homework/session6/session6-homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08b31f4c",
   "metadata": {},
   "source": [
    "# California Housing Prices\n",
    "\n",
    "The goal of this homework is to create a regression model for predicting housing prices (column `median_house_value`).\n",
    "\n",
    "In this homework we'll again use the California Housing Prices dataset - the same one we used in homework 2 and 3.\n",
    "\n",
    "We can take it from [Kaggle](https://www.kaggle.com/datasets/camnugent/california-housing-prices) or download using the wget link mentioned below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "916dc5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.isfile('housing.csv'):\n",
    "    !wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c99326",
   "metadata": {},
   "source": [
    "## Load the Data\n",
    "\n",
    "We need to import the required libraries and then load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39d0c751",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d14eea5",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('housing.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3dbfac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check num of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16e7009b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             float64\n",
       "latitude              float64\n",
       "housing_median_age    float64\n",
       "total_rooms           float64\n",
       "total_bedrooms        float64\n",
       "population            float64\n",
       "households            float64\n",
       "median_income         float64\n",
       "median_house_value    float64\n",
       "ocean_proximity        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673ef09e",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "\n",
    "- Fill NAs with `0`.\n",
    "- Apply the log transform to `median_house_value`.\n",
    "- Do train/validation/test split with 60%/20%/20% distribution.\n",
    "- Use the `train_test_split` function and set the `random_state` parameter to 1.\n",
    "- Use `DictVectorizer` to turn the dataframe into matrices.\n",
    "\n",
    "Let's perform these tasks on one at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c178e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the missing values in the df\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb9598e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'total_bedrooms' column with '0'\n",
    "df['total_bedrooms'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff97af9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the dataframe again\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ebb9586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        452600.0\n",
       "1        358500.0\n",
       "2        352100.0\n",
       "3        341300.0\n",
       "4        342200.0\n",
       "           ...   \n",
       "20635     78100.0\n",
       "20636     77100.0\n",
       "20637     92300.0\n",
       "20638     84700.0\n",
       "20639     89400.0\n",
       "Name: median_house_value, Length: 20640, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'median_house_value' before log transformation\n",
    "df.median_house_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16a61cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        13.022766\n",
       "1        12.789687\n",
       "2        12.771673\n",
       "3        12.740520\n",
       "4        12.743154\n",
       "           ...    \n",
       "20635    11.265758\n",
       "20636    11.252872\n",
       "20637    11.432810\n",
       "20638    11.346883\n",
       "20639    11.400887\n",
       "Name: median_house_value, Length: 20640, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply log tranformation to column 'median_house_value'\n",
    "df.median_house_value = np.log1p(df.median_house_value)\n",
    "df.median_house_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99a2cf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train/val/test with 60/20/20 distribution and 'random_state' to 1\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_full_train, df_test = train_test_split(df, test_size=0.2, random_state=1)\n",
    "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72bbb849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12384, 10), (4128, 10), (4128, 10))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the data distribution\n",
    "df_train.shape, df_val.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea4c6f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the indexes\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_val = df_val.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee9647f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create target variables from dfs\n",
    "y_train = df_train.median_house_value.values\n",
    "y_val = df_val.median_house_value.values\n",
    "y_test = df_test.median_house_value.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "751c8ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove target variables from dfs\n",
    "del df_train['median_house_value']\n",
    "del df_val['median_house_value']\n",
    "del df_test['median_house_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f3f6284",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply DictVectorizer on df_train and df_val\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "\n",
    "# Prepare train data\n",
    "train_dicts = df_train.to_dict(orient='records')\n",
    "dv = DictVectorizer(sparse=False)\n",
    "X_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "# Prepare validation data\n",
    "val_dicts = df_val.to_dict(orient='records')\n",
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ffa5f23",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "Let's train a decision tree regressor to predict the `median_house_value` variable.\n",
    "\n",
    "- Train a model with `max_depth=1`\n",
    "\n",
    "Which feature is used for splitting the data?\n",
    "\n",
    "- `ocean_proximity=INLAND`\n",
    "- `total_rooms`\n",
    "- `latitude`\n",
    "- `population`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81272af5",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Create decision tree model with max depth of 1\n",
    "dt = DecisionTreeRegressor(max_depth=1)\n",
    "\n",
    "# Train the model\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4622fad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\repos\\ml-engineering\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize which feature is used for splitting the data\n",
    "from sklearn import tree\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "tree.plot_tree(dt, feature_names=dv.get_feature_names())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe349c1",
   "metadata": {},
   "source": [
    "`ocean_proximity=INLAND` is used for data split.\n",
    "\n",
    "## Question 2\n",
    "\n",
    "Train a random forest model with these parameters:\n",
    "\n",
    "- `n_estimators=10`\n",
    "- `random_state=1`\n",
    "- `n_jobs=-1` (optional - to make training faster)\n",
    "\n",
    "What's the RMSE of this model on validation?\n",
    "\n",
    "- 0.05\n",
    "- 0.25\n",
    "- 0.55\n",
    "- 0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ba09918",
   "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>RandomForestRegressor(n_estimators=10, n_jobs=-1, random_state=1)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, n_jobs=-1, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, n_jobs=-1, random_state=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Create random forest model\n",
