analysis_4_dataAll.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis on All Data Files\n",
    "\n",
    "On this notebook, I will apply the best optimized models on all five datasets.\n",
    "\n",
    "## Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import base libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.io import arff\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, auc\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "from functions import *\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "There are five data files: \n",
    "    * data1, 1year.arff \n",
    "    * data2, 2year.arff \n",
    "    * data3, 3year.arff\n",
    "    * data4, 4year.arff\n",
    "    * data5, 5year.arff\n",
    "\n",
    "Note: No cleaning applied to data. XGBoost Classifier can handle the missing values and outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load all five data files\n",
    "\n",
    "data1 = arff.loadarff('data/1year.arff')\n",
    "df1 = pd.DataFrame(data1[0])\n",
    "\n",
    "data2 = arff.loadarff('data/2year.arff')\n",
    "df2 = pd.DataFrame(data2[0])\n",
    "\n",
    "data3 = arff.loadarff('data/3year.arff')\n",
    "df3 = pd.DataFrame(data3[0])\n",
    "\n",
    "data4 = arff.loadarff('data/4year.arff')\n",
    "df4 = pd.DataFrame(data4[0])\n",
    "\n",
    "data5 = arff.loadarff('data/5year.arff')\n",
    "df5 = pd.DataFrame(data5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert class/label type to binary\n",
    "\n",
    "df1['class'] = df1['class'].astype('int64')\n",
    "df2['class'] = df2['class'].astype('int64')\n",
    "df3['class'] = df3['class'].astype('int64')\n",
    "df4['class'] = df4['class'].astype('int64')\n",
    "df5['class'] = df5['class'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of datasets\n",
      "Data 1 (Year1): 7027\n",
      "Data 2 (Year2): 10173\n",
      "Data 3 (Year3): 10503\n",
      "Data 4 (Year4): 9792\n",
      "Data 5 (Year5): 5910\n"
     ]
    }
   ],
   "source": [
    "# Size of datasets\n",
    "\n",
    "print('Size of datasets')\n",
    "print(\"Data 1 (Year1):\", len(df1))\n",
    "print(\"Data 2 (Year2):\", len(df2))\n",
    "print(\"Data 3 (Year3):\", len(df3))\n",
    "print(\"Data 4 (Year4):\", len(df4))\n",
    "print(\"Data 5 (Year5):\", len(df5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imbalance Information\n",
    "\n",
    "I am using both sample_weight and scale_pos_weight parameters to deal with the class imbalance.\n",
    "\n",
    "* sample_weight: The weights for training sample are calculated for each dataset seperately and used when during training.\n",
    "\n",
    "\n",
    "* scale_pos_weight: I provide certain values to initiate the classifier. I either use the imbalance ratio or square root of the imbalance ratio. These values are not exactly same for the datasets, but close enough to use a constant about average number.\n",
    "    * Model 7 (max_depth=5): scale_pos_weight=20 (~imbalance ratio)\n",
    "    * Model 8 (max_depth=4): scale_pos_weight=4.5 (~square root of imbalance ratio)\n",
    "    * Model 9 (max_depth=6): scale_pos_weight=20 (~imbalance ratio)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Weights:\n",
      "Data 1: Ratio=24.93, sqrt(ratio)=4.993, class_weights=[ 0.52005625 12.96494465]\n",
      "Data 2: Ratio=24.432, sqrt(ratio)=4.943, class_weights=[ 0.52046455 12.71625   ]\n",
      "Data 3: Ratio=20.218, sqrt(ratio)=4.496, class_weights=[ 0.52473022 10.60909091]\n",
      "Data 4: Ratio=18.014, sqrt(ratio)=4.244, class_weights=[0.52775682 9.50679612]\n",
      "Data 5: Ratio=13.415, sqrt(ratio)=3.663, class_weights=[0.53727273 7.20731707]\n"
     ]
    }
   ],
   "source": [
    "# Imbalance info using class_weight.compute_class_weight\n",
    "\n",
    "print('Class Weights:')\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "for i, df in enumerate(df_list, start=1):\n",
    "    class_weights = class_weight.compute_class_weight(class_weight='balanced', classes=[0,1], y=df['class'])\n",
    "    ratio = class_weights[1]/class_weights[0]\n",
    "    sqrt_ratio = np.sqrt(class_weights[1]/class_weights[0])\n",
    "    print(f'Data {i}: Ratio={round(ratio,3)}, sqrt(ratio)={round(sqrt_ratio,3)}, class_weights={class_weights}')\n",
    "    \n",
    "# The values are very similar for train/test/whole datasets.\n",
    "# Training weights are used for data training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imbalance Ratio, based on class value_counts:\n",
      "Data 1: Ratio=24.93, sqrt(ratio)=4.993\n",
      "Data 2: Ratio=24.432, sqrt(ratio)=4.943\n",
      "Data 3: Ratio=20.218, sqrt(ratio)=4.496\n",
      "Data 4: Ratio=18.014, sqrt(ratio)=4.244\n",
      "Data 5: Ratio=13.415, sqrt(ratio)=3.663\n"
     ]
    }
   ],
   "source": [
    "# Imbalance info using class value_counts\n",
    "\n",
    "print('Imbalance Ratio, based on class value_counts:')\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "for i, df in enumerate(df_list, start=1):\n",
    "    val_counts = df['class'].value_counts()\n",
    "    ratio= val_counts[0]/val_counts[1]\n",
    "    sqrt_ratio= np.sqrt(val_counts[0]/val_counts[1])\n",
    "    print(f'Data {i}: Ratio={round(ratio,3)}, sqrt(ratio)={round(sqrt_ratio,3)}')\n",
    "    \n",
    "# The values are very similar for train/test/whole datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Model 7, 9, 10 Performance on All Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------model_9------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9520</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.7590</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.7810</td>\n",
       "      <td>0.9840</td>\n",
       "      <td>0.9630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8590</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9240</td>\n",
       "      <td>0.9930</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4890</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>0.5520</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7190</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8360</td>\n",
       "      <td>0.9820</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5540</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.6260</td>\n",
       "      <td>0.9560</td>\n",
       "      <td>0.9160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8530</td>\n",
       "      <td>0.9820</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4960</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>0.5670</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.9420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9740</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>0.7330</td>\n",
       "      <td>0.6950</td>\n",
       "      <td>0.9510</td>\n",
       "      <td>0.9580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8448</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9126</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5916</td>\n",
       "      <td>0.7102</td>\n",
       "      <td>0.6442</td>\n",
       "      <td>0.9604</td>\n",
       "      <td>0.9406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.9520  1.0000  0.9760    0.9980  1.0000\n",
       "Data 1    Test     0.7590  0.8040  0.7810    0.9840  0.9630\n",
       "Data 2   Train     0.8590  1.0000  0.9240    0.9930  1.0000\n",
       "Data 2    Test     0.4890  0.6340  0.5520    0.9640  0.9240\n",
       "Data 3   Train     0.7190  1.0000  0.8360    0.9820  1.0000\n",
       "Data 3    Test     0.5540  0.7200  0.6260    0.9560  0.9160\n",
       "Data 4   Train     0.7440  1.0000  0.8530    0.9820  1.0000\n",
       "Data 4    Test     0.4960  0.6600  0.5670    0.9470  0.9420\n",
       "Data 5   Train     0.9500  1.0000  0.9740    0.9960  1.0000\n",
       "Data 5    Test     0.6600  0.7330  0.6950    0.9510  0.9580\n",
       "Average  Train     0.8448  1.0000  0.9126    0.9902  1.0000\n",
       "Average   Test     0.5916  0.7102  0.6442    0.9604  0.9406"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model 9: Best Model for Data 3, max_depth=4\n",
    "\n",
    "xgbParams_m9 = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 4.5,\n",
    "    'n_estimators': 125, \n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 1,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1  \n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "#df_list = [df1, df2]\n",
    "\n",
    "model_9_df = compare_datafiles_perf(df_list, xgbParams_m9, 'model_9', 1, 1, 1)\n",
    "model_9_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------model_7------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "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>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.9950</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.7120</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0.9690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8730</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4840</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>0.5490</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.9320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.6160</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>0.9270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8930</td>\n",
       "      <td>0.9870</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.6110</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9070</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6070</td>\n",
       "      <td>0.7890</td>\n",
       "      <td>0.6860</td>\n",
       "      <td>0.9450</td>\n",
       "      <td>0.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8316</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9076</td>\n",
       "      <td>0.9896</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5624</td>\n",
       "      <td>0.7294</td>\n",
       "      <td>0.6348</td>\n",
       "      <td>0.9588</td>\n",
       "      <td>0.9474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.8800  1.0000  0.9360    0.9950  1.0000\n",
       "Data 1    Test     0.6270  0.8240  0.7120    0.9760  0.9690\n",
       "Data 2   Train     0.8730  1.0000  0.9320    0.9940  1.0000\n",
       "Data 2    Test     0.4840  0.6340  0.5490    0.9640  0.9320\n",
       "Data 3   Train     0.7700  1.0000  0.8700    0.9860  1.0000\n",
       "Data 3    Test     0.5380  0.7200  0.6160    0.9540  0.9270\n",
       "Data 4   Train     0.8060  1.0000  0.8930    0.9870  1.0000\n",
