06.05-Ordered-Integer-Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target guided encodings\n",
    "\n",
    "In the previous lectures in this section, we learned how to convert a label into a number, by using one hot encoding, replacing by a digit or replacing by frequency or counts of observations. These methods are simple, make (almost) no assumptions and work generally well in different scenarios.\n",
    "\n",
    "There are however methods that allow us to capture information while pre-processing the labels of categorical variables. These methods include:\n",
    "\n",
    "- Ordering the labels according to the target\n",
    "- Replacing labels by the target mean (mean encoding / target encoding)\n",
    "- Replacing the labels by the probability ratio of the target being 1 or 0\n",
    "- Weight of evidence.\n",
    "\n",
    "All of the above methods have something in common:\n",
    "\n",
    "- the encoding is **guided by the target**, and\n",
    "- they create a **monotonic relationship** between the variable and the target.\n",
    "\n",
    "\n",
    "### Monotonicity\n",
    "\n",
    "A monotonic relationship is a relationship that does one of the following:\n",
    "\n",
    "- (1) as the value of one variable increases, so does the value of the other variable; or\n",
    "- (2) as the value of one variable increases, the value of the other variable decreases.\n",
    "\n",
    "In this case, as the value of the independent variable (predictor) increases, so does the target, or conversely, as the value of the variable increases, the target value decreases.\n",
    "\n",
    "\n",
    "\n",
    "### Advantages of target guided encodings\n",
    "\n",
    "- Capture information within the category, therefore creating more predictive features\n",
    "- Create a monotonic relationship between the variable and the target, therefore suitable for linear models\n",
    "- Do not expand the feature space\n",
    "\n",
    "\n",
    "### Limitations\n",
    "\n",
    "- Prone to cause over-fitting\n",
    "- Difficult to cross-validate with current libraries\n",
    "\n",
    "\n",
    "### Note\n",
    "\n",
    "The methods discussed in this and the coming 3 lectures can be also used on numerical variables, after discretisation. This creates a monotonic relationship between the numerical variable and the target, and therefore improves the performance of linear models. I will discuss this in more detail in the section \"Discretisation\".\n",
    "\n",
    "===============================================================================\n",
    "\n",
    "## Ordered Integer Encoding\n",
    "\n",
    "Ordering the categories according to the target means assigning a number to the category from 1 to k, where k is the number of distinct categories in the variable, but this numbering is informed by the mean of the target for each category.\n",
    "\n",
    "For example, we have the variable city with values London, Manchester and Bristol; if the default rate is 30% in London, 20% in Bristol and 10% in Manchester, then we replace London by 1, Bristol by 2 and Manchester by 3.\n",
    "\n",
    "## In this demo:\n",
    "\n",
    "We will see how to perform one hot encoding with:\n",
    "- pandas\n",
    "- Feature-Engine\n",
    "\n",
    "And the advantages and limitations of these implementations using the House Prices dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to split the datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# for encoding with feature-engine\n",
    "from feature_engine.categorical_encoders import OrdinalCategoricalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Neighborhood</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CollgCr</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Veenker</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CollgCr</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NoRidge</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Exterior1st Exterior2nd  SalePrice\n",
       "0      CollgCr     VinylSd     VinylSd     208500\n",
       "1      Veenker     MetalSd     MetalSd     181500\n",
       "2      CollgCr     VinylSd     VinylSd     223500\n",
       "3      Crawfor     Wd Sdng     Wd Shng     140000\n",
       "4      NoRidge     VinylSd     VinylSd     250000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "\n",
    "data = pd.read_csv(\n",
    "    '../houseprice.csv',\n",
