/l5DXjpgUtZv3Ezn42ewpwFyyhTqhh1axxC88Z1FSxFwGMP92Dy5EmULFmSylWq8MfvvyetP2TwIC5pdxFFixalTduLKVuuHEM/HcK/7ryNMWO+4+13389U/+bONzD+h3Ecd9zxtGp9ISVKlmTypJ95/713GfjhB7z1zgBatb4g0zGfDB7ExAnjOb7+CVSqdAiFCxfmzz9nMPjjgXww4D1eeKkPV3W6JmEf16xZwzVXd6R48eKsXbt21y+OSB5RsCSSRIUDS9GlQ1P+XrqaE9s9zJIVO/6hP7V+TYb/91YeuPGc7cHS599N5ak3RjDpj/mZ2ml8fA0+femfPNylNR+N+Im/l67evq/hMdXp0qEpf85dQuPLH2flmg0A9Oo3ku/f7sojt13A0G9+Ze7C7EHRm4PHMXpi4tGqWCf+XzVeeuBSpvy5kFadX2DRsjWZ9qel6X6PKHj8qV5UrlyFI2rUYPQ3X9PszNMT1l29ejWdb7iWggULMnzkqO0jPN3//R+an3UGAz/8gAHvvUu7i9tvP6b9JZfxRr+3OKJGjUxtvfP2/7i64+V0vuFaWrQ8h8KFC2/f1/uFlyhatGi28//6yy80PukEunW9k8uu6JDpmFh33nYrq1et4q6u3eh+/707dT3yHQ0sRZL+dRRJ4rBK5ShYsADjf52dKVAC+GbCdFav3UD5siW3l701ZFy2QAng24kz+GbCdIoULkTDYw7PtO+aNo0BeOy14dsDJYC5C5fzyoBvKFqkEB1aNdztz9KzS2vS0gpy9b39sgVKAOnp23b7HLL7TmtyOjVq1kxpOmbghx+wZMkS2rZrn2kqrGjRotunuf77ykuZjrnpnzdnC5QALrn0MmrUrMmyZcv49ZdfMu2LFygBHP1//0ft2nVYtWoVS5bEH80cMngQ/fu9wVO9elOp0iE5fqZ8zYJpuP39tS9SsCSSxIy5S9i0eQv161blwANKZNrX6LgjKF2yGF+N+yOltrakbwUgfevWTOVNTqgFwIjvp2Y75vPvpmaqk9XJxx7OrVecwR1Xnkmbs4/L1scMlQ8+gMbH1WDilDlM/XMhp9avSZcrmnLrFWfQ5MRa++w/YPndqK++BOCsZtkfa9X4lFMpXrw4Y8d8z6ZNm1Jqr1ChQgCkpaU26TB92jSmTfuD8uXLU6lSpWz7Fy9eTOcbruW8Vq255LLLU2ozv8vrQEbBUnyahhNJYsXq9dz37CAeu+NCfvzwPoZ8NZnlq9ZxeJXynHPa/zFyzG/8s8c7ObZzWKWynH7ikazbsIlvJ87YXl68aGEqVyjLmnUbM03NZZgxN1hzrUbVg7PtA3iw83mZtjdu2kKv/iN56MVPM5UfX/ewsL0lDOtzC6dlCb5+mfYX7e/8LzPnLc3xs0h0TJsWBOo1a2YPptPS0qhWvTpTp0xh1syZ1K5TJ2lbP4wbx29Tp3JI5crUPTr+TUJffjGS77/7ls2bNzN79iyGfjIEgBdfeZUCBbL/7d35xuvYtm0bz73w8s5+tHxrXw0m9ncKlkRy8Pzbo5izYDkvP3gZnS5qtL18xtzFvDV4XLbpuawKF0rjjZ5XUrRIIe7pNTDTVFuZUsUAWL12Q9xjV4XlB4T1MvwybT7XdX+LbyZM5++lqzi4XCmanlSb7jedR7drW1CwQAG6Pz9ke/2DypUC4KKzjmXpynVcfHsfvvphGuXLluTe61tw2bkNGNj7Ruq3fXj7CJhE3+rVqwAoU6ZM3P2lSwflK1euTNrOihUr6HTVFQA89sTT2e6gy/DlFyN56onHtm9XrFiRPq/15ayzm2Wr2++N1/lk8CDefPs9KlSokPOHES0dEGGahhPJwe0dz+TtJzrx1pBx1Dm3O+VOuo2TLnmUWfOX0feRK+l5a6uExxYoYLzeowMnH3sE7w+fSK/+X+xSHzzLU4sGfzU5vBNuGZs2pzPv7xX0HTiGC25+kc1b0unSoWmmKbmC4V/9aWkFuemhtxn81WTWrNvIrPlLueb+N5k4ZQ61qlXggjPr7VL/JJo8/MFJ9gt43bp1tLngfGZMn87td/6LNm3bJazb4+FH2bDFWbpyLWN++JHTmpxBq3Nb8NgjPTPVmzN7Nnfd0YUL27RN2p7Ekdera0drBe/IULAkksQpx9ekZ5fWfPr1L3R96iNm/7WMDRu38PPv87n4jj78tWgFt17RlGqVD8x2bIECxhs9O3LR2cfxwfCJXHVvv2x1VoWjTKVLFsu2D6BMWL4qwchTVj//Pp8Jv86hcKE0Gvyj+vbylWvWA8E03bBvp2Q7bsioyQDUr1s1pfNINGSMHK1atSru/jVrgqndRCNP69at44Lzz+H7777lli630/ORx+LWy6pEiRLUO/ZY+r75P846uxn/7n4/E8aP377/+muvplixYjz73Is783FECd6RtV8HS2Y228zOzOt+pMrMqpmZm1meTI+G585+m8yO/fvU9cwNLU8Ncje+jrOY5IaNW5jw6xwKFixAvdqHZtpXsGAB+j9yFe2a1+fdoePpeE9ftm7NfrfZ+o2b+WvRCkqVKErF8qWz7a9xWJCrNGPO4mz7ElkaTguWKFZke9m02cFzKNes37h9tCHWytVBMFa0aPxbvyWaatU6EoDp06dl25eens7sWbNIS0uj+uGHZ9u/Zs0aWp3bgtHffM3td/6Lx554apf6cNbZzXF3Ro/esX7Yzz/9yOLFizm00kEUK2TbX9ddcxUAjz3Sk2KFjLYXtd6lc4rsbXs9WDKz9mY2zszWmdni8P1NtpfDTTPrGwYH52cpfyYsvzIXznGlmX27u+3EtJctWMntc8S029fM8v0Su0UKB3Fr7PIAsTLKN29J315WKK0g7zzRiYvOPo63hozj6vv6s21b9gAlw6jxwS+6s04+Ktu+sxsdlalOTtLSClCvThC4zZq/I1n7l+kLWLJiDQeVLcXBYf5SrKNqBHcyzV2wLKXzSDQ0Of0MAEYMH5Zt37ejv2H9+vU0POlkihQpkmnfqlWrOLfF2Xz37Wi6drs35RGleBYs+AuAtII7/sa79PIOXHlVp2yvxqecCsAxx9Tjyqs60bTpWbt83v1VXo/6aGQpvr0aLJnZHcCzwBNARaACcAPQCMiLP2mnseMJxoQjOm2BP/OgLxJB3/0Y3Ll29YWNOOSgzFMZZzc6ipPqHc6GjZsZOyl45EnhQmm89/S1nHf6Mbwx8Huu6/5W3JGcWK9+EMS6XTs1y5TIfVilclzf7lQ2btpC/0Fjt5eXLF6Ef9SqnK2dQmkFeeLONhxWqRy/z/ybiVPnbt+3des2XvvgOyBYbyn2H6y6NQ7hivMasGXLVj4a+VNK10Wi4YKL2lC+fHneH/AuEydM2F6+ceNGHnzgPgCuvf7GTMesWLGCls3O5IdxY7m/+79zfOzIpk2bGPP993H3TRg/nlf7vEyBAgU4O2b5gqef6c1LfV7N9urQMRhZat7yHF7q8yo33BT3cV75Wl4HMgqW4ttr0z1mVgZ4COjg7h/G7PoJuCxLveeAFgRPD/4v8LC7bzOzAsA9wLUETwoeBtzs7qvCY68AegAlgadT6NYQ4HIzK+vuK4DmwGQg05/eZnY1cBdBgPcDcJ27zwn3OcHTi+8AygNvA/8EagMvA4XMbC2Q7u4HmNk5YR+PAFYBr7n7gyn0NSVmdgjB9TsVWAv0cvfe4b4TCYLVOsAG4EPgdnffnKWN6wi+J25mXYCv3D3jHvV6ZvY0UJXg+nd094251f+o+Wjkz3wx9neaNqzNTx/dx+CvJrNo6WqOPLwiLU+pS4ECBbi/92CWr1oHwHP3tqfFKUezZMUaFixeyT3XtcjW5jcTpmdadXvspFk8++YX3HpFU8YPuIeBI3+iUKG07esm3fbogEyrd5crU4Jx73Xj59/n8ev0Bfy9dBXly5bktPq1qF6lPEtWrKFjtzeyBWmPvz6c0xscyeXnNaBujUMYPXE65Q8oSeum9ShWtDBdn/pISwdEwOBBHzNk0McALFr0NwDjxo3h2quvBODA8uV59PEnAShdujQvvPxfLr24Dc3ObELbdu0pW64cn34ymGl//MEFF7WhbbuLM7Xfvu2F/DhxAocfcQTbtm2jx0MPZuvDeee35ph6QbL/hg0bOOO0RhxZuzb16h1H5SpVWL9+PX/8/tv2dZ4efuwJjqxde09cjvxn34wl9nt7MzfmJKAIMCiHes8BZYDDCZ44/DmwEHgNuDJ8nQ4sBvoDzwNXmNlRwEtAS2Ac8AhQJYdzbQQGA+3DYzuEbW7/c8fMWhMEaOcB04G7gXeAk2PaORc4ASgNTASGuPswM7sBuMbdG8fUXReeZwrBE49HmNnP7v5xDn3NURhMDiG4xpcQfP6RZvaHuw8HtgK3ARPCfZ8BNwHPxLbj7n3M7GRgvrvfl+U07QiCyo3AdwTfj2yLqIQB13UAFIo/hbUvcHda3/wiN7Q7lbbNjuf80/9B8aKFWb56PcO+ncqL74zii7E7nt2Vkeh9UJdL/kQAACAASURBVNlS3Ht9/Afd9nh5aLZHlNz99EB+mb6AGy8+lasvasS2bc7Pv8+jV7/sD9JdsXo9L74zivp1q3LmSXUoV6Y4m7dsZeb8JTz5xuf0fvPLuMsZbNi4hRbX9+b2jmfRttlxXN/uFDZuSmfs5Jk8++aX2xfAlLw1edLPvPVm5psBZs2cyayZMwE4rGrV7cESwPmtWvP5F1/z+KM9+Xjgh9sfpPvYE0/T+eZbsv0lP3t2MAo6888/6fmff8ftQ9Wq1bYHSyVKlOCBBx9i9DdfM3r01yxbuhQz45DKlbnk0su5/sbOnNigQa59/vxuXx152d9ZTlMEuXYis8uBJ929YkzZ98BRBEFUM4JfvuuBY919aljneuASd29iZl8AH7r7i+G+I4FfCUaZ7gGOcvf24b4SwAqgpbuPjNOfvsB8gtGRJwgCgGlANWAE8Kq79zWzz4AP3P218LgCBCM2ddx9TjiydIq7fxvuHwD86O6PWpD3lDVYytqPZwB399vMrBowCyjk7ulx6s4mGL2K3Vc4PF9jM2sAvO/uh8Uc0w2o5e5XxWmvC3Cau18QbjtQ091nZFyf2GApPP997v5WuP04UNrdb0j0+QAKFD/Yixyp24dl71gx/vm87oLkE40a1GfixAm5Ft0UqVDTK16cyqTIvm3uc+dPdPf6OdeMjr05srQMKG9maRmBgLufDGBm8wnyp8oT/PKfE3PcHCAjQeOQOPvSCHKfDgHmZexw93VmlmO2qrt/a2YHAfcBn7j7hiyRfVXgWTOLvVXEwj5l9OXvmH3rCaYB4woDmkcJRpUKEwSK7yeqH0fr2OAvIyCL6eshZha7Al1BYHRYtxbB9GR9oDjBtZu4E+eG7J9VD3sSEcklGlmKpr2Z4D0G2AQkXsEPlgJbCH7pZzgM+Ct8vyDOvnRgEcFU3fb7t82sOME0XireIsg56h9n3zzgenc/IOZVzN3jZzxmFm/Y7m2Cqb9D3b0MwRRWbv3fMQ+YlaWvpdw9Yz7oJeB3gtGj0gSjcYnOvXeGHEVEZLu8Tr5Wgnd8ey1YcveVwL+BF82sjZmVNLMCZlYPKBHW2QoMAHqaWSkzqwrcThDMQJArdJuZVTezksDDwHvhSNUHwLlm1tjMChMkk6f6+XoDZwHfxNn3MtDNzOpCkIBuZm1TbHcRUCXsT4ZSwHJ33xgmXF+aYlup+AFYbWZdzayYmRU0s6PN7ISYc68G1ppZbYLE9GR9z744i4iISD6zV5cOcPfHCYKffxEkaC8CXgG6AhkjNTcTJEHPBL4lGIl5Pdz3OvAmQVAziyDJ+Oaw7SkEidlvE4wyrSDISUqlX8vd/QuPk8Dl7gOBx4B3zWw1QY5U9luc4vuSIJH7bzPLuM3oJuAhM1sDPEAQHOaKMNg8D6hHcH2WAq8SJMwD3EkQnK0huMvwvSTNvQYcZWYrzWy3k89FRCQFef0oEj3uJK69luAt+ZcSvGVvUoK37C17IsG78mXP5lZzkTWr1zlK8BYREZFdYErwjioFSyIiIhFggGKlaFKwJCIiEgn77t1i+zsFSyIiIhGhWCmaFCyJiIhEhEaWommvLh0gIiIisq/RyJKIiEgUmKbhokrBkoiISAQYUKCAoqUoUrAkIiISERpZiiYFSyIiIhGhBO9oUrAkIiISBcpZiiwFSyIiIhEQrOCtaCmKtHSAiIhIJAQreO/vr5SuhNkBZvaBmf1uZr+Z2UlmVs7MRpjZ9PBr2bCumVlvM5thZpPN7LiYdjqG9aebWceY8uPN7JfwmN6WQ8cULImIiEjUPAsMc/fawDHAb8DdwBfuXhP4ItwGaAHUDF/XAS8BmFk5oDvQADgR6J4RYIV1ros5rnmyzihYEhERiQiz/f+V8zWw0sCpwGsA7r7Z3VcCrYB+YbV+QOvwfSugvwfGAgeYWSWgGTDC3Ze7+wpgBNA83Ffa3ce4uwP9Y9qKSzlLIiIiEZFPcpbKm9mEmO0+7t4nZvtwYAnwhpkdA0wEbgUquPtCAHdfaGYHh/UrA/Nijp8fliUrnx+nPCEFSyIiIlGQf+6GW+ru9ZPsTwOOA25293Fm9iw7ptziiXfVfBfKE9I0nIiISARk3A23v79SMB+Y7+7jwu0PCIKnReEUGuHXxTH1D405vgqwIIfyKnHKE1KwJCIiEhF5nU8UhZwld/8bmGdmR4ZFTYGpwGAg4462jsCg8P1goEN4V1xDYFU4XTccONvMyoaJ3WcDw8N9a8ysYXgXXIeYtuLSNJyIiEhE5JOcpVTcDPzPzAoDM4GrCAZ4BphZJ2Au0DasOxRoCcwA1od1cfflZvYfYHxY7yF3Xx6+vxHoCxQDPgtfCSlYEhERiQjFSgF3/xmIl9fUNE5dBzonaOd14PU45ROAo1Ptj6bhRERERJLQyJKIiEgUmKbhokrBkoiISAQEd8PldS8kHgVLIiIikZD6s9Nk71KwJCIiEhGKlaJJwZKIiEhEaGQpmhQsiYiIREH+edzJPkfBkoiISARkPO5EoidhsGRmLXemIXcfuvvdEREREYmWZCNLn5D46bxZOVAwV3okIiKST2lkKZqSBUvV91ovRERERDlLEZUwWHL3OXuzIyIiIvmdRpaiKeUEbzMrAlxN8GC7Q4HO7j7dzC4GJrv7b3uojyIiIvs/3Q0XWSkFS2ZWCxgBlAEmAk2AUuHuU4BzgA57oH8iIiL5gmkF78hKdWSpNzAXOA9YC2yO2fc18Fgu90tERCTfUawUTakGS6cAbd19pZllvettEVApd7slIiKS/xRQtBRJBVKstxEolmBfZWBl7nRHREREJFpSHVkaAdxjZiMJpuEAPEz6vhnQgpQiIiK7SQNL0ZRqsHQX8B0wgyBwcuABoC5QGLhwj/ROREQknzDT0gFRlVKw5O7zzOwY4HagKfAnQZ7S+8DT7r5sz3VRREQkfyigWCmSUl5nyd1XAPeHLxEREcllGlmKppSDJQAzOwA4mmBUaQEwxd2V3C0iIpILFCtFU6qLUqYBPYHOQPGYXevN7EXgXnffsgf6JyIiki8YwcKUEj2pjiw9DVwHPAR8BCwGDgYuIpiWKwrcsic6KCIikl8oZymaUg2WrgDucfenY8qWAz3NbCNwHwqWREREZD+UarC0DZiSYN+vBEsJiIiIyK4yPRsuqlINlt4ErgGGx9l3LfBWrvVIREQkn1KsFE0JgyUzuylmczbQxsymAIPZkbPUCigFPLkH+ygiIrLfM/RsuKhKNrL0fJyyQ4A6ccqfBp7NlR6JiIjkU4qVoilhsOTuqT5kV0RERHKBcpaiaacWpRQREZE9I3g2XF73QuLZ2RW8qwC1CNZVysTdh+ZWp0RERPIj5SwFzGw2sAbYCqS7e30zKwe8B1QjyKVu5+4rLBiOexZoCawHrnT3H8N2OhIsbwTQw937heXHA32BYsBQ4FZ3T3hnf6oreJcCBgBnZxSFX2MbLphKWyIiIiIpON3dl8Zs3w184e6Pmtnd4XZXoAVQM3w1AF4CGoTBVXegPkG8MtHMBofPun2JYLHtsQTBUnPgs0QdSTUv6RHgMOAUgkDpAqAJ8BowC2iYYjsiIiKSgOWD125oBfQL3/cDWseU9/fAWOAAM6sENANGuPvyMEAaATQP95V29zHhaFL/mLbiSnUariXBMNa4cHuBu48HvjGzp4C7gHYptiUiIiJxKMF7Owc+NzMHXnH3PkAFd18I4O4LzezgsG5lYF7MsfPDsmTl8+OUJ5RqsFQBmOfuW81sHVAuZt9Q4MMU2xEREZE4gnWW8roXe0V5M5sQs90nDIZiNXL3BWFANMLMfk/SXryr5rtQnlCqwdI8oHz4fjpwLjtW824AbEyxHREREYkn/zzuZKm7109Wwd0XhF8Xm9lA4ERgkZlVCkeVKhEskA3ByNChMYdXARaE5U2ylI8Ky6vEqZ9QqjlLI4Azw/e9gM5m9r2ZfQX8h2C+T0RERHZDxvIB+/Mr52tgJcIbyzCzEgQ3l/1K8ASRjmG1jsCg8P1goIMFGgKrwum64cDZZlbWzMqG7QwP960xs4bhnXQdYtqKK9WRpa5AcQB3f9PM1gJtCG65+yfwSortiIiISAL5ZGQpJxWAgeG1SAPedvdhZjYeGGBmnYC5QNuw/lCC3OoZBEsHXAXg7svN7D/A+LDeQ+6+PHx/IzuWDviMJHfCZXQiR+6+PuxAxvZAYGAqx4qIiEjO8lHOUlLuPhM4Jk75MqBpnHIHOido63Xg9TjlE4CjU+2TVvAWERGJCI0sRVPCYMnMlpBDdngsdz8451oiIiIi+5ZkI0svsBPBkoiIiOwejStFU8Jgyd0f3Iv9EBERydfM9Gy4qFLOkoiISEQoVoomBUsiIiIRoQTvaFKwJCIiEhGKlaJJwZKIiEgEGKacpYhSsCQiIhIFKT4ORPa+VJ8NJyIiIpIvaVFK2eOOqlmFAZ8+ltfdkHxi3rL1OVcSyQWb07fleptK8I4mLUopIiISEZruiSYtSikiIhIBhkaWokoJ3iIiIhFRQLFSJKUcLJnZSUAnoBZQNOt+dz8xF/slIiKS7yhYiqaUgiUzOwsYCnwBNAY+A4oBjYD5wNd7qoMiIiL5gZmm4aIq1ZGlh4Bnga7AFuB+d//RzKoCw4FRe6Z7IiIi+YdGlqIp1cT7owhGk7YR3CFXAsDd5wAPAvfuic6JiIiI5LVUR5Y2AgXc3c1sIXAEMDrctxqosic6JyIikp9oFi6aUg2WJgFHAiMI8pa6mdlfwGaCKbpf9kz3RERE8gcDPRsuolINlp4Bqofv7wGGEOQqQZDgfUEu90tERCTf0aKU0ZRSsOTuQ2Pe/2VmxwM1CO6I+93dN++h/omIiOQbGliKpl1alNLdHZiey30RERHJt8xM03ARleo6S4/nVMfd/7X73REREcm/FCtFU6ojS23jlJUFSgOrgBWAgiUREZHdoHWWoinVnKXq8crNrAHQB7ghNzslIiIiEhW79SBddx9nZk8AzwPH506XRERE8h8tHRBduxUshZYRrMEkIiIiu0GxUjSlmuBdPE5xYaAOwaKUU3KzUyIiIvmOKWcpqlIdWVpL8Ey4rAz4C2idaz0SERHJpwxFS1GUarB0NdmDpY0Eq3f/4O5bcrVXIiIi+UyQs5TXvZB4Ur0bru8e7oeIiEi+p2ApmlJ6DI2ZbTWzExPsO97MtuZut0RERPIfM9vvXztxLQqa2U9m9km4Xd3MxpnZdDN7z8wKh+VFwu0Z4f5qMW10C8v/MLNmMeXNw7IZZnZ3Tn1J9Zl9yT5dISA9xXZEREREUnEr8FvM9mNAL3evSbAYdqewvBOwwt1rAL3CepjZUUB7oC7QHHgxDMAKAi8ALYCjgEvCugklnIYzs8OAajFFx5pZ0SzVigIdgVnJTiIiIiLJKWdpBzOrApwD9ARut2BI6gzg0rBKP+BB4CWgVfge4APg+bB+K+Bdd98EzDKzGUDGLNkMd58ZnuvdsO7URP1JlrN0FdCdILHbww7FswG4Jkk7IiIikhPLN+sslTezCTHbfdy9T5Y6zxA8Rq1UuH0gsNLdM2ay5gOVw/eVgXkA7p5uZqvC+pWBsTFtxh4zL0t5g2QdThYsvUgQoRkwGbgs/BprMzA3jNpERERkN+STFbyXunv9RDvN7FxgsbtPNLMmGcVxqnoO+xKVx0tBirc80nYJgyV3XwIsgSCpCligJQJERET2DE3DbdcION/MWhKk+5QmGGk6wMzSwtGlKsCCsP584FBgvpmlAWWA5THlGWKPSVQeV6oJ3icBXeLtMLM7zaxdiu2IiIhIAmb7/ysn7t7N3au4ezWCBO0v3f0y4CugTVitIzAofD843Cbc/6W7e1jePrxbrjpQE/gBGA/UDO+uKxyeY3CyPqW6KGU34NUE+9aH+wek2JaIiIhkYxTQCt7JdAXeNbMewE/Aa2H5a8CbYQL3coLgB3efYmYDCBK304HO7r4VwMz+CQwHCgKvu3vSx7alGizVAH5NsO83gmhNREREdpGRbxK8U+buo4BR4fuZ7LibLbbORqBtguN7EtxRl7V8KDA01X6kOg23nmBOL55DASV4i4iIyH4p1ZGlkcD9Zjbc3RdnFJrZQcC9wOd7onMiIiL5hinBO6pSDZa6EqxV8KeZDQMWApWAZsBKgrUQREREZDfkk6UD9jmpPkh3rpkdA9wOnA7UA5YBzxEsPb50z3VRRERk/6ecpehKdWQpY92lbvH2mVkhrcEkIiKyezSyFE0pB0tZhc9dOR24BLiQYGlxERER2UWKlaJpp4MlM2tAECC1AyoQrGnwbi73S0REJF8xUr9FXfaulIIlMzuaIEBqD1QjeCZcYYIcphdiHmwnIiIiu8LANLQUSQmDWDM73MzuMbNfgEnAnQQLUHYgWITSgJ8UKImIiMj+LNnI0gyCp/COA64HPnT3FQBmVmYv9E1ERCRf0bhSNCULluYAVYGjgSbAwnBRSo0kiYiI5DJDd8NFVcJgyd2rm9lJwKUET/G9FFhhZh8BnxGMOomIiEguUagUTUkTvN19DDDGzG4FmhIkeV8EdCIIlq41s/XuPmGP91RERGQ/p4GlaEp1Be9twAhghJndALQkuDPuAuBSM5vm7nX2XDdFRET2d6a74SJqp9dZcvfNwMfAx2ZWAmhNEDiJiIjILtI6S9G1yyt4A7j7OuB/4UtERER2g0aWoklBrIiIiEgSuzWyJCIiIrlH40rRpGBJREQkCvS4k8hSsCQiIhIBSvCOLgVLIiIiEaGRpWhSsCQiIhIRCpWiScGSiIhIRGhgKZoULImIiERAkLOkaCmKlEsmIiIikoRGlkRERCJC03DRpGBJREQkEgzTNFwkKVgSERGJCI0sRZOCJRERkQhQgnd0KVgSERGJAtPIUlQpWBIREYkIBUvRpGBJREQkIpTgHU1aZ0lEREQiw8yKmtkPZjbJzKaY2b/D8upmNs7MppvZe2ZWOCwvEm7PCPdXi2mrW1j+h5k1iylvHpbNMLO7c+qTgiUREZEIMKCA7f+vFGwCznD3Y4B6QHMzawg8BvRy95rACqBTWL8TsMLdawC9wnqY2VFAe6Au0Bx40cwKmllB4AWgBXAUcElYNyFNw4kksXL5MkYOG8I3Xwxj2u9TWfz3AgoVKkzN2nW54OLLueDiKyhQYMffHHNmzmDEZ4P5btRI5s76k6VLF1OmzAH847gTueKam2jQ6LRs55gw9ls++F9ffpsyiSWLFrFhwzoOOrgiNWvX5YpON9LwlNOzHTP5pwl8MWwIv0+ZzG+/TmLZksVUqHgIX06clvCzDP9kIBPGfsvvUybzx9RfWbd2DedeeDGPPfdarlwr2X0rli9jxGdDGDVyGNN+m8Ki8OetVp26XNT+Ci5qn/nnLcOP48fyYq/HmPTjeDZt2kjVaodz0SUduKLTjRQsWDDpOd2dqy4+j++/+QqAqfNXkZaW/VfDxg0beOW5pxg66AP+mj+XkiVL0eDkU7n5rnupUat2jp9t/JhvueKiFmzbto0bu/yL2+7unuJVyV80DQfu7sDacLNQ+HLgDODSsLwf8CDwEtAqfA/wAfC8mVlY/q67bwJmmdkM4MSw3gx3nwlgZu+Gdacm6pOCJZEkhn8ykIe6deGgChU58eRTqVT5UJYtWczIzwbzwJ2dGf3l5/Tq8xYWZmU+98R/+GzwhxxRqzannHE2ZcqWY/af0/jq86F89fmndHvocS7vdFOmc4z77mvGff81/zi2Pg0anUax4iVY+Nc8vvp8KKNGDOX6W//FLf96INMxnw4cwFuvvUhaoUIcXuNIli1ZnONneeXZx/lj6i8UL1GSipUOYeaMNbl3oSRXDBsykO5db+XgChVp0GjHz9vnQwdz7+038c0Xn9P71R0/bwAjh33CzZ0upUiRorRsdRFlDijLlyM+4+EHuvLjD2Pp/epbSc/55msvM+67byhStCibNm6MW2fzpk1cefF5/PjDGI4+5jg6XnMTCxfMZ9iQgYwaOYz+Hw7lmONOSHiOtWvX0PWW6yhWrDjr1q1NWE/yTYJ3eTObELPdx937xFYIR38mAjUIRoH+BFa6e3pYZT5QOXxfGZgH4O7pZrYKODAsHxvTbOwx87KUN0jWYQVLIklUPbwGz78xgNPObJ7pL/pb736Q9uecxoihgxgxdBBnn9MagEZNzqJT59upc/QxmdoZP2Y011xyPk/2uI9m517IQRUqbt93Tec76HzHvdnOvWjhAto2b8R/n3uSSzpel+mY1u0up3W7yziiVh0KFy5M3colc/wsXR98lIqVKnNY9SMYP2Y0V7VtudPXQ/asakfU4OX+79Mky8/b7fc8SJsWpzH804/5/NNBNDs3+Hlbu2Y1993RmQIFC/LmR8P4v3rHAdCl6wN0aNOSYZ8M5JOP3+fc1m3jnm/mjGk82fN+rr7xVoZ+HIwYxfP6K7358YcxND/3Ap7p039731q2asNNV15Mty438smoH+KOegH0vO8u1qxZzXW33EmvRx7c1cuTL+STkaWl7l4/WQV33wrUM7MDgIFAnXjVwq/xLponKY/3g+pxyrZTzpJIEg0bN+H0s1tm+yVw0MEVuPiKYLp8/JjR28svuPjybIESwAknncKJJ53Cls2b+WnC2Ez7ihQtGvfcFSodQr36Ddi2bRvz5s7KtK/O0f+gztHHULhw4ZQ/S4NGp1H18BqZRiUkWk5q3IQz4v68VaR9h2sAGPf9jp+3YUMGsnzZUs5p1WZ7oATBz1SXu4PRyHf6vhr3XOnp6dz1z2uocmg1br3rvoR9cnfe7RdM1d51f49MfTuz+bnUb9iIGdN+44eYfsUaOewTPnz3Te7r8QQVYgJ+yU45S9m5+0pgFNAQOMDMMgZ5qgALwvfzgUMBwv1lgOWx5VmOSVSekIIlkV2UVqgQAAULpjZAm5ZWKPyaWv1lSxcz+acJFC5ShOpH1Ny1Tsp+o1D4c5OWtiMHaex3XwNw6hlnZat/QsPGFCtWnJ8mjGXzpk3Z9r/Y6zF++3USj/XuQ+EiRRKed+7smSz4ax7Vj6jJoVWrZdufce6MvsRatmQx993RmTNbnEerNpck/4BCxrPh9vf/crwKZgeFI0qYWTHgTOA34CugTVitIzAofD843Cbc/2WY9zQYaB/eLVcdqAn8AIwHaoZ31xUmSAIfnKxPmoYT2QXp6ekM/uBtABqfnv0XVVYL5s9l7HejKFasOMc3aBS3zq+TfuTrkZ+Rnp7OooUL+GrEUNatWc09/3mSsuXK52r/Zd+Snp7Ox+8HP2+nxPy8zZwxHYBqh2cPptPS0qhyWFWm//Ebc+fMypSEPfmnibz87ONcd/MdmUak4tlxjhpx91erHpTP+nNGtn333XUz29x56PFnk55DQpZvcpZyUgnoF+YtFQAGuPsnZjYVeNfMegA/ARl3p7wGvBkmcC8nCH5w9ylmNoAgcTsd6BxO72Fm/wSGAwWB1919SrIOKVgS2QW9Hn6A6b9P5dQzmtG4yZlJ627etIl//bMTmzdt4o77elDmgLJx602Z9CMvPv3I9u0SJUvR4+mXOV9/ked7T/a4n2m/T+W0ps0yBUtr16wGoFTp0nGPK1W6DABrVq/aXrZxwwbuuvkajqhVm863d8vx3GvXrMrhHKXDc6zMVP7B2/34YtgnPPNKf8ofVCHH80hAsRK4+2Tg2DjlM9lxN1ts+UYgbmKeu/cEesYpHwoMTbVPmoYT2UlvvfYifV/pzeE1avFI7/8mrbt161buvuUafho/hhbnX8RVN9yasO7FHa5hyl9r+fHPpQweNYELLr6cbrdey7+73pLbH0H2If1ffZHXX+7N4TWP5Inn4+cfJRLMRJApT+3x/9zH/DmzeKx3HwqFU8m7I9455s+dQ88HutLivAtp2eqi3T6HSF7TyJLITni77ys88sC/OKJWbV5771MOKFsuYd2tW7fS9eZODP9kIM3Pu5BHn3stpeTqIkWLckTN2nR76Ak2b9rEgLdep+Epp9Ps3Aty86PIPuCt11+hx313UaNWHfp98Em2n7eSpTJGdVbHPT5j5Cmj3g/fj+Z/b7zCzXfey1FxbkSIp2SpjNGpROdYk6kewD233UDRokXp/mivlM4hgSDBW2NLUaSRpYgxs9lmlnxeZ8+de5SZXZNkf99wrjhf6v/fF+h57x3UrH0Ub7w/lIMOTjy1kJ6ezl03Xclngz7gnAva8fgLb6Sc2B3rlDPOBjLfcSf5Q98+z/PQPbdTq/ZRvPnhUA46OPudZIfXCHKVZs+cnm1feno68+fOIS0tjcOqVgdg6q+TcHd6P9GDWhVLZHplLBtwVJUy1KpYgqm/Tspyjuw5SQCzZwXl1Y/YkdM05ZdJLFu6hIZ1q2Y6x91dbgDgpWcep1bFEtx45cW7dG32Z5YPXvuiPB9ZMrPZQAVgK7COYA7xZnffoyuXmdmVwBsES6ffHlPemmBNh37ufuVunqMaMAsoFLOQ1u601xeY7+73xZTl6jli2r0SuMbdG+dWm/uyV194ml4PP0Dtuv/g1XcHJ0243rx5M3fc0IEvh3/C+W0upWevlxOuP5OTRQuDu1lTveNO9g99nnuKJ3s+QJ2j/8Eb7w2h3IHxf94aNjqNwR++xzdfjuDcC9pl2jd+7Lds2LCeExo23n63W83aR9Hm0o7xmuKzQR+ybt1a2lzSAcwoW/ZAAA6rdvj/t3fncXbN9x/HX+9EIrGLoCJBrdVGEPuuqF3RVu1qK0qLNtWirdZWaidKqZ1WbbX8bJHEVrGUxFZFbYnYSYglkUjy+f3x/d44GZObmYg55868n33MY+4958y5n7k9cj/z/X7O50uvxfvwyksvMHrUyC/cEXf/3YOnxVKzw067MWHC+C+8xqiXX+LRhx9gxb79+Fa/VVs8utWhNGo20c5V5V/g7SJiiKTFSdXpvwVmurBdS0maYwaJxEvAzpJ+Vdi/FzDjNSOsIdYMkgAAIABJREFUwzn/zJM597QT+Fa/Vbnw7zfXnXqbNHEih+2/G/ffPYjv7/oj/nDKwJkmSo8+9C9WW2u9Lxz36siXuXDgqQBstNmWX/4XsYbw5zNO5uxTjqdvv1W55Jpb6l5vW263I6eecAy33Xw9e+73k2l3tk389FPOOvk4AHbd+/PB4vU23IT1Ntyk2XM9dP89fPLJxxx36sDpRkElscuP9uOMP/6BU4//7XRNKYfceSuPPTyMZZdfkTXX3WDaz/zuxNOafY1//uNKHn34ATbebEsvdzIDHaQpZcOpSrIEQES8LukOoC+ApF7AX4D1SbcD/iki/pr3zUlaLK/259S1wK8jYqKkjYGrgIHAz4HBwJ7NvORbpPVntgBuk9QDWBe4Eli4dlBewO8M0oJ7o4DDIuLevO9e4F+kNWv6AQ8Bu0XEe8D9+RQf5FqV7wDvAH8FViZ1DB1Eup1x+ltJZlF+X04kvS9zkkbJfh4REyQtmH+3tUj/3w8DDoqI15qcY0XS+95F0sfA5IhYIO9eUNJtwIak2zF3i4iXZkfsVXTTtX/j3NNOoHPnzvRfc13+dsn5XzimV+8l2XHnPQA49sjDuP/uQSzYYyEW+dpinH/mSV84fo11NmDNdTec9vxn++7CvPPNT79VV+drvXozefJkRo96hWH3Dmby5Mnsvu9BrNvkA+7lF5/nonPPmG7bh+M+4OjDD5z2/IhjTpxuBGzonf/H0DtvBeC9d98G4Mnh/572Mwv2WIgjjvljq94fm73+ec1VnH3K8XTu3JnV116XKy764vXWu88SfG+X9M/ZPPPOxwmnn8uh++/Ont/bkq13+AELLLAgQ++6nVde/B9bbrsj22z/gy+co7X2PfBQ7h18J3feeiM/2Goj1tlgY958fTR3/t+NdO8+Fyeddf4sj57a9FyyVE2VSpYk9QG2Bv6ZN10NPAP0Ar4BDJb0ckQMBX5D6ui5CinpuJk0IvW7/LNfA3oAS1K/NusK0mjSbaTeDDeTVjyuxbR43rcncCewKXCDpG9ExLv5sN1IqxePBu4AfkkaGduQNEW2QG3kStKywEmkRGo+4AbSAoCHt/iNqu9PwNKk9+Uz4O/AMcBRpPfhUlIi1Rm4BDgX2KF4goh4VtJBND8Ntytp9eYRpIUMTyT3tCiSdABwAMBii/dpurthvD56JJCKta+86M/NHrPGOutPS5Zqx78/dgznn3lys8cf/AumS5YOGXA0D953N0+OeJR7B9/BlKlTWKjnImyyxbZ8f7e9m21N8N47b3PzdX+bbtuECeOn23bIgKOnS5aee+apL/zM6FGvMHpU6g7eq/cSTpZK9tqro4B0vV12YfPX25rrbDAtWQL4zlbbcdWNgzj/rFO467abpy2ke9SxJ7PX/gfPlo7tXeeck8uuvZULBp7OrTdey2UXnss888zHZltux6FH/IZlV2huJQqbFc6Vqkm12z5LCyDVLPUkNYwaR0pMBuRtI0mJxkf52JOAxSJib0kvkWqbbs/7tgAuiIil8sjSXcB8uf9Cc6+7N7A/abRnJLA8aZRnACnx6Z1f59dA34jYs/Czg4C/R8TleWRpSESckPcdDHw3IrZsST1RrpH6fUSsWng/9o+IIc0cexkpMSn+Tp2AeUmrMk8hjZT1q432SFonx/r1Zs63CnBPRCyYn98LXBURFzVXs5Rff3JE7J+fbw2cERF1lxzvu3L/uPYOFyhb2+jS2R831ja+t/n6PP3kiNl2wa240qpxxS33zq7TVdaaSy8wfGZrw1VNVUaWdmiaHOQpuLG1RCkbBdTe4F75eXFfr8Lzd2eUKBXl6anbSKNSPSNimKStCocsCewkabvCti6ktus1bxUejwdmuKqppEWAc4ANSElOJ+D9mcVZcNoMCrwhTR3OBQwv/DUp0igSkuYCziSNDNU6I84rqXOtq2kLtPh3NTMzaw+qkiw15w2gh6R5CwnTEsDrhf1LkqbpavuKC+G1ZsjsCuBu4Nhm9o0GroyIH7fifPViOClv7xcRY/LI0rmzcO7mvAdMAL4VEa83s38AsAKwVkS8lUeWHmfGKzObmVkbSbfWe2S0iipbkRcRo4EHgZMkdZPUD9gPqBVdXA38Ni+415NUl3PVLL7cfaTpuIHN7LsK2E7SFpI651g2ltS7Bed9F5hKqiGqmZc0VfZBroc6YhZj/oKImEoqHj8zj2AhafE8RVl77Qn5tXsA9W5HeRvonRcZNDOzr1peG669fzWiyiZL2a7AUqQRoxtJtT2D874TgMeAp4CnSQXHs9QwMZKhETG2mX2jge2Bo0nJz2hSgjPT9y4ixpMKoIdJ+iDfVXcs0J/P67P+WecUs+LXwIvAw5I+BIaQRpMAzgK6k0agHiYVrM/I3aRRu7ckvTebYzQzs2aU3TCyLb4aUekF3tb+ucDb2pILvK2tzO4C72/2WzWu+r/7ZtfpKmu1peZ3gbeZmZnNCrlmqaKcLJmZmVVEo9b0tHdOlszMzCqgkWt62ruqF3ibmZmZlcojS2ZmZlXhoaVKcrJkZmZWES7wriYnS2ZmZhXhAu9qcrJkZmZWEc6VqsnJkpmZWRX4drjKcrJkZmZWEa5ZqiYnS2ZmZhUgXLNUVe6zZGZmZlaHR5bMzMwqwgNL1eRkyczMrCqcLVWSkyUzM7OKcIF3NTlZMjMzqwgXeFeTkyUzM7OKcK5UTU6WzMzMqsLZUiW5dYCZmVkFpAbe7f9/M30fpD6S7pH0rKRnJB2Wt/eQNFjSC/n7gnm7JJ0j6UVJT0nqXzjXj/LxL0j6UWH7apKezj9zjlR/AtTJkpmZmVXJZGBARKwIrA0cIumbwJHA0IhYDhianwNsBSyXvw4AzoeUXAG/B9YC1gR+X0uw8jEHFH5uy3oBOVkyMzOrAqUC7/b+NTMR8WZEjMiPPwKeBRYHtgcuz4ddDuyQH28PXBHJw8ACkhYDtgAGR8TYiHgfGAxsmffNFxEPRUQAVxTO1SzXLJmZmVWES5amJ2kpYFXgEWDRiHgTUkIlaZF82OLA6MKPvZa31dv+WjPbZ8jJkpmZWVV0jGypp6THCs8vjIgLmx4kaR7gBuDwiPiwTllRcztiFrbPkJMlMzOzSmhZAXQ78F5ErF7vAEldSInS3yLin3nz25IWy6NKiwHv5O2vAX0KP94beCNv37jJ9nvz9t7NHD9DrlkyMzOriLLriapQs5TvTLsYeDYizijsugWo3dH2I+Dmwva98l1xawPj8nTdIGBzSQvmwu7NgUF530eS1s6vtVfhXM3yyJKZmVkFiI4yCzdT6wF7Ak9LeiJvOxo4GbhW0n7Aq8BOed/twNbAi8B4YB+AiBgr6Xjg0XzccRExNj/+CXAZ0B24I3/NkJMlMzOzqnC2REQ8wIzfiU2bOT6AQ2ZwrkuAS5rZ/hjQt6UxeRrOzMzMrA6PLJmZmVVEBynwbjhOlszMzCqiJQXQ1vacLJmZmVWEc6VqcrJkZmZWBS28td7anpMlMzOzynC2VEVOlszMzCpAeGSpqpwsmZmZVYRzpWpynyUzMzOzOjyyZGZmVhGehqsmJ0tmZmYV4aaU1eRkyczMrCqcK1WSkyUzM7OKcK5UTU6WzMzMKkBuSllZTpbMzMwqwjVL1eRkyczMrCqcK1WS+yyZmZmZ1eGRJTMzs4rwwFI1OVkyMzOrCBd4V5OTJTMzs0qQC7wrysmSmZlZBQiPLFWVC7zNzMzM6vDIkpmZWUV4ZKmanCyZmZlVhGuWqsnTcGZmZmZ1eGTJzMysCrw2XGU5WTIzM6sA4aaUVeVkyczMrCqcLVWSkyUzM7OKcIF3NTlZMjMzqwjXLFWTkyUzM7OKcK5UTU6WzMzMqsLZUiW5z5KZmZlZHR5ZMjMzqwgXeFeTkyUzM7MKeHzE8EFzdVXPsuNoA++VHUBrOVkyMzOrgIjYsuwYrHmuWTIzMzOrw8mSmZmZWR1OlszMzMzqcLJkZmZmVoeTJTMzM7M6nCyZmZmZ1eFkyczMzKwOJ0tmZmZmdSgiyo7B2jlJ7wKjyo6jAfWkATvdWkPytTZrloyIhcsOwr56TpbMKkrSYxGxetlxWPvna82sPk/DmZmZmdXhZMnMzMysDidLZtV1YdkBWIfha82sDtcsmZmZmdXhkSUzMzOzOpwsmZmZmdXhZMnMzMysDidLZmZmZnU4WTKzmZLUpewYrH1T0rnsOMya47vhzKxZkpYAFgWejIhJ+YPsRGAs8HhEDC41QGsXJC0ITI2Icfl5J2BNYHxEPFVqcGaZkyUza5ak84DNgD2Ax4GHSeuHzQl8ADwYEaeUF6E1upwonQ28BPwxIj6T9ATpGvuIlKj/uMwYzcDJkpnVIekyYEXSB9oGEfETSfMDewLfAR6KiJNLDNEanKTfAP1JyXhPoDtwJLAB8Dvg9YjYubwIzVyzZGaZJDV9HBF7k0aVTgeWljRvni65CrgbWEPScSWEaw2qdm3V6pMi4kRgGLBG/hoUEeOBe4GfA0tK+ls50ZolTpbMDIDIw8yStik8PjgiDgKuB5YC+krqEhEfAJcBjwHLSFqonKit0cTn0xnzFbadAQwC+gA7SJo/IiYCI4BDgNUlXdLmwZplnoYzs2kkbQUMAAaTptoejIgD8r4rgBVIf+0/mutL5gM6R8T7ZcVsjUfST4EdSfVwjwJnRMQZkg4FNgUeAC6KiPclzQGsBLwfESPLitk6No8smXVwkpaVdDpARNwB3AkcB0yqJUp5317A88ApwLqS5oiID50oWUtIWlTSavnpg8AY4ElSQn4GQEScA9wHrAfsK2mhiJgcEY87UbIyOVkys6nAzyXVVp5/hVSP9KKkwyUtXDswJ0xvAH8A5mjrQK0x5dGhs4GjJK0SESOArkCXtFvz1o7NidP9wHbAbrmVgFmpPA1n1oFJ6hwRUyQtCwwHLgeOiIiJkg4jTYncD1ycp0RWiYgnJC0aEW+XGbs1FknLA6eRRpT+DLwNzA8cBcwL/Lh4TUnaHbg/IkaXEK7ZdJwsmXVweTptsqTlSAnTdRGxX943gHQL91PAJGB/YJ2IeLO0gK3hSOoUEVMlLQMMBN4CBkbE45LWAQ4ltQz4PnAg6Rrbs7yIzabnZMmsg6qNKjXZtjyp4PbaWjNASQcDq5F64ewbEY+3ebDWsCQpIqKQMC0LnEMaWRoYESMkrUXqrbQyaXp3p4h4pMSwzabjZMmsAyqMJolUTPseMC4i3pS0Ap8nTPvXjge6R8RH5UVtjaZ2neXHAuaKiE/yNXYWn48wjZA0F+la/F9EjCovarMvcrJk1sEU/sLvBPwLmAeYAjwL/CkinsofZg8Cd0XEriWGaw2qUA/XCfgb0I1U0H1MTo5qCdPrwKURMazEcM3q8l0GZh1MTpRE6pA8KiJWBg4CvgmcKWm1iHge2BBYT9LXyovWGlUhUXqUNLV2HfBfYEjhGjuMtJzOLpK6lRetWX2+9desY1oZeIbUHZn8vTvpr/w/Sjoq//W/TER8VlaQ1vCOAt6KiJ0AJG1EWiR3mKQNIuJRSXuRenp9WmagZvV4ZMmsA2imV83LwPl5lOlUYKWI+AZp+ZJVgGPzX/pTMGuh4vqC2bXAyXnfpcA6wMKkRP0RSWtGxEtuD2BV55Els3auSTH3N4APgLG5NmlOYGnS6u6Q/k04F/iL/9K31igWc+fnXSLiBUmvSFqftGTJGrmH1z3AQ4BvGLCG4GTJrB3Lt21PziNL9wOTSY0AH5R0NekDqwfwXUmbktbq2jAi3i0taGs4uZi7dp39hdSd+2NJV0fEMEkTgYnA2rmwe1tSLyUvlWMNwcmSWTtS62nT9DGpuHZkROwhaXXS3UkTI+IBSWeTmgEuCmweEc+VErw1rFzMLWAY8BJwB9Ab+JeklYAPSfVwJwJLADs6UbJG4tYBZu2QpLkiYnyhVuk6YEBEjJR0ManJ5JqkUab38jFzuJjbZqZJEl6bbvtM0neAIyNi07z9AmBdYOVcG9cDWBCYEBFvlBK82SxygbdZO5M7bt8qaYGImAr0BPoCfSSdBawBrBURk4D9gE0icaJkM1UYuTxV0oKF62YSMD7vu5JUzL1aTpQOAjrlYm4nStZwnCyZtT8vkkaLLpTUIyLeAS4GbgW+GxH9cpHtT4EDSNMmZi2WRyzXAUZImj9v/pRUk3Q38HVSMfckSb8EtgemlhOt2ZfnaTizBtZ0SqS2Dfg2sA+pd9I+pA+q44FtSIlTL+CHwDZe681ao7DW2xzADcCqQL+I+EDSIaSFcncmLcq8LXAMsGlEPFla0GZfkpMls3ZA0sJN72CTtAtwHnA7cHBEfJi3rQG8AdyauyibtUpxEebcBmBBYKOIGCfpQGBXUouKAP7gRMkanZMlswYn6XfA+sBPgFfzLdy9SW0BhpDW45oTOMB3INmskvQTYHJE/LWwrTfwAOnO6iCNML2fl8gZA3SNiE9KCdhsNnLNklnjuxBYgHRbdjdJS5EWwT0/IvbJ+6cA1xbqS8xaTNI8pBHJzSTtmbctRkrI/xoRvYERwGO56PutiPjMiZK1Fx5ZMmtghe7cPUm9bd4G+pMSpeNrxwAbA7sDv4uI18qK1xqXpIWAo0n9uIYDvwDOi4iT8v45gUGkKblVmtbSmTUyJ0tmDahJzUjn3BSwJ3ANsBiweu6zVCvGFdAtIiaUGbc1JkmdcguAnqSEaUfgiYjYMe/vmu98mwNYzGu9WXvjaTizBiFpGUnnw7SOyZ2LjyPiPdIdbp8AF0tatvbXfe6j5ETJZkpSL0lLF7flRKlTvsaOJ90F97Gk3fL2Sfn7ZCdK1h45WTJrHJ2A/SRdBjNMmMYAW5OWlDg71y+ZtUgegbwY+IukZYv7CgnT+8DJpF5e25FaU5AboJq1S06WzBpERLxAWrl9i9wheUYJ07ukaZI5AXflthbLI5G7A92AkyUt12R/cYTpBOAjYH1J87V9tGZtxzVLZg2iUH/0DeAeYEhE1O5Maq6GqZP/2rfWKFw7C5D6c70BHJUT9eJxtWuxB9AlIt4uI16ztuJkyazimkt6csJ0LzC4kDBNd1xz3b3NZqSQANUSpgVJCdPrpAVyX2zu+FKCNWtjnoYzq7DcGmCqknUlLZ/Xe3uOtKTJ5oUpual5zS7yc3+QWYvk66x2vUyVNE+uTdoG6E2akmtaw+TryzoMjyyZVVThdu1OwDCgK6mHzS3AlRExXNKKwGBgeERsX2K41qCK07bAFaQGp2OAqyJicO6vdBswkrR0yXPlRWtWDo8smVVUbUQJuA8YFRGrAXsDGwG/kbR2RDwLbAl8Q1Kv8qK1RlVIlP5NWhrneuAV4EJJmxfusFwFOFJSl/KiNSuHkyWzCshJUXP6A09HxC75+V7AfMBEYEBOmP4DrBQRb7RBqNbAitdZbiBZcwTwVkTsHBGXAcsA3YFLcsI0FliHNLLkOyytw3GyZFYBtfoPSdvWtkn6QUQMBy7Nz08HVouIZYBHgA2BH0vqjlsEWAsU64zyMjlz5DvfbgOOAZB0MdCPtBbcaOAWSVtHxPsRMbKEsM1KN8fMDzGztiBpY+AkSSsBuwH/I02JDM9TH32AAfnwqcCVwJ/cmdtaQ9JAYP6I2At4GjiWdJ2FpPWBVYF1I+ITSY+TauJemOEJzToAJ0tm1fEocDZwPvB4RHw/b+8MzAV8C9hN0neAfYENcgNKs9Y4B3hI0mvA3RHxj9oOSZ+ROnOvkpP2jYGNfJ1ZR+dpOLOS1epIIuITUi3SCOATSQPy9s8iYhxwOGnF928CW0bE/0oK2RpUvvPtBeA10oLLHxb2CfiA9Lnwe9K03B5OlMzcOsCsVLm/zeSm24BdgYOBmyLiT3l7r4h4Q1IXF9laaxQ7vOfn3wTGA48BN0fEfoV9KwCTgfER8WabB2tWQZ6GMytJ7oA8Od+2fTmph9JNpGLbf5D6Ku0jqSvwNjAwtwcYW1bM1nia9FE6EBgFvBoRIyVtAdwlaWpE/FjSoaQ+S8e76aTZ55wsmZWgyYjS3cBbwJOkEaUVgfOAy4BPgJ+RbuNeN/e8MWuR3Ni0lig9Rqp/Gw+Mk3RqRAyVtAlwv6RvkRZq3tiJktn0PA1nVpL8AXYIsHhEHJm3bQPsB7wKnBsRL0rqTLp7ySNKNkskHQisHBEHS+oP7ABsBhwTEUNy+4DNgEcjYlSZsZpVkZMls5JI6gc8AbxD6p/0et6+Lan55DjgrIh4prwordFJOouUCA2KiAF52wqkbvDrAqdGxK3lRWhWfb4bzqyN5BGiaSLiKWA9Uo3IfoXttwLXkGqW3mvLGK3xFRdTzq4lTfMulxN0IuJ54BJSsv4zSXPV6SJv1uF5ZMmsDTQpst2BVDvyRES8IGkDUt3SCRFxbOFn5omIj0sK2RpQsRZO0vxAp4h4X1JfYCDwFHBJRDyZj1kWGOf2AGb1OVky+4rlu94iJ0qPkAps3yM1/PtlRFwqaUPgTmBgRPy6vGitUeVi7qn5OhsCTAD6AieRRir7AGeRRpOuiogRpQVr1mA8DWc2m9WZzjgOeD4iNsrdud8FtsmjAfeTRpz2kdSzrWK19iMipuaHd5Hq4LYDfklazmS3PO17BClJ30nSnGXEadaI3DrAbDYrLIq7BvDf3JkbYHFgWN53JWnx212A3pI+jYi7JC0VEePLiNsaR220Mj+eO6/jJmB+4JOI2CXv24jUo+sCSfNGxHBJ+wNjImJiab+AWYPxyJLZV0DSucAtwLa5dgTgZWCBnCj1Jd0BNxn4CfDTXAD+aSkBW0MpJEoXA2dJWjhvmx9YR1J/SRcAGwJr5OvsZ5K+GREj3B7ArHU8smQ2m0maC1gY6AFsnzbpBlJTwPOAADaMiEmSDgP2ADYtLkdhNjOSlgd+ALwE/E7SKRExStJlwBVAl4hYIR/7M2DPvN3MWskF3mZfgbxi+/nAx6QR3Isi4lpJuwAHkTpzfwSsBXwvIh4vLVhrSJLmI93+Pw/wH6Ab8FtgaeB4Uk3cA6QFc39KWnzZ15nZLHCyZPYlNKkd6R4RE/LjeYEBpNGkfsC2wJkRcV2+XXtJYArwYkS8Vk701iiK11mT7d8GzgT+CaxASpB+R6qP2wjYAngW+Lubm5rNOidLZrOBpMtJf9kPjYgL87YfkdZ1WwM4BtgUOAe4o1D0bdZieTrt3Yj4R6FVwGmkVgFzAPsDI4ETI+Ld2jElhmzWLrhmyexLkrQy6Xbsj0nFtRuQ7nr7B7AKsGtEHJtv1T4amCLpJi9Waq2R72I7GwhJqwP/lXQdaeToFxGxuaQ5gJ2BUyQdGRFvlxiyWbvhZMnsS4qIJyUNIPVJeoH039XXgOGkaZGlSNMgR0uaCIxwomSz4N/AqcAGQE/SVNu/gV+RljLZOiJuyrVMG5UXpln742TJ7EuoTXNExPWS5iHViLxMmm4bAuwOjK0tXVJczsSsNSLiqcK6b2uTirifBDYnJU+98nFXSLoxIj4qJ1Kz9sc1S2atkBOiSRExqbCtWOS9J+l27v8AZ+W6kWnrdZnNiibX2MrAgcCKwAF5fcGlSWu8jSkzTrP2yk0pzVpIUg/SgrfTLRWR131TfnwlaZX3vqQmgMs4UbJZkZuUAtOusc758ZOkthT/Bf4qaZWIeBkYW06kZu2fkyWzFoqIscBQUi+b7SR1L+wrJkx/IxV3rw/smotuzVosjyRNkdRF0g4AxaalEfE08BfSNNxlkvq6Ds7sq+N/xM1aoFCbdJSkT0itAKZKurU2JVdLmCK5WtJk4CGPLFlrSOpcSIyGkm4UuKnpcRHxdO7WPZHU4NTMviKuWTJrgWK/mlyXNIDUGflnwE1Napjc28a+lDxK+WugR0T8aibHdi1ef2Y2+zlZMmuh/AH2DPAocDvpzretgENJCdNnJYZn7UjuzH078L+IWDlvcxJuVhLXLJm13Mqk7sk/iohrImJfUqHtRaQapm7lhmeNqljMnQ0D9iX1T/o5QO7W7X+zzUrg//DMZqBWsF0wFVhN0vq1DRFxHKkR5UXAd9owPGsncmuJKUo2lNQfWDQiria1CDg8Nz11wmRWEhd4mzWjSZEtkrrkpoB/B34g6e2IeCHvvotUv/SfMmK1xpVvCJicE6BhpMWVuwHdJP0iIq6UNBU4Li/UfIKn4szanmuWzJqoJUr5A2wg0BnoTmoZsBSpRmkKcA8wP+mv/7Uj4t1yIrZGJ+kCYM6I2FvSMqSlc04ANo+If0naB/g5aQ3C990mwKxteTjXrInalAjwCLAAqfmfgOeA8aTFcP8L7AOsC/zQiZJ9SQuQrjeAlyPidNI6cAdL6gpcDawfEWOdKJm1PU/DmTVvE+DDiNi9tkHSSNKU2zcj4jhJpwBTfdu2tUbTu9pyMvQhsCSkfl151/+A1QvX16dtGqiZTeORJbPmTQT6SFqhtiEijgGeBTbMzz91omStkYu5p+Zi7mUlLZ6vocuBQyTtl5fVgTTFO1XS3OVFbGbgkSWzLxRzZ6OA14G1Jb0eER/n7ePaNjprL5oUc98HTAaWljQY+COwE2kJkz0kfQCsA2wZEZ+UFrSZAS7wtg6uSTH30aTakReA64FvA8eT/ur/H9CTtMzJBhHxSkkhWwOqLYOTr7PrgE8iYi9J3yWNVPYH9gAWBL4FzA3clxfINbOSOVmyDi9/gD3G5wXc8wLLAduSGlHuCixLqis5KiIeLylUaxC15Cg/7kT6t7aWlN8MnBoR9+f9KwJHAE9FxFmlBW1mM+RpOOuwCoW2uwMfRMRueXtv4JfApcCOpMVMOwOdPCViM9MkUTqNlHiPkXRDRNwmqQ+wEnB/vgaflfQm6aYCJ0tmFeQCb+sQit24JX09T7/V7kiaAEySNEfe/hpwJbAQsGxETIqICU6UbGaaJEo3A2uT6pPmJnXingc4E/ijpK0K1+D7wFuS/AesWQX5P0xr95p8gJ0DLA48L+m3+cPqfVK/pDUi4iGAiBiSXtCpAAAHeElEQVQu6RNSDZNZixSus2uBPhHRPz/fhFS83SsiLpe0MHCLpGtIhd5bA5tFxOSSQjezOpwsWbtX+AC7iVSPtA9Qu4W7a0QMzdMl90jajVTgvSap782LZcVtjUnSIqSi7WskzR8R44BXSCOYXQEi4jRJTwDrkdpUnBwRz5UVs5nV52TJOoTcQLJ3RKxe2LYUcJ6ks3KTyQ+B44AxpGmT70XEG2XEa40rIt6RtBlwDTBR0vXADcC5EfEfmDbaOQQYUmKoZtZCvhvO2j1J3Ug1SJdGxO15W2/gQdIUyEfA4RFxT94+gTQgNbasmK3xSVoJuBb4OnB2RPw6b2+ur5eZVZgLvK0j6Ab0BeYqbOsPnBIRSwP3A3+X1DciXouIMU6U7MuKiKeB75KmcqdIWjSPKDlRMmswnoazjmAKqS5kFVKzSSLiltrOiPiZpOWAecoJz9qriHhB0q6kRpRzA6eQOsObWQPxyJK1exHxEfAn4Je5gHs6kg4EegDulmyzXR5h+j6wC3CopM4lh2RmreSRJesobiRNxZ0vaTHSX/pdSetx/QrYOCLeKTE+a8ci4hlJGwOTPA1n1nhc4G0dRm4IuDdpvbcxwFhgEvDTiHiixNDMzKzCnCxZhyOpF/A1YBwwJiI+KDkkMzOrMCdLZmZmZnW4wNvMzMysDidLZmZmZnU4WTIzMzOrw8mSmZmZWR1OlszMzMzqcLJkZmZmVoeTJTMzM7M6nCyZdTCS/iApCl9vSLpB0jJf8eteL+neJnG814qf75p/ZpXZGNNPJdVtNtfaOAs/F5J+OuvRTTvPUvlc237Zc5nZrHGyZNYxjQPWyV+/BFYBhkqauw1juAjYohXHdwV+T4rVzKzNeCFds45pckQ8nB8/LOlV4F/A1qRFhqcjqTPQOSImza4AIuI14LXZdT4zs6+KR5bMDGB4/r4UgKTLJD0maQdJzwCfAmvlfUtI+oeksZLGSxokaYXiyST1kXS7pAmSRkrav+kLNje9JWkhSRdIelPSp5Kel3R43v1R/n5pYQqxFm83SadIGi1poqQnJW3d5NxzSjpX0gc59jOBLq19oyTNnc/zfP79X5H0Z0nzNXN4V0ln59f7QNJASV2bnG+m76eZlcsjS2YGOUkC3mqy7RTgOOBt4BVJPYAHgDHAQcB44EhgiKTlI2KCJAE3Az2B/UiJ1rFAD+CFGQUgqTtwL7BIPv45YNn8BbAJcDdwAnBb3vZm/n49sCZpmu4l4IfALZJWj4gn8jEnA/sDvwH+C/wY2KkF701TcwGd83neBfrkx9fxxWnFAcDDwO7At4ATSe/HEfl3nun7OQvxmdls5mTJrIOSVPvvf2ngPNLIzZDCIQsBmxWSDSQdD8wNrBIRY/O2YcBIYF/gz8BWwKrA2hHxSD5mOCmJmWGyBOxFSij6F17z7sL+R/P3lwpTiEjaFNgG2Dgi7sub75K0PCmJ2UnSQqRk5PcRcXr+uUGkpKlVIuJd4CeF158DeAV4QNISEfFq4fCPgJ0iYipwh6Q5gd9IOim/fz9n5u+nmZXM03BmHdNCwGf563lSwrRzRLxZOOb1YqKUbQYMBj6UNEdOFD4iTeOtno9ZE3i7ligBRMQoPp/qm5FNgMebec2Z2Yw0IjasFlOOa2ghppWAbqQRr1pMU4vPW0PSnpIel/Qx6T18IO9avsmhN+fXqfkn0B3oW4h9Zu+nmZXMI0tmHdM40gd1kBKNNyKi6S30bzfzcz2BtYGdm9k3NH//GvBOM/vfAeatE9NCfD6t1ho982t+1sy+KYWYajE0jalVJO0IXAGcDxwNjAUWA24kJWT1zl97vlj+3pL308xK5mTJrGOaHBGPzeSY5voPjQVuAY5vZl+tAPstUt1RU4sA9WpwxvB5fVJrjAVeB3aoc0ytFmuRfHwxptbaCXgkIg6ubZC00QyObXr+2vNaUtiS99PMSuZkycxaYyipePqZOsXHjwK/l7RWoWZpCaA/MGwm595JUr+IeKqZ/bW2BU1Hb4aSCqk/jojnZnDup0mF1duTCseR1Ck/b63uwMQm23afwbHbSzqqMBX3PVLC+J9C7DN7P82sZE6WzKw1zgD2AO6WNJA0orMosBHwQERcDdwOPAlcJ+nXpCTlOGY+5XUFcAipOPsPpFqqrwPLR8SRETFJ0ivADyX9J5/3KVLNzyBgsKQ/Ac8A85GaV3aLiKMiYoykC4FjJU3Ox/wYmGcW3oPBwJ8l/QZ4hNSbatMZHDtvfh/+SipePwY4t1bMTcveTzMrmZMlM2uxiHhP0tqkW+DPBBYgTSk9QEpciIiQ9F3gQuASUpL0R+A7pBqdGZ37U0mbkG7xP46U8Iwk3alXcxBwGumuvTmBr0fESEnfI9UPHQ4sQZreegIYWPjZX5H6Kh0DTAWuIiUrp7fybbiAVBB/GGmUazCwG6lFQFOn52OvJt1Qc1GOs/Y7z/T9NLPy6Ys1nWZmZmZW49YBZmZmZnU4WTIzMzOrw8mSmZmZWR1OlszMzMzqcLJkZmZmVoeTJTMzM7M6nCyZmZmZ1eFkyczMzKyO/wfVewiKMnv4TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "############################################################################################################\n",
