.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_preprocessing_plot_function_transformer.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_preprocessing_plot_function_transformer.py:


=========================================================
Using FunctionTransformer to select columns
=========================================================

Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you can use the FunctionTransformer to select all but the first column of the
PCA transformed data.



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/preprocessing/images/sphx_glr_plot_function_transformer_001.png
          :alt: plot function transformer
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/preprocessing/images/sphx_glr_plot_function_transformer_002.png
          :alt: plot function transformer
          :class: sphx-glr-multi-img






.. code-block:: default

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.model_selection import train_test_split
    from sklearn.decomposition import PCA
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import FunctionTransformer


    def _generate_vector(shift=0.5, noise=15):
        return np.arange(1000) + (np.random.rand(1000) - shift) * noise


    def generate_dataset():
        """
        This dataset is two lines with a slope ~ 1, where one has
        a y offset of ~100
        """
        return np.vstack((
            np.vstack((
                _generate_vector(),
                _generate_vector() + 100,
            )).T,
            np.vstack((
                _generate_vector(),
                _generate_vector(),
            )).T,
        )), np.hstack((np.zeros(1000), np.ones(1000)))


    def all_but_first_column(X):
        return X[:, 1:]


    def drop_first_component(X, y):
        """
        Create a pipeline with PCA and the column selector and use it to
        transform the dataset.
        """
        pipeline = make_pipeline(
            PCA(), FunctionTransformer(all_but_first_column),
        )
        X_train, X_test, y_train, y_test = train_test_split(X, y)
        pipeline.fit(X_train, y_train)
        return pipeline.transform(X_test), y_test


    if __name__ == '__main__':
        X, y = generate_dataset()
        lw = 0
        plt.figure()
        plt.scatter(X[:, 0], X[:, 1], c=y, lw=lw)
        plt.figure()
        X_transformed, y_transformed = drop_first_component(*generate_dataset())
        plt.scatter(
            X_transformed[:, 0],
            np.zeros(len(X_transformed)),
            c=y_transformed,
            lw=lw,
            s=60
        )
        plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.117 seconds)


.. _sphx_glr_download_auto_examples_preprocessing_plot_function_transformer.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_function_transformer.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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