    "    usecols=['Neighborhood', 'Exterior1st', 'Exterior2nd', 'SalePrice'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighborhood :  25  labels\n",
      "Exterior1st :  15  labels\n",
      "Exterior2nd :  16  labels\n",
      "SalePrice :  663  labels\n"
     ]
    }
   ],
   "source": [
    "# let's have a look at how many labels each variable has\n",
    "\n",
    "for col in data.columns:\n",
    "    print(col, ': ', len(data[col].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CollgCr', 'Veenker', 'Crawfor', 'NoRidge', 'Mitchel', 'Somerst',\n",
       "       'NWAmes', 'OldTown', 'BrkSide', 'Sawyer', 'NridgHt', 'NAmes',\n",
       "       'SawyerW', 'IDOTRR', 'MeadowV', 'Edwards', 'Timber', 'Gilbert',\n",
       "       'StoneBr', 'ClearCr', 'NPkVill', 'Blmngtn', 'BrDale', 'SWISU',\n",
       "       'Blueste'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore the unique categories\n",
    "data['Neighborhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['VinylSd', 'MetalSd', 'Wd Sdng', 'HdBoard', 'BrkFace', 'WdShing',\n",
       "       'CemntBd', 'Plywood', 'AsbShng', 'Stucco', 'BrkComm', 'AsphShn',\n",
       "       'Stone', 'ImStucc', 'CBlock'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Exterior1st'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['VinylSd', 'MetalSd', 'Wd Shng', 'HdBoard', 'Plywood', 'Wd Sdng',\n",
       "       'CmentBd', 'BrkFace', 'Stucco', 'AsbShng', 'Brk Cmn', 'ImStucc',\n",
       "       'AsphShn', 'Stone', 'Other', 'CBlock'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Exterior2nd'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding important\n",
    "\n",
    "We select which digit to assign each category using the train set, and then use those mappings in the test set.\n",
    "\n",
    "**Note that to do this technique with pandas, we need to keep the target within the training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 4), (438, 4))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[['Neighborhood', 'Exterior1st', 'Exterior2nd', 'SalePrice']],  # this time we keep the target!!\n",
    "    data['SalePrice'],  # target\n",
    "    test_size=0.3,  # percentage of obs in test set\n",
    "    random_state=0)  # seed to ensure reproducibility\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore original relationship between categorical variables and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's explore the relationship of the categories with the target\n",
    "\n",
    "for var in ['Neighborhood', 'Exterior1st', 'Exterior2nd']:\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig = X_train.groupby([var])['SalePrice'].mean().plot()\n",
    "    fig.set_title('Relationship between {} and SalePrice'.format(var))\n",
    "    fig.set_ylabel('Mean SalePrice')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the relationship between the target and the categories of the categorical variables goes up and down, depending on the category.\n",
    "\n",
    "\n",
    "## Ordered Integer encoding with pandas\n",
    "\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- quick\n",
    "- returns pandas dataframe\n",
    "\n",
    "### Limitations of pandas:\n",
    "\n",
    "- it does not preserve information from train data to propagate to test data\n",
    "\n",
    "We need to store the encoding maps separately if planing to use them in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "IDOTRR      92174.166667\n",
       "BrDale      99440.000000\n",
       "MeadowV    104441.666667\n",
       "Edwards    125082.676056\n",
       "BrkSide    125438.414634\n",
       "OldTown    130958.328767\n",
       "Sawyer     136596.442623\n",
       "Blueste    137500.000000\n",
       "SWISU      139154.666667\n",
       "NPkVill    140107.142857\n",
       "NAmes      146435.264901\n",
       "Mitchel    153070.444444\n",
       "SawyerW    179463.088889\n",
       "Gilbert    192662.290909\n",
       "NWAmes     192788.235294\n",
       "Blmngtn    198164.500000\n",
       "CollgCr    203086.371429\n",
       "ClearCr    210034.666667\n",
       "Crawfor    217453.914286\n",
       "Somerst    226644.196429\n",
       "Veenker    229500.000000\n",
       "Timber     238700.100000\n",
       "NridgHt    307418.137255\n",
       "StoneBr    309105.937500\n",
       "NoRidge    336264.666667\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's order the labels according to the mean target value\n",
    "\n",
