
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_display_object_visualization.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_display_object_visualization.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py:


===================================
Visualizations with Display Objects
===================================

.. currentmodule:: sklearn.metrics

In this example, we will construct display objects,
:class:`ConfusionMatrixDisplay`, :class:`RocCurveDisplay`, and
:class:`PrecisionRecallDisplay` directly from their respective metrics. This
is an alternative to using their corresponding plot functions when
a model's predictions are already computed or expensive to compute. Note that
this is advanced usage, and in general we recommend using their respective
plot functions.

.. GENERATED FROM PYTHON SOURCE LINES 19-26

Load Data and train model
-------------------------
For this example, we load a blood transfusion service center data set from
`OpenML <https://www.openml.org/d/1464>`. This is a binary classification
problem where the target is whether an individual donated blood. Then the
data is split into a train and test dataset and a logistic regression is
fitted with the train dataset.

.. GENERATED FROM PYTHON SOURCE LINES 26-38

.. code-block:: Python

    from sklearn.datasets import fetch_openml
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler

    X, y = fetch_openml(data_id=1464, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

    clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
    clf.fit(X_train, y_train)



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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-5bAKby/scikit-learn-1.4.2+dfsg/examples/miscellaneous/plot_display_object_visualization.py", line 32, in <module>
        X, y = fetch_openml(data_id=1464, return_X_y=True)
               ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-5bAKby/scikit-learn-1.4.2+dfsg/.pybuild/cpython3_3.13/build/sklearn/utils/_param_validation.py", line 213, in wrapper
        return func(*args, **kwargs)
      File "/build/scikit-learn-5bAKby/scikit-learn-1.4.2+dfsg/.pybuild/cpython3_3.13/build/sklearn/datasets/_openml.py", line 992, 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 39-44

Create :class:`ConfusionMatrixDisplay`
#############################################################################
 With the fitted model, we compute the predictions of the model on the test
 dataset. These predictions are used to compute the confusion matrix which
 is plotted with the :class:`ConfusionMatrixDisplay`

.. GENERATED FROM PYTHON SOURCE LINES 44-52

.. code-block:: Python

    from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix

    y_pred = clf.predict(X_test)
    cm = confusion_matrix(y_test, y_pred)

    cm_display = ConfusionMatrixDisplay(cm).plot()



.. GENERATED FROM PYTHON SOURCE LINES 53-58

Create :class:`RocCurveDisplay`
#############################################################################
 The roc curve requires either the probabilities or the non-thresholded
 decision values from the estimator. Since the logistic regression provides
 a decision function, we will use it to plot the roc curve:

.. GENERATED FROM PYTHON SOURCE LINES 58-65

.. code-block:: Python

    from sklearn.metrics import RocCurveDisplay, roc_curve

    y_score = clf.decision_function(X_test)

    fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1])
    roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()


.. GENERATED FROM PYTHON SOURCE LINES 66-70

Create :class:`PrecisionRecallDisplay`
#############################################################################
 Similarly, the precision recall curve can be plotted using `y_score` from
 the prevision sections.

.. GENERATED FROM PYTHON SOURCE LINES 70-75

.. code-block:: Python

    from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve

    prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1])
    pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()


.. GENERATED FROM PYTHON SOURCE LINES 76-82

Combining the display objects into a single plot
#############################################################################
 The display objects store the computed values that were passed as arguments.
 This allows for the visualizations to be easliy combined using matplotlib's
 API. In the following example, we place the displays next to each other in a
 row.

.. GENERATED FROM PYTHON SOURCE LINES 82-90

.. code-block:: Python


    import matplotlib.pyplot as plt

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))

    roc_display.plot(ax=ax1)
    pr_display.plot(ax=ax2)
    plt.show()


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

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


.. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_display_object_visualization.zip <plot_display_object_visualization.zip>`


.. only:: html

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

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