ch02-categorical-encoding/Recipe-08-Grouping-rare-categories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rare category grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# to split the datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# for encoding using feature-engine\n",
    "from feature_engine.encoding import RareLabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15  target\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  202.0    0       1\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g   43.0  560       1\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  280.0  824       1\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  100.0    3       1\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  120.0    0       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the data set\n",
    "\n",
    "data = pd.read_csv(\"credit_approval_uci.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((483, 15), (207, 15))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(labels=[\"target\"], axis=1),  # predictors\n",
    "    data[\"target\"],  # target\n",
    "    test_size=0.3,  # percentage of observations in test set\n",
    "    random_state=0,  # seed to ensure reproducibility\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping categories with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v          0.573499\n",
       "h          0.209110\n",
       "ff         0.084886\n",
       "bb         0.080745\n",
       "z          0.014493\n",
       "dd         0.010352\n",
       "j          0.010352\n",
       "Missing    0.008282\n",
       "n          0.006211\n",
       "o          0.002070\n",
       "Name: A7, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the frequency of labels in A7\n",
    "\n",
    "freqs = X_train[\"A7\"].value_counts(normalize=True)\n",
    "\n",
    "freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['v', 'h', 'ff', 'bb']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# frequent categories in A7\n",
    "\n",
    "frequent_cat = [x for x in freqs.loc[freqs > 0.05].index.values]\n",
    "\n",
    "frequent_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dd', 'z', 'j', 'Missing', 'n', 'o']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rare categories in A7\n",
    "\n",
    "[x for x in X_train[\"A7\"].unique() if x not in frequent_cat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's group rare labels into a new category called rare\n",
    "\n",
    "X_train[\"A7\"] = np.where(X_train[\"A7\"].isin(frequent_cat), X_train[\"A7\"], \"Rare\")\n",
    "\n",
    "X_test[\"A7\"] = np.where(X_test[\"A7\"].isin(frequent_cat), X_test[\"A7\"], \"Rare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v       0.573499\n",
       "h       0.209110\n",
       "ff      0.084886\n",
       "bb      0.080745\n",
       "Rare    0.051760\n",
       "Name: A7, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the results\n",
    "\n",
    "X_train[\"A7\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping categories with Feature-engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's separate into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(labels=[\"target\"], axis=1),  # predictors\n",
    "    data[\"target\"],  # target\n",
    "    test_size=0.3,  # percentage of observations in test set\n",
    "    random_state=0,  # seed to ensure reproducibility\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the encoder to group categories present\n",
    "# in less than 5% of the observations.\n",
    "\n",
    "# Group only categories in variables with more\n",
    "# than 4 unique categories\n",
    "\n",
    "rare_encoder = RareLabelEncoder(tol=0.05, n_categories=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A1 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A4 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A5 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A9 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A10 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A12 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n",
      "/home/bjpcjp/.local/lib/python3.10/site-packages/feature_engine/encoding/rare_label.py:199: UserWarning: The number of unique categories for variable A13 is less than that indicated in n_categories. Thus, all categories will be considered frequent\n",
      "  warnings.warn(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "RareLabelEncoder(n_categories=4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's fit the encoder to the train set\n",
    "\n",
    "rare_encoder.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1', 'A4', 'A5', 'A6', 'A7', 'A9', 'A10', 'A12', 'A13']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_encoder.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A1': array(['a', 'b', 'Missing'], dtype=object),\n",
       " 'A4': array(['u', 'y', 'Missing', 'l'], dtype=object),\n",
       " 'A5': array(['g', 'p', 'Missing', 'gg'], dtype=object),\n",
       " 'A6': Index(['c', 'q', 'w', 'i', 'ff', 'k', 'aa', 'cc', 'm'], dtype='object'),\n",
       " 'A7': Index(['v', 'h', 'ff', 'bb'], dtype='object'),\n",
       " 'A9': array(['t', 'f'], dtype=object),\n",
       " 'A10': array(['t', 'f'], dtype=object),\n",
       " 'A12': array(['t', 'f'], dtype=object),\n",
       " 'A13': array(['g', 's', 'p'], dtype=object)}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the encoder_dict_ we can observe the number of\n",
    "# observations per category for each variable\n",
    "\n",
    "rare_encoder.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's transform the train and test sets\n",
    "\n",
    "X_train_enc = rare_encoder.transform(X_train)\n",
    "X_test_enc = rare_encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
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       "      <th>A5</th>\n",
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       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
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       "      <th>A15</th>\n",
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       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>a</td>\n",
       "      <td>46.08</td>\n",
       "      <td>3.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.375</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>8</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>396.0</td>\n",
       "      <td>4159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>a</td>\n",
       "      <td>15.92</td>\n",
       "      <td>2.875</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>v</td>\n",
       "      <td>0.085</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>b</td>\n",
       "      <td>36.33</td>\n",
       "      <td>2.125</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>0.085</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>b</td>\n",
       "      <td>22.17</td>\n",
       "      <td>0.585</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>0.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>b</td>\n",
       "      <td>57.83</td>\n",
       "      <td>7.040</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>14.000</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "    A1     A2     A3 A4 A5  A6  A7      A8 A9 A10  A11 A12 A13    A14   A15\n",
       "596  a  46.08  3.000  u  g   c   v   2.375  t   t    8   t   g  396.0  4159\n",
       "303  a  15.92  2.875  u  g   q   v   0.085  f   f    0   f   g  120.0     0\n",
       "204  b  36.33  2.125  y  p   w   v   0.085  t   t    1   f   g   50.0  1187\n",
       "351  b  22.17  0.585  y  p  ff  ff   0.000  f   f    0   f   g  100.0     0\n",
       "118  b  57.83  7.040  u  g   m   v  14.000  t   t    6   t   g  360.0  1332"
      ]
     },
     "execution_count": 15,
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