    "X_train.groupby(['Neighborhood'])['SalePrice'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above cell, we ordered the categories from the neighbourhood where the houses sale prices are cheaper (IDOTRR), to the neighbourhood where the house prices are, on average, more expensive (NoRidge).\n",
    "\n",
    "In the next cells, we will replace those categories, ordered as they are, by the numbers 0 to k, where k is the number of different categories minus 1, in this case 25 - 1 = 24.\n",
    "\n",
    "So IDOTRR will be replaced by 0 and NoRidge by 24, just to be clear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IDOTRR', 'BrDale', 'MeadowV', 'Edwards', 'BrkSide', 'OldTown',\n",
       "       'Sawyer', 'Blueste', 'SWISU', 'NPkVill', 'NAmes', 'Mitchel', 'SawyerW',\n",
       "       'Gilbert', 'NWAmes', 'Blmngtn', 'CollgCr', 'ClearCr', 'Crawfor',\n",
       "       'Somerst', 'Veenker', 'Timber', 'NridgHt', 'StoneBr', 'NoRidge'],\n",
       "      dtype='object', name='Neighborhood')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first  we generate an ordered list with the labels\n",
    "\n",
    "ordered_labels = X_train.groupby(['Neighborhood'\n",
    "                                  ])['SalePrice'].mean().sort_values().index\n",
    "\n",
    "ordered_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'IDOTRR': 0,\n",
       " 'BrDale': 1,\n",
       " 'MeadowV': 2,\n",
       " 'Edwards': 3,\n",
       " 'BrkSide': 4,\n",
       " 'OldTown': 5,\n",
       " 'Sawyer': 6,\n",
       " 'Blueste': 7,\n",
       " 'SWISU': 8,\n",
       " 'NPkVill': 9,\n",
       " 'NAmes': 10,\n",
       " 'Mitchel': 11,\n",
       " 'SawyerW': 12,\n",
       " 'Gilbert': 13,\n",
       " 'NWAmes': 14,\n",
       " 'Blmngtn': 15,\n",
       " 'CollgCr': 16,\n",
       " 'ClearCr': 17,\n",
       " 'Crawfor': 18,\n",
       " 'Somerst': 19,\n",
       " 'Veenker': 20,\n",
       " 'Timber': 21,\n",
       " 'NridgHt': 22,\n",
       " 'StoneBr': 23,\n",
       " 'NoRidge': 24}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# next let's create a dictionary with the mappings of categories to numbers\n",
    "\n",
    "ordinal_mapping = {k: i for i, k in enumerate(ordered_labels, 0)}\n",
    "\n",
    "ordinal_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now, we replace the labels with the integers\n",
    "\n",
    "X_train['Neighborhood'] = X_train['Neighborhood'].map(ordinal_mapping)\n",
    "X_test['Neighborhood'] = X_test['Neighborhood'].map(ordinal_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64      16\n",
       "682     17\n",
       "960      4\n",
       "1384     3\n",
       "1100     8\n",
       "416      6\n",
       "1034    18\n",
       "853     10\n",
       "472      3\n",
       "1011     3\n",
       "Name: Neighborhood, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore the result\n",
    "\n",
    "X_train['Neighborhood'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can turn the previous commands into 2 functions\n",
    "\n",
    "\n",
    "def find_category_mappings(df, variable, target):\n",
    "\n",
    "    # first  we generate an ordered list with the labels\n",
    "    ordered_labels = X_train.groupby([variable\n",
    "                                      ])[target].mean().sort_values().index\n",
    "\n",
    "    # return the dictionary with mappings\n",
    "    return {k: i for i, k in enumerate(ordered_labels, 0)}\n",
    "\n",
    "\n",
    "def integer_encode(train, test, variable, ordinal_mapping):\n",
    "\n",
    "    X_train[variable] = X_train[variable].map(ordinal_mapping)\n",
    "    X_test[variable] = X_test[variable].map(ordinal_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and now we run a loop over the remaining categorical variables\n",
    "\n",
    "for variable in ['Exterior1st', 'Exterior2nd']:\n",
    "\n",
    "    mappings = find_category_mappings(X_train, variable, 'SalePrice')\n",
    "\n",
    "    integer_encode(X_train, X_test, variable, mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Neighborhood</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>219500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>173000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>116500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood  Exterior1st  Exterior2nd  SalePrice\n",
       "64              16           11           13     219500\n",
       "682             17            5            7     173000\n",
       "960              4            5            8     116500\n",
       "1384             3            4            4     105000\n",