       "Data 4    Test     0.5560  0.6800  0.6110    0.9550  0.9490\n",
       "Data 5   Train     0.8290  1.0000  0.9070    0.9860  1.0000\n",
       "Data 5    Test     0.6070  0.7890  0.6860    0.9450  0.9600\n",
       "Average  Train     0.8316  1.0000  0.9076    0.9896  1.0000\n",
       "Average   Test     0.5624  0.7294  0.6348    0.9588  0.9474"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model 7, max_depth=5\n",
    "\n",
    "xgbParams_m7 = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 20,\n",
    "    'n_estimators': 125, \n",
    "    'max_depth': 5,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 0,\n",
    "    'reg_alpha': 5,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 0.7\n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_7_df = compare_datafiles_perf(df_list, xgbParams_m7, 'model_7', 1, 1, 1)\n",
    "model_7_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------model_10------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "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>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9780</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6900</td>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.7340</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>0.9560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9510</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5810</td>\n",
       "      <td>0.6060</td>\n",
       "      <td>0.5930</td>\n",
       "      <td>0.9710</td>\n",
       "      <td>0.9060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.9950</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6360</td>\n",
       "      <td>0.7010</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.9310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8980</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9460</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5640</td>\n",
       "      <td>0.6410</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9850</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6370</td>\n",
       "      <td>0.7220</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.9510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9394</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9684</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6216</td>\n",
       "      <td>0.6908</td>\n",
       "      <td>0.6542</td>\n",
       "      <td>0.9634</td>\n",
       "      <td>0.9384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.9780  1.0000  0.9890    0.9990  1.0000\n",
       "Data 1    Test     0.6900  0.7840  0.7340    0.9790  0.9560\n",
       "Data 2   Train     0.9510  1.0000  0.9750    0.9980  1.0000\n",
       "Data 2    Test     0.5810  0.6060  0.5930    0.9710  0.9060\n",
       "Data 3   Train     0.9000  1.0000  0.9470    0.9950  1.0000\n",
       "Data 3    Test     0.6360  0.7010  0.6670    0.9640  0.9310\n",
       "Data 4   Train     0.8980  1.0000  0.9460    0.9940  1.0000\n",
       "Data 4    Test     0.5640  0.6410  0.6000    0.9550  0.9480\n",
       "Data 5   Train     0.9700  1.0000  0.9850    0.9980  1.0000\n",
       "Data 5    Test     0.6370  0.7220  0.6770    0.9480  0.9510\n",
       "Average  Train     0.9394  1.0000  0.9684    0.9968  1.0000\n",
       "Average   Test     0.6216  0.6908  0.6542    0.9634  0.9384"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model 10, max_depth=6\n",
    "\n",
    "xgbParams_m10 = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 20,\n",
    "    'n_estimators': 80, \n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.25,\n",
    "    'max_delta_step': 4,\n",
    "    'reg_lambda': 1,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1,\n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_10_df = compare_datafiles_perf(df_list, xgbParams_m10, 'model_10', 1, 1, 1)\n",
    "model_10_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare models\n",
    "\n",
    "model_df_list = [model_9_df, model_7_df, model_10_df]\n",
    "\n",
    "model_names_list = ['Model 9', 'Model 7', 'Model 10']\n",
    "\n",
    "plot_compare_model_metricsAvg(model_df_list, model_names_list, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments\n",
    "\n",
    "Overall the performance of the models are similar, especially recall.\n",
    "\n",
    "Model 9 (max_depth=4):\n",
    "    * Previously Selected performance for data 3\n",
    "    * Overfitting is least on Data 3\n",
    "\n",
    "Model 7 (max_depth=5):\n",
    "    * Performance is similar to Model 9\n",
    "    * Recall is sligtly larger than Model 9\n",
    "    * More smooth performance on all datasets\n",
    "\n",
    "Model 10 (max_depth=6):\n",
    "    * Highest metrics\n",
    "    * But overfitting is larger\n",
    "    \n",
    "Selected model: Model 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Tuning and Comparing\n",
    "\n",
    "Try some more tuning..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------noSpw------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "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",
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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>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9780</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.8720</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.8370</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>0.9660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9590</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6450</td>\n",
       "      <td>0.5630</td>\n",
       "      <td>0.6020</td>\n",
       "      <td>0.9740</td>\n",
       "      <td>0.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8860</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9390</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>0.6360</td>\n",
       "      <td>0.6510</td>\n",
       "      <td>0.9650</td>\n",
       "      <td>0.9290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9260</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9610</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6090</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>0.5580</td>\n",
       "      <td>0.9570</td>\n",
       "      <td>0.9450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9820</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9910</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.7590</td>\n",
       "      <td>0.7330</td>\n",
       "      <td>0.7460</td>\n",
       "      <td>0.9620</td>\n",
       "      <td>0.9630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9462</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9718</td>\n",
       "      <td>0.9972</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.7104</td>\n",
       "      <td>0.6502</td>\n",
       "      <td>0.6788</td>\n",
       "      <td>0.9694</td>\n",
       "      <td>0.9486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.9780  1.0000  0.9890    0.9990  1.0000\n",
       "Data 1    Test     0.8720  0.8040  0.8370    0.9890  0.9660\n",
       "Data 2   Train     0.9590  1.0000  0.9790    0.9980  1.0000\n",
       "Data 2    Test     0.6450  0.5630  0.6020    0.9740  0.9400\n",
       "Data 3   Train     0.8860  1.0000  0.9390    0.9940  1.0000\n",
       "Data 3    Test     0.6670  0.6360  0.6510    0.9650  0.9290\n",
       "Data 4   Train     0.9260  1.0000  0.9610    0.9960  1.0000\n",
       "Data 4    Test     0.6090  0.5150  0.5580    0.9570  0.9450\n",
       "Data 5   Train     0.9820  1.0000  0.9910    0.9990  1.0000\n",
       "Data 5    Test     0.7590  0.7330  0.7460    0.9620  0.9630\n",
       "Average  Train     0.9462  1.0000  0.9718    0.9972  1.0000\n",
       "Average   Test     0.7104  0.6502  0.6788    0.9694  0.9486"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample_weight only, no scale_pos_weight\n",
    "\n",
    "xgbParams = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    #'scale_pos_weight': 4.5,\n",
    "    'n_estimators': 125, \n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 1,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1  \n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_noSpw_df = compare_datafiles_perf(df_list, xgbParams, 'noSpw', 1, 0, 0)\n",
    "model_noSpw_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------nEst100------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9400</td>\n",
       "      <td>0.9950</td>\n",
       "      <td>1.000</td>\n",
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       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
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       "      <td>0.9770</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8210</td>\n",
       "      <td>0.9820</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.6620</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.9570</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6090</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4910</td>\n",
       "      <td>0.7480</td>\n",
       "      <td>0.5930</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7690</td>\n",
       "      <td>0.9680</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4270</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.5240</td>\n",
       "      <td>0.9350</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8420</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9140</td>\n",
       "      <td>0.9870</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6110</td>\n",
       "      <td>0.7330</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>0.9440</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7320</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8402</td>\n",
       "      <td>0.9804</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5194</td>\n",
       "      <td>0.7214</td>\n",
       "      <td>0.6016</td>\n",
       "      <td>0.9522</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy    auc\n",
       "Data                                                      \n",
       "Data 1   Train     0.8870  1.0000  0.9400    0.9950  1.000\n",
       "Data 1    Test     0.6450  0.7840  0.7080    0.9770  0.964\n",
       "Data 2   Train     0.6970  1.0000  0.8210    0.9820  1.000\n",
       "Data 2    Test     0.4230  0.6620  0.5160    0.9570  0.923\n",
       "Data 3   Train     0.6090  1.0000  0.7570    0.9700  1.000\n",
       "Data 3    Test     0.4910  0.7480  0.5930    0.9480  0.916\n",