    "rf = RandomForestRegressor(n_estimators=10, random_state=1, n_jobs=-1)\n",
    "\n",
    "# Train the model\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "006365a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on validation data\n",
    "y_pred = rf.predict(X_val)\n",
    "\n",
    "# Calculate RMSE on validation\n",
    "rmse = mean_squared_error(y_val, y_pred, squared=False) # squared=False returns rmse value\n",
    "rmse.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d416c2",
   "metadata": {},
   "source": [
    "The rmse is `0.25`.\n",
    "\n",
    "## Question 3\n",
    "\n",
    "Now let's experiment with the `n_estimators` parameter.\n",
    "\n",
    "- Try different values of this parameter from 10 to 200 with step 10.\n",
    "- Set `random_state` to `1`.\n",
    "- Evaluate the model on the validation dataset.\n",
    "\n",
    "After which value of `n_estimators` does RMSE stop improving?\n",
    "\n",
    "- 10\n",
    "- 50\n",
    "- 70\n",
    "- 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5625c5b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "401c2884c8c945ff8489f9afa271f0d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "# Experiment random forest model with different n_estimators\n",
    "scores = []\n",
    "\n",
    "for est in tqdm(range(10, 201, 10)):\n",
    "    rf = RandomForestRegressor(n_estimators=est, random_state=1, n_jobs=-1)\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = rf.predict(X_val)\n",
    "    rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "    \n",
    "    scores.append((est, rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b202272",
   "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>n_estimators</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.245454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.239133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0.235631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0.234526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>0.233065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>0.232812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>0.232360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>0.232504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>0.232179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>0.231951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>0.231520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>120</td>\n",
       "      <td>0.231622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>130</td>\n",
       "      <td>0.231723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>140</td>\n",
       "      <td>0.231707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>150</td>\n",
       "      <td>0.231741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>160</td>\n",
       "      <td>0.231740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170</td>\n",
       "      <td>0.231684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>180</td>\n",
       "      <td>0.231841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>190</td>\n",
       "      <td>0.231810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>200</td>\n",
       "      <td>0.231708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_estimators      rmse\n",
       "0             10  0.245454\n",
       "1             20  0.239133\n",
       "2             30  0.235631\n",
       "3             40  0.234526\n",
       "4             50  0.233065\n",
       "5             60  0.232812\n",
       "6             70  0.232360\n",
       "7             80  0.232504\n",
       "8             90  0.232179\n",
       "9            100  0.231951\n",
       "10           110  0.231520\n",
       "11           120  0.231622\n",
       "12           130  0.231723\n",
       "13           140  0.231707\n",
       "14           150  0.231741\n",
       "15           160  0.231740\n",
       "16           170  0.231684\n",
       "17           180  0.231841\n",
       "18           190  0.231810\n",
       "19           200  0.231708"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe from scores\n",
    "df_scores = pd.DataFrame(scores, columns=['n_estimators', 'rmse'])\n",
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8a18c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_scores.n_estimators, df_scores.rmse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc67b306",
   "metadata": {},
   "source": [
    "The rmse doesn't improve at all and keeps dropping. So, at `n_estimators=10` the rmse stops improving.\n",
    "\n",
    "## Question 4\n",
    "\n",
    "Let's select the best `max_depth`:\n",
    "\n",
    "- Try different values of `max_depth`: `[10, 15, 20, 25]`\n",
    "- For each of these values, try different values of `n_estimators` from 10 till 200 (with step 10)\n",
    "- Fix the random seed: `random_state=1`\n",
    "\n",
    "What's the best `max_depth`:\n",
    "\n",
    "- 10\n",
    "- 15\n",
    "- 20\n",
    "- 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82d808c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7833ee055ca449da6e8f781d87f79a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c0c9119d95249129ee9c6ac96b67627",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36f323cffb9943e487929b67ed47d6bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d272d79ee79c4d86bf834c5d821ba45f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7d5b16cee8a486095a3cbc3a2d022a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train rf model on max depth values of [10, 15, 20, 25]\n",
    "\n",
    "scores = []\n",
    "\n",
    "for depth in tqdm([10, 15, 20, 25]):\n",
    "    # build rf model and set 'warm_start=True' to reuse solution of the previous call\n",
    "    # and add more estimators to the ensemble\n",
    "    rf = RandomForestRegressor(n_estimators=10,\n",
    "                               max_depth=depth,\n",
    "                               random_state=1,\n",
    "                               n_jobs=-1,\n",
    "                               warm_start=True)\n",
    "    \n",
    "    for est in tqdm(range(10, 201, 10)):\n",
    "        rf.n_estimators = est\n",
    "        rf.fit(X_train, y_train)\n",
    "        \n",
    "        y_pred = rf.predict(X_val)\n",
    "        rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "        \n",
    "        scores.append((depth, est, rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83dae688",
   "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>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.255042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.252784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.251647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>0.251062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>0.250192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>25</td>\n",
       "      <td>160</td>\n",
       "      <td>0.231503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.231458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>25</td>\n",
       "      <td>180</td>\n",
       "      <td>0.231613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>25</td>\n",
       "      <td>190</td>\n",
       "      <td>0.231616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>25</td>\n",
       "      <td>200</td>\n",
       "      <td>0.231493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  n_estimators      rmse\n",
       "0          10            10  0.255042\n",
       "1          10            20  0.252784\n",
       "2          10            30  0.251647\n",
       "3          10            40  0.251062\n",
       "4          10            50  0.250192\n",
