prediction_model.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from utils.preparedata import PrepareData\n",
    "# Pipeline and transformations\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.model_selection import train_test_split \n",
    "# Regressors\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "#Metrics\n",
    "from sklearn import metrics\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset\n",
    "parser= lambda x: pd.to_datetime(x)\n",
    "df = pd.read_csv('http://huy302.github.io/interview_dataset.csv', parse_dates=['date on production'], date_parser=parser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's separate dataset in train and test\n",
    "X = df.drop('production', axis=1)\n",
    "y = df['production']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_columns = ['azimuth', 'md (ft)', 'tvd (ft)', 'well spacing', 'porpoise deviation', \n",
    "        'porpoise count', 'shale footage', 'acoustic impedance', 'log permeability', 'porosity', \n",
    "        'poisson ratio', 'toc', 'vcl', 'p-velocity', 's-velocity', 'youngs modulus', 'isip', 'pump rate',\n",
    "       'total number of stages', 'proppant volume', 'proppant fluid ratio']\n",
    "\n",
    "num_transform = MinMaxScaler()\n",
    "\n",
    "cat_columns = ['year on production', 'month on production']\n",
    "\n",
    "cat_transform = OneHotEncoder()\n",
    "\n",
    "column_transform = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", num_transform, num_columns),\n",
    "        (\"cat\", cat_transform, cat_columns)\n",
    "        ]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        ('features', PrepareData()),\n",
    "        ('col_transform', column_transform),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]\n",
    ")\n",
    "\n",
    "grid_param = [\n",
    "    {\n",
    "        \"regressor\": [XGBRegressor()]\n",
    "    },\n",
    "    {\n",
    "        \"regressor\": [RandomForestRegressor()]\n",
    "    },\n",
    "    {\n",
    "        \"regressor\": [BaggingRegressor()]\n",
    "    }   \n",
    "]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evalaute models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridsearch = GridSearchCV(pipe, grid_param, cv=5, verbose=0, n_jobs=-1)\n",
    "best_model = gridsearch.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "                (&#x27;col_transform&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;,\n",
       "                                                   &#x27;tvd (ft)&#x27;, &#x27;well spacing&#x27;,\n",
       "                                                   &#x27;porpoise deviation&#x27;,\n",
       "                                                   &#x27;porpoise count&#x27;,\n",
       "                                                   &#x27;shale footage&#x27;,\n",
       "                                                   &#x27;acoustic impedance&#x27;,\n",
       "                                                   &#x27;log permeability&#x27;,\n",
       "                                                   &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;,\n",
       "                                                   &#x27;toc&#x27;, &#x27;vcl&#x27;, &#x27;p-velocity&#x27;,\n",
       "                                                   &#x27;s-velocity&#x27;,\n",
       "                                                   &#x27;youngs modulus&#x27;, &#x27;isip&#x27;,\n",
       "                                                   &#x27;pump rate&#x27;,\n",
       "                                                   &#x27;total number of stages&#x27;,\n",
       "                                                   &#x27;proppant volume&#x27;,\n",
       "                                                   &#x27;proppant fluid ratio&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;year on production&#x27;,\n",
       "                                                   &#x27;month on production&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, RandomForestRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;features&#x27;, PrepareData()),\n",
       "                (&#x27;col_transform&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;,\n",
       "                                                   &#x27;tvd (ft)&#x27;, &#x27;well spacing&#x27;,\n",
       "                                                   &#x27;porpoise deviation&#x27;,\n",
       "                                                   &#x27;porpoise count&#x27;,\n",
       "                                                   &#x27;shale footage&#x27;,\n",
       "                                                   &#x27;acoustic impedance&#x27;,\n",
       "                                                   &#x27;log permeability&#x27;,\n",
       "                                                   &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;,\n",
       "                                                   &#x27;toc&#x27;, &#x27;vcl&#x27;, &#x27;p-velocity&#x27;,\n",
       "                                                   &#x27;s-velocity&#x27;,\n",
       "                                                   &#x27;youngs modulus&#x27;, &#x27;isip&#x27;,\n",
       "                                                   &#x27;pump rate&#x27;,\n",
       "                                                   &#x27;total number of stages&#x27;,\n",
       "                                                   &#x27;proppant volume&#x27;,\n",
       "                                                   &#x27;proppant fluid ratio&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;year on production&#x27;,\n",
       "                                                   &#x27;month on production&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, RandomForestRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PrepareData</label><div class=\"sk-toggleable__content\"><pre>PrepareData()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_transform: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                 [&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;, &#x27;tvd (ft)&#x27;,\n",
       "                                  &#x27;well spacing&#x27;, &#x27;porpoise deviation&#x27;,\n",
       "                                  &#x27;porpoise count&#x27;, &#x27;shale footage&#x27;,\n",
       "                                  &#x27;acoustic impedance&#x27;, &#x27;log permeability&#x27;,\n",
       "                                  &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;, &#x27;toc&#x27;, &#x27;vcl&#x27;,\n",
       "                                  &#x27;p-velocity&#x27;, &#x27;s-velocity&#x27;, &#x27;youngs modulus&#x27;,\n",
       "                                  &#x27;isip&#x27;, &#x27;pump rate&#x27;, &#x27;total number of stages&#x27;,\n",
       "                                  &#x27;proppant volume&#x27;, &#x27;proppant fluid ratio&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;year on production&#x27;,\n",