       "Data 4   Train     0.6250  1.0000  0.7690    0.9680  1.000\n",
       "Data 4    Test     0.4270  0.6800  0.5240    0.9350  0.938\n",
       "Data 5   Train     0.8420  1.0000  0.9140    0.9870  1.000\n",
       "Data 5    Test     0.6110  0.7330  0.6670    0.9440  0.959\n",
       "Average  Train     0.7320  1.0000  0.8402    0.9804  1.000\n",
       "Average   Test     0.5194  0.7214  0.6016    0.9522  0.940"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n_estimators=100\n",
    "\n",
    "xgbParams_11 = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 4.5,\n",
    "    'n_estimators': 100, \n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 1,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1  \n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_nEst100_df = compare_datafiles_perf(df_list, xgbParams_11, 'nEst100', 1, 0, 0)\n",
    "model_nEst100_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------lambda5------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.9090</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9520</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>1.0000</td>\n",
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       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6890</td>\n",
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       "      <td>0.9660</td>\n",
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       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
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       "      <td>0.8800</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4800</td>\n",
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       "      <td>0.5560</td>\n",
       "      <td>0.9630</td>\n",
       "      <td>0.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6470</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7850</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5070</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.9810</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4770</td>\n",
       "      <td>0.6120</td>\n",
       "      <td>0.5360</td>\n",
       "      <td>0.9440</td>\n",
       "      <td>0.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6190</td>\n",
       "      <td>0.7780</td>\n",
       "      <td>0.6900</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.9610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7882</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8784</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5544</td>\n",
       "      <td>0.7192</td>\n",
       "      <td>0.6254</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.9484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.9090  1.0000  0.9520    0.9960  1.0000\n",
       "Data 1    Test     0.6890  0.8240  0.7500    0.9800  0.9660\n",
       "Data 2   Train     0.7850  1.0000  0.8800    0.9890  1.0000\n",
       "Data 2    Test     0.4800  0.6620  0.5560    0.9630  0.9480\n",
       "Data 3   Train     0.6470  1.0000  0.7850    0.9750  1.0000\n",
       "Data 3    Test     0.5070  0.7200  0.5950    0.9500  0.9240\n",
       "Data 4   Train     0.7400  1.0000  0.8500    0.9810  1.0000\n",
       "Data 4    Test     0.4770  0.6120  0.5360    0.9440  0.9430\n",
       "Data 5   Train     0.8600  1.0000  0.9250    0.9890  1.0000\n",
       "Data 5    Test     0.6190  0.7780  0.6900    0.9470  0.9610\n",
       "Average  Train     0.7882  1.0000  0.8784    0.9860  1.0000\n",
       "Average   Test     0.5544  0.7192  0.6254    0.9568  0.9484"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reg_lambda=5\n",
    "\n",
    "xgbParams = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 4.5,\n",
    "    'n_estimators': 125, \n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 5,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1  \n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_lambda5_df = compare_datafiles_perf(df_list, xgbParams, 'lambda5', 1, 0, 0)\n",
    "model_lambda5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------nEst100_lambda5------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "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>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8460</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9170</td>\n",
       "      <td>0.9930</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6090</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.9740</td>\n",
       "      <td>0.9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6280</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7710</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4050</td>\n",
       "      <td>0.6900</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>0.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4180</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>0.9340</td>\n",
       "      <td>0.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.5930</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4240</td>\n",
       "      <td>0.6990</td>\n",
       "      <td>0.5270</td>\n",
       "      <td>0.9340</td>\n",
       "      <td>0.9370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7860</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0.9820</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.8330</td>\n",
       "      <td>0.7040</td>\n",
       "      <td>0.9470</td>\n",
       "      <td>0.9610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.6784</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8024</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4932</td>\n",
       "      <td>0.7606</td>\n",
       "      <td>0.5958</td>\n",
       "      <td>0.9486</td>\n",
       "      <td>0.9466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.8460  1.0000  0.9170    0.9930  1.0000\n",
       "Data 1    Test     0.6090  0.8240  0.7000    0.9740  0.9670\n",
       "Data 2   Train     0.6280  1.0000  0.7710    0.9760  1.0000\n",
       "Data 2    Test     0.4050  0.6900  0.5100    0.9540  0.9480\n",
       "Data 3   Train     0.5390  1.0000  0.7000    0.9600  1.0000\n",
       "Data 3    Test     0.4180  0.7570  0.5380    0.9340  0.9200\n",
       "Data 4   Train     0.5930  1.0000  0.7440    0.9640  1.0000\n",
       "Data 4    Test     0.4240  0.6990  0.5270    0.9340  0.9370\n",
       "Data 5   Train     0.7860  1.0000  0.8800    0.9820  1.0000\n",
       "Data 5    Test     0.6100  0.8330  0.7040    0.9470  0.9610\n",
       "Average  Train     0.6784  1.0000  0.8024    0.9750  1.0000\n",
       "Average   Test     0.4932  0.7606  0.5958    0.9486  0.9466"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n_estimators=100\n",
    "# reg_lambda=5\n",
    "\n",
    "xgbParams = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 4.5,\n",
    "    'n_estimators': 100, \n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 5,\n",
    "    'reg_alpha': 0,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 1  \n",
    "}\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "model_nEst100_lambda5_df = compare_datafiles_perf(df_list, xgbParams, 'nEst100_lambda5', 1, 0, 0)\n",
    "model_nEst100_lambda5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_df_list2 = [model_7_df, model_noSpw_df, model_nEst100_df, model_lambda5_df, model_nEst100_lambda5_df]\n",
    "\n",
    "model_names_list2 = ['Model 7', 'nsSpw', 'nEst100', 'lambda5', 'nEst100_lambda5']\n",
    "\n",
    "plot_compare_model_metricsAvg(model_df_list2, model_names_list2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments\n",
    "\n",
    "After some tuning, still best performance is Model 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model\n",
    "\n",
    "Model 7 has the best performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Model Parameters\n",
    "# Model 7, max_depth=5\n",
    "\n",
    "xgbParams_final = {\n",
    "    'eval_metric': 'logloss', \n",
    "    'random_state': 42,\n",
    "    'scale_pos_weight': 20,\n",
    "    'n_estimators': 125, \n",
    "    'max_depth': 5,\n",
    "    'min_child_weight': 3,\n",
    "    'gamma': 0,\n",
    "    'learning_rate': 0.20,\n",
    "    'max_delta_step': 0,\n",
    "    'reg_lambda': 0,\n",
    "    'reg_alpha': 5,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 0.7\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------final_model------\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n",
      "Sample weights are used!\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.9950</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.7120</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0.9690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8730</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 2</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.4840</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>0.5490</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.9320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 3</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.6160</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>0.9270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8930</td>\n",
       "      <td>0.9870</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 4</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.6110</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8290</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9070</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data 5</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.6070</td>\n",
       "      <td>0.7890</td>\n",
       "      <td>0.6860</td>\n",
       "      <td>0.9450</td>\n",
       "      <td>0.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.8316</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9076</td>\n",
       "      <td>0.9896</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.5624</td>\n",
       "      <td>0.7294</td>\n",
       "      <td>0.6348</td>\n",
       "      <td>0.9588</td>\n",
       "      <td>0.9474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sample  precision  recall      f1  accuracy     auc\n",
       "Data                                                       \n",
       "Data 1   Train     0.8800  1.0000  0.9360    0.9950  1.0000\n",
       "Data 1    Test     0.6270  0.8240  0.7120    0.9760  0.9690\n",
       "Data 2   Train     0.8730  1.0000  0.9320    0.9940  1.0000\n",
       "Data 2    Test     0.4840  0.6340  0.5490    0.9640  0.9320\n",
       "Data 3   Train     0.7700  1.0000  0.8700    0.9860  1.0000\n",