    "# CLASSIFICATION REPORT ###\n",
    "\n",
    "print('\\n')\n",
    "print(\"=== CLASSIFICATION REPORT ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"ROC AUC score:\",metrics.roc_auc_score(y_test, probs))\n",
    "\n",
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "# True Positives\n",
    "TP = confusion[1, 1]\n",
    "# True Negatives\n",
    "TN = confusion[0, 0]\n",
    "# False Positives\n",
    "FP = confusion[0, 1]\n",
    "# False Negatives\n",
    "FN = confusion[1, 0]\n",
    "print(\"Sensitivity:\",TP / float(TP + FN))\n",
    "print(\"Specificity:\",TN / float(TN + FP))\n",
    "print(\"Precision:\",TP / float(TP + FP))\n",
    "print('\\n')\n",
    "\n",
    "#####################################################################################################\n",
    "# CONFUSION MATRIX\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion Matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    # Plot the confusion matrix\n",
    "    plt.figure(figsize = (8, 8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 25)\n",
    "    plt.colorbar(aspect=5)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 12)\n",
    "    plt.yticks(tick_marks, classes, size = 12)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(False)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual label', size = 15)\n",
    "    plt.xlabel('Predicted label', size = 15)\n",
    "    plt.ylim([1.5, -.5])\n",
    "    \n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plot_confusion_matrix(cm, classes = ['Good Mental Health', 'Poor Mental Health'],\n",
    "                      title = 'Confusion Matrix')\n",
    "\n",
    "\n",
    "############################################################################################\n",
    "# ROC CURVE PLOT\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, probs)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, probs)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr, tpr, label='Baseline Model (AUC = %0.3f)' % roc_auc, color='midnightblue')\n",
    "#plt.fill_between(fpr, tpr, alpha=0.2, color='orange', **step_kwargs)\n",
    "\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', size = 15)\n",
    "plt.ylabel('True Positive Rate', size = 15)\n",
    "plt.title('Receiver Operating Characteristic (ROC Curve)', size = 25)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('ROC')\n",
    "plt.show()\n",
    "\n",
    "###########################################################################################\n",
    "# PRECISION-RECALL CURVE\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from inspect import signature\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "average_precision = average_precision_score(y_test, probs)\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, probs)\n",
    "\n",
    "step_kwargs = ({'step': 'post'}\n",
    "               if 'step' in signature(plt.fill_between).parameters\n",
    "               else {})\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.step(recall, precision, color='navy', alpha=0.7,\n",
    "         where='post', label='Baseline Model (AP = %0.3f)' % average_precision)\n",
    "plt.fill_between(recall, precision, alpha=0.7, color='navy', **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall', size=15)\n",
    "plt.ylabel('Precision', size=15)\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall Curve', size=25)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with imbalanced data:\n",
    "\n",
    "## The data is imbalanced, indicted by two things: \n",
    "\n",
    "### (1) MENTHLTH2 value counts show twice as many '0' than '1' rows\n",
    "\n",
    "### (2) The accuracy scores in the baseline model for the '1' values are far lower than the '0', showing the model is biased. It's good at predicting 'Good Mental Health', but not 'Poor Mental Health'. I ran the Random Forest with class_weight option, giving 'Poor Mental Health' a higher weight. This improved scores a bit, but not by much.\n",
    "\n",
    "### There are various re-sampling methods for dealing with unbalanced data. We will utilize the 'Under-Sampling' technique. This technique drops rows at random from the 'majority class', or the over-represented value. In this case, the '0' rows will be dropped at random until both value's are equal. This can lead to a loss of information, if there is not enough data. Since we have almost 500,000 total rows, this should not be a significant problem. I also tried adjusting class_weight in the baseline model and also used SMOTE-NC for Over-Sampling, however, the Under-Sampling provided the best results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    307337\n",
       "1.0    142679\n",
       "Name: MENTHLTH2, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check value counts for each class of the target\n",
    "LLCP2.MENTHLTH2.value_counts().plot(kind='bar', title='Count (MENTHLTH2)');\n",
    "LLCP2['MENTHLTH2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are roughly twice as many value counts for the target in 'class 0' compared with 'class 1'...this is why the previous model was better at predicting 'class 0'. We'll now use under-sampling to balance the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Class count\n",
    "count_class_0, count_class_1 = LLCP2.MENTHLTH2.value_counts()\n",
    "\n",
    "# Divide by class\n",
    "Good_MH = LLCP2[LLCP2['MENTHLTH2'] == 0]\n",
    "Poor_MH = LLCP2[LLCP2['MENTHLTH2'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random under-sampling:\n",
      "1.0    142679\n",
      "0.0    142679\n",
      "Name: MENTHLTH2, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Good_MH_under = Good_MH.sample(count_class_1)\n",
    "LLCP2_under = pd.concat([Good_MH_under, Poor_MH], axis=0)\n",
    "\n",
    "print('Random under-sampling:')\n",
    "print(LLCP2_under.MENTHLTH2.value_counts())\n",
    "\n",
    "LLCP2_under.MENTHLTH2.value_counts().plot(kind='bar', title='Count (MENTHLTH2)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can see above that we now have an equal amount of observations for both classes of the target MENTHLTH2. We did lose a lot of information using this method, however, we still have a pretty large dataset to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 Under-Sampled Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's re-run the same model now, using the under-sampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_under = LLCP2_under[['SEX','_AGE_G','_BMI5CAT','_EDUCAG','_INCOMG','_RFDRHV5','_PACAT1','_RFHLTH','_HCVU651','EMPLOY1',\n",
    "           'VETERAN3','MARITAL','ADDEPEV2','POORHLTH','PHYSHLTH']].values\n",
    "\n",
    "y_under = LLCP2_under['MENTHLTH2'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows/columns in X_test_under dataset:  (85608, 15)\n",
      "Number of rows/columns in y_test_under dataset:  (85608,)\n",
