ColumnTranformer_FunctionalTransformer_ML_Pipeline.ipynb

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  "metadata": {
    "colab": {
      "name": "ColumnTranformer_FunctionalTransformer_ML_Pipeline.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNVO1FJ/f/tzCgNQvxXXeSE"
    },
    "kernelspec": {
      "name": "python3",
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOls_jEU38sE"
      },
      "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"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H18mxoed4Y4m"
      },
      "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)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6iFwRdT4a8s",
        "outputId": "17331c8f-2ff8-49fa-c9ec-104a3c53d404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "X.head(5)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "\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  ...                        home.dest\n",
              "0     1.0  ...                     St Louis, MO\n",
              "1     1.0  ...  Montreal, PQ / Chesterville, ON\n",
              "2     1.0  ...  Montreal, PQ / Chesterville, ON\n",
              "3     1.0  ...  Montreal, PQ / Chesterville, ON\n",
              "4     1.0  ...  Montreal, PQ / Chesterville, ON\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7amCbUw46rO",
        "outputId": "8dc16585-1a94-47e7-8b89-3cd5b1c8b7c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1309 entries, 0 to 1308\n",
            "Data columns (total 13 columns):\n",
            " #   Column     Non-Null Count  Dtype   \n",
            "---  ------     --------------  -----   \n",
            " 0   pclass     1309 non-null   float64 \n",
            " 1   name       1309 non-null   object  \n",
            " 2   sex        1309 non-null   category\n",
            " 3   age        1046 non-null   float64 \n",
            " 4   sibsp      1309 non-null   float64 \n",
            " 5   parch      1309 non-null   float64 \n",
            " 6   ticket     1309 non-null   object  \n",
            " 7   fare       1308 non-null   float64 \n",
            " 8   cabin      295 non-null    object  \n",
            " 9   embarked   1307 non-null   category\n",
            " 10  boat       486 non-null    object  \n",
            " 11  body       121 non-null    float64 \n",
            " 12  home.dest  745 non-null    object  \n",
            "dtypes: category(2), float64(6), object(5)\n",
            "memory usage: 115.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-EJ_tXE48Vk",
        "outputId": "0f9702e5-fc02-4416-bb52-589cec27494e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     1309\n",
              "unique       2\n",
              "top          0\n",
              "freq       809\n",
              "Name: survived, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tmth5gOk4nDR"
      },
      "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": "code",
      "metadata": {
        "id": "KjbUB4Tp4eb0"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTr8xstI4qov"
      },
      "source": [
        "Select cat and Continuous Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rWXJean4iKv"
      },
      "source": [
        "numeric_features = ['age', 'fare']\n",
        "categorical_features = ['embarked', 'sex', 'pclass']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2x88OyCX41HO"
      },
      "source": [
        "Create a pipeline of both Continuous and Categorical Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ln1jPng44tMK"
      },
      "source": [
        "cnts_pipeline = Pipeline([\n",
        "    ('impute', SimpleImputer(strategy='mean')),\n",
        "    ('scale', StandardScaler())\n",
        "])\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pb1t58BQ5Q8L"
      },
      "source": [
        "categ_pipeline = Pipeline([\n",
        "    ('impute', SimpleImputer(strategy='most_frequent')),\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO-NhY9e5cUK"
      },
      "source": [
        "#Apply Column TRansformers\n",
        "List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.<br>\n",
        "\n",
        "\n",
        "ColumnTransformer [('name', pipeline_name, features)]¶"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPgMyWmT5Thm"
      },
      "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"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bKLZ5o457-e"
      },
      "source": [
        "#Apply Fit_transform(Data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nt7HLJ651eB"
      },
      "source": [
        "X_train_processed = preprocess_pipeline.fit_transform(X)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xrN4Yno6L55"
      },
      "source": [
        "#Functionaltransformer\n",
        "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",
      "metadata": {
        "id": "JVDpeOLD6KOW"
      },
      "source": [
        "from sklearn.preprocessing import FunctionTransformer"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDIfmtHy6P4f"
      },
      "source": [
        "cnts_pipeline = Pipeline([\n",
        "    ('impute', SimpleImputer(strategy='mean')),\n",
        "    ('log', FunctionTransformer(np.log1p))   \n",
        "])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lmb5WS5d6Uvf"
      },
      "source": [
        "preprocess_pipeline = ColumnTransformer([\n",
        "    ('continuous', cnts_pipeline, numeric_features)\n",
        "    ])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b62vxG376XRN"
      },
      "source": [
        "X_train_processed = preprocess_pipeline.fit_transform(X)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LakpDLa6jGB",
        "outputId": "d146a903-4d19-4a2b-9880-fd228a474207",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X_train_processed"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    }
  ]
}