experimental_feature_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling Process With Experimental Feature Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In this notebook we use our experimental database to identify key features and tune the hyperparameters of several high preforming models. For model selection, we where looking for models where we could best improve the precision(reduce false positives) while not drastically reducing recall (false negatives).  To evaluate the models we used sklearn metrics and also viewed the confusion matrix to see how the models predictions on the test set preformed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/heymanjake/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "# Import Packages\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sklearn Packages\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, precision_score, confusion_matrix, accuracy_score\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import set_config\n",
    "set_config(print_changed_only=False)\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.utils.testing import ignore_warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "from xgboost import plot_importance\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "pd.set_option('display.max_columns', 300)\n",
    "% matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in dataframe\n",
    "exp_df = pd.read_csv('experiment_features.csv',index_col=0)\n",
    "exp_df.head()\n",
    "\n",
    "vanilla_df = pd.read_csv('../Phase_3_Project/data.csv/fetal_health.csv')\n",
    "X = vanilla_df.drop('fetal_health', axis =1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation function\n",
    "def evaluation(y_true, y_pred):\n",
    "    \n",
    "# Print Accuracy, Recall, F1 Score, and Precision metrics.\n",
    "    print('Evaluation Metrics:')\n",
    "    print('Accuracy: ' + str(metrics.accuracy_score(y_test, y_pred)))\n",
    "    print('Recall: ' + str(metrics.recall_score(y_test, y_pred)))\n",
    "    print('F1 Score: ' + str(metrics.f1_score(y_test, y_pred)))\n",
    "    print('Precision: ' + str(metrics.precision_score(y_test, y_pred)))\n",
    "    \n",
    "# Print Confusion Matrix\n",
    "    print('\\nConfusion Matrix:')\n",
    "    print(' TN,  FP, FN, TP')\n",
    "    print(confusion_matrix(y_true, y_pred).ravel())\n",
    "    \n",
    "# Function Prints best parameters for GridSearchCV\n",
    "def print_results(results):\n",
    "    print('Best Parameters: {}\\n'.format(results.best_params_))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split of data\n",
    "X = exp_df.drop('fetal_health', axis =1)\n",
    "y = exp_df.fetal_health\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standared scaler for predicting features\n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class imbalance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To handle class imbalance we oversampled the minority class using SMOTE(Synthetic Minority Oversampling Technique), this balanced the minority class by sampling the nearest neighboors and adding points between the neighbors.  We used both a SMOTE sampled database and unbalanced database to compare the effect of the metrics for each experimental baseline model.  The Smote Database preformed better on every baseline model, we chose to use the SMOTE database for our final models.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Used smote to oversample minority class\n",
    "sm = SMOTE(random_state=25)\n",
    "smX_train, smy_train = sm.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here we ran a basline model of knn on the experimental database and compared the metrics results to the database treated with smote.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#baseline KNN with class imbalance\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9172932330827067\n",
      "Recall: 0.9682151589242054\n",
      "F1 Score: 0.9473684210526316\n",
      "Precision: 0.927400468384075\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[396  13  31  92]\n"
     ]
    }
   ],
   "source": [
    "#prediction metrics and confusion matrix of base KNN with class imbalance\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Base KNN model with Smote oversampled class\n",
    "smknn = KNeighborsClassifier(n_neighbors=1)\n",
    "smknn.fit(smX_train,smy_train)\n",
    "y_pred = smknn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9172932330827067\n",
      "Recall: 0.9584352078239609\n",
      "F1 Score: 0.9468599033816426\n",
      "Precision: 0.9355608591885441\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[392  17  27  96]\n"
     ]
    }
   ],
   "source": [
    "#Prediction metrics and confusion matrix of smote base KNN\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Basline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here are two Logistic regression Models comparing the effect of SMOTE on the metric scores.  SMOTE greatly improved the precision of the model, however Recall was greatly reduced.  While one of the aims for our project was to improve precision as best we can, too many false negatives are not ideal for the overal fetal mortality rate.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit Train set with Logistic Regression model\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.8778195488721805\n",
      "Recall: 0.941320293398533\n",
      "F1 Score: 0.9221556886227544\n",
      "Precision: 0.903755868544601\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[385  24  41  82]\n"
     ]
    }
   ],
   "source": [
    "#Regression model evaluation metrics and confusion matrix\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "smlr = LogisticRegression(solver='liblinear')\n",
    "smlr.fit(smX_train,smy_train)\n",
    "y_predsm = smlr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.8853383458646616\n",
      "Recall: 0.8801955990220048\n",
      "F1 Score: 0.9218950064020486\n",
      "Precision: 0.967741935483871\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[360  49  12 111]\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test, y_predsm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Below is a table of feature coefficients for the Logistic Regression.  The greatest feature Coefficients are sqrt_total_change, quant_acc_1, quant_hist_mean(1 2 and 3) and tendancy_1.  All of these features are ones that we engineered.  Looking at what these features represent, for this model, the change in the rate of FHR and the average change in that rate seem to be the biggest factors for classifying fetal health.  We wanted to try and improve the Logistic regression by tuning the hyperparameters to improve both the recall and precision.  To attempt this we used a bagging classifer to train the logistic regression on multiple random samples and aggrigate the predictions to see how that effected the evaluation metrics.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>Coefs</th>\n",
       "      <th>sm_Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>0.113121</td>\n",
       "      <td>0.112113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>-0.003969</td>\n",
       "      <td>-0.224252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>0.010522</td>\n",
       "      <td>0.313262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>-0.005162</td>\n",
       "      <td>-0.055485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>-0.002154</td>\n",
       "      <td>-0.036389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.044586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>0.066841</td>\n",
       "      <td>0.084350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>-0.693812</td>\n",
       "      <td>-0.626913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>0.037476</td>\n",
       "      <td>0.039607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>0.028843</td>\n",
       "      <td>0.045947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>-0.013947</td>\n",
       "      <td>-0.009616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>-0.001040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>-0.007793</td>\n",
       "      <td>-0.010656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>0.244311</td>\n",
       "      <td>0.200837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>-0.023230</td>\n",
       "      <td>-0.009779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>0.032645</td>\n",
       "      <td>-0.059707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>-0.151405</td>\n",
       "      <td>-0.031339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>0.034361</td>\n",
       "      <td>0.045463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>-0.309730</td>\n",
       "      <td>-3.329097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>0.039742</td>\n",
       "      <td>0.062281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>-0.047973</td>\n",
       "      <td>-1.245735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>0.062709</td>\n",
       "      <td>-0.514483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.019549</td>\n",
       "      <td>-0.583633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>-0.002612</td>\n",
       "      <td>-1.085404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.017099</td>\n",
       "      <td>0.384853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>-0.002186</td>\n",
       "      <td>-0.626686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.010689</td>\n",
       "      <td>0.195844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>-0.000754</td>\n",
       "      <td>-0.629847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>-0.074348</td>\n",
       "      <td>-0.752577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>0.089239</td>\n",
       "      <td>-1.254315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>-0.003675</td>\n",
       "      <td>-0.238835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>-0.228912</td>\n",
       "      <td>-3.312252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>-0.334322</td>\n",
       "      <td>-1.669820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>-0.453612</td>\n",
       "      <td>-2.735225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>-0.247562</td>\n",
       "      <td>-2.348351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>-0.345712</td>\n",
       "      <td>-2.624426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>0.054544</td>\n",
       "      <td>-1.194180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>0.415900</td>\n",
       "      <td>-0.125200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>0.580449</td>\n",
       "      <td>0.816205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             features     Coefs  sm_Coefs\n",
       "0                                      baseline value  0.113121  0.112113\n",
       "1                                       accelerations -0.003969 -0.224252\n",
       "2                                      fetal_movement  0.010522  0.313262\n",
       "3                                uterine_contractions -0.005162 -0.055485\n",
       "4                                 light_decelerations -0.002154 -0.036389\n",
       "5                            prolongued_decelerations  0.000782  0.044586\n",
       "6                     abnormal_short_term_variability  0.066841  0.084350\n",
       "7                mean_value_of_short_term_variability -0.693812 -0.626913\n",
       "8   percentage_of_time_with_abnormal_long_term_var...  0.037476  0.039607\n",
       "9                 mean_value_of_long_term_variability  0.028843  0.045947\n",
       "10                                    histogram_width -0.013947 -0.009616\n",
       "11                                      histogram_min  0.006153 -0.001040\n",
       "12                                      histogram_max -0.007793 -0.010656\n",
       "13                          histogram_number_of_peaks  0.244311  0.200837\n",
       "14                                     histogram_mode -0.023230 -0.009779\n",
       "15                                     histogram_mean  0.032645 -0.059707\n",
       "16                                   histogram_median -0.151405 -0.031339\n",
       "17                                 histogram_variance  0.034361  0.045463\n",
       "18                               uterine_cont_per_min -0.309730 -3.329097\n",
       "19                                       total_change  0.039742  0.062281\n",
       "20                                  sqrt_total_change -0.047973 -1.245735\n",
       "21                                     hist_zeros_1.0  0.062709 -0.514483\n",
       "22                                     hist_zeros_2.0  0.019549 -0.583633\n",
       "23                                     hist_zeros_3.0 -0.002612 -1.085404\n",
       "24                                     hist_zeros_4.0  0.017099  0.384853\n",
       "25                                     hist_zeros_5.0 -0.002186 -0.626686\n",
       "26                                     hist_zeros_7.0  0.000000  0.000000\n",
       "27                                     hist_zeros_8.0  0.010689  0.195844\n",
       "28                                    hist_zeros_10.0 -0.000754 -0.629847\n",
       "29                                  hist_tendancy_0.0 -0.074348 -0.752577\n",
       "30                                  hist_tendancy_1.0  0.089239 -1.254315\n",
       "31                                    sev_decel_0.001 -0.003675 -0.238835\n",
       "32                                        quant_acc_1 -0.228912 -3.312252\n",
       "33                                  quant_light_dec_1 -0.334322 -1.669820\n",
       "34                                  quant_hist_mean_1 -0.453612 -2.735225\n",
       "35                                  quant_hist_mean_2 -0.247562 -2.348351\n",
       "36                                  quant_hist_mean_3 -0.345712 -2.624426\n",
       "37                                  quant_hist_mean_4  0.054544 -1.194180\n",
       "38                                  quant_hist_mean_5  0.415900 -0.125200\n",
       "39                                  quant_hist_mean_6  0.580449  0.816205"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a table of logistic regression coefficients and comparing the coefficients \n",
    "#of the SMOTE and inbalanced datasets\n",
    "coef_table = pd.DataFrame(list(X_train.columns)).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",lr.coef_.transpose())\n",
    "\n",
    "coef_table_2 = pd.DataFrame(list(smX_train.columns)).copy()\n",
    "coef_table_2.insert(len(coef_table_2.columns),'sm_Coefs',smlr.coef_.transpose())\n",
    "\n",
    "smote_vs_coef = pd.concat([coef_table,coef_table_2],axis=1)\n",
    "smote_vs_coef.columns = ['features','Coefs','del','sm_Coefs']\n",
    "del smote_vs_coef['del']\n",
    "smote_vs_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bagging classifier for legistic regression\n",
    "bag_log = BaggingClassifier(\n",
    "    base_estimator=LogisticRegression(\n",
    "        random_state=1),n_estimators=200,\n",
    "    max_samples=.85,\n",
    "    max_features=10,oob_score=True,\n",
    "    n_jobs=-1,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   2 out of   8 | elapsed:    1.5s remaining:    4.4s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    1.5s finished\n",
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   2 out of   8 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "#Fit bagging classifier\n",
    "bag_log.fit(smX_train, smy_train)\n",
    "y_pred = bag_log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.8796992481203008\n",
      "Recall: 0.8899755501222494\n",
      "F1 Score: 0.9191919191919191\n",
      "Precision: 0.9503916449086162\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[364  45  19 104]\n"
     ]
    }
   ],
   "source": [
    "#Evaluation Metrics\n",
    "evaluation(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Bagging classifer made only minute changes to the evaluation metrics.  We decided to look at some other models and compare the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The next model we tried was a decision tree.  Again we compared the imbalanced and SMOTE data sets on baseline models to observe the effect on the metrics.  With the SMOTE data set, decision tree produced very good metrics with all default hyperparamers.  The most important features where abnormal_short_term_variability, and mean_value_of_short_term_variability\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train decision tree with train set and predict on the test set\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "tree = tree.fit(X_train,y_train)\n",
    "\n",
    "y_pred = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9154135338345865\n",
      "Recall: 0.9364303178484108\n",
      "F1 Score: 0.9445129469790383\n",
      "Precision: 0.9527363184079602\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[383  26  19 104]\n"
     ]
    }
   ],
   "source": [
    "#Evaluation metrics\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision tree with smote dataset\n",
    "smtree = DecisionTreeClassifier()\n",
    "smtree.fit(smX_train,smy_train)\n",
    "y_pred = smtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9398496240601504\n",
      "Recall: 0.9633251833740831\n",
      "F1 Score: 0.9609756097560975\n",
      "Precision: 0.9586374695863747\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[394  15  17 106]\n"
     ]
    }
   ],
   "source": [
    "#evaluation Metrics\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>features</th>\n",
       "      <th>importance</th>\n",
       "      <th>sm_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>0.033734</td>\n",
       "      <td>0.033734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>0.020453</td>\n",
       "      <td>0.020453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>0.016659</td>\n",
       "      <td>0.016659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>0.007839</td>\n",
       "      <td>0.007839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>0.004911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>0.028585</td>\n",
       "      <td>0.028585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>0.340669</td>\n",
       "      <td>0.340669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>0.107879</td>\n",
       "      <td>0.107879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>0.048263</td>\n",
       "      <td>0.048263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>0.012098</td>\n",
       "      <td>0.012098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>0.014281</td>\n",
       "      <td>0.014281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>0.042490</td>\n",
       "      <td>0.042490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>0.033694</td>\n",
       "      <td>0.033694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>0.019438</td>\n",
       "      <td>0.019438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>0.015507</td>\n",
       "      <td>0.015507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>0.173347</td>\n",
       "      <td>0.173347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>0.011743</td>\n",
       "      <td>0.011743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>0.010128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>0.009152</td>\n",
       "      <td>0.009152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>0.037034</td>\n",
       "      <td>0.037034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>0.008482</td>\n",
       "      <td>0.008482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.003614</td>\n",
       "      <td>0.003614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             features  importance  \\\n",
       "0                                      baseline value    0.033734   \n",
       "1                                       accelerations    0.020453   \n",
       "2                                      fetal_movement    0.016659   \n",
       "3                                uterine_contractions    0.007839   \n",
       "4                                 light_decelerations    0.004911   \n",
       "5                            prolongued_decelerations    0.028585   \n",
       "6                     abnormal_short_term_variability    0.340669   \n",
       "7                mean_value_of_short_term_variability    0.107879   \n",
       "8   percentage_of_time_with_abnormal_long_term_var...    0.048263   \n",
       "9                 mean_value_of_long_term_variability    0.012098   \n",
       "10                                    histogram_width    0.014281   \n",
       "11                                      histogram_min    0.042490   \n",
       "12                                      histogram_max    0.033694   \n",
       "13                          histogram_number_of_peaks    0.019438   \n",
       "14                                     histogram_mode    0.015507   \n",
       "15                                     histogram_mean    0.173347   \n",
       "16                                   histogram_median    0.011743   \n",
       "17                                 histogram_variance    0.010128   \n",
       "18                               uterine_cont_per_min    0.009152   \n",
       "19                                       total_change    0.037034   \n",
       "20                                  sqrt_total_change    0.008482   \n",
       "21                                     hist_zeros_1.0    0.000000   \n",
       "22                                     hist_zeros_2.0    0.000000   \n",
       "23                                     hist_zeros_3.0    0.000000   \n",
       "24                                     hist_zeros_4.0    0.003614   \n",
       "25                                     hist_zeros_5.0    0.000000   \n",
       "26                                     hist_zeros_7.0    0.000000   \n",
