91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
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"""
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===================================
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Visualizations with Display Objects
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===================================
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.. currentmodule:: sklearn.metrics
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In this example, we will construct display objects,
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:class:`ConfusionMatrixDisplay`, :class:`RocCurveDisplay`, and
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:class:`PrecisionRecallDisplay` directly from their respective metrics. This
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is an alternative to using their corresponding plot functions when
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a model's predictions are already computed or expensive to compute. Note that
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this is advanced usage, and in general we recommend using their respective
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plot functions.
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"""
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# %%
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# Load Data and train model
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# -------------------------
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# For this example, we load a blood transfusion service center data set from
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# `OpenML <https://www.openml.org/d/1464>`. This is a binary classification
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# problem where the target is whether an individual donated blood. Then the
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# data is split into a train and test dataset and a logistic regression is
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# fitted with the train dataset.
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from sklearn.datasets import fetch_openml
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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X, y = fetch_openml(data_id=1464, return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)
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clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
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clf.fit(X_train, y_train)
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# %%
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# Create :class:`ConfusionMatrixDisplay`
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##############################################################################
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# With the fitted model, we compute the predictions of the model on the test
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# dataset. These predictions are used to compute the confusion matrix which
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# is plotted with the :class:`ConfusionMatrixDisplay`
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from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
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y_pred = clf.predict(X_test)
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cm = confusion_matrix(y_test, y_pred)
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cm_display = ConfusionMatrixDisplay(cm).plot()
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# %%
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# Create :class:`RocCurveDisplay`
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##############################################################################
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# The roc curve requires either the probabilities or the non-thresholded
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# decision values from the estimator. Since the logistic regression provides
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# a decision function, we will use it to plot the roc curve:
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from sklearn.metrics import RocCurveDisplay, roc_curve
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y_score = clf.decision_function(X_test)
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fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1])
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roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()
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# %%
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# Create :class:`PrecisionRecallDisplay`
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##############################################################################
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# Similarly, the precision recall curve can be plotted using `y_score` from
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# the prevision sections.
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from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve
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prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1])
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pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()
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# %%
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# Combining the display objects into a single plot
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##############################################################################
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# The display objects store the computed values that were passed as arguments.
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# This allows for the visualizations to be easliy combined using matplotlib's
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# API. In the following example, we place the displays next to each other in a
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# row.
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# sphinx_gallery_thumbnail_number = 4
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import matplotlib.pyplot as plt
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
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roc_display.plot(ax=ax1)
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pr_display.plot(ax=ax2)
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plt.show()
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