.. 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

        Click :ref:`here <sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.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_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 16-18

.. code-block:: default

    print(__doc__)








.. 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 wtih the train dataset.

.. GENERATED FROM PYTHON SOURCE LINES 26-38

.. code-block:: default

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

    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)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-b83547b9-b57e-44d1-913e-08fd994fead6 {color: black;background-color: white;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 pre{padding: 0;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-toggleable {background-color: white;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-item {z-index: 1;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-parallel-item:only-child::after {width: 0;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b83547b9-b57e-44d1-913e-08fd994fead6 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-b83547b9-b57e-44d1-913e-08fd994fead6" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="668ff78b-d10b-4d3f-ad5f-96fe1578eb2b" type="checkbox" ><label class="sk-toggleable__label" for="668ff78b-d10b-4d3f-ad5f-96fe1578eb2b">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('logisticregression', LogisticRegression(random_state=0))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="bf411e64-88f6-49a3-a37b-ae113e2247be" type="checkbox" ><label class="sk-toggleable__label" for="bf411e64-88f6-49a3-a37b-ae113e2247be">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0d3b23d8-9838-4cd1-9535-85064420dca4" type="checkbox" ><label class="sk-toggleable__label" for="0d3b23d8-9838-4cd1-9535-85064420dca4">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. 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 confustion matrix which
 is plotted with the :class:`ConfusionMatrixDisplay`

.. GENERATED FROM PYTHON SOURCE LINES 44-53

.. code-block:: default

    from sklearn.metrics import confusion_matrix
    from sklearn.metrics import ConfusionMatrixDisplay

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

    cm_display = ConfusionMatrixDisplay(cm).plot()





.. image:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_001.png
    :alt: plot display object visualization
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 54-59

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 59-66

.. code-block:: default

    from sklearn.metrics import roc_curve
    from sklearn.metrics import RocCurveDisplay
    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()




.. image:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_002.png
    :alt: plot display object visualization
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 67-71

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

.. GENERATED FROM PYTHON SOURCE LINES 71-78

.. code-block:: default

    from sklearn.metrics import precision_recall_curve
    from sklearn.metrics import PrecisionRecallDisplay

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




.. image:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_003.png
    :alt: plot display object visualization
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 79-85

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 85-92

.. code-block:: default


    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()



.. image:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_004.png
    :alt: plot display object visualization
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  1.877 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py:


.. only :: html

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