       "27                                     hist_zeros_8.0    0.000000   \n",
       "28                                    hist_zeros_10.0    0.000000   \n",
       "29                                  hist_tendancy_0.0    0.000000   \n",
       "30                                  hist_tendancy_1.0    0.000000   \n",
       "31                                    sev_decel_0.001    0.000000   \n",
       "32                                        quant_acc_1    0.000000   \n",
       "33                                  quant_light_dec_1    0.000000   \n",
       "34                                  quant_hist_mean_1    0.000000   \n",
       "35                                  quant_hist_mean_2    0.000000   \n",
       "36                                  quant_hist_mean_3    0.000000   \n",
       "37                                  quant_hist_mean_4    0.000000   \n",
       "38                                  quant_hist_mean_5    0.000000   \n",
       "39                                  quant_hist_mean_6    0.000000   \n",
       "\n",
       "    sm_importance  \n",
       "0        0.033734  \n",
       "1        0.020453  \n",
       "2        0.016659  \n",
       "3        0.007839  \n",
       "4        0.004911  \n",
       "5        0.028585  \n",
       "6        0.340669  \n",
       "7        0.107879  \n",
       "8        0.048263  \n",
       "9        0.012098  \n",
       "10       0.014281  \n",
       "11       0.042490  \n",
       "12       0.033694  \n",
       "13       0.019438  \n",
       "14       0.015507  \n",
       "15       0.173347  \n",
       "16       0.011743  \n",
       "17       0.010128  \n",
       "18       0.009152  \n",
       "19       0.037034  \n",
       "20       0.008482  \n",
       "21       0.000000  \n",
       "22       0.000000  \n",
       "23       0.000000  \n",
       "24       0.003614  \n",
       "25       0.000000  \n",
       "26       0.000000  \n",
       "27       0.000000  \n",
       "28       0.000000  \n",
       "29       0.000000  \n",
       "30       0.000000  \n",
       "31       0.000000  \n",
       "32       0.000000  \n",
       "33       0.000000  \n",
       "34       0.000000  \n",
       "35       0.000000  \n",
       "36       0.000000  \n",
       "37       0.000000  \n",
       "38       0.000000  \n",
       "39       0.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Table for decision tree feature coefficients\n",
    "coef_table = pd.DataFrame(list(X_train.columns)).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",tree.feature_importances_.transpose())\n",
    "\n",
    "coef_table_2 = pd.DataFrame(list(smX_train.columns)).copy()\n",
    "coef_table_2.insert(len(coef_table_2.columns),'sm_Coefs',tree.feature_importances_.transpose())\n",
    "\n",
    "smote_vs_coef = pd.concat([coef_table,coef_table_2],axis=1)\n",
    "smote_vs_coef.columns = ['features','importance','del','sm_importance']\n",
    "del smote_vs_coef['del']\n",
    "smote_vs_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As we did above we used the SMOTE and imbalanced data and this time trained a random forest classifer.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done  50 out of  50 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "#Random Forest classifier using 50 estimators and a max depth of 3\n",
    "rfc = RandomForestClassifier(random_state =1, n_estimators= 50, max_depth = 3, n_jobs =-1,verbose=1)\n",
    "rfc.fit(X_train,y_train)\n",
    "y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.900375939849624\n",
      "Recall: 0.9828850855745721\n",
      "F1 Score: 0.9381563593932323\n",
      "Precision: 0.8973214285714286\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[402   7  46  77]\n"
     ]
    }
   ],
   "source": [
    "#Evaluation metrics for random forest\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done  50 out of  50 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "#Random Forest classifier using 50 estimators and a max depth of 3 using SMOTE dataset\n",
    "smrfc = RandomForestClassifier(random_state =1, n_estimators= 50, max_depth = 3, n_jobs =-1,verbose=1)\n",
    "smrfc.fit(smX_train,smy_train)\n",
    "y_pred = smrfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[380  29]\n",
      " [ 18 105]]\n",
      "Evaluation Metrics:\n",
      "Accuracy: 0.9116541353383458\n",
      "Recall: 0.9290953545232273\n",
      "F1 Score: 0.9417596034696406\n",
      "Precision: 0.9547738693467337\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[380  29  18 105]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "evaluation(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>features</th>\n",
       "      <th>importance_baseline</th>\n",
       "      <th>sm_importance_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>9.926765e-03</td>\n",
       "      <td>0.017274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>1.035325e-01</td>\n",
       "      <td>0.201070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>4.708818e-03</td>\n",
       "      <td>0.002493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>1.058922e-02</td>\n",
       "      <td>0.020314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>6.170803e-04</td>\n",
       "      <td>0.001409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>8.764041e-02</td>\n",
       "      <td>0.085824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>1.576849e-01</td>\n",
       "      <td>0.173725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>1.492258e-01</td>\n",
       "      <td>0.104371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>8.983866e-02</td>\n",
       "      <td>0.080703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>1.227784e-02</td>\n",
       "      <td>0.009671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>2.413741e-02</td>\n",
       "      <td>0.013937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>3.454206e-02</td>\n",
       "      <td>0.045037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>3.475578e-03</td>\n",
       "      <td>0.007946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>1.292067e-03</td>\n",
       "      <td>0.000498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>2.993087e-02</td>\n",
       "      <td>0.013993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>1.002393e-01</td>\n",
       "      <td>0.062982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>5.230935e-02</td>\n",
       "      <td>0.022024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>4.127107e-02</td>\n",
       "      <td>0.039004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>3.162570e-02</td>\n",
       "      <td>0.041517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>2.676057e-02</td>\n",
       "      <td>0.015820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>1.688455e-02</td>\n",
       "      <td>0.008602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>4.751671e-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>4.129194e-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>5.073501e-03</td>\n",
       "      <td>0.024165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>2.024508e-03</td>\n",
       "      <td>0.000977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>6.361066e-07</td>\n",
       "      <td>0.000289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>2.716066e-04</td>\n",
       "      <td>0.004286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>1.834956e-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>9.625095e-04</td>\n",
       "      <td>0.000334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>2.085230e-03</td>\n",
       "      <td>0.001047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             features  importance_baseline  \\\n",
       "0                                      baseline value         9.926765e-03   \n",
       "1                                       accelerations         1.035325e-01   \n",
       "2                                      fetal_movement         4.708818e-03   \n",
       "3                                uterine_contractions         1.058922e-02   \n",
       "4                                 light_decelerations         6.170803e-04   \n",
       "5                            prolongued_decelerations         8.764041e-02   \n",
       "6                     abnormal_short_term_variability         1.576849e-01   \n",
       "7                mean_value_of_short_term_variability         1.492258e-01   \n",
       "8   percentage_of_time_with_abnormal_long_term_var...         8.983866e-02   \n",
       "9                 mean_value_of_long_term_variability         1.227784e-02   \n",
       "10                                    histogram_width         2.413741e-02   \n",
       "11                                      histogram_min         3.454206e-02   \n",
       "12                                      histogram_max         3.475578e-03   \n",
       "13                          histogram_number_of_peaks         1.292067e-03   \n",
       "14                                     histogram_mode         2.993087e-02   \n",
       "15                                     histogram_mean         1.002393e-01   \n",
       "16                                   histogram_median         5.230935e-02   \n",
       "17                                 histogram_variance         4.127107e-02   \n",
       "18                               uterine_cont_per_min         3.162570e-02   \n",
       "19                                       total_change         2.676057e-02   \n",
       "20                                  sqrt_total_change         1.688455e-02   \n",
       "21                                     hist_zeros_1.0         4.751671e-04   \n",
       "22                                     hist_zeros_2.0         0.000000e+00   \n",
       "23                                     hist_zeros_3.0         0.000000e+00   \n",
       "24                                     hist_zeros_4.0         0.000000e+00   \n",
       "25                                     hist_zeros_5.0         0.000000e+00   \n",
       "26                                     hist_zeros_7.0         0.000000e+00   \n",
       "27                                     hist_zeros_8.0         0.000000e+00   \n",
       "28                                    hist_zeros_10.0         0.000000e+00   \n",
       "29                                  hist_tendancy_0.0         0.000000e+00   \n",
       "30                                  hist_tendancy_1.0         4.129194e-04   \n",
       "31                                    sev_decel_0.001         0.000000e+00   \n",
       "32                                        quant_acc_1         5.073501e-03   \n",
       "33                                  quant_light_dec_1         2.024508e-03   \n",
       "34                                  quant_hist_mean_1         6.361066e-07   \n",
       "35                                  quant_hist_mean_2         0.000000e+00   \n",
       "36                                  quant_hist_mean_3         2.716066e-04   \n",
       "37                                  quant_hist_mean_4         1.834956e-04   \n",
       "38                                  quant_hist_mean_5         9.625095e-04   \n",
       "39                                  quant_hist_mean_6         2.085230e-03   \n",
       "\n",
       "    sm_importance_baseline  \n",
       "0                 0.017274  \n",
       "1                 0.201070  \n",
       "2                 0.002493  \n",
       "3                 0.020314  \n",
       "4                 0.001409  \n",
       "5                 0.085824  \n",
       "6                 0.173725  \n",
       "7                 0.104371  \n",
       "8                 0.080703  \n",
       "9                 0.009671  \n",
       "10                0.013937  \n",
       "11                0.045037  \n",
       "12                0.007946  \n",
       "13                0.000498  \n",
       "14                0.013993  \n",
       "15                0.062982  \n",
       "16                0.022024  \n",
       "17                0.039004  \n",
       "18                0.041517  \n",
       "19                0.015820  \n",
       "20                0.008602  \n",
       "21                0.000000  \n",
       "22                0.000000  \n",
       "23                0.000000  \n",
       "24                0.000000  \n",
       "25                0.000000  \n",
       "26                0.000000  \n",
       "27                0.000000  \n",
       "28                0.000000  \n",
       "29                0.000000  \n",
       "30                0.000000  \n",
       "31                0.000000  \n",
       "32                0.024165  \n",
       "33                0.000977  \n",
       "34                0.000289  \n",
       "35                0.000687  \n",
       "36                0.004286  \n",
       "37                0.000000  \n",
       "38                0.000334  \n",
       "39                0.001047  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create table of feature coefficients\n",
    "coef_table = pd.DataFrame(list(X_train.columns)).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",rfc.feature_importances_.transpose())\n",
    "\n",
    "coef_table_2 = pd.DataFrame(list(smX_train.columns)).copy()\n",
    "coef_table_2.insert(len(coef_table_2.columns),'sm_Coefs',smrfc.feature_importances_.transpose())\n",
    "\n",
    "smote_vs_coef = pd.concat([coef_table,coef_table_2],axis=1)\n",
    "smote_vs_coef.columns = ['features','importance_baseline','del','sm_importance_baseline']\n",
    "del smote_vs_coef['del']\n",
    "smote_vs_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search Random Forest\n",
    "- We ran a gridsearch on the random forest to identify what the best hyperparamters where for the model.  We checked several estimator sizes, max depth and min weight fraction leaf to find the ideal parameters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameter grid for grid search with lists of estimators, both criterion, a list of max depths sqrt max features \n",
    "#and a list of min weight fraction leaf\n",
    "parameters = {\n",
    "    'n_estimators': [25,50,100,300,500],\n",
    "    'criterion' : ['gini','entropy'],\n",
    "    'max_depth' : [8,9,10,11,12],\n",
    "    'max_features' : ['sqrt'],\n",
    "    'min_weight_fraction_leaf' : [0,0.1,0.3,0.5],\n",
    "    \n",
    "    \n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gridsearch with random forest\n",
    "grid_tree=GridSearchCV(RandomForestClassifier(), parameters, cv=15, scoring='f1', verbose=1, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 200 candidates, totalling 3000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    4.0s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   21.5s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   48.0s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2434 tasks      | elapsed:  4.4min\n",
      "[Parallel(n_jobs=-1)]: Done 3000 out of 3000 | elapsed:  5.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=15, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [8, 9, 10, 11, 12],\n",
       "                         'max_features': ['sqrt'],\n",
       "                         'min_weight_fraction_leaf': [0, 0.1, 0.3, 0.5],\n",
       "                         'n_estimators': [25, 50, 100, 300, 500]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit random forest grid serch to SMOTE train set\n",
    "grid_tree.fit(smX_train, smy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9762215043444111\n",
      "{'criterion': 'gini', 'max_depth': 11, 'max_features': 'sqrt', 'min_weight_fraction_leaf': 0, 'n_estimators': 500}\n",
      "RandomForestClassifier(max_depth=11, max_features='sqrt',\n",
      "                       min_weight_fraction_leaf=0, n_estimators=500)\n"
     ]
    }
   ],
   "source": [
    "#Find the best score, best parameters and best estimator for grid search\n",
    "print(grid_tree.best_score_)\n",
    "print(grid_tree.best_params_)\n",
    "print(grid_tree.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9492481203007519\n",
      "Recall: 0.9755501222493888\n",
      "F1 Score: 0.9672727272727273\n",
      "Precision: 0.9591346153846154\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[399  10  17 106]\n"
     ]
    }
   ],
   "source": [
    "#evaluation metrics for random forest gridsearch best parameters\n",
    "y_pred = grid_tree.best_estimator_.predict(X_test)\n",
    "evaluation(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings:** \n",
    "- The best parameters for the random forest was a max depth of 11, min weight fraction leave of 0, and 500 estimators.  The Evaluation metrics where our highest score with the experimental set so far.  Below we compared feature importance for the three random forest models we ran and then graphed the top ten important features to visualize there importance for fetal health classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>0</th>\n",
       "      <th>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>0.026225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>0.113580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>0.012005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>0.025464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>0.007497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>0.056724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>0.160471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>0.097464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>0.068753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>0.022311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>0.025999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>0.035609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>0.020700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>0.014845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>0.045093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>0.071337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>0.049617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>0.037920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>0.026603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>0.019211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>0.020624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>0.001479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.000521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>0.002650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>0.003001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>0.017910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>0.002388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>0.002426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>0.001914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>0.002389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>0.001540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>0.003400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    0     Coefs\n",
       "0                                      baseline value  0.026225\n",
       "1                                       accelerations  0.113580\n",
       "2                                      fetal_movement  0.012005\n",
       "3                                uterine_contractions  0.025464\n",
       "4                                 light_decelerations  0.007497\n",
       "5                            prolongued_decelerations  0.056724\n",
       "6                     abnormal_short_term_variability  0.160471\n",
       "7                mean_value_of_short_term_variability  0.097464\n",
       "8   percentage_of_time_with_abnormal_long_term_var...  0.068753\n",
       "9                 mean_value_of_long_term_variability  0.022311\n",
       "10                                    histogram_width  0.025999\n",
       "11                                      histogram_min  0.035609\n",
       "12                                      histogram_max  0.020700\n",
       "13                          histogram_number_of_peaks  0.014845\n",
       "14                                     histogram_mode  0.045093\n",
       "15                                     histogram_mean  0.071337\n",
       "16                                   histogram_median  0.049617\n",
       "17                                 histogram_variance  0.037920\n",
       "18                               uterine_cont_per_min  0.026603\n",
       "19                                       total_change  0.019211\n",
       "20                                  sqrt_total_change  0.020624\n",
       "21                                     hist_zeros_1.0  0.001479\n",
       "22                                     hist_zeros_2.0  0.000521\n",
       "23                                     hist_zeros_3.0  0.000441\n",
       "24                                     hist_zeros_4.0  0.000303\n",
       "25                                     hist_zeros_5.0  0.000012\n",
       "26                                     hist_zeros_7.0  0.000000\n",
       "27                                     hist_zeros_8.0  0.000006\n",
       "28                                    hist_zeros_10.0  0.000013\n",
       "29                                  hist_tendancy_0.0  0.002650\n",
       "30                                  hist_tendancy_1.0  0.003001\n",
       "31                                    sev_decel_0.001  0.000228\n",
       "32                                        quant_acc_1  0.017910\n",
       "33                                  quant_light_dec_1  0.002388\n",
       "34                                  quant_hist_mean_1  0.002426\n",
       "35                                  quant_hist_mean_2  0.001914\n",
       "36                                  quant_hist_mean_3  0.002389\n",
       "37                                  quant_hist_mean_4  0.001325\n",
       "38                                  quant_hist_mean_5  0.001540\n",