       "                                  &#x27;month on production&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;, &#x27;tvd (ft)&#x27;, &#x27;well spacing&#x27;, &#x27;porpoise deviation&#x27;, &#x27;porpoise count&#x27;, &#x27;shale footage&#x27;, &#x27;acoustic impedance&#x27;, &#x27;log permeability&#x27;, &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;, &#x27;toc&#x27;, &#x27;vcl&#x27;, &#x27;p-velocity&#x27;, &#x27;s-velocity&#x27;, &#x27;youngs modulus&#x27;, &#x27;isip&#x27;, &#x27;pump rate&#x27;, &#x27;total number of stages&#x27;, &#x27;proppant volume&#x27;, &#x27;proppant fluid ratio&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year on production&#x27;, &#x27;month on production&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('features', PrepareData()),\n",
       "                ('col_transform',\n",
       "                 ColumnTransformer(transformers=[('num', MinMaxScaler(),\n",
       "                                                  ['azimuth', 'md (ft)',\n",
       "                                                   'tvd (ft)', 'well spacing',\n",
       "                                                   'porpoise deviation',\n",
       "                                                   'porpoise count',\n",
       "                                                   'shale footage',\n",
       "                                                   'acoustic impedance',\n",
       "                                                   'log permeability',\n",
       "                                                   'porosity', 'poisson ratio',\n",
       "                                                   'toc', 'vcl', 'p-velocity',\n",
       "                                                   's-velocity',\n",
       "                                                   'youngs modulus', 'isip',\n",
       "                                                   'pump rate',\n",
       "                                                   'total number of stages',\n",
       "                                                   'proppant volume',\n",
       "                                                   'proppant fluid ratio']),\n",
       "                                                 ('cat', OneHotEncoder(),\n",
       "                                                  ['year on production',\n",
       "                                                   'month on production'])])),\n",
       "                ('regressor', RandomForestRegressor())])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.67\n"
     ]
    }
   ],
   "source": [
    "print('R2: {:.2f}'.format(best_model.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = best_model.predict(X_test)\n",
    "sns.distplot(y_test - y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :  531.33\n",
      "RMSE: 806.50\n",
      "MSE :  650439.41\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE :  {:.2f}\".format(metrics.mean_absolute_error(y_test, y_pred)))\n",
    "print(\"RMSE: {:.2f}\".format(np.sqrt(metrics.mean_squared_error(y_test, y_pred))))\n",
    "print(\"MSE :  {:.2f}\".format(metrics.mean_squared_error(y_test, y_pred)))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start=100, stop=1200, num=12)]\n",
    "max_depth = [int(x) for x in np.linspace(3, 30, num=10)] + [None]\n",
    "min_samples_split  = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "\n",
    "random_grid = {\n",
    "               'regressor__n_estimators': n_estimators,\n",
    "               'regressor__max_depth': max_depth,\n",
    "               'regressor__min_samples_split': min_samples_split,\n",
    "               'regressor__min_samples_leaf': min_samples_leaf\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr_random = RandomizedSearchCV(estimator=best_model.best_estimator_, param_distributions=random_grid, \n",
    "                                scoring='neg_mean_squared_error', n_iter=10, cv=5, n_jobs=-1, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;features&#x27;, PrepareData()),\n",
       "                                             (&#x27;col_transform&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               MinMaxScaler(),\n",
       "                                                                               [&#x27;azimuth&#x27;,\n",
       "                                                                                &#x27;md &#x27;\n",
       "                                                                                &#x27;(ft)&#x27;,\n",
       "                                                                                &#x27;tvd &#x27;\n",
       "                                                                                &#x27;(ft)&#x27;,\n",
       "                                                                                &#x27;well &#x27;\n",
       "                                                                                &#x27;spacing&#x27;,\n",
       "                                                                                &#x27;porpoise &#x27;\n",
       "                                                                                &#x27;deviation&#x27;,\n",
       "                                                                                &#x27;porpoise &#x27;\n",
       "                                                                                &#x27;count&#x27;,\n",
       "                                                                                &#x27;shale &#x27;\n",
       "                                                                                &#x27;footage&#x27;,\n",
       "                                                                                &#x27;acoustic &#x27;\n",
       "                                                                                &#x27;impedance&#x27;,\n",
       "                                                                                &#x27;log &#x27;\n",
       "                                                                                &#x27;permeability&#x27;,\n",
       "                                                                                &#x27;porosity&#x27;,\n",
       "                                                                                &#x27;poisson &#x27;\n",
       "                                                                                &#x27;ratio&#x27;,\n",
       "                                                                                &#x27;toc&#x27;,\n",
       "                                                                                &#x27;vcl&#x27;,\n",
       "                                                                                &#x27;p-velocity&#x27;,...\n",
       "                                                                                &#x27;on &#x27;\n",
       "                                                                                &#x27;production&#x27;])])),\n",
       "                                             (&#x27;regressor&#x27;,\n",
