ColumnTransformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data from https://www.openml.org/d/40945\n",
    "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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",
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       "\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>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass                                             name     sex      age  \\\n",
       "0     1.0                    Allen, Miss. Elisabeth Walton  female  29.0000   \n",
       "1     1.0                   Allison, Master. Hudson Trevor    male   0.9167   \n",
       "2     1.0                     Allison, Miss. Helen Loraine  female   2.0000   \n",
       "3     1.0             Allison, Mr. Hudson Joshua Creighton    male  30.0000   \n",
       "4     1.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.0000   \n",
       "\n",
       "   sibsp  parch  ticket      fare    cabin embarked  boat   body  \\\n",
       "0    0.0    0.0   24160  211.3375       B5        S     2    NaN   \n",
       "1    1.0    2.0  113781  151.5500  C22 C26        S    11    NaN   \n",
       "2    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
       "3    1.0    2.0  113781  151.5500  C22 C26        S  None  135.0   \n",
       "4    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column Tranformation\n",
    "\n",
    "##### This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select cat and Continuous Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['age', 'fare']\n",
    "categorical_features = ['embarked', 'sex', 'pclass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a pipeline of both Continuous and Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnts_pipeline = Pipeline([\n",
    "    ('impute', SimpleImputer(strategy='mean')),\n",
    "    ('scale', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "categ_pipeline = Pipeline([\n",
    "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Apply Column TRansformers\n",
    "#### List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.\n",
    "\n",
    "#### ColumnTransformer [('name', pipeline_name, features)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_pipeline = ColumnTransformer([\n",
    "    ('continuous', cnts_pipeline, numeric_features),\n",
    "    ('cat', categ_pipeline, categorical_features)\n",
    "    ])  ##remainder is used to get all the columns irrespective of transormation happened or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply Fit_transform(Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_processed = preprocess_pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.06842022,  3.4424802 ,  0.        , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-2.24909235,  2.28647622,  0.        , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-2.16497396,  2.28647622,  0.        , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-0.26254556, -0.50407824,  1.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-0.22372049, -0.50407824,  1.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-0.06842022, -0.49151035,  0.        , ...,  0.        ,\n",
       "         0.        ,  1.        ]])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functionaltransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### applies a function to a column which isn't technically a transformer is common, so Scikit-learn introduced a FunctionTransformer. A FunctionTransformer takes a function (such as np.log) and makes a transformer that does nothing when fit is called, but calls the function when transform is called."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "cnts_pipeline = Pipeline([\n",
    "    ('impute', SimpleImputer(strategy='mean')),\n",
    "    ('log', FunctionTransformer(np.log1p))\n",
    "     \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_pipeline = ColumnTransformer([\n",
    "    ('continuous', cnts_pipeline, numeric_features)\n",
    "    ]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_processed = preprocess_pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.40119738, 5.35817699],\n",
       "       [0.65060496, 5.02749241],\n",
       "       [1.09861229, 5.02749241],\n",
       "       ...,\n",
       "       [3.314186  , 2.1071783 ],\n",
       "       [3.33220451, 2.1071783 ],\n",
       "       [3.40119738, 2.18323834]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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