      "Number of rows/columns in X_train_under dataset:  (199750, 15)\n",
      "Number of rows/columns in y_train_under dataset:  (199750,)\n"
     ]
    }
   ],
   "source": [
    "# 70/30 train-test split\n",
    "X_train_under, X_test_under, y_train_under, y_test_under = train_test_split(X_under, y_under, test_size=0.3, random_state=0)\n",
    "\n",
    "# describes info about train and test set \n",
    "print(\"Number of rows/columns in X_test_under dataset: \", X_test_under.shape) \n",
    "print(\"Number of rows/columns in y_test_under dataset: \", y_test_under.shape) \n",
    "print(\"Number of rows/columns in X_train_under dataset: \", X_train_under.shape) \n",
    "print(\"Number of rows/columns in y_train_under dataset: \", y_train_under.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 99974, 1.0: 99776}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the unique counts for the target classes\n",
    "unique, counts = np.unique(y_train_under, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# ---UNDER-SAMPLED MODEL---\n",
    "\n",
    "RFC_under = RandomForestClassifier()\n",
    "RFC_under.fit(X_train_under, y_train_under)\n",
    "y_pred_under = RFC_under.predict(X_test_under)   #yields predicted class 0/1\n",
    "probs_under = RFC_under.predict_proba(X_test_under)\n",
    "probs_under = probs_under[:,1]    #yields probability of either class 0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=== CLASSIFICATION REPORT ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.68      0.77      0.72     42705\n",
      "         1.0       0.73      0.64      0.68     42903\n",
      "\n",
      "   micro avg       0.70      0.70      0.70     85608\n",
      "   macro avg       0.71      0.70      0.70     85608\n",
      "weighted avg       0.71      0.70      0.70     85608\n",
      "\n",
      "Accuracy: 0.7019554247266611\n",
      "ROC AUC score: 0.761725666334119\n",
      "Sensitivity: 0.6370883155024124\n",
      "Specificity: 0.7671232876712328\n",
      "Precision: 0.733220666344761\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "############################################################################################################\n",
    "# CLASSIFICATION REPORT ###\n",
    "\n",
    "print('\\n')\n",
    "print(\"=== CLASSIFICATION REPORT ===\")\n",
    "print(classification_report(y_test_under, y_pred_under))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test_under, y_pred_under))\n",
    "print(\"ROC AUC score:\",metrics.roc_auc_score(y_test_under, probs_under))\n",
    "\n",
    "confusion1 = confusion_matrix(y_test_under, y_pred_under)\n",
    "# True Positives\n",
    "TP_under = confusion1[1, 1]\n",
    "# True Negatives\n",
    "TN_under = confusion1[0, 0]\n",
    "# False Positives\n",
    "FP_under = confusion1[0, 1]\n",
    "# False Negatives\n",
    "FN_under = confusion1[1, 0]\n",
    "print(\"Sensitivity:\",TP_under / float(TP_under + FN_under))\n",
    "print(\"Specificity:\",TN_under / float(TN_under + FP_under))\n",
    "print(\"Precision:\",TP_under / float(TP_under + FP_under))\n",
    "\n",
    "#####################################################################################################\n",
    "# CONFUSION MATRIX\n",
    "\n",
    "print('\\n')\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion Matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "\n",
    "    # Plot the confusion matrix\n",
    "    plt.figure(figsize = (8, 8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 25)\n",
    "    plt.colorbar(aspect=5)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 12)\n",
    "    plt.yticks(tick_marks, classes, size = 12)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(False)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual label', size = 15)\n",
    "    plt.xlabel('Predicted label', size = 15)\n",
    "    plt.ylim([1.5, -.5])\n",
    "\n",
    "cm = confusion_matrix(y_test_under, y_pred_under)\n",
    "plot_confusion_matrix(cm, classes = ['Good Mental Health', 'Poor Mental Health'],\n",
    "                      title = 'Confusion Matrix')\n",
    "plt.show()\n",
    "############################################################################################\n",
    "# ROC CURVE PLOT\n",
    "\n",
    "roc_auc_under = roc_auc_score(y_test_under, probs_under)\n",
    "fpr_under, tpr_under, thresholds = roc_curve(y_test_under, probs_under)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr, tpr, label='Baseline Model (AUC = %0.3f)' % roc_auc, color='midnightblue')\n",
    "#plt.fill_between(fpr, tpr, alpha=0.2, color='midnightblue', **step_kwargs)\n",
    "plt.plot(fpr_under, tpr_under, label='Under-Sampled Model (AUC = %0.3f)' % roc_auc_under, color='royalblue')\n",
    "#plt.fill_between(fpr_under, tpr_under, alpha=0.2, color='royalblue', **step_kwargs)\n",
    "\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', size = 15)\n",
    "plt.ylabel('True Positive Rate', size = 15)\n",
    "plt.title('Receiver Operating Characteristic (ROC Curve)', size = 25)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "###########################################################################################\n",
    "# PRECISION-RECALL CURVE\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from inspect import signature\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "average_precision_under = average_precision_score(y_test_under, probs_under)\n",
    "\n",
    "precision_under, recall_under, _ = precision_recall_curve(y_test_under, probs_under)\n",
    "\n",
    "step_kwargs = ({'step': 'post'}\n",
    "               if 'step' in signature(plt.fill_between).parameters\n",
    "               else {})\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.step(recall, precision, color='navy', alpha=0.7,\n",
    "         where='post', label='Baseline Model (AP = %0.3f)' % average_precision)\n",
    "plt.fill_between(recall, precision, alpha=0.7, color='navy', **step_kwargs)\n",
    "\n",
    "plt.step(recall_under, precision_under, color='royalblue', alpha=0.7,\n",
    "         where='post', label='Under-Sampled Model (AP = %0.3f)' % average_precision_under)\n",
    "plt.fill_between(recall_under, precision_under, alpha=0.7, color='royalblue', **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall', size=15)\n",
    "plt.ylabel('Precision', size=15)\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall Curve', size=25)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For this model, the average accuracy score dropped, though the classes are much more balanced. The AUC is similar. This model is better at predicting both classes, which is important. Let's tune the hyper-parameters, to see if we can improve the model further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Under-Sampled Model with Hyper-Parameters Tuned (FINAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We'll use RandomizedSearchCV for tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows/columns in X_test_final dataset:  (85608, 15)\n",
      "Number of rows/columns in y_test_final dataset:  (85608,)\n",
      "Number of rows/columns in X_train_final dataset:  (199750, 15)\n",