       "39                                  quant_hist_mean_6  0.003400"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Best feature table for random forest gridsearch best parameters\n",
    "coef_table = pd.DataFrame(list(X_train.columns)).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",grid_tree.best_estimator_.feature_importances_.transpose())\n",
    "coef_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "#table comparing random forest best features between imbalanced data set, smote and smote with gridsearch best features\n",
    "coef_table.columns = ['features','grid_search_importance']\n",
    "del coef_table['features']\n",
    "best_features_rfc = pd.concat([smote_vs_coef,coef_table],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>features</th>\n",
       "      <th>importance_baseline</th>\n",
       "      <th>sm_importance_baseline</th>\n",
       "      <th>grid_search_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>9.926765e-03</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.026225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>1.035325e-01</td>\n",
       "      <td>0.201070</td>\n",
       "      <td>0.113580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>4.708818e-03</td>\n",
       "      <td>0.002493</td>\n",
       "      <td>0.012005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>1.058922e-02</td>\n",
       "      <td>0.020314</td>\n",
       "      <td>0.025464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>6.170803e-04</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.007497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>8.764041e-02</td>\n",
       "      <td>0.085824</td>\n",
       "      <td>0.056724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>1.576849e-01</td>\n",
       "      <td>0.173725</td>\n",
       "      <td>0.160471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>1.492258e-01</td>\n",
       "      <td>0.104371</td>\n",
       "      <td>0.097464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>8.983866e-02</td>\n",
       "      <td>0.080703</td>\n",
       "      <td>0.068753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>1.227784e-02</td>\n",
       "      <td>0.009671</td>\n",
       "      <td>0.022311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>2.413741e-02</td>\n",
       "      <td>0.013937</td>\n",
       "      <td>0.025999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>3.454206e-02</td>\n",
       "      <td>0.045037</td>\n",
       "      <td>0.035609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>3.475578e-03</td>\n",
       "      <td>0.007946</td>\n",
       "      <td>0.020700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>1.292067e-03</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.014845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>2.993087e-02</td>\n",
       "      <td>0.013993</td>\n",
       "      <td>0.045093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>1.002393e-01</td>\n",
       "      <td>0.062982</td>\n",
       "      <td>0.071337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>5.230935e-02</td>\n",
       "      <td>0.022024</td>\n",
       "      <td>0.049617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>4.127107e-02</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>0.037920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>3.162570e-02</td>\n",
       "      <td>0.041517</td>\n",
       "      <td>0.026603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>2.676057e-02</td>\n",
       "      <td>0.015820</td>\n",
       "      <td>0.019211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>1.688455e-02</td>\n",
       "      <td>0.008602</td>\n",
       "      <td>0.020624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>4.751671e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>4.129194e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>5.073501e-03</td>\n",
       "      <td>0.024165</td>\n",
       "      <td>0.017910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>2.024508e-03</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.002388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>6.361066e-07</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.002426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.001914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>2.716066e-04</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.002389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>1.834956e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>9.625095e-04</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.001540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>2.085230e-03</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>0.003400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             features  importance_baseline  \\\n",
       "0                                      baseline value         9.926765e-03   \n",
       "1                                       accelerations         1.035325e-01   \n",
       "2                                      fetal_movement         4.708818e-03   \n",
       "3                                uterine_contractions         1.058922e-02   \n",
       "4                                 light_decelerations         6.170803e-04   \n",
       "5                            prolongued_decelerations         8.764041e-02   \n",
       "6                     abnormal_short_term_variability         1.576849e-01   \n",
       "7                mean_value_of_short_term_variability         1.492258e-01   \n",
       "8   percentage_of_time_with_abnormal_long_term_var...         8.983866e-02   \n",
       "9                 mean_value_of_long_term_variability         1.227784e-02   \n",
       "10                                    histogram_width         2.413741e-02   \n",
       "11                                      histogram_min         3.454206e-02   \n",
       "12                                      histogram_max         3.475578e-03   \n",
       "13                          histogram_number_of_peaks         1.292067e-03   \n",
       "14                                     histogram_mode         2.993087e-02   \n",
       "15                                     histogram_mean         1.002393e-01   \n",
       "16                                   histogram_median         5.230935e-02   \n",
       "17                                 histogram_variance         4.127107e-02   \n",
       "18                               uterine_cont_per_min         3.162570e-02   \n",
       "19                                       total_change         2.676057e-02   \n",
       "20                                  sqrt_total_change         1.688455e-02   \n",
       "21                                     hist_zeros_1.0         4.751671e-04   \n",
       "22                                     hist_zeros_2.0         0.000000e+00   \n",
       "23                                     hist_zeros_3.0         0.000000e+00   \n",
       "24                                     hist_zeros_4.0         0.000000e+00   \n",
       "25                                     hist_zeros_5.0         0.000000e+00   \n",
       "26                                     hist_zeros_7.0         0.000000e+00   \n",
       "27                                     hist_zeros_8.0         0.000000e+00   \n",
       "28                                    hist_zeros_10.0         0.000000e+00   \n",
       "29                                  hist_tendancy_0.0         0.000000e+00   \n",
       "30                                  hist_tendancy_1.0         4.129194e-04   \n",
       "31                                    sev_decel_0.001         0.000000e+00   \n",
       "32                                        quant_acc_1         5.073501e-03   \n",
       "33                                  quant_light_dec_1         2.024508e-03   \n",
       "34                                  quant_hist_mean_1         6.361066e-07   \n",
       "35                                  quant_hist_mean_2         0.000000e+00   \n",
       "36                                  quant_hist_mean_3         2.716066e-04   \n",
       "37                                  quant_hist_mean_4         1.834956e-04   \n",
       "38                                  quant_hist_mean_5         9.625095e-04   \n",
       "39                                  quant_hist_mean_6         2.085230e-03   \n",
       "\n",
       "    sm_importance_baseline  grid_search_importance  \n",
       "0                 0.017274                0.026225  \n",
       "1                 0.201070                0.113580  \n",
       "2                 0.002493                0.012005  \n",
       "3                 0.020314                0.025464  \n",
       "4                 0.001409                0.007497  \n",
       "5                 0.085824                0.056724  \n",
       "6                 0.173725                0.160471  \n",
       "7                 0.104371                0.097464  \n",
       "8                 0.080703                0.068753  \n",
       "9                 0.009671                0.022311  \n",
       "10                0.013937                0.025999  \n",
       "11                0.045037                0.035609  \n",
       "12                0.007946                0.020700  \n",
       "13                0.000498                0.014845  \n",
       "14                0.013993                0.045093  \n",
       "15                0.062982                0.071337  \n",
       "16                0.022024                0.049617  \n",
       "17                0.039004                0.037920  \n",
       "18                0.041517                0.026603  \n",
       "19                0.015820                0.019211  \n",
       "20                0.008602                0.020624  \n",
       "21                0.000000                0.001479  \n",
       "22                0.000000                0.000521  \n",
       "23                0.000000                0.000441  \n",
       "24                0.000000                0.000303  \n",
       "25                0.000000                0.000012  \n",
       "26                0.000000                0.000000  \n",
       "27                0.000000                0.000006  \n",
       "28                0.000000                0.000013  \n",
       "29                0.000000                0.002650  \n",
       "30                0.000000                0.003001  \n",
       "31                0.000000                0.000228  \n",
       "32                0.024165                0.017910  \n",
       "33                0.000977                0.002388  \n",
       "34                0.000289                0.002426  \n",
       "35                0.000687                0.001914  \n",
       "36                0.004286                0.002389  \n",
       "37                0.000000                0.001325  \n",
       "38                0.000334                0.001540  \n",
       "39                0.001047                0.003400  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features_rfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f88927e4780>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(grid_tree.best_estimator_.feature_importances_, index=X.columns).nlargest(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**findings**\n",
    "- In the Above graphe you can see the top ten importanct features for our grid search random foret model.  Abnormal_short_term_variatability, acceleration and mean_value_of_short_term_varitability seem to be key factors in health classification.  We can infer from this that extended rapid extreme changes in fetal heart rate are detrimental to fetal health."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We wanted to try an XGboost classifer to attempt a better model than the gridsearch random forest.  XGboost uses gradiant descent and boosting principles to regression trees.  We first tried an XGboost model with some parametrs we thought would be ideal for the model.  Next we ran a grid search on the XGboost parameters to produce our best possible model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_clf = xgb.XGBClassifier(objective ='binary:logistic', \n",
    "                           colsample_bytree = 0.75, \n",
    "                           subsample = 0.85,\n",
    "                           learning_rate = 0.1,\n",
    "                           max_depth = 11, \n",
    "                           alpha = 1, \n",
    "                           n_estimators = 1000,\n",
    "                          verbose=1, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(alpha=1, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.75, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=11,\n",
       "              min_child_weight=1, missing=None, n_estimators=1000, n_jobs=-1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=0.85, verbose=1, verbosity=1)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_clf.fit(smX_train,smy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xg_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9642857142857143\n",
      "Recall: 0.9902200488997555\n",
      "F1 Score: 0.97708082026538\n",
      "Precision: 0.9642857142857143\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[405   4  15 108]\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_table = pd.DataFrame(list(X_train.columns)).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",xg_clf.feature_importances_.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>0</th>\n",
       "      <th>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baseline value</td>\n",
       "      <td>0.015132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerations</td>\n",
       "      <td>0.169203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fetal_movement</td>\n",
       "      <td>0.016855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uterine_contractions</td>\n",
       "      <td>0.020800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>light_decelerations</td>\n",
       "      <td>0.015470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prolongued_decelerations</td>\n",
       "      <td>0.099882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abnormal_short_term_variability</td>\n",
       "      <td>0.149758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_value_of_short_term_variability</td>\n",
       "      <td>0.024495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>percentage_of_time_with_abnormal_long_term_var...</td>\n",
       "      <td>0.022075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean_value_of_long_term_variability</td>\n",
       "      <td>0.008179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>histogram_width</td>\n",
       "      <td>0.008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>histogram_min</td>\n",
       "      <td>0.025074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>histogram_max</td>\n",
       "      <td>0.024256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>histogram_number_of_peaks</td>\n",
       "      <td>0.012114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>histogram_mode</td>\n",
       "      <td>0.041893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>histogram_mean</td>\n",
       "      <td>0.055668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>histogram_median</td>\n",
       "      <td>0.014260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>histogram_variance</td>\n",
       "      <td>0.014404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>uterine_cont_per_min</td>\n",
       "      <td>0.027773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_change</td>\n",
       "      <td>0.008287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sqrt_total_change</td>\n",
       "      <td>0.010150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hist_zeros_1.0</td>\n",
       "      <td>0.024100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hist_zeros_2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hist_zeros_3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hist_zeros_4.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hist_zeros_5.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>hist_zeros_7.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hist_zeros_8.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>hist_zeros_10.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hist_tendancy_0.0</td>\n",
       "      <td>0.007253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>hist_tendancy_1.0</td>\n",
       "      <td>0.008355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sev_decel_0.001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>quant_acc_1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>quant_light_dec_1</td>\n",
       "      <td>0.010097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>quant_hist_mean_1</td>\n",
       "      <td>0.017187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>quant_hist_mean_2</td>\n",
       "      <td>0.013594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>quant_hist_mean_3</td>\n",
       "      <td>0.061120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>quant_hist_mean_4</td>\n",
       "      <td>0.024476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>quant_hist_mean_5</td>\n",
       "      <td>0.043454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>quant_hist_mean_6</td>\n",
       "      <td>0.005938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    0     Coefs\n",
       "0                                      baseline value  0.015132\n",
       "1                                       accelerations  0.169203\n",
       "2                                      fetal_movement  0.016855\n",
       "3                                uterine_contractions  0.020800\n",
       "4                                 light_decelerations  0.015470\n",
       "5                            prolongued_decelerations  0.099882\n",
       "6                     abnormal_short_term_variability  0.149758\n",
       "7                mean_value_of_short_term_variability  0.024495\n",
       "8   percentage_of_time_with_abnormal_long_term_var...  0.022075\n",
       "9                 mean_value_of_long_term_variability  0.008179\n",
       "10                                    histogram_width  0.008700\n",
       "11                                      histogram_min  0.025074\n",
       "12                                      histogram_max  0.024256\n",
       "13                          histogram_number_of_peaks  0.012114\n",
       "14                                     histogram_mode  0.041893\n",
       "15                                     histogram_mean  0.055668\n",
       "16                                   histogram_median  0.014260\n",
       "17                                 histogram_variance  0.014404\n",
       "18                               uterine_cont_per_min  0.027773\n",
       "19                                       total_change  0.008287\n",
       "20                                  sqrt_total_change  0.010150\n",
       "21                                     hist_zeros_1.0  0.024100\n",
       "22                                     hist_zeros_2.0  0.000000\n",
       "23                                     hist_zeros_3.0  0.000000\n",
       "24                                     hist_zeros_4.0  0.000000\n",
       "25                                     hist_zeros_5.0  0.000000\n",
       "26                                     hist_zeros_7.0  0.000000\n",
       "27                                     hist_zeros_8.0  0.000000\n",
       "28                                    hist_zeros_10.0  0.000000\n",
       "29                                  hist_tendancy_0.0  0.007253\n",
       "30                                  hist_tendancy_1.0  0.008355\n",
       "31                                    sev_decel_0.001  0.000000\n",
       "32                                        quant_acc_1  0.000000\n",
       "33                                  quant_light_dec_1  0.010097\n",
       "34                                  quant_hist_mean_1  0.017187\n",
       "35                                  quant_hist_mean_2  0.013594\n",
       "36                                  quant_hist_mean_3  0.061120\n",
       "37                                  quant_hist_mean_4  0.024476\n",
       "38                                  quant_hist_mean_5  0.043454\n",
       "39                                  quant_hist_mean_6  0.005938"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_xgb = xgb.XGBClassifier(objective = 'binary:logistic')\n",
    "param_dist = {'n_estimators': [500,1000,1500],\n",
    "              'learning_rate': [0.1,0.07,0.05,0.03,0.01],\n",
    "              'max_depth': [9,10,11,12,13],\n",
    "              'colsample_bytree': [0.5,0.45,0.4],\n",
    "              'min_child_weight': [1, 2, 3]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_xg = GridSearchCV(estimator=clf_xgb,\n",
    "                      param_grid= param_dist,\n",
    "                      scoring='f1',\n",
    "                      n_jobs=-1,\n",
    "                      verbose=1,\n",
    "                      iid=False,\n",
    "                      cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 675 candidates, totalling 6750 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   33.4s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed: 12.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed: 20.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed: 30.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2434 tasks      | elapsed: 43.5min\n",
      "[Parallel(n_jobs=-1)]: Done 3184 tasks      | elapsed: 54.9min\n",
      "[Parallel(n_jobs=-1)]: Done 4034 tasks      | elapsed: 69.6min\n",
      "[Parallel(n_jobs=-1)]: Done 4984 tasks      | elapsed: 86.6min\n",
      "[Parallel(n_jobs=-1)]: Done 6034 tasks      | elapsed: 103.0min\n",