       "1100             8            5            7      60000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see the result\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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by778Qh6+ZBiNUmp2Y1aVRWdmWWb2aXi8E1gMdI1TZQwwxcz2mtkKIAM4JtwyzGy5me0DpgBjJAk4BXg+1J8EnBuzrEnh8fPAqDC/c87VORNnruT9jM3837cG0Tu1RbLDKVO1pMXQ1HUU8FEoul7SPEkTJBU1RHYF1sRUywxlpZW3B7abWX6x8gOWFaZnh/mLxzVeUrqk9E2bNh3UNjrnXDJ8sWEnD72xhFEDOnLZMd2THU5CqjzxSGoJ/Au42cx2AI8ChwHDgCzgt0WzllDdKlAeb1kHFpg9bmZpZpbWoUOHuNvhnHM1zb78Qm6eModWTRry0AVDqS0NO1WaeCQ1Iko6T5nZCwBmtsHMCsysEPgbUVMaREcssem6G7AuTvlmoK2khsXKD1hWmN4G2Fq5W+ecc8n1u2lfsChrB7+8YCgdWjVJdjgJq8pebQKeABab2e9iyjvHzHYesCA8fgW4NPRI6w30Az4GPgH6hR5sjYk6ILxiZga8DVwY6o8FXo5Z1tjw+ELgf2F+55yrEz5avoW/vruMy47pzqmDOiU7nHKpyn8XjQSuAOZLmhPK7iLqlTaMqOlrJXANgJktlDQVWETUI+46MysAkHQ98CaQAkwws4VhebcDUyT9DPiMKNER7v8pKYPoSOfSKtxO55yrVjty87jly9EJBpVdoYaRHwhE0tLSLD09PdlhOOdcmW6ZOoeX56zjuWuP42tJ/qOopNlmllaeOjW7s7dzzrkDvDY/ixc+Xct13+ib9KRTUZ54nHOultiwI5e7XpzPkd3acEMNH50gHk88zjlXCxSNTrA3r3aMThBP7Y3cOefqkckfruS9pZu5+5sD6dOhZbLDOSieeJxzroZbumEnD76+hFMGdOQ7x/ZIdjgHzROPc87VYPvyC7n52Tm0aNKQhy44otaMThBPzbxKkHPOOQB+/98vWLhuB49fMZyOrZomO5xK4Uc8zjlXQ32yciuPvbOMS9K6c/rgQ5MdTqXxxOOcczXQztw8fvjsHLod0pz/O7v2jU4Qjze1OedcDfTTfy9i3fY9PHft8bRsUre+qv2Ixznnapg3FmTx/OxMrvtGX4b3rJ2jE8Tjicc552qQjTtyufOF+Qzt1oYbR/VLdjhVwhOPc87VEGbGbc/PY09eQa0fnSCeurlVzjlXC/1z1ire+WITd581kMNq+egE8Xjicc65GiBj4y5+/upiTj68A5eP6JnscKpUVV6BtLuktyUtlrRQ0k2h/NeSlkiaJ+lFSW1DeS9JeyTNCbfHYpY1XNJ8SRmS/hiuboqkdpKmSVoa7g8J5QrzZYT1fK2qttM55w7WvvxCfvjsHJo3TuFXFwytE6MTxFOVRzz5wK1mNhAYAVwnaRAwDRhiZkOBL4A7Y+osM7Nh4XZtTPmjwHiiy2H3A0aH8juA6WbWD5gengOcGTPv+FDfOedqpD9OX8r8tdk8eP5QOrauG6MTxFNlicfMsszs0/B4J7AY6Gpmb5lZfphtFtAt3nIkdQZam9mHFl0udTJwbpg8BpgUHk8qVj7ZIrOAtmE5zjlXo8xetZVHZmRw0fBujB5Sd0YniKdazvFI6gUcBXxUbNJ3gddjnveW9JmkdySdGMq6Apkx82SGMoBOZpYFUaIDOsbUWVNKndi4xktKl5S+adOmcm+Xc84djF178/nhs3Ppekgz7j1ncLLDqTZV/ndYSS2BfwE3m9mOmPK7iZrjngpFWUAPM9siaTjwkqTBQEmNnVbWahOpY2aPA48DpKWllbVM55yrFLl5BUxbtIEJH6wgc1sOU685rs6NThBPlW6ppEZESecpM3shpnws8C1gVGg+w8z2AnvD49mSlgH9iY5WYpvjugHrwuMNkjqbWVZoStsYyjOB7qXUcc65pFi4Lpvn0jN58bO1ZO/Jo2vbZjx0/lDSerVLdmjVqsoST+h59gSw2Mx+F1M+GrgdOMnMcmLKOwBbzaxAUh+ijgHLzWyrpJ2SRhA11V0J/ClUewUYCzwU7l+OKb9e0hTgWCC7qEnOOeeqU3ZOHi/PXcuzn6xh4bodNG7YgDMGH8olad05/rD2NGhQt3uwlaQqj3hGAlcA8yXNCWV3AX8EmgDTQpfBWaEH29eB+yXlAwXAtWa2NdT7ATARaEZ0TqjovNBDwFRJ44DVwEWh/DXgLCADyAGurqJtdM65rygsNGYu28LU9DW8sXA9+/ILGdylNT89ZzBjhnWhbfPGyQ4xqRRauuq9tLQ0S09PT3YYzrlaLHNbDs/PzuS59EzWbt9Dm2aNOHdYFy5K686Qrm2SHV6VkDTbzNLKU6f+nM1yzrkqkJtXwFuLNvBc+hrez9gMwAl9U7n9zAGcPqgTTRulJDnCmscTj3POVcCCtdk8l76Gl+as+7KjwE2j+nHh8G50O6R5ssOr0TzxOOdcgrbn7OPlOeuYmr6/o8DowYdycT3uKFARnniccy6OwkLjg2WbmZqeyZuho8CQrq25f8xgzjnSOwpUhCce55wrwbbd+3j649U8/dHqLzsKfPuYHlyU1o3BXepmR4Hq4onHOediLN+0iwkfrOD52Znk5hVyQt9U7jhzAKd5R4FK44nHOVfvmRmzlm/lifeX89/FG2mc0oDzjurKd0/ozeGHtkp2eHWOJx7nXL21L7+QV+ev4+/vrWDhuh20a9GYm0b14/IRPenQqkmyw6uzPPE45+qd7Jw8nv54NZNmrmT9jlwO69CCB88/gvOO6urNadXAE49zrt5YuXk3//hgBVPTM9mTV8AJfVN58IIjOKlfB+8KXY088Tjn6jQzI33VNv727nKmLd5AwwbinCO7Mu6E3gzq0jrZ4dVLnnicc3VSXkEhry9YzxPvLWduZjZtmzfiupP7cuVxPevF5aVrMk88zrk6ZUduHlM+Xs3ED1ayLjuX3qkteODcIVz4tW40a+znb2oCTzzOuTphzdYc/vHBSp79ZDW79xUwok877h8zhFMGdPTzNzWMJx7nXK02e9U2nnh/OW8sWE8DiW8N7cy4E/pwRDcfXaCm8sTjnKuVcvMKuONf83hpzjpaN23I+K8fxtjje9K5TbNkh+bK0KCqFiypu6S3JS2WtFDSTaG8naRpkpaG+0NCuST9UVKGpHmSvhazrLFh/qWSxsaUD5c0P9T5Y7jcdqnrcM7VDZt37eU7f/+Il+as48ZR/fjwzlHcceYATzq1RJUlHiAfuNXMBgIjgOskDQLuAKabWT9gengOcCbQL9zGA49ClESAe4FjgWOAe2MSyaNh3qJ6o0N5aetwztVySzfs5Ny/fMCCtdk88p2vcctp/WnRxBtvapMqSzxmlmVmn4bHO4HFQFdgDDApzDYJODc8HgNMtsgsoK2kzsAZwDQz22pm24BpwOgwrbWZfWjR9bsnF1tWSetwztVi7y3dxPmPzCQ3r5BnrzmOs47onOyQXAVU5RHPlyT1Ao4CPgI6mVkWRMkJ6Bhm6wqsiamWGcrilWeWUE6cdRSPa7ykdEnpmzZtqujmOeeqwZOzVnHVPz6h6yHNePn6kQzr3jbZIbkKqvLEI6kl8C/gZjPbEW/WEsqsAuUJM7PHzSzNzNI6dOhQnqrOuWpSUGjc/+9F3PPSAk7q34Hnf3A8Xdv6uZzarMzEI6mTpCckvR6eD5I0LpGFS2pElHSeMrMXQvGG0ExGuN8YyjOB7jHVuwHryijvVkJ5vHU452qRXXvzGT85nQkfrODqkb3425VptPTzObVeIkc8E4E3gS7h+RfAzWVVCj3MngAWm9nvYia9AhT1TBsLvBxTfmXo3TYCyA7NZG8Cp0s6JHQqOB14M0zbKWlEWNeVxZZV0jqcc7XEuu17uOixD5nxxSYeGDOYe88eTIr/EbROSOSnQ6qZTZV0J4CZ5UsqSKDeSOAKYL6kOaHsLuAhYGo4aloNXBSmvQacBWQAOcDVYX1bJT0AfBLmu9/MtobHPyBKjM2A18ONOOtwztUC8zK3M25SOrn7Cphw1dGc1N+bwuuSRBLPbkntCedPio5GyqpkZu9T8nkYgFElzG/AdaUsawIwoYTydGBICeVbSlqHc67me2NBFjc/O4f2LZrw1P87lv6d/AqgdU0iiecWoqarwyR9AHQALqzSqJxz9Y6Z8eg7y/jVG59zVI+2PH5Fml8FtI4qM/GY2aeSTgIOJzqC+dzM8qo8MudcvbEvv5C7X5zPc7MzOfvILvz6wqF+JdA6LJFebdcBLc1soZktAFpK+n9VH5pzrj7YnrOPKyd8xHOzM7lxVD/+eOkwTzp1XCK92r5vZtuLnoTRA75fdSE55+qLFZt3c94jM/l01XYevuRIbjmtP2HIRVeHJXKOp4EkhZP/SEoBGldtWM65um7W8i1c++RsGkg89f1jObpXu2SH5KpJIonnTaKuyY8R9Wy7FnijSqNyztVpz6Wv4a4X59OjXXMmXHU0Pdu3SHZIrholknhuB64h+s+MgLeAv1dlUM65uqmw0PjNW5/zyIxljOzbnke+M5w2zRolOyxXzRLp1VZIdPmBR6s+HOdcXbVnXwG3PjeH1+av57JjenD/mME0SqmWcYpdDVNq4pE01cwuljSfEgbfNLOhVRqZc67O2Lgjl+9PTmfe2mzu+eZAxp3Q2zsR1GPxjnhuCvffqo5AnHN106J1O/jepE/YviePx69I47RBnZIdkkuyUhOPmWWFHmxPmNmp1RiTc66WKyw05mRuZ/riDUz8YCWtmjZi6jXHMaRrm2SH5mqAuOd4zKxAUo6kNmZW5vhszrn6a/fefN5bupnpizfw9ucb2bxrHykNxAl9U/nlBUM5tE3TZIfoaohEerXlEo0wPQ3YXVRoZjdWWVTOuVph3fY9TF+ykemLNzBz2Rb25RfSqmlDTj68I6cO7MjJ/TvSprn3WnMHSiTxvBpuzrl6rrDQWLAum/8u2sB/F29kUVZ0UeGe7ZtzxYiejBrYkaN7tfPeai6uuIlH0lFERzkLzWxx9YTknKtJ9uwr4IOMzUxfsoHpizeycedeGgiG9zyEO84cwKkDO3JYh5beS80lLF536p8AlwOzgV9JetDM/pbogiVNIOoRt9HMhoSyZ4lGuQZoC2w3s2GSegGLgc/DtFlmdm2oM5z9F3t7DbjJzExSO+BZoBewErjYzLaFq5H+geiicjnAVWb2aaJxO+ei7s/Tl2zkv4s28MGyzeTmFdKySUNO6t+BUQM7cvLhHWnXwkfOchUT74jnEmCYmeWEC8G9ASSceIiSxZ+ByUUFZnZJ0WNJv+XAC8otM7NhJSznUWA8MIso8YwmutLoHcB0M3tI0h3h+e3AmUC/cDs21D+2HHE7V++YGQvX7WD64o1MX7KBeZnRR7PbIc249OgejBrYkWN7t6dxQ29CcwcvXuLJNbMciK7oKalce5yZvRuOZL4iHJVcDJwSbxmSOgOtzezD8HwycC5R4hkDnBxmnQTMIEo8Y4DJYVDTWZLaSupsZlnlid+5+mB+ZjbPpq9m+uKNZGXnIsGw7m257YzDOXVgJ/p38iY0V/niJZ7DJL0SHqvYc8zsnINY74nABjNbGlPWW9JnwA7gHjN7D+gKZMbMkxnKADoVJZPwn6OOobwrsKaEOl9JPJLGEx1N0aNHj4PYHOdqj4JCY9qiDUx4fwUfr9xKs0YpfL1/Kj88rT+nDOhIaku/6qerWvESz5hiz39Tieu9DHgm5nkW0CMcWQ0HXpI0mCjhFfeV4XuKSbiOmT0OPA6QlpZW1nKdq9V27c1n6idrmDhzJau35tDtkGbc882BXHx0d1o39S7PrvrEG7ngnaLHkpoRJYbPS5s/UZIaAucDw2PWtRfYGx7PlrQM6E90tNItpno3YF14vKGoCS00yW0M5ZlA91LqOFfvrNmaw6SZK3n2kzXs3JtPWs9DuPPMAZw2qBMNvduzS4Iy/8cj6Wyio53GRM1hw4D7D6Kp7VRgiZl92YQmqQOwNYyU0IeoY8ByM9sqaaekEcBHwJXAn0K1V4CxwEPh/uWY8uslTSHqVJDt53dcfWNmzF61jSfeX8GbC9fTQOKsIzoz7oTeHNm9bbLDc/VcIn8gvQ84hujkPWY2p7ROA7EkPUN08j9VUiZwr5k9AVzKgc1sAF8H7peUDxQA15rZ1jDtB+zvTv0YU8QnAAAeUElEQVR6uEGUcKZKGgesBi4K5a8RdaXOIOpOfXUC2+hcnZBXUMhr87OY8P4K5mZm06ZZI8Z//TDGHt+Tzm2aJTs85wBQuKJ16TNIH5nZsZI+M7OjQtm8unZZhLS0NEtPT092GM5VSHZOHk9/vJrJH64kKzuXPqktuHpkLy4Y3o3mjRP5felcxUiabWZp5amTyB65QNK3gRRJ/YAbgZkVCdA5V7mWb9rFPz5YyfOzM9mTV8DIvu35+XlDOLl/Rxo08G7QrmZKJPHcANxNdPL/GeBN4IGqDMo5VzozY+ayLTzx/gr+t2QjjVMaMGZYF757Qm8Gdm6d7PCcK1Mil77OIUo8d1d9OM650uTmFfDK3HVMeH8FS9bvpH2Lxtw0qh+Xj+hJh1b+3xtXe8Qbq+3fxPnPzEH+gdQ5l6DNu/by5KxVPDlrFZt37ePwTq341QVDOWdYF5o2Skl2eM6VW7wjnsr8w6hzrpwKCo2/vbec3037gn35hXzj8A6MO6EPI/u292FsXK2W0B9InXPVa83WHG6dOpePV27ljMGduO2MAfTt2DLZYTlXKRL5A2k/4EFgEPDltWvNrE8VxuVcvWRmPD87k5/+exEAv73oSM7/Wlc/wnF1SiK92v4B3As8DHyD6A+Z/ilwrpJt2bWXu16cz5sLN3BM73b89qIj6d6uebLDcq7SJZJ4mpnZdEkys1XAfZLeI0pGzrlKMH3xBm7/13x27MnjrrMGMO6EPqT4/3BcHZVI4skN1+JZKul6YC3QsYw6zrkE7N6bz89eXcwzH69mwKGtePJ7xzDgUP8vjqvbEkk8NwPNiUYseIDo4m1jqzIo5+qD2au2ccvUOazemsM1J/XhltP606Shd492dV8ifyD9JDzcJekWYLuVNcCbc65U+/IL+eP0pTwyI4MubZsx5fsjOLZP+2SH5Vy1ifcH0p8AU81siaQmwBvAkUC+pG+b2X+rK0jn6oqMjTu5+dk5LFi7g4uGd+MnZw+ilV+EzdUz8Y54LmH/mGxFTWsdiC7QNgnwxONcggoLjUkfruSh15fQoklDHrt8OKOHHJrssJxLiniJZ19Mk9oZwBQzKwAWh6uIOucSkJW9hx89N5cPMrYwakBHHrzgCDq2alp2RefqqHgJZK+kIcAGov/v/Chmmv+5wLkEvDxnLf/30gLyC40Hzz+CS4/u7n8GdfVevAuu3wQ8DywBHjazFQCSzgI+K2vBkiZI2ihpQUzZfZLWSpoTbmfFTLtTUoakzyWdEVM+OpRlSLojpry3pI8kLZX0rKTGobxJeJ4RpvdK+NVwrpJsz9nHDc98xk1T5tC3Y0teu/FELjumhycd54g/VttHwIASyl8jurx0WSYCfwYmFyt/2MwOGIBU0iCiS2IPBroA/5XUP0z+C3AakAl8IukVM1sE/DIsa4qkx4BxwKPhfpuZ9ZV0aZjvkgTida5SvLd0E7c9N4/Nu/Zy2xmHc83X+9AwJd5vPOfqlyr7NJjZu8DWBGcfQ3QOaW84ssoAjgm3DDNbbmb7gCnAGEU/G08hOiKDqLPDuTHLmhQePw+Mkv/MdNVgz74C7ntlIVc88TEtmzbkpetGct03+nrSca6YZHQSuF7SlUA6cKuZbQO6ArNi5skMZQBripUfC7Qn+j9Rfgnzdy2qY2b5krLD/JuLByJpPDAeoEePHge/Za7empe5nR8+O4dlm3Zz9che3D56gF8rx7lSVHfieZSoi7aF+98C36XkQUeNko/ILM78lDHtwEKzx4HHAdLS0vxPsa5M+QWF7MjNZ3vOPrbvySM7J49PV2/j0RnLSG3ZhCfHHcsJ/VKTHaZzNVpCiUfS8UCv2PnNrPi5mzKZ2YaYZf4N+E94mgl0j5m1G7AuPC6pfDPQVlLDcNQTO3/RsjJDt+82JN7k5+qJ3LwCduzJY/uePLbn5B2QSLbv2ReVFXuenZPHzr35JS7vnCO78MCYIbRp7n8Gda4siVyP55/AYcAcoCAUG1/tNFAmSZ3NLCs8PQ8o6vH2CvC0pN8RdS7oB3xMdPTST1JvosFJLwW+bWYm6W3gQqLzPmOBl2OWNRb4MEz/nw/xU/+YGcs27WLmsi18vGIrm3buJbsoyezZR25eYal1GzYQbZo1ok3zRrRt1oiOrZrSv2Or8LwxbZs3om3zRrRp1oi2zRvTvkVjv3yBc+WQyBFPGjCovF/ekp4BTgZSJWUSXUbhZEnDiBLXSuAaADNbKGkqsAjIB64Lf1YljIj9JpACTDCzhWEVtwNTJP2MqHv3E6H8CeCfkjKIjnQuLU/crvbK3JbDzGVbmJmxmZnLtrBx514AurRpSvd2zenRrjlDu0XJIkoa+xPJl8+bN6ZF4xTv9uxcFVJZ+UTSc8CNMUcqdVJaWpqlp6cnOwxXDpt37eXDZVuYuSxKNKu25ACQ2rIxxx2WyvGHtWfkYal0b9fME4lzVUTSbDNLK0+dRI54UoFFkj4G9hYVmtk55YzPuYOyIzePj5dv5YNlm/lw2RaWrN8JQKsmDTm2T3vGHteLkX1T6d+ppSca52qwRBLPfVUdhHMlyc0rYPaqbXwQms7mZW6n0KBJwwYc3asdt53RhZF9UxnSpbX/V8a5WiSR6/G8Ux2BOJdXUMi8zOwvz9HMXr2NffmFpDQQw7q35bpv9OX4w1I5qkdb/4+Mc7VYIr3aRgB/AgYCjYlO8