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.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# 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.
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)

# %%
# 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`
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()


# %%
# 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:
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()

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

# %%
# 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.

# sphinx_gallery_thumbnail_number = 4
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()