       "Data 3    Test     0.5380  0.7200  0.6160    0.9540  0.9270\n",
       "Data 4   Train     0.8060  1.0000  0.8930    0.9870  1.0000\n",
       "Data 4    Test     0.5560  0.6800  0.6110    0.9550  0.9490\n",
       "Data 5   Train     0.8290  1.0000  0.9070    0.9860  1.0000\n",
       "Data 5    Test     0.6070  0.7890  0.6860    0.9450  0.9600\n",
       "Average  Train     0.8316  1.0000  0.9076    0.9896  1.0000\n",
       "Average   Test     0.5624  0.7294  0.6348    0.9588  0.9474"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tIiIiIgGgpE1EREQkAJS0iYiIiASAkjYRERGRAFDSJiIiIhIAStpEREREAkBJm4iIiEgAKGkTERERCQAlbSIiIiIBoKRNREREJACUtImIiIgEgJI2ERERkQBQ0iYiIiISAEraRERERAJASZuIiIhIAChpExEREQkAJW0iIiIiAaCkTURERCQAlLSJiIiIBICSNhEREZEAUNImIiIiEgBK2kREREQCQEmbiIiISAAoaRMREREJACVtIiIiIgGgpE1EREQkAJS0iYiIiASAkjYRERGRAFDSJiIiIhIAStpEREREAkBJm4iIiEgAxC1pM7MZZvaFmX1Uw3Yzs/81s3Vm9qGZHRuv2ERERESSXTxH2mYCg2vZPgTo4z9+Bjwch5hEREREAuGgeHXknFtsZtm17DIceMI554BlZtbezLo657bW1u4XG3fx4Ji3ABj7yBmV66dfNZN9KT2qfU3K3ne5dubNALz26INs+Fduje1nH1PI0LyxAMy4+nd8e/APq92vzbdFXDfr6srlipiqk9pmAVdPuxOAmT+fwO59Z9W4r45JxwSJOabft/+28vnErdtqPKbW377L77r+AIBhxcvILT29xjYLD1rEq2kDALh56/uUpNR8TJO7ZlYu//qblBrb3Ff6Ov/b2evz/325iDYHnVvjviIiQZZM17R1AzaFLG/21x3AzH5mZsvNbHlcIhMRERFJMPMGtuLUmTfS9qpz7qhqtr0GTHHO/c1fXgiMd86tqK3NHpl9XdG2NTGIVqR5yL7xNQA2TB2a4Ehio7qReBGAfrP6AbDyqpU17lOY442G564ujEtMEkOTMvyfOxIahpmtcM4d35DXJtNI22bg8JDl7sBnCYpFREREJKkkU9L2MnClfxfpAGBHXdeziYiIiDQXcbsRwcyeBgYBnc1sM3Ar0ArAOfcI8DpwLrAO2ANcE0m7LVvsjEW4IiIiIkklnnePjqxjuwPG1rfdTodXe6+CiIiISJOSTKdHRURERKQGcRtpi5Xtm7YkOgQRkbgoystj9zuLEx1Gk/Gs/7NwSs31EkWSSeCTtrLydokOQURi7NWCD9j40fZEh5FwStgSI/W0gYkOQQRoAkmbiDR9jU3Yso7qFKVIkoNqhkUuklpsIkGhpE1EAkMFckWkOVPSJiIi0hhzLoG1byY6CmkGdPeoiIhIYyhhC44+Zyc6gkbRSJuIiEg0JHhOS2n6NNImIiJNUv6C/ESHIBJVgR9p65KVnugQRCJ2zePvsWjNtkSHwW3vTueEz1cDMM9fV/jiuMQFVJdBDwJQmKN6WhK5JVuWAHBqt1MTHIlIdGikTSSOkiFhAyoTNgke1Qyrv4fOeijRIYhEReBH2kSC4tWCD/j1NymJDgOAt/yRq6BRfTIRac4CP9L25QZVSZdgUEX/xmlqBXJFROor8CNt5dY60SGI1Mvv23/LhqlDExpDxbVhGrkSEQmOwI+0iYiIiDQHStpEREREAiDwp0dFoq1o2H+xe11x9Bv2L/6f9+K45CmvMSkj0RFIE5B/SCZL2ibHTTbV0u+5NBEaaRMJE5OELQmldt2b6BCkiUjmhO3UPd/Gp6OAT48kwaCRNpEaRPsi/bfGvAXAkAvuSfiNCCJRNasfACuvWpngQESatsCPtLVwzWNURERERJq3wCdtnbO7JjoEERERkZgLfNImIiIi0hwEPmnbse2LRIcgIiIiEnOBvxFh357kvWtJRCTZ5C/IZ8mWJYkOQ0QaIPBJm0g0FeXlNbqNax5/j0Vrth2w/tfoPxiSeLFK2E7tdmpM2hWR7yhpEwmx+53FQONqmFWXsIU6vW9mg9sWiRaV5xAJHiVtItXocdpXjW4jvBbbg36dtsevOaHRbYuISPMT+BsRRERERJoDJW0i1dGUNCIikmR0elSSzqsFH7Dxo+2J6dyf1P2tJcCStxrURMUNBxWnQ0VERKIh8Elbm7ZxmgxY4iZhCVscZB3VKdEhBJZKVYhIcxf4pC0js0uiQ5AYGfvIGXHvszAnF2jcZPHZN74GHHgjgjSOErboUXkOkWAKfNImUpeivLzKUh4SfCpVISLNVeBvRPhyw9ZEhyBJrr4JW2NqtImIiMRK4Efayi0t0SFIQER0ynNSRuwDERERaYDAj7SJiIiINAdK2kREREQCIPCnRyV4ElqHTUREJKCUtEncRZKw1VrPbM4lsPbNevR4mPdD16vVSnXQRESSm5I2SZgG12GrV8LWAM10CqsgJGyqLyYizVngk7YWriTRIUicFb3Tkd1bD67/CyftaHCf1zz+HovWbGvw64NEddBERJJT4G9E6JytaYGam4YkbKmnDWxUn/VN2E7vm9mo/kRERMIFfqRNmq/GTDXVUJqaSkREEiXwI20iIiIizUHgk7YvNu5KdAgiIiIiMafToyJxoHIaIiLSWErapFFUKDcyQUnYVFJDRCR5KWmTRmlowlZr8VygKC+P3e8sblDbyUzlNEREpKGUtElUNLhQbg3qSthSu+6Nan8iIiLJTkmbxE69p5sK5U09lTvis+jFIyIiEmCBv3tUklgsp5tqplNNiYhI8xX4kbaWLXYmOgSpS0Omj5qb2/DXioiINEGBH2nrdHi3RIcgIiIiEnOBH2kTqa+G1ExL9wf++s26MQYRiYiI1C3wI23bN21JdAgSMImqmaYaaCIi0hiBH2krK2+X6BCi5prH32PRmm2JDqNefk0KANk3vnbAtg0HU+O2Cre9O50TPl9d4/baXttQFaNmuwqn1vu1mjBeREQSJfAjbU1J0BK2aKgtYXvvkJw4RlK30/tmJjoEERFpxgI/0tYUBWk058ExbwE1xDyJmrf5Cl8cB0Du6sIDtuUCVzU2wGpUXJcWpPdZREREI20iIiIiAaCkTURERCQAdHpUmqyGlPYQERFJVhppkyartoRN5TdERCRoAj/S1iUrPdEhSJJbedXKRIcgIiLSaIFP2iQYivLy2P3O4kSHISIiElhK2qSKVws+YONH26Pebm0JW+ppA6Pen4iISFMT+KTtyw3RTzCas4YkbFlHdYp43+rqsYmIiEjdAp+0lVvrRIfQJI195IxEhyAiIiIhAp+0SfOiMh4iItJcqeSHBEp9EzaV9hARkaZCI20SSCrjISIizU1ckzYzGwxMA1oC051zU8O2ZwEzgEzgK2CUc25zPGOU2tWvdMdh3o+5uTGLR0REpLmIW9JmZi2BB4EfAZuBf5jZy865VSG73QM84ZybZWZnAFOA0fGKMVquefw9Fq3ZlugwYqIxtdZU2kNERKTh4jnSdgKwzjm3HsDM5gLDgdCk7fvAL/3ni4AX62q0hSuOcpiN15iE7fS+mVGMJHYiKt0xKcP/uSO2wYiIiDQD8UzaugGbQpY3AyeG7fMBcDHeKdQLgXQz6+Scq7F4WOfsrtGOM2o2TB0a1/5iVRhXREREEi+ed49aNetc2PI44DQz+xdwGrAFKD2gIbOfmdlyM1u+bVvTPA3ZENFK2OpTLFdERETiI54jbZuBw0OWuwOfhe7gnPsMuAjAzNKAi51zB5xbc849BjwG0CerR3ji1+wFtTCuarCJiIjULJ4jbf8A+phZTzNrDYwAXg7dwcw6m1lFTDfh3Ulaq317UqIeqCRGpAmbaq+JiEhzFLeRNudcqZldD7yBV/JjhnPuYzO7HVjunHsZGARMMTMHLAbGxis+SR6qwSYiInKguNZpc869Drwetu63Ic//DPw5njFJ7epXl01ERERiRdNYSa2qS9hUb01ERCT+NI2VRCSiumwiIiISM0rakpRqromIiEgoJW1JqqEJW6JqrFVbrqNnD+/nrH7xD0hERKSJCXzS1qbtt4kOIaaSsubanEtg7ZtVVi2pSNAaSeU8REREqhf4pC0js0uiQ2h+whK2UCv/XVR1RZ+z4YrnYhyQiIhI0xf4pE0SKHQi+IpToJocXkREJCYCn7R9uWFrokOodM3j77FoTfLOhaqaayIiIsEV+Dpt5ZaW6BAqhSZsp/fNTGAk1Wtowqa6bCIiIokX+JG2ZLRh6tBEh1Ar1VwTEREJnsCPtImIiIg0BxppkwartjabiIiIxIRG2qTBwhM21VgTERGJHY20SaOtvGplokMQERFp8gKftLVwJYkOISmonIeIiEjTFvikrXN2YubaTDaRJmz1Kt9RzXRVIiIikhiBT9qkqqiW86gtYetzNpSujl5fIiIiUislbVK3mqamqpi6SkRERGIu8HePfrFxV6JDaJbyF+QnOgQREZFmJfBJmyRGRbkPlfkQERGJD50eTSKvFnzAxo+2JzqMennorIcSHYKIiEizoJG2JBKesGUdpTtjRURExKORtiQ09pEzKp+r/pqIiIiAkrYqrnn8PRat2ZboMKqoT8JWrxpsIiIiEihK2kJEI2E7vW9mFCI5UFTrr4mIiEjgBD5pa9liZ9Tb3DB1aNTbbAryF+QfMEm8iIiIxEfgk7ZOh3dLdAjBF+F0VeEJm8p9iIiIxE/gkzaJgrqmqwqz8qqVMQxGREREqhP4pG37pi2JDqHpqGm6KhEREUm4wCdtZeXtEh1CowWxqK6IiIjEV+CTtiAKr722cdCDlc87bf+IwpyxiQhLREREkljESZuZtQLOAb4HPO6c22lmhwM7nHPRv4WzCaup9toZb1efrKn+moiIiESUtJlZNjAfOARoC7wC7AR+BaQAebEJr2mrqL321pi3qiyLiIiIhIt07tFpwP8BnYBvQ9a/AJwZ7aAkueQvyKffrH6JDkNERKRZi/T06MnAyc65/WYWun4jcFjUo5KkElqfTbXZREREEiPSpK2l/wjXHdgVvXAkmak+m4iISOJEenp0PnBDyLIzs1TgVuCvUY+qHrpkpSeyexEREZG4iHSkbRzwtpl9CBwMPAEcgTfKNjpGsTVJRXl5fNDvv9ne6ajKGxBERERE6hJR0uacKzKzo/EStOPwRuieAWY55wJ1evSax99j0ZptCet/9zuL2T7osgPWZx3VKQHRiIiISFBEWvLjBGCFc+7hsPUtzewE59x7MYkuAl9uqN9MAnUlbKf3zWxMOPUy9pEz4taXiIiIBFukp0ffBboCX4Stb+9vq+4mhbgot9YNet2GqUOjHEnTkr8gv8pdoyIiIpJYkd6IYICrZn0HYE/0wpFkEZ6wqdSHiIhIYtU60mZmz/pPHTDdzPaFbG4J9AeWxSg2SQIq8yEiIpIc6hppK/MfBpSHLJcBxcAcdPeoiIiISMzVOtLmnBsJYGYbgMnOud3xCEpEREREqoq05MdNsQ4kqIry8tj9zuJEhyEiIiJNXKR3j2JmI4GRQA+gyi2bzrnvRzmuiLVwxYnqGqDWhK2iiK6IiIhIY0Vap+0XwG3ADOBsYDrQBzgR+N+YRReBztldE9l9pdzVhQesq23Gg2QspqsyHyIiIskr0pG2/wZ+5px7xsyuA+5zzq03s9uB+FWjDaiEFtGdcwmsfTOiXVXmQ0REJHlFmrQdznelPb4FKmZpf9Jf/99RjitiO7aF1/uVKiJM2OhzNpSuBlTmQ0REJBlFmrR9DnQENgJFwAnAB0AWXjmQhNm3JyWR3QfHpB117zOrX+zjEBERkQaJdEaERcAw//ks4A9mNg94FngpFoGJiIiIyHciHWkbU7Gvc+4BM9sJnAwsBB6IUWwiIiIi4ou0TlsJUBKyPAtvxE1ERERE4iDiOm3VMbNhwO3OuWOjFE+DXfP4eyxasy3RYYiIiIjERJ1Jm5mNxqvNth8ocM7908wGAH8AjgHmxjbEyNQnYTu9b+yqlLxa8AEbP9oes/ZrU22dtZ49vJ+6yUBERCTQak3azOznwD3AGuB7wCVmNhG4FXgUuMg591nMo6yHDVOHJrT/8IQtnkV0o1EYV7XZREREklNdI20/A653zj1qZj8C3gAuAo5wzn0Z8+gi0Kbtt4kOoVqJLKhbpc7apAz/ZwQlP0RERCRp1VXyIxv4K4Bzbj5QCtyYLAkbQEZml0SHICIiIhJzdSVtKXgzIFTYh1doV0RERETiKJK7R682s+KQ/UeZWZWRNufcQ1GPLEJfbtiakH6L8vLY/c7ihPQtIiIizU9dSdsXwC9Dlr/hwHlGHZCwpK3c0hLSb2jClnrawITEICIiIs1HrS3wE5oAACAASURBVEmbc+7QeAUSVLmrC6PaXrVlO0RERKTZi3TuUYmTxiZsKtkhIiLSNDVqRoTmotqCuYMeBOCtMW/FpM8qZTtERESk2dNIWwTqO8NBPAvqioiISPOgkbZ6CC2YW5iTC0T/mjYRERGR6gR+pK2FK0l0CCIiIiIxF3HSZmatzGyYmf3czNr56w6veJ4onbN1KlJERESavohOj5pZNjAfOARoC7wC7AR+hTdrQl5swhMRERERiPyatmnA/wE/Bb4KWf8C8KdIOzOzwX5bLYHpzrmpYdt7ALOA9v4+NzrnXo+0/aCKSm22OZfA2jejE5CIiIgknUiTtpOBk51z+80sdP1G4LBIGjCzlsCDwI+AzcA/zOxl59yqkN0mAs865x42s+8Dr+NNWl+jLzbuivAQkld4wtagWmu1JWx9zq5/eyIiIpJUIk3aWvqPcN2BSLOmE4B1zrn1AGY2FxgOhCZtDqi4Ri4D+CzCtpuEqNRmm7Sj8W2IiIhI0ok0aZsP3MB38446M0sFbgX+GmEb3YBNIcubgRPD9pkEvGlmNwCpwFkRth0T1RbVFREREUmASO8eHQecY2YfAgcDTwDrgZ7AbyJsw6pZ58KWRwIznXPdgXOBJ83sgBjN7GdmttzMlkfYd4OEJmwqmCsiIiKJFNFIm3OuyMyOBq4EjsVL9p4BZjnnIj09uhk4PGS5Owee/vwJMNjv810zOxjoDHwRFs9jwGMAPTL7hid+URdaVFdEREQkESIt+dHOObcTeKgRff0D6GNmPYEtwAjg8rB9ioAzgZlmlos3qretEX2KiIiINAmRnh793MyeM7PzzaxBU18550qB64E3gEK8u0Q/NrPbzex8f7dfAT81sw+Ap4GrnXMxH0kTERERSXaRJmCX4Y2KPQ18a2bPAU8655bWpzO/5trrYet+G/J8FV55kYi1bLGzPruLiIiIBFJEI23OuZedcyPwZkT4FdALeMfM1pvZ7bEMsC6dDu+WyO5FRERE4qJeE8Y754qdc7Occ+cARwM7gJtjEpmIiIiIVKrX9Wlm1gY4D7gCGIJ3k8A9MYgrYts3bYl6m6rPJiIiIskm0rtHz8RL1C7yV/0Fr47aokTfKFBW3q7uneopMPXZNN+oiIhIsxHpSNvreHd9/gx4yTm3L3YhJY+a6rMV5eXFOZIahCdsmmNURESkyYo0aevqnPsqppEEyO53FgOQetrAiF+TvyD/gInho0bzjYqIiDR5NSZtZtbWObfHX9xrZm1r2jdkv4TIvvG1hPTb49FHI963roTt1G6nNjYcERERacJqG2nbZWZdnXNfAMUcOE9oqJbRDathTu+bmegQ6rTyqpWJDkFEREQCqLak7Vzgq5DnSTszwYapQxMdgoiIiEhM1Zi0OefeCHn+1/iEIyIiIiLViai4rpntMbMDzj2aWUczS+j1bJ8fVJ7I7kVERETiItIZEQ4GrIb19ZpVIdm9WvBBokMQEREROUCtJT/MLN9/6oCrzaw4ZHNL4DTgkxjFlhAVhXXDi+oW5eVVlvoQERERibe66rTd4v80vIniQ89FlgAbgHwSqOv+0pi0O+z6/lWWwxO2SGu0xbQ+m4iIiDQbtSZtzrmuAGb2LnCuc+7ruERVD+XWOq795a4urNf+oQlbtbXYNBWViIiIRCCiGRGccz+MdSBNXY312RqbsGnqKhERkWahthkR7gZuc87t9p/XyDk3PuqRNTeaikpERERqUdtI26lAq5DnNUnaorsiIiIiTUVtxXV/WN1zEREREYm/BtdYM7PuZhbRNXEiIiIi0jgRJV1mNglY55yb7S+/ij83qZkNds4tj12ItWvhiuveKQKvFnxQWaMtVCT12VTWQ0RERGIt0pG2q4FPAczsHOCHwCDgOWBqLAKL1NZWbaPSTmjCFlpYNzRhq6k2W10JW7WlPkRERETqIdLTm4cCm/3n5wLPOecWm9lW4L2YRJYgYx85o9r1kdRnq7Gsh4iIiEgjRTrS9hXQ3X9+DrDQf25401klTEbZ7kR2LyIiIhIXkY60vQjMNrNCoAvwV3/9D4B1sQgsUge79ER2LyIiIhIXkY60/QKYAWwBBjvndvnrs4DpsQisWZhzSaIjEBERkYCIdBqrEuB31az/fdQjak4qprDSVFQiIk3Szp07+eKLL9i/f3+iQ5E4adWqFV26dKFdu3ZRbzviOmtm1hEYA3wfbxaEj4HHnHNfRT2qJFGUlxefjq54Lj79iIhI3OzcuZPPP/+cbt26kZKSgpklOiSJMecc3377LVu2bAGIeuIWaZ22E/GuY9sF/N1fnQ+MN7NznHP/iGpUCdRvVr/K58++UwrAP79nXBqyXkREpC5ffPEF3bp1o23b6JSmkuRnZrRt25Zu3brx2WefJSZpA+7Fuxnhp865Uj+wg/CuZ7sfOCWqUcVJTQV1w029tO4bZFWLTUREQu3fv5+UlJREhyEJkJKSEpNT4pEmbccB11UkbADOuVIzuxtI2GwIjRWesG1s/zHwXb21wim5VZZFRETqQ6dEm6dYfe6RJm27gMOB1WHru/vbEmavNb77ioK6/Wb9vNFtiYiIiMRCpCU/ngX+ZGYXm1lXMzvUzH4M/NHfljA7WqYmsnsRERFpgvr27cvzzz+f6DCqiDRpGwfMA+biTWe1BXgaeB34dWxCExERkXgYNGgQbdq0IT09nYyMDHr16sXo0aNZsWJFvdrJzs5m9uzZjYpl4cKFnHnmmXTs2BEzY/PmzTXuO2fOHNLS0iofZkZKSkrl8pgxYxocx5o1a7j44osb/PpYiChpc87tdc7lAZnAALwJ4zOdc//tnNsbywDr0nX/nkR2LyIi0iTccsst7Nq1ix07drBo0SKysrIYMGAAL7zwQlzjSE1N5corr+TJJ5+sc98rrriC4uLiykfLli2ZN29e5fIjjzxywGuCXDOvzqTNzA4zs9Fmdi3Q2Tn3D+fce865b+IQX53KLS0q7eQvyI9KOyIiIkGXlZXF5MmTufLKK7nhhhtwzgEwbdo0cnJySE9Pp0ePHtx0002UlZUBcN5551FUVMR1111HWloaZ5/tFY6fO3cu/fv3p127dnTt2pW8vDx276553vABAwZw1VVXceSRR0blWBYsWMDBBx/MzJkz6dmzJ126dAHg/vvvp2/fvqSnp5OVlcXEiRMpLy+vfF337t2ZO3dulTaeeuopevXqRUZGBpdddlmtxxELtd6IYGYn4Z0CrSg0UmJmo5xzf455ZHG2ZMsSoBGlO+Zc8t0MByIiItXIvvG1GrfdeWE/Lj+xBwBP/b2ICS/UXLlgw9Shlc+HPbCEj7bsrHF7Y4wYMYIZM2awZs0acnJy6N69O/PmzSM7O5v333+fwYMHk52dTV5eHq+88grZ2dlMnjyZUaNGVbaRkZHBU089RW5uLuvXr+f8889n8uTJTJkyJSoxRqKkpIQFCxbwwQcfcNBBXupz+OGH89e//pXs7Gz+9a9/cc4559CzZ09+8pOf1NjG22+/zcqVK9m5cyennHIKBQUF/OY3v4nbcdQ10jYZWAb0xrtT9CngnlgHlUgPnfVQw17Y0IRNU1iJiEiS6t69OwDbt3slsi6++GJ69uyJmXHMMccwevRoFi5cWGsbQ4YM4cgjj6RFixb07t2b/Pz8Ol8Tbc457r77btq1a1dZ7PjHP/5x5bEce+yxXHHFFbXG5Zxj6tSppKam0rVrV84//3yWL49v1bO6Sn70B053zq0HMLOfA9+YWftkOT3aEJEW1W2QSTti066IiARepCNgl5/Yo3LUrS6v3hC74u4VNwF06tQJgKeffpr77ruP9evXU1paSklJCQMGDKi1jfnz53P77bezevVq9u3bR1lZWeUpynhp1aoVhx12WJV1c