       "..        ...           ...       ...\n",
       "75         25           160  0.231503\n",
       "76         25           170  0.231458\n",
       "77         25           180  0.231613\n",
       "78         25           190  0.231616\n",
       "79         25           200  0.231493\n",
       "\n",
       "[80 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.DataFrame(scores, columns=['max_depth', 'n_estimators', 'rmse'])\n",
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c62911c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10\n",
       "1     10\n",
       "2     10\n",
       "3     10\n",
       "4     10\n",
       "      ..\n",
       "75    25\n",
       "76    25\n",
       "77    25\n",
       "78    25\n",
       "79    25\n",
       "Name: max_depth, Length: 80, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores.max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0547c4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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+qwdJmtUnIZx/Xz+W2ycNwGYx8en6fCY99gXvr80JdmkiItJJlZSUABAbGwvAypUrcblcZGZm+tsMHDiQnj17kp2d3WZ1WNvslaVTsFrMTD+9Lz8bmMitb6zhh9xSZrzyLR+ty+P+c4YQE2YPdokiItIEwzCoch/7MaQh1hBMJlOLtvV6vdx8882MGzeOIUOGAJCXl4fdbic6OjqgbVJSEnl5eUdbbrMUkOSwDOoWyTvTx/HEZ5t4MmsL76/N5Zufiph7/lB+flxSsMsTEZFGqtxVZLySccz3u/TSpYTaQlu07fTp01m3bh1fffVVK1d15HSKTQ6b3Wpm1sQBvH3jSfRNDKewvIZr/rmCW99YQ0mVK9jliYhIBzZjxgzef/99Fi9eTI8ePfzLk5OTqa2tpbi4OKB9fn4+ycnJbVaPepDkiA3rEc37M0/m0YUbefbLn/jPql0s2VLIgxcM45T+CcEuT0RE8J3qWnrp0qDs90gYhsHMmTN5++23ycrKIj09PWD9yJEjsdlsLFq0iAsuuACADRs2sGPHDsaOHdtqdTemgCQt4rRZ+P0Zg5h4XBK3vbmGbXsrmfb8MqZm9OSOSQOJCrUFu0QRkS7NZDK1+FTXsTR9+nReeeUV3n33XSIiIvzjiqKioggJCSEqKoqrrrqKWbNmERsbS2RkJDNnzmTs2LFNfoOttehr/i3UFb7mf7gqa908+OGPvJS9HQC7xcypAxI4e3gKmYOSCLFbglyhiEjn1pG/5t/cgO4XXniBK664AvAd36233sqrr75KTU0NkyZN4m9/+1uzp9ha42v+CkgtpIB0oK83F3L/+z/wY16Zf1mo3cLE45I4+/gUxvdLwGbRsDcRkdbWkQNSW2iNgKRTbNJqxvWN56ObT+HHvFLeW53De2ty2LWvindW5/DO6hyiQ21MGdKNs4enkJEei9ncsq+BioiItDUFJGl1A5MjGTg5ktsnDWDVjmL+uyaH99fmUlhew6vLdvDqsh0kRzr5xbBunH18CkO7R7X4mhkiIiJtQQFJ2ozJZGJkrxhG9orhj2cOIvunvby3OoePvs8jr7Saf3y1lX98tZW0uFDOHp7C2cen0DcxIthli4iIaAxSS2kMUsvVuD1kbdjDe2tyWLQ+n2qX17/uuG6RnH18CmcNT6F79JF9VVREpKvSGKRAGoMkHZLDamHS4GQmDU6mvMbNwh/yeG91Dl9uKuSH3FJ+yC1l3oc/MqpXDGcfn8IZQ7sRH+4IdtkiItKFKCBJUIU7rJw3ogfnjehBUUUtH67L5b3VOSzbVsSK7ftYsX0f9/73B8b1jefs4SlMGpxEhFPXWBIRkbalgCTtRmyYnakZvZia0YvckireX5PLe2ty+G53CV9s3MMXG/fwh7fNTM3oxY2n91GvkoiItBkFJGmXukWFcM0pvbnmlN78tKec/67J5b01u9myp4Lnv97Ka8t3cOW4NK4d30dX7RYRkVanq/ZJu9c7IZzfZvbj01mn8tJvxjCsRxSVtR6eXLyFk+d/xl8XbaK8xh3sMkVEpBNRQJIOw2QycWr/BN6dPo5nLhvJgKQIyqrdPLJwI6fMX8yzX/xEtcsT7DJFRKQTUECSDsdkMjFpcDIf/nY8f7n4eNLjwyiqqOWBD9ZzyvzF/F/2Nmrd3kO/kIiIBN3cuXMZPXo0ERERJCYmcu6557Jhw4aANqeddhomkylguv7669u0LgUk6bDMZhPnHN+dhbecwvwLhtE9OoSCshruevd7Tn84izdW7MTtUVASEWnPPv/8c6ZPn84333zDwoULcblcTJw4kYqKioB211xzDbm5uf5p/vz5bVqXBmlLh2e1mPnV6FTOGZHC68t38tfPNrO7uIo7/r2Wp7O2cPPP+/OLod107zcRkXboo48+Cnj+4osvkpiYyMqVKznllFP8y0NDQ0lOTj5mdakHSToNh9XCtLFpfHH76fz+jIHEhNr4qbCCm179ljMe/5JPvs9DF44Xka7CMAy8lZXHfDrav7MlJSUAxMbGBix/+eWXiY+PZ8iQIdx5551UVlYe1X4ORT1I0umE2C1ce0ofLhnTkxe+3sazX/zEj3llXPt/KxmeGs1tE/tzct943SBXRDo1o6qKDSeMPOb7HbBqJabQ0BZt6/V6ufnmmxk3bhxDhgzxL7/00kvp1asXKSkprF27ltmzZ7Nhwwbeeuut1ir7AApI0mlFOG3cNKEf08b24pkvfuLFr7exZmcxlz23jDHpsdw+aQCj02IP/UIiInJMTJ8+nXXr1vHVV18FLL/22mv980OHDqVbt25MmDCBLVu20KdPnzapRQFJOr3oUDuzJw/kN+PSeSprC/9aup1lW4v45dPZnNo/gVsn9mdYj+hglyki0qpMISEMWLUyKPttiRkzZvD+++/zxRdf0KNHj4O2zcjIAGDz5s0KSCJHKyHCwZyzjuPq8en89bPNvLliJ59v3MPnG/cwaXASs34+gAHJEcEuU0SkVZhMphaf6jqWDMNg5syZvP3222RlZZGenn7IbVavXg1At27d2qyuoA/SfvLJJ0lLS8PpdJKRkcGyZcuabfvss88yfvx4YmJiiImJITMz84D2V1xxxQHXSpg8eXJAm6KiIqZOnUpkZCTR0dFcddVVlJeXt8nxSfuTEh3C3POHsujWUzl/RHdMJvj4+3wm/+ULLntuKbNeX83d767j4Y838PTnW3h56XbeW5PD4h8LWLGtiA15ZeQUV1Fa7cLr1aBvEZGjMX36dP71r3/xyiuvEBERQV5eHnl5eVRVVQGwZcsW7r//flauXMm2bdt47733mDZtGqeccgrDhg1rs7pMRhC/1vP6668zbdo0nn76aTIyMliwYAFvvvkmGzZsIDEx8YD2U6dOZdy4cZx00kk4nU4efPBB3n77bb7//nu6d+8O+AJSfn4+L7zwgn87h8NBTEyM//mUKVPIzc3lmWeeweVyceWVVzJ69GheeeWVw669tLSUqKgoSkpKiIyMPIqfggTbpvwyHvt0Ix98l3fE25pMEG63Eu60EuG0EuG0Ee5omI90WgOex4bb6RblJDnSSVSITQPFRaRVVFdXs3XrVtLT03E6ncEu54g093fwhRde4IorrmDnzp38+te/Zt26dVRUVJCamsp5553HH//4x2Y/fw/28zjcz++gBqSMjAxGjx7NE088AfhGr6empjJz5kx+97vfHXJ7j8dDTEwMTzzxBNOmTQN8Aam4uJh33nmnyW3Wr1/Pcccdx/Llyxk1ahTguwbDGWecwa5du0hJSTms2hWQOp8f80pZtb2YsmoXZdVuymvclNbNl1W7KK9x1837nrs8R/dPx2kzkxzpJCnSSbcoJ0lRTrpFOkmOql8WQny4Hasl6B29ItLOdeSA1BZaIyAFbQxSbW0tK1eu5M477/QvM5vNZGZmkp2dfVivUVlZicvlOuBaCVlZWSQmJhITE8PPfvYz/vSnPxEXFwdAdnY20dHR/nAEkJmZidlsZunSpZx33nlN7qumpoaamhr/89LS0sM+VukYBiZHMjD58MNutcvTRHhyUVrtpny/IFW/rrTaxZ6yGvJLq9lX6aLa5WXb3kq27W3+Wh5mEyRG+MJTcqSDblEhDYGqLkwlRzoJsVta40cgIiJ1ghaQCgsL8Xg8JCUlBSxPSkrixx9/PKzXmD17NikpKWRmZvqXTZ48mfPPP5/09HS2bNnC73//e6ZMmUJ2djYWi4W8vLwDTt9ZrVZiY2PJy2v+FMvcuXO59957j+AIpbNz2iw4bRYSIhxHvG21y0N+aTV5JdXkNfNYUFaDx2v4npdWs+YgrxcVYqNblJP+SREM6R7JkJQoBqdEERVqa/kBioh0YR32W2zz5s3jtddeIysrK6D77OKLL/bPDx06lGHDhtGnTx+ysrKYMGFCi/d35513MmvWLP/z0tJSUlNTW/x60rU5bRZ6xYXRKy6s2TYer8He8hpyG4Wn/JJqckuqyS/1PVa5PJRUuSipcvFjXhnvrcnxv0aPmBCGpEQxpHskg1OiGNw9ksQIdb+LiBxK0AJSfHw8FouF/Pz8gOX5+fmHvNfKww8/zLx58/j0008POYK9d+/exMfHs3nzZiZMmEBycjIFBQUBbdxuN0VFRQfdr8PhwOE48p4CkZaymE0kRjpJjHQyvJk2hmFQWu0mv7SaXfsqWZ9bxrrdJXyfU8qOokp27ati174qPvq+oXc0McLBkO5RDEmJ5Li68NQ9OkQDxkVE9hO0gGS32xk5ciSLFi3i3HPPBXyDtBctWsSMGTOa3W7+/Pk88MADfPzxxwHjiJqza9cu9u7d679WwtixYykuLmblypWMHOm7BPtnn32G1+v1X3hKpKMwmUxEhdiICrHRPymCnw1sOGVdUuni+9wSvt9dyvc5JazLKWXLnnIKymr47McCPvux4T8K0aE232m5up6mISmRpMWF6Qa/ItJlBfUU26xZs7j88ssZNWoUY8aMYcGCBVRUVHDllVcCMG3aNLp3787cuXMBePDBB5kzZw6vvPIKaWlp/jFD4eHhhIeHU15ezr333ssFF1xAcnIyW7Zs4Y477qBv375MmjQJgEGDBjF58mSuueYann76aVwuFzNmzODiiy8+7G+wiXQEUaE2TuoTz0l94v3LKmrc/JhXyrr60LS7lI35ZRRXuvhqcyFfbS70tw2zWxicEsVxKZEM6R7F4JRIeieE4bBqQLhIe6Ubcvu0xs8hqAHpoosuYs+ePcyZM4e8vDyOP/54PvroI//A7R07dmA2N3zF+amnnqK2tpYLL7ww4HXuvvtu7rnnHiwWC2vXruWll16iuLiYlJQUJk6cyP333x9weuzll19mxowZTJgwAbPZzAUXXMDjjz9+bA5aJIjCHFZG9oplZK+Gb37WuD1szCtnXU6JPzStzy2lotbDsm1FLNtW5G9rMZvoFRtKv6Rw+iVG0C8pnL6J4fRJCMdpU3ASCRabzfeFjMrKSkJaeKuPzqSy0vft4PqfS0sE9TpIHZmugySdmdvjZcueCn9gWpdTwvrcUsqq3U22N5mgZ2wo/RLD6ZcU4XtMjKBPYhih9g77XRCRDiU3N5fi4mISExMJDQ3tkuMKDcOgsrKSgoICoqOjm7wVSYe4UGRHpoAkXY1hGBSU1bApv5xNBWVsKihnU34ZG/PLKalyNbtdj5iQwOCUFEHfxHDCHQpOIq3JMAzy8vIoLi4OdilBFx0dTXJycpMhUQGpjSkgifgYhkFheS2bCsrYXFDeEKDyy9lbUdvsdilRTvr6e5vC6RUXRmKkg8QIB+EOa5f8369Ia/B4PLhczf+npbOz2WxYLM2f8ldAamMKSCKHtre8xheaCsrrHn3BqaCs5qDbOW1mEiIcJEY4SQh31M37Hv3LIxzEhdux6VYsInIE2v2tRkSk84sLdxAX7iCjd1zA8pJKF5v3+E7P1fc47d5XxZ6yGspq3FS7vOwsqmJnUdUh9xEbZm8IT+EOEiId+4Uqpz9URTrVK9UaDMOgstZ3cdLSahelVW5K6+ZLquqeV7v8y0qr3HgNA7PJhMVswmQCs8mEue7RZDJhMdcva1jfVFuzeb/5urZWs4kQm4VQh5Uwu4UQu+8x1GEl1G4h1G4hzF4377ASarPo8hVyWNSD1ELqQRJpG1W1HvaU1bCnvJo9ZTUUlNX4njeaLyyvwe09/D9fkU4r6Qnh9I4PI73RFNbFxkN5vQZl1W6KKmvZV1lLSeX+Acd3P8HmQ48bzxH83NujEJulLjD5wlPI/iHKHhi2okJs9IgJITUmlNTYECKcun0P+IKyy2Pg8Rp4DAOPp+7R29QyLx4vgevqlrm9XrxeGrVrWDdhUFKrj1fUKbY2poAkElxer8G+ylr2lNeFp9KahvmyGvaUNQSs5r59Vy8p0lEXlvYLUAlhpMaEYre271N4hmFQVuOmuMLlCzwVtRRV+IJP/eO+/dbtq6xlX6XrqEOOzeK7SGmk00ZEiI1Ip5XIuouWRjptRIZYfeucVqxmM17DaJi84DUMDMP3wehb7jsWr9fAUz9ft9xbt9w/X7e+/gO3yuWhosZDZa2bylrfY0WNp255w7LWyHXRofsHplBSY0LoUReeesSEtrvLXbg8Xv/xV9Z6qKr1UFnroaLW7Z+vqnVTsd98Zd18420Cnrs8xyQof3brqfROCG/V19QpNhHp1Mxmk/8U3sCD352IqloPO4oq2VpYzk+FFWzdU8HWQt+0t6KW/NIa8ktr+OanooDtLGYTqTEh/vCUnhBGn7rwlBThPKpTNV6vQY3bS5XL90FeVeuhum6+uu55/Xx5jWe/cFMXfPYLPUfSk7a/cIeV6FAb0aH7BZu6cBMVYiMypNFzZ8Myp83coU5ZGobv590QmBpCwv7L6kNARa2byhoPRRW17NpXyc59VRRV1FJc6aK40sW63aVN7ichwhHQ45QaE+oPUCnRIUc8Zs7r9QXg0qqGHr6S/Xr36nv4ShotL61bXuvxtsaP74hYzb5TpBazCYvJhMVS92huNDW1rNFyRxADp3qQWkg9SCKdQ0mli617K9haWM7WPRW+AFU3VdZ6mt0uxGYhLT6M3vFh9IgNwTA4MOT4g4+34Xndshp3635whdgsxIbZiQmzERNq982H2uvmbcSE2YkNtfsew+xEh9p0VfQjVF7jZve+KnYWVbJzX6VvnNy+unseFlVSVnPwnkqzCZIjnfSIDfUHKJvFHBB8AsJPpYuyGjet8SltMZsaTiHudzqxfsxWSDPrQm0Wwhx183YLITYLYXXju5w2ywFhqCOM79IptjamgCTSudVf9+knf29TOVsLfQFqx97KFvfaNMVuNRNis+C01T/6PrDq50PtFn/g8YWg+rBj84egELvCTjAZhkFJlYudRVV1PU6NAtS+SqpdLQ/FTpvZ34tX37sX5T+l6Tu9GbjM1/MX7vCNsbJbOlaPX1tSQGpjCkgiXZfL42X3vip+Kiznpz0V7C6uwmYx47SacdYFm5C6kOOsDzz+ZeaAZU6bBUsH+F+3HB3DMNhTXuMPULvqeqIMA/8pTP9pzf1Cjm+ZVb19rUgBqY0pIImIiHQ8h/v53b6/niEiIiISBApIIiIiIo0oIImIiIg0ooAkIiIi0ogCkoiIiEgjCkgiIiIijSggiYiIiDSigCQiIiLSiAKSiIiISCMKSCIiIiKNKCCJiIiINKKAJCIiItKIApKIiIhIIwpIIiIiIo0oIImIiIg0ooAkIiIi0ogCkoiIiEgjCkgiIiIijSggiYiIiDSigCQiIiLSiAKSiIiISCMKSCIiIiKNKCCJiIiINKKAJCIiItKIApKIiIhIIwpIIiIiIo0oIImIiIg0ooAkIiIi0ogCkoiIiEgjCkgiIiIijSggtUOGYQS7BBERkS5NAakd8RpeZn42k/GvjyevIi/Y5YiIiHRZCkjtiNlkJq8ij5KaEtbsWRPsckRERLosa7ALkAaG18sV/6vGts7ND92/YVLapGCXJCIi0iWpB6kdMZnNdN9aRmohlKxaFuxyREREuiwFpHYmbMQJAISs30GtpzbI1YiIiHRNCkjtTELGeAD67nTzw94fglyNiIhI16SA1M6EjhgBQJ9cWJuzKsjViIiIdE0KSO2MrVcvXBEh2D2w+9uvgl2OiIhIl6SA1M6YTCZMwwYC4FnzvS4aKSIiEgQKSO1Q/OiTAei+tUwXjBQREQkCBaR2KGrUGAAG7DJYU7A6uMWIiIh0QQpI7ZBzyBC8FhMxFbBx/dfBLkdERKTLUUBqh8xOJ7V9egBQvmpFkKsRERHpehSQ2qmIkaMBCPtxFzWemiBXIyIi0rUoILVTCWN8A7X77vLogpEiIiLHmAJSO1V/wci0Ali7XfdlExEROZYUkNopW3IyNfGRmA3IX6ELRoqIiBxLQQ9ITz75JGlpaTidTjIyMli2rPnekmeffZbx48cTExNDTEwMmZmZB21//fXXYzKZWLBgQcDytLQ03wUZ95vmzZvXWofUaizDjgPA+O5HXTBSRETkGApqQHr99deZNWsWd999N6tWrWL48OFMmjSJgoKCJttnZWVxySWXsHjxYrKzs0lNTWXixIns3r37gLZvv/0233zzDSkpKU2+1n333Udubq5/mjlzZqseW2tIzDgFgB7bKsipyAlyNSIiIl1HUAPSo48+yjXXXMOVV17Jcccdx9NPP01oaCjPP/98k+1ffvllbrzxRo4//ngGDhzIP/7xD7xeL4sWLQpot3v3bmbOnMnLL7+MzWZr8rUiIiJITk72T2FhYa1+fEcrcqTvgpH9cwzW5H0b5GpERES6jqAFpNraWlauXElmZmZDMWYzmZmZZGdnH9ZrVFZW4nK5iI2N9S/zer1cdtll3H777QwePLjZbefNm0dcXBwjRozgoYcewu12t/xg2ohzQH/cdivh1fDT2i+DXY6IiEiXYQ3WjgsLC/F4PCQlJQUsT0pK4scffzys15g9ezYpKSkBIevBBx/EarVy0003NbvdTTfdxAknnEBsbCxLlizhzjvvJDc3l0cffbTZbWpqaqipabgeUWlp6WHVeDRMNhuugb2wrt1Cxber4Pw236WIiIgQxIB0tObNm8drr71GVlYWTqcTgJUrV/KXv/yFVatWYTKZmt121qxZ/vlhw4Zht9u57rrrmDt3Lg6Ho8lt5s6dy7333tu6B9GYxwWfPwibFsLl/wVnJNEjM6hZu4XIDblUuasIsYa0bQ0iIiISvFNs8fHxWCwW8vPzA5bn5+eTnJx80G0ffvhh5s2bxyeffMKwYcP8y7/88ksKCgro2bMnVqsVq9XK9u3bufXWW0lLS2v29TIyMnC73Wzbtq3ZNnfeeSclJSX+aefOnYd1nEfEbIV1b0HuatjyGQAJY8YD0G+Xh+8Lv2/9fYqIiMgBghaQ7HY7I0eODBhgXT/geuzYsc1uN3/+fO6//34++ugjRo0aFbDusssuY+3ataxevdo/paSkcPvtt/Pxxx83+5qrV6/GbDaTmJjYbBuHw0FkZGTA1OpMJhgwxTe/4UMAQkccD0D3Ivh+y+GNzRIREZGjE9RTbLNmzeLyyy9n1KhRjBkzhgULFlBRUcGVV14JwLRp0+jevTtz584FfOOL5syZwyuvvEJaWhp5eXkAhIeHEx4eTlxcHHFxcQH7sNlsJCcnM2DAAACys7NZunQpp59+OhEREWRnZ3PLLbfw61//mpiYmGN49M0YMAWyn4BNn4DHjSU6mqoecYTs2kvhiiUwrv1djkBERKSzCWpAuuiii9izZw9z5swhLy+P448/no8++sg/cHvHjh2YzQ2dXE899RS1tbVceOGFAa9z9913c8899xzWPh0OB6+99hr33HMPNTU1pKenc8sttwSMSwqq1BPBGQ1VRbBrGfQ6CfvwYbBrMebvNmIYxkHHV4mIiMjRMxm6RHOLlJaWEhUVRUlJSeufbvvPNfDdG3DSTTDxfva8+TqFd93DD6kw5q1PSI1Ibd39iYiIdBGH+/kd9FuNSBMGTPY91o1Dihw5GoC+ubAmd1WwqhIREekyFJDao76Zvm+07d0EhZuxp6VRG+bA7obtK7KCXZ2IiEinp4DUHjmjIO1k3/zGDzGZzbgH9wGgZvWaIBYmIiLSNSggtVf9A7/uHzvad+mD6E35VLoqg1WViIhIl6CA1F7Vj0Pa8Q1UFpE45hQA+u3y8v1eXTBSRESkLSkgtVcxaZB4HBge2LSQkKFD8JpNxJfB+vW6ca2IiEhbanFA+r//+z/GjRtHSkoK27dvB2DBggW8++67rVZcl1d/Ve2NH2IODaUq3Xd9qH3LdUVtERGRttSigPTUU08xa9YszjjjDIqLi/F4PABER0ezYMGC1qyvaxtwhu9x06fgrsV5/PEAWL7fgi5fJSIi0nZaFJD++te/8uyzz/KHP/wBi8XiXz5q1Ci+++67Viuuy0s5AcISobYMtn9NtxNPA6DX9ip2lO0Ibm0iIiKdWIsC0tatWxkxYsQByx0OBxUVFUddlNQxm6H/JN/8hg/9F4xMz4e1O5cHsTAREZHOrUUBKT09ndWrVx+w/KOPPmLQoEFHW5Psb0DD1/2tyclUxYRiMWDXsqygliUiItKZtehmtbNmzWL69OlUV1djGAbLli3j1VdfZe7cufzjH/9o7Rq7tt6ngdUJJTsw7VmPMaQ/fLka1xqdyhQREWkrLQpIV199NSEhIfzxj3+ksrKSSy+9lJSUFP7yl79w8cUXt3aNXZs9DNJPhU0fw4YPiB9zMpVfriZ28x4qXBWE2cKCXaGIiEin0+Kv+U+dOpVNmzZRXl5OXl4eu3bt4qqrrmrN2qSe/zTbRyRmjAeg/y6DdXvUiyQiItIWWhSQqqqqqKz03e4iNDSUqqoqFixYwCeffNKqxUmd/nVX1d69AmdqHG6bmYhq2Lg2K6hliYiIdFYtCkjnnHMO//znPwEoLi5mzJgxPPLII5xzzjk89dRTrVqgAJHdIMX3rUHTT59S1a8HAMXLlwazKhERkU6rRQFp1apVjB/vO9Xz73//m+TkZLZv384///lPHn/88VYtUOrUXzRy40eEjTgBAMcPW3XBSBERkTbQooBUWVlJREQEAJ988gnnn38+ZrOZE0880X/bEWll9afZtiym+5iTAUjfUcO20m3Bq0lERKSTalFA6tu3L++88w47d+7k448/ZuLEiQAUFBQQGRnZqgVKneShENkD3FWEx1YB0GMvfPeT7ssmIiLS2loUkObMmcNtt91GWloaGRkZjB07FvD1JjV1hW1pBSaT/9ts1twvKE+OAiB36efBrEpERKRTalFAuvDCC9mxYwcrVqzgo48+8i+fMGECjz32WKsVJ40MqDvNtvFjzEMHAuBZ+30QCxIREemcWnShSIDk5GSSk5MDlo0ZM+aoC5KDSBsP9nAozyNxUG/KFy4lYUsR5bXlhNvDg12diIhIp9GigFRdXc1f//pXFi9eTEFBAV6vN2D9qlWrWqU4acTqgD4/g/XvkRhZSjnQN8dgbf63nJQ6PtjViYiIdBotCkhXXXUVn3zyCRdeeCFjxozBZDK1dl3SnAFnwPr3sJdmUxNixVnlZt2KzxSQREREWlGLAtL777/PBx98wLhx41q7HjmUfhPBZMZUsI7q/ifiWLODslUr4LxgFyYiItJ5tGiQdvfu3f3XQZJjLCwOUjMAiOgVDUDI+u14De9BNhIREZEj0aKA9MgjjzB79mxdFDJY6r7u3yOsCIDeO11sK9kWxIJEREQ6lxYFpFGjRlFdXU3v3r2JiIggNjY2YJI21t8XkMLdq/GaILEEvv/xiyAXJSIi0nm0aAzSJZdcwu7du/nzn/9MUlKSBmkfa/H9ILYPlqItlKeEE7m7nPxlX8KJVwS7MhERkU6hRQFpyZIlZGdnM3z48NauRw5H/VW1s5/AkmKH3WCsXR/sqkRERDqNFp1iGzhwIFVVVa1dixyJunFI3ULzAUj8aR+ltaXBrEhERKTTaFFAmjdvHrfeeitZWVns3buX0tLSgEmOgdQTwRlNQkTdQO08+G7XyiAXJSIi0jm06BTb5Mm+e4JNmDAhYLlhGJhMJjwez9FXJgdnsUK/idiq3qAy3EJouYdtyxYxrvfpwa5MRESkwzvigORyuQB4+umnGTBgQKsXJEdgwBRM371BbbJB6Gao+HYVXBzsokRERDq+Iw5INpuNuLg4Tj/9dPr169cWNcnh6jsBzFaio0uBcMLX78JreDGbWnTmVEREROq06JP017/+Nc8991xr1yJHyhkFaSfTI9o3YL7PThdb9m0JclEiIiIdX4vGILndbp5//nk+/fRTRo4cSVhYWMD6Rx99tFWKk8PQfwphm7JwWyCyCtav/Yx+p6lnT0RE5Gi0KCCtW7eOE044AYCNGzcGrNNFI4+xAZMxfzSbsgQPMXkWCpd9BaddF+yqREREOrQWBaTFixe3dh3SUjFpkDgYe3wO5IVgXrfxkJuIiIjIwWk0b2cwYDLdY6oB6La1lJKakiAXJCIi0rEpIHUGA84gIa4GgB574Lut3wS5IBERkY5NAakzSDkBa1w8ZVFezMCObxYFuyIREZEOTQGpMzCbof8k3AluAKpXrw5uPSIiIh2cAlJnMeAMYutOs0VsyMHj1e1eREREWkoBqbPofRo9470A9NntYdPeDUEuSEREpONSQOos7KGEjDiZGrtBSC1sXPlpsCsSERHpsBSQOhHTwCmUJ/p6kYqWZwe5GhERkY5LAakz6T8ZZ3wtANbvNwW5GBERkY5LAakziexGau9kALpvq6C4uji49YiIiHRQCkidTPwpv8CLQXIxfLfxy2CXIyIi0iEpIHUyluPPoSTOAGDX158EuRoREZGOSQGps0kagreb7211rVoZ5GJEREQ6JgWkzsZkImHwAACifyrG7XUHuSAREZGORwGpE+o14VcApOUZbMpdF+RqREREOh4FpE7IMfZ8KkINbB7YvPhfwS5HRESkw1FA6oRMNieVqSEAlCz/JsjViIiIdDwKSJ1U6JBBANi3FgW5EhERkY5HAamT6vnzqQCk7jbYm7c2yNWIiIh0LApInVT82EzcZoiugO8//0ewyxEREelQFJA6KbPDQXF33zik3BW6ca2IiMiRCHpAevLJJ0lLS8PpdJKRkcGyZcuabfvss88yfvx4YmJiiImJITMz86Dtr7/+ekwmEwsWLAhYXlRUxNSpU4mMjCQ6OpqrrrqK8vLy1jqkdsM01Hc9JM/2UqguDXI1IiIiHUdQA9Lrr7/OrFmzuPvuu1m1ahXDhw9n0qRJFBQUNNk+KyuLSy65hMWLF5OdnU1qaioTJ05k9+7dB7R9++23+eabb0hJSTlg3dSpU/n+++9ZuHAh77//Pl988QXXXnttqx9fsCWdPBmA2Dwz7s0Lg1yNiIhIB2IE0ZgxY4zp06f7n3s8HiMlJcWYO3fuYW3vdruNiIgI46WXXgpYvmvXLqN79+7GunXrjF69ehmPPfaYf90PP/xgAMby5cv9yz788EPDZDIZu3fvPuzaS0pKDMAoKSk57G2OtZq8XOOHAQONdQMHGt//86JglyMiIhJ0h/v5HbQepNraWlauXElmZqZ/mdlsJjMzk+zswxszU1lZicvlIjY21r/M6/Vy2WWXcfvttzN48OADtsnOziY6OppRo0b5l2VmZmI2m1m6dGmz+6qpqaG0tDRgau/sScmUxNgwG7Dlu1Xg0W1HREREDkfQAlJhYSEej4ekpKSA5UlJSeTl5R3Wa8yePZuUlJSAkPXggw9itVq56aabmtwmLy+PxMTEgGVWq5XY2NiD7nfu3LlERUX5p9TU1MOqMdiqj0sHoCzfgF3Nj9cSERGRBkEfpN1S8+bN47XXXuPtt9/G6XQCsHLlSv7yl7/w4osvYjKZWnV/d955JyUlJf5p586drfr6bSXihNEAOPOtsOHDIFcjIiLSMQQtIMXHx2OxWMjPzw9Ynp+fT3Jy8kG3ffjhh5k3bx6ffPIJw4YN8y//8ssvKSgooGfPnlitVqxWK9u3b+fWW28lLS0NgOTk5AMGgbvdboqKig66X4fDQWRkZMDUEaTVDdTulQt7NvwvyNWIiIh0DEELSHa7nZEjR7Jo0SL/Mq/Xy6JFixg7dmyz282fP5/777+fjz76KGAcEcBll13G2rVrWb16tX9KSUnh9ttv5+OPPwZg7NixFBcXs3LlSv92n332GV6vl4yMjFY+yuCLHXw8NXYToTXwQ04eFG4OdkkiIiLtnjWYO581axaXX345o0aNYsyYMSxYsICKigquvPJKAKZNm0b37t2ZO3cu4BtfNGfOHF555RXS0tL8Y4bCw8MJDw8nLi6OuLi4gH3YbDaSk5MZMMB3TaBBgwYxefJkrrnmGp5++mlcLhczZszg4osvbvKSAB2dyWqluE8CSesLKChywsYPIX5msMsSERFp14I6Bumiiy7i4YcfZs6cORx//PGsXr2ajz76yD9we8eOHeTm5vrbP/XUU9TW1nLhhRfSrVs3//Twww8f0X5ffvllBg4cyIQJEzjjjDM4+eST+fvf/96qx9aeWIb5vs1nFNg0DklEROQwmAzDMIJdREdUWlpKVFQUJSUl7X480uaP3sB1893kxcDJk/Ox3b4ZQmMPvaGIiEgnc7if3x32W2xy+HqNnQhA8j7Y6LbCJl1VW0RE5GAUkLoAW1Q0e7uFAbC1JAQ2fBDkikRERNo3BaQuorbugpGVhXbY+DEU/RTkikRERNovBaQuImqU7xIGoQU2cFfBf64BjyvIVYmIiLRPCkhdRJ+TzwAgNdegwB4Nu1fAFw8FtygREZF2SgGpi4jqO4iKUAt2D6zvfq5v4RcPwfbDuzGwiIhIV6KA1EWYTCZK+vtupVK4owSGXwKGF966FqpLglydiIhI+6KA1IXYhg8FwLRuI0yZD9G9oGQH/O+2IFcmIiLSviggdSGpYzMB6LZ5Hzur9sEF/wCTBb57A9a+EeTqRERE2g8FpC6k19hMKsOsRFfAh0//DlLHwKl3+Fb+71bYtz24BYqIiLQTCkhdiNnhIPSKSwHo/+5q1uWthvG3QY8xUFPqG4/kcQe3SBERkXZAAamLGXD1zVRFOkgqhoVP/QHDbIELngV7BOz8Br56LNglioiIBJ0CUhdjDgkh5uqrABj54U98sfUziEmDMx/xNciaCzuXB69AERGRdkABqQvqNe0aqqNDSSiFr/9+H26vG4b9CoZcCIYH3roaasqCXaaIiEjQKCB1QWank6QbbgRg/KIC3vnhDTCZfL1IUamwbxt8ODu4RYqIiASRAlIXlXzxr6mNiyC+DNa9sIBKVyWERMP5fweTGVa/DOveCnaZIiIiQaGA1EWZHQ6633gTAD//vIz/+/Y534peJ8HJs3zz798MJbuCU6CIiEgQKSB1YbG//BXuxBhiy2Hnv56jsKrQt+K030H3kb5bkLx1HXg9wS1URETkGFNA6sLMdjs9pt8MwJlf1fDM0sd9Kyw2OP9ZsIXB9q9gyePBK1JERCQIFJC6uOjzz8PbLYHoSqh84y1+Kv7JtyKuD5wx3zf/2Z9g96rgFSkiInKMKSB1cSabje4zbgbg7GwPf13yUMPK46fCceeA1w3/uRpqK4JTpIiIyDGmgCREnXM29OhGZBWEvfcFK/JW+FaYTPCLBRCRAkVb4KM7g1qniIjIsaKAJJisVlJm/haAs7/x8tev5mMYhm9laCyc/wxgglUvwfr/Bq9QERGRY0QBSQCIPPNMLGk9iaiGtE++5+NtHzesTD8FxvkuCcB7M6E0NzhFioiIHCMKSAL4epGSZvhC0C+WeXn660ep9dQ2NDj9j9BtOFTtg3euB683SJWKiIi0PQUk8YucMhlbn96EV8MJWbt4fcPrDSutdrjgObCGwE9Z8M3fglaniIhIW1NAEj+TxULizJkAnLnc4P++eYrS2tKGBvH9YPJc3/yieyF3bRCqFBERaXsKSBIgYuJE7P37EVoDp3xVzD+++0dgg5FXwIAzwVNb99X/yqDUKSIi0pYUkCSAyWwmoa4X6YwVBu+u+Bc55Tn7NTDB2X+F8CQo3AAL7wpSpSIiIm1HAUkOEJGZiWPQIEJqYXJ2NU98+0Rgg7A4OPcp3/zyf8CGj459kSIiIm1IAUkOYDKZ/L1Ik1cafPHdf1m/d31go74T4MTpvvl3p0NZ/jGuUkREpO0oIEmTwk8/DeeQIThdcPY3Hh5Z+UjDxSPrTZgDSUOgshDevREarxcREemgFJCkSSaTiYSbfL1Ik1YZbNj0DV/nfB3YyOaEC/4BVids/hSW/T0IlYqIiLQ+BSRpVtj48YQMH47dDedke3lkxSN4vJ7ARomDYOKffPOf3AX5Pxz7QkVERFqZApI0y2QyEV/XizTxW4O9Ozfx3pb3Dmw4+mroNxE8NfCfq8BVfYwrFRERaV0KSHJQYSedRMjIkdg8cN4SL098+wRV7qrARiYTnPMkhCVAwQ/wxjQozWn6BUVERDoABSQ5qP2/0Za5xsCTl8+/fvjXgQ3DE+Hcp8FshU0fw5MZsOxZ3bNNREQ6JAUkOaSwEzMIHTMGqwfOz/by3Lrn2Fu198CG/TLh2s+h+yioKYUPboPnJ2lckoiIdDgKSHJYEmbOAOBnayF0TznPrH2m6YbJQ+CqT2DKQ2CPgF3L4JnxsOg+cFU1vY2IiEg7o4AkhyV09GjCThqLxWNw/hIvb254k20l25pubLZAxrUwfSkM/AV43fDlI/DUSfBT1rEsW0REpEUUkOSwxc/wjUU6/TuIK3Lxl1V/OfgGUd3h4pfhon9BRDco+gn+eQ68fQNUNHGKTkREpJ1QQJLDFnrCCMLGj8fsNbhgicGnOz5ldcHqQ2846CyYvgzGXAuYYM0r8MQoWPOarr4tIiLtkgKSHJH6sUinfGeQXGTw8IqHD7wFSVOckXDGQ3DVQkgcDFVF8PZ1vh6lvVvauGoREZEjo4AkRyRk2DDCTzsNs2Fw0RITa/asYdGORYf/Aqmj4brPYcLdvluUbP3cNzbpy0fA42q7wkVERI6AApIcsfi6XqST1nnoXmiwYNUCXN4jCDcWG4yfBTdmQ+/TwF3t+5bbM6fCzuVtU7SIiMgRUECSIxYyeDDhmRMwGQaXZlvZXrqdNze8eeQvFNsbLnsHzvs7hMZBwffw3M/hf7dBdWmr1y0iInK4FJCkRRJm+HqRRn1fQ+oeg6fXPE15bfmRv5DJBMMvgunL4fipgAHLn4Unx8D6/7Zu0SIiIodJAUlaxDlwIBGTJmEy4PJvnOyr2cfz655v+QuGxcG5f4Np7/l6lspy4fVfw2tToWR36xUuIiJyGBSQpMXip98IJhPD1lXQK9/gnz/8k7yKvKN70d6nwg1LYPxtvvu6/fi+775uS58Br6d1ChcRETkEBSRpMWf//kROmQLANcsjqfHU8OTqJ4/+hW0hMOEuuO5L6DEGasvgwzt845Py1h3964uIiByCApIclfgZ08Fspv93+0jPNXh387ts3LexdV486Tj4zcdw5qPgiITdK+GZU+Dzh9SbJCIibUoBSY6Ko3dvIn9xJgDTV8ZhYHDdwuv4eNvHh3cByUMxm2H0Vb4rcQ86GwwPLP4TvHQWFO88+tcXERFpggKSHLWEG28Ei4We3xUwviSZwqpCbvv8NqYvms7u8lYaYB3ZDS76P98lAezhsP1reHocfP9O67y+iIjIfhSQ5KjZ09KIOvtsAO5Y24sbht+AzWzjy91fct675/HiuheP7EKSBzP8Irj+S+g+EqpL4M3L4d0ZUFvROq8vIiKCApK0kvgbbwCLhaqvvuYK71j+ffa/GZU0iip3FY+sfIRL3r+EtXvWts7OYnv7xiaNvxUwwbf/5xublLO6dV5fRES6PAUkaRX21FSizz8PgF0zZ5KwchvPT3qe+066jyhHFBv2beDXH/yaB755gLLasqPfocUGE+bA5f+FiBTYuxn+kQlfPw5e79G/voiIdGkmo1VG0nY9paWlREVFUVJSQmRkZLDLaRfce/aw4zdXUbNpEwBRF5xP0p13UmKt5ZEVj/DelvcASAhJ4HdjfsfPe/0ck8l09DuuLIL/3tRw5e3ep8N5T0NE8tG/toiIdCqH+/mtgNRCCkhN89bUsOcvj1P0wgtgGNi6dydl3lxCR49mae5S7v/mfraXbgfglB6n8IeMP5ASnnL0OzYMWPUSfPg7cFf57u12zt9gwOSjf20REek0FJDamALSwVUuX07O7N/hyskBk4nYK68k4bc34bLCs2uf5bl1z+H2ugmxhjD9+OlMHTQVq9l69DvesxH+8xvI+873fMy18PP7fBefFBGRLk8BqY0pIB2ap7yc/LlzKfnPWwA4+vUjZf6DOAcN4qfin7g3+15WFawCYGDsQOacOIehCUOPfsfuGlh0H2Q/4XueeBxc8JzvwpMiItKlHe7nd9AHaT/55JOkpaXhdDrJyMhg2bJlzbZ99tlnGT9+PDExMcTExJCZmXlA+3vuuYeBAwcSFhbmb7N06dKANmlpaZhMpoBp3rx5bXJ8XZklPJyUBx6gx5NPYImNpWbTJrb+6iIKn/k76RG9eGHyC9x70r1E2iP5sehHpn4wlblL51JeW350O7Y6YNID8Ov/QFgiFPwAfz8Nlj3rOxUnIiJyCEENSK+//jqzZs3i7rvvZtWqVQwfPpxJkyZRUFDQZPusrCwuueQSFi9eTHZ2NqmpqUycOJHduxsuRti/f3+eeOIJvvvuO7766ivS0tKYOHEie/bsCXit++67j9zcXP80c+bMNj3WrixiwgR6//c9wjMngMvFnsceY/uvL8O9cxfn9zuf9859j1/0/gUGBq/8+ArnvHMOn27/9OivxN0303fj234TwVMDH9wGr14MFYWtc2AiItJpBfUUW0ZGBqNHj+aJJ3ynQrxeL6mpqcycOZPf/e53h9ze4/EQExPDE088wbRp05psU9+V9umnnzJhwgTA14N08803c/PNN7e4dp1iO3KGYVDy9jvkP/AA3ooKTKGhJM2eTfSvfonJZCI7J5v7v7mfnWW+W4ic1uM0fp/xe7qFdzvaHcOyv8Mnd/mCUniS71tufX7WCkclIiIdSbs/xVZbW8vKlSvJzMxsKMZsJjMzk+zs7MN6jcrKSlwuF7Gxsc3u4+9//ztRUVEMHz48YN28efOIi4tjxIgRPPTQQ7jd7oPuq6amhtLS0oBJjozJZCL6/PPo/d67hI4Zg1FZSd7dd7Pz+utxFRQwNmUsb539FtcMvQar2UrWrizOefcc/vn9P3F7D/7+HGLHkHEdXPMZJAyE8nz4v/Pgkz+Cu7b1DlBERDqNoAWkwsJCPB4PSUlJAcuTkpLIy8s7rNeYPXs2KSkpASEL4P333yc8PByn08ljjz3GwoULiY+P96+/6aabeO2111i8eDHXXXcdf/7zn7njjjsOuq+5c+cSFRXln1JTUw/zSKUxW/fu9HzxBRJnz8Zkt1Px+RdsPfscSj/6GKfVyU0n3MSbv3iTExJPoMpdxUMrHuLS/13K94XfH92Ok4fAtVkw+mrf8yV/hecyoXDzUR+TiIh0LkE7xZaTk0P37t1ZsmQJY8eO9S+/4447+Pzzzw8YWN3YvHnzmD9/PllZWQwbNixgXUVFBbm5uRQWFvLss8/y2WefsXTpUhITE5t8reeff57rrruO8vJyHA5Hk21qamqoqanxPy8tLSU1NVWn2I5SzaZN7L5jNjXr1wMQefZZJP/xj1giI/EaXt7e9DaPrHyEstoyzCYzlwy8hN+e8FtCrEf5tf0fP4B3p0NVEdhCYcqDMOIyX2+TiIh0Wu3+FFt8fDwWi4X8/PyA5fn5+SQnH/wKyA8//DDz5s3jk08+OSAcAYSFhdG3b19OPPFEnnvuOaxWK88991yzr5eRkYHb7Wbbtm3NtnE4HERGRgZMcvQc/fqR/vprxF1/HZjNlL73X346+xwqsrMxm8xc0P8C3jv3Pc7sfSZew8vL61/m6k+uZl/1vqPb8cAzfAO4008FVyW8N9N349uqo3xdERHpFFrhynwtY7fbGTlyJIsWLeLcc88FfIO0Fy1axIwZM5rdbv78+TzwwAN8/PHHjBo16rD25fV6A3p/Glu9ejVms7nZHiZpWya7ncSbbyb81FPJ+d3vcG3fwY4rf0PMZZeReOss4kPimTd+Hmf1Pos7vriDtXvWctmHl/HUhKdIjTyKU52R3eCydyD7r77rJv3wrq9nyREB9nCwh/p6l+xhdY+hYAvzPffPN24T1kT7ukdz0K+qISIihymo32J7/fXXufzyy3nmmWcYM2YMCxYs4I033uDHH38kKSmJadOm0b17d+bOnQvAgw8+yJw5c3jllVcYN26c/3XCw8MJDw+noqKCBx54gLPPPptu3bpRWFjIk08+ySuvvMLKlSsZPHgw2dnZLF26lNNPP52IiAiys7O55ZZbmDJlCi+99NJh165vsbUNb2Ul+Q89RPGrrwFg792blAcfJGToEAB+Kv6JGz69gZyKHGKdsTw54UmGxA85+h3vXgVvXeO76W1bsYb4LlY56Gw47myI7d12+xIRkSZ1mCtpP/HEEzz00EPk5eVx/PHH8/jjj5ORkQHAaaedRlpaGi+++CLg+3r+9u3bD3iNu+++m3vuuYfq6mouvfRSli5dSmFhIXFxcYwePZo//vGPjB49GoBVq1Zx44038uOPP1JTU0N6ejqXXXYZs2bNanb8UVMUkNpW+Zdfkvv7P+DeswesVuJvuJ74a6/FZLOxp3IP0xdNZ33RekKsITx86sOc0uOUo9+p1wMlu3yn3GorwVXhe6wtb2JZRcO8q/553aN/vq59c5KGwnHn+MJSwoCjr19ERA6pwwSkjkoBqe259+0j7777KPvwIwCcQ4eS8uCDOHqnU+GqYFbWLJbkLMFisnDXiXdxQf8LglxxE7xe381zayuhphR+yoL178HWL8HwNLRLGFjXs3QOJA3WYHERkTaigNTGFJCOnZL3/0fefffhLS3F5HSSMv9BIidOxOV1ce+Se3l3y7sAXD/8em4cfiOmjhAuKvbChg98455+ygKvq2FdbG9fUBp0NqSMUFgSEWlFCkhtTAHp2HLl55N75++pWLIELBZS5j9I1JlnYhgGT65+kmfWPgPAuX3PZc7YOdjMtiBXfASqimHjx76wtPlT39W+60X19J2CG3Q29Bitgd4iIkdJAamNKSAde4bHQ+7v/0DJu++C2Uy3Bx4g+rxzAXhz45v86Zs/4TW8jEsZxyOnPUKYLSy4BbdETRls+gR+eM/36KpsWBfRDQad5etd6jkWzJbg1Ski0kEpILUxBaTgMLxe8u6+h+I33wSTieR77iHmol8B8PnOz7n9i9upclcxKHYQf8v8G/Eh8Yd4xXasthK2LPKFpQ0fQm1Zw7qwBBj4C1/vUtp4sHSgHjMRkSBSQGpjCkjBYxgG+Q/8mX3/+hcASX/4A7GX/RqAdYXrmL5oOkXVRXQP785TmU+RHpUezHJbh7vGN1bph/fgx/ehurhhXUgMDDjT17uUMADC4n3XcdLYJRGRAyggtTEFpOAyDIOChx6m6PnnAUi8/TbirroKgJ2lO7n+0+vZUbaDKEcUf/3ZXxmROCKY5bYujwu2fekbs7T+fagsPLCN1enrZQqLh9D4hvmmHkPjweY89schIhIECkhtTAEp+AzDYM/jj7P3qacBiL9pJgk33ghAUXURMxfNZG3hWuxmOw+e8iCZvTIP9nIdk9cDO7IbBniX5vouK3Ck7BGNglP9fF2ACouH8CSI7gmO8NY/DhGRY0QBqY0pILUfhU89xZ6/PA5A3PXXkfDb32IymahyV3HHF3eQtTMLEyZmj5nN1EFTg1vssVBbARV7fJcSqNjTMFU2el6/fv9LDByO0HiISdtv6tUwH9ldg8dFpF1TQGpjCkjty97nnqfgoYcAiL3yShLvuB2TyYTb62bu0rm8sfENAK4cfCU3j7wZs0lflwfAMKC6pPnwVLHHdwqvohDKcg99M1+zDaJTIbpXoxCV5gtSITFtf0wiIgehgNTGFJDan6J/vUz+n/4EQMyll5L0xz9gMpsxDIPn1j3HX1b9BYAp6VP407g/YbfYg1lux1RdAvu2w75tvql4//kd4Kk9+PbOqMDQtH+QikoFq94TEWlbCkhtTAGpfdr3+hvk3XMPGAbRv7yQ5HvvxVR3ccX/bvkvc76eg9twMzp5NAtOX0CkXe9dq/F6fL1M+weo/YNUef7BtzeZfSEptnejKd0XoGwhbX4IItL5KSC1MQWk9qv47XfI/cMfwOsl6pyz6fbAA5isVgCyc7K5JesWKlwV9I3uy1OZT5EclhzkiruI2gpfL5M/ODUKUocaXB7ZvSEwxfaGmPSG546INi9fRDoHBaQ2poDUvpV+8AG7b78DPB4iz5hCyoMPYrL5Lqa4oWgDN356IwVVBSSGJvJU5lP0j+kf5Iq7OMPw9TAVbYWinxpNW6Gm5ODbhyUG9jjt/6hxTyKyHwWkNqaA1P6VLlzI7lm3gstFeOYEuj/6KGa7b4xLbnkuN3x6A1tKthBuC2fB6QvI6JYR5IqlSYYBlUWwr6nw9JNvgPnBhMT4glLSYN/97HqMhvgBuq+dSBelgNTGFJA6hrKsLHbf9FuM2lrCThlPj8cfx+z0XRSxpKaE3y7+LSvzV2I1W/nTuD9xZu8zg1yxHLHqkkY9T/vNl+c1vY0jErqfAN1H1YWmUb5rPYlIp6eA1MYUkDqO8q+/Ztf0GRjV1YSdNJYeTzyBOTQUgBpPDX/46g98vO1jAC4deCkJoQl4vB7chtv36HXjNty4vb7nHsODy+vCY3j8y/Zf33i7+nmP4aF7RHdO6nYS47qPo3dUb0y6HUjbqq3wjW/auxlyvoVdK2D3KnBVHNg2Jr2hh6nHKEgaom/VidTzenw3064ugZpSqC4FV5XvyxP2UN/tjexhYKubt1iDXXGzFJDamAJSx1KxbBk7r78Bo7KS0FGj6PH001jCwwDwGl4eWfEI//zhn8e0pqTQJE5KOYmTup/E2G5jiXJEHdP9d1keN+xZD7uW+wLTrhVQuOHAdlYndBveEJh6jPYNFO8oodbj9n2Q1ZTWfbDVPdYv2/+51w0Wu+86Vhabb95SN29u9NxiB7O1bpnd90F4qG2tDt/P0+roOD+/zsQwfP9ZqH/f/SGnJDDwHGx+/5tlHw6LoyE42UJ94anxZKufbyJg1S9PGNjq32BVQGpjCkgdT+Wqb9l57bV4y8sJGT6c1Gf/jmW/9+79n95nye4lWMwWrGYrFpMFm9mGxWTxL7OarL51ZgsWU9PLbGabr73J6n+0mq2YTCZ+2PsDS3KWsDJ/JTWeGv++TZgYEj/EF5hSTmJowlBsZlswfkxdU9U+X8/SrhV1wWl54A2B60V0awhL3UdByvG+P+RHwzB8NyP21Pge6ydPDbirwV0LrspDBJ0m1rkqj66utmJ1Nkw2J1hDfMHJVvfY1HObs4ntmnq+3/b7L7c4Os+Ys9pKqCryjb2rLKqbL/L9Dh+wrKguBJWC4Wmd/Vsc4Iz0XdPMGuL79mltpS+A1Za33n7q3bgUEge26ksqILUxBaSOqeq7dey4+mq8JSU4Bw+m53P/wBIdfczrqHZXsyp/FV/nfM2SnCVsLt4csD7cFs6Y5DGM6z6Ok1JOokdEj2NeY5dmGLB3S0NY2rUc8r8/8I+/yeIb/J1yvK+3JCDYNA47TT2vPvTFNY+WNcR3GQRnpO/RUffojGp4brb6bjnjqfX1PHlq657XL6ubb9zGU+vrfWqyzX7b0w4+Ziz79WL5w5MjMGgFBLP6cGXfrzfM6vtZHTBv891ixz9ft65+O7O1rn0T867KunBT1EzwqXusn3dXt/xnYLI0hBtH3aN/PnK/ZY3noxrmrY7mX98wfO93bUXg5Ko4yLL9wpWr8sB2Vy+EqNb9+6eA1MYUkDqu6vXr2fGbq/Ds24djwAB6vvA81tjYoNaUX5HPkpwlZOdkk52bTXFNccD6nhE9/b1LY7qNIcx2lL0WcuRqKyBnNeyu62Xaubz5QeBHo77Hw/8hbfeFnAMCzv7PI5sPQJYg90Qahi8ouasbJle1r+fBXeMbx+Kuafq5q7qJ7Zp5nfpQuv/2hje4x95WzDYIjYWQWN/j/vMhsRAa1zBfH2ycUb7TVzrFqYDU1hSQOraaTZvYfuVv8BQWYu/bh57PP48tMTHYZQHg8XpYX7SeJTlL+Hr316zdsxa34favt5qsDE8czriUcZzU/SQGxQ7SveWCwTCgdHdD75LJUhdm6nod/L0S9obeCX/wcTSxzOkLM/oAaz3+YLZ/cDpI4GqybY2vl8zr8g1U9veS1T1vct5d17PW1LzL1wNXP28NqQszMYHBJiDsxOwXhuJ8Y3T0e9JiCkhtTAGp46vZupUdV1yJOz8fe69e9HzxBWzdugW7rAOU15azLG8ZS3KWsCRnCTvLdgasj3HEcGLKiYztNpak0CRCbCGEWkMJsYYETBazJUhHICLSfiggtTEFpM6hdudOdlx+Ba6cHGzdu9PzpRex92jf4312lu709S7lfM2yvGVUNPWV9SbYzXZCbCEHBKcDwpSt6eWR9kh6RvYkMTRRPVYi0mEpILUxBaTOw5WTw/YrrsS1YwfmsDAcAwZgT0vDnp6GIz0de3o69tRUTPb2d00cl9fF2j1r/d+MK60tpcpVRZW7YTJaeYBsiDWEXpG9SItM8z1GpZEemU5aVJrGRolIu6eA1MYUkDoXV34BO6++ippNm5tuYDZj69HDF5rS6kJTejr2tDSsiQnt9oKPhmFQ46kJCEz7T5XuSn+gqnRXHthmv7BVXFPMrrJdAeOhGksISSAtKo20yLqpLjylhKfoFJ+ItAsKSG1MAanzMVwuajZtombrVmq3bqN261bftG0b3srmryljDgur63FKx56ehj2trucpLc1/xe7OwuV1sbtsN9tKt7GtZJvvsW5+b3Xz90SzmW30jOjZEJ7qHtOj0nWBTBE5phSQ2pgCUtdhGAbugj11YckXnmrqHl27doG3+a8SW5OTA0JTyLBhOIcOxWTpfL0ppbWlbC/ZzrbSbWwt2eoPTztKdwRcFLOxaEc0aZFppEakEh8S758SQhOIC4kjISSBcFt4u+2lE5GORQGpjSkgCYC3thbXzp3Ubt3a0PO0zdf75Nm3r8ltLLGxhJ96KuGnn0b4uHGYwzr3uB2v4SW3Itff4+QPTyXbyK/MP6zXcFqcxIXE+YJTSENwahykYp2xWM3t9x5QIhJ8CkhtTAFJDsVTXOwLTdu2+wLUli1ULluGt6zhnkYmm43QjAzCTz+NiNNPx5aSEryCg6DSVcmOsh1sK9lGTkUOhVWFFFYWUlhdyJ7KPeyt2kuZ6/DvAWXCRIwzJqAnqj5U9YzsSb/ofiSHJas3SqQLU0BqYwpI0hKGy0XlypWUL15M2eIsXDt2BKx3DBjgD0vOoUMxdZb7Rx2FKncVe6v2+sJTVSF7qvb45wurGoLU3uq9eA7jPlDhtnD6RvelX0w/3xTte9RYKJGuQQGpjSkgydEyDIPaLVsoW7yY8sVZVK1eHTCeyRIfT/ippxBx+umEnXRSpxvw3do8Xg/FNcVNBqmCygLfqb2Sbc1+Cy8xJNEfmuoDVJ/oPjgsB7n3lIh0OApIbUwBSVqbe98+yj//nPLFWVR89RXeioYLQJrsdkLHnkjE6acTftpp2JKTg1hpx+XyuNhaupXN+zazqXgTm/b5ppyKnCbbm01mekb0DOhp6hfTjx7hPXTZApEOSgGpjSkgSVsyamupWL6c8sVZlC9ejGv37oD1juMGEXHa6YSffjrOwcfpVNxRKq8tZ3NxYGjaVLyJkpqSJts7LU76RPcJ6GkKsYZgMVmwmCyYzWasJitmkxmLuW6ZqZll5rplpoZlzY2R8ng91HhqqPZUU+uppdpdTY2nxj9Vu+uWexoea9z7rW9mO6fFSUp4im8KS/HPR9ojNV5LOh0FpDamgCTHimEY1Gza5A9LVWvW+G6UWseakED4aacRfvpp2Hr0wBIZiSUiAlNoqD7cjoJhGBRWFfrD0sZ9G9lcvJktxVsOetmC1lAflCwmi7+nqsZdc9CLdLaFUGuoPyx1C+t2QIiKc8bpdyyIDMOgtLaUgsoC8ivzfY8V+f75wqpCLCZLwP0ZQ22hAbcSqn/e1LL9n9vMtk7zXisgtTEFJAkW9969lH/+BeWLF1P+9dcYzV3E0mLBEhGBuS4wmSMjsERE+h8tkRGY/Y8RWKKiAtqbQkI6zR/E1uTxethZttPf27S5eDPbSrfh8rhwe914DS8ew4PH8OA1vIHLvHXLWiHo2Mw2HBYHDosDp9WJ3WLHaWl4dFgd/vX+yeposk2Fq4Kc8hxyynPIrchld/luiqqLDlmD3Ww/IDz558NSSAxNbPZUpNfwUuuppcZTg8vrosZTQ62ntmHy1q3z1K3zNqzbfxu31+3vnavvmbOarf5w2fj5odo1Xm81W3FYHIRYQ/w/x2Px78Lj9VBUXUR+pS/w5FfkBwahuscqd1Wb1wK+0H5AgLKFEuOIIcYZQ7QjmlhnLNHOaGIddY/OWGKcMe1uHJ8CUhtTQJL2wFtbS+XSZZQvXkzF0qV4iorwlJWBuxV6GqzWgGBliY7GltoDe2pP7L16Yu/ZE1tqKman8+j31QV5DS8e735BynDj9dY9Gt6AcGVg+IJQXbhxWBxtPgaq2l1NbkWuLzhV5JBbnktORY4/SBVUFhzyPn9Wk9UfkhqHHbf32PaGtRYTJn8o3T84Oa1OnBanf3n98/pQuv96fxuLkwpXhT/w1E8FlQXsqdxzWN/KBN/FVhNDE0kKTfI/JoUlER8Sj2EY/tsIVboqA24rdDjPXV7XUf/MQqwhvvDkiCbGGRMwXx+w9p+PsEe06Q2xFZDamAKStFeGYWBUVeEpK8NbWoqnrAxPaSnegMcyvGWlAY+eslK8pWVHHLCsSUm+sNQzFXvPXth7+YKTvWdPLBERbXikEkwuj4u8yjx/cMot9/U81YeqvIq8w+4pM2HCbrH7JrPdHwJtFhsOs6NhXf3yut4zu8WO1WwNCJtur9vfW+c23A3L95vff119++a2c3ldVLurDzustCazyUx8SDzJocm+4BOWFBCEkkOTSQhNwGltu/+kuLwu/30ZGwepclc5xTXF7Kve55tqAh+Lq4tb1FtqMVn8AeqR0x6hd1TvVj2mw/381iVnRToZk8mEKTTUd1mApKQj3r65gOXZu5fanTtx7dhB7fYd1O7YgbesDHd+Pu78fFi+/IDXssTE+MJTr56BPU89e2KJidEpvA7MZrGRGpFKakRqk+s9Xg97qvaQV5Hnb99U2LGbfSGnvf8uuLwuaty+ge7V7mr/QHf/c89+y+qe17hrqPJU+QfKV7mrAtZXu6sJsYYEBJ/6KTE0kbiQuKBfGd5mtmGz24i0H3lHgGEYlLnKKK4upqi6yBeaanzz+z/uH7AqXBV4DA97q33XNrOb7W1wVIdHPUgtpB4k6eoMw8BTXOwLTDt2Urtj+37zO/Dsbf7mtQDm8HB/WLL37ImtW7Jv/FNUNJaoSCyRkb7nkZGd8t51InKgGk8NxdXF/l6oUUmjsFlsrboPnWJrYwpIIgfnKa/AtbOht8k/v3Mn7tzcI3otc0SELzBFRfoGk0dG+b6tFxWJuf55faiKiqprE4k5PFyXQJAuy6itxfDUnRqs/6ivewz85DeabNPcsvrYYLJYOuS/MZ1iE5GgsoSHYRk0COegQQes81ZX49q1K6Dnyb1nD57iEt/pvNJSvCUleOu+oectK/Pdw67R9aAOyWz2DTSPisLeqxfOQYNwHjcI53HHYUtNbfenddqaYRi4C/ZgVFX6T8uaQ0LUY9eOGS4X7qIi3IWFeAoLcRcW4i7cW/e4B49/vjDgvo9txmTy/WclOnq/Kco/b46Kwrr/uijfuo5wGRL1ILWQepBE2p5RW+sbA1VSire0BE9JXYAqKcVTWoK3pKRu/sDnRnX1QV/bHB6Oc+BAnIOPwzFoEM5Bx+Ho0xuTtXP9v9HweHDl5uHasd1/+rN2x3Zcdb15Tf2cTA6HLyiFhmAOaQhO/mWhob7lISGY656bQurahu3XNiQUc2gIeDx4a2oxamswamrwVtc0zNfUYNTUYtRU7zfvW++tqcGorp+vW+7fpm5yucBiwWSxYLJawGrzzVssYLVislrr5i2YLL7n/nmLBWzWhnmrBVPd9vu3N9ntdZMNk92O2f+8brLZ9ptvaBvQ7iC/V4bXi2ffPn+wOVjw8ezb15a/LseMyWZrCFNR0VhiAgNU/RSakdHqX/bQKbY2poAk0r55a2rwlJT4Bprv20fNli1U/7Ce6vXrqdmwAaO29oBtTA4Hjv79A3qaHP37t/tLGRguF67du33hp/6U5g7fY+2uXeA6yFe1LRbMTifeqqqAewFKKzObmwhVNozKKtxFReA5gm/JWSxY4+KwxMdhjY/HGp+ANS4Oa0I81vh4LHHxvvnYWEz2/QY51/fYNH7cb97UuO3BtnO7ff9pKSnBU1zcMDV+vi9wmXGw38dGev/3PRz9+h12+8OhU2wi0qWZHQ7MiYmQmAhA6OjR/nWGy0XNT1upXv8D1T/8QE1dcPJWVFD93XdUf/ddwwtZLDh6p/vC0iBfaHIOGnTML2HgranBtXPnfiGooRfIlZNz0A9Yk83mv/SCvVf9wPi6SzJ064bJZvN9e7GmBm9VFUZlJd7KSrxVVXgrq/BW+Z4b/udVdevrllXUta3ytfVtX+VfZrJYMDkcmBx2zHYHJqezYd7hm8wOOyZH3XKHA5N/nR2zw7nffN1yuwOz04HJasXwGuBxY7jdGG4PhscNHg+Gy90w7/ZguF0N8x437NfecLvB7fGN2fG467b1YLhcvqm29sDJ5cJw1eKtrcWoPbBNwFgerxejurr5nk2TCUtMTF3gicNSH3zqnlvj4+uWxftOUbWHcT92O9aEBKwJCYe9iWEYGJWVhw5Vxb7nlri4NjyAg1MPUgupB0mkczG8Xlw7d1K9fj3V3//ge/zhBzxFTV9R2paaWtfT5OtlMlktDR+k9R+qjZ/X+j5QfY9NrG/qucuFp7TUdymFg/y5NoWE+AJQz9SAAGTv2RNrUpLGFQWB4Xb7w1JAiPL/DtRicjh8ISM2ttOd3m2vdIqtjSkgiXR+vkHMBVT/0BCYan5Y7+uxCQJzeDj2XvUX4+wZ0CNkTUho94NeRdoDnWITETlKJpMJW1IStqQkIk4/3b/cU1xcF5jqxjT9tAUTpoaxJfXT/s/t9cvt+83bDr5N3XNzaKjv4prR0QpBIseIApKIyBGyREcTNnYsYWPHBrsUEWkj7WCUl4iIiEj7ooAkIiIi0ogCkoiIiEgjCkgiIiIijSggiYiIiDSigCQiIiLSiAKSiIiISCMKSCIiIiKNKCCJiIiINKKAJCIiItKIApKIiIhIIwpIIiIiIo0oIImIiIg0ooAkIiIi0og12AV0VIZhAFBaWhrkSkRERORw1X9u13+ON0cBqYXKysoASE1NDXIlIiIicqTKysqIiopqdr3JOFSEkiZ5vV5ycnKIiIjAZDIFu5w2U1paSmpqKjt37iQyMjLY5bQpHWvn1ZWOV8faeXWl423LYzUMg7KyMlJSUjCbmx9ppB6kFjKbzfTo0SPYZRwzkZGRnf4fZD0da+fVlY5Xx9p5daXjbatjPVjPUT0N0hYRERFpRAFJREREpBEFJDkoh8PB3XffjcPhCHYpbU7H2nl1pePVsXZeXel428OxapC2iIiISCPqQRIRERFpRAFJREREpBEFJBEREZFGFJCEuXPnMnr0aCIiIkhMTOTcc89lw4YNAW1OO+00TCZTwHT99dcHqeKWu+eeew44joEDB/rXV1dXM336dOLi4ggPD+eCCy4gPz8/iBUfnbS0tAOO12QyMX36dKBjv69ffPEFZ511FikpKZhMJt55552A9YZhMGfOHLp160ZISAiZmZls2rQpoE1RURFTp04lMjKS6OhorrrqKsrLy4/hURyegx2ry+Vi9uzZDB06lLCwMFJSUpg2bRo5OTkBr9HU78K8efOO8ZEcnkO9t1dcccUBxzJ58uSANp3hvQWa/PdrMpl46KGH/G06ynt7OJ81h/M3eMeOHZx55pmEhoaSmJjI7bffjtvtbvV6FZCEzz//nOnTp/PNN9+wcOFCXC4XEydOpKKiIqDdNddcQ25urn+aP39+kCo+OoMHDw44jq+++sq/7pZbbuG///0vb775Jp9//jk5OTmcf/75Qaz26CxfvjzgWBcuXAjAL3/5S3+bjvq+VlRUMHz4cJ588skm18+fP5/HH3+cp59+mqVLlxIWFsakSZOorq72t5k6dSrff/89Cxcu5P333+eLL77g2muvPVaHcNgOdqyVlZWsWrWKu+66i1WrVvHWW2+xYcMGzj777APa3nfffQHv9cyZM49F+UfsUO8twOTJkwOO5dVXXw1Y3xneWyDgGHNzc3n++ecxmUxccMEFAe06wnt7OJ81h/ob7PF4OPPMM6mtrWXJkiW89NJLvPjii8yZM6f1CzZEGikoKDAA4/PPP/cvO/XUU43f/va3wSuqldx9993G8OHDm1xXXFxs2Gw248033/QvW79+vQEY2dnZx6jCtvXb3/7W6NOnj+H1eg3D6DzvK2C8/fbb/uder9dITk42