      "[Parallel(n_jobs=-1)]: Done 6750 out of 6750 | elapsed: 117.0min finished\n",
      "/Users/heymanjake/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=XGBClassifier(), iid=False, n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.5, 0.45, 0.4],\n",
       "                         'learning_rate': [0.1, 0.07, 0.05, 0.03, 0.01],\n",
       "                         'max_depth': [9, 10, 11, 12, 13],\n",
       "                         'min_child_weight': [1, 2, 3],\n",
       "                         'n_estimators': [500, 1000, 1500]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xg.fit(smX_train,smy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.45,\n",
       " 'learning_rate': 0.07,\n",
       " 'max_depth': 12,\n",
       " 'min_child_weight': 1,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_xg.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "Accuracy: 0.9661654135338346\n",
      "Recall: 0.9902200488997555\n",
      "F1 Score: 0.9782608695652174\n",
      "Precision: 0.9665871121718377\n",
      "\n",
      "Confusion Matrix:\n",
      " TN,  FP, FN, TP\n",
      "[405   4  14 109]\n"
     ]
    }
   ],
   "source": [
    "evaluation(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8892929eb8>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oUc3E+8yZM1hbW3PixAlq164NoPV5q5o1a3LixAkcHR3566+/sLKyAkBP7/HXFYp6VisiIgIzMzP8/Py4evUqGzZseKIXLTZs2MDUqVMpU6YMgwYN4vfff9c6lr29PT/88AM5OTno6+tz/PhxPv74Y86fP/9YnCqVqshnxvX09DSf16hRg5kzZ1K1alVOnjxJYmLiU+ddGGdnZ/r3749KpSIsLIyUlBTmzZvHL7/8AsCAAQM0x+ff4wH4+/sze/Zsatasybx584iPjwfg5s2b3L59m0qVKnHq1Ck++eQToqOjC43j0ePRqVMnwsLCsLCwYO7cuU+dkxBCCPE8XLt2lapVbTRfe3lNICgoELVajaWlJePGTcLExJRatWrz+ecDUKlUtGr1Dk2bNi+xmGUyLorUvn17fH192bp1KxYWFujr65OVlUVUVBTLly/HxMSEmTNncv78ea3bjxs3jilTphAWFkZOTg7+/v5PHUPr1q3x9PTk5MmTGBsbU716dW7fvl3sdnXr1qVnz56UL18ea2trGjduTEREhNb1OnfuTJ8+fcjLy6N58+Y4OTkVyKlFixYMHTqUFStWaJ1E16lTh9DQUBo2bIivry/e3t7k5uYCDyfATxLzkzAxMaFevXrk5ORgamqKoig0a9aM7t27U7ZsWczNzbl9+za2trZat//www/54osvsLS0pHLlyiQlJQEPn22fNm0at27domnTprz//vtFTsbffPNNAgMDqVmzJq1ataJly5bcvXsXCwuL55KnEEII8bRcXd0f+7pu3XosWBBWYL2BA4dqnisvaSqlJGu5CCFKDV9fX/7v//6P1q1bP9H6cltWd0heukXy0i2Sl+6Qx1SE0OLGjRuaKimPatmyJaNHj37u42VlZTFo0KACy2vUqIGfn99zGyc6OppZs2YVWN65c2dcXV2f2zjPy8CBA6lUqdITT8SFEOJ5GjDAFROT/PdfbHB3H4i/vy8qlQp7+5p4enqjp6fHqlXLiIqKxMTEBFdX9xJt9CJEPpmMC51WtWpVVq5c+dLGMzQ0pHv37ly+fBkvLy/N8vyGOtrqeCcnJ3Pw4EG6dev2xOM4ODi81LyeRWZmJp07d2bv3r2EhRW8BSiEEC9D/gvvISGLNMu8vT0YMmQ4zZq1YNasAA4e3I+tbTV2797FokXLABg+fCDNm7fUFBwQoqRI0x8hnoOiGupcuHDhle7MKYQQuiwm5hIZGRl4eIxg9OhhnD17hgsXzmteyGvV6h1OnPiNK1f+pmnT5pQpU4YyZcpga2tHTMylEo5eCLkyLsQzOX36NAMHDuTu3bv06dOHhQsXsnPnTvbv38/ixYtRq9XY2NgQGBjIggULOH/+POvXr6dNmzZamyCFh4ezevVqypUrh4GBAc7OzgBs3LiRvLw8Ro8eTWxsLJGRkeTk5GBmZkZwcDDbtm1j3759ZGRkkJiYiLu7O3v27OHSpUuMGzcOJycnrfFHREQUu92WLVtYvnw5hoaGvPHGG/j5+ZGVlYWXlxf379/Hzs5Os78LFy4UaHJkZlZ4LVapMy6EeB7CxnfAyMiIPn3c6NbtY+LiruHlNRpFUTQv2pcta8KDB6nUrFmLVat+IC3tAdnZ2Zw9G82HH3Yv4QyEkMm4EM9ErVazdOlS4uPjGTr0f29jb9u2jf79+9OlSxc2b95Mamoqw4YNY926dfTu3ZvRo0cXaIK0ZMkSlixZoqkd7u7+vzfBzc3NCQ0NJS8vj5MnT7Js2TL09PQYNGgQZ86cAeDBgweaDqLLli1jw4YNHDt2jBUrVhQ6GS9uu+bNmxMcHMymTZswNTUlICCA9evXAw+rxnh4eHD69GmOHTsGwJQpUwo0OfLw8HgRh14IITQqVjSjXLmGNGlSHyMjIypVehNLywr8+eefmpfl1Oo8KlasQMuWDnz2mTvjx3tQvXp1mjZtQo0aNi+kicvTehVieBFKY17S9EeIV0SDBg1QqVRUrFiRjIwMzfIJEyawcOFC1q5di729fYHJsLYmSNeuXaNmzZoYGxsDDxvu5Mtv2qOnp4eBgQGenp6ULVuWmzdvahow5XfqNDMzo2bNmqhUKsqVK1dk46DitouLi6NWrVqahkAtW7bk0KFDALz33sMXnho3boxardbkpa3JUWGk6Y9ukbx0y+uUV2JiCps2/UhsbAxeXuO5cyeRe/fu07JlK3bt2kezZi2IjNxDs2YtuHjxGtev32TevEWkpqbi4TECC4vKJX6sXqfzpeukmooQr5DCmvWsX7+eUaNGYWlpiY+PD7t378bW1lbTHEdbEyQ7OzsuX75MRkYGhoaGREdHa7qQ5jftOX/+PFFRUYSHh5Oenk6PHj00jX2epXFQcdvZ2toSGxtLWloaZcuW5bfffqNGjRqoVCr++OMPnJycOHfunOYPgsKaHAkhxIvWtetH+Pv7Mnz4IFQqFRMm+FCunAWBgf4sXPg91au/Qbt2jujp6XHjRjyDB7tjYKBmxIgx6Ovrl3T4QshkXIjnycHBgQEDBmBhYYGJiQnt2rUjKyuLixcvsmzZMq1NkCpUqMCQIUNwdXXFwsKCzMxM1Gq1ZqILUL16dYyNjenRoweGhoZUrFjxuTUR0qZChQqMGjUKd3d39PT0sLOzw8vLC319fSZMmECfPn2wt7fHwMAAQGuTIyGEeBkMDAzw9S34O+fR6ir5xo2b9DJCEuKpSNMfIUpYTk4OixcvZvjw4QD07duXsWPHah5nKa1K2+1LKJ23ZUHy0jWlOa+LF68yaJAbQUHfk5GRwezZARgYGFK7dh3GjPFCT0+PNWtWEhW1Cz09PdzcBvD+++1LOvQilebzVdryksdUhNAhERERWmuRz5w5s0AJRLVaTVJSEh06dMDKygoHBwdatGjxXOLw9fUlNja2wPLFixc/l9q6I0eOJCQk5D/vRwghipOdnU1gYACGhmUACAz0Z+xYLxo1asyiRfPZvftn3nnnPX78cR3r128mPT2dAQNcX/nJuBAyGRfiJQkKCir0M0dHRxITE4tc51n4+vo+1/39m0zEhRAvy8yZM/n4409YufIHABITb9OoUWMAGjVqzKFD+3F07EjlylVIT08nIyNd896NEK8ymYwL8YK8DrXI27Rpw+HDh3Fzc6NevXpcunSJ1NRU5s6di42Nzcs83EKIUmzHjq1UqFCBt99urZmMV61qw++/n6Rp0+YcPnyQjIx0ACpVssbNzYXc3Dzc3PqXYNRCPBmZjAvxgpT2WuT/3s7BwYFJkyYRFBTE9u3bH8v536TpjxDiaZjf3I5KpeLIkSPExl5ixoypeHuPY+HChfz44xoaNWpESkoK586d4v79JPbt2wfAoEGDaNv2HRwcHEo4g6KVxnrcUDrzkjrjQuiQ0l6LXFu+AJUrV+bOnTtF7lfqjOsWyUu3lM68OmjyGjlyKF99NZEjRw7h5TUJK6uKBAUF0qrVOyiKAXp6au7dy0SlUlGmjDFxcbeoUuXVPR6l83yVzrzkBU4hdExpr0UuhBAlydbWDi+vMRgZGdG0aXNat34XgBMnfmPo0P7o6enh4NCEli3fLuFIhSiaTMaFeMlKSy1yIYQoCfn1w6tXf4N3321b4PNBgz5n0KDPX3ZYQjwzqTMuhA4ojbXIS9vtSyidt2VB8tI1kpdukbx0hzymIsRrTK1Wk56eTvfu3TEwMNCpWuRCCPFfJCXdZdAgN5Yt+4GEhLtaG/2sX7+aqKhIAFq3bsPAgYW/QC7Eq0Ym40I8g6dp6gOQnJzMwYMH6dat2zOP6enpiaen5zNvXxhttcgTExOZMWNGgc9mz56Nvb09PXr0YNWqVfTr10/rsRBCiOchJyen2EY/b77pQGTkzyxatAyVSsWIEYNp27Y9tWrVLuHohXgyUg1fiOckKChI60Qc4MKFC+zdu/clR/TsKlasWGzDoNDQ0JcTjBDitRUS8h0ff/wJVlZWQMFGP9HRf2BtXZk5c4LR19dHT0+PnJycQn8XC/EqkivjQjwjXW/q0717d5YsWYK5uTlvv/02q1atokGDBnTv3p05c+Ywfvx4NmzYwK5duwgNDaVChQpkZ2djb29PaGgo9+7dw9fXFwcHhwLHonfv3kUeO6kzLoQoStj4DuzYsRULC4tiG/2o1WosLCxQFIXvv59L7dp1sbOrXsIZCPHkZDIuxDPS9aY+jo6OHDx4kMqVK2Nra8vhw4cxNDTkjTfeeOyq0qxZswgPD8fCwkKT5/Dhw1m1ahW+vr5EREQUOBbFTcaFEKIoFSuaERn5sNGPp+cpYmMv4e3trbXRT8WKZmRmZjJx4kRMTEyYMcMffX39kk7hqZTG5jhQOvOSpj9CvEJ0valPx44dWbBgAVWqVMHDw4OVK1eiKAodO3bUrHPnzh1MTU0pX758gbie5FgURpr+6BbJS7eUhrwSE1P47rsFmq9HjhxKQMA0duyILNDo5/bt+3z55SiaNWtBv379uXs3rQQjf3ql4XxpUxrzelHVVOSZcSGeUXFNfVatWgXA7t270dPTK9DUB9Da1CcvL4/o6GjN/v7d1Oe7775jypQp5OXl/aemPnXq1OH69etER0fz/vvvk5aWxp49e2jb9n91ey0sLEhJSeHu3bsAmivxAI9WRZXmQEKIlyG/0c+wYQMpW9aE1q3f5cCBX/jjj1McPforI0cOZeTIoZw9G138zoR4RciVcSGeM11q6tOyZUuuX7+Onp4eLVu2JCYmBhMTE5KSkoCHj+JMnz6dQYMGUa5cOdTq//3KqFmzJl5eXrzzzjv/KQYhhChOSMgiKlY0w9y8UoFGP++/3569e38tociE+O+k6Y8Qr4DS2NSnOKXt9iWUztuyIHnpmtKWV2mvM17azle+0piXNP0R4hXxImqMS1MfIYQoSOqMi9eBTMaFeA6CgoIK/Sy/xnhxDX9eZlMfIYTQBfl1xvNLG/67zvihQ/txdOyoqTMOSJ1xoXNkMi7EM9D1GuMRERHFbrdz505NGcXmzZvj5eXFzZs38fX1JTMzk+TkZEaMGIGTkxPdunXjrbfe4sKFC6hUKubPn4+ZWekraSWEeHmkzrh4XchkXIhnoOs1xovbrkWLFgQHB7Nx40aMjY356quvOHz4MCqVigEDBvD2229z6tQpgoODcXJy4sGDB3Tp0oUpU6bw5ZdfcuDAAbp06VLo2NL0RwhRlLDxHdi+fQsqlYoTJ34jJuYi3t7efP75SFauXMaaNSuoV68BhoYGAGRmZjJ9uh9ly5blyy/Hl3D0QjwdmYwL8Qx0vcZ4cdtdu3aNu3fvav7QePDgAXFxcTRv3pzQ0FB+/PFHVCrVY9VeGjRoAECVKlWKHXvrnI+K/FwIITZsWKf5t5ubG76+vhw4cIDZs2dibW3NN998g5NTe6ysTBk8eCxvv/32YxdHdElpbI4DpTMvafojxCuiuBrjlpaW+Pj4sHv3bmxtbQvUGHd0dNRaY9zQ0JDo6Gjs7e2BgjXGw8PDSU9Pp0ePHv+pxnhx29na2lKlShXCwsIwMDAgIiKC+vXrM3fuXFxcXHj//ffZuHEjmzZteqL9aVPa3rKH0lk9ACQvXVMa88rKeviHv4VFJQYOHIyRkRFNmzanQYNmbNy4ld9++43U1DT27NkHwLBhI3nzTYeSDPmJlcbzBaUzL6mmIoQO0KUa40WpUKEC/fv3x83NjdzcXGxsbOjcuTOdOnXC39+fhQsXUqVKFU09ciGEeJGkzrgozaTOuBAl7HWsMQ5yZVyXSF66RfLSLZKX7pAr40KUUlJjXAjxOslv4hMU9D3m5ubMnDmNlJQU8vJymTzZDxsbW1atWkZUVCQmJia4urrTps17JR22EC+MTMbFS9GmTRsOHz78wvafm5vL0KFDSUtLY8GCBZQrV67AOh06dGDnzp2UKVPmhcWxfv16evTogYGBwVNt9zJqjB84cICEhAR69+6tdd3g4GCsrKzo06fPY8tHjhxJSEiI5gWq06dPU65cORwdHVm1ahX9+vV77nELIUqnfzfxmT9/Hh980BlHxw84deoEV69eISMjg927d7Fo0TIAhg8fSPPmLYHS9zKgEAB6JR2AEM9DYmIiSUlJrF27VutE/GVZuHCh5mXNV03btm0LnYgXJU0hQ5UAACAASURBVCQk5LGve/TogaOjIwChoaHPJTYhxOshv4mPlZUVAGfOnCYx8RZjxnxBZOROmjZtzpUrf9O0aXPKlClDmTJlsLW1IybmUglHLsSLI1fGS4HiGrhkZ2c/9+Yt2dnZODs789NPP1G2bFmWLFmCWq3mnXfeYcaMGeTl5XH//n0mT55Ms2bNNNvlX12tWbMma9eu5c6dO4waNYqVK1eybds2VCoVzs7Oj9Xa/rctW7awfPlyDA0NeeONN/Dz82PKlClcuXIFHx8f/Pz8ijxe169f19p4p2PHjjRr1oy///4bS0tLgoODyc7OZty4cdy+fZsqVapw/PhxDh06pHW/4eHhJCYm4uHhwfz585kzZw7Hjx9HURT69+9P586dcXNzo3z58ty/f58uXbpw4MCBp27YM3LkSNzd3XnrrbeIjo4mNDSUWbNmMWnSJFJSUkhKSsLFxQVXV9cC4129ehUvLy/mzJnD2bNnefDgATVr1mT69OkAREVFsXPnTjIyMpg8eTIODg4F7mrkX0FPTk7m3r17+Pr6kpKSQrdu3WjXrh2xsbHMnDmTRYsWFXoOpM64EK+XsPEdtDbxSUi4gZmZOXPnzueHHxazevVynJz+j1WrfiAt7QHZ2dmcPRvNhx92L+EMhHhxZDJeShTWwGXZsmVcu3btuTdvMTAwoGPHjkRGRvLxxx+zY8cOli5dypEjR/D29qZu3bps3bqViIiIxybj2sTExLBjxw7WrFmDSqWif//+vPvuu5ryfo9KSkoiODiYTZs2YWpqSkBAAOvXr+frr7/G09Oz2Ik4QGBgYIHGOxEREcTFxbF8+XKqVKnCp59+ypkzZzh9+jS2trbMmzeP2NhYunbtWuh+XVxcCA0NJSgoiP3793P9+nXWrVtHZmYmvXr1ok2bNgB069aNDz74gIiIiGdq2OPi4sKmTZt466232LRpE7169eLq1at06dKFjh07cuvWLdzc3HB1dS0wHkBqairm5ub88MMP5OXl0aVLF27dugWAjY0Nfn5+mj8IHi1d+G/Dhw9n1apV+Pr6cvToUdauXUu7du348ccf6dmzZ7HnQQjx+qhY0YzIyO2oVCo8PU8RG3uJGTOmoq+vz0cfOVO+vBldu3YiKCiIli2/4rPP3Bk/3oPq1avTtGkTatSw0eynNJK8dIfUGReFKqyBS1pa2gtr3uLi4oKvry/29va88cYblC9fnkqVKjF//nyMjIx48OABpqamhW6fX8jn4sWL3Lhxg/79+wNw7949rl27pnUyHhcXR61atTT7bdmyJYcOHaJdu3ZPfKy0Nd4BKF++PFWqVHks99jYWNq2fVhGq2bNmlSoUOGJxrh48SJ//vknbm5uwMPnJG/cuAH8r5FP/vjwdA173nvvPWbNmkVycjInTpxg8uTJ3Llzh+XLlxMZGYmpqelj5/PR8QDKlCnD3bt3NQ2E0tLSyM7OBtAcl9q1a5OYmPhEuQK8/fbb+Pv7888//3D48OFin3/fOuejUveWPZTO6gEgeemaVzGvxMQUvvtugebrkSOH8tVXE1m8eD7btu2iU6cu7Nt3CBub6ly8eI3r128yb94iUlNT8fAYgYVFZc1+SptX8Xw9D6UxrxdVTUWeGS8lCmu4olKpNM1bVq5cSb9+/WjcuDFz587lo48+YtasWbz99ts8WuHySZu3vPHGGyiKwpIlS3BxcQHA39+f0aNHM3PmTOrUqcO/K2caGhpqJnnnzp0DwN7enlq1arFixQpWrlxJjx49qFOnjtYxbW1tiY2NJS0tDYDffvutwGSzOPmNdwBN453C8q5Tpw6///47ANeuXSu2rrZKpSIvLw97e3vefvttVq5cyfLly+ncuTO2trYFxnmWhj16enp06tQJX19fnJyc0NfXJywsjCZNmjB79mw6depU5PnMf5Hz22+/xdPTk4yMDM360dHRAFy4cIGqVasWG8ujjYe6deuGv78/bdq0eeoXWIUQr6eRIz34+eftDBs2kGPHjuDm9rBPw40b8Qwe7M5XX41mxIgx6Ovrl3SoQrwwcmW8lFOr1S+0eUvPnj2ZO3curVq1AuDDDz/kiy++wNLSksqVKxfYr7u7O35+flSpUoVKlSoBUK9ePVq3bk2fPn3IysrCwcEBa2trreNVqFCBUaNG4e7ujp6eHnZ2dnh5eT3VVVxtjXeKym/8+PH07duXqlWrFluJpUWLFgwdOpQVK1bw22+/4erqSlpaGk5OTkXeJXhan3zyCU5OTuzatQuA9u3b4+vry9atD5/J1NfXJysrS+u2Dg4OzJ8/n169emFoaEi1atU0DYSuX7+Ou7s7WVlZT/TIT82aNfHy8mL27Nn06NGDdu3a8dNP8jy4EKJoISH/e6fku+/mF/h83LhJLzMcIUqUNP0RoginTp0iLS2Nd999lytXrjB48GCioqJKOqxX0q1btxg3bhzLly9/ovVL2+1LKJ23ZUHy0jWSl26RvHSHNP0RJSIrK4tBgwYVWF6jRo0nunL6rPbs2cOyZcsKLHd3d+eDDz4odLvo6GhmzZpVYHnnzp01LzQ+jWrVquHp6UlISAg5OTn4+Piwfv16tm3bVmBdT09PmjZt+tRjFEaXGvbs2rWLkJCQIu8yCCFKv0cb+mRkZODt7YGtbTUAunfviaNjR9avX01UVCQArVu3YeDAoSUZshAlrtRdGT9+/DhmZmbUq1fvpY+9evVqNmzYwOeff46zs3OBz5OTkzl48CDdunVj0aJFtGrVCgcHh5cWX/6YdevWZcuWLbi4uBTa6OVpPFqusCSNHz8eZ2dnzQuX//Yy4ty9e3eRj9mUpL/++os9e/YwcuRIrZ9HRERw+fJlvLy8Hlvu4eHBzJkz8fHx0Xxf5zcPetYmRyBXxnWJ5KVbSiqvnJwcpkwZz99/X2bmzG+Jjv6D1NRU+vT5X2Ow+Pjr+PhMYNGiZahUKkaMGMyXX06gVq3axe5fzpduKY15yQucT2jjxo2a519ftt27dxMYGKh1Ig4PX4rbu3cvAEOHDn2pE/FHx0xMTCQ8PPyljv26WLFiBampqSUdhlb169cvdCJelKCgIAwNDTVfP9o86FVuciSEeLn+3dDnwoW/OHLkECNGDGH6dD/S0h5gbV2ZOXOC0dfXR09Pj5ycnMd+vwjxOnphj6lERESwZ88eUlNTSUpKYsSIEZQvX56goCD09fWpVq0afn5+bN26lY0bN5KXl8fo0aO5fv06a9euJS8vD0dHR0aNGsXOnTsLNK0JDg7m+vXr/PPPP9y4cYMJEyZQvnx5Dh48yJ9//kmtWrXYu3cvkZGR5OTkYGZmRnBwMHl5eVqbuFy4cIFp06YBYGFhQUBAgNZmN6C9aczp06c5e/YskyZNIigoiGrVqhXYbsGCBZw/f57169fz+++/4+zszJ07d4ps2OPk5KQ1f22ioqL49ddf8fHxYeHChfzxxx+Ehoby008/kZCQwJUrV3B2diYyMpKYmBhNZ8U9e/bw888/k5yczJgxY+jQoYPW/aempmptLAMwb948kpKSMDQ0JDAwkEuXLrF48WIMDAy4fv06zs7ODB8+vNCGO+3bt8fe3h57e3tSUlJQq9XcuHGDrKwsnJ2d2bdvHwkJCcyfPx8bGxt8fHy4efMmSUlJtG3blrFjxz7x9+b9+/f56quvSE1NJTc3lzFjxtC6dWutDY9MTU2ZOnUqZ8+excrKivj4eEJDQzWVUR71yy+/8Ndff+Ht7c2aNWs0j7M82sho/PjxJCcnk5yczKBBg1i3bh0GBgbcvHmTTz/9lKNHj3L+/Hnc3d0Lfaxm+vTp1KtXj+7du5OYmMjnn39OeHi41mPy7/F27NhBUFAQq1atKvCzAfDHH3/w2WefkZqayqhRo2jXrh0dOnRg586dmvHzr6BXr15d0+SoVq1aWFtb07dvX+7du8eAAQM0dc21kaY/QpQehTX0qV+/IV27fky9evVZvnwpYWGLGTlyLBYWFiiKwvffz6V27brY2VUv4QyEKGHKC7Jx40alf//+Sm5urpKYmKi0a9dO6dChg3Lnzh1FURQlKChIWb9+vbJx40Zl2LBhiqIoyp07d5QPPvhASU9PV3JzcxV/f38lPj5e6dy5s5KWlqYoiqJ4eXkphw4dUubNm6dMnjxZURRFOXTokDJw4EBFURTF29tb2b9/v5Kbm6sEBwcrubm5iqIoysCBA5UTJ04oy5YtU2bOnKkoiqLExMQo9erVUxRFUVxcXJRLly4piqIoGzZsUL799ttCcxs1apSye/duRVEU5dy5c0r37t0VRVGUfv36KTExMYVud/ToUWXs2LGPxblx40ZlwIABiqIoyrZt25SePXsqeXl5ypEjR5Thw4crSUlJWvPXJj09XenatauiKIoyePBg5eOPP1ays7OVMWPGKDExMZox4+LiFBcXF0VRFGXevHnKxIkTNfENHjy40PjPnj2r7Nq1S1EURbl586bywQcfaPLetm2boiiKsmrVKiUgIEA5evSo0rlzZyU7O1t58OCB0qxZsyKPXd26dZW7d+9qjs38+fMVRVGUKVOmaM7X3LlzlR9++EGJi4tTNmzYoCiKomRkZChvvfXWY8e0MPnnZ8aMGcqyZcs0ebRv317Jzc1V2rdvr5w8eVJRFEXx9PRUtm3bpuzevVsZM2aMoiiK8s8//yjNmzdX4uLiih3j0qVLyqeffqrk5OQoubm5ipubmxIbG6t4e3srP/zwg+Z4Ozs7K1lZWcrvv/+utG3bVsnMzFSuXbumfPjhh4WOERMTo7i5uSmKoiiLFy9WVq5cWeQxeXS8sWPHFvqzsXHjRmXw4MFKXl6ecufOnceOS0ZGxmPfs7NmzVIURdF8du3aNaVnz56a74GwsLBC4xdClD6urq5K3759lX79+inNmzdXPvnkE+X27duazy9duqS4u7srivLwd5Snp6cyZcoUJScnp6RCFuKV8UJf4GzZsiV6enpYWVlhbGzM1atXNVcwMzIyaNOmDXZ2dpo60XFxcdSuXVvzctrEiROJjo7W2rQG/tcwpXLlygXKuOnp6WFgYKBpbHLz5k1ycnIKbeISGxvL1KlTgYet3ouqXV1Y05hnVVTjl2vXrhWa/78ZGRlRo0YNoqOjUavVNGnShOPHj5OQkFDkc9INGzYEwMrKioyMjELXs7KyKrSxTIsWLQBo1qwZ+/fvBx7W6Far1ajVas05LarhTvny5TX7y288ZG5urmn+Y25uTlZWFhYWFpw5c4ajR49iampaaAm/wsTGxtKtWzcArK2tMTU15e7du4+Nm9/0Jz4+niZNmgAPyypqa0SkTWGNjODxJjy1a9fGwMAAMzMz7OzsMDQ0LLbpT82aNcnNzSU+Pp4dO3Zo7poUdkz+/b1c2M8GQPPmzVGpVFhaWmJmZkZycvIT5VutWjVMTEyIiYlh69atzJ9fsFTZv5W2ZwmhdD4jCZKXrimJvLQ19Bky5HM8PL6iQYM32b17H/b2tbl9+z5ffjmKZs1a0K9ff+7eTXviMeR86ZbSmJdOVlP5888/Abhz5w6ZmZnY2dkxf/58zMzM2LNnD2XLliUhIQE9vYePrtvZ2XH58mWysrIwNDRk9OjReHt7a5rWGBgYEBERQf369YmKitLaMEWlUqEoCufPnycqKorw8HDS09Pp0aMHiqJomrg4OTk91sSlRo0azJw5k6pVq3Ly5Mki61bnN41xdHR8rGlMcfT09LQ+X1tU4xdbW1ut+RfGycmJWbNm4ejoSLVq1QgKCuKdd94pMo4nbTyT31jG1dWVo0ePaibdAGfOnMHa2poTJ05Qu3btQvdb2LHL/x54kpgiIiIwMzPDz8+Pq1evsmHDhgLNhYqSH0ODBg24desW9+/fx8LCQuu4tWvX1tTNvnfvHleuXCly3/nff/mNjJYsWYJKpWLZsmXUqVOHn3/++T83/YGH9c9nzZpFrVq1MDc3Z8WKFYUek3+PUdjPBjw8jwCJiYmkpaU99gdSYfnmfy/16tWL0NBQrK2tn7hTqRCi9PLymkBQUCBqtRpLS0vGjZvEgQO/8Mcfp8jKyuLo0V8BGDZsJG+++XLfoRLiVfJCJ+N37tzhs88+IyUlha+//ho9PT2GDh2KoiiYmJgQGBhIQkKCZv0KFSowZMgQ+vXrh0qlon379tjY2GhtWlOYxo0bM3v2bL799luMjY3p0aMHhoaGVKxYkdu3bxfaxMXX1xdvb29yc3MBiizR9jRNYx5lZ2fHxYsXtZbsK0yFChWeKv/27dszceJEvv76aypXrsyYMWPw9fV9bB1LS0uys7OZNWvWU5XIK6qxTFRUFMuXL8fExISZM2dy/vx5rft41mP3qNatW+Pp6cnJkycxNjamevXqT/XS7ueff87EiRPZtWsXGRkZ+Pn5oVZr/1Fo164dBw4c4NNPP8XKygojI6MiK4c0bdqUcePGERYW9sSNjJ5FftOm0NBQ4OmOSfXq1bX+bMDDO1bu7u6kpaXh5+dX7B8LjzY5cnJyws/PT2tpSSHE6+PRhj4LFoQ99tn777dn795fX3ZIQrzSXlhpw8LKpJU0aeIinkZsbCznz5+nS5cuJCUl0bVrV/bt2ydv/2uRnp5Ov379CA8PL3CnQ5vSdvsSSudtWZC8dM2LzuvRWuJ5eXkEBvoDCjVr1sHD4yv09fVZtWoZUVGRmJiY4OrqTps27/3nceV86ZbSmJdOPqbyKtLWxEWb/9LsZuTIkdy7d++xZaamppqrmP/V825s82+61Gzm327cuIG3t3eB5S1btmT06NFPvb8qVaowe/Zsli9fTm5uLl5eXhw8eFDr3Y06depgbGz82B+g+TW6tU3eH607r01ISAjHjh0rsDwgIEBrtZ6SdOrUKb7++mvGjh37RBNxIYRuysnJITAwAEPDh3eVFy36ns8/H0GTJs3w9/fl0KED2NpWY/fuXSxatAyA4cMH0rx5y1f+/x9ClJRS1/RHiJLytHeDjh07xrp16wgKCnrBkb2aStsVEyidV4JA8tI1LzKv776bTevWbVi58ge++moitrbV0NfXJzs7G29vTwYMGMzt27c5c+Y0Y8c+/F3o4zOBXr1cefPNRv9pbDlfuqU05iVXxoXQAadPn2bgwIHcvXuXPn36sHDhQnbu3Mn+/ftZvHgxarUaGxsbAgMDH6s736ZNG63118PDw1m9ejXlypXDwMBA01Dq0dr8sbGxBWqGb9u2rdj69dpEREQUu522GuXh4eGcOnWKOXPm4O3tjYODA3379i30OEmdcSF0S2G1xPX19bl5M4GxY7/AxMQUO7vqmJmZs2rVD6SlPSA7O5uzZ6P58MPuJZyBEK8umYwL8Ryp1WqWLl1KfHy8phwlwLZt2+jfvz9dunRh8+bNpKamMmzYMNatW0fv3r0ZPXo0bm5uODk58ddffzFx4kSWLFnCkiVL2Lx5M4aGhri7u2v2Z25uTmhoKHl5eZw8eVJT3nDQoEGaiigPHjwgLCyM7du3s2zZMjZs2MCxY8c0L1sWpqjtOnToQHJycoHx+vbty+HDhxk/fjzZ2dlFTsSFELqnYkUzIiO3o1Kp8PQ8RWzsJWbMmEpoaCiNGtVhz56HFZoWLw5h5syZfPaZO+PHe1C9enWaNm1CjRo2hV4VfNo4SiPJS3e8iJxkMi7Ec9SgQQNUKhUVK1Z8rGb7hAkTWLhwIWvXrsXe3r7AZFhb/fVr165Rs2ZNjI2NgYeVWvLl1w4vqmZ4UfXri1LUdkWNN3ToUHr37l1k5818W+d8VOpuX0LpvC0LkpeueRF5JSamaK0l7u09gZEjPahWzY7cXD2ysnK5ePEa16/fZN68RaSmpuLhMQILi8r/OSY5X7qlNOYlj6kIoQMKKwW4fv16Ro0ahaWlJT4+PuzevRtbW1tNjW5t9dfz6+5nZGRgaGhIdHS0pulQ/kuSRdUMf9Ya5kVtV9h4WVlZBAQE4Ofnh6+vL6tXr5aKM0K8Bvr1609AgC9qtQFGRkZ4e0/BwsKCGzfiGTzYHQMDNSNGjEFfX7+kQxXilSWTcSFeAgcHBwYMGICFhQUmJia0a9eOrKwsTd15bfXX8+vuu7q6YmFhQWZmJmq1+rHOp0XVDH8RChtv9uzZtGvXjt69e3P79m3mzJnDhAkTXlgcQoiS9Wgt8dDQsAKfjxs36WWGI4ROk2oqQryicnJyWLx4McOHDwegb9++jB07VvM4i64rbbcvoXTelgXJS9dIXrpF8tId8piKEK8ZtVpNeno63bt3x8DAAAcHB1q0aPFc9q3LteSFEIXLzc1lwoQJXLoUg56ePhMnfk1qaiqzZwdgYGBI7dp1GDPGi9jYS8ydO0ez3blzZwkImE2rVu+UYPRCvJ7kyrgQL8iL6kI7fvx4TYnDhIQEevfu/Vz3X9SYbdu2fW77LG1XTKB0XgkCyUuXHDjwCydO/Iqn50ROnTrBhg1rSExMZOxYLxo1asyiRfOpXv0N/u//nDXb7N0bxYED+/D19S/ByItXGs8XSF66RK6MCyEe8zwnxkKI0qFt23Z89FFnkpLSuXXrJuXLW3Lu3J80atQYgEaNGnPo0H7NZDw9PZ2wsIWEhCwuybCFeK3JZFyIF+iPP/7gs88+IzU1lVGjRtGuXTt+/vlnVq9erVln7ty5AIwdOxZFUcjOzmbq1KnUrVuXlStXsm3bNlQqFc7Ozo/VGs+/8v7pp5/y5ZdfUrlyZeLi4mjUqBFTp04lJSWFSZMmkZSUBMDkyZOpW7euZvvp06dTr149unfvTmJiIp9//jnh4eH4+Phw8+ZNkpKSaNu2LWPHji0wppeXF5mZmXTu3Jm9e/dy4cIFpk2bBoCFhQUBAQGYmRVei1Wa/gjx/IWN7wA8fMRt2rSvOXDgF6ZNm8nff8fy++8nadq0OYcPHyQjI12zzbZtP9G+vRMWFhYlFbYQrz2ZjAvxAhkbG7No0SLu3r2Li4sLbdu25cqVKyxatAhjY2N8fHw4dOgQ5ubmmJmZMWfOHGJiYkhNTSUmJoYdO3awZs0aVCoV/fv3591339U6zpUrV1i6dCnGxsY4OTmRmJjIsmXLaNWqFa6urly5coUJEyawdu1azTa9evVi6tSpdO/enZ9++okePXqQkJBAkyZNcHFxITMzs8BkvDBTpkwhICCAWrVqER4ezpIlS/Dw8Hhux1EIUbxHb4HPnfstiYmJ9OrVi9DQUGbNmsWPP66hUaNGpKSkaNbdty+SefPm6UxzFl2J82lJXrpDmv4IoWOaN2+OSqXC0tISMzMzkpOTsbS0xNvbGxMTEy5fvkyTJk00k/QvvvgCtVrN8OHDuXjxIjdu3KB///4A3Lt3j2vXrmkdx87ODlNTUwAqVqxIZmYmFy9e5OjRo+zcuROA+/fvP7ZNzZo1yc3NJT4+nh07dmi6ap45c4ajR49iampKVlZWobk9+rpJbGwsU6dOBSA7O1vTlKgw0vRHt0heuiExMYWff95OWto9evRw5cGDHBQFtm/fhZfXJKysKhIUFEirVu+QmJhCamoqaWkZqNWmOnEcStv5yid56Q55ZlwIHZTfmj4xMZG0tDQMDAyYN28ev/zyCwADBgxAURSOHTtGpUqVCAsL4/fff+fbb79l0qRJ1KpViyVLlqBSqVi2bBl16tTh559/LjCOtkY99vb2fPjhh3Tr1o1//vmH8PDwAuv07NmTWbNmUatWLczNzVmxYgVmZmb4+flx9epVNmzY8Niku0yZMiQmJgLw559/apbXqFGDmTNnUrVqVU6ePKlZRwjxcr3/fgfmzPFnz54h5OTkMHq0JyqVHl5eYzAyMqJp0+a0bv3wDltc3FWqVKlSwhELIWQyLsQLlJGRgbu7O2lpafj5+WFqakqzZs3o3r07ZcuWxdzcnNu3b9OhQwc8PDxYvnw5enp6jBgxgnr16tG6dWv69OlDVlYWDg4OWFtbP/HYw4YNY9KkSWzYsIHU1FRGjhxZYJ1OnTrh7+9PaGgoAK1bt8bT05OTJ09ibGxM9erVH2si9N5777F27Vr69OlDw4YNMTExAR6WSvT29iY3NxcAf/9XuyqDEKWVsbExc+fOLXD17t13C77wXb9+Q6ZPn1NguRDi5ZLShkKIElHabl9C6bwtC5LXqyo3N5eZM6cRF3dVU1M8Le0B330XSF4eVKtmx/jxU9DT02PNmpVERe1CT08PN7cBvP9++5IO/6np+vkqjOSlO+QxFSGEEEJoHD58EHjYjv7UqRMEB3+LSvXwzlqDBs2YOnUyv/56iMaNm/Ljj+tYv34z6enpDBjgqpOTcSFKK72SDkCIV8GFCxc4fvz4E6+v7ZGPkvK0sWtz/Phxzp8/D7xauQkhCte2bTvGjZsEoKkpXqdOXZKTk1EUhbS0B6jVaoyNjalcuQrp6elkZKSjpyf/6xfiVSJXxoUAIiMjsbKyomXLlk+0fkhIyAuO6Mk9bezabNy4EWdnZ+rVq/dScpM640L8N4XVFL93Lxl/f3/KlfseExNTmjZtDkClSta4ubmQm5uHm1v/EoxcCPFvMhkXr51/N65p1qwZ1tbWGBgY0LBhQzIyMggKCkJfX59q1arh5+fH1q1b2bhxI3l5eYwePRovLy8OHz6Mm5sb9erV49KlS6SmpjJ37lxsbGyKbNbzb+Hh4axdu5a8vDwcHR0ZNWoUW7ZsYfny5RgaGvLGG29oYti/fz8ZGRlcu3aNIUOG0KZNGzZt2qSJfeLEibzxxhsYGhoybtw4fH19yczMJDk5mREjRuDk5MS+ffs0E+4GDRrQu3dvDh48yJ9//kmtWrVwcXHh8OHDnDt3jm+++QZ9fX3KlCnDN998Q15entYGQydPnmTmzJmo1WrMzc2ZPXu2ptSiEOL5K6ymeHp6OqtXr6Z27dqsXr2apUu/59133+X+/ST27dsHwKBBg2jb9h0cHBxKKvxnVhrrVoPkpUukzrgQL4C1tTXdQD3yzQAAIABJREFUu3fHysqKRo0a0alTJ9asWYOlpSXfffcdmzZt0kwy86uOPMrBwYFJkyYRFBTE9u3b6dChg9ZmPfb29gW2/eeff1i8eDFbtmzB0NCQGTNmEB8fT3BwMJs2bcLU1JSAgADWr19P2bJlSU1NZenSpVy5coVhw4bRo0cPTewODg6kpaXxxRdf0KBBA3799VcGDBjA22+/zalTpwgODqZdu3Z88803hIeHY2lpSUhICBUqVOC9997D2dmZqlWramKbPHky/v7+1K9fn6ioKGbMmMG4ceO0NhiKiorigw8+YNCgQezdu5f79+8XORmXOuO6RfJ69eTXFE9MvI2b2wBNTXEzM3NMTR/WDS9Txozbt/9BUQzQ01Nz714mKpWKMmWMiYu7RZUqupW7Lp+vokheukNe4BTiBfh3MaG7d+9y+/ZtTdfJjIwM2rRpg52dXaGNbBo0aABA5cqVuXPnTqHNerRNxuPi4qhduzZGRkYATJw4kejo/8fenUdFVf9/HH/OsCmbICAgYgaouIBpUqi5W1+FNDVEwVCS3FIwDEVFlFRUcBdSUSTcdzDX3PKrZbnmN8sVMHcEFJBN9vn9wY+bxIBoKg5+Hud0Dty59877M9OpD/d+7ut9ARsbG2ky6+DgwM8//0yrVq2wtbUFwNzcvMKGPKV1mpiYsHz5crZv345MJqOwsJC0tDT09fUxMjICKl8fnpycTLNmzaQaFiwoiUBT1mBo1KhRrFixgqFDh2JqaqqSV9wEQdV07tyN2bO/YcyYvzPF9fUN8PX1RaGQoa6ujr//VMzN63P27GlGjPBELpdjb/8ODg7vV3f5giD8PzEZF944yhrXyGQyiouLMTQ0xMzMjGXLlqGnp8eRI0fQ1tYmMTGxyg89WVlZKW3Wo0zDhg25fv06+fn5aGpq4uPjg7+/PwkJCeTk5KCtrc3p06elCbay5j6ltZcqrXPJkiUMGDCAzp07s2PHDmJjYzEyMiIjI4P09HQMDAyYNWsWffr0QSaTlfvDpF69ely5cgVbW1vOnDlDo0aNKqxh9+7d9OvXD39/fyIiIti6dat4EFQQXrLatWszc+bccts3b95c7uqdl9dIvLxGvqrSBEF4BmIyLrxxlDWuadmyJaGhoVhbWxMQEMCIESNQKBTo6OgQGhpKYmJilc//LM166taty/Dhw/nss8+QyWR07doVCwsLvL29GTJkCHK5nIYNG+Ln58fevXuVnuPJ2p9U2tAnIiICc3Nz0tLSkMvlTJ8+nZEjRyKXy2nevDl2dnZcunSJ+fPn06BBA+n4WbNmMXPmTBQKBWpqasyePbvCMdvZ2TFp0iS0tbXR0NBgxowZVf68BEEQBOFNJpr+CIJQLWraWkKomWskQYyrOihr6LNy5bc8fPgQgPv3E2nRoiXffDOHXbti+f77GNTU1Bg61Iu+fZ1e23H9G6/z9/VviHGpjpe1ZlyEjQovTYcOHV7q+YuKivDy8sLNzY1Hjx5V+TgPDw8SEhKe6z3Xr19f6esVZX4fOXIEDw+Pcv8cOnTouep4XpUtHblz5w6urq7ltq9cuZILFy4QExPD/PnzSUlJISgoCCibTy4IwovzZEMfL6+RhIUt5Jtv5hAevpLZs+ejq6uHt/fXPHz4gO3bN7N8+WoWLgwnIiK8wudJBEF4PYllKoLKSklJIS0tjZiYmFf2nsuXL+ezzz6r8PWKMr+7d+9O9+7dX3Z5T/U8GeIjRowAID4+Hih5aLN0Mv5kPrkgCC9Op05daN/+A+Dvhj6loqIicHFxxdjYmJ9/PoadXSs0NTXR1NTEwsKSK1euYG6u/IFzQRBeP2IyrqJiYmI4evQoubm5pKSkMGTIEI4cOUJcXBwTJ06koKCA6Oho5HI57777Ln5+fty/f19p7nTv3r157733uHr1KjKZTHp48Z8KCgpwcnLi+++/R1tbm8jISNTV1Wnfvj1z586luLiYjIwMpk6dSps2baTjPDw8CAoKwtramk2bNvHgwQO8vb2fKYtbWe52YGAgN27cYNq0aRWuUV60aBEnT56kuLgYZ2dnKeHk22+/5cGDBzx+/JiFCxdiaWnJ3LlzOXfuHAAff/wxQ4cOZdKkSaSnp5Oenk7nzp159OgRQUFB0mT0SUlJSWUyv6uSVz5t2jRat27NzZs3cXR0JDMzkwsXLvD2228zb948pWM6cuQIhw8fZs6cOQD07duX1atXs3//fg4ePEhhYSF6enqEhYWxZ88epfnop0+flibmubm5hISEoKGhQWpqKqNGjSI1NZXOnTszZswYJk2ahJOTk/T+d+7cYfz48UybNk3KJ09NTeXHH39k6dKlAAwaNIilS5dSr149pWMQTX8EoWIVNfQBSEtL5ezZM3h7jwcgOzsbHZ2/Y0RLI1AFQVAdYjKuwrKzs4mKimLv3r1ER0ezdetWTp06RXR0NLdu3WLHjh3Url2bCRMmcOLECWQyWbnc6R49epCdnY2zszOBgYF8/fXXHD9+HGdn53Lvp6GhwUcffcTBgwfp27cv+/btY/Xq1fz666/4+/vTtGlTdu/eTUxMTJnJuDLx8fFVzuJOS0tTmrs9ffp0xo8fX+nDgjt37mT9+vWYmpqWuYLeuXNnPvnkE8LCwvjhhx+wsbHhzp07bN26lcLCQtzd3XF0dATA0dFRmsSvX79e6UQcni+v/O7du6xZswYTExPee+89tm3bRmBgIN27dycjIwN9ff1y79OlSxfmzZtHTk4O8fHxNGzYEENDQ9LT06U/wLy8vPjjjz8AlOajx8XFMW/ePExNTVmxYgU//PADvXv3Jicnh3nz5qGtrc3gwYMrvZrfsmVLKZ+8Y8eOrFixgkePHpGSkoKhoWGFE3FBECpXUUOfvXv3cvDgz/Tt2wczMwMAzM2NiY+/LB1TVJSPnp5ejWy2AjWziQyIcakS0fRHKKM0A1pPTw9ra2tkMhl16tQhJyeH1NRUaXlBdnY2t2/f5t133y2XO12qNCvb3NycvLy8Ct9zwIABBAUFYWVlRaNGjaRJ17Jly6hVqxbZ2dmVNnspfV74WbO4leVud+nS5amf0cKFC1m4cCEPHjygY8eO0vaWLVsCYGxszIMHD0hISKBt27bIZDI0NDRo1aqVtK68onzxylQ1r9zAwEBqtKOtrY2NjQ1Q8p1W9D2oqanxn//8h4MHD/K///2PAQMGIJfL0dDQYPz48Whra3P//n3p+1VWv6mpKcHBwWhra5OUlCT98WRrayvdFbGzs+Ovv/6q0nhlMhl9+vRhz5493LlzBxcXl0r3F01/VIsY16tVUUOf1NQc/vvfnxg61Euq28LCilOnFnDnzgMKCgq4di2OJk2avJbj+rde1+/r3xLjUh2i6Y9QjrK859Lt5ubmREVFoaGhQUxMDM2aNVOaO/20c/1To0aNUCgUREZG4ubmBkBwcDDz58/H2tqapUuXcvfu3TLHaGpqkpKSgrW1NZcuXcLU1PSZsrgbNGhQYe52ZfLz8/nhhx9YuHAhCoUCZ2dnpVf8AaytrYmJicHT05OCggLOnz9Pv379yn02Twsfeta88qp+7v/k4uLC9OnTSUtLY9q0aVy5coXDhw+zbds2Hj9+TP/+/aValeWjT506lcOHD6Orq4u/v7+0b0JCAtnZ2WhpaXHhwgUGDhzIsWPHKh1v6bGffvopfn5+PH78mK+//vq5xiUIQgllDX20tLS4desm9etbSPsZGRnj4jKIMWOGU1xczIgRX6KlpQWIhzgFQVWIyXgNpK6ujqenJx4eHhQVFWFhYUGvXr2U5k4/DxcXF5YsWSIt4+jTpw9ffvklRkZGmJmZlTvvkCFDmDFjBubm5tLShWfN4laWu13auKcimpqa1KlTh08++YQ6derQoUOHMu3en9S1a1dOnz7NwIEDKSgooGfPnrRo0aLcftbW1vj5+TF//nyl53mReeWVsbS0BEoeDJXL5bz11lvUrl2b/v37o6mpiYmJCcnJyRUe/8knn+Dq6oq+vj7GxsbSvnXq1MHX15fU1FScnJykK/UVadWqlZRPbm1tjY6ODu+88w7q6uI/LYLwb1TU0Gf9+q3ltvXp048+ffq9irIEQXgJRM64IAgvzMiRI5kyZQpvvfXWU/etabcvoWbelgUxrldNWca4trY2ISGzyMzMpLi4iKlTZ2Bh0YCNG9dx+PAB5HI5Hh6f07lz19d2XP+WGJdqqYnjEstUhFcmPz8fLy+vctvffvvtl9pZ8ciRI0RHR5fbPmTIED788MMKj7tw4YLS5JFevXrh7u7+zHXExMRw/fp1/Pz8pG2+vr6EhISgqanJvXv38Pf3l14rLCwkPT2dXr164ePj88zvV5Hn/TxelISEBIKCgli3bl2Z8SuTm5uLu7s7HTt2rNJEXBCEij2ZMf7bb2cJC1uInp4+H37Yi+7dP+S3385y8+YN9PXrsH37ZrZs2cnjx4/5/HN3OnfuWs3VC4LwrMRkXChHU1OTdevWvfL3fd4sbnt7+5de76JFi6Sf69evX+b9Tp06xebNm1/oRBxen2xyKDt+ZWrVqvVK894FoSZTljF+/vxZrK1tGDfuS8zNzRk3zg8NDQ3MzMx5/PgxubmPlT4fIgjC609MxgVBid9//51hw4aRmpqKm5sbERER7N+/n2PHjrFq1SrU1dWxsLAgNDSUFStWcOXKFbZs2UKHDh0ICAigsLAQmUzG1KlTsbW1Zdu2bWzYsIE6deqgoaEh5XY/mQGekJCgNCe8sjz5Hj16KK3/aTn0PXr0YP/+/eWy6JOTk/Hz80OhUGBiYiKdr1u3buzfv5+bN28qzZT/6KOPaNOmDX/99RdGRkaEhYWhpqZW4ecrcsYFQbmKMsb37duFnp4+S5Ys47vvVrFhwxo8Pb+gXj1TPDwGUFRUjIeHZ/UWLwjCcxGTcUFQQl1dndWrV3P37l0pIhJgz549eHp64uzszM6dO8nKymLUqFFs3ryZgQMH4uPjg4eHBz169ODy5ctMmTKFyMhIIiMj2blzJ5qammWaG5VmgBcXF3Pu3DmlOeEV5cmvXbu2wsn4045r27YtYWFh5bLof/75Zz7++GNcXV3Zt28fmzZtKnPO+Ph4pZnyt2/fZs2aNZibmzNo0CD++OMP3nnnnRf8rQhCzVdRxri+vj6ffOKEoaEeH3/ck0WLFnHp0m9kZKRx9OhRALy8vOjUqT0mJvY1Mt8ZamZuNYhxqRKRMy4Ir0jz5s2RyWSYmJiQm5srbZ88eTIRERFs2rQJKyurcpPhhIQEHBwcgJIc+Pv373Pr1i2sra2pXbs2AK1bt5b2L41orCwnvKI8+cry4J923K1bt5Rm0cfFxfHJJ58A0KZNm3KT8Yoy5Q0NDTE3NweenlUPImdc1YhxvToVZYy3atWaPXsO0LOnM0eP/oyFxVsoFBrI5eo8epSHTCZDS6s2t28nYW8vHpBWJWJcqkM8wCkIr1BF+d9btmzB29sbIyMjpk2bxqFDh2jQoAHFxcVASfTh2bNn6d69O5cvX8bY2JiGDRty/fp1cnNz0dTU5MKFC1Jzo9I1npXlhD9vFnllxzVo0EBpFv3169c5f/48tra20pX5J1WUKf+8NQqCUJ6yjPHGjZsyd+5Mdu7cgY6OLtOnz0JfX5+zZ08zYoQncrkce/t3cHB4v7rLFwThGYnJuCA8A3t7ez7//HMMDAzQ0dGhS5cu5Ofnc+3aNaKjo5k4cSKBgYFERUVRWFhIcHAwdevWZfjw4bi7u2NgYEBeXh7q6uplOqA+a074v1W3bl2lWfTjxo3D19eXffv20aBBg3LHPS1TXhCEf6+ijPHFi5eV2+blNRIvr5GvoixBEF4SkTMuCC9ZYWEhq1atYvTo0QAMHjyYr776SlrO8qaqabcvoWbelgUxLlUjxqVaxLhUh1imIggqSl1dncePH9OvXz80NDSwt7enbdu2L+TcQUFBJCQklNu+atUqatWq9ULeQxCEf0dZEx8Li5I7TwcP/sCOHVuIiPiOuLirLFmyQDru0qU/mT17Po6O7aurdEEQXgExGRfeaE9r8PNP6enp/PTTT/Tu3fuZ3mf8+PGMHz/+X9f7T0FBQc997PHjx0lMTGTgwIEvriBBEMpR1sRn7tyFxMVdZe/e76XnQxo3bkp4+EoAfvzxMMbGJmIiLghvANEhQBD+YdGiRRV2mrx69So//vjjK67o5ejUqZOYiAvCK9CpUxcmTgwA/m7i8+hROsuXh+Pj83W5/R8/fkxUVARffTXhVZcqCEI1EFfGhTeeqjf4GTt2LEOGDOG9997jwoULLF++nHnz5hEQEEBmZiZpaWkMGDAAd3d3PDw8MDQ0JCMjA2dnZ27evImfnx8LFizgzz//JDs7G2tra+bMmUNYWBh37tzh4cOH3Lt3j8mTJ9OxY0eOHj1KeHg4UBIB+c0333D27FkWLVqEmpoalpaWzJgxAw0NjQo/c9H0R3hTKGviM3PmXObOnYmPz3i0tLTKHbNnz/d07doDAwODV12uIAjVQEzGhTeeqjf4GTBgALGxsbz33nvExsbi6urKzZs3cXZ25qOPPiIpKQkPDw/c3d0B6N27Nx9++KHUvj4rKwt9fX2+++47iouLcXZ2JikpCQBNTU0iIyM5ceIEUVFRtGvXjpkzZ7Jt2zaMjIwIDw8nMTGRwMBANm7ciJGREYsXL5bqEIQ3nbImPt27d8fY2JilS+eRl5fHzZt/sXLlUgICSq6eHz16kKVLl/6r5iI1sdkKiHGpmpo4LtH0RxBeAlVv8NOxY0fmzZtHeno6Z8+eZerUqTx48IA1a9Zw8OBBdHV1y8QoltZRSktLi9TUVKmenJwcCgoKytRjZmZGfn4+aWlp6OvrY2RkBJRclX/48CHJycl89dVXAOTm5tKhQ4dKP3PR9Ee1iHE9P2VNfOrWNWLt2q1oaWmRmHiP6dOnMGKEDykpmWRlZZGTk4u6uu5z1ya+L9UixqU6RJqKILwkqt7gRy6X07NnT4KCgujRowdqampERUXxzjvv4O7uzsmTJzl27FiF4y19kHPx4sWkpqZy6NChCusxMjIiIyOD9PR0DAwMmDVrFn369MHMzIxly5ahp6fHkSNH0NbWfuZxCEJNpayJj7LlKQC3b9+UutkKgvBmEJNxQaiAKjX4+fTTT+nRowcHDhwAoGvXrgQFBbF7924MDAxQU1MjPz+/wnEuW7YMV1dXNDU1sbS0rLAeuVzO9OnTGTlyJHK5nObNm2NnZ0dAQAAjRoxAoVCgo6NDaGjovxqPINQkFTXxATA3r8/KldHS782atWDOnAVK9xUEoWYSTX8E4QUSDX6qrqbdvoSaeVsWxLieR1WzxUulpaUxevQw1qzZXOFV86oS35dqEeNSHWKZiiCogKo2+MnLy2PXrl0MGDBA6XnOnDmDnp4etra20rYnG/ykpKSQm5uLpaVllRv8hIWFYWxsjJub23OOThCEqqpqtjjAqVO/smJFGKmpqdVVriAI1UhMxgXhBatKg5+UlBS2bdtW4WR8x44dODk5lZuMl1LWrEgQhNdHp05daN/+A0B5tnhIyCxpX7lcxuLFy/Dy8qiucgVBqEZiMi4I1WDFihXEx8cTHh7OH3/8QVZWFkVFRYwbNw49PT1++uknLl68iI2NDT/++GO5TPKnefjwIZMmTSIzMxOFQkFISAgAR44c4YcffiA9PZ1x48bRrVs31q9frzTz/NixY+Tm5nLr1i2GDx9O//79uXDhAt988w06OjoYGRmhpaXF3LlzWbduHXv27EEmk+Hk5FQm0lEQ3lRVzRZ3cHCspgoFQXgdiMm4IFSDUaNGce3aNbKzs2nfvj1Dhw4lKSkJNzc3Dh8+TMeOHXFycsLMzIz09HSlmeSVWb58Od26dcPNzY1ff/2VCxcuAGBqakpwcDCnTp0iMjKSLl26VHj+rKwsVq9ezY0bNxg1ahT9+/dn+vTphIaG0rhxYxYtWkRSUhLx8fHs27ePjRs3IpPJ8PT05IMPPpBSZJQRTX+Emmz3gk+kn6uaLQ6gpibHxETvX68Zh5qZ7wxiXKqmJo5L5IwLQg2TkJBA7969gZKJsq6ubpl1o5Vlklfmr7/+wsXFBYB27doBJWvGW7RoAYCxsTG5ubmVnr90iYy5ubmUxJKcnEzjxo0BePfdd9m3bx/Xrl3j3r17eHp6AvDo0SNu3bpV6WRc5IyrFjGuZ/Os2eKlioqKSUnJREtLefJRVYnvS7WIcakO8QCnINQgcrmc4uJiKau8efPmJCUlkZGRgYGBATKZDIVCUWkmeWWsra35448/sLW15cyZM/z3v/+lVq1a5XLDnzXz3MzMjPj4eGxsbPj9998BsLKywsbGhsjISGQyGdHR0TRp0uQFfEqCoLqeJVtcEIQ3m5iMC0I1MDIyoqCggMzMTG7evMmBAwfIzc1lxowZqKur06pVK+bPn8/ChQufK5N81KhRTJkyhV27dgEwe/Zsdu7cWW6/Z808nz59OlOmTEFbWxsNDQ1MTU2xtbWlXbt2uLm5kZ+fj729Paamps//4QhCDfAs2eKltm/f/ZKrEgThdSRyxgVBqLINGzbQq1cv6taty6JFi9DQ0GDs2LHPda6advsSauZtWRDjUjViXKpFjEt1iGUqgiCUM3bsWB49elRmm66uLsuXL38p72dkZMSwYcPQ1tZGT0+PuXOVX/kThJqmsiY+S5cuoGHDt+jbt+Q5jfXrozl8+CA6Ojq4uw+hQ4eO1Vm6IAivOXl1FyDUPDExMcyfP7/MNl9f3wrbsaenp7N7d82+Pevh4SE17HmRwsPDWbdunfRPmzZtSEpK4tSpUy/8vQB69uxJSkoKGzduJCIiAkNDw5fyPoLwunmyiY+X10jCwhaSlpbG11/78PPPx6X9EhLiOXToABER37FwYTirV68gNze3usoWBEEFiCvjwiuxaNGiCl+7evUqP/74o5QqIjy/ffv2ERsbi66ubnWXIgg1irImPo8f5zBs2AhOnjwh7Xfjxl+0bv2u9LBmgwYNiY+Po2VLu2qpWxCE15+YjAsvxe+//86wYcNITU3Fzc2NiIgI9u/fz7Fjx1i1ahXq6upYWFgQGhrKihUruHLlClu2bKFDhw4EBARQWFiITCZj6tSp2Nrasm3bNjZs2ECdOnXQ0NDAyckJKOlUWVxcjI+PDwkJCUqb1xw9epTc3FxSUlIYMmQIR44cIS4ujokTJ9KjRw+l9cfExChteuPh4UFQUBDW1tZs2rSJBw8e0K9fP3x9fTE3N+fOnTs4OzsTFxfHpUuX6NKli9SNc+nSpaSlpaGpqUloaCh169ZlwYIFnDlzBoVCgaenJ7169cLDwwNDQ0MyMjJYvXo1ampq5eq7dOkSM2fORE1NDS0tLWbOnElMTAz3799n5MiRrF69mlq1apU7btKkSSgUChITE8nJySEkJARra2ulTXuuXbvG3LlzKS4uJiMjg6lTp9KmTRvpXAsXLiQzM5Np06YxefJkbt26RV5eHl5eXtL3UxGRMy6okqhJ3YCyTXxmzQqhfn0L6te3KDMZt7a2Yf3678jJyaagoIA//7xAnz79qqt0QRBUgJiMCy+Furo6q1ev5u7du4wYMULavmfPHjw9PXF2dmbnzp1kZWUxatQoNm/ezMCBA/Hx8cHDw4MePXpw+fJlpkyZQmRkJJGRkezcuRNNTc0y3R319fVZvnw5xcXFnDt3TmnzmuzsbKKioti7dy/R0dFs3bqVU6dOsXbt2gon46C86U1Fbt++TVRUFLm5uXTv3p3jx49Tu3ZtunbtKk3GP/roI5ydndmwYQMRERG0b9+eO3fusHnzZvLy8nB1daVDhw4A9O7dmw8//LDC95s6dSrBwcE0a9aMw4cPM3fuXJYuXUpMTAxRUVGVRqhZWloSEhLCsWPHmDdvHn5+fkqb9sTHx+Pv70/Tpk3ZvXs3MTEx0mQ8JCQEmUzG9OnTycrK4tSpU+zYsQOAEydOVPjegqCKnnzoqrSJj6urK3v37kVbWxsdHS10dWthYqKHiYk9Q4cOYdIkX9566y1at36Ht9+2eOXNT2pisxUQ41I1NXFcoumPoDKaN2+OTCbDxMSkzHrJyZMnExERwaZNm7Cysio3GU5ISMDBwQGAZs2acf/+fW7duoW1tTW1a9cGoHXr1tL+b7/9NlB5c5xmzZoBoKenh7W1NTKZjDp16pCXl1fpGJQ1vXnSk0FElpaW6OnpoampibGxMQYGBkDZrO62bdsC0KZNG44dO4axsTEXL17Ew8MDgMLCQu7du1dmXBVJTk6WxuXg4MCCBQsq3f9Jjo4lrbdbt27N7NmzK2zaU69ePZYtW0atWrXIzs6Wlr48ePCAq1ev0rBhQ6DkgdHAwEACAwPJysqiT58+T61BNP1RLW/6uJQ18VEoIDU1h+zsIrKz86hVK5eUlEzS0tK4c+c+S5euJCsrC1/fMRgYmL3Sz+9N/75UjRiX6nhZaSpVeoDzwoULfPfdd+Tn5zNs2DAcHR05fvz40w8U3ljKGsYAbNmyBW9vb9avXw/AoUOHpAY4gNQEB+Dy5csYGxvTsGFDrl+/Tm5uLsXFxVJrdyiZhMPfzWsWL15MYGAgxcXFlTaved4xaGpqkpKSApQsFXnaeJ9UeqX+7NmzNG7cGCsrK95//33WrVvHmjVr6NWrFw0aNKjS+erVq8eVK1cAOHPmDI0aNarSmAAuXrwIwG+//SbVYWNjw9q1a1m3bh39+/enSZMmBAcH4+PjQ0hICE2aNJE+T2NjY1avXk18fDzHjx8nOTmZixcv8u2337Jy5UrmzZtXpS6hgqBKOnfuxrVrVxkzZjjjx3tX2MTHwMCAe/fu8sUXQ5gwwYcxY8YpXWomCIJQqkpXxmfNmoWPjw8HDhygVq1axMbGMnbsWDp16vSy6xNqGHt7ez7//HMMDAzQ0dGhS5cu5Ofnc+3aNaJKSTAHAAAgAElEQVSjo5k4cSKBgYFERUVRWFhIcHAwdevWZfjw4bi7u2NgYEBeXh7q6uplJnzP2rzmeQ0ZMoQZM2Zgbm5OvXr1nunYw4cPs2bNGnR0dAgJCUFfX5/Tp0/j7u5OTk4OPXr0qPKDl7NmzWLmzJkoFArU1NSYPXt2les4fvw4R44cobi4mDlz5mBpaam0aU+fPn348ssvMTIywszMjLS0NOkcMpmM2bNn4+XlxdatW0lJSaFv375oa2szbNgw1NXFTTehZqmsiY+X10jpZ5lMxsSJAa+qLEEQaoAqNf1xcXFh+/btfP3113Ts2JG+ffvSt29fpR39BOFFKywsZNWqVYwePRqAwYMH89VXX0nLWYSqmzRpEk5OTq/FH9I17fYl1MzbsqA64yosLGTOnG9ITEykoCCfoUO9OHToBx4+fAjA/fuJtGjRkm++mcOvv55g/fooCgqKaNLElq+/9n/uu2ivG1X5vp6VGJdqqYnjqtamP7Vr1yYqKoqTJ08ybdo01q5di46OznMXIwjPQl1dncePH9OvXz80NDSwt7eX1l//W0FBQUrzv1etWqU0jeRVunfvHv7+/uW2Ozg44OPjA8CpU6fYvHlzmejI/Px8vLy8yh1X0Tr09PR0fvrpJ3r37s3KlStxdHTE3t7+BY1CEF6dAwf2oa9vQGDgTB49SufzzwcTE7MXgIyMDHx8RuHt/TU5OdksW7aEjRs3UFSkwYYNa0hPTxe5+YIgVIsqTcbnz5/Ptm3bCA8Pp06dOiQlJT3TA2OC8G+NHz9eSiV5kYKCgl74OV+U+vXrs27dumc+TlNT85mOezLn/cnkG0FQNV279qBr1+7S72pqf/8vLioqAhcXV4yNjTl16lesrGwICQnh+vUb9O7dV0zEBUGoNlWajJuamuLo6MiVK1do0aIFXbp0wczM7GXXJgg1TlZWFgEBAWRmlqQuDBgwgBYtWhAcHIxCocDU1JT58+dz9erVcttu3rzJrFmzgJKHxP65Tnz//v1StOO7776Ln58fYWFhnD9/npycHIKDg9m5cyd//vkn2dnZWFtbM2fOnDI57+fPn8fJyYl27doxZcoUbt++TVFREZ9//jlOTk54eHhga2tLXFwcWVlZLFmyBGNjY8aNG0dWVha5ublMmDCB999/vzo+XuENp62tDUBOTjZTp/ozfHjJ0ra0tFTOnj2Dt3fJH/SPHqVz/vw5du36npycYsaM+YIWLexo2PCtaqtdEIQ3V5Um42vWrOHw4cMkJyfTs2dPpk2bhouLi9Jb4YIgVOzmzZs4Ozvz0UcfkZSUhIeHB7Vq1WLRokVYW1uzYcMGEhISCAwMLLftm2++Yfbs2djY2LBt2zYiIyNp3749ULLUJCwsjB07dlC7dm0mTJgg5X1bWVkxdepUsrKy0NfX57vvvqO4uBhnZ2eSkpLK5LyfP38eKEm9MTQ0ZN68eWRlZdG/f38pEtHe3p6AgAAWLVrE3r176dq1Kw8ePCA6OpqHDx9y48aNp34OoumP8KLtXvAJAImJifj6jsHd3R0XFxcADh7cRd++fTAzK4kctbQ0o1Ure0xMTABwdHyfpKRbvPtuy+op/iWoifnOIMalamriuKotZzw2NpatW7fi6uqKoaEh27dvZ8CAAWIyLgjPyNjYmDVr1nDw4EF0dXUpLCzk4cOHWFtbAyUPpwJKt5VOyAEKCgrKrAG/desWqamp0jKT7Oxsbt++Dfy9VlxLS4vU1FQpiz0nJ4eCggKldSYkJEgTfV1dXaytraXzNW/eHAAzMzMePHhA48aNGTx4MOPHj6ewsFDKTa+MyBlXLaowrpSUTFJTH+LtPRJf34m0bfueVPN///sTQ4d6Sb+bmTXiypWrpKamkpcn4+zZ3/jwQ+fXfoxVpQrf1/MQ41ItNXFc1foAp1wuR1NTU/pdS0tL5KYKwnOIiorinXfewd3dnZMnT3Ls2DHq1avHjRs3aNSoEStXruTtt99Wuu3tt98mJCSE+vXrc+7cOSnvHKBBgwaYm5sTFRWFhoYGMTExUnfO0iz248ePk5iYyOLFi0lNTeXQoUMoFIoyOe+lSvPeP/zwQ7Kysrh27ZqUgf5PV69eJTs7m5UrV5KcnMygQYPo2rXry/sQBaECa9d+R2ZmJtHRkURHRwKwYMFSbt26Sf36FtJ+hoaGjBw5hi+++IKCgiK6deuBlZVNdZUtCMIbrkqT8ffee4+QkBAeP37M4cOH2bJli3TLWhCEquvatStBQUHs3r0bAwMD1NTUCAoKYsqUKcjlckxMTPD09MTU1LTcNnNzc/z9/SkqKgIgODhYylKvW7cunp6eeHh4UFRUhIWFBb169Srz3vb29ixbtgxXV1c0NTWxtLQkOTmZhg0bSjnvpVxdXQkMDMTNzY28vDzGjh2LkZGR0jE1atSIb7/9lp07d6KhoSElvQjCq/bVV3589ZVfue3r128tt61Hj//g5uZS467cCYKgeqqUM15cXMzWrVv55ZdfKC4uxtHRkUGDBonGHoIgPLeaOAmqibdlQYxL1YhxqRYxLtVRrctUhg8fzurVqxk0aNBzFyAIgiAIz0NZMx8TE1P8/X1p0MASgH79XOje/SM2blzH4cMHkMvleHh8TufOYsmUIAivN3lVdnr8+DGJiYkvuxZBqBHWr19f6etXr17lzJkz/+ocHh4eSpsVVaZbt27k5eU90zGC8DoobeazbFkk8+cvZeHCUK5du8LAgYMJD19JePhKunf/iMzMTLZv30xExHcsXBjO0qWiH4YgCK+/Kl0ZT01NpVu3bhgZGaGlpYVCoUAmk3HkyJGXXZ8gqJzly5fz2WefVfj6wYMHMTY2xsHB4bnPIQhvEmXNfK5evcytWzf5+edjNGhgybhxX1O7dm3MzMx5/PgxubmPpYeXBUEQXmdVmoyvXr36ZdchCK+dv/76i8mTJ6Ouro6amhqhoaEsXryYhIQELC0t+eOPPzh48CCTJk0iPT2d9PR0OnfuzKNHjwgKClLa3TMpKYnY2Fg0NDRo0aIFmZmZLF68GC0tLamRz4YNG6Rz+Pn5lWsS5O7u/tTajx49Snh4OFASRVgaiRgUFMSdO3cACA8PR01NTen5lTX3sbCw4Ntvv+Xw4cPUrVuXx48fM27cOJo3b05AQABpaWkATJ06laZNm1Zan8gZF6oqalI3pc18Cgry+fjjvtjaNmPNmtVERa1i1Kix1KtniofHAIqKivHw8Kze4gVBEKqgSpPxim6pW1hYKN0uCDXBL7/8QosWLZg0aRJnz55l27Zt5Ofns3XrVm7fvl0mrcTR0RFPT0+gZImJsok4lHSz7devH8bGxtjZ2dG9e3c2bdqEqakpa9asYfny5fj7+0vnuHjxYrkmQU+bjBcWFjJz5ky2bduGkZER4eHh3L9/H4BPP/2Utm3bMmnSJE6cOMFbb71V4fn/2dynU6dO/PTTT2zfvp2CggJ69+4NwIoVK3B0dMTd3Z0bN24wefJkNm3a9C8/fUEoUfrA0z+b+WRkZKCvrw9A374fM3PmTC5d+o2MjDSOHj0KgJeXF506tcfe3v6p569pxLhUixiX6qi2pj+nTp2Sfi4oKODcuXO0bduWvn37vvCCBOF14eLiwqpVq/jiiy/Q09PD1tZW+p+6paVlmT9Gn2zAU1VpaWno6upiamoKgIODAwsXLiyzj7ImQVU5r76+vhRFOHbsWOm1li1bSufNzc2t9Pz/bO6TkJCAnZ0dampqqKmpSee6du0aJ0+eZP/+/QBkZGQ8tUbR9Ee1VOe4KmrmM3y4J76+E2jevCWHDh3FyqoxCoUGcrk6jx7lIZPJ0NKqze3bSZibK69dfF+qRYxLtdTEcVVrmsqcOXPK/J6eno6vr+9zFyMIquDIkSO8++67jB07lj179rBw4ULs7OwYOnQoDx48ICkpSdpXJpNJPz8tLVQmk1FcXIyhoSFZWVkkJydTr149Tp8+TaNGjcqcQ1mToKcxMjIiIyOD9PR0DAwMmDVrFn369ClX57Oe38bGhnXr1lFcXExhYSGXLl0CwMrKij59+tC7d28ePnzItm3bnlqjIDwLZc18vL19Wbp0Ierq6hgZGTFxYgA6OrqcPXuaESM8kcvl2Nu/g4PD+9VcvSAIQuWeKyhcW1ubu3fvvuhaBOG10rJlSyZMmEBYWBhyuZywsDD27NnDoEGDMDU1rTBn39raGj8/P+bPn1/heUNDQ7G2tmbWrFl4e3sjk8moU6eO9Idv6TlcXFzKNQnKz8+vtG65XM706dMZOXIkcrmc5s2bY2dnp3RfZU2IKjp/06ZN6dy5M66urhgaGqKhoYG6ujqjRo0iICCArVu3kpWVVeZKvCC8CBU181mxIqrcNi+vkXh5jXwVZQmCILwQVWr64+HhIV1RUygU3Llzh06dOkkPhQnCm6hDhw6cOHGiust4ZR4+fMgPP/zA4MGDyc/Px9nZmTVr1lC/fv3nOl9Nu30JNfO2LIhxqRoxLtUixqU6qnWZire3t/SzTCbD0NAQGxub5y5GEGq6e/fu4e/vX267g4PDC2sXf+HCBebNm1due69evaqUuPKsDA0N+fPPP/n000+RyWQMGDDguSfigupS1oCnUSMrgoODkMlkWFlZM368P3K5nB07trJ//x5kMvD0HE6HDh2ru3xBEITXTpWujM+cOZPAwMAy2/z9/QkJCXlphQmCKigqKmLEiBHk5OSwYsUK6tSpU+b1Q4cOYW9vLz2k+U9hYWEYGxvj5ub2Ksp96e7du8eVK1fo1q3bU/etaVdMoGZeCYKy49q7dxfx8XGMG/c1jx6l8/nng2ncuAkDBw6mTZu2zJs3m/fea0erVq0ZO3YE0dEbyc/P47PPXNmxY0+55xaq05vwfdUkYlyqpSaOq1qujAcEBHD79m3+/PNP4uLipO2FhYVkZtasD1gQnkdKSgppaWnExMQofX3t2rUEBQVVOBmvaU6ePMn169erNBkXVJPyBjxXaN36XQAcHdtz+vQpOnfuSnT0RtTV1UlMvIeuru5rNREXBEF4XVQ6GR89ejR3794lODi4zENZampqWFtbv/TiBOF1FxgYKGVrZ2dnl2l8k5iYyOXLl/H392fjxo2EhYXx559/kp2djbW1dbmUImUmTZqEuro69+7dIz8/HycnJ44ePUpiYiLLli2jYcOGzJ07l3PnzgHw8ccf4+7ujpOTE99//z3a2tpERkairq7Of/7zHwIDA8nLy0NLS4uZM2dSVFSEr68v5ubm3LlzB2dnZ+Li4rh06RJdunRh/PjxXL16lVmzZgFIjYkuXbrEqlWr0NDQ4M6dOzg5OTFixAhWrlxJbm4urVu3pnv37hWOSzT9UU0VNeD59tvF0kRbW1uH7OwsANTV1dmxYwurV6/ExWVgtdUtCILwWlNUUVpamuLevXuKu3fvKm7duqX45ZdfqnqoINRYt2/fVgwYMEARGhqq2LBhg0KhUCj++usvxaBBgxQKhULx2WefKeLj4xWZmZmKlStXKhQKhaKoqEjRs2dPxf379xVLly5VbNy4scLz+/v7K5YtW6ZQKBSKwMBARUhIiEKhUCiWLFmi+O677xQ//vijYsyYMYri4mJFfn6+wsXFRXHlyhVFaGioIjY2VqFQKBT9+vVTpKamKsaNG6f473//q1AoFIpffvlFMX78eMXt27cV77//viIjI0ORnJyssLOzU6SlpSlyc3MV7dq1UygUCsWAAQMUcXFxCoVCodi6dati4cKFipMnTyp69eqlKCgoUGRnZyvatGmjUCgUih07dijmzZv3Qj9j4fVz7949Rb9+/RTbtm1TKBQKRceOHaXXDh06pPjmm2/K7J+Xl6cYMmSI4tdff32ldQqCIKiCKj3AGRYWRnR0NIWFhRgYGJCcnEzLli1FnrAg/L+nNb7R0tIiNTWV8ePHo62tTU5ODgUFBVU6d2nzHX19faysrKSf8/PzSUhIoG3btshkMjQ0NGjVqhUJCQkMGDCAoKAgrKysaNSoEYaGhly7do2IiAgiIyNRKBRoaGgAJQ2M9PT00NTUxNjYGAMDA+DvTPKEhAQpOamgoEBqcNSkSRPU1dVRV1enVq1az/yZ1bS1hFAz10hC2XEpa8Bjbd2EAweO0qZNWw4ePEKbNm05d+4PVqz4luDg0P8/i5yMjNzX6vN5E76vmkSMS7XUxHFVa5pKbGwsx44dIzg4mNGjR3P9+nU2btz43MUIQk1TUeMbmUyGQqHg+PHjJCYmsnjxYlJTUzl06NBTmwOVqmydrbW1NTExMXh6elJQUMD58+fp168fjRo1QqFQEBkZKT0camVlxbBhw2jTpg0JCQmcOXPmqeeHku6iISEh1K9fn3PnzpGSklLhcXK5nOLi4iqNS1BNyhrwjBvnx5Il84mI+Ja33mpEly7dUVNTw8amMSNHfo5MJsPRsb20rlwQBEH4W5Um4/Xq1UNXV5fGjRtz5coVPvroIxYsWPCyaxMElVFR45vWrVszceJEli9fzrJly3B1dUVTUxNLS0uSk5P/9ft27dqV06dPM3DgQAoKCujZsyctWrQAwMXFhSVLluDo6AiUJCAFBQWRl5dHbm4uAQEBVXqPoKAg/P39KSoqAiA4OLjC2ps0acLy5ctp0aIFzs7O/3p8wuunogY84eEry20bNmwEw4aNeBVlCYIgqKwqRRt+8cUXfPzxx5ibm7N+/Xq8vLyYMGEChw4dehU1CoJQA9W025dQM2/LQtlx1aSc8Tfh+6pJxLhUS00cV7UuUwkODmbv3r307duXo0ePMm3aNL766qvnLkYQqsvVq1fJyMjAwcGhSvuPHTuW8PDwl1pTfn4+Xl5e5ba//fbbzJgxQ/r9WWt/FXx9fQkJCUFTU7O6SxFekQMH9qGvb0Bg4MwyOePDh4+WcsZ/+ukYrVq1JjZ2e5mc8fbtPxDxhoIgCP9Qpcm4qakpgwYN4sqVK0ycOJHc3Fwp3koQVMnBgwcxNjau8oT2ZU/EATQ1NVm3bt1T93vW2l+FRYsWVXcJwismcsYFQRBerCpNxn/99VemTZtGUVERW7ZsoXfv3syfP58PPvjgZdcnCM8tJiaG69ev4+fnR15eHm3atMHU1BQNDQ1atGhBbm4uixYtQk1NDUtLS2bMmMHu3bvZsWMHxcXF+Pj44Ofnx4kTJ/Dw8MDW1pa4uDiysrJYsmQJFhYWrFu3jj17SroKOjk5MWTIkArr2bZtG5s2baK4uJju3bvj7e3Nrl27WLNmDZqamjRq1Eiq4dixY+Tm5nLr1i2GDx9Ohw4diI2NlWq3t7cvd/5Tp06xYsUK5HI5KSkpDBw4kMGDB1eYEz5//nw0NDRwdXWlb9++Ss+3cuVKNDQ0uH//PoMGDeLkyZNcuXKFIUOG4O7uTrdu3di/fz/Tp09HU1OTu3fvkpyczNy5c6W168qInHHVJHLGBUEQXrwqTcYXLlzIxo0bGT58OCYmJqxfv57x48eLybigUkxNTenXrx/GxsbY2dnRs2dPNm7ciJGREYsXLyY2NhZ1dXX09fVZvnx5uePt7e0JCAhg0aJF7N27l27durFv3z42btyITCbD09OTDz74QIoffNLDhw9ZtWoVu3btQlNTk7lz53L37l3CwsKIjY1FV1eX2bNns2XLFrS1tcnKymL16tXcuHGDUaNG0b9/f6l2ZRPxUklJSezcuZPi4mJ69+5Nz549CQwMZPbs2djY2LBt2zYiIyNp3749eXl5T40nvX//Pjt37uTixYuMGzeOQ4cOkZSUxNixY3F3dy+zb/369ZkxYwZbt25ly5YtZZbYCDVD6XrHxMREfH3H4O7ujouLCxERYdJr6urFmJjUlX4fNeoLhg0bwvDhw0lIuCg9UPy6qGgNp6oT41ItYlyq42WMqUqT8eLiYkxMTKTfbWxsXnghgvAy/fM55dTUVJKTk6VnH3Jzc+nQoQMNGzaUcrT/qTTv28zMjAcPHnDt2jXu3buHp6cnAI8ePeLWrVtKJ+O3b9+mcePGUh73lClTuHDhAjY2Nujq6gLg4ODAzz//TKtWrbC1tQXA3Nyc/Pz8Ko+zdevW0vrtxo0bc+vWrQpzwisa55MaN26MhoYGenp6NGzYEE1NTerUqUNeXl65fZs1ayZ9Pr/99lul59294JMa92AP1MwHluDvcaWkZIqccRUgxqVaxLhUR7U+wGlmZsbRo0eRyWRkZGSwYcMG6tev/9zFCMKroKWlJWViX7x4ESjJxi4uLsbQ0BAzMzOWLVuGnp4eR44cQVtbm8TERORyeZXOb2VlhY2NDZGRkchkMqKjo2nSpInSfRs2bMj169fJz89HU1MTHx8f/P39SUhIICcnB21tbU6fPi1NkJWtrS2tvTKXL1+mqKiI/Px84uPjeeuttyrMCa/KOJ9lja9YD/xmEDnjgiAIL1alk/GkpCRMTU2ZMWMGwcHBJCYm8uGHH/L++++LW9DCa69jx45s2rQJNzc3WrRogY6ODi1btiQ0NBRra2sCAgIYMWIECoUCHR0dQkNDSUxMrPL5bW1tadeuHW5ubuTn52Nvb4+pqanSfevWrcvw4cP57LPPkMlkdO3aFQsLC7y9vRkyZAhyuZyGDRvi5+fH3r17lZ7jydorutVfWFjI8OHDSU9PZ/To0dStW/eZcsIF4WlEzrggCMKLVWnOeL9+/YiNjQUgKiqKYcOGvbLCBEF4NqdOnWLz5s0qk3BS025fQs28LQtiXKpGjEu1iHGpjmpZpvLkPH337t1iMi4IT3HkyBGio6PLbR8yZAgffvjhC3mP8PBwTp06VW67skSUf3O+2bNnY2lp+VznFFTfxYt/snz5UsLDV3Lx4kUCAqaioaFJ48ZNGDfOD7lczq5dsXz/fQxqamoMHer12jX1EQRBUAWVTsafXANahUadglBtSiP2tLS0qq2G48ePc+jQoSplhufl5dGrVy9+/PHHZ36fsWPHMnbsWOl3Dw8PgoKCsLa25tNPP32u8zVt2hR7e3vkcjnffvstQUFBz3weoebYsGENBw7so1at2gAEBgbi4/M1dnatWLlyGYcO/UDbtu+xfftmIiPXkZ+fz5dfeuHg8L5oACUIgvCMqvakGuLhLEGoydauXUtWVhYmJiZiIi5gYdGA4OB50u9JSUnY2bUCwM6uFRcu/I/Lly9iZ9cKTU1NdHV1sbCwJCEhrrpKFgRBUFmVXhmPi4uje/eSTmtJSUnSzwqFAplMxpEjR15+hcIbJSYmhiNHjpCVlUVaWhpjxowhLCyMRo0aoampSVBQEBMmTCArK4uioiLGjRtHu3btpOPv3LlDQEAAhYWFyGQypk6diq2tLR999BFt2rThr7/+wsjIiLCwMAoKCpg4cSLJycmYm5tz5swZfv755zJXmjdt2sSDBw/w9vZW2uAnISGBKVOmULt2bWrXrk2dOnUqHFt2djZ+fn5kZGTQsGFDabuypjw6OjrMmjWLCxcuUFBQgLe3Nz169GDBggWcOXMGhUKBp6cnvXr1ks6TmZlJQEAAaWlpAEydOpWmTZvStWtXrKyssLKyYsCAAcydO5fi4mIyMjKYOnUqGRkZXL58GX9/f+bNm4e/vz9bt27lxIkTLF68GC0tLamuy5cvs2rVKjQ0NLhz5w5OTk6MHj2agwcPsmrVKtTV1bGwsCA0NLTStBbR9Of1FTWpG126dCcx8Z60zdLSkvPnz9G69bucOPETubmPyc7ORkdHV9qnNB9fEARBeDaVTsYPHDjwquoQBElOTg7fffcdqampDBgwgKKiIr788kuaN29OSEgI7du3Z+jQoSQlJeHm5sbhw4elY0NDQ/Hw8KBHjx5cvnyZKVOmEBMTw+3bt1mzZg3m5uYMGjSIP/74g99//50GDRqwdOlSEhIS+PjjjyusKT4+XmmDnyVLluDj40OHDh1YuXIl169fr/AcsbGxNGnSBF9fX37//XdpnbaypjwtW7YkLS2N7du3k5KSwvr166UJ8ObNm8nLy8PV1ZUOHTpI51+xYgWOjo64u7tz48YNJk+ezKZNm0hMTCQmJgZDQ0P27duHv78/TZs2Zffu3cTExDBr1iyaNWtGUFAQGhoaQMkf3IGBgWzatAlTU1PWrFnD8uXL6dKlC/fu3WPXrl3k5+fTsWNHRo8ezZ49e/D09MTZ2ZmdO3eSlZWFvr7+v/1XQagGpQ8Y5eXpoKGhhomJHrNnzyY4OJjt2zdiZ2dHZmYm5ubGxMdflvYvKsrH0tJU5Zp8qFq9VSXGpVrEuFTHK2/6Y2Fh8cLfUBCexsHBAblcjrGxMfr6+iQkJEj52wkJCfTu3Rso6aipq6tLamqqdGxCQgIODg5ASROa+/fvA2BoaIi5uTlQ0kgnLy+PhIQEOnXqBIC1tTV169YtV0vpsxIVNfiJi4uTOmK2adOm0sl4XFwcHTuWPODWqlUr1NXVpZr/2ZTnr7/+4p133gHAxMQEX19fVq1axcWLF/Hw8ABKYgzv3fv76uW1a9c4efIk+/fvByAjI0Mau6GhIQD16tVj2bJl1KpVi+zsbKnh0D+lpaWhq6srRTU6ODiwcOFCunTpQpMmTVBXV0ddXV1qYjR58mQiIiLYtGkTVlZW9OjRo8LPAUTTn9dZaf2pqdkUFBSRkpLJsWPH8PMLwNjYhEWLQnF0bI+FhRWnTi3gzp0HFBQUcO1aHIaG5io1/prwfSkjxqVaxLhUx8tKU6nymnFBeFVKG/Q8ePCArKwsjIyMpCUP1tbWnD17FihZOpWRkYGBgYF07JOvX758GWNjY0D5Mw9NmjTh/PnzANy6dUta3qGpqSk1xrl06RLwd4OftWvXsm7dOvr370+TJk2wsrKSzvHnn39WOi4rKyv+97//SectLCwEkJryrFu3jgkTJtC5c2esrKz4448/gJLlJ15eXlhZWfH++++zbt061qxZQ69evWjQoEGZ83t6erJu3ToWL14s/Ra5Cx0AACAASURBVNHy5HKR4OBgfHx8CAkJoUmTJtIfGzKZrMxD2oaGhmRlZUl55KdPn6ZRo0YVfpZbtmzB29ub9evXA3Do0KFKPwtBtbz11lv4+Y1j1KhhaGvr0K7dBxgZGePiMogxY4bj4zOKESO+rNYHqAVBEFRVlTpwCsKr9ODBA4YOHUpmZibTp08v80DhyJEjmTJlCgcOHCA3N5cZM2ZIV5gBJk6cSGBgIFFRURQWFhIcHFzh+7i4uDBp0iQGDx5M/fr1pYnEkCFDmDFjBubm5tSrVw+ouMHP9OnT8fX1ZfXq1dStW7fSycjgwYOZPHkybm5uWFlZSUtClDXladSoEb/++itubm4UFRUxZswYOnXqxOnTp3F3dycnJ4cePXqUubI9atQoAgIC2Lp1K1lZWWUSV0r16dOHL7/8EiMjI8zMzKQ/QFq3bs3EiROZOXMmUDLhnjVrFt7e3shkMurUqcOcOXOIi1P+gJ69vT2ff/45BgYG6Ojo0KVLlwo/B0E1mJvXZ+XKaKAkrcjOzqHcPn369KNPn36vuDJBEISapdKmP4LwqsXExHD9+nX8/Mp3+HvRfvvtN3Jycvjggw+4ceMGX3zxRZn158LLVdNuX8Krvy37ZBZ4qYMHf2DHji1ERHxHXNxVlixZIL126dKfzJ49H0fH9s/0PjXxdjOIcakaMS7VUhPHVS1NfwShJrO0tGT8+PGEh4dTWFjItGnTiImJ4X//+x9yubzCiL+KOl1evXqVjIwM9u7dS0JCQrnjVq1aJa2xBujQoQMnTpz41+OYNGkSTk5O0vr353HmzBn09PSwtbVl7NixhIeH/+u6hJfrn1ngAHFxV9m793tpyVHjxk2lifqPPx7G2NjkmSfigiAIwsslJuPCa6V///6v7L1MTEzKNeiJiYlBX1//ua7MHzx4EGNjY5XM6d6xYwdOTk7Y2tqKibiKKM0CnzlzGgCPHqWzfHk4Pj5fExIyq8y+jx8/JioqgvDwVdVRqiAIglAJMRkXhH+4e/curq6ubN26laNHj7J06VJ0dXWpU6cOTZs25b333uPmzZt88cUXpKam0rVrV1xdXYmNjUVDQ4MWLVpICStPKioqIjAwkPj4eCwtLcnPzwcgMTGRwMBA8vLy0NLSYubMmZibm7Ns2TIOHz5MUVERbm5uDBo0SGnWeamCggKmT5/OzZs3KS4u5quvvuL999/n448/lnLaJ06cSFBQEHl5eaSnpzNmzBjMzMz46aefuHjxIjY2/9fencdFWe7/H38BAyigoUIdFfQraqalpplZaWpuhXWyTi6Yu2mkuIAYbhQmmihGLkfFvbAUt1M/s1TKjlvm8WjqKXPJfUtRXABjEeb3hw8mFVDEgeEe38+/nPueue/rPRpd3Pd1fz416NixI1u3bmXfvn2MGzcOJycny7iys7MZNmwYf/vb3zh58iR169Zl7Nix7Ny5k6ioKEwmE2XLliU6OjrfSi2gOuP36/Za4FlZWUycOI7Bg0PyfG7h66+/omXL1rc87CwiIiWDJuMi+cjKyiIyMpL4+Hi8vLwYNmyYZV96ejozZ84kKyuLFi1aMGjQIF5//XW8vLzynIgDbNq0ifT0dJYtW8aZM2csdfyjoqLo3r07zZs3Z9u2bURHR9O3b182bdrE8uXLycjIYMqUKRw6dCjPWuc5li9fTrly5ZgwYQKXLl2iW7durFmzhmvXrlnqtP/444/07t2bZ555hl27djF9+nQWLlxIs2bN8Pf3p1KlSpbjjRkzhvHjx1O7dm2+++47Jk6cyHvvvcexY8eYP38+pUuXpnXr1iQmJvLdd9/Rpk0b+vbty4YNG7h69eodJ+Nyf26vBX7+/AnOnj3NtGmTSU9P5/jxo8yZM43Ro0cD8MMP65k2bdp91ce1x3rBoFxGo1zGYo+5ir3OuMiDLCkpCQ8PD0t5xEaNGnHhwgUAatasiYuLC8At1Vzu5Oaa5JUqVbLUPT948CCxsbHMmzcPs9mMs7MzR48epV69ejg5OVG6dGnGjBnDN998k2et8xwHDx5k586d7N27F7hRhzynWkpOnXZvb29mzZrFihUrcHBwsJRXzMv58+epXbs2cKPO+JQpNx4ErFKlimWi7e3tTXp6OoGBgcyePZuePXvyyCOP5PsLSQ7VGb8/t9cCr1ixGosWLQXg7NkzfPDBKPr3H0xiYjIpKSlcu5aGyeRR6LHZ44NYoFxGo1zGYo+5VGdcpJhVqFCB1NRUS1OhPXv2WPblVWvbwcGB7OzsfI93c53xc+fOce7cOcv20NBQ4uLiGDt2LO3atcPPz499+/aRnZ1NZmYmvXv3zrfW+c3Hb9++PXFxccydO5eXXnqJhx56CPir1vjUqVN57bXXmDx5Ms8880y+dcbhRoOg/fv3Azce8LxTnfHVq1fz+uuvExcXR82aNVm2bNkdvlkpTidPHrf84iciIiWProyL5MPR0ZHw8HD69etHmTJlyM7OpmrVqvm+/4knnmDSpElUr16dJk2a5NrfunVrdu7cSceOHalUqZKlK2ZYWJhlHXdaWhqjR4+mdu3aNGvWjICAALKzswkICMi31nmOLl26MGbMGLp160ZKSgpdu3a9peEPwEsvvcT48eOJjY2lYsWKlivn9evXJzo6+pYmQpGRkYwbNw6z2YyTkxMTJkzIN3vdunUZMWIEbm5uODs78+GHHxbsS5b7cnMt8Py21a79OB99NAURESmZVGdc5A5iY2Pp3bs3Li4uhIaG0rRpUzp06GDrYdkFe7t9CfZ5WxaUy2iUy1iUyzhUZ1zEBtzd3enUqROlSpWicuXK+Pv73/UzM2bMYPv27bm2T5gwAV9f36IYptjA9evXiYz8gD/+OIujoyNhYWM4f96R0aPH4OzsQs2ajzJkSGiuuxMiIiI302RcJA83dwLt1q0bAMHBwfm+//Lly2zevJlXX32VoKCgPFvRJyQk4OLicsvSknsVHBxMly5deOaZZwp9jMJKSEhg7dq1lgc5b7Zs2TKWLl2KyWTi3XffpWXLlsU+vuK2bdsWsrKymD17ATt2/MScOf/kwoXzDB48jLp16zNnzkwSEtbSrt3df4ETEZEHly7ZiBRQTEyMpYLK7Q4cOMCGDRvu+PnPPvuMlJSUohhakYuMjGTKlCl5PqCamJhIXFwcS5cuZf78+Xz88ceWGur2zNe3KllZWWRnZ5OamorJZOLcuXPUrVsfgLp167N3724bj1JEREo6XRkXyceePXvo06cPSUlJBAQEEBsby7fffsvGjRuZO3cuJpOJypUrM2nSJGbPns3+/fuJj4+nc+fOuY7173//m99++42wsDC++OIL4uPjczXvGTFiBC4uLpw+fZrz588zceJEHn/8cT7//HOWL1+Ot7c3Fy9eBCAlJYXRo0eTnJzMpUuX6NixI127dqV79+489thjHDp0iJSUFKZOnUrlypVzNRBycHDg2LFjhIWFkZWVRYcOHVi5cmW+v2w0bNiQ1q1bEx8fn2vf3r17adCgAS4uLri4uFClShX2799/x/KGRm/6s2DEi5QuXZo//jhD165vcuXKZSZNiiEp6QI//7yTBg2eYuvWzaSl/WnroYqISAmnybhIPkwmE/Pnz+f06dP079/fsv3rr7+mV69etG/fni+//JKUlBQCAwNZunRpnhNxgBYtWlC7dm0iIiI4ceJEvs17KlWqxIcffsiyZcuIj49n+PDhfPbZZ6xevRoHBwfeeOMNAI4fP0779u1p27Yt586do3v37nTt2hWAevXqMXr0aGJiYlizZg1NmzbN1UBo6NChvPHGG4SGhrJ582aeeeaZfCfiAP7+/nmug4cbvxiUKfPXQynu7u6GvQNQUN7eZZg3bwYtWjRn2LBhnD17lp49ezJt2jQmT57MihVfULduXZKTk+2m6YW95LidchmLchmLPeZS0x+RYlSnTh0cHBzw9vYmLS3Nsn3kyJHExsayZMkS/Pz8aN269T0d9+DBg/k278lpsvO3v/2NXbt2ceTIEWrUqGGZKOdcbfby8uLTTz9l/fr1eHh43NK8p06dOpZjXLhwIc8GQnCjkc+WLVtYtWoVAwYMKMQ3dIOHhwepqamW16mpqbdMzvNi9KY/iYnJmEylcHAwkZiYTGamE+npGfzwww+Eho7Gy8ubmJhJNGnynKFz5rDHqgigXEajXMZij7nU9EekmOXV3AYgPj6eQYMGsXjxYuDGg42Ojo53bPiTczyz2XzH5j23n9PX15fff/+dtLQ0srKy+O233wBYsGABTz75JNHR0bz00ku5GvbcLK8GQhkZGXTq1Inly5dz8eJFHnvssQJ/L7erV68eO3fuJD09neTkZA4fPnxLMyJ71