+bM4f7772f9+vWUlZWxb98+Tj215veyTZs2dOzYsXI5NTWVXbviW/WsrpG2DsB/Khacc7uAPf76wApN2LKO6pTASERERJLXM888Q7du3ejbty+bNm1i1KhRTJw4ka1bt7Jjxw7Gjh1beb0bQIsWVdOKkpISLrjgAkaMGEFRURE7d+7krrvuqvKaeAiP69///jejR49m0qRJfP755+zYsYMxY8bEPa76iqS47hFm1jlk2YA+ZlY5N4dzblXUI4uDiqK6IiIi8p1NmzYxffp0Zs6cyTPPPIOZUVxcTHl5OZmZmbRq1Yply5bx5JNPkpubW/m6Qw89lLVr11Yul5SUsHfvXjp06EBKSgqrVq2ioKCg1r7Ly8spKSlh3759AOzbt4+9e/fSunXrA5KvhiouLsY5R2ZmJgcddBBLly5lzpw59O/fPyrtx0okR/8OsDLk0RZv8viVwEf+z4Rp4UoS2b2IiEiTcMcdd5Cenk67du0YOHAg69atY+nSpZW1ynJzc7ntttsYPnw47du3Z+rUqYwcObJKGxMnTmT27Nl06NCBIUOGkJaWxsMPP8z48eNJS0tj7NixXH755bXGsXjxYlJSUsjJyQGgd+/epKSksHjx4qgda79+/bjlllsYOnQo7du355577jngWJKR1TYUaGZ9I2nEObcmahHVU5uufdy+rWvr3jHEg2PeAqqOtPWb1Q+oOs9oYY73v4fc1YV1Nzopw/+pa9pERAQKCwurjEJJ81LT529mK5xzxzekzVpPjyYyGUu0ory8RIcgIiIiUik6J4eboN3veMOwqacNTHAkIiIiIk0gaTukNLaH0OPRR2PavoiIiEgkIrl7tMmIaX02ERERkRhqVklb1OuzaeoqERERiZN6JW1mlgZ8D1jlnNsfm5BiL2r12cITNk1JJSIiIjESUdJmZqnAw8AooBw4AlhvZgXAVufc72IXYgCozIeIiIjEWKRX8U8BcoCTgL0h698ELol2UCIiIiJSVaSnR4cDlzrn/m5modV4VwG9oh+WiIiIiISKdKQtE/iimvWpUYylQRw6NSkiItIYgwYNok2bNqSnp5ORkUGvXr0YPXo0K1asqFc72dnZzJ49u1GxLFy4kDPPPJOOHTtiZmzevLnGfefMmUNaWlrlw8xISUmpXB4zZkyjYvnrX/9KWlpao9qIpkiTthXAuSHLFaNt1wLvRjWievrioPREdi8iItIk3HLLLezatYsdO3awaNEisrKyGDBgAC+88EJc40hNTeXKK6/kySefrHPfK664guLi4spHy5YtmTdvXuXyI488EoeI4yfSpO1m4C4zewDvlOpYM5sH/Ay4JVbBiYiISPxlZWUxefJkrrzySm644QYq5imfNm0aOTk5pKen06NHD2666SbKysoAOO+88ygqKuK6664jLS2Ns8/2KirMnTuX/v37065dO7p27UpeXh67d++use8BAwZw1VVXceSRR0bteN5//33OOussOnfuTFZWFr/97W8pLS0F4Ntvv+Xaa68lMzOTdu3a0bdvX15++WXWr1/PhRdeyO7duytH7p555pmoxdQQESVtzrnFwGlAF2ALcBGwGzjZOfde7MKrW5fSXRHt92rBBzGOREREpGkZMWIEW7ZsYc0abyry7t27M2/ePHbu3MlLL73EjBkzmD59OgCvvPIKPXr0YPr06RQXF/Pmm15ZrIyMDJ566im++eYblixZwpIlS5g8eXLcjmHLli0MGjSIUaNGsXXrVpYsWcJLL73EfffdB8Af//hHPv74Yz755BN27NjBm2++yRFHHEGvXr144YUXSE1NrRy5u+yyy+IWd3UirtPmnFsBJDbaahgZEe1XUVg3KkV1RURE6in7xtfi2t+GqUMb3Ub37t0B2L7d+w69+OKLK7cdc8wxjB49moULF5KXl1djG0OGDKl83rt3b/Lz83niiScaHVukHn/8cU466SSuvvpqAHr06MH48eOZMmUK48ePp3Xr1uzatYvCwkJOPPFEsrKy4hZbfUVap61tbdudc3uiE07sDbu+/wHr8hfkJyASERGR5FZxE0CnTt6Ax9NPP819993H+vXrKS0tpaSkhAEDBtTaxvz587n99ttZvXo1+/bto6ysjC5dusQ89gr//ve/WbhwIe3bt69cV15eTqtWrQD4yU9+wldffcX111/P+vXr+dGPfsTdd99Nz5494xZjpCIdaSvmu5sPqtMyCrEkzJItSwA4tdupFOXlsfudxXW/aI7K04mISOSiMfIVb8888wzdunWjb9++bNq0iVGjRvGXv/yFIUOG0Lp1a8aNG8fy5csr92/RoupVVyUlJVxwwQXcfffdXHvttaSkpFBQUMA999wTt2PIyspi2LBhPP/889Vub9WqFRMmTGDChAl89dVXjBkzhry8PN58880DjifRIo1mCN7doxWP4cAkYDNwTUwiS4CHznqoSsKWetrAmneumMJKU1eJiEgTs2nTJm699VZmzpzJtGnTMDOKi4spLy8nMzOTVq1asWzZsgPu8Dz00ENZu3Zt5XJJSQl79+6lQ4cOpKSksGrVKgoKCmrtu7y8nL1797Jv3z4A9u3bx969eykvL2/QsVxzzTUsXryY2bNns2/fPsrLy1m3bl3lNXfz58/n/fffp7S0lLZt29K2bVsOOuigyuPZs2cPn332WYP6jrZIb0R4I+zxqnPuDmA8TXRGhNzVhfR49NG6d7ziudgHIyIiEmN33HEH6enptGvXjoEDB7Ju3TqWLl1aeR1bbm4ut912G8OHD6d9+/ZMnTqVkSNHVmlj4sSJzJ49mw4dOjBkyBDS0tJ4+OGHGT9+PGlpaYwdO5bLL7+81jgWL15MSkoKOTk5gHcdXEpKCosXR3AWrBqHH344Cxcu5Omnn6ZHjx507NiRH//4x2zcuBGArVu3MnLkSNq3b0+3bt3Ytm0bDz74IABHH3001157LUcffTTt27fn2WefbVAM0WIVt/E26MVm3wM+dM4lrMhuj8y+rmjbmjr3e3DMW0D1k8X3m9UPgJVXraQwJxfwkrZaTfJvgNC8oyIiUo3CwkJyc3MTHYYkSE2fv5mtcM4d35A2G3yy1sxaA2PxSoCIiIiISAxFevfoNqreiGBAe6AEuDIGcUXs84Mado5bREREJEgivXt0YthyObANWOqcq25OUhERERGJojqTNjM7CNgPvO6c+0/sQxIRERGRcHVe0+acKwUKgDaxD6f+uu4vTXQIIiIiIjEX6Y0I7wEHTiWQBMqtdaJDEBEREYm5SK9pKwDuNbPDgBV4k8VXcs6tinZgIiIiIvKdSJO2impyD/k/K+4kNf95oKexqjdNYSUiIiJxFmnSFpXqgGY2GJiGl+RNd85NDdt+P3C6v9gW6OKca0+y0RRWIiIiEme1Jm1mNgP4uXOu7ikH6mBmLYEHgR/hzVn6DzN7OfTUqnPulyH73wAc09h+Y0pTWImIiDQ5ZWVlZGRksGjRIv7rv/4r0eFUqutGhKuAlCj1dQKwzjm33jlXAszFm3i+JiOBp6PUt4iIiNRg0KBBtGnThvT0dDIyMujVqxejR49mxYoV9WonOzub2bNnNyqWe++9l2OPPZaMjAwOOeQQLr30UoqKiqrd98477yQtLY20tDRSU1MxM1JTUyvX3XnnnQ2KoWXLlhQXFydVwgZ1J20Wxb66AZtCljf76w7s1CwL6Am8VVejLVxxVIITERFpzm655RZ27drFjh07WLRoEVlZWQwYMIAXXnghrnGUlJTwwAMP8Pnnn7Nu3TpSU1MZNmxYtftOmDCB4uJiiouLWbPGOyn48ccfV66bMGHCAa/Zv39/TOOPpUhKfjR8RvmqqksAa2p7BPBn51xZtQ2Z/czMlpvZ8q2t2kYpPBEREQHIyspi8uTJXHnlldxwww04531dT5s2jZycHNLT0+nRowc33XQTZWXeV/V5551HUVER1113HWlpaZx9tnfd99y5c+nfvz/t2rWja9eu5OXlsXv37hr7vummmzj55JM5+OCDSU9PZ9y4caxcuZKvv/66Qccyffp0cnJymDp1Kt27d+e4444D4De/+Q09e/YkLS2N3r1788ADD1S+prS0FDNj2bJlVdq4//776datGx07diQ/P5/y8vhOpRnJjQj/Mat9wM05F8ndo5uBw0OWuwOf1bDvCLzJ6Gvq7zHgMYA2XftEK6kUERGJrUkZNW8b9gc4/hrv+fLH4dVf1NLOju+ePzoQtn5Q8/ZGGDFiBDNmzGDNmjXk5OTQvXt35s2bR3Z2Nu+//z6DBw8mOzubvLw8XnnlFbKzs5k8eTKjRo2qbCMjI4OnnnqK3Nxc1q9fz/nnn8/kyZOZMmVKRDEsXLiQ7t2706FDhwYfx7p16/jyyy9Zu3ZtZQJ61FFH8Ytf/IJDDz2UhQsXMmzYML7//e9z5plnVtvGp59+ytdff8369evZuHEjJ5xwAqeddhqXXXZZg+Oqr0iStp8B30Shr38AfcysJ7AFLzG7PHwnM+sLdADejaTRjLKas3URERFpuO7duwOwfft2AC6++OLKbccccwyjR49m4cKF5OXl1djGkCFDKp/37t2b/Px8nnjiiYj6X7p0KTfffDNz585tSPiVDj74YKZMmUKrVq0q140ePbry+VlnncXgwYNZuHBhjUlbWloakyZNokWLFhxxxBGcfvrpLF++POmStleiMSm8c67UzK4H3sAr+THDOfexmd0OLHfOvezvOhKY6ypS4Toc7NIbG5qIiEh8RDoCdvw134261SVvccPjqcPmzZsB6NSpEwBPP/009913H+vXr6e0tJSSkhIGDBhQaxvz58/n9ttvZ/Xq1ezbt4+ysjK6dOlSZ99Llixh+PDhPPbYYwwdOrRRx3HYYYdVSdgA/vCHP/CnP/2JTZu8y+337NlDZmZmjW0ccsghtGjx3VVlqamp7Nq1q1Fx1Vdd17RF9dSjc+5159wRzrnvOed+56/7bUjChnNuknPuxmj2KyIiIvX3zDPP0K1bN/r27cumTZsYNWoUEydOZOvWrezYsYOxY8cSOsYSmtSAd1PBBRdcwIgRIygqKmLnzp3cdddd1DUu88Ybb3Deeecxffp0Ro4c2ejjCI/rnXfe4eabb+aPf/wj27dv55tvvuHcc8+tM65Ei+fdo0kpf0F+okMQERFJKps2beLWW29l5syZTJs2DTOjuLiY8vJyMjMzadWqFcuWLePJJ5+s8rpDDz2UtWvXVi6XlJSwd+9eOnToQEpKCqtWraKgoKDWvp9//nkuueQSZs+ezUUXXRST49u5cyctW7YkMzMTM+OVV17hzTffjElf0VRr0uacaxGNU6PJbMmWJQCc2u3U2necc4l3AWltF5GKiIgE1B133EF6ejrt2rVj4MCBrFu3jqVLl1Zex5abm8ttt93G8OHDad++PVOnTj1gFGzixInMnj2bDh06MGTIENLS0nj44YcZP348aWlpjB07lssvP+By9irGjRvHnj17GDFiRGW9tbS0tBprtTXEueeey8iRIznuuOPIzMzkxRdfZPjw2krHJgdL9qHAuvTI7OuKttU+YcOrBR+w8SPvIsqxj5xRZVu/Wf248dkyjv206vuQu7qwaiPhyVqfszUjgoiI1KiwsJDc3KjMAikBVNPnb2YrnHPHN6TNSOceDbSKhC3rqE7Vbg9P2FJPG1hzY1G6jVpERESkPppF0lZh2PX9a91+wOiaiIiISJKIZEaEpLbX4nu7rYiIiEgiBD5p29EyNdEhiIiIiMRc4JM2ERERkeYg8Ne0dd2/p0Gvy1+QX1nuQ0RERCTZBX6krdzSGvQ6JWwiIiISJIFP2hrrtaUnJToEERERkToF/vRobUKL6tZk9zveRLu11mYTERERSbAmPdIWmrDVVFi3Qo9HH411OCIiIklp0KBBtGnThvT0dDIyMujVqxejR49mxYoV9WonOzub2bNnNyqWe++9l2OPPZaMjAwOOeQQLr300hqnsLrzzjsrp7lKTU3FzEhNTa1cd+eddzYqlkceeYSjjjqqUW1EU5NO2iqMfeSMOgvrVhE6z6jmGxURkWbglltuYdeuXezYsYNFixaRlZXFgAEDeOGFF+IaR0lJCQ888ACff/4569atIzU1lWHDhlW774QJEyguLqa4uJg1a7wpLT/++OPKdRMmTIhn6DHXLJK2elv7ZvXr+5wd3zhEREQSICsri8mTJ3PllVdyww03UDFP+bRp08jJySE9PZ0ePXpw0003UVZWBsB5551HUVER1113HWlpaZx9tvedOXfuXPr370+7du3o2rUreXl57N69u8a+b7rpJk4++WQOPvhg0tPTGTduHCtXruTrr79u8PEsWrSIk046iQ4dOtC7d28eeOCBym1ffvklF154IR07diQjI4N+/frx97//nbfffptf/OIXFBYWVo7cvfvuuw2OIRoCf01bC1dS79fkL8iPbEfNMyoiIs3YiBEjmDFjBmvWrCEnJ4fu3bszb948srOzef/99xk8eDDZ2dnk5eXxyiuvkJ2dzeTJkxk1alRlGxkZGTz11FPk5uayfv16zj//fCZPnsyUKVMiimHhwoV0796dDh06NOgY3n//fYYPH87cuXM555xzWL16NYMHD6489VpxCnXTpk2kpKTwySefkJKSQlZWFn/4wx8oKCjgo48+alDf0Rb4pG1rq/ofQkW5j1O7nQosinJEIiIi1Yj3pTZRGHjo3r07ANu3e9eIX3zxxZXbjjnmGEaPHs3ChQvJy8ursY0hQ4ZUPu/duzf5+fk88cQTEfW/dOlSbr75ZubOnduQ8AEoKChg1KhRnHvuuQAceeSRjBkzhieeeIJLL72U1q1bs337dtauXUv//v3JyclpcF+xFvikraFufLaMYz9VwiYiIlKTzZs3A9Cpk3cz39NPP819993H+vXrKS0tpaSkhAEDBtTaxvz587n99ttZvXo1+/bto6ysjC5dutTZ95IlSxg+fDiPPfYYQ4cObfAx/Pvf/2bp0qU89dRTlevKysro06cPADfffDO/+93vuPzyy9m2bRvnn38+d911F507d25weEfPWwAAIABJREFUn7HSbJO2Yz91lc9V7kNERGIugJfcPPPMM3Tr1o2+ffuyadMmRo0axV/+8heGDBlC69atGTduHMuXL6/cv0WLqpfKl5SUcMEFF3D33Xdz7bXXkpKSQkFBAffcc0+t/b7xxhtcdtllzJgxg4suuqhRx5CVlcUPfvAD7r333mq3p6enM3XqVKZOncpnn33GiBEjmDBhAo899tgBx5NoyRVNAxxS2rhDyF1dqHIfIiIiITZt2sStt97KzJkzmTZtGmZGcXEx5eXlZGZm0qpVK5YtW8aTTz5Z5XWHHnooa9eurVwuKSlh7969dOjQgZSUFFatWkVBQUGtfT///PNccsklzJ49u9EJG8D111/PrFmzmDdvHvv376e0tJSPPvqIv/3tbwC8+OKLrFmzhvLyctLS0mjTpg0HHXRQ5fFs3bq11hsn4inwSZuIiIg03h133EF6ejrt2rVj4MCBrFu3jqVLl1Zex5abm8ttt93G8OHDad++PVOnTmXkyJFV2pg4cSKzZ8+mQ4cODBkyhLS0NB5++GHGjx9PWloaY8eO5fLLL681jnHjxrFnzx5GjBhReddmWlpajbXa6nLsscfyl7/8hbvuuouuXbvSpUsXrrvuusrr9D755BPOPfdc0tPT+d73vkfnzp2ZPHkyAOeccw6nnHIKPXr0oH379ixbtqxBMUSLVdzGG1Q9Mvu6om1rqt324Ji3AK9OW6h+s/rx7JRSwBtpO0DFxaIBHMoWEZHkUFhYSG5ubqLDkASp6fM3sxXOueMb0mazuaYtf0G+JokXERGRwGo2p0eVsImIiEiQNZuRtgorr1oJQOGUsCHLOZfUPBOCiIiISII1m5G2UEXVFQEMT9g0ZZWIiIgkkcCPtDnqf7PA7ncWAzXUZ9PNByIiIpKEAj/S9sVB6Q1+reqziYiISFAEPmkTERERaQ4Cn7R1Kd1V7fpXCz6IcyQiIiIisRP4pM3IqHb9xo+8SsdZR3Uif0F+PEMSERERibrAJ211GXZ9/8oabad2OzXB0YiIiEiyKysrIy0tjX/84x+JDqWKJp+0hXrorIcSHYKIiEjSGTRoEG3atCE9PZ2MjAx69erF6NGjWbFiRb3ayc7OZvbs2Y2K5YEHHuCII46gffv2dOrUiXPOOYcPP/yw2n3vvPPOyrlJU1NT+f/t3Xt8VeWd9/3Pj1PA7JyAYCIxCRggAXwQsTR0KELlRpIbCN6MGijh6e3Qwk2gw9PHaolQOUQITEUY0qKIFCHl5JRDmZbXiBS9sRgrPMM4QkAwasLhnpJIgGAhJFnPH3uzm2ASQg477PB9v17rxV5rXetavzVXwd9c61rXZWYEBgZ6jy1evLhBMbRt25bS0lK+9a1vNeZRmtxdlbQVTJtGXrzWgRMREbnZvHnzuHz5MhcvXmT//v3ExMSQmJjIjh07fBpHcnIyBw8epKSkhHPnzjFq1CiSk5Opaa30jIwMSktLKS0t5cQJ9zrkR48e9R7LyMj4xjXXr19v9mdoLndV0nZjfjaoZY42ERERISYmhszMTKZMmcKsWbO8CdPKlSuJj48nKCiI6Oho5syZQ0VFBQBjx46loKCAqVOn4nK5GDXKPUn9li1bGDBgAMHBwURGRjJt2jSuXLlS670feOABunbt6t1v27YtZ86c4fLlmj88vJW1a9cSHx9PVlYWUVFRDBo0CIDnn3+eHj164HK5iIuLY9WqVd5rysvLMTNyc3Or1fHKK6/QvXt3OnfuzIwZM6isrGxQTA3l95PrNkTC8Tz3Dy1dJSIiPvTgmw/Weu7nQ37Ok72fBOCtT99i4QcLay17Y0lGgKd2P0XeV3m1nm+M1NRU1q1bx4kTJ4iPjycqKoo9e/YQGxvLkSNHGD16NLGxsUybNo3du3cTGxtLZmYmkydP9tYREhLCpk2bSEhIID8/n3HjxpGZmcmSJUtqve/777/PmDFjuHTpEgA//elPCQ4ObvBznDp1iqKiIk6ePOlNQPv378/s2bOJiIhg3759jBkzhr59+/LYY4/VWMdnn33GhQsXyM/P58svv2Tw4ME8+uijPP300w2O63bdVT1t36Clq0RERGoVFRUFQHGxe0aGCRMm0KNHD8yMgQMHkpaWxr59++qsIykpiX79+tGmTRvi4uKYMWPGLa8ZOnQoJSUlfPXVV7zyyit8+9vfbtRzdOzYkSVLltCpUyfuueceANLS0oiMjMTMGDlyJKNHj64zLpfLxfz58wkICKB3796MGDGCQ4cONSqu2+X3PW3/1a4Juia1dJWIiPhAfXvAnuz9pLfX7Va2jd3WmJDqdPr0aQC6dOkCwObNm1m+fDn5+fmUl5dTVlZGYmJinXXs3buXhQsXcvz4ca5du0ZFRQXdunWr1/1DQ0OZNWsWnTt3pm/fviQkNGxc+n333Uf79u2rHVuxYgVvvPEGhYWFAHz99deEh4fXWse9995LmzZ/6+sKDAxs8Cvbhrq7e9pERESkVlu3bqV79+706dOHwsJCJk+ezNy5czl37hwXL14kPT292gcCVZMagLKyMsaPH09qaioFBQVcunSJpUuX1vhRQW0qKyspKyvjs88+a/Bz3BzXe++9xwsvvMDrr79OcXExJSUltX7scCdR0iYiIiLVFBYW8uKLL7J+/XpWrlyJmVFaWkplZSXh4eG0b9+e3NxcNm7cWO26iIgITp486d0vKyvj6tWrhIWF0alTJ44dO0Z2dnad937ttdc4ffo0juNQVFTEzJkz6dixY6NfkVZ16dIl2rZtS3h4OGbG7t27efvtO3+Mu98nbZHXy1s6BBEREb+3aNEigoKCCA4OZtiwYZw6dYqDBw8yYcIEABISEliwYAEpKSmEhoaSlZXFxIkTq9Uxd+5ccnJyCAsLIykpCZfLxerVq3nuuedwuVykp6czadKkOuP485//zODBg3G5XPTv35+zZ8/yzjvv1Pnq8nYlJyczceJEBg0aRHh4ODt37iQlJaXJ6m8udqd3Bd5KdHgfp+D8iW8c/+X0PwKQ/ur3vF/rbFviTvC8X4/O9yyBpTFtIiLSxPLy8ho8Bkv8X23tb2aHHcd5pCF1+n1Pm4iIiMjdQEmbiIiIiB9Q0iYiIiLiB1p90lbX7NMiIiIi/sLvJ9etr+92/y6w371z4wMEERERET/h90lbG6f0lmVuzECdRw1f8WjpKhEREfEDfp+0nWt/T8Mu1DQfIiIi4kda/Zg2ERERkdbA75O2kIorLR2CiIiISLPz+6StoxPU0iGIiIj4teHDhxMQEEBQUBAhISH07NmTtLQ0Dh8+fFv1xMbGkpOT06hYVq1aRe/evQkNDaVLly48/vjjfPzxxzWWXbx4MS6XC5fLRWBgIGZGYGCg99jixYsbFcurr75K//79G1VHU/L7pE1EREQab968eVy+fJmLFy+yf/9+YmJiSExMZMeOHT6NIzk5mYMHD1JSUsK5c+cYNWoUycnJ1LTsZkZGBqWlpZSWlnLihHtJy6NHj3qPZWRk+DT25qakTURERKqJiYkhMzOTKVOmMGvWLG/CtHLlSuLj4wkKCiI6Opo5c+ZQUVEBwNixYykoKGDq1Km4XC5GjXLPzrBlyxYGDBhAcHAwkZGRTJs2jStXah/a9MADD9C1a1fvftu2bTlz5gyXL19u8PPs37+f73znO4SFhREXF8eqVau854qKinjiiSfo3LkzISEhPPjgg3z44Ye8++67zJ49m7y8PG/P3QcffNDgGJqCkjYRERGpUWpqKmfOnPH2YkVFRbFnzx4uXbrErl27WLduHWvXrgVg9+7dREdHs3btWkpLS3n77bcBCAkJYdOmTZSUlHDgwAEOHDhAZmZmnfd9//33CQ0NpWPHjvzkJz/hpz/9KcHBwQ16hiNHjpCSksLcuXMpKipi165dLFu2jG3btgF4X6EWFhZy4cIF3nrrLSIiIhg+fDgrVqwgISHB23M3ZMiQBsXQVPx+yo+b/Wv2f/DlJ8UtHYaIiEg1vl6h58YcpY0RFRUFQHGx+7+rEyZM8J4bOHAgaWlp7Nu3j2nTptVaR1JSkvd3XFwcM2bMYMOGDXXed+jQoZSUlFBSUsKbb77pjaMhsrOzmTx5MsnJyQD069eP6dOns2HDBp566ik6dOhAcXExJ0+eZMCAAcTHxzf4Xs2t1SVtVRO2L0OPen8X1PE/KBEREfmm06dPA9ClSxcANm/ezPLly8nPz6e8vJyysjISExPrrGPv3r0sXLiQ48ePc+3aNSoqKujWrVu97h8aGsqsWbPo3Lkzffv2JSGhhknyb+Hzzz/n4MGDbNq0yXusoqKCXr16AfDCCy/w0ksvMWnSJM6fP8+4ceNYunRptVe0d4pWl7TdkP7q93jwzX9078wP4cp79wEQGHm1BaMSEZG7VVP0fPna1q1b6d69O3369KGwsJDJkyezfft2kpKS6NChA88++yyHDh3ylm/Tpvqoq7KyMsaPH8+yZct45pln6NSpE9nZ2fziF7+odwyVlZWUlZXx2WefNShpi4mJ4aGHHuLll1+u8XxQUBBZWVlkZWVx9uxZUlNTycjIYM2aNd94npZ2Z0XTAFft9gYmRj/6lZauEhERqUNhYSEvvvgi69evZ+XKlZgZpaWlVFZWEh4eTvv27cnNzWXjxo3VrouIiODkyZPe/bKyMq5evUpYWBidOnXi2LFjZGdn13nv1157jdOnT+M4DkVFRcycOZOOHTvy7W9/u0HPMnPmTN5880327NnD9evXKS8v55NPPuH9998HYOfOnZw4cYLKykpcLhcBAQG0a9fO+zznzp2r88MJX/L7pO1i28Aaj894Z0bNF8y/CN9/qxkjEhER8T+LFi0iKCiI4OBghg0bxqlTpzh48KB3HFtCQgILFiwgJSWF0NBQsrKymDhxYrU65s6dS05ODmFhYSQlJeFyuVi9ejXPPfccLpeL9PR0Jk2aVGccf/7znxk8eDAul4v+/ftz9uxZ3nnnHcLDwxv0XA8//DDbt29n6dKlREZG0q1bN6ZOneodp/fpp5+SnJxMUFCQ98vVGx9KPP744wwdOpTo6GhCQ0PJzc1tUAxNxWqa98SfBET2cq6d+1tW/8vpfwTg1SHuV6Pf/fqv/Oq/zpO3xf16NOF4nu+DFBGRu05eXl6DXudJ61Bb+5vZYcdxHmlInX7f0xZ5/es6z//qv877KBIRERGR5uP3SVuluVo6BBEREZFm5/dJm4iIiMjdoFUlbf+a/R8tHYKIiIhIs2hVSduNiXVj+ndp4UhEREREmlarStpuGDNzQEuHICIiItKkWmXSJiIiItLa+H3S1sYpa+kQRERERJqd3ydt59q32uVTRURERLx8mrSZ2WgzO2Fmp8zsZ7WUecrMjpnZUTPb5Mv4RERERCoqKnC5XHz00UctHUo1PkvazKwt8EsgCegLTDSzvjeV6QXMAf7OcZx+wOymuHfBe52bohoREZFWafjw4QQEBBAUFERISAg9e/YkLS2Nw4cP31Y9sbGx5OTkNFlcTz/9NGbmXdz9ZosXL8blcuFyuQgMDMTMCAwM9B5bvHhxg+7btm1bSktL+da3vtWY8JucL3vaBgOnHMfJdxynDNgCpNxU5ofALx3HuQDgOM5fblXpveW3foQr5zoCEPjosNsMWURE5O4wb948Ll++zMWLF9m/fz8xMTEkJiayY8eOFoln+/bt3kXda5ORkUFpaSmlpaWcOHECgKNHj3qPZWRkfOOa69evN0u8vuDLpK07UFhl/7TnWFW9gd5m9iczyzWz0fWp+F+z/8O7UHxdol97rb6xioiI3LViYmLIzMxkypQpzJo1C8dxAFi5ciXx8fEEBQURHR3NnDlzqKioAGDs2LEUFBQwdepUXC4Xo0aNAmDLli0MGDCA4OBgIiMjmTZtGleuXKnz/sXFxTz77LOsWbOm0c+ydu1a4uPjycrKIioqikGDBgHw/PPP06NHD1wuF3Fxcaxatcp7TXl5OWZGbm5utTpeeeUVunfvTufOnZkxYwaVlZWNju92+HIUv9VwzLlpvx3QCxgORAEHzKy/4zgl1Soy+xHwI4D7u/b2TqoLmlhXRETuXHnxCbWei1iwgLCnnwLgwtZt/J8XX6y1bMLxPO/vz//HBK4eO1br+cZITU1l3bp1nDhxgvj4eKKiotizZw+xsbEcOXKE0aNHExsby7Rp09i9ezexsbFkZmYyefJkbx0hISFs2rSJhIQE8vPzGTduHJmZmSxZsqTW+86cOZNZs2bRs2fPJnmOU6dOUVRUxMmTJ70JaP/+/Zk9ezYRERHs27ePMWPG0LdvXx577LEa6/jss8+4cOEC+fn5fPnllwwePJhHH32Up59+uklirA9f9rSdBu6vsh8FnK2hzC7Hca47jvM5cAJ3EleN4zhrHMd5xHGcR6oeT3/1e5pYV0REpIlERUUBeF9TTpgwgR49emBmDBw4kLS0NPbt21dnHUlJSfTr1482bdoQFxfHjBkz6rxm586d5Ofn84//+I9N9hwdO3ZkyZIldOrUiXvuuQeAtLQ0IiMjMTNGjhzJ6NGj64zL5XIxf/58AgIC6N27NyNGjODQoUNNFmN9+LKn7SOgl5n1AM4AqcCkm8rsBCYC682sK+7Xpfk+jFFERKTZ1LcHLOzpp7y9brfSY/tvGxNSnU6fPg1Aly7ut1ibN29m+fLl5OfnU15eTllZGYmJiXXWsXfvXhYuXMjx48e5du0aFRUVdOvWrcayX331FT/+8Y/5/e9/T5s2TdevdN9999G+fftqx1asWMEbb7xBYaF75NbXX39NeHh4rXXce++91WIKDAzk8uXLTRZjffisp81xnHJgJvBvQB6wzXGco2a20MzGeYr9G1BsZseA/cBPHcepexSiiIiINIutW7fSvXt3+vTpQ2FhIZMnT2bu3LmcO3eOixcvkp6e7n3dCHwj0SorK2P8+PGkpqZSUFDApUuXWLp0abVrqvr44485e/YsI0aMoGvXrnTt2hWAMWPG8LOf1ThTWL3cHNd7773HCy+8wOuvv05xcTElJSUkJyfXGtedwqcz0zqO8wfgDzcd+3mV3w7wE88mIiIiLaCwsJC1a9eyfv16tm7diplRWlpKZWUl4eHhtG/fntzcXDZu3EhCwt/G6UVERHDy5EnvfllZGVevXiUsLIxOnTpx7NgxsrOza73vkCFD+OKLL6odu//++/n1r3/NiBEjmuz5Ll26RNu2bQkPD8fM2L17N2+//TaTJt38AvDO4vcrIjhcbOkQRERE/N6iRYsICgoiODiYYcOGcerUKQ4ePMiECRMASEhIYMGCBaSkpBAaGkpWVhYTJ06sVsfcuXPJyckhLCyMpKQkXC4Xq1ev5rnnnsPlcpGenl5nYhQQEEBUVFS1DSA8PJzQ0NAme9bk5GQmTpzIoEGDCA8PZ+fOnaSk3DwL2Z3H7vSuwFsJiOzlLE9xT+WR/ur3vMcffPNBAP7z8wLyttwHNN3XNCIiIreSl5dXrRdK7i61tb+ZHb75Q8r68vueNhEREZG7gd8nbd3Kv/nlxox3ZrRAJCIiIiLNx++TNiPkG8cOnDkAwHe7f9fX4YiIiIg0C79P2uryq5G/aukQRERERJpEq07aRERERFoLJW0iIiIifqDVJ20F73Vu6RBEREREGq3VJ21XznUEIPDRYS0ciYiIiEjDtfqk7Ybo115r6RBEREREGszvk7Zr5t8rOoiIiLS04cOHExAQQFBQECEhIfTs2ZO0tDQOHz58W/XExsaSk5PTZHE9/fTTmBnvv/9+jecXL16My+XC5XIRGBiImREYGOg9tnjx4kbd/9VXX6V///6NqqMp+X3SFuAYADH9u3zz5PxvzuEmIiIi3zRv3jwuX77MxYsX2b9/PzExMSQmJrJjx44WiWf79u0UFxfXWSYjI4PS0lJKS0s5ceIEAEePHvUey8jI8EWoPuP3SdsNY2YOaOkQREREWoWYmBgyMzOZMmUKs2bN4sY65StXriQ+Pp6goCCio6OZM2cOFRUVAIwdO5aCggKmTp2Ky+Vi1KhRAGzZsoUBAwYQHBxMZGQk06ZN48qVK3Xev7i4mGeffZY1a9Y0yfPs37+f73znO4SFhREXF8eqVau854qKinjiiSfo3LkzISEhPPjgg3z44Ye8++67zJ49m7y8PG/P3QcffNAk8TRUq0naREREpGmlpqZy5swZby9WVFQUe/bs4dKlS+zatYt169axdu1aAHbv3k10dDRr166ltLSUt99+G4CQkBA2bdpESUkJBw4c4MCBA2RmZtZ535kzZzJr1ix69uzZ6Gc4cuQIKSkpzJ07l6KiInbt2sWyZcvYtm0bgPcVamFhIRcuXOCtt94iIiKC4cOHs2LFChISErw9d0OGDGl0PI3RrkXv3sw03YeIiNwp8uITfHq/hON5ja4jKioKwPuacsKECd5zAwcOJC0tjX379jFt2rRa60hKSvL+jouLY8aMGWzYsKHW8jt37iQ/P5/f/OY3jQ0fgOzsbCZPnkxycjIA/fr1Y/r06WzYsIGnnnqKDh06UFxczMmTJxkwYADx8fFNct/m0KqTNk33ISIi0nCnT58GoEsX97jxzZs3s3z5cvLz8ykvL6esrIzExMQ669i7dy8LFy7k+PHjXLt2jYqKCrp161Zj2a+++oof//jH/P73v6dNm6Z5Gfj5559z8OBBNm3a5D1WUVFBr169AHjhhRd46aWXmDRpEufPn2fcuHEsXbqUrl27Nsn9m1KrTtpu0HQfIiLS0pqi58vXtm7dSvfu3enTpw+FhYVMnjyZ7du3k5SURIcOHXj22Wc5dOiQt/zNiVZZWRnjx49n2bJlPPPMM3Tq1Ins7Gx+8Ytf1Hi/jz/+mLNnzzJixIhqx8eMGcP06dPJysq67WeIiYnhoYce4uWXX67xfFBQEFlZWWRlZXH27FlSU1PJyMhgzZo1TZY4NpW7ImkTERGR+issLGTt2rWsX7+erVu3YmaUlpZSWVlJeHg47du3Jzc3l40bN5KQ8LfXvhEREZw8edK7X1ZWxtWrVwkLC6NTp04cO3aM7OzsWu87ZMgQvvjii2rH7r//fn79619/I5Grr5kzZzJq1ChGjhzJyJEjMTOOHz9OSUkJQ4cOZefOnSQkJNCrVy9cLhcBAQG0a9fO+zznzp3jypUrBAYGNuj+TenOSiFFRESkRSxatIigoCCCg4MZNmwYp06d4uDBg95xbAkJCSxYsICUlBRCQ0PJyspi4sSJ1eqYO3cuOTk5hIWFkZSUhMvlYvXq1Tz33HO4XC7S09OZNGlSrTEEBAQQFRVVbQMIDw8nNDS0Qc/18MMPs337dpYuXUpkZCTdunVj6tSp3nF6n376KcnJyQQFBfHAAw/QtWtX74cSjz/+OEOHDiU6OprQ0FByc3MbFENTsRuf8fqr6PA+zvMTVpP+6ve8xx5880EAti0pB/yzS1pERPxbXl5etV4oubvU1v5mdthxnEcaUqd62kRERET8gJI2ERERET/Q+pK23zwJwM+2VbRwICIiIiJNp/UlbSfdMzA//Jl7rJ7maBMREZHWoPUlbTfRHG0iIiLSGrT6pE1ERESkNVDSJiIiIuIHlLSJiIiI+AElbSIiIiJ+QEmbiIiISBUVFRW4XC4++uijlg6lGiVtIiIid7nhw4cTEBBAUFAQISEh9OzZk7S0NA4fPnxb9cTGxpKTk9OoWObPn0+7du1wuVze7fnnn6+x7OLFi71lAgMDMTMCAwO9xxYvXtygGNq2bUtpaSnf+ta3GvMoTU5Jm4iIiDBv3jwuX77MxYsX2b9/PzExMSQmJrJjxw6fxzJ8+HBKS0u929KlS2ssl5GR4S1z4sQJAI4ePeo9lpGR8Y1rrl+/3qyxNyclbSIiIlJNTEwMmZmZTJkyhVmzZuE47gnrV65cSXx8PEFBQURHRzNnzhwqKtwrEI0dO5aCggKmTp2Ky+Vi1KhRAGzZsoUBAwYQHBxMZGQk06ZN48qVKz57lrVr1xIfH09WVhZRUVEMGjQIgOeff54ePXrgcrmIi4tj1apV3mvKy8sxM3Jzc6vV8corr9C9e3c6d+7MjBkzqKys9NlzALTz6d2am2cJKxERkTvRL6f/sdZzw7/fh37f7Q7A0QNnePc3J2otm/7q97y/ty3+iPMFl2s93xipqamsW7eOEydOEB8fT1RUFHv27CE2NpYjR44wevRoYmNjmTZtGrt37yY2NpbMzEwmT57srSMkJIRNmzaRkJBAfn4+48aNIzMzkyVLltR639zcXLp27UpQUBAjR45k8eLFhIeHN/g5Tp06RVFRESdPnvQmoP3792f27NlERESwb98+xowZQ9++fXnsscdqrOOzzz7jwoUL5Ofn8+WXXzJ48GAeffRRnn766QbHdbtaV0+bZwkrERERabyoqCgAiouLAZgwYQI9evTAzBg4cCBpaWns27evzjqSkpLo168fbdq0IS4ujhkzZtR5zZNPPsmxY8c4f/48f/zjHzlz5gwpKSneZKshOnbsyJIlS+jUqRP33HMPAGlpaURGRmJmjBw5ktGjR9cZl8vlYv78+QQEBNC7d29GjBjBoUOHGhxTQ7SunjYREZE7WH17wPp9t7u31+1WnspovsHyp0+fBqBLly4AbN68meXLl5Ofn095eTllZWUkJibWWcfevXtZuHAhx48f59q1a1RUVNCtW7day/fr18/7u0ePHrz++utERUWRn5/PAw880KDnuO+++2jfvn21YytWrOCNN96gsLAQgK+//rrO3rx7772XNm3+1tcVGBjI5cuXay3fHFpXT5uIiIg0ma1bt9K9e3f69OlDYWEhkydPZu7cuZw7d46LFy+Snp5erQesalIDUFZWxvjx40lNTaWgoIBLly6xdOnS2+o1u1FnY3rabo7rvffe44UXXuD111+nuLiYkpISkpOTG3UPX1DSJiIiItUUFhby4osjkF48AAATB0lEQVQvsn79elauXImZUVpaSmVlJeHh4bRv357c3Fw2btxY7bqIiAhOnjzp3S8rK+Pq1auEhYXRqVMnjh07RnZ2dp333r59O+fPnwfgzJkzTJ8+nUGDBjW4l60mly5dom3btoSHh2Nm7N69m7ffvvOHWClpExERERYtWkRQUBDBwcEMGzaMU6dOcfDgQSZMmABAQkICCxYsICUlhdDQULKyspg4cWK1OubOnUtOTg5hYWEkJSXhcrlYvXo1zz33HC6Xi/T0dCZNmlRnHL/97W/p27cv99xzD4mJiXTp0oXdu3djZk32rMnJyUycOJFBgwYRHh7Ozp07SUlJabL6m4vd6V2BtxId3sd5fsJq9ziB+SEAPNgjmm1LygFIOJ7XkuGJiMhdKi8vj4SEhJYOQ1pIbe1vZocdx3mkIXWqp01ERETEDyhpExEREfEDStpERERE/ICSNhERERE/0HqSNi1hJSIiIq1Y60nabixh1WsUP9tW0bKxiIiIiDSx1pO03fD9t3j4M/c0JoGPDmvhYERERESaRutL2qqIfu21lg5BREREpEm06qRNREREpLVQ0iYiInKXGz58OAEBAQQFBRESEkLPnj1JS0vj8OHDt1VPbGwsOTk5jYpl/vz5tGvXDpfL5d2ef/75GssuXrzYWyYwMBAzIzAw0Hts8eLFjYrl1VdfpX///o2qoykpaRMRERHmzZvH5cuXuXjxIvv37ycmJobExER27Njh81iGDx9OaWmpd1u6dGmN5TIyMrxlTpw4AcDRo0e9xzIyMnwZdrNT0iYiIiLVxMTEkJmZyZQpU5g1axY31ilfuXIl8fHxBAUFER0dzZw5c6iocM/YMHbsWAoKCpg6dSoul4tRo0YBsGXLFgYMGEBwcDCRkZFMmzaNK1eu+PR59u/fz3e+8x3CwsKIi4tj1apV3nNFRUU88cQTdO7cmZCQEB588EE+/PBD3n33XWbPnk1eXp635+6DDz7wadw3U9ImIiIiNUpNTeXMmTPeXqyoqCj27NnDpUuX2LVrF+vWrWPt2rUA7N69m+joaNauXUtpaSlvv+2eiiskJIRNmzZRUlLCgQMHOHDgAJmZmXXeNzc3l65du9KjRw9++MMfcv78+QY/w5EjR0hJSWHu3LkUFRWxa9culi1bxrZt2wC8r1ALCwu5cOECb731FhEREQwfPpwVK1aQkJDg7bkbMmRIg+NoCu1a9O4iIiJ3iV9O/6NP75f+6vcaXUdUVBQAxcXFAEyYMMF7buDAgaSlpbFv3z6mTZtWax1JSUne33FxccyYMYMNGzbUWv7JJ5/kmWee4f777+eLL74gPT2dlJQU/vSnP2Fmt/0M2dnZTJ48meTkZAD69evH9OnT2bBhA0899RQdOnSguLiYkydPMmDAAOLj42/7Hr6ipE1ERERqdPr0aQC6dOkCwObNm1m+fDn5+fmUl5dTVlZGYmJinXXs3buXhQsXcvz4ca5du0ZFRQXdunWrtXy/fv28v3v06MHrr79OVFQU+fn5PPDAA7f9DJ9//jkHDx5k06ZN3mMVFRX06tULgBdeeIGXXnqJSZMmcf78ecaNG8fSpUvp2rXrbd+ruSlpExER8YGm6Pnyta1bt9K9e3f69OlDYWEhkydPZvv27SQlJdGhQweeffZZDh065C3fpk31UVdlZWWMHz+eZcuW8cwzz9CpUyeys7P5xS9+Ue8YbtR5Y1zd7YqJieGhhx7i5ZdfrvF8UFAQWVlZZGVlcfbsWVJTU8nIyGDNmjXfeJ6WdmdFIyIiIi2usLCQF198kfXr17Ny5UrMjNLSUiorKwkPD6d9+/bk5uaycePGatdFRERw8uRJ735ZWRlXr14lLCyMTp06cezYMbKzs+u89/bt271j2M6cOcP06dMZNGhQg3rZAGbOnMmbb77Jnj17uH79OuXl5XzyySe8//77AOzcuZMTJ05QWVmJy+UiICCAdu3aeZ/n3LlzPv9wojZK2kRERIRFixYRFBREcHAww4YN49SpUxw8eNA7ji0hIYEFCxaQkpJCaGgoWVlZTJw4sVodc+fOJScnh7CwMJKSknC5XKxevZrnnnsOl8tFeno6kyZNqjOO3/72t/Tt25d77rmHxMREunTpwu7duxs0ng3g4YcfZvv27SxdupTIyEi6devG1KlTveP0Pv30U5KTkwkKCuKBBx6ga9eu3g8lHn/8cYYOHUp0dDShoaHk5uY2KIamYg3tbrxTRIf3cZ6fsJr0iCfcB+ZfJC8+AYCE43ktGJmIiNzN8vLySEhIaOkwpIXU1v5mdthxnEcaUqd62kRERET8gJI2ERERET/Q6pK2Ge/MaOkQRERERJpcq0vaDpw50NIhiIiIiDS5Vpe0iYiIiLRGStpERESaSWVlZUuHIC2gudpdSZuIiEgzCAwM5MyZM5SVlTV4Nn/xL47jUFZWxpkzZwgMDGzy+rWMlYiISDOIioqiqKiIL7/8kvLy8pYOR3ykXbt2hISENMvapa0raes1CsqPt3QUIiIitGnThm7dutW5OLrI7Whdr0e//xY/21bR0lGIiIiINDmfJm1mNtrMTpjZKTP7WQ3nf2Bm583siGeberv3ePgz97iBwEeHNUHEIiIiIncGn70eNbO2wC+B/wacBj4ys985jnPspqJbHceZ2dj7Rb/2WmOrEBEREblj+LKnbTBwynGcfMdxyoAtQIoP7y8iIiLit3yZtHUHCqvsn/Ycu9kEM/vYzP7FzO73TWgiIiIidzbz1dwxZvYk8LjjOFM9+2nAYMdxZlUp0wUodRznmplNB55yHOd7NdT1I+BHnt3+wCfN/gDSXLoCRS0dhDSI2s6/qf38l9rOv/VxHCeoIRf6csqP00DVnrMo4GzVAo7jFFfZfR1YWlNFjuOsAdYAmNkhx3EeadpQxVfUfv5Lbeff1H7+S23n38zsUEOv9eXr0Y+AXmbWw8w6AKnA76oWMLPIKrvjgDwfxiciIiJyx/JZT5vjOOVmNhP4N6AtsM5xnKNmthA45DjO74Afm9k4oBz4CviBr+ITERERuZP5dEUEx3H+APzhpmM/r/J7DjDnNqtd0wShSctR+/kvtZ1/U/v5L7Wdf2tw+/nsQwQRERERabjWtYyViIiISCvlN0lbPZbACjCzrZ7zH5pZrO+jlJrUo+1+YmbHPPPz7TOzmJaIU2p2q/arUu