How-to-encode-ordinal-features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A common mistake to avoid when encoding ordinal features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook version of the blog post [A common mistake to avoid when encoding ordinal features](https://medium.com/@alexandr.nixon/a-common-mistake-to-avoid-when-encoding-ordinal-features-79e402796ab4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import category_encoders as ce\n",
    "from pylab import rcParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordinal features are a very common type of categorical data in which its different categories present a natural order. An example could be a feature indicating the temperature taking the categories cool, mild and hot. \n",
    "A common issue in these cases is choosing an appropriate way to encode these features. This is a very important aspect to take into account, which will determine the value that the feature will be adding to a model.\n",
    "\n",
    "A fairly common mistake, is to just assign a unique numerical value to each category of a feature, disregarding any order it may have. The motivation behind this post really comes from answering this question in stack overflow where, since I gave quite a detailed explanation of the issue, I thought it would be also worth writing a post about it here. The idea in this post is to explore and understand why this is not a good idea, and which other approaches we should be using instead.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mapping the inherent ordinality of a feature to a wrong scale will have a very negative impact on the performance of the model (that is, proportional to the relevance of the feature).\n",
    "\n",
    "As mentioned, ordinal features have a natural ordering, or to put it another way, they have an ordinal scale with a **ranking**. So if we want to preserve the features' value after encoding it, we want to make sure that it resembles their ranking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An intuitive way to think about it, is by the way a decision tree sets its boundaries. During training, a decision tree will learn the optimal features to set at each node, as well as an optimal threshold whereby unseen samples will follow a branch or another depending on these values.\n",
    "\n",
    "If we encode an ordinal feature using a simple `LabelEncoder` for instance, that could lead to a feature having say 1 representing warm, 2 which maybe would translate to hot, and a 0 which may translate to boiling. In such case, instead of having a higher degree of certainty in which path a sample should take due to a higher value, the optimal threshold just ends up being meaningless, and we lose its predictable power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though seeing why this is so will be more intuitive than just words (decision trees to the rescue :). Let's use the good ol' Play Tennis example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "        {'Hours_of_dedication': pd.Categorical(\n",
    "              values =  ['20-25', '20-25', '5-10', '5-10', '40-45', \n",
    "                         '0-5', '15-20', '20-25', '30-35', '5-10',\n",
    "                         '10-15', '45-50', '20-25', '40-45', '30-35'],\n",
    "              categories=['0-5', '5-10', '10-15', '15-20', '20-25', \n",
    "                          '25-30','30-35','40-45', '45-50']),\n",
    "\n",
    "         'Assignments_avg_grade': pd.Categorical(\n",
    "             values =  ['B', 'C', 'F', 'C', 'B', \n",
    "                        'D', 'C', 'A', 'B', 'B', \n",
    "                        'D', 'A', 'D', 'D', 'C'],\n",
    "             categories=['F', 'D', 'C', 'B','A']),\n",
    "\n",
    "         'Result': pd.Categorical(\n",
    "             values = ['Pass', 'Pass', 'Fail', 'Fail', 'Pass', \n",
    "                       'Fail', 'Fail','Pass','Pass', 'Fail', \n",
    "                       'Fail', 'Pass', 'Pass', 'Pass', 'Pass'], \n",
    "             categories=['Fail', 'Pass'])\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've defined all columns as `pd.Categorical`, having all categories start from the most *negative* value (that is, those that push the prediction towards No).\n",
    "\n",
    "The advantage of defining a categorical column as a pandas' categorical, is that we get to establish an *order* among its categories. This allows for much faster sorting based on the established order rather than lexical sorting. And it can also be used as a simple way to get codes for the different categories according to their order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the dataframe we'll be using looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Hours_of_dedication</th>\n",
       "      <th>Assignments_avg_grade</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-25</td>\n",
       "      <td>B</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20-25</td>\n",
       "      <td>C</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-10</td>\n",
       "      <td>F</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5-10</td>\n",
       "      <td>C</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40-45</td>\n",
       "      <td>B</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0-5</td>\n",
       "      <td>D</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15-20</td>\n",
       "      <td>C</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20-25</td>\n",
       "      <td>A</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30-35</td>\n",
       "      <td>B</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5-10</td>\n",
       "      <td>B</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10-15</td>\n",
       "      <td>D</td>\n",
       "      <td>Fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45-50</td>\n",
       "      <td>A</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20-25</td>\n",