u82M78+r6sUU9PX8MaC9Xy0fAu790UdJwd1bs2VI3oysm8qR/duR8smPiC6c3VFIp/mPxP1DHuOqIfblUTdnZ07aC98msmPn59Hz/bNOfeorozsm8qIPu1p16JxskNzzlWRhH5GmlmGpJTQxfkfkmZWcVyuHlixeTf3vLSAY3q345nvjyClgXcIcK4+SCTx5IRLDsyR9CsgC2hRtWG5um5vfgE3PPMpjVIa8PtLhnnSca4eSaQr0BVhvuuB3UTD0VxQlUG5uu/Xb3zOgrU7+PWFQ+nStlmyw3HOVaNEerWtktQM6GxmP62GmFwd9/bnG/n7+yu48rienD740GSH45yrZmUe8Ug6m2ictjfC82GSXqnqwFzdtHFHLj+aOpcBh7birrMGJjsc51wSJNLUdh/RBdm2A5jZHKKRqp0rl8JC45apc9m9L58/XXaU/xfHuXoqkcSTb2bZVR6Jq/Mee3cZ72ds5t6zB9OvU6tkh+OcS5JEerUtkPRtIEVSP+BGwLtTu3L5dPU2fvvWF3zziM5cenT3sis45+qsRI54bgAGEw0Q+gywA7i5KoNydcuO3DxufOYzDm3dlF+cf4QP4OlcPZdIr7Yc4O5wc65czIy7XphPVnYuU685jjbN/AqdztV3pSaesnqulXVZBEkTgG8BG81sSCj7NXA2sA9YBlxtZtsl9QIWA5+H6rPM7NpQZzgwEWgGvAbcFK5A2g54lqijw0rgYjPbpujn9B+As4Ac4Coz+zRerK7qPJeeyX/mZXHbGYczvOchyQ7HOVcDxGtqOw7oBrwH/Ab4bbFbWSYCo4uVTQOGmNlQ4Avgzphpy8xsWLhdG1P+KDCeaHy4fjHLvAOYbmb9gOnhOcCZMfOOD/VdEmRs3MW9ryzkuD7tufakw5IdjnOuhoiXeA4F7gKGEB1BnAZsNrN3ErlUgpm9S3Tp6diyt8wsPzydRZTYSiWpM9DazD4Ml96eDJwbJo8BJoXHk4qVT7bILKBtWI6rRrl5BdzwzGc0bdSA31/qQ+I45/YrNfGYWYGZvWFmY4ERQAYwQ9INlbTu7wKvxzzvLekzSe9IOjGUdQUyY+bJDGUAnYouxx3uO8bUWVNKnQNIGi8pXVL6pk2bDm5r3AEeen0Ji7N28JuLjqRT66bJDsc5V4PE7VwgqQnwTeAyonMpfwReONiVSrobyAeeCkVZQA8z2xLO6bwkaTBQ0s9kK2vxidYxs8eBxwHS0tLKWq5L0H8XbWDizJVcPbIXowZ2SnY4zrkaJl7ngklEzWyvAz81swWVsUJJY4k6HYwKzWeY2V6i7tqY2WxJy4D+REcrsc1x3YB14fEGSZ3NLCs0pW0M5ZlEA5mWVMdVsfXZudz2/FwGdW7NHWcOSHY4zrkaKN45niuIvvxvAmZK2hFuOyXtqMjKJI0GbgfOCd20i8o7SEoJj/sQdQxYHprQdkoaEXqrXQm8HKq9AowNj8cWK79SkRFAdlGTnKtaBYXGzc9+xt78Qv707aNo0tCHxHHOfVWpRzxmlsifS0sl6RngZCBVUiZwL1EvtibAtPAnwqJu018H7peUDxQA15pZUceEH7C/O/Xr7D8v9BAwVdI4YDVwUSh/jagrdQZRd+qrD2Y7XOIeeTuDWcu38usLh3JYh5bJDsc5V0MptHbVe2lpaZaenp7sMGqt9JVbueTxWXzziM784dJhPjqBc/WEpNlmllaeOgd1VOMcQHZOHjdNmUOXtk35+XlDPOk45+JKZJBQ50plZtzxwjw27Mjl+R8cT6umPiSOcy4+P+JxB+WZj9fw+oL1/OiMwxnWvW2yw3HO1QKeeFyFfbFhJz/990JO7JfK+BP7JDsc51wt4YnHVUhuXgHXP/0prZo25LcXH0kDHxLHOZcgP8fjKuRnry7iiw27mHj10XRs5UPiOOcS50c8rtzeWJDFk7NW8/0Te3Py4R3LruCcczE88bhyWbt9Dz9+fh5HdG3DbWf4kDjOufLzxOMSll9QyM1TPqOg0PjTZUfRuKHvPs658vNzPC5hf/pfBp+s3MbDlxxJr9QWyQ7HOVdL+U9Wl5BZy7fwp/8t5fyjunLeUXGv3+ecc3F54nFl2rZ7HzdPmUOPds25/9whyQ7HOVfLeVNbHWRm7CsoJDevkL35BewN97HPc4vf5xWwN39/ndj7xVk72LJ7Ly/8YCQtm/gu45w7OP4tUgvlFRTy+fqdzMvMZv7a7czLzGbzrr37E0t+IQcz6HijFNG0YQpNGjWgScMUmjZqwC/OO4IjurWpvI1wztVbnnhquIJCY9mmXcxds535a7OZm5nN4qwd7MsvBKBNs0Yc0bUNQ7q0oUmjBjRtlEKThvvvm8Q8bxqef3kfk1hi71N8FALnXBXyxFODFBYaq7bmMC8zOoqZn5nNgnXZ5OwrAKBF4xSGdG3D2ON6MrRbW4Z2a0OPds39MgTOuVqlShOPpAnAt4CNZjYklLUDngV6ASuBi81sW7i09R+Irh6aA1xlZp+GOmOBe8Jif2Zmk0L5cPZfnfQ14CYzs9LWUZXbWl5mxtrte5iXmR1u0RHNztx8AJo0bMDgLq25OK07Q7u1YWi3NvRJbeljojnnar2qPuKZCPwZmBxTdgcw3cweknRHeH47cCbQL9yOBR4Fjg1J5F4gDTBgtqRXQiJ5FBgPzCJKPKOJLo1d2jqSZuOOXOZmZjM/c3t0vzabrbv3AdE5lQGHtuacI7swtFsbjujalv6dWtIwxTsdOufqnipNPGb2rqRexYrHACeHx5OAGURJYQww2aJrcc+S1FZS5zDvNDPbCiBpGjBa0gygtZl9GMonA+cSJZ7S1lHtzIzrn/6MV+dnAZDSQPTr2JJTB3bkiG5tObJbGw4/tBVNGqYkIzznnKt2yTjH08nMsgDMLEtS0SiTXYE1MfNlhrJ45ZkllMdbxwEkjSc6YqJHjx4Hs02lenV+Fq/Oz2LscT05Z1gXBnVuQ7PGnmScc/VXTepcUNLJC6tAecLM7HHgcYC0tLSD6IBcsp25edz/70UM7tKan5w92HuLOeccyRm5YENoQiPcbwzlmUD3mPm6AevKKO9WQnm8dVSrh6ctZdOuvfz8vCM86TjnXJCMxPMKMDY8Hgu8HFN+pSIjgOzQXPYmcLqkQyQdApwOvBmm7ZQ0IvSIu7LYskpaR7VZuC6biTNX8O1jejCse9vqXr1zztVYVd2d+hmik/ypkjKJeqc9BEyVNA5YDVwUZn+NqCt1BlF36qsBzGyrpAeAT8J89xd1NAB+wP7u1K+HG3HWUS0KC417XlrAIc0b82O/Zo1zzh2gqnu1XVbKpFElzGvAdaUsZwIwoYTydOAro1aa2ZaS1lFdpqav4bPV2/nNRUfSpnmjZIXhnHM1kv9RpJJt3b2Ph95YwjG92nHB17qWXcE55+oZTzyV7KHXF7MrN5+fnTfEh7JxzrkSeOKpROkrtzI1PZNxJ/amf6dWyQ7HOedqJE88lSSvoJC7X1xAlzZNufGUfskOxznnaixPPJVk0syVfL5hJz85ezAt/GJpzjlXKk88lSArew8PT/uCUwZ05IzBnZIdjnPO1WieeCrBA/9ZRH6hcd/Zg71DgXPOlcETz0Ga8flGXpu/nhtO6UuP9s2THY5zztV4nngOQm5eAfe+spA+qS34/tf7JDsc55yrFfws+EF4dMYyVm3J4anvHevX03HOuQT5EU8Frdi8m0dnLOOcI7swsm9qssNxzrlawxNPBZgZP3l5AU0aNuCebw5MdjjOOVereOKpgNfmr+e9pZu59fT+dGzdNNnhOOdcreKJp5x25uZx/38WMrhLay4f0TPZ4TjnXK3jnQvK6ff/XcrGnXt57PLhNEzxvO2cc+VV7d+ckg6XNCfmtkPSzZLuk7Q2pvysmDp3SsqQ9LmkM2LKR4eyDEl3xJT3lvSRpKWSnpXUuDJiX7RuBxNnruSyY3pwVI9DKmORzjlX71R74jGzz81smJkNA4YTXW30xTD54aJpZvYagKRBwKXAYGA08IikFEkpwF+AM4FBwGVhXoBfhmX1A7YB4w427uiqovNp26wRPz7j8INdnHPO1VvJbisaBSwzs1Vx5hkDTDGzvWa2gujS2MeEW4aZLTezfcAUYIyiMWtOAZ4P9ScB5x5soM/NXsOnq7dz51kDadu8Ug6gnHOuXkp24rkUeCbm+fWS5kmaIKmoLasrsCZmnsxQVlp5e2C7meUXK/8KSeMlpUtK37RpU6lBbt29jwdf96uKOudcZUha4gnnXc4BngtFjwKHAcOALOC3RbOWUN0qUP7VQrPHzSzNzNI6dOhQaqy/fH0Ju3LzeeBcv6qoc84drGQe8ZwJfGpmGwDMbIOZFZhZIfA3oqY0iI5YusfU6wasi1O+GWgrqWGx8gpJX7mVZ9PXMO6E3hx+qF9V1DnnDlYyE89lxDSzSeocM+08YEF4/ApwqaQmknoD/YCPgU+AfqEHW2OiZrtXzMyAt4ELQ/2xwMsVCTC/oJB7XgpXFR3lVxV1zrnKkJT/8UhqDpwGXBNT/CtJw4iaxVYWTTOzhZKmAouAfOA6MysIy7keeBNIASaY2cKwrNuBKZJ+BnwGPFGROCfOXMmS9Tt57PLhflVR55yrJIoOEFxaWpqlp6d/+Twrew+n/vYdjundjglXHe3ndpxzrgSSZptZWnnqJLtXW431s/8sJr/Q+Ok53qHAOecqkyeeErzzxSZenZ/F9d/wq4o651xl88RTTG5eAfe+vIA+qS0Yf5JfVdQ55yqbnzEv5rF3lrFySw5PjvOrijrnXFXwI54YKzfv5pEZyzj7yC6c0M+vKuqcc1XBE0+Mn7yykMYpflVR55yrSp54guw9ebz7xSZuPb0/nfyqos45V2U88QRZ23MZ1Lk1V/hVRZ1zrkp54gnyCgv5+XlD/KqizjlXxfxbNkht2cSvKuqcc9XAE0/QuY2f13HOuergicc551y18sTjnHOuWnnicc45V6088TjnnKtWnnicc85Vq6QlHkkrJc2XNEdSeihrJ2mapKXh/pBQLkl/lJQhaZ6kr8UsZ2yYf6mksTHlw8PyM0Jdv6iOc87VAMk+4vmGmQ2LuXrdHcB0M+sHTA/PAc4E+oXbeOBRiBIVcC9wLHAMcG9RsgrzjI+pN7rqN8c551xZkp14ihsDTAqPJwHnxpRPtsgsoK2kzsAZwDQz22pm24BpwOgwrbWZfWjRtb0nxyzLOedcEiXzejwGvCXJgL+a2eNAJzPLAjCzLEkdw7xdgTUxdTNDWbzyzBLKDyBpPNFREcBeSQsOequSJxXYnOwgDoLHnzy1OXbw+JPt8PJWSGbiGWlm60JymSZpSZx5Szo/YxUoP7AgSnaPA0hKj2nyq3U8/uSqzfHX5tjB40+2onP05ZG0pjYzWxfuNwIvEp2j2RCayQj3G8PsmUD3mOrdgHVllHcrodw551ySJSXxSGohqVXRY+B0YAHwClDUM20s8HJ4/ApwZejdNgLIDk1ybwKnSzokdCo4HXgzTNspaUTozXZlzLKcc84lUbKa2joBL4Yezg2Bp83sDUmfAFMljQNWAxeF+V8DzgIygBzgagAz2yrpAeCTMN/9ZrY1PP4BMBFoBrwebvE8XgnblUwef3LV5vhrc+zg8SdbueNX1OnLOeecqx41rTu1c865Os4Tj3POuWrliQeQNFrS52F4nTvKrlEzSOou6W1JiyUtlHRTsmOqCEkpkj6T9J9kx1JektpKel7SkvA+HJfsmMpD0g/DvrNA0jOSavQVESVNkLQx9j93pQ21VROVEv+vw/4zT9KLktomM8Z4Soo/ZtqPJJmk1LKWU+8Tj6QU4C9Ew/IMAi6TNCi5USUsH7jVzAYCI4DralHssW4CFic7iAr6A/CGmQ0AjqQWbYekrsCNQJqZDQFSgEuTG1WZJvLV4a9KG2qrJprIV+OfBgwxs6HAF8Cd1R1UOUykhOHHJHUHTiPqFFamep94iP4/lGFmy81sHzCFaIieGs/Msszs0/B4J9GX3ldGaKjJJHUDvgn8PdmxlJek1sDXgScAzGyfmW1PblTl1hBoJqkh0Jwa/n83M3sX2FqsuLShtmqckuI3s7fMLD88ncWB/0GsUUp5/QEeBn5MCX/UL4knntKH3alVJPUCjgI+Sm4k5fZ7oh22MNmBVEAfYBPwj9BU+Pfwv7RawczWAr8h+pWaRfT/uLeSG1WFHDDUFtCxjPlrsu9S9l8/ahRJ5wBrzWxuonU88SQ4vE5NJqkl8C/gZjPbkex4EiXpW8BGM5ud7FgqqCHwNeBRMzsK2E3NbuY5QDgXMgboDXQBWki6PLlR1V+S7iZqPn8q2bEkSlJz4G7gJ+Wp54mn9GF3agVJjYiSzlNm9kKy4ymnkcA5klYSNXGeIunJ5IZULplAppkVHWU+T5SIaotTgRVmtsnM8oAXgOOTHFNFlDbUVq0RriX2LeA7Vrv+XHkY0Q+XueFz3A34VNKh8Sp54olGPegnqbekxkQnV19JckwJCcMBPQEsNrPfJTue8jKzO82sm5n1Inrd/2dmteYXt5mtB9ZIKhqddxSwKIkhlddqYISk5mFfGkUt6hwRo7ShtmoFSaOB24FzzCwn2fGUh5nNN7OOZtYrfI4zga+Fz0ap6n3iCSf1rica920xMNXMFiY3qoSNBK4gOlKYE25nJTuoeuYG4ClJ84BhwC+SHE/CwpHa88CnwHyi74MaPXyLpGeAD4HDJWWG4bUeAk6TtJSoZ9VDyYwxnlLi/zPQimiU/jmSHktqkHGUEn/5l1O7juqcc87VdvX+iMc551z18sTjnHOuWnnicc45V6088TjnnKtWnnicc85VK088zlWQpIKYbuxzyhrZXNJdFVzP38s7+Kuk68No62WOFiypl6RvVyQ25yrCu1M7V0GSdplZy6qaP9RJMbOC8tYBhgLbgBlEo09vjjP/ycCPzOxb5VmPcxXlRzzOVSJJbcK1nQ4Pz5+R9H1JDxGNAj1H0lNh2uWSPg5lfw0JA0m7JN0v6SPgOEkzJKWFaZdJmh+un/PLmPUeUMfMPjOzlSXEd1LMEdpnkloR/eHyxFD2w6p+jZzzxONcxTXTgU1tl5hZNtFIGBMlXQocYmZ/M7M7gD1mNszMviNpIHAJMNLMhgEFwHfCclsAC8zsWDN7v2hlkroAvwROIRol4WhJ58arU4IfAdeFdZ4I7CEa2PS9ENvDlfPSOFe6hskOwLlabE/4Aj+AmU2TdBHRBQaPLKXuKGA48Ek0TBrN2D+4ZQHRwK/FHQ3MMLNNAOHI6evAS3HqFPcB8LtQ9wUzywzrd67aeOJxrpJJagAMJDqaaEc0cOJXZgMmmVlJV5vMLeW8TrwMUVqdA5jZQ5JeBc4CZkk6taw6zlU2b2pzrvL9kGjA2cuACeHSFQB5MY+nAxdK6gggqZ2knmUs9yPgJEmp4XzQZcA75QlM0mFhROFfAunAAGAn0SCVzlULTzzOVVzxczwPSeoPfA+41czeA94F7gnzPw7Mk/SUmS0K5W+Fka2nAZ3jrSxcXfNO4G1gLvCpmZV4CQBJN0rKJLo+yjxJRZcWvzl0TJhLdET2OjAPyJc01zsXuOrg3amdc85VKz/icc45V6088TjnnKtWnnicc85VK088zjnnqpUnHuecc9XKE49zzrlq5YnHOedctfr/Oxf3J7vgQPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's inspect the newly created monotonic relationship\n",
    "# between the variables and the target\n",
    "\n",
    "for var in ['Neighborhood', 'Exterior1st', 'Exterior2nd']:\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig = X_train.groupby([var])['SalePrice'].mean().plot()\n",
    "    fig.set_title('Monotonic relationship between {} and SalePrice'.format(var))\n",
    "    fig.set_ylabel('Mean SalePrice')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see from the plots above that the relationship between the categories and the target is now monotonic, and for the first 2 variables, almost linear, which helps improve linear models performance.\n",
    "\n",
    "### Note\n",
    "\n",
    "Monotonic does not mean strictly linear. Monotonic means that it increases constantly, or it decreases constantly.\n",
    "\n",
    "Replacing categorical labels with this code and method will generate missing values for categories present in the test set that were not seen in the training set. Therefore it is extremely important to handle rare labels before-hand. I will explain how to do this, in a later notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integer Encoding with Feature-Engine\n",
    "\n",