7vzcwxM33VdgepT/uZ2VOev4NHzWyTr2OUmtXj385oM9tvZv/u+fczuSXilG8ys3Vm9hczq3FKMnP7Z0/bfmxmD9enXr9I2qosgZUE9AUmmlnfm4r9A3DBcZw44BVqmS5EfKuebffvwCOO4/xfwL8Ay3wbpdSmnu2HmQUBPwY+9G2EUpf6tJ+Z9cI9lvjvHMfpB8z2eaDyDfX8uzcX2OY4zkDcMzL8yrdRSh3WA6PrOJ8E9PJsPwJW16dSv0jaqN8SWCnAm57f/wI8ZmbmwxilZrdsO8dx9juO87VnNxf3HH5yZ6jv8nOLcCfbV30ZnNxSfdrvh8AvHce5AOA4zl98HKPUrD5t5wDBnt8h3DT3qbQcx3H+N+5ZMGqTAmxw3HKB0JumPauRvyRt9VkCy1vGcZxy4CLQxSfRSV3qu3zZDf8A7GnWiOR23LL9zGwgcL/jOP/qy8CkXurz96830NvM/mRmuWZWV++A+E592m4+MNnMTuOemWEW4i9u97+NgI+n/GiEmnrMbv7stT5lxPfq3S5mNhl4BHi0WSOS21Fn+5lZG9zDEX7gq4DkttTn71873K9ohuPu5T5gZv0dxylp5tikbvVpu4nAesdxXjazIcBGT9tVNn940kgNyln8paftlktgVS1jZu1wdxXX1TUpvlGftsPMRgIvAOMcx7nmo9jk1m7VfkG41/9918y+ABKB3+ljhDtGff/t3OU4znXHcT4HTuBO4qRl1aft/gHYBuA4zgdAR9zrksqdr17/bbyZvyRtt1wCy7P/f3t+/z3wR0eT0N0J6rN82UDgNdwJm8bT3FnqbD/HcS46jtPVcZxYx3FicY9JHOc4ToPX1pMmVZ9/O3cCIwDMrCvu16X5Po1SalKftisAHgMwswTcSdt5n0YpDfU7YIrnK9JE4KLjOOdudZFfvB6t5xJYb+DuGj6Fu4ctteUilhvq2Xb/BLiAtzzfjhQ4jjOuxYIWr3q2n9yh6tl+/waMMrNjQAXwU8dxilsuaoF6t93/C7xuZv8P7ldrP1BnxZ3BzDbjHnLQ1TPm8EWgPYDjOK/iHoOYDJwCvgb+Z73qVfuKiIiI3Pn85fWoiIiIyF1NSZuIiIiIH1DSJiIiIuIHlLSJiIiI+AElbSIiIiJ+QEmbiDQ5M2tnZo6ZjW/pWBrKzOI8z/DQLcrlmNlOX8UlIncvJW0i8g1mtt6TsNy81ZnA+JKZZVaJq8LMCsxsjZk11ZrDnwORwCee+4303Cv0pnLpNPMyXlXufWMrNrN9nkk5b6cev0+mRe5mStpEpDbv4E5aqm6ftGhE33QUd1zRwEzgCWB9U1TsOE6F4zj/x3Gc8luUu+jDdTr74H7eEcAFYI9nFQMRuQsoaROR2lzzJC1Vt3IAM0s2s/fNrMTMvjKzPWbWp7aKPEu1zDezL83smpmdM7NfVznfxszmmFm+mf3VzP7TzCbWI8ZyT1xnPDPEZwNJZhbgqXeAmf3RU2exma0zs+Aq971x/pKZXTazI2b2qOec9/WomcUBez2XXfAcX+sp5309ambpZnbWzKr922pm28zst1X2U8zs/zOzq2b2uZkt8ixVdCt/8Tzvx8BLQCjwrSr1ftvM9ppZkZldNLMDZja4yvVfeP7c4XmGU00Qk4j4iJI2EWmIQGA57oRhBO5lWHabWftayj8FzAam416MfBzutRVvWAJMAf4X0BdYCrxhZqNvM66/4v53ra2ZuXAvAXQBGAxMAIYBr1cpvwUo9JwfCCwErtZQ7+eeZ4C/9Xb9pIZyW3Av2P29Gwc8SeJYIMeznwxsAP4Z6Id70e9Uz73rxcwC+dsr2etVTgUBbwLfBRKB/8TdGxfmOX8jwfufnmdIbKqYRMQHHMfRpk2btmob7leM5UBplW1PHeWDgUog0bPfDvdaiOM9+88Bx4B2NVwbhDtRGnLT8Wzgd3XcMxM4UmU/AfgM+JNn/3/hXoc4sEqZkZ64enj2rwDfr6X+OE/Zh266NvSmcjnAzir7u4FfV9n/gSeODp79g8Ccm+r4e9wLRtf2rDfufaMtHM/2YU3/N61yneFeQDy1pnapUu62Y9KmTZvvN/W0iUht/jfwUJVt6o0TZtbLzDZ7XmdeAs7iThCia6lrK+7k7HMzW2tmf1/l1Vt/IADYa2alNzbgh8ADt4jxQU/5v+Ie3/YFkOY5lwD8h+M4V6qU/1OVc+DuLVxvZu+YWYaZ9b7F/eojB/gfZtbRs/994C3Hcco8+4OAn9/0rBuAYDMLv0Xd3wUeBibi7v2b4lQZc2dm93o+xvjUzC4Cl4Eu1N4uNzQmJhHxkXYtHYCI3LG+dhznVC3nfo87afgh7oStEndPWo1joBzH+dKTEI0EHgNeAeaZ2RD+NkzjvwNnbrq0jLqdwP2qtQI46zjOtSrnDHevUrVQqv7pOM48M9sIJAOjgPlm9kPHcd68xX3rsgtYA4w1s/dxvz6u+prRgBeB7TVc+9Ut6v7ccX/08KnnFekOMxvgOM6NV6Q5uMe5zQa+BK4B71JLuzRRTCLiI0raROS2mNm9uMel/YPjOAc8xwZzizGyjuP8Fferw91m9k/Aadxjqg7jTs6iHcd57zbDKasjsTwGfN/MAqv0tg31/JlXJa5PgU+BFWb2Ou7xXDUlbTcSyLZ1BeQ4zlUz2467hy0K93O+X6XIvwN96oi7vtYD83C/Bv5nz7GhwI8cx/kDgJlFAhFVrqnwbDc/Q1PFJCLNSEmbiNyuIty9Lz8ys3O4E5N/wt3bViMze8bz88+4x5FNwj2A/pTjOBfN7BXgFTNrCxzAPUZuCO6kbG0D49yIu/foTTObj/sDgdXANsdxvvB8qLAE+Bfcr1Ujgb/D/Vq4Jl96/vzvZrYH+KvjOKW1lM0B/oD7o4XfOI5TtcdvAbDLzAqBt3AnUQ8CgxzH+Vl9H85xnAozWwnMMbO1juN8jTv5TDOzQ7hfR/8T7t62G9c4ZlYAPGZmf8L9hfCFpopJRJqXxrSJyG1xHKcCeBr32KpPgFXAHKp/xXizEuBHuHuc/hNIwT0YvsBzfg7uDwuex90L9jYwHvcr2IbGWQo8DnTG/aXqdtwJ4Q89RcpxJ3IbcCc7N84/W0t9X+JObpYB/wWsqOP2+4G/APF4vhqtUs8fcH9N+t88cf0Z94caBdy+tUAn3HPUgfujh1DcPWebgNdwfx1b1U889y703L+pYxKRZmLV/x9AEREREbkTqadNRERExA8oaRMRERHxA0raRERERPyAkjYRERERP6CkTURERMQPKGkTERER8QNK2kRERET8gJI2ERERET+gpE1ERETED/z/eNaJpnQhcYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Final Model Results for All data files\n",
    "\n",
    "df_list = [df1, df2, df3, df4, df5]\n",
    "\n",
    "final_model_df = compare_datafiles_perf(df_list, xgbParams_final, 'final_model', 1, 1, 1)\n",
    "final_model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample weights are used!\n",
      "Data 1 Classification Report:\n",
      "\n",
      "Training Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00      5401\n",
      "           1       0.88      1.00      0.94       220\n",
      "\n",
      "    accuracy                           0.99      5621\n",
      "   macro avg       0.94      1.00      0.97      5621\n",
      "weighted avg       1.00      0.99      0.99      5621\n",
      "\n",
      "Testing Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      1355\n",
      "           1       0.63      0.82      0.71        51\n",
      "\n",
      "    accuracy                           0.98      1406\n",
      "   macro avg       0.81      0.90      0.85      1406\n",
      "weighted avg       0.98      0.98      0.98      1406\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data 1 Final Model\n",
    "d1_final_model = xgb_model_report2(1, df1, xgbParams_final, 'final_model', 1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample weights are used!\n",
      "Data 2 Classification Report:\n",
      "\n",
      "Training Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00      7809\n",
      "           1       0.87      1.00      0.93       329\n",
      "\n",
      "    accuracy                           0.99      8138\n",
      "   macro avg       0.94      1.00      0.96      8138\n",
      "weighted avg       0.99      0.99      0.99      8138\n",
      "\n",
      "Testing Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98      1964\n",
      "           1       0.48      0.63      0.55        71\n",
      "\n",
      "    accuracy                           0.96      2035\n",
      "   macro avg       0.74      0.80      0.76      2035\n",
      "weighted avg       0.97      0.96      0.97      2035\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data 2 Final Model\n",
    "d2_final_model = xgb_model_report2(2, df2, xgbParams_final, 'final_model', 1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample weights are used!\n",
      "Data 3 Classification Report:\n",
      "\n",
      "Training Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99      8014\n",
      "           1       0.77      1.00      0.87       388\n",
      "\n",
      "    accuracy                           0.99      8402\n",
      "   macro avg       0.88      0.99      0.93      8402\n",
      "weighted avg       0.99      0.99      0.99      8402\n",
      "\n",
      "Testing Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98      1994\n",
      "           1       0.54      0.72      0.62       107\n",
      "\n",
      "    accuracy                           0.95      2101\n",
      "   macro avg       0.76      0.84      0.80      2101\n",
      "weighted avg       0.96      0.95      0.96      2101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data 3 Final Model\n",
    "d3_final_model = xgb_model_report2(3, df3, xgbParams_final, 'final_model', 1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample weights are used!\n",
      "Data 4 Classification Report:\n",
      "\n",
      "Training Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99      7421\n",
      "           1       0.81      1.00      0.89       412\n",
      "\n",
      "    accuracy                           0.99      7833\n",
      "   macro avg       0.90      0.99      0.94      7833\n",
      "weighted avg       0.99      0.99      0.99      7833\n",
      "\n",
      "Testing Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98      1856\n",
      "           1       0.56      0.68      0.61       103\n",
      "\n",
      "    accuracy                           0.95      1959\n",
      "   macro avg       0.77      0.82      0.79      1959\n",
      "weighted avg       0.96      0.95      0.96      1959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data 4 Final Model\n",
    "d4_final_model = xgb_model_report2(4, df4, xgbParams_final, 'final_model', 1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample weights are used!\n",
      "Data 5 Classification Report:\n",
      "\n",
      "Training Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99      4408\n",
      "           1       0.83      1.00      0.91       320\n",
      "\n",
      "    accuracy                           0.99      4728\n",
      "   macro avg       0.91      0.99      0.95      4728\n",
      "weighted avg       0.99      0.99      0.99      4728\n",
      "\n",
      "Testing Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97      1092\n",
      "           1       0.61      0.79      0.69        90\n",
      "\n",
      "    accuracy                           0.95      1182\n",
      "   macro avg       0.79      0.87      0.83      1182\n",
      "weighted avg       0.95      0.95      0.95      1182\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data 5 Final Model\n",
    "d5_final_model = xgb_model_report2(5, df5, xgbParams_final, 'final_model', 1, 1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Predictor Atttributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Data1</th>\n",
       "      <th>Data2</th>\n",
       "      <th>Data3</th>\n",
       "      <th>Data4</th>\n",
       "      <th>Data5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Attr34</td>\n",
       "      <td>Attr59</td>\n",
       "      <td>Attr52</td>\n",
       "      <td>Attr62</td>\n",
       "      <td>Attr62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Attr6</td>\n",
       "      <td>Attr34</td>\n",
       "      <td>Attr34</td>\n",
       "      <td>Attr33</td>\n",
       "      <td>Attr34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attr22</td>\n",
       "      <td>Attr10</td>\n",
       "      <td>Attr10</td>\n",
       "      <td>Attr31</td>\n",
       "      <td>Attr64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Attr24</td>\n",
       "      <td>Attr5</td>\n",
       "      <td>Attr26</td>\n",
       "      <td>Attr6</td>\n",
       "      <td>Attr7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Attr25</td>\n",
       "      <td>Attr27</td>\n",
       "      <td>Attr27</td>\n",
       "      <td>Attr34</td>\n",
       "      <td>Attr27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Attr27</td>\n",
       "      <td>Attr7</td>\n",
       "      <td>Attr59</td>\n",
       "      <td>Attr27</td>\n",
       "      <td>Attr6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Attr26</td>\n",
       "      <td>Attr26</td>\n",
       "      <td>Attr31</td>\n",
       "      <td>Attr26</td>\n",
       "      <td>Attr42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Attr8</td>\n",
       "      <td>Attr25</td>\n",
       "      <td>Attr16</td>\n",
       "      <td>Attr42</td>\n",
       "      <td>Attr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Attr45</td>\n",
       "      <td>Attr24</td>\n",
       "      <td>Attr35</td>\n",