alTVw4e3M+AAW8zeHAg/fsPpFatWoSGDiEwsA9ubu48+2xTWw9TRERKOF0ZF7lH9erVo3fv3nh6euLu7k6LFi3IyMjg4MGDLFq0yHLF+3YNGjTgvffeY8GCBXds3nOz8uXLM2TIELp06UL58uUpXbo0AC1btiQiIoLVq1fj6emJk5NTvg9N5tVAyMXFhfr163P8+HHeeuutQn0PCxcupEqVKrRq1cqyTMZsNhMcHIyrq2uhjmkkbm5ujBs38ZZt3t5lqFv3aRuNSEREjEhNf0QeUDkT8/nz5+Ph4VHs5y+pty+/+WY133yzGoCMjAx+//0gM2fOY+rUKTg4OODnV52QkLA864fb421ZUC6jUS5jUS7jUNMfkWJ0c53xHMHBwURFReX5oGNOnXE3NzcWLVqUa3+PHj0A7ngVvCCsVWf85MmTBAUF0blzZ8tEPCgoiCtXrtzyPg8PD2JiYhg+fDgXL17E3d2dqKgoypcvf8v77KnOuL//q/j7vwrAlClRtG//dxYunEu/fu/SsGEjJk+ewObNG2ne3LgZRUSk5NBkXKSAYmJi8t2XU2c8JiaGVq1a5fme7t27ExERcV+TcWvx9fXlq69uLS84Y8aMPN+7cOFCHn30UQYNGsSaNWuYOXOm5SFQ+KvO+MqVK0lPT6dr1648//zzd6zOYgT79+/j6NHDDBsWxsKFc2nQ4CkAmjR5jv/8Z7sm4yIiYhWajIvkQ3XGb9i5cydvv/02AC+88AIzZ868Zb891RlfMOJFy58/+2whffrcKGlpNpstD9e6ubmTmmrfpRtFRKT4aDIukg/VGb/h5jri7u7uJCcn57s/5z1GrTOes57v6tWrnD59gnbtblz9NpmcLPtMpmy8vcvnu/bPHuvqgnIZjXIZi3IZh+qMixQj1Rm/4eY64qmpqZQtWzbf/TnvMWqd8ZwxbdmyiQYNGlleV6/+KOvW/UDDho1Yv/57GjZslOf47fGBJVAuo1EuY1Eu41CdcZFipjrjNzRs2JCNGzcCsGnTJp566qlb9ttjnfETJ45TqVJly+ugoKEsWDCHd97pTWZmJi1a5P1cgIiIyL3SlXGRe/Sg1RkPCAggLCyMgIAAnJ2dmTJlCmDfdca7du1xy+sqVaoyY8YcG41GRETsmeqMizygVGfc+uzxtiwol9Eol7Eol3GozriIAXz//ff51hlv06ZN8Q8oH/dSZ3zWrFm2GGKxiotbyJYtm8jMzOSNN96kVq3axMRMxtHRERcXF8aMGUv58hVsPUwREbFDmoyLWFGrVq3yrTO+Y8cOypQpk+/67JxGQz179uSf//wnEREReb5v+/btLF26NFfd8wMHDnD16lWefvru7dh9fX25cOGCpQIL5F9nvKASEhJYu3atZRmLUeza9V/+97+9zJo1n7S0NJYsiWPt2m8IDh5OzZq1+PLLlXz++acMGhRi66GKiIgd0mRcpJisXLkSf3//Oz4sCeDt7Z3vRPxO1q9fj5eXV4Em49YWGRnJli1bLNVgjOQ///mJ6tVrMGpUKKmpqQwcOITXXvsHXl5eAGRlZeHiYuw18CIiUnJpMi5ym9TUVIYNG8bVq1epUaMGP//8M56enkRERFC9enWWLFnChQsXGDRoEFOmTOGXX34hNTWV6tWr89FHHzF9+nROnTrFxYsXOXPmDCNHjqRcuXJs3ryZX3/9lRo1alCpUqV8z3/q1ClCQkJYtmwZP/zwA9OmTcPDw4OHHnqIWrVq0bhxY44fP87bb79NUlISLVu2pFOnTvzrX//C2dmZxx9/PM+GO1lZWYSHh/P777/j6+treeDz7NmzhIeHk56ejqurK+PGjaNixYq5GgV16dIl3zE3bNiQ1q1bEx8fX6DvuKQ0/Vkw4kWuXLnMH3+cZdKkTzh79jRhYSF88cVKAP73vz2sWrWMGTPm2nikIiJirzQZF7nNF198Qa1atQgODmbXrl1s2bIFT0/PXO9LSUmhbNmyLFy4kOzsbNq3b8+5c+cAcHFxYd68eWzdupUFCxYwf/58mjVrhr+//x0n4jfLysoiMjKS+Ph4vLy8GDZsmGVfeno6M2fOJCsrixYtWjBo0CBef/11vLy88u18uWnTJtLT01m2bBlnzpxh3bp1AERFRdG9e3eaN2/Otm3biI6Opm/fvrkaBd3chfJ2/v7+bN++vUC5ShJv7zL87W/e1KlTi0qVylOpUnnc3Erj5JTJ9u3bmTVrFvPnz8PX1/eejmmPlMtYlMtYlMs41PRHpBicOnWKZs2aATeu+N7emTKnAJGrqytJSUmEhITg5ubGtWvXyMzMBG5t3pNfycG7SUpKwsPDw7JcolGjRly4cAGAmjVrWsZlMhXsP+NDhw5ZJuqVKlWiYsWKwI0mRLGxscybNw+z2Yyzs3O+jYKspaQ0/UlMTKZGjTosX76EV155k4sXL5CSksqaNetZvfpLPvlkFqVKPVTgsdpj9QBQLqNRLmNRLuNQ0x+RYlKrVi127doF3HgoMiMjAxcXFxITEwHYt28fcONK89mzZ/n4448JCQkhLS3NMlHP6wpyTtOfgqpQoQKpqakkJSUBsGfPnluOldfx79R4yM/Pj927dwNw7tw5y1V8Pz8/QkNDiYuLY+zYsbRr1y7fRkH26Pnnm/Hoo7Xo168n770XTEhIGNOmfcy1a9cYNWo4QUH9mT8/1tbDFBERO6Ur4yK36dixI6NHj+att96yLCnp0aMHH374IRUrVuThhx8GbjT/mTlzJp06dcLFxQVfX1/Onz+f73Hr169PdHQ0Pj4+VK9e/a7jcHR0JDw8nH79+lGmTBmys7OpWrVqvu9/4oknmDRpEtWrV6dJkya59rdu3ZqdO3fSsWNHKlWqRLly5QAICwsjIiKC9PR00tLSGD16dL6NguzVgAFDbnn97bcbbDQSERF50Kjpj8gdpKen8/LLL7Nhg20mZ7GxsfTu3RsXFxdCQ0Np2rQpHTp0sMlYrK2ob19eupRE377diYn5J05OTowfH4GDgwN+ftUJCQnD0dH6Nwbt8bYsKJfRKJexKJdxqOmPiEHcrZ54jx49OHHiRK6HAsuVK0d0dPQtV6Dd3d3p1KkTzs7OODk54e/vf9fzz5gxI8+HKSdMmHBPDyLe7k5NgQIDA7l8+TLOzs64uroyb968Qp/HGq5fv86kSRMsJQmnT/+Yfv3epWHDRkyePIHNmzfSvHlLm45RREQENBkXuSNXV9d7vip+t3riHTp04MiRI4SGht71WN26daNbt26WRj8FWSoSFBREUFDQPY25IO7UFOjEiROsWbMm32orxW3GjE/o0OEfxMUtBODAgf00aPAUAE2aPMd//rNdk3ERESkRNBmXB5Yt64nv2bOHPn36kJSUREBAAJ07d+bFF1/k22+/ZePGjcydOxeTyUTlypWZNGkSs2fPZv/+/cTHx9O5c+c8j9mmTRsaNGjA8ePHadKkCcnJyezdu5dq1aoxefLkfOuJFzRbToWZ2124cIGrV68SGBjI1atX6d+/Py1b2m6i+803q/H09OSZZ561TMZvLsvo5uZOamqKzcYnIiJyM03G5YFly3riJpOJ+fPnc/r0afr373/LBPvrr7+mV69etG/fni+//JKUlBQCAwNZunRpvhNxgNOnT/Ppp5/i7e1N48aNWb58OeHh4bRq1YqrV6/mWU987NixBc6W32Q8MzOTPn360KNHD65cuUJAQAD16tWjQoUK+Y61qJr+rJ7yGuvX37hCHxKyi8OHDzFx4lguX75kWatnMmXj7V2+yOrf2mNdXVAuo1EuY1Eu41CdcRErsmU98Tp16uDg4IC3tzdpaWm37Bs5ciSxsbEsWbIEPz8/WrduXaBjenp6Wn4BcHNzo0aNGgCUKVOG9PT0POuJWyObl5cXXbp0wWQyUaFCBWrXrs3Ro0fvOBkvqjrjiYnJfPLJbMvroKD+DB8+in/+cyrr1v1Aw4aNWL/+exo2bFQk57fHB5ZAuYxGuYxFuYxDdcZFrMyW9cTvtLY6Pj6eQYMGsXjxYgASEhJwdHS8Yw3xux0T8q4nfq/Z8vLjjz8ydOhQ4MbSn0OHDuHn51egzxaXoKChLFgwh3fe6U1mZiYtWrSy9ZBEREQAXRmXB1hJqSd+u3r16tG7d288PT1xd3enRYsWZGRkcPDgQRYtWkSvXr0KlTeveuI+Pj73lC0vzZs3Z8uWLXTq1AlHR0dCQkIoX758ocZobTNmzMnzzyIiIiWF6oyLYPt64g8ie7t9CfZ5WxaUy2iUy1iUyzhUZ1zEYCIiIjh8+HCu7XPnzqVUqVKFOub333/PokWLcm3v0aMHbdq0KdQxC6qo6pcX1vXr1/noo7GcPXuWzMwMevbsi7f3I0RHT8DZ2YWaNR9lyJDQImnuIyIiYi26Mi5iZXdr+rNq1ao864wHBwcTFRWVZy3xy5cvs3nzZl599dUiGbM1/Pnnn3Tp0oVhw4bxwgsv3PX993vFZM2a/8fvvx9iyJBhXLlymd6936JcufIMHRpK3br1mTNnJlWr/h/t2t29UZK12OOVIFAuo1EuY1Eu49ADnCIGsXLlyntedw0QExOTb1OfAwcOlPglNB9++GGxNv1p2bI1/foFWl47OZlITDxP3br1Aahbtz579+4utvGIiIgUhpapyANLTX+s0/QHYP78+TRo0OCuVWRy3G+d8QUjXsTNzQ2Aa9dSGTMmjH793mXVqmX8/PNOGjR4iq1bN5OW9ud9nUdERKSoaTIuDyw1/bFO059t27Zx/PhxPvzwQ0upyKKWc6vv7NmzBAcPpGvXrrz55ps8++xTjB8/nhUrvqBu3bokJycXe9MJe2xyAcplNMplLMplHGr6I2JFavpjnaY/K1as4PTp03Tv3p0jR47w66+/4u3tbfl8Xu636U9iYjJJSRcZNOgdgoPfo1GjxiQmJrNmzTpCQ0fj5eVNTMwkmjR5rljXLNrjGklQLqNRLmNRLuNQNRURK8tp+tO6detcTX+qV6/Ovn37eOSRRyyNcT755BOSkpJISEgolqY/FSpU4P333ychIQEfHx+rNP3p06cPDRs25PDhw+zYseOes+VlypQplj+PGDECf3//O07EreWzzxaSnJzMokXzWLRoHgBdunQjNHQIpUqVokGDp3j22aZFPg4REZH7ocm4PLDU9Mc6TX9sZejQUIYODc21vWnTu1dyERERKSlU2lAENf2xhcLe6suvvnhYWDA+Pjfqnb/++pu0atXWmsMtEHu8LQvKZTTKZSzKZRxapiJiEDl1xpcuXZpn059XXnmFkydPFqrOuJubW4ls+rNjxw6WLFlCVlYWrVq1YuDAgUU2jnXrvqFsWU/Cw8dZ6ov37t2Pzp3fIiCgW5GdV0REpChoMi7CjYc0rXVVfOXKlfj7+xMREZHn/lWrVuW5PSYmJt9j5tQZj4mJoVWrVtYY5j0LCgoiKCgo1/YTJ06wZMkS4uLicHFxYdq0aWRmZuLs7Fwk42jZsjUtW/71HTg5mThw4DdOnDjOli0b8fHxZciQYbi5uRfJ+UVERKxJk3F5YKnOuHXqjP/444888cQThIWFkZiYSGBgYJFNxIE864tnZmbwyisdeOyx2nz66XwWLJhLUNDQIhuDiIiItWgyLg8s1Rm3Tp3xS5cu8d///pclS5aQnp5OQEAAK1asoGzZsvmOtbBNf1ZPeQ3IXV/86tWrlvN16PAK48aNs1l9W3usqwvKZTTKZSzKZRyqMy5iRaozbp06456enjRu3BgPDw88PDyoXr06x44do169evl+prB1xvOrL96vXy+Cg4dTp84TJCT8gJ9fTZs8OGSPDyyBchmNchmLchmHHuAUsTLVGbdOnfGGDRvyxRdfkJ6eTlZWFocPH6ZKlSoF+mxh5FVffNCgYKZN+xiTyUSFChV4773RRXZ+ERERa9JkXB5YqjNunTrjtWrV4h//+AcBAQGYzWYGDBiQ53Ifa8mvvvjs2QuK7JwiIiJFRXXGRVCdcVuwt9uXYJ+3ZUG5jEa5jEW5jEPLVEQMJiIiIs8643PnzqVUqVKFOub3339fIuuM+/r6Fum5AX799RdmzZrGjBlzOHBgP9HRE3B2dqFmzUcZMiQUR0fHIh+DiIiItenKuIgdyWk49NhjjxX7uZOSkujSpQurV6/G1dX1ru+/l6sLn3/+KevWfUOpUqWZM2cRfft2Z+jQUOrWrc+cOTOpWvX/aNfO/36GbxX2eCUIlMtolMtYlMs4iurKuC4lidiRlStX3vOab2vYvHkzffr04cKFC0Vy/MqVfRg/frLldWLieerWrQ9A3br12bt3d5GcV0REpKhpmYqIDdiq4VBKSgqjR48mOTmZS5cu0bFjR7p27cqePXsYP348ZrOZRx55hOjoaA4cOJBrW37LaxwdHVm4cCH/+Mc/CpT/XuqMLxjxIi1atOLs2TOWbZUqVebnn3fSoMFTbN26mbS0Pwt8PBERkZJEk3ERG7BVw6Hjx4/Tvn172rZty7lz5+jevTtdu3YlPDycmJgYqlevzueff87hw4fz3Pb444/nedznn3/eel/ObXJu66Wnu+Ps7IS3dxkmT45i/PjxrFjxBXXrfA5EWQAAGdJJREFU1iU5ObnENJcoKeOwNuUyFuUyFuUyDjX9EbETtmo45OXlxaeffsr69evx8PDg+vXrAFy8eNFShvGtt97Kd5u13EvTn5z3JSWlkpmZRWJiMmvWrCM0dDReXt7ExEyiSZPnSsTaRHtcIwnKZTTKZSzKZRxaMy5iR3IaDgG5Gg4B7Nu3D8DSlOfjjz8mJCSEtLS0+2o4tGDBAp588kmio6N56aWXLO99+OGHOXbsGABz5swhISEhz20lhY9PFUJDhxAY2Ac3N3eefbaprYckIiJSKLoyLmIDtmo41LJlSyIiIli9ejWenp44OTmRkZHB2LFjGTVqFI6Ojnh7e9OrVy8eeeSRXNtsqWLFSsyZswiApk1foGnTF2w6HhEREWtQaUMRG3tQGw4V9FbfzfXFc6xfv5aVK+OJjV1YVMMrFHu8LQvKZTTKZSzKZRxq+iNiEHer9b1q1SqOHDlCaOitLd2Dg4OJiorKtX4c4PLly2zevJlXX321QGMoioZD8fHxfP3117m2h4SE8O9//5sff/wRBwcHxowZQ7169Qp1jtvdXF88x6FDB1iz5qs7LscRERExCk3GRaxs5cqV+Pv7F7jxjqur612vih84cIANGzbc02Tc2jp37kznzp1zbd+3bx+7d+9m2bJlnD59mgEDBvD//t//s8o5c+qLjxv3PgBXrlxm1qwZDB48jKioSKucQ0RExJY0GZcHlq1qfQPs2bOHPn36kJSUREBAAJ07d+bFF1/k22+/ZePGjcydOxeTyUTlypWZNGkSs2fPZv/+/cTHx+c5IQZo06YNDRo04Pjx4zRp0oTk5GT27t1LtWrVmDx5MmfPniU8PJz09HRcXV0ZN24cFStWLHC2nOovt6tTpw7z58/HwcGBM2fO4OXlZbW/o5vri2dlZTFx4jgGDw4pUIdPERERI9BkXB5Ytqr1DWAymZg/fz6nT5+mf//+t0ywv/76a3r16kX79u358ssvSUlJITAwkKVLl+Y7EQc4ffo0n376Kd7e3jRu3Jjly5cTHh5Oq1atuHr1KlFRUXTv3p3mzZuzbds2oqOjGTt2bIGz5TcZz8kTExPDZ599Rnh4+F2/+7s1/Vkw4sVc2w4c+I2TJ08SHf0RGRkZHDt2lKlTpzBkyLC7nk9ERKSk0mRcHli2qvUNN64mOzg44O3tTVpa2i37Ro4cSWxsLEuWLMHPz4/WrVsX6Jienp6WXwDc3NyoUaMGAGXKlCE9PZ2DBw8SGxvLvHnzMJvNODs7WzVbcHAw/fr1o3PnzjRq1IgqVark+97VU14rUCb4q9lP8+bP0rz5t8CNv7uQkBAiIyMKfJziYo9NLkC5jEa5jEW5jENNf0SsKKfWd+vWrXPV+q5evTr79u3jkUcesdT6/uSTT0hKSiIhIeG+an3n97kc8fHxDBo0iAoVKvD++++TkJCAj48P2dnZhT4mgJ+fH3369KFhw4YcPnyYHTt23HO2vGzbto3169fzwQcf4OrqislkKtBnC/pE+s3Nfu60rSSwx+oBoFxGo1zGolzGoWoqIlZmq1rfd1OvXj169+6Np6cn7u7utGjRgoyMDA4ePMiiRYsKXe87LCyMiIgI0tPTSUtLY/To0fj4+NxTtrw0btyYtWvX0qVLF7Kzs3nrrbfw9fUt1BjzcnN98TttExERMSLVGRfhwa31bUv2dsUE7PNKECiX0SiXsSiXcejKuIjBFEWt7++//55Fixbl2t6jRw/atGlTqGMW1IwZM9i+fXuu7RMmTLDqlfAceTX7mTZtClWqVKVDhzetfj4RERFb0GRchILV+i6onKY/+dX6vt+mP61atbLKOO9VUFAQQUFBee6Liopi165dXL9+nc6dO9OpU6f7OtftzX4uXbpEZOQHnDx5nK5du9/XsUVEREoSR1sPQMTerFy58p7XXQPExMTkORGHv5r+lEQ//fQTJ06cID4+niVLljB37lyuXLlyX8fMafaT488/r9GnT3/atfO/3+GKiIiUKLoyLg8sNf2xTtOfBg0aWMogwo3mPCbTnX+05FdnPKe++M3NfgAqVapMpUqV+emnrXc8roiIiNFoMi4PLDX9sU7TH1dXV1xdXcnMzGTEiBF07twZd3f3Av0d3O7mh1ty6ovfvM3d3RUPj1IlunZtSR7b/VAuY1EuY1Eu41CdcRErUtMf6zX9uXLlCoMHD6Zx48a88847dx3r6imv5flE+t1qiaemplOqVFqJfULfHqsHgHIZjXIZi3IZh6qpiFiZmv5Yp+lPWloavXr1onfv3vz9738v0GdERETkBk3G5YGlpj/WafqzdOlSTp48yfLly1m+fDlgnXKHeTX26dv37lfdRUREjERNf0RQ0x9bsLfbl2Cft2VBuYxGuYxFuYxDy1REDEZNf+5dRkYGEyaM5cyZ07i7uxMSEoavbxWrHFtERKQk0pVxESvLafrz2GOP5bn/fpv+lFTHjx9n4MCBfP311wV6f15XF1aujOf3338nLGw0J04c45NPovn44xnWHmqRsccrQaBcRqNcxqJcxlFUV8bV9EfEyh60pj8AX375JcHBwVy6dOm+jnP06FGaNHkOgCpV/o9jx45aY3giIiIllpapyANLTX+s0/QH4KGHHmLx4sUFXiqTV9OfBSNepGbNR/nxx8288EILfv31Fy5cSCQrKwsnJ6cCHVdERMRoNBmXB5aa/lin6Q9Ay5Yt7/p932z1lNfy3N6r11tMmjSJYcMG0rBhQx5//HH+9rfcfyclmT02uQDlMhrlMhblMg41/RGxIjX9sV7Tn8LIa93dL7/spWbNOvTrN4j9+/dx6NBhQ605tMc1kqBcRqNcxqJcxqFqKiJWpqY/1mn6Y00+PlWYO3c2S5YsxsOjDCNHhhf7GERERIqTJuPywFLTH+s0/bEmT09Ppk6dabPzi4iIFDeVNhRBTX9sIa9bfdevXycy8gP++OMsjo6OhIWNoWrV/yv+wRWSPd6WBeUyGuUyFuUyDi1TETEYNf25d9u2bSErK4vZsxewY8dPzJnzT8aPn2yVY4uIiJREmoyLcOMhTWteFV+1ahUeHh7ExcVZtlmjqU+rVq1o1aqV1cZ5L65evYq/vz8BAQG3bM/Ozub999/nwIEDuLi4EBkZSdWqVQt1Dl/fqmRlZZGdnU1qaiomk35EiYiIfdP/6USKSUxMTL77cpr6lMQOm0lJSbz33nscO3aMvn375tr/3XffkZGRQXx8PLt372bixInMmjXrjsfMr8546dKl+eOPM3Tt+iZXrlxm0qT8vzMRERF7oMm4SBG5vbFPbGxsoZv6nDx5klGjRgE3mhUdOXKEbdu28e9//5tFixbh6OjIU089RWhoKNOnT+fnn3/m2rVrjB8/no0bN7JmzRpMJhONGjVi+PDh7Ny5k6ioKEwmE2XLliU6OhoPD488c6SmpjJo0CA2bdqU5/6dO3daSkQ++eST/PLLL4X6vry9yzBv3gxatGjOsGHDOHv2LD179mT16tW4uroW6pi2YI91dUG5jEa5jEW5jEN1xkUM5PbGPjkK09TH19eXuLg4MjIyCAwMZOrUqaSnpzN9+nRWrlxJ6dKlGT58OFu3bgVulDEcM2YMBw4c4Ntvv2Xp0qWYTCYGDRrEDz/8wH/+8x/atGlD37592bBhA1evXs13Mu7r64uvr2++k/GUlJRbPuvk5MT169fvuMRk9ZTXcj0Ek5iYjMlUCgcHE4mJyWRmOpGensG5c1coXbp0vscqSezxgSVQLqNRLmNRLuMoqgc4HQt9RBG5o/wa+4wcOZIdO3bQrVs3du3ahaNjwf4zzMrKIjg4mL///e80b96cEydOkJSURP/+/enevTuHDx/m5MmTAFSrVg2AI0eOUL9+fZydnXFwcKBRo0YcOnSIwMBAkpKS6NmzJ2vXrr2vtdkeHh6kpqZaXmdnZxf6eJ06deXgwf0MGPA2gwcH0r//QMNMxEVERApDk3GRIpJf05ycpj6LFy8GICEhAUdHxzs29TGbzYwaNYoGDRrQoUMHAHx8fKhYsSILFiwgLi6Obt26Ub9+fQDLBN/Pz4+9e/dy/fp1zGYzO3bsoFq1aqxevZrXX3+duLg4atasybJlywqds2HDhpar5rt37+bRRx8t9LHc3NwYN24iM2fOY+7cT2nb9qVCH0tERMQItExFpJgVpqnP2rVrWb9+PefOnWPjxo0AfPDBB/Tq1Yvu3buTlZVF5cqVefnll2/5XK1atXj55ZcJCAggOzubp556itatW7N3715GjBiBm5sbzs7OfPjhh/ec47333mPo0KG0adOGrVu30qVLF8xmMxMmTCjU9yIiIvIgUtMfEbGJnHV3WVlZREVFcvLkcRwdnRg16gMqV/ax8egKxx7XSIJyGY1yGYtyGYea/og8AGzV1OfMmTOEhYXl2v70008zePDgIjsvwNatmwGYNWsBu3b9l+nTP2bixI+L9JwiIiIlhSbjIkVg1apVHDlyhNDQUMu2gjb9sUVTn0qVKt3SoOhmv/32G+PGjcPJyQkXFxeioqLw8vKy7M/OziYiIqLQTX9eeKEFzz3XFIBz5/6gXLkK9xdGRETEQPQAp0gxiYmJyXMiDn81/SmJxo8fT3h4OHFxcbRp04a5c+fesv/mpj/Dhg1j4sSJdz3mq8O+os/Ev/KaTCYiIz8gJmYyLVvapsOoiIiILejKuEgRsZemPx9//DEPP/wwcGN99+0NeO6n6c/N6+emTv2YxMREOnXqxJo1a3BzcyvwcUoSe2xyAcplNMplLMplHGr6I2Ig9tL0J2civmvXLhYvXsznn39+y/77afqTmJjM2rVrSEw8T/fuvUlNvY7ZDElJ10hNzSrYF12C2OMDS6BcRqNcxqJcxqGmPyIGY09Nf7755hs++OAD5syZQ/ny5W/Zd79Nf5o3f5GDBw8wcGA/QkIGMXhwSK6r7yIiIvZKk3GRImIvTX+++uorFi9eTFxcHL6+vrn232/Tn9KlSzNu3ET++c+5xMYupFmzFvf0eRERESPTMhWRYmakpj9ZWVmMHz+eihUrMmjQIOCvcofWbPrz66+/MGvWNGbMmHPPnxURETEyNf0REZvIWXf3+eefsm7dN5QqVZo5cxbZdlD3yR7XSIJyGY1yGYtyGYea/ogYSGHrjLu5uZW4pj/+/v6Eh4djNpt57LHHCA8Px8nJyfKe+60zXrmyD+PHT2bcuPetkkVERMRINBkXKSYxMTH57supMx4TE1Pimv4MGDCAkJAQnn76aUaMGMGGDRtu+cXg5jrju3fvZuLEicyaNeuO53t12FcsGPEiAC1atOLs2TPWCyMiImIgmoyLFBF7qTM+ffp0nJycyMjIIDExkQoVbu2QWdg64zffrktPd8fZ2ckuatLaQ4a8KJexKJexKJdxqM64iIHYS51xJycnTp8+Te/evfHw8LCUTcxxP3XGcyQlpZKZmWX49YX2uEYSlMtolMtYlMs4VGdcxGDsqc545cqVWb9+PQEBAbna3d9vnXEREZEHmSbjIkXEXuqMBwYGcuzYMQDc3d1z/fJwv3XGASpWrGT4SioiIiKFoctXIsXMSHXGAfr378+IESNwdnamdOnSREZGAli1zriIiMiDSnXGRcQm7G0tIdjnGklQLqNRLmNRLuNQnXGRB8D3339f4uqMDx48uMjOKyIi8qDTZFykBGnVqlWJqzMuIiIiRUcPcIqIiIiI2IjWjIuIiIiI2IiujIuIiIiI2Igm4yIiIiIiNqLJuIiIiIiIjWgyLiIiIiJiI5qMi4iIiIjYiCbjIiIiIiI2oqY/IlJssrOziYiI4MCBA7i4uBAZGUnVqlVtPax7tmfPHqKjo4mLi+P48eOMGDECBwcHatasyQcffICjoyPLli1j6dKlmEwm3n33XVq2bGnrYecrMzOTUaNGcfr0aTIyMnj33XepUaOG4XNlZWUxZswYjh49ipOTEx999BFms9nwuXJcvHiRN954gwULFmAymewiV4cOHShT5kbLcB8fHwIDA+0iV2xsLBs2bCAzM5OAgAAaN25s+FyrVq3iX//6FwDp6en89ttvfPHFF0yYMMGwuTIzMxkxYgSnT5/G0dGRcePGFc9/W2YRkWKybt06c1hYmNlsNpt//vlnc2BgoI1HdO/mzJljfuWVV8wdO3Y0m81m8zvvvGP+6aefzGaz2RweHm5ev369+fz58+ZXXnnFnJ6ebr569arlzyXVihUrzJGRkWaz2WxOSkoyN2/e3C5yJSQkmEeMGGE2m83mn376yRwYGGgXucxmszkjI8M8YMAAc9u2bc2///67XeRKS0szv/baa7dss4dcP/30k/mdd94xZ2VlmVNSUszTpk2zi1w3i4iIMC9dutTwuRISEsyDBw82m81m85YtW8xBQUHFkknLVESk2OzcuZNmzZoB8OSTT/LLL7/YeET3rkqVKkyfPt3y+tdff6Vx48YAvPDCC/z444/s3buXBg0a4OLiQpkyZahSpQr79++31ZDv6qWXXmLIkCGW105OTnaRq3Xr1owbNw6AM2fO4OXlZRe5AKKioujSpQsPP/wwYB//Dvfv38+ff/5Jnz596NGjB7t377aLXFu2bOHRRx9l4MCBBAYG0qJFC7vIleN///sfv//+O507dzZ8rmrVqpGVlUV2djYpKSmYTKZiyaRlKiJSbFJSUvDw8LC8dnJy4vr165hMxvlR1K5dO06dOmV5bTabcXBwAMDd3Z3k5GRSUlIst9pztqekpBT7WAvK3d0duPH3M3jwYIYOHUpUVJThcwGYTCbCwsJISEhg2rRp/PDDD4bPtWrVKsqXL0+zZs2YM2cOYB//DkuVKkXfvn3p2LEjx44do1+/fnaR69KlS5w5c4bZs2dz6tQp3n33XbvIlSM2NpaBAwcCxv936ObmxunTp3n55Ze5dOkSs2fPZseOHUWeyTj/BxQRw/Pw8CA1NdXyOjs721AT8bw4Ov51gzE1NZWyZcvmypmamnrLD+6S6OzZswwcOJCuXbvy6quvMnnyZMs+I+eCG1eRQ0ND6dSpE+np6ZbtRs21cuVKHBwc2LZtG7/99hthYWEkJSVZ9hs1V7Vq1ahatSoODg5Uq1YNT09Pfv31V8t+o+by9PTEz88PFxcX/Pz8cHV15Y8//rDsN2ougKtXr3LkyBGaNGkCGP/n4aJFi2jatCnDhg3j7Nmz9OzZk8zMTMv+osqkZSoiUmwaNmzIpk2bANi9ezePPvqojUd0/+rUqcP27dsB2LRpE40aNaJevXrs3LmT9PR0kpOTOXz4cInOeuHCBfr06cPw4cN58803AfvI9eWXXxIbGwtA6dKlcXBw4IknnjB8rs8//5zFixcTFxdH7dq1iYqK4oUXXjB8rhUrVjBx4kQAzp07R0pKCs8//7zhcz311FNs3rwZs9nMuXPn+PPPP3n22WcNnwtgx44dPPfcc5bXRv+5UbZsWcuk+qGHHuL69evFksnBbDabrZJAROQucqqpHDx4ELPZzIQJE6hevbqth3XPTp06RUhICMuWLePo0aOEh4eTmZmJn58fkZGRODk5sWzZMuLj4zGbzbzzzju0a9fO1sPOV2RkJN9++y1+fn6WbaNHjyYyMtLQua5du8bIkSO5cOEC169fp1+/flSvXt3wf1836969OxERETg6Oho+V0ZGBiNHjuTMmTM4ODgQGhpKuXLlDJ8LYNKkSWzfvh2z2UxwcDA+Pj52kWvevHmYTCZ69eoFYPifh6mpqYwaNYrExEQyMzPp0aMHTzzxRJFn0mRcRERERMRGtExFRERERMRGNBkXEREREbERTcZFRERERGxEk3ERERERERvRZFxERERExEaM3W1DRETkAXDq1CleeumlXKVAZ8+eTcWKFW00KhGxBk3GRUREDODhhx/mq6++svUwRMTKNBkXERGxE6tXr2bevHk4OTnh4+PD5MmTcXFxITo6mu+++w4nJyc6d+5Mz549OXr0KO+//z6XL1/Gzc2N0aNHU69ePUaMGMHly5c5fvw4w4cPx8vLi48++oi0tDTKlSvH2LFj8fX1tXVUEbuhybiIiIgBnD9/ntdee83y+tVXX+Xtt9++5T2ffPIJy5Yto0KFCkRFRXHkyBGOHTvGrl27WL16NZmZmXTt2hV/f3+GDx9O//79adu2Lbt372bIkCGsW7cOAE9PT2bPnk1GRgZvvvkms2fPplKlSmzevJnw8HAWLVpUnNFF7Jom4yIiIgZQkGUqLVu2JCAggNatW9OuXTtq167N8uXLefnll3FxccHFxYWvvvqK1NRUTpw4Qdu2bQF48skneeihhzhy5AgA9erVA+DYsWOcPHmSd99913KOlJSUIkoo8mDSZFxERMROjBkzhv3797Nx40aGDx9OUFAQJpMJBwcHy3tOnTrFQw89lOuzZrOZrKwsAEqVKgVAdnY2Pj4+ll8CsrKyuHDhQjEkEXlwqLShiIiIHbh+/Tpt27alXLlyvPPOO7z22mv89ttvPP3006xfv57MzEz+/PNP3n77bS5cuICPjw/r168HYPfu3Vy4cIGaNWveckw/Pz+uXLnCf//7XwBWrlxJaGhosWcTsWe6Mi4iImIHTCYTgwcPpk+fPri6ulKhQgUmTpxIhQoV+OWXX3jjjTfIzs6mR48eVKtWjcmTJxMREcH06dNxdnZm+vTpuLi43HJMFxcXpk6dyvjx40lPT8fDw4OoqCgbJRSxTw5ms9ls60GIiIiIiDyItExFRERERMRGNBkXEREREbERTcZFRERERGxEk3ERERERERvRZFxERERExEY0GRcRERERsRFNxkVEREREbESTcRERERERG/n/mQYin/ggdqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "plot_importance(grid_xg.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f889251b240>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(grid_xg.best_estimator_,max_num_features=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**findings**\n",
    "- The grid search xgboost produced our overall best model.  We where extremely pleased with the highest recall and accuracy score.  The precision, which was our target metric was also very high.  When graphing the featrue importance, the most important features where abnormal_short_term_variatability and the histogram mean, min, width  and mode.  Our engineered features where not as important as some of the unchanged features.  Our best preforming engineered feature was sqrt_total_change, but no engineered feature was in the top ten.  Just like our random forest model short term varitability of the FHR is a key feature in classifying fetal health."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: scikit-learn==0.20.4 in /Users/heymanjake/opt/anaconda3/lib/python3.8/site-packages (0.20.4)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.13.3 in /Users/heymanjake/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==0.20.4) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /Users/heymanjake/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==0.20.4) (1.18.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-learn==0.20.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /Users/heymanjake/opt/anaconda3\n",
      "\n",
      "  removed specs:\n",
      "    - numpy\n",
      "    - scikit-learn\n",
      "    - scipy\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    argon2-cffi-20.1.0         |   py38haf1e3a3_1          44 KB\n",
      "    async_generator-1.10       |             py_0          24 KB\n",
      "    attrs-20.3.0               |     pyhd3eb1b0_0          43 KB\n",
      "    backports-1.0              |     pyhd3eb1b0_2         210 KB\n",
      "    backports.functools_lru_cache-1.6.1|     pyhd3eb1b0_0          12 KB\n",
      "    beautifulsoup4-4.9.3       |     pyhb0f4dca_0          91 KB\n",
      "    bleach-3.2.1               |             py_0         112 KB\n",
      "    brotlipy-0.7.0             |py38h9ed2024_1003         331 KB\n",
      "    ca-certificates-2020.10.14 |                0         121 KB\n",
      "    certifi-2020.6.20          |     pyhd3eb1b0_3         155 KB\n",
      "    cffi-1.14.3                |   py38h2125817_2         221 KB\n",
      "    chardet-3.0.4              |py38hecd8cb5_1003         175 KB\n",
      "    conda-4.9.2                |   py38hecd8cb5_0         2.9 MB\n",
      "    conda-package-handling-1.7.2|   py38h22f3db7_0         1.5 MB\n",
      "    cryptography-3.2.1         |   py38hbcfaee0_1         560 KB\n",
      "    dbus-1.13.18               |       h18a8e69_0         445 KB\n",
      "    expat-2.2.10               |       hb1e8313_2         110 KB\n",
      "    freetype-2.10.4            |       ha233b18_0         564 KB\n",
      "    glib-2.66.1                |       h9bbe63b_0         2.7 MB\n",
      "    importlib-metadata-2.0.0   |             py_1          35 KB\n",
      "    importlib_metadata-2.0.0   |                1          11 KB\n",
      "    ipykernel-5.3.4            |   py38h5ca1d4c_0         180 KB\n",
      "    ipython-7.19.0             |   py38h01d92e1_0         982 KB\n",
      "    ipywidgets-7.5.1           |             py_1          98 KB\n",
      "    jedi-0.17.2                |           py38_0         919 KB\n",
      "    jsonschema-3.2.0           |             py_2          47 KB\n",
      "    jupyter_client-6.1.7       |             py_0          77 KB\n",
      "    jupyterlab-2.2.6           |             py_0         3.4 MB\n",
      "    jupyterlab_pygments-0.1.2  |             py_0           8 KB\n",
      "    libxml2-2.9.10             |       h7cdb67c_3         1.1 MB\n",
      "    lz4-c-1.9.2                |       h79c402e_3         130 KB\n",
      "    nbclient-0.5.1             |             py_0          58 KB\n",
      "    nbconvert-6.0.7            |           py38_0         483 KB\n",
      "    nbformat-5.0.8             |             py_0          88 KB\n",
      "    nest-asyncio-1.4.2         |     pyhd3eb1b0_0          11 KB\n",
      "    notebook-6.1.4             |           py38_0         4.1 MB\n",
      "    openssl-1.1.1h             |       haf1e3a3_0         2.2 MB\n",
      "    pandoc-2.11                |       h0dc7051_0        10.8 MB\n",
      "    pandocfilters-1.4.3        |   py38hecd8cb5_1          14 KB\n",
      "    pillow-8.0.1               |   py38h5270095_0         584 KB\n",
      "    pip-20.2.4                 |   py38hecd8cb5_0         1.8 MB\n",
      "    pkginfo-1.6.1              |   py38hecd8cb5_0          43 KB\n",
      "    prompt-toolkit-3.0.8       |             py_0         248 KB\n",
      "    psutil-5.7.2               |   py38haf1e3a3_0         341 KB\n",
      "    pygments-2.7.2             |     pyhd3eb1b0_0         674 KB\n",
      "    pyrsistent-0.17.3          |   py38haf1e3a3_0          89 KB\n",
      "    pyzmq-19.0.2               |   py38hb1e8313_1         402 KB\n",
      "    ripgrep-12.1.1             |                0         1.3 MB\n",
      "    setuptools-50.3.1          |   py38hecd8cb5_1         726 KB\n",
      "    six-1.15.0                 |   py38hecd8cb5_0          27 KB\n",
      "    sqlite-3.33.0              |       hffcf06c_0         1.3 MB\n",
      "    terminado-0.9.1            |           py38_0          27 KB\n",
      "    tqdm-4.51.0                |     pyhd3eb1b0_0          66 KB\n",
      "    traitlets-5.0.5            |             py_0          83 KB\n",
      "    urllib3-1.25.11            |             py_0          99 KB\n",
      "    wheel-0.35.1               |     pyhd3eb1b0_0          38 KB\n",
      "    zeromq-4.3.3               |       hb1e8313_3         414 KB\n",
      "    zipp-3.4.0                 |     pyhd3eb1b0_0          15 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        42.9 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  argon2-cffi        pkgs/main/osx-64::argon2-cffi-20.1.0-py38haf1e3a3_1\n",
      "  async_generator    pkgs/main/noarch::async_generator-1.10-py_0\n",
      "  jupyterlab_pygmen~ pkgs/main/noarch::jupyterlab_pygments-0.1.2-py_0\n",
      "  nbclient           pkgs/main/noarch::nbclient-0.5.1-py_0\n",
      "  nest-asyncio       pkgs/main/noarch::nest-asyncio-1.4.2-pyhd3eb1b0_0\n",
      "\n",
      "The following packages will be REMOVED:\n",
      "\n",
      "  alabaster-0.7.12-py_0\n",
      "  anaconda-2020.07-py38_0\n",
      "  anaconda-project-0.8.4-py_0\n",
      "  applaunchservices-0.2.1-py_0\n",