       "      <td>D</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40-45</td>\n",
       "      <td>D</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30-35</td>\n",
       "      <td>C</td>\n",
       "      <td>Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours_of_dedication Assignments_avg_grade Result\n",
       "0                20-25                     B   Pass\n",
       "1                20-25                     C   Pass\n",
       "2                 5-10                     F   Fail\n",
       "3                 5-10                     C   Fail\n",
       "4                40-45                     B   Pass\n",
       "5                  0-5                     D   Fail\n",
       "6                15-20                     C   Fail\n",
       "7                20-25                     A   Pass\n",
       "8                30-35                     B   Pass\n",
       "9                 5-10                     B   Fail\n",
       "10               10-15                     D   Fail\n",
       "11               45-50                     A   Pass\n",
       "12               20-25                     D   Pass\n",
       "13               40-45                     D   Pass\n",
       "14               30-35                     C   Pass"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as mentioned, we can obtain the assigned codes to each category in each categorical column through the `pd.Series.cat.codes method`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Hours_of_dedication</th>\n",
       "      <th>Assignments_avg_grade</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Hours_of_dedication  Assignments_avg_grade  Result\n",
       "0                    4                      3       1\n",
       "1                    4                      2       1\n",
       "2                    1                      0       0\n",
       "3                    1                      2       0\n",
       "4                    7                      3       1\n",
       "5                    0                      1       0\n",
       "6                    3                      2       0\n",
       "7                    4                      4       1\n",
       "8                    6                      3       1\n",
       "9                    1                      3       0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.apply(lambda x: x.cat.codes)\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fit a `DecisionTreeClassifier`, and see how the tree has defined the splits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = tree.DecisionTreeClassifier()\n",
    "X = X.drop(['Result'],1)\n",
    "y = df.Result\n",
    "dt.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sklearn.tree` module provides a function to plot a fitted decision tree. Let's use it to see what the decision tree looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 10, 10\n",
    "g = tree.plot_tree(dt, \n",
    "                   feature_names = X.columns,\n",
    "                   class_names=['Failed', \"Pass\"],\n",
    "                   filled = True,\n",
    "                   label='all',\n",
    "                   rounded=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is that all?? Well… yes! I've actually set the features in such a way that there is this simple and obvious relation between the Hours of dedication feature, and whether the exam is passed or not, which makes it seem clear that the problem should be very easy to model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to do the same by directly encoding all features with an output we could obtain through a `LabelEncoder`, so disregarding the actual ordinality of the features, and just assigning a value based on the position in which they occur:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wrong = df.copy()\n",
    "df_wrong['Hours_of_dedication'].cat.set_categories(['0-5','40-45', '25-30', '10-15', '5-10',\n",
    "                                            '45-50','15-20', '20-25','30-35'], inplace=True)\n",
    "df_wrong['Assignments_avg_grade'].cat.set_categories(['A', 'C', 'F', 'D', 'B'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 14,18\n",
    "X_wrong = df_wrong.drop(['Result'],1).apply(lambda x: x.cat.codes)\n",
    "y = df_wrong.Result\n",
    "\n",
    "dt_wrong = tree.DecisionTreeClassifier()\n",
    "dt_wrong.fit(X_wrong, y)\n",
    "\n",
    "t = tree.plot_tree(dt_wrong, \n",
    "                   feature_names = X_wrong.columns,\n",
    "                   class_names=[\"Fail\", \"Pass\"],\n",
    "                   filled = True,\n",
    "                   label='all',\n",
    "                   rounded=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the tree structure is way more complex than necessary for the simple problem we're trying to model. In order for the tree to correctly predict all training samples the tree has expanded until a depth of `4`, when a single node should be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_wrong.get_depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to summarise, preserving the ordinality of the features once encoding them is crucial, otherwise as made clear with this example we'll lose all their predictable power and just add *noise* to our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How should Ordinal Features be encoded?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, we've seen a simple way to encode ordinal features using `pd.Categorical`. But what we'll be interested in most of the times is not only to transform a given set of features, but also to replicate the applied transformations on unseen data.\n",
    "\n",
    "For that we have the `OrdinalEncoder` from [Category Encoders](https://contrib.scikit-learn.org/categorical-encoding/). This class enables us to:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Define a dictionary mapping categories to codes\n",