       "                                              RandomForestRegressor())]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;regressor__max_depth&#x27;: [3, 6, 9, 12,\n",
       "                                                                 15, 18, 21, 24,\n",
       "                                                                 27, 30, None],\n",
       "                                        &#x27;regressor__min_samples_leaf&#x27;: [1, 2, 5,\n",
       "                                                                        10],\n",
       "                                        &#x27;regressor__min_samples_split&#x27;: [2, 5,\n",
       "                                                                         10, 15,\n",
       "                                                                         100],\n",
       "                                        &#x27;regressor__n_estimators&#x27;: [100, 200,\n",
       "                                                                    300, 400,\n",
       "                                                                    500, 600,\n",
       "                                                                    700, 800,\n",
       "                                                                    900, 1000,\n",
       "                                                                    1100,\n",
       "                                                                    1200]},\n",
       "                   scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;features&#x27;, PrepareData()),\n",
       "                                             (&#x27;col_transform&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               MinMaxScaler(),\n",
       "                                                                               [&#x27;azimuth&#x27;,\n",
       "                                                                                &#x27;md &#x27;\n",
       "                                                                                &#x27;(ft)&#x27;,\n",
       "                                                                                &#x27;tvd &#x27;\n",
       "                                                                                &#x27;(ft)&#x27;,\n",
       "                                                                                &#x27;well &#x27;\n",
       "                                                                                &#x27;spacing&#x27;,\n",
       "                                                                                &#x27;porpoise &#x27;\n",
       "                                                                                &#x27;deviation&#x27;,\n",
       "                                                                                &#x27;porpoise &#x27;\n",
       "                                                                                &#x27;count&#x27;,\n",
       "                                                                                &#x27;shale &#x27;\n",
       "                                                                                &#x27;footage&#x27;,\n",
       "                                                                                &#x27;acoustic &#x27;\n",
       "                                                                                &#x27;impedance&#x27;,\n",
       "                                                                                &#x27;log &#x27;\n",
       "                                                                                &#x27;permeability&#x27;,\n",
       "                                                                                &#x27;porosity&#x27;,\n",
       "                                                                                &#x27;poisson &#x27;\n",
       "                                                                                &#x27;ratio&#x27;,\n",
       "                                                                                &#x27;toc&#x27;,\n",
       "                                                                                &#x27;vcl&#x27;,\n",
       "                                                                                &#x27;p-velocity&#x27;,...\n",
       "                                                                                &#x27;on &#x27;\n",
       "                                                                                &#x27;production&#x27;])])),\n",
       "                                             (&#x27;regressor&#x27;,\n",
       "                                              RandomForestRegressor())]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;regressor__max_depth&#x27;: [3, 6, 9, 12,\n",
       "                                                                 15, 18, 21, 24,\n",
       "                                                                 27, 30, None],\n",
       "                                        &#x27;regressor__min_samples_leaf&#x27;: [1, 2, 5,\n",
       "                                                                        10],\n",
       "                                        &#x27;regressor__min_samples_split&#x27;: [2, 5,\n",
       "                                                                         10, 15,\n",
       "                                                                         100],\n",
       "                                        &#x27;regressor__n_estimators&#x27;: [100, 200,\n",
       "                                                                    300, 400,\n",
       "                                                                    500, 600,\n",
       "                                                                    700, 800,\n",
       "                                                                    900, 1000,\n",
       "                                                                    1100,\n",
       "                                                                    1200]},\n",
       "                   scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;features&#x27;, PrepareData()),\n",
       "                (&#x27;col_transform&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                                  [&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;,\n",
       "                                                   &#x27;tvd (ft)&#x27;, &#x27;well spacing&#x27;,\n",
       "                                                   &#x27;porpoise deviation&#x27;,\n",
       "                                                   &#x27;porpoise count&#x27;,\n",
       "                                                   &#x27;shale footage&#x27;,\n",
       "                                                   &#x27;acoustic impedance&#x27;,\n",
       "                                                   &#x27;log permeability&#x27;,\n",
       "                                                   &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;,\n",
