
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/compose/plot_column_transformer_mixed_types.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_compose_plot_column_transformer_mixed_types.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py:


===================================
Column Transformer with Mixed Types
===================================

.. currentmodule:: sklearn

This example illustrates how to apply different preprocessing and feature
extraction pipelines to different subsets of features, using
:class:`~compose.ColumnTransformer`. This is particularly handy for the
case of datasets that contain heterogeneous data types, since we may want to
scale the numeric features and one-hot encode the categorical ones.

In this example, the numeric data is standard-scaled after mean-imputation. The
categorical data is one-hot encoded via ``OneHotEncoder``, which
creates a new category for missing values.

In addition, we show two different ways to dispatch the columns to the
particular pre-processor: by column names and by column data types.

Finally, the preprocessing pipeline is integrated in a full prediction pipeline
using :class:`~pipeline.Pipeline`, together with a simple classification
model.

.. GENERATED FROM PYTHON SOURCE LINES 26-31

.. code-block:: default


    # Author: Pedro Morales <part.morales@gmail.com>
    #
    # License: BSD 3 clause








.. GENERATED FROM PYTHON SOURCE LINES 32-44

.. code-block:: default

    import numpy as np

    from sklearn.compose import ColumnTransformer
    from sklearn.datasets import fetch_openml
    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.preprocessing import StandardScaler, OneHotEncoder
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split, GridSearchCV

    np.random.seed(0)








.. GENERATED FROM PYTHON SOURCE LINES 45-46

Load data from https://www.openml.org/d/40945

.. GENERATED FROM PYTHON SOURCE LINES 46-52

.. code-block:: default

    X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)

    # Alternatively X and y can be obtained directly from the frame attribute:
    # X = titanic.frame.drop('survived', axis=1)
    # y = titanic.frame['survived']



.. rst-class:: sphx-glr-script-out

.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-GiwEwR/scikit-learn-1.1.2+dfsg/examples/compose/plot_column_transformer_mixed_types.py", line 46, in <module>
        X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-GiwEwR/scikit-learn-1.1.2+dfsg/.pybuild/cpython3_3.11/build/sklearn/datasets/_openml.py", line 726, in fetch_openml
        raise TimeoutError('Debian Policy Section 4.9 prohibits network access during build')
    TimeoutError: Debian Policy Section 4.9 prohibits network access during build




.. GENERATED FROM PYTHON SOURCE LINES 53-71

Use ``ColumnTransformer`` by selecting column by names

We will train our classifier with the following features:

Numeric Features:

* ``age``: float;
* ``fare``: float.

Categorical Features:

* ``embarked``: categories encoded as strings ``{'C', 'S', 'Q'}``;
* ``sex``: categories encoded as strings ``{'female', 'male'}``;
* ``pclass``: ordinal integers ``{1, 2, 3}``.

We create the preprocessing pipelines for both numeric and categorical data.
Note that ``pclass`` could either be treated as a categorical or numeric
feature.

.. GENERATED FROM PYTHON SOURCE LINES 71-87

.. code-block:: default


    numeric_features = ["age", "fare"]
    numeric_transformer = Pipeline(
        steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
    )

    categorical_features = ["embarked", "sex", "pclass"]
    categorical_transformer = OneHotEncoder(handle_unknown="ignore")

    preprocessor = ColumnTransformer(
        transformers=[
            ("num", numeric_transformer, numeric_features),
            ("cat", categorical_transformer, categorical_features),
        ]
    )


.. GENERATED FROM PYTHON SOURCE LINES 88-90

Append classifier to preprocessing pipeline.
Now we have a full prediction pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 90-99

.. code-block:: default

    clf = Pipeline(
        steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())]
    )

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

    clf.fit(X_train, y_train)
    print("model score: %.3f" % clf.score(X_test, y_test))


.. GENERATED FROM PYTHON SOURCE LINES 100-104

HTML representation of ``Pipeline`` (display diagram)

When the ``Pipeline`` is printed out in a jupyter notebook an HTML
representation of the estimator is displayed:

.. GENERATED FROM PYTHON SOURCE LINES 104-106

.. code-block:: default

    clf


.. GENERATED FROM PYTHON SOURCE LINES 107-115

Use ``ColumnTransformer`` by selecting column by data types

When dealing with a cleaned dataset, the preprocessing can be automatic by
using the data types of the column to decide whether to treat a column as a
numerical or categorical feature.
:func:`sklearn.compose.make_column_selector` gives this possibility.
First, let's only select a subset of columns to simplify our
example.