    "If using Feature-Engine, instead of pandas, we do not need to keep the target variable in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 3), (438, 3))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[['Neighborhood', 'Exterior1st', 'Exterior2nd']], # predictors\n",
    "    data['SalePrice'],  # target\n",
    "    test_size=0.3,  # percentage of obs in test set\n",
    "    random_state=0)  # seed to ensure reproducibility\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_enc = OrdinalCategoricalEncoder(\n",
    "    # NOTE that we indicate ordered in the encoding_method, otherwise it assings numbers arbitrarily\n",
    "    encoding_method='ordered',\n",
    "    variables=['Neighborhood', 'Exterior1st', 'Exterior2nd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalCategoricalEncoder(encoding_method='ordered',\n",
       "                          variables=['Neighborhood', 'Exterior1st',\n",
       "                                     'Exterior2nd'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# when fitting the transformer, we need to pass the target as well\n",
    "# just like with any Scikit-learn predictor class\n",
    "\n",
    "ordinal_enc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Neighborhood': {'IDOTRR': 0,\n",
       "  'BrDale': 1,\n",
       "  'MeadowV': 2,\n",
       "  'Edwards': 3,\n",
       "  'BrkSide': 4,\n",
       "  'OldTown': 5,\n",
       "  'Sawyer': 6,\n",
       "  'Blueste': 7,\n",
       "  'SWISU': 8,\n",
       "  'NPkVill': 9,\n",
       "  'NAmes': 10,\n",
       "  'Mitchel': 11,\n",
       "  'SawyerW': 12,\n",
       "  'Gilbert': 13,\n",
       "  'NWAmes': 14,\n",
       "  'Blmngtn': 15,\n",
       "  'CollgCr': 16,\n",
       "  'ClearCr': 17,\n",
       "  'Crawfor': 18,\n",
       "  'Somerst': 19,\n",
       "  'Veenker': 20,\n",
       "  'Timber': 21,\n",
       "  'NridgHt': 22,\n",
       "  'StoneBr': 23,\n",
       "  'NoRidge': 24},\n",
       " 'Exterior1st': {'BrkComm': 0,\n",
       "  'AsphShn': 1,\n",
       "  'CBlock': 2,\n",
       "  'AsbShng': 3,\n",
       "  'WdShing': 4,\n",
       "  'Wd Sdng': 5,\n",
       "  'MetalSd': 6,\n",
       "  'HdBoard': 7,\n",
       "  'Plywood': 8,\n",
       "  'Stucco': 9,\n",
       "  'BrkFace': 10,\n",
       "  'VinylSd': 11,\n",
       "  'CemntBd': 12,\n",
       "  'Stone': 13,\n",
       "  'ImStucc': 14},\n",
       " 'Exterior2nd': {'AsphShn': 0,\n",
       "  'CBlock': 1,\n",
       "  'AsbShng': 2,\n",
       "  'Brk Cmn': 3,\n",
       "  'Wd Shng': 4,\n",
       "  'MetalSd': 5,\n",
       "  'Stone': 6,\n",
       "  'Wd Sdng': 7,\n",
       "  'Plywood': 8,\n",
       "  'HdBoard': 9,\n",
       "  'Stucco': 10,\n",
       "  'BrkFace': 11,\n",
       "  'ImStucc': 12,\n",
       "  'VinylSd': 13,\n",
       "  'CmentBd': 14,\n",
       "  'Other': 15}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the encoder dict we can observe each of the top categories\n",
    "# selected for each of the variables\n",
    "\n",
    "ordinal_enc.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Neighborhood', 'Exterior1st', 'Exterior2nd']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the list of variables that the encoder will transform\n",
    "\n",
    "ordinal_enc.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>3</td>\n",
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       "      <th>1100</th>\n",
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      "text/plain": [
       "      Neighborhood  Exterior1st  Exterior2nd\n",
       "64              16           11           13\n",
       "682             17            5            7\n",
       "960              4            5            8\n",
       "1384             3            4            4\n",
       "1100             8            5            7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = ordinal_enc.transform(X_train)\n",
    "X_test = ordinal_enc.transform(X_test)\n",
    "\n",
    "# let's explore the result\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "If the argument variables is left to None, then the encoder will automatically identify all categorical variables. Is that not sweet?\n",
    "\n",
    "The encoder will not encode numerical variables. So if some of your numerical variables are in fact categories, you will need to re-cast them as object before using the encoder.\n",
    "\n",
    "Finally, if there is a label in the test set that was not present in the train set, the encoder will through and error, to alert you of this behaviour."
   ]
  }
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