       "                                                   &#x27;toc&#x27;, &#x27;vcl&#x27;, &#x27;p-velocity&#x27;,\n",
       "                                                   &#x27;s-velocity&#x27;,\n",
       "                                                   &#x27;youngs modulus&#x27;, &#x27;isip&#x27;,\n",
       "                                                   &#x27;pump rate&#x27;,\n",
       "                                                   &#x27;total number of stages&#x27;,\n",
       "                                                   &#x27;proppant volume&#x27;,\n",
       "                                                   &#x27;proppant fluid ratio&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;year on production&#x27;,\n",
       "                                                   &#x27;month on production&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, RandomForestRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PrepareData</label><div class=\"sk-toggleable__content\"><pre>PrepareData()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_transform: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, MinMaxScaler(),\n",
       "                                 [&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;, &#x27;tvd (ft)&#x27;,\n",
       "                                  &#x27;well spacing&#x27;, &#x27;porpoise deviation&#x27;,\n",
       "                                  &#x27;porpoise count&#x27;, &#x27;shale footage&#x27;,\n",
       "                                  &#x27;acoustic impedance&#x27;, &#x27;log permeability&#x27;,\n",
       "                                  &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;, &#x27;toc&#x27;, &#x27;vcl&#x27;,\n",
       "                                  &#x27;p-velocity&#x27;, &#x27;s-velocity&#x27;, &#x27;youngs modulus&#x27;,\n",
       "                                  &#x27;isip&#x27;, &#x27;pump rate&#x27;, &#x27;total number of stages&#x27;,\n",
       "                                  &#x27;proppant volume&#x27;, &#x27;proppant fluid ratio&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;year on production&#x27;,\n",
       "                                  &#x27;month on production&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;azimuth&#x27;, &#x27;md (ft)&#x27;, &#x27;tvd (ft)&#x27;, &#x27;well spacing&#x27;, &#x27;porpoise deviation&#x27;, &#x27;porpoise count&#x27;, &#x27;shale footage&#x27;, &#x27;acoustic impedance&#x27;, &#x27;log permeability&#x27;, &#x27;porosity&#x27;, &#x27;poisson ratio&#x27;, &#x27;toc&#x27;, &#x27;vcl&#x27;, &#x27;p-velocity&#x27;, &#x27;s-velocity&#x27;, &#x27;youngs modulus&#x27;, &#x27;isip&#x27;, &#x27;pump rate&#x27;, &#x27;total number of stages&#x27;, &#x27;proppant volume&#x27;, &#x27;proppant fluid ratio&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year on production&#x27;, &#x27;month on production&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('features', PrepareData()),\n",
       "                                             ('col_transform',\n",
       "                                              ColumnTransformer(transformers=[('num',\n",
       "                                                                               MinMaxScaler(),\n",
       "                                                                               ['azimuth',\n",
       "                                                                                'md '\n",
       "                                                                                '(ft)',\n",
       "                                                                                'tvd '\n",
       "                                                                                '(ft)',\n",
       "                                                                                'well '\n",
       "                                                                                'spacing',\n",
       "                                                                                'porpoise '\n",
       "                                                                                'deviation',\n",
       "                                                                                'porpoise '\n",
       "                                                                                'count',\n",
       "                                                                                'shale '\n",
       "                                                                                'footage',\n",
       "                                                                                'acoustic '\n",
       "                                                                                'impedance',\n",
       "                                                                                'log '\n",
       "                                                                                'permeability',\n",
       "                                                                                'porosity',\n",
       "                                                                                'poisson '\n",
       "                                                                                'ratio',\n",
       "                                                                                'toc',\n",
       "                                                                                'vcl',\n",
       "                                                                                'p-velocity',...\n",
       "                                                                                'on '\n",
       "                                                                                'production'])])),\n",
       "                                             ('regressor',\n",
       "                                              RandomForestRegressor())]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'regressor__max_depth': [3, 6, 9, 12,\n",
       "                                                                 15, 18, 21, 24,\n",
       "                                                                 27, 30, None],\n",
       "                                        'regressor__min_samples_leaf': [1, 2, 5,\n",
       "                                                                        10],\n",
       "                                        'regressor__min_samples_split': [2, 5,\n",
       "                                                                         10, 15,\n",
       "                                                                         100],\n",
       "                                        'regressor__n_estimators': [100, 200,\n",
       "                                                                    300, 400,\n",
       "                                                                    500, 600,\n",
       "                                                                    700, 800,\n",
