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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_miscellaneous_plot_pipeline_display.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py:


=================================================================
Displaying Pipelines
=================================================================

The default configuration for displaying a pipeline in a Jupyter Notebook is
`'diagram'` where `set_config(display='diagram')`. To deactivate HTML representation,
use `set_config(display='text')`.

To see more detailed steps in the visualization of the pipeline, click on the
steps in the pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 15-21

Displaying a Pipeline with a Preprocessing Step and Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with a preprocessing
 step, :class:`~sklearn.preprocessing.StandardScaler`, and classifier,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 21-33

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LogisticRegression
    from sklearn import set_config

    steps = [
        ("preprocessing", StandardScaler()),
        ("classifier", LogisticRegression()),
    ]
    pipe = Pipeline(steps)


.. GENERATED FROM PYTHON SOURCE LINES 34-35

To visualize the diagram, the default is `display='diagram'`.

.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: default

    set_config(display="diagram")
    pipe  # click on the diagram below to see the details of each step


.. GENERATED FROM PYTHON SOURCE LINES 39-40

To view the text pipeline, change to `display='text'`.

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default

    set_config(display="text")
    pipe


.. GENERATED FROM PYTHON SOURCE LINES 44-45

Put back the default display

.. GENERATED FROM PYTHON SOURCE LINES 45-47

.. code-block:: default

    set_config(display="diagram")


.. GENERATED FROM PYTHON SOURCE LINES 48-55

Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with multiple
 preprocessing steps, :class:`~sklearn.preprocessing.PolynomialFeatures` and
 :class:`~sklearn.preprocessing.StandardScaler`, and a classifier step,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 55-68

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler, PolynomialFeatures
    from sklearn.linear_model import LogisticRegression

    steps = [
        ("standard_scaler", StandardScaler()),
        ("polynomial", PolynomialFeatures(degree=3)),
        ("classifier", LogisticRegression(C=2.0)),
    ]
    pipe = Pipeline(steps)
    pipe  # click on the diagram below to see the details of each step


.. GENERATED FROM PYTHON SOURCE LINES 69-75

Displaying a Pipeline and Dimensionality Reduction and Classifier
###############################################################################
 This section constructs a :class:`~sklearn.pipeline.Pipeline` with a
 dimensionality reduction step, :class:`~sklearn.decomposition.PCA`,
 a classifier, :class:`~sklearn.svm.SVC`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 75-84

.. code-block:: default


    from sklearn.pipeline import Pipeline
    from sklearn.svm import SVC
    from sklearn.decomposition import PCA

    steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))]
    pipe = Pipeline(steps)
    pipe  # click on the diagram below to see the details of each step


.. GENERATED FROM PYTHON SOURCE LINES 85-91

Displaying a Complex Pipeline Chaining a Column Transformer
###############################################################################
 This section constructs a complex :class:`~sklearn.pipeline.Pipeline` with a
 :class:`~sklearn.compose.ColumnTransformer` and a classifier,
 :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 91-127

.. code-block:: default


    import numpy as np
    from sklearn.pipeline import make_pipeline
    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    from sklearn.linear_model import LogisticRegression

    numeric_preprocessor = Pipeline(
        steps=[
            ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
            ("scaler", StandardScaler()),
        ]
    )

    categorical_preprocessor = Pipeline(
        steps=[
            (
                "imputation_constant",
                SimpleImputer(fill_value="missing", strategy="constant"),
            ),
            ("onehot", OneHotEncoder(handle_unknown="ignore")),
        ]
    )

    preprocessor = ColumnTransformer(
        [
            ("categorical", categorical_preprocessor, ["state", "gender"]),
            ("numerical", numeric_preprocessor, ["age", "weight"]),
        ]
    )

    pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))
    pipe  # click on the diagram below to see the details of each step


.. GENERATED FROM PYTHON SOURCE LINES 128-134

Displaying a Grid Search over a Pipeline with a Classifier
###############################################################################
 This section constructs a :class:`~sklearn.model_selection.GridSearchCV`
 over a :class:`~sklearn.pipeline.Pipeline` with
 :class:`~sklearn.ensemble.RandomForestClassifier` and displays its visual
 representation.

.. GENERATED FROM PYTHON SOURCE LINES 134-181

.. code-block:: default


    import numpy as np
    from sklearn.pipeline import make_pipeline
    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import GridSearchCV

    numeric_preprocessor = Pipeline(
        steps=[
            ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
            ("scaler", StandardScaler()),
        ]
    )

    categorical_preprocessor = Pipeline(
        steps=[
            (
                "imputation_constant",
                SimpleImputer(fill_value="missing", strategy="constant"),
            ),
            ("onehot", OneHotEncoder(handle_unknown="ignore")),
        ]
    )

    preprocessor = ColumnTransformer(
        [
            ("categorical", categorical_preprocessor, ["state", "gender"]),
            ("numerical", numeric_preprocessor, ["age", "weight"]),
        ]
    )

    pipe = Pipeline(
        steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())]
    )

    param_grid = {
        "classifier__n_estimators": [200, 500],
        "classifier__max_features": ["auto", "sqrt", "log2"],
        "classifier__max_depth": [4, 5, 6, 7, 8],
        "classifier__criterion": ["gini", "entropy"],
    }

    grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)
    grid_search  # click on the diagram below to see the details of each step


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_pipeline_display.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/miscellaneous/plot_pipeline_display.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. only:: html

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

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