      "  appscript-1.1.1-py38haf1e3a3_0\n",
      "  argh-0.26.2-py38_0\n",
      "  asn1crypto-1.3.0-py38_1\n",
      "  astroid-2.4.2-py38_0\n",
      "  astropy-4.0.1.post1-py38h01d97ff_1\n",
      "  atomicwrites-1.4.0-py_0\n",
      "  autopep8-1.5.3-py_0\n",
      "  babel-2.8.0-py_0\n",
      "  backports.shutil_get_terminal_size-1.0.0-py38_2\n",
      "  bitarray-1.4.0-py38haf1e3a3_0\n",
      "  bkcharts-0.2-py38_0\n",
      "  blas-1.0-mkl\n",
      "  blosc-1.19.0-hab81aa3_0\n",
      "  bokeh-2.1.1-py38_0\n",
      "  boto-2.49.0-py38_0\n",
      "  bottleneck-1.3.2-py38hf1fa96c_1\n",
      "  cloudpickle-1.5.0-py_0\n",
      "  colorama-0.4.3-py_0\n",
      "  contextlib2-0.6.0.post1-py_0\n",
      "  curl-7.71.1-hb0a8c7a_1\n",
      "  cycler-0.10.0-py38_0\n",
      "  cython-0.29.21-py38hb1e8313_0\n",
      "  cytoolz-0.10.1-py38h1de35cc_0\n",
      "  dask-2.20.0-py_0\n",
      "  dask-core-2.20.0-py_0\n",
      "  diff-match-patch-20200713-py_0\n",
      "  distributed-2.20.0-py38_0\n",
      "  docutils-0.16-py38_1\n",
      "  et_xmlfile-1.0.1-py_1001\n",
      "  fastcache-1.1.0-py38h1de35cc_0\n",
      "  flake8-3.8.3-py_0\n",
      "  flask-1.1.2-py_0\n",
      "  fsspec-0.7.4-py_0\n",
      "  get_terminal_size-1.0.0-h7520d66_0\n",
      "  gevent-20.6.2-py38haf1e3a3_0\n",
      "  gmp-6.1.2-hb37e062_1\n",
      "  gmpy2-2.0.8-py38h6ef4df4_3\n",
      "  greenlet-0.4.16-py38haf1e3a3_0\n",
      "  h5py-2.10.0-py38h3134771_0\n",
      "  hdf5-1.10.4-hfa1e0ec_0\n",
      "  heapdict-1.0.1-py_0\n",
      "  html5lib-1.1-py_0\n",
      "  imageio-2.9.0-py_0\n",
      "  imagesize-1.2.0-py_0\n",
      "  intel-openmp-2019.4-233\n",
      "  intervaltree-3.0.2-py_1\n",
      "  isort-4.3.21-py38_0\n",
      "  itsdangerous-1.1.0-py_0\n",
      "  jbig-2.1-h4d881f8_0\n",
      "  jdcal-1.4.1-py_0\n",
      "  joblib-0.16.0-py_0\n",
      "  jupyter-1.0.0-py38_7\n",
      "  jupyter_console-6.1.0-py_0\n",
      "  keyring-21.2.1-py38_0\n",
      "  kiwisolver-1.2.0-py38h04f5b5a_0\n",
      "  krb5-1.18.2-h75d18d8_0\n",
      "  lazy-object-proxy-1.4.3-py38h1de35cc_0\n",
      "  libcurl-7.71.1-h8a08a2b_1\n",
      "  libgfortran-3.0.1-h93005f0_2\n",
      "  libllvm9-9.0.1-h21ff451_1\n",
      "  libspatialindex-1.9.3-h0a44026_0\n",
      "  libssh2-1.9.0-ha12b0ac_1\n",
      "  libxslt-1.1.34-h83b36ba_0\n",
      "  llvm-openmp-10.0.0-h28b9765_0\n",
      "  llvmlite-0.33.0-py38ha11be7d_1\n",
      "  locket-0.2.0-py38_1\n",
      "  lxml-4.5.2-py38h63b7cb6_0\n",
      "  lzo-2.10-h1de35cc_2\n",
      "  matplotlib-3.2.2-0\n",
      "  matplotlib-base-3.2.2-py38h5670ca0_0\n",
      "  mccabe-0.6.1-py38_1\n",
      "  mkl-2019.4-233\n",
      "  mkl-service-2.3.0-py38hfbe908c_0\n",
      "  mkl_fft-1.1.0-py38hc64f4ea_0\n",
      "  mkl_random-1.1.1-py38h959d312_0\n",
      "  mock-4.0.2-py_0\n",
      "  more-itertools-8.4.0-py_0\n",
      "  mpc-1.1.0-h6ef4df4_1\n",
      "  mpfr-4.0.2-h9066e36_1\n",
      "  mpmath-1.1.0-py38_0\n",
      "  msgpack-python-1.0.0-py38h04f5b5a_1\n",
      "  multipledispatch-0.6.0-py38_0\n",
      "  networkx-2.4-py_1\n",
      "  nltk-3.5-py_0\n",
      "  nose-1.3.7-py38_1004\n",
      "  numba-0.50.1-py38h959d312_1\n",
      "  numexpr-2.7.1-py38hce01a72_0\n",
      "  numpy-1.18.5-py38h1da2735_0\n",
      "  numpy-base-1.18.5-py38h3304bdc_0\n",
      "  numpydoc-1.1.0-py_0\n",
      "  openpyxl-3.0.4-py_0\n",
      "  pandas-1.0.5-py38h959d312_0\n",
      "  partd-1.1.0-py_0\n",
      "  path-13.1.0-py38_0\n",
      "  path.py-12.4.0-0\n",
      "  pathlib2-2.3.5-py38_1\n",
      "  pathtools-0.1.2-py_1\n",
      "  patsy-0.5.1-py38_0\n",
      "  pep8-1.7.1-py38_0\n",
      "  pluggy-0.13.1-py38_0\n",
      "  ply-3.11-py38_0\n",
      "  prompt_toolkit-3.0.5-0\n",
      "  py-1.9.0-py_0\n",
      "  pycodestyle-2.6.0-py_0\n",
      "  pycurl-7.43.0.5-py38ha12b0ac_0\n",
      "  pydocstyle-5.0.2-py_0\n",
      "  pyflakes-2.2.0-py_0\n",
      "  pylint-2.5.3-py38_0\n",
      "  pyodbc-4.0.30-py38h0a44026_0\n",
      "  pytables-3.6.1-py38h4727e94_0\n",
      "  pytest-5.4.3-py38_0\n",
      "  python-jsonrpc-server-0.3.4-py_1\n",
      "  python-language-server-0.34.1-py38_0\n",
      "  pywavelets-1.1.1-py38h1de35cc_0\n",
      "  qdarkstyle-2.8.1-py_0\n",
      "  qtawesome-0.7.2-py_0\n",
      "  qtconsole-4.7.5-py_0\n",
      "  regex-2020.6.8-py38haf1e3a3_0\n",
      "  rope-0.17.0-py_0\n",
      "  rtree-0.9.4-py38_1\n",
      "  scikit-image-0.16.2-py38h6c726b0_0\n",
      "  scikit-learn-0.23.1-py38h603561c_0\n",
      "  scipy-1.5.0-py38hbab996c_0\n",
      "  seaborn-0.10.1-py_0\n",
      "  simplegeneric-0.8.1-py38_2\n",
      "  singledispatch-3.4.0.3-py38_0\n",
      "  snappy-1.1.8-hb1e8313_0\n",
      "  snowballstemmer-2.0.0-py_0\n",
      "  sortedcollections-1.2.1-py_0\n",
      "  sortedcontainers-2.2.2-py_0\n",
      "  sphinx-3.1.2-py_0\n",
      "  sphinxcontrib-1.0-py38_1\n",
      "  sphinxcontrib-applehelp-1.0.2-py_0\n",
      "  sphinxcontrib-devhelp-1.0.2-py_0\n",
      "  sphinxcontrib-htmlhelp-1.0.3-py_0\n",
      "  sphinxcontrib-jsmath-1.0.1-py_0\n",
      "  sphinxcontrib-qthelp-1.0.3-py_0\n",
      "  sphinxcontrib-serializinghtml-1.1.4-py_0\n",
      "  sphinxcontrib-websupport-1.2.3-py_0\n",
      "  spyder-4.1.4-py38_0\n",
      "  spyder-kernels-1.9.2-py38_0\n",
      "  sqlalchemy-1.3.18-py38haf1e3a3_0\n",
      "  statsmodels-0.11.1-py38haf1e3a3_0\n",
      "  sympy-1.6.1-py38_0\n",
      "  tbb-2020.0-h04f5b5a_0\n",
      "  tblib-1.6.0-py_0\n",
      "  threadpoolctl-2.1.0-pyh5ca1d4c_0\n",
      "  toml-0.10.1-py_0\n",
      "  toolz-0.10.0-py_0\n",
      "  typing_extensions-3.7.4.2-py_0\n",
      "  ujson-1.35-py38h1de35cc_0\n",
      "  unicodecsv-0.14.1-py38_0\n",
      "  unixodbc-2.3.7-h1de35cc_0\n",
      "  watchdog-0.10.3-py38haf1e3a3_0\n",
      "  werkzeug-1.0.1-py_0\n",
      "  wrapt-1.11.2-py38h1de35cc_0\n",
      "  wurlitzer-2.0.1-py38_0\n",
      "  xlrd-1.2.0-py_0\n",
      "  xlsxwriter-1.2.9-py_0\n",
      "  xlwings-0.19.5-py38_0\n",
      "  xlwt-1.3.0-py38_0\n",
      "  yapf-0.30.0-py_0\n",
      "  zict-2.0.0-py_0\n",
      "  zope-1.0-py38_1\n",
      "  zope.event-4.4-py38_0\n",
      "  zope.interface-4.7.1-py38h1de35cc_0\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  attrs                                         19.3.0-py_0 --> 20.3.0-pyhd3eb1b0_0\n",
      "  beautifulsoup4     pkgs/main/osx-64::beautifulsoup4-4.9.~ --> pkgs/main/noarch::beautifulsoup4-4.9.3-pyhb0f4dca_0\n",
      "  bleach                                         3.1.5-py_0 --> 3.2.1-py_0\n",
      "  brotlipy                          0.7.0-py38haf1e3a3_1000 --> 0.7.0-py38h9ed2024_1003\n",
      "  ca-certificates                               2020.6.24-0 --> 2020.10.14-0\n",
      "  certifi            pkgs/main/osx-64::certifi-2020.6.20-p~ --> pkgs/main/noarch::certifi-2020.6.20-pyhd3eb1b0_3\n",
      "  cffi                                1.14.0-py38hc512035_1 --> 1.14.3-py38h2125817_2\n",
      "  conda                                        4.8.3-py38_0 --> 4.9.2-py38hecd8cb5_0\n",
      "  conda-package-han~                   1.6.1-py38h1de35cc_0 --> 1.7.2-py38h22f3db7_0\n",
      "  cryptography                         2.9.2-py38ha12b0ac_0 --> 3.2.1-py38hbcfaee0_1\n",
      "  dbus                                   1.13.16-h18a8e69_0 --> 1.13.18-h18a8e69_0\n",
      "  expat                                    2.2.9-hb1e8313_2 --> 2.2.10-hb1e8313_2\n",
      "  freetype                                2.10.2-ha233b18_0 --> 2.10.4-ha233b18_0\n",
      "  glib                                    2.65.0-hc5f4afa_0 --> 2.66.1-h9bbe63b_0\n",
      "  importlib-metadata pkgs/main/osx-64::importlib-metadata-~ --> pkgs/main/noarch::importlib-metadata-2.0.0-py_1\n",
      "  importlib_metadata                                1.7.0-0 --> 2.0.0-1\n",
      "  ipykernel                            5.3.2-py38h5ca1d4c_0 --> 5.3.4-py38h5ca1d4c_0\n",
      "  ipython                             7.16.1-py38h5ca1d4c_0 --> 7.19.0-py38h01d92e1_0\n",
      "  ipywidgets                                     7.5.1-py_0 --> 7.5.1-py_1\n",
      "  jedi                                        0.17.1-py38_0 --> 0.17.2-py38_0\n",
      "  jsonschema         pkgs/main/osx-64::jsonschema-3.2.0-py~ --> pkgs/main/noarch::jsonschema-3.2.0-py_2\n",
      "  jupyter_client                                 6.1.6-py_0 --> 6.1.7-py_0\n",
      "  jupyterlab                                     2.1.5-py_0 --> 2.2.6-py_0\n",
      "  libxml2                                 2.9.10-h3b9e6c8_1 --> 2.9.10-h7cdb67c_3\n",
      "  lz4-c                                    1.9.2-h0a44026_0 --> 1.9.2-h79c402e_3\n",
      "  nbconvert                                    5.6.1-py38_1 --> 6.0.7-py38_0\n",
      "  nbformat                                       5.0.7-py_0 --> 5.0.8-py_0\n",
      "  notebook                                     6.0.3-py38_0 --> 6.1.4-py38_0\n",
      "  openssl                                 1.1.1g-h1de35cc_0 --> 1.1.1h-haf1e3a3_0\n",
      "  pandoc                                             2.10-0 --> 2.11-h0dc7051_0\n",
      "  pandocfilters                                1.4.2-py38_1 --> 1.4.3-py38hecd8cb5_1\n",
      "  pillow                               7.2.0-py38ha54b6ba_0 --> 8.0.1-py38h5270095_0\n",
      "  pip                                         20.1.1-py38_1 --> 20.2.4-py38hecd8cb5_0\n",
      "  pkginfo                                    1.5.0.1-py38_0 --> 1.6.1-py38hecd8cb5_0\n",
      "  prompt-toolkit                                 3.0.5-py_0 --> 3.0.8-py_0\n",
      "  psutil                               5.7.0-py38h1de35cc_0 --> 5.7.2-py38haf1e3a3_0\n",
      "  pygments                                       2.6.1-py_0 --> 2.7.2-pyhd3eb1b0_0\n",
      "  pyrsistent                          0.16.0-py38h1de35cc_0 --> 0.17.3-py38haf1e3a3_0\n",
      "  pyzmq                               19.0.1-py38hb1e8313_1 --> 19.0.2-py38hb1e8313_1\n",
      "  ripgrep                                 11.0.2-he32d670_0 --> 12.1.1-0\n",
      "  setuptools                                  49.2.0-py38_0 --> 50.3.1-py38hecd8cb5_1\n",
      "  sqlite                                  3.32.3-hffcf06c_0 --> 3.33.0-hffcf06c_0\n",
      "  terminado                                    0.8.3-py38_0 --> 0.9.1-py38_0\n",
      "  tqdm                                          4.47.0-py_0 --> 4.51.0-pyhd3eb1b0_0\n",
      "  traitlets          pkgs/main/osx-64::traitlets-4.3.3-py3~ --> pkgs/main/noarch::traitlets-5.0.5-py_0\n",
      "  urllib3                                       1.25.9-py_0 --> 1.25.11-py_0\n",
      "  wheel               pkgs/main/osx-64::wheel-0.34.2-py38_0 --> pkgs/main/noarch::wheel-0.35.1-pyhd3eb1b0_0\n",
      "  zeromq                                   4.3.2-hb1e8313_2 --> 4.3.3-hb1e8313_3\n",
      "  zipp                                           3.1.0-py_0 --> 3.4.0-pyhd3eb1b0_0\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  six                     pkgs/main/noarch::six-1.15.0-py_0 --> pkgs/main/osx-64::six-1.15.0-py38hecd8cb5_0\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "  backports                                        1.0-py_2 --> 1.0-pyhd3eb1b0_2\n",
      "  backports.functoo~                             1.6.1-py_0 --> 1.6.1-pyhd3eb1b0_0\n",
      "  chardet                                   3.0.4-py38_1003 --> 3.0.4-py38hecd8cb5_1003\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqlite-3.33.0        | 1.3 MB    | ##################################### | 100% \n",
      "ripgrep-12.1.1       | 1.3 MB    | ##################################### | 100% \n",
      "ipython-7.19.0       | 982 KB    | ##################################### | 100% \n",
      "zeromq-4.3.3         | 414 KB    | ##################################### | 100% \n",
      "glib-2.66.1          | 2.7 MB    | ##################################### | 100% \n",
      "dbus-1.13.18         | 445 KB    | ##################################### | 100% \n",
      "pyrsistent-0.17.3    | 89 KB     | ##################################### | 100% \n",
      "certifi-2020.6.20    | 155 KB    | ##################################### | 100% \n",
      "argon2-cffi-20.1.0   | 44 KB     | ##################################### | 100% \n",
      "ipykernel-5.3.4      | 180 KB    | ##################################### | 100% \n",
      "tqdm-4.51.0          | 66 KB     | ##################################### | 100% \n",
      "psutil-5.7.2         | 341 KB    | ##################################### | 100% \n",
      "expat-2.2.10         | 110 KB    | ##################################### | 100% \n",
      "openssl-1.1.1h       | 2.2 MB    | ##################################### | 100% \n",
      "six-1.15.0           | 27 KB     | ##################################### | 100% \n",
      "beautifulsoup4-4.9.3 | 91 KB     | ##################################### | 100% \n",
      "pip-20.2.4           | 1.8 MB    | ##################################### | 100% \n",
      "prompt-toolkit-3.0.8 | 248 KB    | ##################################### | 100% \n",
      "conda-4.9.2          | 2.9 MB    | ##################################### | 100% \n",
      "terminado-0.9.1      | 27 KB     | ##################################### | 100% \n",
      "nest-asyncio-1.4.2   | 11 KB     | ##################################### | 100% \n",
      "zipp-3.4.0           | 15 KB     | ##################################### | 100% \n",
      "attrs-20.3.0         | 43 KB     | ##################################### | 100% \n",
      "nbformat-5.0.8       | 88 KB     | ##################################### | 100% \n",
      "cryptography-3.2.1   | 560 KB    | ##################################### | 100% \n",
      "async_generator-1.10 | 24 KB     | ##################################### | 100% \n",
      "pandoc-2.11          | 10.8 MB   | ##################################### | 100% \n",
      "jupyterlab_pygments- | 8 KB      | ##################################### | 100% \n",
      "brotlipy-0.7.0       | 331 KB    | ##################################### | 100% \n",
      "ipywidgets-7.5.1     | 98 KB     | ##################################### | 100% \n",
      "nbclient-0.5.1       | 58 KB     | ##################################### | 100% \n",
      "urllib3-1.25.11      | 99 KB     | ##################################### | 100% \n",
      "backports.functools_ | 12 KB     | ##################################### | 100% \n",
      "chardet-3.0.4        | 175 KB    | ##################################### | 100% \n",
      "bleach-3.2.1         | 112 KB    | ##################################### | 100% \n",
      "setuptools-50.3.1    | 726 KB    | ##################################### | 100% \n",
      "pygments-2.7.2       | 674 KB    | ##################################### | 100% \n",
      "backports-1.0        | 210 KB    | ##################################### | 100% \n",
      "pkginfo-1.6.1        | 43 KB     | ##################################### | 100% \n",
      "pillow-8.0.1         | 584 KB    | ##################################### | 100% \n",
      "jsonschema-3.2.0     | 47 KB     | ##################################### | 100% \n",
      "importlib_metadata-2 | 11 KB     | ##################################### | 100% \n",
      "libxml2-2.9.10       | 1.1 MB    | ##################################### | 100% \n",
      "importlib-metadata-2 | 35 KB     | ##################################### | 100% \n",
      "jupyterlab-2.2.6     | 3.4 MB    | ##################################### | 100% \n",
      "cffi-1.14.3          | 221 KB    | ##################################### | 100% \n",
      "jupyter_client-6.1.7 | 77 KB     | ##################################### | 100% \n",
      "nbconvert-6.0.7      | 483 KB    | ##################################### | 100% \n",
      "pandocfilters-1.4.3  | 14 KB     | ##################################### | 100% \n",
      "notebook-6.1.4       | 4.1 MB    | ##################################### | 100% \n",
      "pyzmq-19.0.2         | 402 KB    | ##################################### | 100% \n",
      "jedi-0.17.2          | 919 KB    | ##################################### | 100% \n",
      "freetype-2.10.4      | 564 KB    | ##################################### | 100% \n",
      "lz4-c-1.9.2          | 130 KB    | ##################################### | 100% \n",
      "ca-certificates-2020 | 121 KB    | ##################################### | 100% \n",
      "wheel-0.35.1         | 38 KB     | ##################################### | 100% \n",
      "traitlets-5.0.5      | 83 KB     | ##################################### | 100% \n",
      "conda-package-handli | 1.5 MB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: - WARNING conda.core.path_actions:verify(962): Unable to create environments file. Path not writable.\n",
      "  environment location: /Users/heymanjake/.conda/environments.txt\n",
      "\n",
      "done\n",
      "Executing transaction: | WARNING conda.core.envs_manager:register_env(50): Unable to register environment. Path not writable or missing.\n",
      "  environment location: /Users/heymanjake/opt/anaconda3\n",
      "  registry file: /Users/heymanjake/.conda/environments.txt\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "!conda uninstall numpy scipy scikit-learn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /Users/heymanjake/opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - numpy\n",
      "    - scikit-learn\n",
      "    - scipy\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    blas-1.0                   |              mkl           6 KB\n",
      "    joblib-0.17.0              |             py_0         206 KB\n",
      "    llvm-openmp-10.0.0         |       h28b9765_0         236 KB\n",
      "    mkl_fft-1.2.0              |   py38hc64f4ea_0         143 KB\n",
      "    numpy-1.19.2               |   py38h456fd55_0          21 KB\n",
      "    numpy-base-1.19.2          |   py38hcfb5961_0         4.0 MB\n",
      "    scikit-learn-0.23.2        |   py38h959d312_0         4.7 MB\n",
      "    scipy-1.5.2                |   py38h2515648_0        13.3 MB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        22.6 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  blas               pkgs/main/osx-64::blas-1.0-mkl\n",
      "  intel-openmp       pkgs/main/osx-64::intel-openmp-2019.4-233\n",
      "  joblib             pkgs/main/noarch::joblib-0.17.0-py_0\n",
      "  libgfortran        pkgs/main/osx-64::libgfortran-3.0.1-h93005f0_2\n",
      "  llvm-openmp        pkgs/main/osx-64::llvm-openmp-10.0.0-h28b9765_0\n",
      "  mkl                pkgs/main/osx-64::mkl-2019.4-233\n",
      "  mkl-service        pkgs/main/osx-64::mkl-service-2.3.0-py38hfbe908c_0\n",
      "  mkl_fft            pkgs/main/osx-64::mkl_fft-1.2.0-py38hc64f4ea_0\n",
      "  mkl_random         pkgs/main/osx-64::mkl_random-1.1.1-py38h959d312_0\n",
      "  numpy              pkgs/main/osx-64::numpy-1.19.2-py38h456fd55_0\n",
      "  numpy-base         pkgs/main/osx-64::numpy-base-1.19.2-py38hcfb5961_0\n",
      "  scikit-learn       pkgs/main/osx-64::scikit-learn-0.23.2-py38h959d312_0\n",
      "  scipy              pkgs/main/osx-64::scipy-1.5.2-py38h2515648_0\n",
      "  threadpoolctl      pkgs/main/noarch::threadpoolctl-2.1.0-pyh5ca1d4c_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "mkl_fft-1.2.0        | 143 KB    | ##################################### | 100% \n",
      "numpy-base-1.19.2    | 4.0 MB    | ##################################### | 100% \n",
      "numpy-1.19.2         | 21 KB     | ##################################### | 100% \n",
      "llvm-openmp-10.0.0   | 236 KB    | ##################################### | 100% \n",
      "scipy-1.5.2          | 13.3 MB   | ##################################### | 100% \n",
      "scikit-learn-0.23.2  | 4.7 MB    | ##################################### | 100% \n",
      "blas-1.0             | 6 KB      | ##################################### | 100% \n",
      "joblib-0.17.0        | 206 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: / WARNING conda.core.path_actions:verify(962): Unable to create environments file. Path not writable.\n",
      "  environment location: /Users/heymanjake/.conda/environments.txt\n",
      "\n",
      "done\n",
      "Executing transaction: - WARNING conda.core.envs_manager:register_env(50): Unable to register environment. Path not writable or missing.\n",
      "  environment location: /Users/heymanjake/opt/anaconda3\n",
      "  registry file: /Users/heymanjake/.conda/environments.txt\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "!conda install numpy scipy scikit-learn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "principalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "PrincipalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.06120220e+00, 3.50943021e+00, 1.82518111e+00, 1.49742335e+00,\n",
       "       1.21893236e+00, 1.02065245e+00, 9.82233269e-01, 9.26968480e-01,\n",
       "       7.62786850e-01, 6.40505823e-01, 5.77555709e-01, 4.97548621e-01,\n",
       "       3.88685694e-01, 3.27185462e-01, 2.65555690e-01, 1.80902355e-01,\n",
       "       1.33209984e-01, 1.17583404e-01, 4.95375945e-02, 2.68017419e-02,\n",
       "       9.82848922e-32])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.06120220e+00 3.50943021e+00 1.82518111e+00 1.49742335e+00\n",
      " 1.21893236e+00 1.02065245e+00 9.82233269e-01 9.26968480e-01\n",
      " 7.62786850e-01 6.40505823e-01 5.77555709e-01 4.97548621e-01\n",
      " 3.88685694e-01 3.27185462e-01 2.65555690e-01 1.80902355e-01\n",
      " 1.33209984e-01 1.17583404e-01 4.95375945e-02 2.68017419e-02\n",
      " 9.82848922e-32]\n",
      "[[-2.24145753e-01  4.34230370e-02  8.06447862e-02  1.27397611e-01\n",
      "   2.78643246e-01  5.77440457e-02  2.02816587e-01 -1.45518303e-01\n",
      "   3.16294760e-01 -2.17062104e-01 -8.05393548e-03  3.13552411e-01\n",
      "  -3.32077300e-01  1.33657575e-01  2.51139586e-01  1.23174824e-01\n",
      "  -2.62567322e-01 -3.08814963e-01 -2.76006375e-01  2.90681244e-01\n",
      "  -7.03441819e-02]\n",