.. GENERATED FROM PYTHON SOURCE LINES 115-119

.. code-block:: default


    subset_feature = ["embarked", "sex", "pclass", "age", "fare"]
    X_train, X_test = X_train[subset_feature], X_test[subset_feature]


.. GENERATED FROM PYTHON SOURCE LINES 120-121

Then, we introspect the information regarding each column data type.

.. GENERATED FROM PYTHON SOURCE LINES 121-124

.. code-block:: default


    X_train.info()


.. GENERATED FROM PYTHON SOURCE LINES 125-130

We can observe that the `embarked` and `sex` columns were tagged as
`category` columns when loading the data with ``fetch_openml``. Therefore, we
can use this information to dispatch the categorical columns to the
``categorical_transformer`` and the remaining columns to the
``numerical_transformer``.

.. GENERATED FROM PYTHON SOURCE LINES 132-137

.. note:: In practice, you will have to handle yourself the column data type.
   If you want some columns to be considered as `category`, you will have to
   convert them into categorical columns. If you are using pandas, you can
   refer to their documentation regarding `Categorical data
   <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`_.

.. GENERATED FROM PYTHON SOURCE LINES 137-155

.. code-block:: default


    from sklearn.compose import make_column_selector as selector

    preprocessor = ColumnTransformer(
        transformers=[
            ("num", numeric_transformer, selector(dtype_exclude="category")),
            ("cat", categorical_transformer, selector(dtype_include="category")),
        ]
    )
    clf = Pipeline(
        steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())]
    )


    clf.fit(X_train, y_train)
    print("model score: %.3f" % clf.score(X_test, y_test))
    clf


.. GENERATED FROM PYTHON SOURCE LINES 156-159

The resulting score is not exactly the same as the one from the previous
pipeline because the dtype-based selector treats the ``pclass`` column as
a numeric feature instead of a categorical feature as previously:

.. GENERATED FROM PYTHON SOURCE LINES 159-162

.. code-block:: default


    selector(dtype_exclude="category")(X_train)


.. GENERATED FROM PYTHON SOURCE LINES 163-166

.. code-block:: default


    selector(dtype_include="category")(X_train)


.. GENERATED FROM PYTHON SOURCE LINES 167-175

Using the prediction pipeline in a grid search

Grid search can also be performed on the different preprocessing steps
defined in the ``ColumnTransformer`` object, together with the classifier's
hyperparameters as part of the ``Pipeline``.
We will search for both the imputer strategy of the numeric preprocessing
and the regularization parameter of the logistic regression using
:class:`~sklearn.model_selection.GridSearchCV`.

.. GENERATED FROM PYTHON SOURCE LINES 175-184

.. code-block:: default


    param_grid = {
        "preprocessor__num__imputer__strategy": ["mean", "median"],
        "classifier__C": [0.1, 1.0, 10, 100],
    }

    grid_search = GridSearchCV(clf, param_grid, cv=10)
    grid_search


.. GENERATED FROM PYTHON SOURCE LINES 185-188

Calling 'fit' triggers the cross-validated search for the best
hyper-parameters combination:


.. GENERATED FROM PYTHON SOURCE LINES 188-193

.. code-block:: default

    grid_search.fit(X_train, y_train)

    print("Best params:")
    print(grid_search.best_params_)


.. GENERATED FROM PYTHON SOURCE LINES 194-195

The internal cross-validation scores obtained by those parameters is:

.. GENERATED FROM PYTHON SOURCE LINES 195-197

.. code-block:: default

    print(f"Internal CV score: {grid_search.best_score_:.3f}")


.. GENERATED FROM PYTHON SOURCE LINES 198-199

We can also introspect the top grid search results as a pandas dataframe:

.. GENERATED FROM PYTHON SOURCE LINES 199-212

.. code-block:: default

    import pandas as pd

    cv_results = pd.DataFrame(grid_search.cv_results_)
    cv_results = cv_results.sort_values("mean_test_score", ascending=False)
    cv_results[
        [
            "mean_test_score",
            "std_test_score",
            "param_preprocessor__num__imputer__strategy",
            "param_classifier__C",
        ]
    ].head(5)


.. GENERATED FROM PYTHON SOURCE LINES 213-217

The best hyper-parameters have be used to re-fit a final model on the full
training set. We can evaluate that final model on held out test data that was
not used for hyperparameter tuning.


.. GENERATED FROM PYTHON SOURCE LINES 217-223

.. code-block:: default

    print(
        (
            "best logistic regression from grid search: %.3f"
            % grid_search.score(X_test, y_test)
        )
    )


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.002 seconds)


.. _sphx_glr_download_auto_examples_compose_plot_column_transformer_mixed_types.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_column_transformer_mixed_types.py <plot_column_transformer_mixed_types.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_column_transformer_mixed_types.ipynb <plot_column_transformer_mixed_types.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