      "Number of rows/columns in y_train_final dataset:  (199750,)\n"
     ]
    }
   ],
   "source": [
    "# 70/30 split\n",
    "X_train_final, X_test_final, y_train_final, y_test_final = train_test_split(X_under, y_under, test_size=0.3, random_state=0)\n",
    "\n",
    "# describes info about train and test set \n",
    "print(\"Number of rows/columns in X_test_final dataset: \", X_test_final.shape) \n",
    "print(\"Number of rows/columns in y_test_final dataset: \", y_test_final.shape) \n",
    "print(\"Number of rows/columns in X_train_final dataset: \", X_train_final.shape) \n",
    "print(\"Number of rows/columns in y_train_final dataset: \", y_train_final.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['sqrt', 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
      " 'min_samples_leaf': [1, 2, 4, 6, 8, 10, 20, 30, 40, 50, 60],\n",
      " 'min_samples_split': [2, 3, 5, 6, 8, 10],\n",
      " 'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]}\n"
     ]
    }
   ],
   "source": [
    "# Define parameters to be tuned\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV  \n",
    "from pprint import pprint\n",
    "\n",
    "# Number of trees in RFC\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['sqrt',2,3,4,5,6,7,8,9,10]\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2,3,5,6,8,10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1,2,4,6,8,10,20,30,40,50,60]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False] # Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed: 33.4min\n",
      "[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed: 294.0min\n",
      "exception calling callback for <Future at 0x1c9aaa78c18 state=finished raised TerminatedWorkerError>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\_base.py\", line 625, in _invoke_callbacks\n",
      "    callback(self)\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\", line 309, in __call__\n",
      "    self.parallel.dispatch_next()\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\", line 731, in dispatch_next\n",
      "    if not self.dispatch_one_batch(self._original_iterator):\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\", line 759, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\", line 716, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\", line 510, in apply_async\n",
      "    future = self._workers.submit(SafeFunction(func))\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\reusable_executor.py\", line 151, in submit\n",
      "    fn, *args, **kwargs)\n",
      "  File \"C:\\Users\\Nick\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\process_executor.py\", line 1022, in submit\n",
      "    raise self._flags.broken\n",
      "sklearn.externals.joblib.externals.loky.process_executor.TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n"
     ]
    },
    {
     "ename": "TerminatedWorkerError",
     "evalue": "A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTerminatedWorkerError\u001b[0m                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-2243ed4d4f09>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m                                verbose=1, random_state=42, n_jobs = 3)\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# Fit the random search model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mRFC_hyper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_final\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_final\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    720\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 722\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1513\u001b[0m         evaluate_candidates(ParameterSampler(\n\u001b[0;32m   1514\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1515\u001b[1;33m             random_state=self.random_state))\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    709\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 711\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    929\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 930\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    931\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    932\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    831\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 833\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    834\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    520\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    382\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    383\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    385\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\_base.py\u001b[0m in \u001b[0;36m_invoke_callbacks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    623\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_done_callbacks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m                 \u001b[0mLOGGER\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'exception calling callback for %r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, out)\u001b[0m\n\u001b[0;32m    307\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_next\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_next\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m         \"\"\"\n\u001b[1;32m--> 731\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    732\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    757\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 759\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    760\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    508\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m         \u001b[0mfuture\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_workers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSafeFunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m         \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_future_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\reusable_executor.py\u001b[0m in \u001b[0;36msubmit\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_submit_resize_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m             return super(_ReusablePoolExecutor, self).submit(\n\u001b[1;32m--> 151\u001b[1;33m                 fn, *args, **kwargs)\n\u001b[0m\u001b[0;32m    152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_resize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_workers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\externals\\loky\\process_executor.py\u001b[0m in \u001b[0;36msubmit\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1020\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshutdown_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1021\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbroken\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1022\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbroken\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1023\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1024\u001b[0m                 raise ShutdownExecutorError(\n",
      "\u001b[1;31mTerminatedWorkerError\u001b[0m: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker."
     ]
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "\n",
    "# First create the base model to tune\n",
    "RFC_hyper = RandomForestClassifier()\n",
    "\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# Search across 50 different combinations, and use 3 available cores (n_jobs)\n",
    "RFC_hyper = RandomizedSearchCV(estimator = RFC_hyper, param_distributions = random_grid, n_iter = 30, cv = 5, \n",
    "                               verbose=1, random_state=42, n_jobs = 2)\n",
    "# Fit the random search model\n",
    "RFC_hyper.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'RandomizedSearchCV' object has no attribute 'best_params_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-c9c7490d2b21>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Print the best hyper-parameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mRFC_hyper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'RandomizedSearchCV' object has no attribute 'best_params_'"
     ]
    }
   ],
   "source": [
    "# Print the best hyper-parameters\n",
    "RFC_hyper.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FINAL MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, we'll adjust the hyper-parameters for the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---FINAL MODEL with hyper-parameters tuned---\n",
    "\n",
    "RFC_final = RandomForestClassifier(n_estimators=400, min_samples_split=2, min_samples_leaf=1, max_features=5,\n",
    "                                   max_depth=10,bootstrap=True, random_state=47, oob_score = True)\n",
    "RFC_final.fit(X_train_final, y_train_final)\n",
    "y_pred_final = RFC_final.predict(X_test_final)   #yields predicted class 0/1\n",
    "probs_final = RFC_final.predict_proba(X_test_final)\n",
    "probs_final = probs_final[:,1]    #yields probability of either class 0-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print accuracy reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=== CLASSIFICATION REPORT ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.71      0.80      0.75     42705\n",
      "         1.0       0.77      0.67      0.72     42903\n",
      "\n",
      "   micro avg       0.73      0.73      0.73     85608\n",
      "   macro avg       0.74      0.73      0.73     85608\n",
      "weighted avg       0.74      0.73      0.73     85608\n",
      "\n",
      "Accuracy: 0.7331207363797776\n",
      "OOB score: 0.7330563204005006\n",
      "ROC AUC score: 0.8085406559796223\n",
      "Sensitivity: 0.6707456355033448\n",
      "Specificity: 0.7957850368809273\n",
      "Precision: 0.7674275961384607\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, recall_score, precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import average_precision_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "############################################################################################################\n",
    "# CLASSIFICATION REPORT ###\n",
    "\n",
    "print('\\n')\n",
    "print(\"=== CLASSIFICATION REPORT ===\")\n",
    "print(classification_report(y_test_final, y_pred_final))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test_final, y_pred_final))\n",
    "print(\"OOB score:\", RFC_final.oob_score_)\n",
    "print(\"ROC AUC score:\",metrics.roc_auc_score(y_test_final, probs_final))\n",
    "\n",
    "confusion2 = confusion_matrix(y_test_final, y_pred_final)\n",
    "# True Positives\n",
    "TP_final = confusion2[1, 1]\n",
    "# True Negatives\n",
    "TN_final = confusion2[0, 0]\n",
    "# False Positives\n",
    "FP_final = confusion2[0, 1]\n",
    "# False Negatives\n",
    "FN_final = confusion2[1, 0]\n",
    "print(\"Sensitivity:\",TP_final / float(TP_final + FN_final))\n",
    "print(\"Specificity:\",TN_final / float(TN_final + FP_final))\n",
    "print(\"Precision:\",TP_final / float(TP_final + FP_final))\n",
    "\n",
    "#####################################################################################################\n",
    "# CONFUSION MATRIX\n",
    "\n",
    "print('\\n')\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion Matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "\n",
    "    # Plot the confusion matrix\n",
    "    plt.figure(figsize = (8, 8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 25)\n",
    "    plt.colorbar(aspect=5)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 12)\n",
    "    plt.yticks(tick_marks, classes, size = 12)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(False)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual label', size = 15)\n",
    "    plt.xlabel('Predicted label', size = 15)\n",
    "    plt.ylim([1.5, -.5])\n",
    "\n",
    "cm = confusion_matrix(y_test_final, y_pred_final)\n",
    "plot_confusion_matrix(cm, classes = ['Good Mental Health', 'Poor Mental Health'],\n",
    "                      title = 'Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "############################################################################################\n",
    "# ROC CURVE PLOT\n",
    "\n",
    "roc_auc_final = roc_auc_score(y_test_final, probs_final)\n",
    "fpr_final, tpr_final, thresholds = roc_curve(y_test_final, probs_final)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr, tpr, label='Baseline Model (AUC = %0.3f)' % roc_auc, color='midnightblue')\n",
    "#plt.fill_between(fpr, tpr, alpha=0.2, color='orange', **step_kwargs)\n",
    "plt.plot(fpr_under, tpr_under, label='Under-Sampled Model (AUC = %0.3f)' % roc_auc_under, color='royalblue')\n",
    "#plt.fill_between(fpr_final, tpr_final, alpha=0.2, color='red', **step_kwargs)\n",
    "plt.plot(fpr_final, tpr_final, label='Final Model (AUC = %0.3f)' % roc_auc_final, color='lightsteelblue')\n",
    "#plt.fill_between(fpr_final, tpr_final, alpha=0.2, color='navy', **step_kwargs)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', size = 15)\n",
    "plt.ylabel('True Positive Rate', size = 15)\n",
    "plt.title('Receiver Operating Characteristic (ROC Curve)', size = 25)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('ROC')\n",
    "plt.show()\n",
    "\n",
    "###########################################################################################\n",
    "# PRECISION-RECALL CURVE\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from inspect import signature\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "average_precision_final = average_precision_score(y_test_final, probs_final)\n",
    "\n",
    "precision_final, recall_final, _ = precision_recall_curve(y_test_final, probs_final)\n",
    "\n",
    "step_kwargs = ({'step': 'post'}\n",
    "               if 'step' in signature(plt.fill_between).parameters\n",
    "               else {})\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.step(recall, precision, color='navy', alpha=1,\n",
    "         where='post', label='Baseline Model (AP = %0.3f)' % average_precision)\n",
    "plt.fill_between(recall, precision, alpha=0.7, color='navy', **step_kwargs)\n",
    "\n",
    "plt.step(recall_under, precision_under, color='royalblue', alpha=1,\n",
    "         where='post', label='Under-Sampled Model (AP = %0.3f)' % average_precision_under)\n",
    "plt.fill_between(recall_under, precision_under, alpha=0.7, color='royalblue', **step_kwargs)\n",
    "\n",
    "plt.step(recall_final, precision_final, color='lightsteelblue', alpha=1,\n",
    "         where='post', label='Final Model (AP = %0.3f)' % average_precision_final)\n",
    "plt.fill_between(recall_final, precision_final, alpha=0.7, color='lightsteelblue', **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall', size=15)\n",
    "plt.ylabel('Precision', size=15)\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall Curve', size=25)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that by tuning the hyper-parameters, we were able to increase average accuracy from 70 to 73% and increased AUC from .76 to .81. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=== RFC STATS ===\n",
      "OOB score: 0.7330563204005006\n",
      "Average number of nodes: 1792\n",
      "Average maximum depth: 10\n",
      "Average Sample Split: 5\n",
      "Average Samples Leaf: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######################################################################################################\n",
    "# RFC STATS\n",
    "n_nodes = []\n",
    "max_depths = []\n",
    "\n",
    "for ind_tree in RFC_final.estimators_:\n",
    "    n_nodes.append(ind_tree.tree_.node_count)\n",
    "    max_depths.append(ind_tree.tree_.max_depth)\n",
    "\n",
    "print('\\n')    \n",
    "print(\"=== RFC STATS ===\")   \n",
    "print(f'OOB score:', RFC_final.oob_score_)\n",
    "print(f'Average number of nodes: {int(np.mean(n_nodes))}')\n",
    "print(f'Average maximum depth: {int(np.mean(max_depths))}')\n",
    "print(f'Average Sample Split: {int(np.mean(min_samples_split))}')\n",
    "print(f'Average Samples Leaf: {int(np.mean(min_samples_leaf))}')\n",
    "\n",
    "#####################################################################################################\n",
    "# FEATURE IMPORTANCE GRAPH\n",
    "\n",
    "feature_names = LLCP2.columns # to label features by name, not index\n",
    "importances = RFC_final.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in RFC_final.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.title(\"Feature Importances\", Size=25)\n",
    "plt.xlabel('Feature Importance Score', size=20)\n",
    "plt.ylabel('Features', size=20)\n",
    "plt.barh(range(X.shape[1]), importances[indices],\n",
    "       color=\"royalblue\", xerr=std[indices], align=\"center\")\n",
    "# To define own labels\n",
    "plt.yticks(range(X.shape[1]), [feature_names[i] for i in indices])\n",
    "plt.ylim([-1, X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}