HnroIf+y4uJiw+FwGK+++qphGIbxww8/GICxfPlyf5sPP/zQMJlMxu7du49Z7Ueq8bE2ZdmyZQZgbN++3b+sV69exmOPPda2xbWBpo738ssvN84555xmt+nM7+0555xj/OxnPwtY1lHf28afNYfzN/iDDz4wzGazkZeX52/z1FNPGZGRkUZNTU2r1qceJDlASUkJALGxsQHLX375ZeLj4xkyZAh33nknlZWVwSjvqG3atImUlBR69+7N1KlT2bFjBwArV67E5XKRmZnpbztw4EB69uxJdnZ2sMptNbW1tfzrX//iN7/5TcD9AzvL+7q/rVu3kpeXF/BeRkVFkZGR4X8vs7OziY6OZtSoUf42mZmZmM1mli5desxrbk0lJSWYTCaio6MDls+bN4+4uDhGjBjBQw891CanJY6VrKwsEhMTGTBgADfccAN79+71r+us721+fj7/+9//uOqqqw5Y1xHf28afNYfzNzg7O5uhQ4eSlJTkbzNp0iRKS0v5/vvvW7U+3YtNAni9Xm6++WbGjRvHkCFD/MsvvfRSevXqRUpKCmvXrmX27Nls2LCBt956K4jVHrmMjAxefPFFBgwYQG5uLvfeey/jx49n3bp15OXlYbfbD/hQSUpKIi8vLzgFt6J33nmH4uJirrjiCv+yzvK+Nlb/fu3/R7T+ef26vLw8EhMTA9ZbrVZiY2M79PtdXV3N7NmzueSSSwLuYXXTTTdxwgknEBsby5IlS7jzzjvJzc3l0UcfDWK1LTN58mTOP/980tPT2bJlC7///e+ZMmUK2dnZWCyWTvvevvTSS0RERBxw2r8jvrdNfdYczt/gvLy8Jv9d169rTQpIEmD69OmsW7cuYFwOEHDufujQoXTr1o0JEyawZcsW+vTpc6zLbLEpU6b454cNG0ZGRga9evXijTfeICQkJIiVtb3nnnuOKVOmkJKS4l/WWd5X8XG5XPzqV7/CMAyeeuqpgHWzZs3yzw8bNgy73c51113H3LlzO9yVmS+++GL//NChQxk2bBh9+vQhKyuLCRMmBLGytvX8888zdepUnE5nwPKO+N4291nTnugUm/jNmDGD999/n8WLF9OjR4+Dts3IyABg8+bNx6K0NhMdHU3//v3ZvHkzycnJ1NbWUlxcHNAmPz+f5OTk4BTYSrZv386nn37K1VdffdB2neV9rX+/Gn/7Zf/3Mjk5mYKCgoD1breboqKiDvl+14ej7du3s3DhwkPeAT0jIwO32822bduOTYFtqHfv3sTHx/t/bzvbewvw5ZdfsmHDhkP+G4b2/94291lzOH+Dk5OTm/x3Xb+uNSkgCYZhMGPGDN5++20+++wz0tPTD7nN6tWrAejWrVsbV9e2ysvL2bJlC926dWPkyJHYbDYWLVrkX79hwwZ27NjB2LFjg1jl0XvhhRdITEzkzDPPPGi7zvK+pqenk5ycHPBelpaWsnTpUv97OXbsWIqLi1m5cqW/zWeffYbX6/UHxY6iPhxt2rSJTz/9lLi4uENus3r1asxm8wGnojqiXbt2sXfvXv/vbWd6b+s999xzjBw5kuHDhx+ybXt9bw/1WXM4f4PHjh3Ld999FxCA6/9DcNxxx7V6wdLF3XDDDUZUVJSRlZVl5Obm+qfKykrDMAxj8+bNxn333WesWLHC2Lp1q/Huu+8avXv3Nk455ZQgV37kbr31ViMrK8vYunWr8fXXXxuZmZlGfHy8UVBQYBiGYVx//fVGz549jc8++8xYsWKFMXbsWGPs2LFBrvroeDweo2fPnsbs2bMDlnf097WsrMz49ttvjW+//dYAjEcffdT49ttv/d/cmjdvnhEdHW28++67xtq1a41zzjnHSE9PN6qqqvyvMXnyZGPEiBHG0qVLja+++sro16+fcckllwTrkJp1sGOtra01zj77bKNHjx7G6tWrA/4N13+rZ8mSJcZjjz1mrF692tiyZYvxr3/9y0hISDCmTZsW5CNr2sGOt6yszLjtttuM7OxsY+vWrcann35qnHDCCUa/fv2M6upq/2t0hve2XklJiREaGmo89dRTB2zfkd7bQ33WGMah/wa73W5jyJAhxsSJE43Vq1cbH330kZGQkGDceeedrV6vApIYQJPTCy+8YBiGYezYscM45ZRTjNjYWMPhcBh9+/Y1br/9dqOkpCS4hbfARRddZHTr1s2w2+1G9+7djYsuusjYvHmzf31VVZVx4403GjExMUZoaKhx3nnnGbm5uUGs+Oh9/PHHBmBs2LAhYHlHf18XL17c5O/t5ZdfbhiG76v+d911l5GUlGQ4HA5jwoQJB/wM9u7da1xyySVGeHi4ERkZaVx55ZVGWVlZEI7m4A52rFu3bm323/DixYsNwzCMlStXGhkZGUZUVJThdDqNQYMGGX/+858DAkV7crDjraysNCZOnGgkJCQYNpvN6NWrl3HNNdcEfO3bMDrHe1vvmWeeMUJCQozi4uIDtu9I7+2hPmsM4/D+Bm/bts2YMmWKERISYsTHxxu33nqr4XK5Wr1eU13RIiIiIlJHY5BEREREGlFAEhEREWlEAUlERESkEQUkERERkUYUkEREREQaUUASERERaUQBSURERKQRBSQRERGRRhSQRKRLSktLY8GCBcEuQ0TaKQUkEenUXnzxRaKjow9Yvnz5cq699to237+CmEjHZA12ASIiwZCQkBDsEo5IbW0tdrs92GWIdBnqQRKRY+K0007jpptu4o477iA2Npbk5GTuueeew9q2uLiYq6++moSEBCIjI/nZz37GmjVr/OvXrFnD6aefTkREBJGRkYwcOZIVK1aQlZXFlVdeSUlJCSaTCZPJ5N9n454dk8nEM888wy9+8QtCQ0MZNGgQ2dnZbN68mdNOO42wsDBOOukktmzZ4t9my5YtnHPOOSQlJREeHs7o0aP59NNPA455+/bt3HLLLf791/vPf/7D4MGDcTgcpKWl8cgjjwQcc1paGvfffz/Tpk0jMjKSa6+9ltraWmbMmEG3bt1wOp306tWLuXPnHsG7ICKHSwFJRI6Zl156ibCwMJYuXcr8+fO57777WLhw4SG3++Uvf0lBQQEffvghK1eu5IQTTmDChAkUFRUBMHXqVHr06MHy5ctZuXIlv/vd77DZbJx00kksWLCAyMhIcnNzyc3N5bbbbmt2P/WBZPXq1QwcOJBLL72U6667jjvvvJMVK1ZgGAYzZszwty8vL+eMM85g0aJFfPvtt0yePJmzzjqLHTt2APDWW2/Ro0cP7rvvPv/+AVauXMmvfvUrLr74Yr777jvuuece7rrrLl588cWAeh5++GGGDx/Ot99+y1133cXjjz/Oe++9xxtvvMGGDRt4+eWXSUtLO8J3QUQOiyEicgyceuqpxsknnxywbPTo0cbs2bMPut2XX35pREZGGtXV1QHL+/TpYzzzzDOGYRhGRESE8eKLLza5/QsvvGBERUUdsLxXr17GY4895n8OGH/84x/9z7Ozsw3AeO655/zLXn31VcPpdB603sGDBxt//etfm92PYRjGpZdeavz85z8PWHb77bcbxx13XMB25557bkCbmTNnGj/72c8Mr9d70BpE5OipB0lEjplhw4YFPO/WrRsFBQUH3WbNmjWUl5cTFxdHeHi4f9q6dav/dNesWbO4+uqryczMZN68eQGnwVpaX1JSEgBDhw4NWFZdXU1paSng60G67bbbGDRoENHR0YSHh7N+/Xp/D1Jz1q9fz7hx4wKWjRs3jk2bNuHxePzLRo0aFdDmiiuuYPXq1QwYMICbbrqJTz75pEXHKSKHpkHaInLM2Gy2gOcmkwmv13vQbcrLy+nWrRtZWVkHrKv/dto999zDpZdeyv/+9z8+/PBD7r77bl577TXOO++8FtdXP16oqWX1Nd92220sXLiQhx9+mL59+xISEsKFF15IbW3tEe23OWFhYQHPTzjhBLZu3cqHH37Ip59+yq9+9SsyMzP597//3Sr7E5EGCkgi0q6dcMIJ5OXlYbVaDzrepn///vTv359bbrmFSy65hBdeeIHzzjsPu90e0CvTmr7++muuuOIKfxArLy9n27ZtAW2a2v+gQYP4+uuvD3it/v37Y7FYDrrPyMhILrroIi666CIuvPBCJk+eTFFREbGxsUd/QCLip1NsItKuZWZmMnbsWM4991w++eQTtm3bxpIlS/jDH/7AihUrqKqqYsaMGWRlZbF9+3a+/vprli9fzqBBgwDft8HKy8tZtGgRhYWFVFZWtlpt/fr146233mL16tWsWbOGSy+99IAesbS0NL744gt2795NYWEhALfeeiuLFi3i/vvvZ+PGjbz00ks88cQTBx1ADvDoo4/y6quv8uOPP7Jx40befPNNkpOTm7zOk4gcHQUkEWnXTCYTH3zwAaeccgpXXnkl/fv35+KLL2b79u0kJSVhsVjYu3cv06ZNo3///vzqV79iypQp3HvvvQCcdNJJXH/99Vx00UUkJCQwf/78Vqvt0UcfJSYmhpNOOomzzjqLSZMmccIJJwS0ue+++9i2bRt9+vTxX3vphBNO4I033uC1115jyJAhzJkzh/vuu48rrrjioPuLiIhg/vz5jBo1itGjR7Nt2zY++OADzGb9KRdpbSbDMIxgFyEiIiLSnui/HSIiIiKNKCCJSFC9/PLLAV/f338aPHhwsMsTkS5Kp9hEJKjKysrIz89vcp3NZqNXr17HuCIREQUkERERkQPoFJuIiIhIIwpIIiIiIo0oIImIiIg0ooAkIiIi0ogCkoiIiEgjCkgiIiIijSggiYiIiDSigCQiIiLSyP8D7MJG+IOD+2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for depth in [10, 15, 20, 25]:\n",
    "    df_subset = df_scores[df_scores.max_depth == depth]\n",
    "    plt.plot(df_subset.n_estimators, df_subset.rmse, label=depth)\n",
    "\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('rmse')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb5fb545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pivot table\n",
    "df_scores_pivot = df_scores.pivot(index='n_estimators', columns=['max_depth'], values=['rmse'])\n",
    "\n",
    "# Create heatmap\n",
    "sns.heatmap(df_scores_pivot, annot=True, fmt='.3f', cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820def2c",
   "metadata": {},
   "source": [
    "According to these graphs, we get the better rmse when the max depth size is `25`.\n",
    "\n",
    "## Question 5\n",
    "\n",
    "We can extract feature importance information from tree-based models.\n",
    "\n",
    "At each step of the decision tree learning algorithm, it finds the best split. When doing it, we can calculate \"gain\" - the reduction in impurity before and after the split. This gain is quite useful in understanding what are the important features for tree-based models.\n",
    "\n",
    "In Scikit-Learn, tree-based models contain this information in the `feature_importance_` field.\n",
    "\n",
    "For this homework question, we'll find the most important feature:\n",
    "\n",
    "- Train the model with these parameters:\n",
    "    - `n_estimators=10`\n",
    "    - `max_depth=20`\n",
    "    - `random_state=1`\n",
    "    - `n_jobs=-1` (optional)\n",
    "    \n",
    "- Get the feature importance information from this model\n",
    "\n",
    "What's the most important feature?\n",
    "\n",
    "- `total_rooms`\n",
    "- `median_income`\n",
    "- `total_bedrooms`\n",
    "- `longitude`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "87ae4671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=20, n_estimators=10, n_jobs=-1, random_state=1)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=20, n_estimators=10, n_jobs=-1, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=20, n_estimators=10, n_jobs=-1, random_state=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build rf model and train\n",
    "rf = RandomForestRegressor(n_estimators=10,\n",
    "                           max_depth=20,\n",
    "                           random_state=1,\n",
    "                           n_jobs=-1)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f46a16d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_income                 0.363080\n",
       "ocean_proximity=INLAND        0.310901\n",
       "latitude                      0.101310\n",
       "longitude                     0.096312\n",
       "housing_median_age            0.033105\n",
       "population                    0.030960\n",
       "total_rooms                   0.020512\n",
       "total_bedrooms                0.018634\n",