    "* *Fit* the encoder with the mapping dictionary and *transform* on unseen data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how to use it with an example with several features. The `OrdinalEncoder` receives a mapping parameter expecting a list of dictionaries, each of which containing the keys `col` and `mapping`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "        {'Outlook': pd.Categorical(\n",
    "                  values =  ['Sunny', 'Sunny', 'Overcast', 'Rainy', 'Rainy', \n",
    "                             'Rainy', 'Overcast', 'Sunny', 'Sunny', 'Rainy', \n",
    "                             'Sunny', 'Overcast', 'Overcast', 'Rainy'],\n",
    "                  categories=['Rainy', 'Overcast', 'Sunny']),\n",
    "         \n",
    "         'Temperature': pd.Categorical(\n",
    "                 values =  ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', \n",
    "                            'Cool', 'Cool', 'Mild', 'Cool', 'Mild', \n",
    "                            'Mild', 'Mild', 'Hot', 'Mild'],\n",
    "                 categories=['Cool', 'Mild', 'Hot']),\n",
    "         \n",
    "         'Humidity': pd.Categorical(\n",
    "                 values = ['High', 'High', 'High', 'High', 'Normal',\n",
    "                           'Normal', 'Normal', 'High', 'Normal', 'Normal', \n",
    "                           'Normal', 'High', 'Normal', 'High'],\n",
    "                 categories=['Normal', 'High']),\n",
    "         \n",
    "         'Windy': pd.Categorical(\n",
    "                 values = ['False', 'True', 'False', 'False', 'False',\n",
    "                           'True', 'True', 'False', 'False', 'False', \n",
    "                           'True', 'True', 'False', 'True'],\n",
    "                 categories=['False', 'True']),\n",
    "         \n",
    "         'PlayTennis': pd.Categorical(\n",
    "                 values = ['No', 'No', 'Yes', 'Yes', 'Yes', \n",
    "                           'No', 'Yes','No','Yes', 'Yes', \n",
    "                           'Yes', 'Yes', 'Yes','No'],\n",
    "                 categories=['No', 'Yes'])\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_cols_mapping = [{\n",
    "    \"col\": \"Outlook\",\n",
    "    \"mapping\": {\n",
    "        'Rainy': 0,\n",
    "        'Overcast': 1,\n",
    "        'Sunny': 2\n",
    "    }}, {\n",
    "    \"col\": \"Temperature\",\n",
    "    \"mapping\": {\n",
    "        'Cool': 0,\n",
    "        'Mild': 1,\n",
    "        'Hot': 2,\n",
    "    }}, {        \n",
    "    \"col\": \"Humidity\",\n",
    "    \"mapping\": {\n",
    "        'Normal': 0,\n",
    "        'High': 1\n",
    "    }}, {\n",
    "    \"col\": \"Windy\",\n",
    "    \"mapping\": {\n",
    "        'False': 0,\n",
    "        'True': 1,\n",
    "    }}, {\n",
    "    \"col\": \"PlayTennis\",\n",
    "    \"mapping\": {\n",
    "        'No': 0,\n",
    "        'Yes': 1,\n",
    "    }}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can create a new instance of the encoder with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Outlook</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>PlayTennis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Outlook Temperature Humidity  Windy PlayTennis\n",
       "0      Sunny         Hot     High  False         No\n",
       "1      Sunny         Hot     High   True         No\n",
       "2   Overcast         Hot     High  False        Yes\n",
       "3      Rainy        Mild     High  False        Yes\n",
       "4      Rainy        Cool   Normal  False        Yes\n",
       "5      Rainy        Cool   Normal   True         No\n",
       "6   Overcast        Cool   Normal   True        Yes\n",
       "7      Sunny        Mild     High  False         No\n",
       "8      Sunny        Cool   Normal  False        Yes\n",
       "9      Rainy        Mild   Normal  False        Yes\n",
       "10     Sunny        Mild   Normal   True        Yes\n",
       "11  Overcast        Mild     High   True        Yes\n",
       "12  Overcast         Hot   Normal  False        Yes\n",
       "13     Rainy        Mild     High   True         No"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.OrdinalEncoder(mapping = ordinal_cols_mapping, \n",
    "                         return_df = True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So by transforming on the same dataframe as above, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Outlook</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>PlayTennis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlook  Temperature  Humidity  Windy  PlayTennis\n",
       "0         2            2         1      0           0\n",
       "1         2            2         1      1           0\n",
       "2         1            2         1      0           1\n",
       "3         0            1         1      0           1\n",
       "4         0            0         0      0           1\n",
       "5         0            0         0      1           0\n",
       "6         1            0         0      1           1\n",
       "7         2            1         1      0           0\n",
       "8         2            0         0      0           1\n",
       "9         0            1         0      0           1\n",
       "10        2            1         0      1           1\n",
       "11        1            1         1      1           1\n",
       "12        1            2         0      0           1\n",
       "13        0            1         1      1           0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = encoder.fit_transform(df)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to summarise, preserving the ordinality of the features once encoding them is crucial, otherwise as made clear with this example we'll lose all their predictive power and just add noise to our model."
   ]
  }
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