       "      <td>Attr52</td>\n",
       "      <td>Attr49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Attr50</td>\n",
       "      <td>Attr16</td>\n",
       "      <td>Attr24</td>\n",
       "      <td>Attr13</td>\n",
       "      <td>Attr5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Attr1</td>\n",
       "      <td>Attr22</td>\n",
       "      <td>Attr25</td>\n",
       "      <td>Attr57</td>\n",
       "      <td>Attr48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Attr51</td>\n",
       "      <td>Attr58</td>\n",
       "      <td>Attr20</td>\n",
       "      <td>Attr21</td>\n",
       "      <td>Attr25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Attr5</td>\n",
       "      <td>Attr15</td>\n",
       "      <td>Attr13</td>\n",
       "      <td>Attr64</td>\n",
       "      <td>Attr51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Attr59</td>\n",
       "      <td>Attr11</td>\n",
       "      <td>Attr41</td>\n",
       "      <td>Attr41</td>\n",
       "      <td>Attr43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Attr21</td>\n",
       "      <td>Attr42</td>\n",
       "      <td>Attr5</td>\n",
       "      <td>Attr1</td>\n",
       "      <td>Attr31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Attr49</td>\n",
       "      <td>Attr41</td>\n",
       "      <td>Attr37</td>\n",
       "      <td>Attr5</td>\n",
       "      <td>Attr23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Attr13</td>\n",
       "      <td>Attr28</td>\n",
       "      <td>Attr45</td>\n",
       "      <td>Attr37</td>\n",
       "      <td>Attr2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Attr37</td>\n",
       "      <td>Attr20</td>\n",
       "      <td>Attr58</td>\n",
       "      <td>Attr24</td>\n",
       "      <td>Attr50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Attr38</td>\n",
       "      <td>Attr31</td>\n",
       "      <td>Attr19</td>\n",
       "      <td>Attr59</td>\n",
       "      <td>Attr21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Attr35</td>\n",
       "      <td>Attr37</td>\n",
       "      <td>Attr6</td>\n",
       "      <td>Attr15</td>\n",
       "      <td>Attr14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data1   Data2   Data3   Data4   Data5\n",
       "0   Attr34  Attr59  Attr52  Attr62  Attr62\n",
       "1    Attr6  Attr34  Attr34  Attr33  Attr34\n",
       "2   Attr22  Attr10  Attr10  Attr31  Attr64\n",
       "3   Attr24   Attr5  Attr26   Attr6   Attr7\n",
       "4   Attr25  Attr27  Attr27  Attr34  Attr27\n",
       "5   Attr27   Attr7  Attr59  Attr27   Attr6\n",
       "6   Attr26  Attr26  Attr31  Attr26  Attr42\n",
       "7    Attr8  Attr25  Attr16  Attr42  Attr16\n",
       "8   Attr45  Attr24  Attr35  Attr52  Attr49\n",
       "9   Attr50  Attr16  Attr24  Attr13   Attr5\n",
       "10   Attr1  Attr22  Attr25  Attr57  Attr48\n",
       "11  Attr51  Attr58  Attr20  Attr21  Attr25\n",
       "12   Attr5  Attr15  Attr13  Attr64  Attr51\n",
       "13  Attr59  Attr11  Attr41  Attr41  Attr43\n",
       "14  Attr21  Attr42   Attr5   Attr1  Attr31\n",
       "15  Attr49  Attr41  Attr37   Attr5  Attr23\n",
       "16  Attr13  Attr28  Attr45  Attr37   Attr2\n",
       "17  Attr37  Attr20  Attr58  Attr24  Attr50\n",
       "18  Attr38  Attr31  Attr19  Attr59  Attr21\n",
       "19  Attr35  Attr37   Attr6  Attr15  Attr14"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List important attitudes\n",
    "\n",
    "final_model_list = [d1_final_model, d2_final_model, d3_final_model, d4_final_model, d5_final_model]\n",
    "\n",
    "df_important_Attr = pd.DataFrame()\n",
    "cols = df1.columns\n",
    "\n",
    "for i,df in enumerate(final_model_list, start=1):\n",
    "    importance = df.feature_importances_\n",
    "    attribute_importance = pd.DataFrame([cols, importance], index=['Attribute', 'Importance']).T\n",
    "    attribute_importance.sort_values(by='Importance', ascending=False, inplace=True, ignore_index=True)\n",
    "    df_important_Attr[f'Data{i}'] = attribute_importance['Attribute'][0:20]\n",
    "\n",
    "df_important_Attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Attr27', 'Attr34', 'Attr5'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Common Best predictors\n",
    "\n",
    "set1 = set(df_important_Attr['Data1'])\n",
    "set2 = set(df_important_Attr['Data2'])\n",
    "set3 = set(df_important_Attr['Data3'])\n",
    "set4 = set(df_important_Attr['Data4'])\n",
    "set5 = set(df_important_Attr['Data5'])\n",
    "\n",
    "important_attr_common = set.intersection(set1, set2, set3, set4, set5)\n",
    "important_attr_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Graphs, Data 3\n",
    "\n",
    "plt.figure(figsize=(20, 5)) \n",
    "\n",
    "for i, col in enumerate(important_attr_common, start=1):\n",
    "    plt.subplot(1, 3, i)\n",
    "    plt.scatter(df3[col], df3['class'], alpha=0.1)\n",
    "    plt.title(col)\n",
    "\n",
    "plt.savefig('figures/scatter_d3_importantAttr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms\n",
    "\n",
    "plt.figure(figsize=(20, 5)) \n",
    "\n",
    "for i, col in enumerate(important_attr_common, start=1):\n",
    "    plt.subplot(1, 3, i)\n",
    "    plt.hist(df3[col], bins=100, log=True)\n",
    "    plt.title(col)\n",
    "    \n",
    "plt.savefig('figures/hist_d3_log_importantAttr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plots\n",
    "\n",
    "plt.figure(figsize=(20, 5)) \n",
    "\n",
    "for i, col in enumerate(important_attr_common, start=1):\n",
    "    plt.subplot(1, 3, i)\n",
    "    plt.boxplot(df3[col].dropna()) \n",
    "    plt.title(col)\n",
    "\n",
    "plt.savefig('figures/boxPlot_d3_importantAttr.png')\n",
    "\n",
    "## Boxplot only draws the attribute only when missing entries removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best predictor descriptions**\n",
    "\n",
    "* X27: profit on operating activities / financial expenses\n",
    "* X34: operating expenses / total liabilities\n",
    "* X5: [(cash + short-term securities + receivables - short-term liabilities) / (operating expenses - depreciation)] * 365"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of Results\n",
    "\n",
    "5-year Period (Data 1):\n",
    "* Model correctly identifies the 80.4% of the true bankrupt companies, which will bankrupt 5 years later. (recall)\n",
    "* Among the model predicted bankruptcy companies, 64.1% of them are true bankrupt companies, which will bankrupt 5 years later. (precision)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score) is 71.3%.\n",
    "    \n",
    "4-year Period (Data 2):\n",
    "* Model correctly identifies the 62.0% of the true bankrupt companies, which will bankrupt 4 years later. (recall)\n",
    "* Among the model predicted bankruptcy companies, 50.6% of them are true bankrupt companies, which will bankrupt 4 years later. (precision)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score) is 55.7%.\n",
    "    \n",
    "3-year Period (Data 3):\n",
    "* Model correctly identifies the 72.0% of the true bankrupt companies, which will bankrupt 3 years later. (recall)\n",
    "* Among the model predicted bankruptcy companies, 53.5% of them are true bankrupt companies, which will bankrupt 3 years later. * The Harmonic Mean of Precision and Recall (f1-score) is 61.4%.\n",
    "\n",
    "2-year Period (Data 4):\n",
    "* Model correctly identifies the 68.0% of the true bankrupt companies, which will bankrupt 2 years later. (recall)\n",
    "* Among the model predicted bankruptcy companies, 55.6% of them are true bankrupt companies, which will bankrupt 2 years later. (precision)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score) is 61.1%.\n",
    "    \n",
    "1-year Period (Data 5):\n",
    "* Model correctly identifies the 78.9% of the true bankrupt companies, which will bankrupt 1 years later. (recall)\n",
    "* Among the model predicted bankruptcy companies, 60.7% of them are true bankrupt companies, which will bankrupt 1 years later. (precision)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score) is 68.6%.\n",
    "\n",
    "On Average:\n",
    "* Model correctly identifies the 72.3% of the true bankrupt companies. (recall)\n",
    "* Among the model predicted bankruptcy companies, 56.9% of them are true bankrupt companies. (precision)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score) is 63.6%.\n",
    "    \n",
    "Class 0 predictions:\n",
    "* Model correctly identifies the ~97% of the true still operating companies. (recall, class 0)\n",
    "* Among the model predicted still operating companies, ~98% of them are true still operating companies. (precision, class 0)\n",
    "* The Harmonic Mean of Precision and Recall (f1-score, class 0) is ~98%.\n",
    " \n",
    "\n",
    "## Conclusion\n",
    "\n",
    "I had three main challenges in this project:\n",
    "\n",
    "1. **Class Imbalance:** \n",
    "    * Resolved. \n",
    "    * Balancing the sample increased the recall and so decreased the precision. The balance sample has moderate evaluation scores. See Model 2 for Data 3. \n",
    "    * However overfitting didn't improve much.\n",
    "\n",
    "2. **Low recall score:**\n",
    "    * I managed to improve recall significantly for all datasets. For instance, Data 3 recall score improved from 0.467 to 0.720.\n",
    "    * I couldn't go for higher recall value, because the precision was decreasing dramatically (below 0.5). The recall and precision are inversely proportional.\n",
    "\n",
    "3. **Large overfitting:** \n",
    "    * I did decrease the overfitting, but not on desired level. \n",
    "    * I tuned parameters which are effecive on overfitting, and find the optimum designs that produces low overfitting, large recall and moderate precision. \n",
    "    * However, I couldn't enforce larger reduction in overfitting, since it causes the precision go below 0.5. which is the random guess probability.\n",
    "\n",
    "Overall, my model correctly identifies\n",
    "* 72.3% of the true bankrupt companies\n",
    "* 97% of the true still operating companies\n",
    "\n",
    "## Future Work\n",
    "\n",
    "* Create seperate final models for each dataset; not just one final model that applies on all. \n",
    "\n",
    "* Search for alternative Classifier methods/tools.\n",
    "\n",
    "* Simply/shorten functions that are created during the project. They have repeating codes.\n",
    "\n"
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