       "                                                                    900, 1000,\n",
       "                                                                    1100,\n",
       "                                                                    1200]},\n",
       "                   scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regressor__n_estimators': 900,\n",
       " 'regressor__min_samples_split': 2,\n",
       " 'regressor__min_samples_leaf': 1,\n",
       " 'regressor__max_depth': 24}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gbr_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ETkIIga+zzqK6zooQb1fM+10P3O7vCb2bC4Z07YKn7uoBD40KhtJqbMu5IHe5REQOjyGKyEl8ufccjhWWQ61U4NHoay/ZBelc8WBUKABg+7EinDdWyVEmEZHTYIgicgIXymrwt+8OAwDGRgYgQOfaZLsBoXr0C9HBKoAv957lZT0iolvAEEXkBD7cmYuyajNCvF0xuqf/Dds+OCgEbi4qFJiqkXzY0E4VEhE5H4YoIgdXXmPGx7+dAQDc3TsQKuWNpzDwcnVB7O2+AID3d5yCEKLNayQickYMUUQO7vM9+SitNqO7nwcig72atc2I7r5QKxXYf9aE3bklbVwhEZFzYogicmBmixX/+jUXAPDEHd2bPZGmp1aNIV27AADe/+VUm9VHROTMGKKIHNgPhww4Z6yCr4cGDw8JbdG2o3r4QaEA/ptdhBNF5W1UIRGR82KIInJgn2fkAwCmjegGVxdVi7b199JibGSgzX6IiKj5GKKIHFRRWTV2nigGAExqYS9Uo0eibwMAfLvvPCxWDjAnImoJhigiB/X9gQJYBTC4qze6+XrYtY+7evvDy1UNQ2k1B5gTEbUQQxSRg9q07zwAYOKgELv34eqiwvj+wQCAb/ada5W6iIg6C4YoIgd0urgC+/ONUCkVmDDQ/hAFABMH12+/5WABasyW1iiPiKhTsCtEnTrFW6KJ5PRNQy/UqB5+8PfS3tK+RkT4IkjnitJqM7Yd5YOJiYiaS33zJtfq0aMHxowZg9mzZ+ORRx6Bq2vTz+kiIvttSM+77rqP0+tnKA/w0t6wXXMolQo8GBWC93acwncHzmNc/6Bb2h8RUWdhV0/U3r17MXDgQCQmJiIoKAhPPvkkdu/e3dq1EVETSipqUVRWA6UC6BOka5V9NganHTkXUGvmQ4mJiJrDrhAVFRWFN998E+fPn8eHH36IgoICjB49Gv3798drr72GCxd4SYCorRw1lAIAuvl6wE3TsrmhrmfQbd7w9dCgrMaMjNO8S4+IqDluaWC5Wq3Gww8/jI0bN+KVV17BiRMnsHjxYoSFhWH69OkoKChorTqJqEGOoQwAEBnUvOfkNYdKqcDvIgMAAKlHi1ptv0REzuyWQlRGRgb+9Kc/ITg4GK+99hoWL16MkydPIiUlBefPn8fEiRNbq04iAlBTZ8Gp4goAQGQrXcprNLYxRGUXQghOvElEdDN2DSx/7bXXsG7dOuTk5GD8+PH46KOPMH78eCiV9ZksIiICSUlJCA8Pb81aiTq9ExfKYbEK+Hpo4OepadV939HLHy4qBU5frMSp4grc7u/ZqvsnInI2dvVEvfvuu3jsscdw5swZbNq0Cffff78UoBoFBATgX//6V6sUSUT1jl5xKU+hULTqvj21aozo7gugvjeKiIhuzK4QlZKSgmeffRbBwcE2y4UQyMurv91ao9FgxowZt14hEQEArEJI46F6t/KlvEZ3N1zS+282x0UREd2MXSHq9ttvR3Fx8TXLS0pKEBERcctFEdG1DKZqlNeYoVErEe7n3ibHGBsZCADYe+YSymvMbXIMIiJnYVeIut6g0/Lyck68SdRGTl4oBwBE+HpArWybJzZ19XVHVx93mK0Cu3MvtskxiIicRYsGlicmJgIAFAoFli9fDnf3y/8btlgsSE9PR1RUVKsWSET1GkPU7QFtO+B7VA8/5O3Ow6/HL+Luhp4pIiK6VotCVFZWFoD6nqiDBw9Co7l8d5BGo8GgQYOwePHi1q2QiGC2WpHbMLXB7f4ebXqs0T388MnuPOw8ce0leyIiuqxFIWrbtm0AgFmzZuHNN9+ETtc2g1uJyFZ+SRXqLAIeGhUCdW17yTz2dl8oFEBOYRmKyqoR4MVL9ERETbFrYMW6desYoIja0ZWX8pStPLXB1Xw8NOgbXP/znXaS46KIiK6n2T1RDz/8MJKSkqDT6fDwww/fsO1XX311y4UR0WUnixpCVDtNgDm6hx8Ony/Fr8eLMTEqtF2OSUTkaJodovR6vTS5n16vb7OCiMhWjdmC/EuVANovRI3q4Yf/23EKO08UQwjR6hN7EhE5g2aHqHXr1jX5ZyJqW6eLK2AVQBd3F/h4tO6jXq5nWLgPNColzpuqkVtcge58BAwR0TXsGhNVVVWFyspK6fszZ87gjTfewE8//dTifa1Zswbh4eFwdXVFTEwMdu/efcP2GzduRGRkJFxdXTFgwABs2bLFZr0QAsuXL0dwcDDc3NwQFxeH48eP27QpKSnBtGnToNPp4O3tjdmzZ6O8vPya/bz66qvo1asXtFotQkND8Y9//KPF50d0q3KL63/W2jPIuGlUiOrqDQDYnVvSbsclInIkdoWoiRMn4qOPPgIAGI1GDB8+HKtXr8bEiRPx7rvvNns/n332GRITE7FixQrs3bsXgwYNQnx8PIqKmn7kxK5duzB16lTMnj0bWVlZSEhIQEJCAg4dOiS1WbVqFd566y2sXbsW6enp8PDwQHx8PKqrq6U206ZNw+HDh5GSkoLNmzdjx44dmDt3rs2xFixYgA8++ACvvvoqjh49im+//RbDhw9vydtE1CpOX6yf2iDCr22nNrhaTIQPACCdIYqIqEkKcb3px2/Az88P27dvR79+/fDBBx/g7bffRlZWFr788kssX74c2dnZzdpPTEwMhg0bhnfeeQcAYLVaERYWhvnz52PJkiXXtJ88eTIqKiqwefNmadmIESMQFRWFtWvXQgiBkJAQLFq0SJqvymQyITAwEElJSZgyZQqys7PRt29f7NmzB0OHDgUAJCcnY/z48Th79ixCQkKQnZ2NgQMH4tChQ+jdu3dL3x5JaWkp9Ho9TCYT72akFtuQnodasxUvbj4MqwAW39u71S/nPRbT9brrfj1ejP/5VzpCvd2wc8ndrXpcIqKOrLm/v+3qiaqsrISXlxcA4KeffsLDDz8MpVKJESNG4MyZM83aR21tLTIzMxEXF3e5GKUScXFxSEtLa3KbtLQ0m/YAEB8fL7XPzc2FwWCwaaPX6xETEyO1SUtLg7e3txSgACAuLg5KpRLp6ekAgO+++w7du3fH5s2bERERgfDwcDzxxBMoKbnx/8hrampQWlpq8yK6FfmXKmEVgN7NBV3cXdr12EO6eUOtVOCcsQpnL1XefAMiok7GrhDVo0cPbNq0Cfn5+fjxxx9x7733AgCKioqa3eNSXFwMi8WCwEDbx0oEBgbCYDA0uY3BYLhh+8avN2sTEBBgs16tVsPHx0dqc+rUKZw5cwYbN27ERx99hKSkJGRmZuKRRx654TmtXLkSer1eeoWFhd2wPdHNNM5SHu7r3u53yLlr1BhwW/2duOmneEmPiOhqdoWo5cuXY/HixQgPD0dMTAxiY2MB1PdKDR48uFULlIPVakVNTQ0++ugj3HHHHbjrrrvwr3/9C9u2bUNOTs51t1u6dClMJpP0ys/Pb8eqyRmdbgxR7TweqtHwhnFRHFxORHQtu0LUI488gry8PGRkZCA5OVlaPnbsWLz++uvN2oefnx9UKhUKCwttlhcWFiIoKKjJbYKCgm7YvvHrzdpcPXDdbDajpKREahMcHAy1Wo1evXpJbfr06QMAyMvLu+45abVa6HQ6mxeRvcxWK/JK6i+jRfjKE6IaB5fvPs0QRUR0NbtCFFAfRgYPHgyl8vIuhg8fjsjIyGZtr9FoEB0djdTUVGmZ1WpFamqq1LN1tdjYWJv2AJCSkiK1j4iIQFBQkE2b0tJSpKenS21iY2NhNBqRmZkptdm6dSusVitiYmIAAKNGjYLZbMbJkyelNseOHQMAdOvWrVnnR3Srzl2qgtkq4K5Rwd9LK0sNQ8N9oFDUX1YsKq2++QZERJ1Iix5A3KiiogIvv/wyUlNTUVRUBKvVarP+1KlTzdpPYmIiZsyYgaFDh2L48OF44403UFFRgVmzZgEApk+fjtDQUKxcuRJA/bQDY8aMwerVqzFhwgR8+umnyMjIwHvvvQcAUCgUWLhwIV566SX07NkTERERWLZsGUJCQpCQkACgvkdp3LhxmDNnDtauXYu6ujrMmzcPU6ZMQUhICID6geZDhgzB448/jjfeeANWqxVPP/007rnnHpveKaK21HgpL8LPQ7YZw3WuLugbrMPh86X4LbcEDw4KkaUOIqKOyK4Q9cQTT2D79u34wx/+gODgYLv/gZ88eTIuXLiA5cuXw2AwICoqCsnJydLA8Ly8PJuerpEjR2LDhg14/vnn8dxzz6Fnz57YtGkT+vfvL7V55plnUFFRgblz58JoNGL06NFITk6Gq+vlJ9F//PHHmDdvHsaOHQulUolJkybhrbfektYrlUp89913mD9/Pu688054eHjgvvvuw+rVq+06TyJ7nL5Yfymvm0yX8hoNC/fB4fOlyDzNEEVEdCW75ony9vbG999/j1GjRrVFTU6D80SRvYQQ6Lv8R1TVWfDUmNsR5uPeJse50TxRjTYfOI95G7LQP1SHzfPvaJM6iIg6kjadJ6pLly7w8fGxuzgiurFTxRWoqrNArVQg2Nv15hu0oehuXQAA2QVlqKgxy1oLEVFHYleI+vvf/47ly5fbPD+PiFrP3jOXAACh3m5QK+2+/6NVBOvdEOrtBotVYH++UdZaiIg6ErvGRK1evRonT55EYGAgwsPD4eJiO5Py3r17W6U4os5qb54RANDVt20u47XUkG5dcM5YhYwzlzCyh5/c5RARdQh2hajGO92IqG009kR1baOxUC01tFsXfLf/PDIb6iIiIjtD1IoVK1q7DiJqUFpdh2NFZQA6TohqHBe1N+8SrFYBpVKeKReIiDoSuwdbGI1GfPDBB1i6dKn0YN69e/fi3LlzrVYcUWe0P98IIYAu7i7wcm3fhw5fT2SQF9w1KpRVm3G8qFzucoiIOgS7QtSBAwfQq1cvvPLKK3j11VdhNBoBAF999RWWLl3amvURdTqZHexSHgCoVUpEhXkDAC/pERE1sCtEJSYmYubMmTh+/LjNJJbjx4/Hjh07Wq04os7o8qByeSfZvFrjJb2MM3yOHhERYGeI2rNnD5588slrloeGhsJgMNxyUUSdldUqkJXX8XqigCvGRbEniogIgJ0Dy7VaLUpLS69ZfuzYMfj7+99yUUSd1YkL5SirNsPNRYUgXdtPsrkhPa/ZbatqLVAo6h9Hc6GsRraHIhMRdRR29UQ9+OCDePHFF1FXVweg/sG/eXl5ePbZZzFp0qRWLZCoM2ns5RkUpoeqg90B56ZRoVeAF4D6u/SIiDo7u0LU6tWrUV5eDn9/f1RVVWHMmDHo0aMHvLy88I9//KO1ayTqNBoHbQ/p2kXmSpo2hJf0iIgkdl3O0+v1SElJwc6dO7F//36Ul5djyJAhiIuLa+36iDqVxh6eIV27oKisRuZqrhXdrQs+2Z2HDIYoIqKWhyir1YqkpCR89dVXOH36NBQKBSIiIhAUFAQhBBSKjnUJgshRGCtrcfJCBYD6Hp/kQx3vJo2hDT1RB8+aUGO2QKtWyVwREZF8WnQ5TwiBBx98EE888QTOnTuHAQMGoF+/fjhz5gxmzpyJhx56qK3qJHJ6WQ1TG0T4ecDHQyNvMdfRzdcdvh4a1FqsOHTOJHc5RESyalFPVFJSEnbs2IHU1FT87ne/s1m3detWJCQk4KOPPsL06dNbtUiizqDxUt7grt7yFnIDCoUC0d264Kcjhcg8cwnR3XzkLomISDYt6on65JNP8Nxzz10ToADg7rvvxpIlS/Dxxx+3WnFEnUljiGqcj6mjkibdPM1xUUTUubUoRB04cADjxo277vr77rsP+/fvv+WiiDobi1VgX8PlvI56Z16jKx9GLISQuRoiIvm0KESVlJQgMDDwuusDAwNx6RL/d0rUUjmGMlTUWuCpVaNXoJfc5dxQ/1A9NColistrkVdSKXc5RESyaVGIslgsUKuvP4xKpVLBbDbfclFEnU3jpbyoMO8ON8nm1VxdVOgfqgPAhxETUefWooHlQgjMnDkTWm3Tj3uoqel489oQOYK90iSb3vIW0kzR3bpgb54RmWcu4eEht8ldDhGRLFoUombMmHHTNrwzj6jlpEk2O/ig8kbR3brg/V9y2RNFRJ1ai0LUunXr2qoOok7rYnkNTl+sH1s0OMwxQlTj4PecwjKUVdfBy9VF5oqIiNqfXc/OI6LWs7fhrrweAZ7QuztGGAnQuSLMxw1CAPvzOekmEXVODFFEMpPmh+rgUxtcrbFeXtIjos6KIYpIZtKg8m7e8hbSQo3jtzLzGKKIqHNiiCKSUZ3Fiv1njQA6/iSbV2usN+vMJVitnHSTiDofhigiGR0tKEN1nRU6VzVu9/eUu5wWiQzygrtGhbIaM44XlctdDhFRu2OIIpLR5YcOd4Gyg0+yeTW1SomoMG8AHBdFRJ0TQxSRjBrDR0d/6PD1NNbNEEVEnRFDFJGMpEk2HWw8VKMhVzyMmIios2GIIpJJUWk1zl6qgkIBDArTy12OXYY0TA6aW1yBi+V87BMRdS4MUUQyaey96R3o5bAzfuvdXdAzoH5AfOOkoUREnUWLHvtCRK2nMXQ4yvPyAGBDet41y/Ru9QHw32lncKHMtjfqsZiu7VIXEZEc2BNFJJPGwdiOOh6qUTdfdwBAXkmFzJUQEbUvhigiGdSarTh4rv6Zc456Z16jrj4eAICzl6pgtlplroaIqP0wRBHJ4PB5E2rNVvh4aBDe0JPjqPw8NXBzUcFsFTCYquUuh4io3TBEEcng8qU8bygUjjXJ5tUUCoV0Se/MxUqZqyEiaj8MUUQyyGoYVD7YwcdDNermUx+iTl/kuCgi6jwYoojamRACGWdKAABDHXw8VKNwv/pxUaeLKyAEH0ZMRJ0DQxRROzt7qQqFpTVwUSkwqOHZc44u1NsNaqUCFbUWXOCkm0TUSTBEEbWzxl6ofiF6uLqoZK6mdahVSoQ1XtIr5rgoIuocGKKI2tme0/WDyoeFO8elvEYRjZf0OC6KiDoJhiiidpbZEKKiu/nIXEnrCve9PC6KiKgzYIgiakemyjrkFJYBAIY6WU9UVx93KBWAsaoOlypr5S6HiKjNMUQRtaPGhw5H+HnAz1MrczWtS6NWItTbDQB7o4ioc2CIImpHe04719QGV2uc6iCXIYqIOgGGKKJ2lNEwU7mzXcprFOHLweVE1HkwRBG1k1qzFfvzjQCAoeHONai8UTdfDygAFJfXoqy6Tu5yiIjaFEMUUTs5dN6EmoaHDndvuOzlbNw0KgTqXAEAp/kcPSJycgxRRO3k8tQGXRz+ocM3cuUjYIiInFmHCFFr1qxBeHg4XF1dERMTg927d9+w/caNGxEZGQlXV1cMGDAAW7ZssVkvhMDy5csRHBwMNzc3xMXF4fjx4zZtSkpKMG3aNOh0Onh7e2P27NkoLy9v8ngnTpyAl5cXvL29b+k8qXP7OuscAEClUGBDet5NX46Kk24SUWche4j67LPPkJiYiBUrVmDv3r0YNGgQ4uPjUVRU1GT7Xbt2YerUqZg9ezaysrKQkJCAhIQEHDp0SGqzatUqvPXWW1i7di3S09Ph4eGB+Ph4VFdXS22mTZuGw4cPIyUlBZs3b8aOHTswd+7ca45XV1eHqVOn4o477mj9k6dOQwiBMw2hopuvu8zVtK3whvMzmKphquK4KCJyXgoh8yPXY2JiMGzYMLzzzjsAAKvVirCwMMyfPx9Lliy5pv3kyZNRUVGBzZs3S8tGjBiBqKgorF27FkIIhISEYNGiRVi8eDEAwGQyITAwEElJSZgyZQqys7PRt29f7NmzB0OHDgUAJCcnY/z48Th79ixCQkKkfT/77LM4f/48xo4di4ULF8JoNDb73EpLS6HX62EymaDT6ex5e8hJnLpQjrtXb4daqcDy+/tCrZL9/y9t6rWUHBSX1+LDmUNxd2Sg3OUQEbVIc39/y/oveW1tLTIzMxEXFyctUyqViIuLQ1paWpPbpKWl2bQHgPj4eKl9bm4uDAaDTRu9Xo+YmBipTVpaGry9vaUABQBxcXFQKpVIT0+Xlm3duhUbN27EmjVrmnU+NTU1KC0ttXkRAZenNgjt4ub0AQq4/AiY9NwSmSshImo7sv5rXlxcDIvFgsBA2/+pBgYGwmAwNLmNwWC4YfvGrzdrExAQYLNerVbDx8dHanPx4kXMnDkTSUlJze5FWrlyJfR6vfQKCwtr1nbk/BoHlTeGC2fXOLh8N0MUETkx5/8vsZ3mzJmDxx57DHfeeWezt1m6dClMJpP0ys/Pb8MKyZHsOVMfJrr5OPd4qEaNk24ePGtCRY1Z5mqIiNqGrCHKz88PKpUKhYWFNssLCwsRFBTU5DZBQUE3bN/49WZtrh64bjabUVJSIrXZunUrXn31VajVaqjVasyePRsmkwlqtRoffvhhk7VptVrodDqbF1FJRS1OXagfVN7VyQeVN/J2d4G3uwvMViE96oaIyNnIGqI0Gg2io6ORmpoqLbNarUhNTUVsbGyT28TGxtq0B4CUlBSpfUREBIKCgmzalJaWIj09XWoTGxsLo9GIzMxMqc3WrVthtVoRExMDoH7c1L59+6TXiy++CC8vL+zbtw8PPfRQ67wB1Cnszr0IAAjw0sJdo5a5mvahUCjQw98TALDzRLHM1RARtQ3Z/0VPTEzEjBkzMHToUAwfPhxvvPEGKioqMGvWLADA9OnTERoaipUrVwIAFixYgDFjxmD16tWYMGECPv30U2RkZOC9994DUP+P98KFC/HSSy+hZ8+eiIiIwLJlyxASEoKEhAQAQJ8+fTBu3DjMmTMHa9euRV1dHebNm4cpU6ZId+b16dPHps6MjAwolUr079+/nd4Zcha/narvienu3znGQzW63d8TGWcu4dcTF+UuhYioTcgeoiZPnowLFy5g+fLlMBgMiIqKQnJysjQwPC8vD0rl5Q6zkSNHYsOGDXj++efx3HPPoWfPnti0aZNNuHnmmWdQUVGBuXPnwmg0YvTo0UhOToarq6vU5uOPP8a8efMwduxYKJVKTJo0CW+99Vb7nTh1Gmkn60NEdz9PmStpX42hMbugFBfLa+DrqZW5IiKi1iX7PFHOjPNE0cXyGkS/9F8AwF/H94GHVvb/t7Srj9JO46ihDG9PHYwHBoXcfAMiog7AIeaJInJ2jfMkRQZ5dboABQCjevgBAHad5LgoInI+DFFEbajxUt6I7r4yVyKPUT3qz/tXDi4nIifEEEXUhn471blD1PAIX6iVCuSXVCHvYqXc5RARtSqGKKI2cqGsBseLyqFQADERPnKXIwtPrRqDu3oDAH45cUHeYoiIWhlDFFEbSW+YHyoySIcuHhqZq5HPnT39AQA/5zBEEZFzYYgiaiOXx0N1zl6oRnf1rn9O5a4Txag1W2Wuhoio9TBEEbWRxvFQsZ10PFSjfiE6+HlqUFFrQcYZPgKGiJwHQxRRGygqrcbJCxUN46E6d4hSKhW4s1f9Jb3tvKRHRE6EIYqoDaQ19EL1DdZB7+4iczXya7ykx3FRRORMGKKI2kDj8/I6+6W8Rnf08INSAeQUluG8sUrucoiIWgVDFFEb6OzzQ12ti4cGg8K8AQDbj7E3ioicA0MUUSszmKqRW1wBpQIY1knnh2rK7xou6aVmF8lcCRFR62CIImpljb1Q/UL00LtxPFSjuD6BAIBfT1xAVa1F5mqIiG4dQxRRK2ucHyr2dl7Ku1KfYC+Eeruhus6KX47zkh4ROT6GKKJWJISQHrbLQeW2FAoF7u1X3xv105FCmashIrp1DFFErej0xUqcM1bBRaVATCefqbwp9/StD1FbjxbBYhUyV0NEdGsYooha0a8Nl6miu3WBu0YtczUdz/BwH+jdXFBSUYvMM5fkLoeI6JYwRBG1oh3H6y/l3dHw0F2ypVYpMTay/i69nw4bZK6GiOjWMEQRtZI6i1UaVH4nQ9R1NY6L+vGIAULwkh4ROS6GKKJWsj/fiPIaM7q4u6BfiE7ucjqsO3v5w81FhfySKhw4a5K7HCIiuzFEEbWSxkt5o3r4QalUyFxNx+WuUSOuYYD5d/vPy1wNEZH9GKKIWknj3Ee8lHdzDwwMBgB8f7AAVt6lR0QOiiGKqBWYKuuwP98IABjd00/eYhzAmN7+8NKqUWCqRmYe79IjIsfEEEXUCnYcvwCrAHoFeiLE203ucjo8rVqFe/sFAeAlPSJyXAxRRK1gW079Q3UbH7JLN3f/oPpLelsOFsBsscpcDRFRyzFEEd0iq1Vgx7H68VBjenM8VHON7uGHLu4uKC6vlR6VQ0TkSBiiiG7RofMmFJfXwlOrxtBufNRLc7molJgYFQoA2Jh5VuZqiIhajiGK6Bb9nFPfCzWqhy80av5ItcQj0bcBAFIOF8JYWStzNURELcN/8Ylu0c8cD2W3/qF69AnWodZixbccYE5EDoYhiugWXKqoRVbD1AYcD2Wf3zf0Rm3M4CU9InIsDFFEt2D7sQsQAogM8kKwnlMb2CNhcChcVAocPGfCUUOp3OUQETUbQxTRLUjJLgQAxPUJlLkSx+XjoZHevw3peTJXQ0TUfGq5CyByVB+lncZ/j9SHKItVMADcgj+M6IYfDhnwZeZZ/CW+N7xcXeQuiYjoptgTRWSn08WVqDFb4alVI7QLL+XditjbfdEjwBMVtRZ8yekOiMhBMEQR2Sm7oH78TmSQF5QKhczVODaFQoHpsd0AAB+lneFDiYnIITBEEdlBCCENgu4TrJO5Gufw8JDb4KlV41RxBXae5AzmRNTxMUQR2SGnsAyXKuugVipwu7+n3OU4BU+tGpOG1M9g/q9fc2Wuhojo5hiiiOzQOKC8R4AnZylvRbNGRUCpqJ8F/sh5TndARB0b//UnskPyYQMAXsprbeF+HrhvQDAAYO32kzJXQ0R0YwxRRC2UX1KJQ+dKoQBDVFt4asztAIDNB84j72KlzNUQEV0fQxRRC/3Y0AsV7ucBTy2nWmtt/UP1uLOXP6wC+L8d7I0ioo6LIYqohRpDVL8Q9kK1lT/dVd8btTHjLM5eYm8UEXVMDFFELVBUVo2MM5cAAH15Ka/NxET4ILa7L2otVryVelzucoiImsQQRdQCKUcKIQQwKMwb3u4auctxWgqFAn8Z1xsA8EXmWZy8UC5zRURE1+KADqIWSD5UfylvXL8gmStxDPY8T/CxmK4AgCFduyCuTyD+m12I11KOYc1jQ1q7PCKiW8KeKKJmulheg10nLwIAxvVniGoPi+7tBYUC+P5AAfblG+Uuh4jIBkMUUTMlHzbAYhXoH6pDhJ+H3OV0Cn2CdXhocP0s5i98e5jP1COiDoUhiqiZNu8vAADcPzBE5ko6lyXjIuGhUWFfvhFfZ52TuxwiIglDFFEzFJVVIz23/lLehIYZtal9BOhcMe/ungCAV5KPorzGLHNFRET1GKKImiH5kAHWhrvywnzc5S6n03l8dDjCfd1RVFaD11OOyV0OEREA3p1H1CyNl/IeGMheqLZ2vTv67uodgKRdp/Hhr7nQqpW4rYt9Ybbx7j8iolvVIXqi1qxZg/DwcLi6uiImJga7d+++YfuNGzciMjISrq6uGDBgALZs2WKzXgiB5cuXIzg4GG5uboiLi8Px47YT9pWUlGDatGnQ6XTw9vbG7NmzUV5+eS6an3/+GRMnTkRwcDA8PDwQFRWFjz/+uPVOmhxGgakKe86UAADG81KebHoFeiEqzBsCwFd7z8HCQeZEJDPZQ9Rnn32GxMRErFixAnv37sWgQYMQHx+PoqKiJtvv2rULU6dOxezZs5GVlYWEhAQkJCTg0KFDUptVq1bhrbfewtq1a5Geng4PDw/Ex8ejurpaajNt2jQcPnwYKSkp2Lx5M3bs2IG5c+faHGfgwIH48ssvceDAAcyaNQvTp0/H5s2b2+7NoA7pm33nIUT9LNoh3m5yl9OpjR8QDHeNCobSauw4fkHucoiok1MIIWT971xMTAyGDRuGd955BwBgtVoRFhaG+fPnY8mSJde0nzx5MioqKmzCzIgRIxAVFYW1a9dCCIGQkBAsWrQIixcvBgCYTCYEBgYiKSkJU6ZMQXZ2Nvr27Ys9e/Zg6NChAIDk5GSMHz8eZ8+eRUhI03dfTZgwAYGBgfjwww+bdW6lpaXQ6/UwmUzQ6fiIEEc17o0dOGoow8sPD8CU4ZcvBdkzkSTduqy8S9iYeRZKBfDUXT0Q2sJgy8t5RHQzzf39LWtPVG1tLTIzMxEXFyctUyqViIuLQ1paWpPbpKWl2bQHgPj4eKl9bm4uDAaDTRu9Xo+YmBipTVpaGry9vaUABQBxcXFQKpVIT0+/br0mkwk+Pj7XXV9TU4PS0lKbFzm27IJSHDWUQaNS4j5eyusQosK80S9EB6sAPs/IR53FKndJRNRJyRqiiouLYbFYEBgYaLM8MDAQBoOhyW0MBsMN2zd+vVmbgIAAm/VqtRo+Pj7XPe7nn3+OPXv2YNasWdc9n5UrV0Kv10uvsLCw67Ylx7CpYV6isX0CoHdzkbkaAuqfq5cQFQovrRoXymrww6Gmf2aJiNqa7GOiHMG2bdswa9YsvP/+++jXr9912y1duhQmk0l65efnt2OV1NosVoFN++pDVELDrNnUMXho1ZgUfRsA4LdTF3HkvEnmioioM5I1RPn5+UGlUqGwsNBmeWFhIYKCmn42WVBQ0A3bN369WZurB66bzWaUlJRcc9zt27fjgQcewOuvv47p06ff8Hy0Wi10Op3NixxX2smLKCytgd7NBXf19pe7HLpKr0AvjO7hBwD4Yu9ZlFTUylwREXU2soYojUaD6OhopKamSsusVitSU1MRGxvb5DaxsbE27QEgJSVFah8REYGgoCCbNqWlpUhPT5faxMbGwmg0IjMzU2qzdetWWK1WxMTESMt+/vlnTJgwAa+88orNnXvUOXyRWd+TeP/AYGjVKpmroabE9wtCVx93VNdZ8cnuPJg5PoqI2pHsl/MSExPx/vvvY/369cjOzsZTTz2FiooKaezR9OnTsXTpUqn9ggULkJycjNWrV+Po0aN44YUXkJGRgXnz5gGoHy+xcOFCvPTSS/j2229x8OBBTJ8+HSEhIUhISAAA9OnTB+PGjcOcOXOwe/du7Ny5E/PmzcOUKVOkO/O2bduGCRMm4M9//jMmTZoEg8EAg8GAkpKS9n2DSBamqjpprM2jQzm2raNSKRWYMiwMbi4qnDNW4eusc5D5hmMi6kRkD1GTJ0/Gq6++iuXLlyMqKgr79u1DcnKyNDA8Ly8PBQUFUvuRI0diw4YNeO+99zBo0CB88cUX2LRpE/r37y+1eeaZZzB//nzMnTsXw4YNQ3l5OZKTk+Hq6iq1+fjjjxEZGYmxY8di/PjxGD16NN577z1p/fr161FZWYmVK1ciODhYej388MPt8K6Q3L7bfx41Zit6B3ph4G16ucuhG/B212Dq8K5QKoCsfCN+OV4sd0lE1EnIPk+UM+M8UY5r4ju/Yv9ZE56f0AdP3NG9yTacJ6pjSTtZjO8OFEAB4H9GdEOf4KZ/5jhPFBHdjEPME0XUEeUYyrD/rAlqpQIP8a48hzGiuy+GR/hAAPgsIx+G0uqbbkNEdCsYooiu8nlG/YDyuD6B8PXUylwNNZdCocADA0PQ3c8DtWYr/p12GuU1ZrnLIiInxhBFdIXqOgu+yDwLAJg8jAPKHY1KqcBjw7vCx0ODS5V1+M9vZ1Br5h17RNQ2GKKIrrD5QAFMVXW4rYsb7uzFuaEckbtWjekjusHVRYm8kkp8uicPFiuHfhJR62OIIrrCx+lnAABTh3eFSqmQuRqyV4DOFX8YEQ61UoGjhjJs4tQHRNQGGKKIGhw+b0JWnhFqpYJzQzmBCD8PTB3eFQoAmXmX8NORwptuQ0TUEmq5CyDqKP727REAQJ9gHVL4C9cp9AnW4aHBofgq6xy2H7sAT62aUxwQUathTxQRAFNlHbLyLwEAhkf4yFwNtaah4T64t2/95L3fHyyQbhwgIrpVDFFEAD7LyEOdRSBQp0V3Pw+5y6FWNqaXP0be7gsAeOaL/fh2/3mZKyIiZ8AQRZ2e2WLF+l31A8pH3e4HhYIDyp2NQqHA+AHBGBbeBVYB/L/P9iG54dmIRET2YoiiTu+nI4U4Z6yCu0aFQWHecpdDbUSpUGBiVCgeHhwKi1Vg/id7sfUox74Rkf0YoqjT+/DXXABATIQPXFT8kXBmSoUCqx4ZiPsHBqPOIvDH/+zFjmMX5C6LiBwUf2NQp5aVdwkZZy5BrVQgJsJX7nKoHahVSrw+OQrx/QJRa7ZizkcZ+DmnSO6yiMgBMURRp/a/P58EACQMDoXOzUXmaqi9uKiUeHvqEMT1CUSN2Yq5H2VyWgsiajGGKOq0jheWIeVIIRQK4I9jbpe7HGpnGrUS7/7PEIwfEIRaixVP/ScTWw4WyF0WETkQhijqtN7dXt8LFd83CD0CPGWuhuTgolLirSmDMTEqBGarwLwNe/HNvnNyl0VEDoIhijqls5cq8e2++rmCnrqLvVCdmVqlxGuPRuGR6NtgFcDCz/ZJz1AkIroRhijqlNZsOwmzVWBUD19Oa0BQKRVYNWkgpsV0hRDAX78+hNdTjvGhxUR0QwxR1OnkXazExox8AMDCuF4yV0MdhVKpwEsJ/fHnsT0BAG+mHsdzXx+C2WKVuTIi6qgYoqjTeTP1OMxWgTt7+WNYOJ+TR5cpFAok3tMLLyX0h0IBfLI7D099vBfVdRa5SyOiDoghijqVkxfK8XVW/QNoE+9hLxQ17X9GdMO704ZAo1Yi5UghHnv/NxSVVctdFhF1MAxR1Km89tMxWAUQ1ycQURwLRTcwrn8w/v34cOhc1dibZ8TEd3bi4FmT3GURUQfCEEWdRsbpEnx/sAAKBbDoXvZC0c3FdPfFpqdHobu/BwpM1Xhk7S58u/+83GURUQfBEEWdgtUq8PfvswEAk4eGoU+wTuaKyFF09/fEpqdH4Xe9/VFjtuLPn2ThleSjsFh55x5RZ6eWuwCi9vDdgfPYn2+Eh0aFRPZCdWob0vNa1P6xmK7QubrggxnD8M8fc7B2+0m8+/NJ7Msz4vXJUQjSu7ZRpUTU0bEnipxeRY0ZL/9wFADwp9/1QIAXf+lRy6mUCiy5LxJvTomCu0aFtFMXcd+bO/BfPnOPqNNiiCKn91rKMRSYqhHm44bZoyPkLocc3MSoUGyePxr9Q3W4VFmHJz7KwIpvDnEaBKJOiCGKnNrBsyas25kLAPj7xP5wdVHJXBE5g+7+nvjyqZGYc0d9KF+fdgYPvP0rMs9ckrkyImpPDFHktMwWK577+iCsAnhgUAju6h0gd0nkRLRqFf46oS+SZg2Dn6cGx4vK8cjaXXjh28MorzHLXR4RtQOGKHJa7/58EgfPmeDlqsay+/vIXQ45qbt6ByDl/43BpCG3QQggaddp3Pvadmw9yrFSRM6Od+eRUzpw1og3U48DAP72YD8OJie7NfduvuhuXaBzU2NT1jmcN1Xj8aQM3NM3EH8d3wfhfh6tcoxGj8V0bVF7Imob7Ikip1NVa8HCz/bBbBWYMCAYDw0Olbsk6iR6BnhhwdhemHNHBFRKBVKOFOKe17fj/9uSjdLqOrnLI6JWxhBFTkUIgec3HcKpCxUI1Gnxj4f6Q6FQyF0WdSIatRJ/ndAXyQvuwJ29/FFnEXhvxyn87p8/4z+/nUGdxSp3iUTUShiiyKn8+7cz+HLvWSgVwOuPRsHbXSN3SdRJ9Qz0wkePD8e6WcNwu78HLlbU4vlNhxD32nZ8nXWWM54TOQGGKHIae06X4MXvjgAAltwXiZE9/GSuiAj4Xe8AJC+8Ey880Bd+nhqcuViJ//fZfox7YweSDxVACIYpIkfFgeXkFE4UlWPuRxkwWwXuHxiMOXd0l7skIomLSomZoyLw6LAwJO06jf/bfgrHi8rxx//sRf9QHaK7+qBXoCcvPRM5GIYocngGUzVmfLgblyrrMOg2PVY9MpC/jKhDcteo8ae7emBaTDd88MspfPhrLg6dK8Whc6UI6+KG3/UOQO8gL35+iRwEQxQ5tKKyavzhX+k4Z6xCdz8PfDhzGNw16hbfMk7UnvRuLlh0b2/MHBmOtdtPYt3O08i/VIWPfjuDYL0rxvTyR/9QPZQMU0QdGkMUOawCUxWmvZ+OU8X1d+Ktf3w4fD21cpdF1Gy+nlr8dUJf+HpqsfNEMdJzS1Bgqsane/Lhl12IMb38ERXWBSolwxRRR8QQRQ7pRFEZZq7bg7OXqhDq7YaPn4hBmI+73GUR2UXn6oL7+gdjTC9/pJ28iF0nL6K4vBZf7j2H1OwijLzdF0PDffjsR6IOhiGKHM62nCL8eUMWymrM6Obrjg1zRiDU203usohumbtGjbF9AjG6hx/Sc0vw64liGKvqsOWQAf/NLsKQbt4Y2Z13nRJ1FAxR5DDMFivWbDuJN1OPwSqAYeFdsPZ/onkJj5yO1kWFO3v5I/Z2X2TlGbHrZDGKymrw26kS/HaqBJl5l/Do0DCM7RPA3ikiGTFEkUM4c7ECiZ/vR+aZSwCAyUPD8PeE/tCoOdUZOS8XlRLDI3wwLLwLTl6owK6TxcgxlGH7sQvYfuwC9G4ueHBQCB6Jvg0Db9Pzrj6idqYQnOmtzZSWlkKv18NkMkGn08ldTofS3Lvn6ixW/HL8An45XowasxVeWjX+ntAfE6NCbvgLg3fnkbO6WF6DarMFX+09hwJTtbT8ti5uiOsTiLsjAxDT3Qda9a31UPGhyK3Pnn+X+L7Ko7m/v9kTRR2SVQjszzci9WgRSipqAQAjb/fFK5MGcgA5dWq+nlo8FtMViff0xq6Txfgi8yySDxlw9lIVknadRtKu0/DQqDCqhx+iu3XB4K5dMCBUDzcNL/sRtTaGKOpQzBYr9p81YsfxYlwoqwEAeLmq8Y+HBuCBgcG8XEHUQKVU4I6e/rijpz8qa8349Xgxth4tQurRIlwoq8FPRwrx05FCAIBaqUCfYB16B3mhu78Huvt5oru/B7r6uHNMFdEtYIiiDuFieQ0yz1xCxplLKK8xAwDcXFS4s6cfYm/3w4ODQmSukKjjcteocW+/INzbLwhWq8DBcyb8duoi9uZdwt48Iy6U1eDgORMOnjNds62PhwYBXloE6lwRqGv86oqjBWXQuanh5eoCT62ac1URNYEhimRzqbIWh8+X4vA5E86UVErL9W4uiO3ui+ERnBeHqKWUSgUGhXljUJg3AEAIgfOmauzLM+LkhXKculCOU8UVOHWhAuU1ZpRU1KKkohZHDWXX3acCgLtWDZ2rGl6u9cFK56qGzs0FPu4a+HhoUGu28kYP6nQYoqjdWKwCh8+b8MvxYmxIz8M5Y5W0TgGgR4Anhob7oG+wjv/rJWolCoUCod5u18ylJoSAsbIOhWXVKCytQWFpNQpN1Sgsq4bBVIOjhlKUVtWhvMYMqwAqasyoqDGj4NrOLADA6/89hmC9G7r6uCPMp/GrO7r6uKObrwe6uLvwcjw5HYYoajNWq8DxonKk517EzhPF+O1UCUxVddJ6BYBwPw/0C9GhX4geejeX6+6Ld9sR2a+5Pz++nlr4emrRNxi4OzIAQP1NHpW1FpRW1aGs2oyy6jqUNnw1VdWhpKIWlyprUWcROGeswjljFdJOXbtvrVoJHw+N9BrXPwjdfOrHZYV4u0Ktav1erI5wh6EQAuU1ZpRWm1FgqkJVrQXVdRZUm60QQkAIQAAQAlApAbVKCY1KCReVEi4qBQ6dM8FNo4KXtr7n72a98x3hnDsThihqFUIIFJXV4PB5E/blGbE3z4j9+UaUNYxvauSlVSOmuy+8tGr0CdHBU8uPIFFHplQo4KlV3/BntTEoNF4avFRZi5KKywHLVFWHGrMVBaZqaVqGX44XX3EMwM9TiwCdFoFergjQuUrjtHw8NA2XEOtr8HJ1gZerGlq1sl16tmrNVlTUmFHe8KqoMaOs4aupqg6lVfVfTVV1KK2uQ2nDn+vX1QdOi9X+mYT+b4dtItWoldC7uUgvnava5vsTReVw06jg5qKCq4tK+rObiwqadnrPOpMO8RtszZo1+Oc//wmDwYBBgwbh7bffxvDhw6/bfuPGjVi2bBlOnz6Nnj174pVXXsH48eOl9UIIrFixAu+//z6MRiNGjRqFd999Fz179pTalJSUYP78+fjuu++gVCoxadIkvPnmm/D09JTaHDhwAE8//TT27NkDf39/zJ8/H88880zbvAkO5FJFbcOYinIcKyxDdkEZjhSUSlMRXMnNRYWoMG+M6uGLkT38MDBUD7VKyZ4lIieiUCgawo0Luvl6XLO+zmLFpYpalFTWh6yLFbVwd1HhTEkl8koqUWu2oqisBkVlNTiE0mYd00WlgKtaBbVKAbVKCRelAi5qJdRKBVwaerWMlXVNbitwOdQIUf+yCIF3th5HrUWgzmK94tU6UylqVEpo1MqGcKOEq4sKyoZAo1DU98xbBVBrscLccNxaixUuKgUqay0orzFDiPpQd6GsRrp7uSWUCtQHqysC1s6TxQ1h7HIQ83Z3gbebC/TuLvB216CLuwvcXFQMYE2QPUR99tlnSExMxNq1axETE4M33ngD8fHxyMnJQUBAwDXtd+3ahalTp2LlypW4//77sWHDBiQkJGDv3r3o378/AGDVqlV46623sH79ekRERGDZsmWIj4/HkSNH4OrqCgCYNm0aCgoKkJKSgrq6OsyaNQtz587Fhg0bANRPtHXvvfciLi4Oa9euxcGDB/H444/D29sbc+fObb83qB1ZrQKl1XW4VFmHS5W1KCqtwXljFQpMVThvqsa5S1U4fbHiuv8wKRVAd39PDLrNG4O71r96B3q1STc9ETkOF5WyvndJ5yota7yMZLUKFFfUoKi0BkVXjM8qKqtBoakalyprUV5jRlm1GeXVZpTX1oeJOotAncV8vUO2Oq1aCS9XNTy0anho6nvFdFf2CLmpr+gdqg8gV37v6qLEJ7vzW3zcK9+n8lozTJXX9npd2Ru2/6wR1XUWVNVaUFVnrf9znQUWq4BVAJW1FlTWWqT9nygqb1YdGpUSencXdHF3gbeb5vKf3TXQu7mgi7tGCl/ejX/uBOFL9hnLY2JiMGzYMLzzzjsAAKvVirCwMMyfPx9Lliy5pv3kyZNRUVGBzZs3S8tGjBiBqKgorF27FkIIhISEYNGiRVi8eDEAwGQyITAwEElJSZgyZQqys7PRt29f7NmzB0OHDgUAJCcnY/z48Th79ixCQkLw7rvv4q9//SsMBgM0Gg0AYMmSJdi0aROOHj3arHNrqxnLvz9QALPVCotVNPxgCJitAtaG7y0CsFitsFjrxzOYLQJVdQ3X4Rt+oKpq66/Jl1fXwdgQmkxVdWhur3OI3hUR/h7o4e+JPsE6aQ6a5t5Nx54oIvu1dBxLR/x5s3csjtUqUFFbH6pqzJd7bczWyz1H5obeo61Hi5q1T6USUCkUmDAwuGEsUv24JLVKAQ+NGu5aldS7dSvaY8bypo4hhECdRdj8+9/4O6FviO7ay5JV9b8TjFV1MDaMd7OXRq1sCFb14ctVo4KrWgmtiwpatbLhVd87p1XXX3JUKxVQKhVQKervNlUq6l8qZX2vp0qhgEqpgEJRP1/aff2DW/1mJIeYsby2thaZmZlYunSptEypVCIuLg5paWlNbpOWlobExESbZfHx8di0aRMAIDc3FwaDAXFxcdJ6vV6PmJgYpKWlYcqUKUhLS4O3t7cUoAAgLi4OSqUS6enpeOihh5CWloY777xTClCNx3nllVdw6dIldOnS5ZraampqUFNzuYvVZKq/jaW0tHnd08315492oc5ibdV9Xsldo4TeTQNfTy2C9VoE6d0QpNNKd9508/Vocvbj2qoK1FY1scMmVFZc/3ZqIrqxlv6b0hF/3m7130VPJeCpuXKJsuF12dH8lvzyFwjXNW5vbXgBMNehygw085+2G7Ln76E1/67VALxUgJcbADcFADUmDfC94f5Ew40F9QGrFqbK+rBlrK6VesVMlXUwVl3uGTNW1veU1VkEqmsAQwVgaNFZtEzmsrhbfszR1Rrf95v1M8kaooqLi2GxWBAYGGizPDAw8Lq9PQaDocn2BoNBWt+47EZtrr5UqFar4ePjY9MmIiLimn00rmsqRK1cuRJ/+9vfrlkeFhbW5LkQEdljjtwFtIKOeA6dsaaOeM4tFfBG2+27rKwMer3+uutlHxPlTJYuXWrTS2a1WlFSUgJfX1+HvyZcWlqKsLAw5Ofnd4qHKfN8nRvP17nxfJ1be5yvEAJlZWUICbnx0zJkDVF+fn5QqVQoLCy0WV5YWIigoKAmtwkKCrph+8avhYWFCA4OtmkTFRUltSkqsr1WbjabUVJSYrOfpo5z5TGuptVqodVqbZZ5e3s32dZR6XS6TvFD2ojn69x4vs6N5+vc2vp8b9QD1UjW26Y0Gg2io6ORmpoqLbNarUhNTUVsbGyT28TGxtq0B4CUlBSpfUREBIKCgmzalJaWIj09XWoTGxsLo9GIzMxMqc3WrVthtVoRExMjtdmxYwfq6upsjtO7d+8mL+URERFR5yL7veeJiYl4//33sX79emRnZ+Opp55CRUUFZs2aBQCYPn26zcDzBQsWIDk5GatXr8bRo0fxwgsvICMjA/PmzQNQP3J/4cKFeOmll/Dtt9/i4MGDmD59OkJCQpCQkAAA6NOnD8aNG4c5c+Zg9+7d2LlzJ+bNm4cpU6ZIXXePPfYYNBoNZs+ejcOHD+Ozzz7Dm2++ec2gdiIiIuqkRAfw9ttvi65duwqNRiOGDx8ufvvtN2ndmDFjxIwZM2zaf/7556JXr15Co9GIfv36ie+//95mvdVqFcuWLROBgYFCq9WKsWPHipycHJs2Fy9eFFOnThWenp5Cp9OJWbNmibKyMps2+/fvF6NHjxZarVaEhoaKl19+uXVP3IFUV1eLFStWiOrqarlLaRc8X+fG83VuPF/n1pHOV/Z5ooiIiIgckeyX84iIiIgcEUMUERERkR0YooiIiIjswBBFREREZAeGqE7s9OnTmD17NiIiIuDm5obbb78dK1asQG1trU27AwcO4I477oCrqyvCwsKwatWqa/a1ceNGREZGwtXVFQMGDMCWLVts1gshsHz5cgQHB8PNzQ1xcXE4fvx4m55fU/7xj39g5MiRcHd3v+5EqHl5eZgwYQLc3d0REBCAv/zlLzCbbZ8W//PPP2PIkCHQarXo0aMHkpKSrtnPmjVrEB4eDldXV8TExGD37t1tcEa3zlHqvNqOHTvwwAMPICQkBAqFQnp+ZqPmfOZKSkowbdo06HQ6eHt7Y/bs2Sgvt32qfXM+/21t5cqVGDZsGLy8vBAQEICEhATk5OTYtKmursbTTz8NX19feHp6YtKkSddMGNxan+328O6772LgwIHShIqxsbH44YcfpPXOdr5Xevnll6Xpeho52/m+8MILUCgUNq/IyEhpvcOcr7w3B5KcfvjhBzFz5kzx448/ipMnT4pvvvlGBAQEiEWLFkltTCaTCAwMFNOmTROHDh0Sn3zyiXBzcxP/93//J7XZuXOnUKlUYtWqVeLIkSPi+eefFy4uLuLgwYNSm5dfflno9XqxadMmsX//fvHggw+KiIgIUVVV1a7nvHz5cvHaa6+JxMREodfrr1lvNptF//79RVxcnMjKyhJbtmwRfn5+YunSpVKbU6dOCXd3d5GYmCiOHDki3n77baFSqURycrLU5tNPPxUajUZ8+OGH4vDhw2LOnDnC29tbFBYWtsdpNpuj1NmULVu2iL/+9a/iq6++EgDE119/bbO+OZ+5cePGiUGDBonffvtN/PLLL6JHjx5i6tSp0vrmfP7bQ3x8vFi3bp04dOiQ2Ldvnxg/frzo2rWrKC8vl9r88Y9/FGFhYSI1NVVkZGSIESNGiJEjR0rrW+uz3V6+/fZb8f3334tjx46JnJwc8dxzzwkXFxdx6NAhpzzfRrt37xbh4eFi4MCBYsGCBdJyZzvfFStWiH79+omCggLpdeHCBWm9o5wvQxTZWLVqlYiIiJC+/9///V/RpUsXUVNTIy179tlnRe/evaXvH330UTFhwgSb/cTExIgnn3xSCFE/b1dQUJD45z//Ka03Go1Cq9WKTz75pK1O5YbWrVvXZIjasmWLUCqVwmAwSMveffddodPppPfgmWeeEf369bPZbvLkySI+Pl76fvjw4eLpp5+WvrdYLCIkJESsXLmylc/k1jhKnTdzdYhqzmfuyJEjAoDYs2eP1OaHH34QCoVCnDt3TgjRvM+/HIqKigQAsX37diFE/bm5uLiIjRs3Sm2ys7MFAJGWliaEaL3Ptpy6dOkiPvjgA6c937KyMtGzZ0+RkpIixowZI4UoZzzfFStWiEGDBjW5zpHOl5fzyIbJZIKPj4/0fVpaGu68805oNBppWXx8PHJycnDp0iWpTVxcnM1+4uPjkZaWBgDIzc2FwWCwaaPX6xETEyO16SjS0tIwYMAABAYGSsvi4+NRWlqKw4cPS21udL61tbXIzMy0aaNUKhEXF9ehztdR6rRHcz5zaWlp8Pb2xtChQ6U2cXFxUCqVSE9Pl9rc7PMvB5PJBADSz2pmZibq6upszjcyMhJdu3a1Od9b/WzLxWKx4NNPP0VFRQViY2Od9nyffvppTJgw4ZqanPV8jx8/jpCQEHTv3h3Tpk1DXl4eAMc6X4Yokpw4cQJvv/02nnzySWmZwWCw+ZACkL43GAw3bHPl+iu3a6pNR3Er51taWoqqqioUFxfDYrF0+PN1lDrt0ZzPnMFgQEBAgM16tVoNHx+fm/5dX3mM9ma1WrFw4UKMGjUK/fv3l2rRaDTXjPO7+nxv9bPd3g4ePAhPT09otVr88Y9/xNdff42+ffs65fl++umn2Lt3L1auXHnNOmc835iYGCQlJSE5ORnvvvsucnNzcccdd6CsrMyhzpchygktWbLkmgF7V7+OHj1qs825c+cwbtw4/P73v8ecOXNkqtw+9pwvkaN6+umncejQIXz66adyl9LmevfujX379iE9PR1PPfUUZsyYgSNHjshdVqvLz8/HggUL8PHHH8PV1VXuctrFfffdh9///vcYOHAg4uPjsWXLFhiNRnz++edyl9YiarkLoNa3aNEizJw584ZtunfvLv35/Pnz+N3vfoeRI0fivffes2kXFBR0zR0Rjd8HBQXdsM2V6xuXBQcH27SJiopq/oldR0vP90aCgoKuuTutueer0+ng5uYGlUoFlUp1w/ekI/Dz83OIOu3RnM9cUFAQioqKbLYzm80oKSm56d/1lcdoT/PmzcPmzZuxY8cO3HbbbdLyoKAg1NbWwmg02vzv/eqfw1v9bLc3jUaDHj16AACio6OxZ88evPnmm5g8ebJTnW9mZiaKioowZMgQaZnFYsGOHTvwzjvv4Mcff3Sq822Kt7c3evXqhRMnTuCee+5xmPNlT5QT8vf3R2Rk5A1fjWM8zp07h7vuugvR0dFYt24dlErbj0RsbCx27NiBuro6aVlKSgp69+6NLl26SG1SU1NttktJSUFsbCwAICIiAkFBQTZtSktLkZ6eLrVpr/O9mdjYWBw8eNDml2tKSgp0Oh369u0rtbnR+Wo0GkRHR9u0sVqtSE1NbZXzbS2OUqc9mvOZi42NhdFoRGZmptRm69atsFqtiImJkdrc7PPfHoQQmDdvHr7++mts3boVERERNuujo6Ph4uJic745OTnIy8uzOd9b/WzLzWq1oqamxunOd+zYsTh48CD27dsnvYYOHYpp06ZJf3am821KeXk5Tp48ieDgYMf6+221IerkcM6ePSt69Oghxo4dK86ePWtzq2kjo9EoAgMDxR/+8Adx6NAh8emnnwp3d/drpjhQq9Xi1VdfFdnZ2WLFihVNTnHg7e0tvvnmG3HgwAExceJEWaY4OHPmjMjKyhJ/+9vfhKenp8jKyhJZWVmirKxMCHH5ttl7771X7Nu3TyQnJwt/f/8mb5v9y1/+IrKzs8WaNWuanOJAq9WKpKQkceTIETF37lzh7e1tcydJR+AodTalrKxM+vsDIF577TWRlZUlzpw5I4Ro3mdu3LhxYvDgwSI9PV38+uuvomfPnjZTHDTn898ennrqKaHX68XPP/9s83NaWVkptfnjH/8ounbtKrZu3SoyMjJEbGysiI2Nlda31me7vSxZskRs375d5ObmigMHDoglS5YIhUIhfvrpJ6c836tdeXeeEM53vosWLRI///yzyM3NFTt37hRxcXHCz89PFBUVOdT5MkR1YuvWrRMAmnxdaf/+/WL06NFCq9WK0NBQ8fLLL1+zr88//1z06tVLaDQa0a9fP/H999/brLdarWLZsmUiMDBQaLVaMXbsWJGTk9Om59eUGTNmNHm+27Ztk9qcPn1a3HfffcLNzU34+fmJRYsWibq6Opv9bNu2TURFRQmNRiO6d+8u1q1bd82x3n77bdG1a1eh0WjE8OHDxW+//dbGZ2cfR6nzatu2bWvy73LGjBlCiOZ95i5evCimTp0qPD09hU6nE7NmzZICdaPmfP7b2vV+Tq/83FVVVYk//elPokuXLsLd3V089NBDNv8hEqL1Ptvt4fHHHxfdunUTGo1G+Pv7i7Fjx0oBSgjnO9+rXR2inO18J0+eLIKDg4VGoxGhoaFi8uTJ4sSJE9J6RzlfhRBCtF6/FhEREVHnwDFRRERERHZgiCIiIiKyA0MUERERkR0YooiIiIjswBBFREREZAeGKCIiIiI7MEQRERER2YEhioiIiMgODFFERFcJDw/HG2+80abHOH36NBQKBfbt29emxyGitqOWuwAiImc3c+ZMGI1GbNq0SVoWFhaGgoIC+Pn5yVcYEd0S9kQRkVOqra2Vu4QbUqlUCAoKglrN/8sSOSqGKCJyCHfddRfmzZuHefPmQa/Xw8/PD8uWLUPj4z/Dw8Px97//HdOnT4dOp8PcuXMBAF9++SX69esHrVaL8PBwrF692ma/RUVFeOCBB+Dm5oaIiAh8/PHHNuubuuxmNBqhUCjw888/S8sOHz6M+++/HzqdDl5eXrjjjjtw8uRJvPDCC1i/fj2++eYbKBQKabum9rt9+3YMHz4cWq0WwcHBWLJkCcxms8178Oc//xnPPPMMfHx8EBQUhBdeeKF13mAiajGGKCJyGOvXr4darcbu3bvx5ptv4rXXXsMHH3wgrX/11VcxaNAgZGVlYdmyZcjMzMSjjz6KKVOm4ODBg3jhhRewbNkyJCUlSdvMnDkT+fn52LZtG7744gv87//+L4qKilpU17lz53DnnXdCq9Vi69atyMzMxOOPPw6z2YzFixfj0Ucfxbhx41BQUICCggKMHDmyyX2MHz8ew4YNw/79+/Huu+/iX//6F1566aVr3gMPDw+kp6dj1apVePHFF5GSktKyN5KIWocgInIAY8aMEX369BFWq1Va9uyzz4o+ffoIIYTo1q2bSEhIsNnmscceE/fcc4/Nsr/85S+ib9++QgghcnJyBACxe/duaX12drYAIF5//XUhhBC5ubkCgMjKypLaXLp0SQAQ27ZtE0IIsXTpUhERESFqa2ubrH3GjBli4sSJNsuu3u9zzz0nevfubXN+a9asEZ6ensJisUjvwejRo232M2zYMPHss882eVwialvsiSIihzFixAgoFArp+9jYWBw/fhwWiwUAMHToUJv22dnZGDVqlM2yUaNGSdtkZ2dDrVYjOjpaWh8ZGQlvb+8W1bVv3z7ccccdcHFxaeEZ2dYaGxtrc36jRo1CeXk5zp49Ky0bOHCgzXbBwcEt7jkjotbBEEVETsPDw6PV96lU1v8zKRrGXgFAXV2dTRs3N7dWP+71XB3UFAoFrFZrux2fiC5jiCIih5Genm7z/W+//YaePXtCpVI12b5Pnz7YuXOnzbKdO3eiV69eUKlUiIyMhNlsRmZmprQ+JycHRqNR+t7f3x8AUFBQIC27em6ngQMH4pdffrkmXDXSaDRSb9n19OnTB2lpaTZhbefOnfDy8sJtt912w22JSB4MUUTkMPLy8pCYmIicnBx88sknePvtt7FgwYLrtl+0aBFSU1Px97//HceOHcP69evxzjvvYPHixQCA3r17Y9y4cXjyySeRnp6OzMxMPPHEEzY9S25ubhgxYgRefvllZGdnY/v27Xj++edtjjNv3jyUlpZiypQpyMjIwPHjx/Hvf/8bOTk5AOrvHDxw4ABycnJQXFzcZNj605/+hPz8fMyfPx9Hjx7FN998gxUrViAxMVHqDSOijoU/mUTkMKZPn46qqioMHz4cTz/9NBYsWCBNZdCUIUOG4PPPP8enn36K/v37Y/ny5XjxxRcxc+ZMqc26desQEhKCMWPG4OGHH8bcuXMREBBgs58PP/wQZrMZ0dHRWLhw4TV3zPn6+mLr1q0oLy/HmDFjEB0djffff1+69DZnzhz07t0bQ4cOhb+//zW9YwAQGhqKLVu2YPfu3Rg0aBD++Mc/Yvbs2dcENiLqOBTiyr5jIqIO6q677kJUVFSbP46FiKi52BNFREREZAeGKCIiIiI78HIeERERkR3YE0VERERkB4YoIiIiIjswRBERERHZgSGKiIiIyA4MUURERER2YIgiIiIisgNDFBEREZEdGKKIiIiI7PD/A5L8/qN4Y1dTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(y_test - predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(y_test, predictions, alpha=0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :  532.16\n",
      "RMSE: 803.20\n",
      "MSE :  645132.42\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE :  {:.2f}\".format(metrics.mean_absolute_error(y_test, predictions)))\n",
    "print(\"RMSE: {:.2f}\".format(np.sqrt(metrics.mean_squared_error(y_test, predictions))))\n",
    "print(\"MSE :  {:.2f}\".format(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16,8), sharey=True)\n",
    "axs[0].scatter(y_test, predictions, alpha=0.5)\n",
    "axs[0].set_title(\"Tuning Hyperparameter\")\n",
    "axs[1].scatter(y_test, y_pred, alpha=0.5)\n",
    "axs[1].set_title(\"Deafault Hyperparameters\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isn't a great difference between default params and tunning params, this must be due to there's little information for high productions and the model can't learn about this cases."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file\n",
    "file = open('models/model.pkl', 'wb')\n",
    "\n",
    "# dump information\n",
    "pickle.dump(gbr_random, file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "77655e78348545ef1ffa39f4f4948f0117d7a823bc7c5854f9f740fa12ddc614"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}