.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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.
.. code-block:: default
print(__doc__)
Load Data and train model
-------------------------
For this example, we load a blood transfusion service center data set from
`OpenML `. 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.
.. 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)
.. only:: builder_html
.. raw:: html
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`
.. 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
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:
.. 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
Create :class:`PrecisionRecallDisplay`
#############################################################################
Similarly, the precision recall curve can be plotted using `y_score` from
the prevision sections.
.. 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
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.
.. 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 0.312 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.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/miscellaneous/plot_display_object_visualization.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_display_object_visualization.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_display_object_visualization.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_