       "households                    0.017169\n",
       "ocean_proximity=NEAR OCEAN    0.004243\n",
       "ocean_proximity=<1H OCEAN     0.002988\n",
       "ocean_proximity=NEAR BAY      0.000429\n",
       "ocean_proximity=ISLAND        0.000357\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Array of features importance\n",
    "importances = rf.feature_importances_\n",
    "# Store importances array to Series and set index as feature names\n",
    "forest_importance = pd.Series(importances, index=dv.get_feature_names_out())\n",
    "\n",
    "# View results in descending order\n",
    "forest_importance.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72e949ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the results\n",
    "fig, ax = plt.subplots()\n",
    "forest_importance.sort_values(ascending=False).plot.barh(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a857402",
   "metadata": {},
   "source": [
    "`median_income` is the most important feature.\n",
    "\n",
    "## Question 6\n",
    "\n",
    "Now let's train an XGBoost model! For this question, we'll tune the `eta` parameter:\n",
    "\n",
    "- Install XGBoost (if not already)\n",
    "- Create DMatrix for train and validation\n",
    "- Create a watchlist\n",
    "- Train a model with these parameters for 100 rounds:\n",
    "```python\n",
    "xgb_params = {\n",
    "    'eta': 0.3, \n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "    \n",
    "    'objective': 'reg:squarederror',\n",
    "    'nthread': 8,\n",
    "    \n",
    "    'seed': 1,\n",
    "    'verbosity': 1,\n",
    "}\n",
    "```\n",
    "\n",
    "Now change `eta` from `0.3` to `0.1`.\n",
    "\n",
    "Which eta leads to the best RMSE score on the validation dataset?\n",
    "\n",
    "- 0.3\n",
    "- 0.1\n",
    "- Both gives same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed661561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['households', 'housing_median_age', 'latitude', 'longitude',\n",
       "       'median_income', 'ocean_proximity=<1H OCEAN',\n",
       "       'ocean_proximity=INLAND', 'ocean_proximity=ISLAND',\n",
       "       'ocean_proximity=NEAR BAY', 'ocean_proximity=NEAR OCEAN',\n",
       "       'population', 'total_bedrooms', 'total_rooms'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract feature names from dv\n",
    "features = dv.get_feature_names_out()\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ca77b7",
   "metadata": {},
   "source": [
    "One of the features contain `<`, remove it from the otherwise it will cause `ValueError` when creating xgboost dmatrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "276835b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['households',\n",
       " 'housing_median_age',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'median_income',\n",
       " 'ocean_proximity=_1H OCEAN',\n",
       " 'ocean_proximity=INLAND',\n",
       " 'ocean_proximity=ISLAND',\n",
       " 'ocean_proximity=NEAR BAY',\n",
       " 'ocean_proximity=NEAR OCEAN',\n",
       " 'population',\n",
       " 'total_bedrooms',\n",
       " 'total_rooms']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Regex pattern\n",
    "pattern = re.compile(r\"\\[|\\]|<\", re.IGNORECASE)\n",
    "\n",
    "# List to store feature names after applying 'pattern'\n",
    "feature_list = []\n",
    "for col in features:\n",
    "    if any(x in str(col) for x in set(('[', ']', '<'))):\n",
    "        col = pattern.sub(\"_\", col)\n",
    "    else:\n",
    "        col\n",
    "    feature_list.append(col)\n",
    "    \n",
    "feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d8e89e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Create DMatrix for train and validation\n",
    "dtrain = xgb.DMatrix(data=X_train, label=y_train, feature_names=feature_list)\n",
    "dval = xgb.DMatrix(data=X_val, label=y_val, feature_names=feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "80e8959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create watchlist\n",
    "watchlist = [(dtrain, 'train'), (dval, 'val')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "725609f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to parse xgb output and return dataframe\n",
    "def parse_xgb_output(output):\n",
    "    # list to store iteration num, train and val rmse\n",
    "    results = []\n",
    "    for line in output.stdout.strip().split('\\n'):\n",
    "        it_line, train_line, val_line = line.split('\\t')\n",
    "\n",
    "        it = int(it_line.strip('[]'))\n",
    "        train = float(train_line.split(':')[1])\n",
    "        val = float(val_line.split(':')[1])\n",
    "\n",
    "        results.append((it, train, val))\n",
    "    \n",
    "    # create dataframe with iteration number, train and val rmse\n",
    "    columns = ['num_iter', 'train_rmse', 'val_rmse']\n",
    "    df_results = pd.DataFrame(results, columns=columns)\n",
    "    \n",
    "    return df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e3142742",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict to store rmse on different eta values\n",
    "scores = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e4a502",
   "metadata": {},
   "source": [
    "Train the model with eta `0.3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9a8d1fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "# Set parameters\n",
    "xgb_params = {\n",
    "    'eta': 0.3, \n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "    \n",
    "    'objective': 'reg:squarederror',\n",
    "    'eval_metric': 'rmse',\n",
    "    'nthread': 8,\n",
    "    \n",
    "    'seed': 1,\n",
    "    'verbosity': 1,\n",
    "}\n",
    "\n",
    "# Build and train model for 100 iterations\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=100,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4165822d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the value of each eta from 'xgb_params'\n",
    "key = f\"eta={xgb_params['eta']}\"\n",
    "\n",
    "# Store the information in the 'scores' dictionary\n",
    "scores[key] = parse_xgb_output(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947b2f9f",
   "metadata": {},
   "source": [
    "Train the model with eta `0.1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "94e66f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1, \n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "    \n",
    "    'objective': 'reg:squarederror',\n",
    "    'eval_metric': 'rmse',\n",
    "    'nthread': 8,\n",
    "    \n",
    "    'seed': 1,\n",
    "    'verbosity': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=100,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bb798ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the value of each eta from 'xgb_params'\n",
    "key = f\"eta={xgb_params['eta']}\"\n",
    "\n",
    "# Store the information in the 'scores' dictionary\n",
    "scores[key] = parse_xgb_output(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e38c795a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['eta=0.3', 'eta=0.1'])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Learning rates in the scores dict\n",
    "scores.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50d72a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scores dataframe to make comparison on different eta\n",
    "for eta, df_score in scores.items():\n",
    "    plt.plot(df_score.num_iter, df_score.val_rmse, label=eta)\n",
    "    \n",
    "plt.xlabel('num of iterations')\n",
    "plt.ylabel('rmse scores')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44209540",
   "metadata": {},
   "source": [
    "`Both` have the same rmse score."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}