      " [ 2.77316446e-01  2.78636681e-01  3.84251949e-02  2.71923188e-02\n",
      "   2.06127824e-02 -7.82640157e-02 -1.13614984e-01 -1.22292605e-01\n",
      "   1.35010939e-01 -1.40811126e-01  9.63764186e-02  2.82859103e-01\n",
      "  -1.42433491e-01  3.79432605e-01  2.39990048e-01  1.27096505e-01\n",
      "   3.67238583e-01  3.25682062e-01  3.76157794e-01  1.00137897e-01\n",
      "   2.25861588e-01]\n",
      " [ 3.40074811e-01 -1.86841014e-01  1.01702096e-01 -1.20826675e-01\n",
      "   2.00835900e-01  7.11304601e-02  3.36745905e-01  4.91747685e-01\n",
      "  -7.45912052e-02  3.20632851e-01 -4.32171762e-01  6.38890031e-02\n",
      "   2.90830365e-02  1.86608724e-01  1.07529488e-01 -3.77185705e-03\n",
      "   2.32712747e-02 -2.21530099e-02  6.56427107e-02  2.65595784e-01\n",
      "  -3.92122984e-02]\n",
      " [ 5.95429042e-02 -4.35785372e-01 -4.77297897e-03 -2.97930736e-01\n",
      "   7.79512505e-02  3.62922498e-02 -9.63714729e-02  9.25206932e-02\n",
      "  -4.29943723e-02  2.12110582e-01  3.78401040e-01  1.05237373e-01\n",
      "  -2.82823844e-01 -2.37443969e-01  1.57216214e-01  2.95636981e-01\n",
      "  -1.73290671e-02 -5.28171141e-02 -4.31122833e-02 -9.52684633e-02\n",
      "   4.78440901e-01]\n",
      " [-8.20063573e-02  1.05234139e-01  6.33874508e-01 -4.61379619e-01\n",
      "  -3.54252906e-01 -2.00270100e-01  2.82723861e-01 -5.24176065e-02\n",
      "  -7.05352241e-02  5.96135952e-03  1.91268575e-01  5.81888029e-02\n",
      "  -3.81460073e-02  6.34846991e-02  9.87539926e-02 -2.02983019e-01\n",
      "  -4.42942827e-02  2.05134483e-02 -3.91510727e-02 -2.81942650e-02\n",
      "  -1.14535219e-01]\n",
      " [-2.57218454e-02  1.07714045e-01 -1.80036027e-01 -3.42191632e-01\n",
      "  -1.78517858e-01  7.06668999e-01 -1.60033654e-01  8.63705130e-02\n",
      "  -5.00962468e-02  2.61707028e-02  1.63256600e-01  8.76588168e-02\n",
      "   3.03516117e-02  2.40301273e-01  6.66096045e-02  1.83147126e-01\n",
      "  -1.08505383e-01  4.48006195e-02 -1.04895385e-02 -2.33508538e-02\n",
      "  -3.53970181e-01]\n",
      " [ 2.45070907e-01 -4.30842707e-01  2.49389615e-02  2.29474390e-01\n",
      "  -3.98325132e-02 -2.02919799e-01  2.12827397e-01 -1.44734314e-01\n",
      "   1.04429638e-01  7.02199935e-03  3.99557444e-01 -8.16633794e-02\n",
      "   2.39365264e-01  2.17030469e-01 -8.20697857e-02  3.61834896e-01\n",
      "   3.39343295e-03  3.91456507e-02  1.88155704e-02  4.85983010e-02\n",
      "  -4.10414715e-01]\n",
      " [ 1.88532880e-01 -1.18177888e-01  4.83887957e-01  2.83271425e-01\n",
      "   8.71160593e-02  5.99790484e-01  1.07634458e-01 -2.06991641e-01\n",
      "   1.21011657e-02 -1.36871793e-01  6.35976462e-02 -1.24002049e-01\n",
      "   6.58326448e-02 -1.60751748e-01 -2.01689149e-01 -1.30754177e-01\n",
      "   5.79866357e-02  4.77161776e-02  6.50665054e-02  1.44917850e-01\n",
      "   2.39175031e-01]\n",
      " [-9.64925996e-02  2.65038762e-01  2.53602045e-01 -9.11396000e-02\n",
      "  -6.54192703e-03 -2.91380229e-02  1.63667696e-02 -1.59133678e-01\n",
      "  -3.13136882e-02  6.80003127e-02 -3.69861250e-01 -1.44014837e-01\n",
      "   8.35760827e-02 -1.74968735e-01 -8.11273558e-02  7.81716029e-01\n",
      "   7.25973522e-03  2.53630670e-02  1.33083931e-03 -3.67250577e-02\n",
      "   2.98441776e-02]\n",
      " [ 7.68636943e-02  1.44962871e-01 -3.82746979e-01  3.61020139e-02\n",
      "  -3.93633781e-01  4.55016853e-02  6.06221609e-01  1.25864530e-01\n",
      "  -1.77986877e-01 -3.42610947e-01  8.15638973e-02  1.11678640e-03\n",
      "  -7.04555589e-02 -1.13639951e-01 -1.08006807e-01  1.28198501e-01\n",
      "  -4.65204913e-02  2.54570900e-02 -7.71411554e-03  1.25187247e-01\n",
      "   2.54317178e-01]\n",
      " [ 1.85263714e-01 -5.72455029e-02 -2.42057986e-01 -6.05880277e-01\n",
      "   2.77459465e-01 -4.59730353e-02  1.03004971e-01 -4.07620378e-01\n",
      "   2.81169615e-01 -1.00067799e-01 -9.02203509e-02 -8.60444567e-02\n",
      "   7.31634483e-02 -6.62718591e-02 -3.29785842e-01 -1.21535546e-01\n",
      "   3.69018450e-02  1.79322306e-02  3.70247496e-02  1.99759987e-01\n",
      "   3.12179350e-03]\n",
      " [-1.18277916e-01  9.79707720e-02 -1.72714569e-01  1.54893146e-01\n",
      "  -2.85989077e-01  6.46443923e-02  1.31565146e-01 -3.99723051e-01\n",
      "   8.88300437e-02  7.20238290e-01  2.18346758e-02 -1.02474363e-02\n",
      "  -5.34431852e-03 -3.10504618e-02  1.28592789e-01 -9.19666818e-02\n",
      "   5.88583866e-03  5.77939845e-02 -9.09464489e-03  3.10240412e-01\n",
      "   8.13896972e-02]\n",
      " [-1.03544567e-01  2.13376802e-01  1.08472893e-01  2.95808288e-02\n",
      "  -1.68401622e-02 -1.25204116e-01 -2.39250912e-01  3.67932961e-01\n",
      "   8.31577432e-02  1.25014632e-01  2.79589942e-01  9.25148406e-02\n",
      "  -9.10573361e-02  5.08412971e-02 -6.09982225e-01  7.84492570e-02\n",
      "  -8.85931236e-02  7.32439441e-03  1.03075713e-02  4.62797971e-01\n",
      "   4.42636711e-02]\n",
      " [-1.25228474e-01 -1.36590272e-01 -1.81443425e-02 -4.64874557e-02\n",
      "   1.67961853e-01 -7.72743977e-02 -1.38856992e-01 -2.48794423e-01\n",
      "  -7.84203344e-01 -1.34990503e-01 -8.58442795e-03  4.02894596e-02\n",
      "  -2.75841030e-02  4.20254226e-02  8.29802698e-02  7.49309659e-03\n",
      "   1.05288022e-02  8.58956392e-02 -5.15948399e-03  4.34710644e-01\n",
      "  -8.34035704e-02]\n",
      " [-7.49018834e-02  3.84843170e-02 -1.92378181e-03 -8.56192000e-02\n",
      "  -4.92998595e-02 -2.65032399e-02 -1.50191635e-01  2.29704421e-01\n",
      "   2.47124188e-01 -1.95465005e-01  1.33095047e-01 -3.83374978e-01\n",
      "   3.35467708e-01 -2.79652811e-01  4.90212399e-01  8.38478245e-03\n",
      "   4.21782679e-02  2.09883227e-02  6.08632509e-02  4.53929699e-01\n",
      "   7.30552613e-03]\n",
      " [-2.30236640e-01 -5.03959419e-01  2.47810948e-02 -3.50587209e-02\n",
      "  -5.01765890e-01  3.76613860e-03 -2.27119909e-01 -4.54106260e-02\n",
      "   1.71620638e-01 -1.93263990e-01 -3.89321544e-01  9.07776169e-02\n",
      "   5.08394341e-02  2.80822448e-01 -1.04594217e-01  2.52140315e-02\n",
      "   5.13042936e-02 -5.67378029e-02  2.20581396e-02  1.45612524e-01\n",
      "   1.96379942e-01]\n",
      " [ 3.56729009e-01  1.30019440e-01  1.90047106e-02  4.53257550e-03\n",
      "  -3.94048129e-02 -9.13794346e-02 -1.71909794e-01 -1.03371063e-01\n",
      "  -6.38041486e-02 -4.15240626e-03 -6.82669984e-03 -7.21354123e-02\n",
      "   2.57443408e-01  2.67495979e-01  7.93357811e-02 -1.20200864e-02\n",
      "  -7.35376425e-01  1.23123768e-02 -8.33600669e-02 -2.85291087e-02\n",
      "   3.20959593e-01]\n",
      " [ 4.76779792e-01 -4.69662660e-02  1.83623300e-02  6.42484731e-02\n",
      "  -2.94413787e-01 -5.96237820e-02 -2.37549344e-01 -5.15478328e-02\n",
      "   5.46564624e-02 -7.06082411e-02 -1.74272608e-01  1.20643847e-01\n",
      "  -4.31255063e-01 -4.48514512e-01  3.68214582e-02 -1.95076840e-02\n",
      "  -1.71164094e-01  1.07379033e-01  2.33781699e-02  1.16081687e-01\n",
      "  -3.46494756e-01]\n",
      " [-3.73268737e-01 -1.74541416e-01  2.23049697e-04  1.90023320e-02\n",
      "   1.55749618e-01  1.63628831e-02  1.64484622e-01  6.73212392e-02\n",
      "   1.18151780e-01 -1.21437407e-02 -5.28940268e-02  2.65669664e-02\n",
      "  -8.68025660e-02 -8.53183274e-02 -6.05619573e-03 -1.74747601e-02\n",
      "  -3.88545957e-01  7.04190293e-01  2.96785680e-01 -5.34812930e-02\n",
      "  -5.63100095e-02]\n",
      " [-9.06028031e-02 -6.01687768e-03 -5.57001714e-03 -1.79098547e-03\n",
      "  -5.80831247e-03  8.05182240e-04  3.81385292e-02 -4.91860592e-02\n",
      "  -5.23566587e-02  2.08416787e-02  3.07921559e-02  5.48425368e-04\n",
      "  -3.54605137e-02 -5.72250080e-02 -3.19804262e-03  1.55506893e-03\n",
      "  -2.03015839e-01 -5.15205515e-01  8.17625759e-01 -2.05697732e-02\n",
      "  -5.96363646e-02]\n",
      " [-3.00099695e-17  3.06088474e-17  7.85743122e-17 -1.33066150e-16\n",
      "   1.61298755e-16 -1.26183359e-16  3.82970020e-18  2.24460053e-16\n",
      "   4.05089581e-16 -6.86904694e-17  1.96072741e-16  7.47856643e-01\n",
      "   5.67485762e-01 -3.44485633e-01  2.76199332e-17  6.31956668e-17\n",
      "  -1.87563235e-17  3.00957657e-16 -2.44421446e-16  1.85421215e-16\n",
      "   3.51448529e-17]]\n"
     ]
    }
   ],
   "source": [
    "eig_values = pca.explained_variance_\n",
    "eig_vectors = pca.components_\n",
    "print(eig_values)\n",
    "print(eig_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.22414575,  0.04342304,  0.08064479,  0.12739761,  0.27864325,\n",
       "        0.05774405,  0.20281659, -0.1455183 ,  0.31629476, -0.2170621 ,\n",
       "       -0.00805394,  0.31355241, -0.3320773 ,  0.13365758,  0.25113959,\n",
       "        0.12317482, -0.26256732, -0.30881496, -0.27600638,  0.29068124,\n",
       "       -0.07034418])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eig_vectors[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1 = pca.components_[0]\n",
    "pc2 = pca.components_[1]\n",
    "pc3 = pca.components_[2]\n",
    "pc4 = pca.components_[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(X.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explain how much variatability is each PC explaing:\n",
    "- 21 PCA componants\n",
    "- PCA1 ~29% of the variance\n",
    "- PCA2 is ~ 16%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>baseline value</th>\n",
       "      <th>accelerations</th>\n",
       "      <th>fetal_movement</th>\n",
       "      <th>uterine_contractions</th>\n",
       "      <th>light_decelerations</th>\n",
       "      <th>severe_decelerations</th>\n",
       "      <th>prolongued_decelerations</th>\n",
       "      <th>abnormal_short_term_variability</th>\n",
       "      <th>mean_value_of_short_term_variability</th>\n",
       "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
       "      <th>mean_value_of_long_term_variability</th>\n",
       "      <th>histogram_width</th>\n",
       "      <th>histogram_min</th>\n",
       "      <th>histogram_max</th>\n",
       "      <th>histogram_number_of_peaks</th>\n",
       "      <th>histogram_number_of_zeroes</th>\n",
       "      <th>histogram_mode</th>\n",
       "      <th>histogram_mean</th>\n",
       "      <th>histogram_median</th>\n",
       "      <th>histogram_variance</th>\n",
       "      <th>histogram_tendency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>130.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
       "0           120.0          0.000             0.0                 0.000   \n",
       "1           132.0          0.006             0.0                 0.006   \n",
       "2           133.0          0.003             0.0                 0.008   \n",
       "\n",
       "   light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
       "0                0.000                   0.0                       0.0   \n",
       "1                0.003                   0.0                       0.0   \n",
       "2                0.003                   0.0                       0.0   \n",
       "\n",
       "   abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
       "0                             73.0                                   0.5   \n",
       "1                             17.0                                   2.1   \n",
       "2                             16.0                                   2.1   \n",
       "\n",
       "   percentage_of_time_with_abnormal_long_term_variability  \\\n",
       "0                                               43.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "\n",
       "   mean_value_of_long_term_variability  histogram_width  histogram_min  \\\n",
       "0                                  2.4             64.0           62.0   \n",
       "1                                 10.4            130.0           68.0   \n",
       "2                                 13.4            130.0           68.0   \n",
       "\n",
       "   histogram_max  histogram_number_of_peaks  histogram_number_of_zeroes  \\\n",
       "0          126.0                        2.0                         0.0   \n",
       "1          198.0                        6.0                         1.0   \n",
       "2          198.0                        5.0                         1.0   \n",
       "\n",
       "   histogram_mode  histogram_mean  histogram_median  histogram_variance  \\\n",
       "0           120.0           137.0             121.0                73.0   \n",
       "1           141.0           136.0             140.0                12.0   \n",
       "2           141.0           135.0             138.0                13.0   \n",
       "\n",
       "   histogram_tendency  \n",
       "0                 1.0  \n",
       "1                 0.0  \n",
       "2                 0.0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.88492915e-01, 1.67037118e-01, 8.68725052e-02, 7.12723339e-02,\n",
       "       5.80170957e-02, 4.85796368e-02, 4.67510123e-02, 4.41205936e-02,\n",
       "       3.63060981e-02, 3.04859310e-02, 2.74897164e-02, 2.36816472e-02,\n",
       "       1.85001366e-02, 1.55729317e-02, 1.26395610e-02, 8.61034594e-03,\n",
       "       6.34034886e-03, 5.59657603e-03, 2.35782351e-03, 1.27567311e-03,\n",
       "       4.67803154e-33])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'proportion of explained variance')"
      ]
     },
     "execution_count": 92,
     "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": [
    "index = np.arange(21)\n",
    "plt.bar(index, pca.explained_variance_ratio_,)\n",
    "plt.title('Scree plot for PCA')\n",
    "plt.xlabel('Num of components')\n",
    "plt.ylabel('proportion of explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'explained variance ratio')"
      ]
     },
     "execution_count": 93,
     "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": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('explained variance ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name First PC and what observations are describing\n",
    "- healthy heart rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_value_of_short_term_variability                      0.778702\n",
       "histogram_width                                           0.771951\n",
       "histogram_variance                                        0.715643\n",
       "light_decelerations                                       0.686006\n",
       "histogram_number_of_peaks                                 0.618293\n",
       "prolongued_decelerations                                  0.499324\n",
       "histogram_max                                             0.329058\n",
       "uterine_contractions                                      0.313647\n",
       "histogram_number_of_zeroes                                0.303250\n",
       "fetal_movement                                            0.198544\n",
       "severe_decelerations                                      0.142163\n",
       "accelerations                                             0.106905\n",
       "mean_value_of_long_term_variability                      -0.019828\n",
       "histogram_tendency                                       -0.173184\n",
       "abnormal_short_term_variability                          -0.358259\n",
       "percentage_of_time_with_abnormal_long_term_variability   -0.534396\n",
       "baseline value                                           -0.551836\n",
       "histogram_mode                                           -0.646428\n",
       "histogram_median                                         -0.679514\n",
       "histogram_mean                                           -0.760287\n",
       "histogram_min                                            -0.817558\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_1 = pc1* np.sqrt(eig_values[0])\n",
    "str_loading_1 = pd.Series(structure_loading_1, index=features)\n",
    "str_loading_1.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  spikes high heart rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "histogram_max                                             0.934144\n",
       "histogram_median                                          0.926082\n",
       "histogram_mode                                            0.904123\n",
       "histogram_mean                                            0.801813\n",
       "histogram_width                                           0.696385\n",
       "accelerations                                             0.685990\n",
       "baseline value                                            0.682739\n",
       "histogram_number_of_peaks                                 0.590844\n",
       "histogram_tendency                                        0.556060\n",
       "mean_value_of_short_term_variability                      0.332390\n",
       "histogram_number_of_zeroes                                0.312905\n",
       "histogram_variance                                        0.246535\n",
       "mean_value_of_long_term_variability                       0.237274\n",
       "fetal_movement                                            0.094601\n",
       "uterine_contractions                                      0.066946\n",
       "light_decelerations                                       0.050748\n",
       "severe_decelerations                                     -0.192682\n",
       "prolongued_decelerations                                 -0.279715\n",
       "abnormal_short_term_variability                          -0.301078\n",
       "percentage_of_time_with_abnormal_long_term_variability   -0.346670\n",
       "histogram_min                                            -0.350664\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_2 = pc2* np.sqrt(eig_values[0])\n",
    "str_loading_2 = pd.Series(structure_loading_2, index=features)\n",
    "str_loading_2.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(str_loading_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "abnormal_short_term_variability                           1.210659\n",
       "baseline value                                            0.837247\n",
       "prolongued_decelerations                                  0.829052\n",
       "percentage_of_time_with_abnormal_long_term_variability    0.789382\n",
       "histogram_variance                                        0.653884\n",
       "light_decelerations                                       0.494448\n",
       "histogram_max                                             0.459422\n",
       "histogram_number_of_peaks                                 0.264732\n",
       "fetal_movement                                            0.250386\n",
       "severe_decelerations                                      0.175120\n",
       "histogram_median                                          0.161609\n",
       "histogram_width                                           0.157292\n",
       "histogram_min                                             0.071601\n",
       "histogram_mode                                            0.057293\n",
       "histogram_number_of_zeroes                               -0.009286\n",
       "histogram_mean                                           -0.054540\n",
       "histogram_tendency                                       -0.096539\n",
       "mean_value_of_short_term_variability                     -0.183640\n",
       "uterine_contractions                                     -0.297469\n",
       "accelerations                                            -0.459993\n",
       "mean_value_of_long_term_variability                      -1.063986\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_3 = pc3* np.sqrt(eig_values[0])\n",
    "str_loading_3 = pd.Series(structure_loading_3, index=features)\n",
    "str_loading_3.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "histogram_tendency                                        1.177898\n",
       "mean_value_of_long_term_variability                       0.931605\n",
       "histogram_number_of_zeroes                                0.727844\n",
       "percentage_of_time_with_abnormal_long_term_variability    0.522206\n",
       "histogram_number_of_peaks                                 0.387059\n",
       "histogram_width                                           0.259089\n",
       "abnormal_short_term_variability                           0.227781\n",
       "light_decelerations                                       0.191912\n",
       "baseline value                                            0.146592\n",
       "severe_decelerations                                      0.089350\n",
       "fetal_movement                                           -0.011751\n",
       "histogram_mode                                           -0.042663\n",
       "mean_value_of_short_term_variability                     -0.105850\n",
       "histogram_median                                         -0.106140\n",
       "histogram_mean                                           -0.130033\n",
       "histogram_variance                                       -0.234546\n",
       "prolongued_decelerations                                 -0.237262\n",
       "histogram_max                                            -0.584575\n",
       "histogram_min                                            -0.696298\n",
       "uterine_contractions                                     -0.733491\n",
       "accelerations                                            -1.072882\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_loading_4 = pc4* np.sqrt(eig_values[0])\n",
    "str_loading_4 = pd.Series(structure_loading_4, index=features)\n",
    "str_loading_4.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "pca = PCA()\n",
    "components = pca.fit_transform(X)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(\n",
    "    components, x=0, y=1, z=2, color=vanilla_df['fetal_health'],\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'healthy': 'PC 1', 'stressed': 'PC 2', 'pathological': 'PC 3'}\n",
    ")\n",
    "fig.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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