87 lines
3.0 KiB
ReStructuredText
87 lines
3.0 KiB
ReStructuredText
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.. _visualizations:
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==============
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Visualizations
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==============
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Scikit-learn defines a simple API for creating visualizations for machine
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learning. The key feature of this API is to allow for quick plotting and
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visual adjustments without recalculation. We provide `Display` classes that
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expose two methods for creating plots: `from_estimator` and
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`from_predictions`. The `from_estimator` method will take a fitted estimator
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and some data (`X` and `y`) and create a `Display` object. Sometimes, we would
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like to only compute the predictions once and one should use `from_predictions`
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instead. In the following example, we plot a ROC curve for a fitted support
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vector machine:
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.. plot::
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:context: close-figs
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:align: center
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from sklearn.model_selection import train_test_split
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from sklearn.svm import SVC
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from sklearn.metrics import RocCurveDisplay
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from sklearn.datasets import load_wine
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X, y = load_wine(return_X_y=True)
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y = y == 2 # make binary
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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svc = SVC(random_state=42)
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svc.fit(X_train, y_train)
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svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
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The returned `svc_disp` object allows us to continue using the already computed
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ROC curve for SVC in future plots. In this case, the `svc_disp` is a
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:class:`~sklearn.metrics.RocCurveDisplay` that stores the computed values as
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attributes called `roc_auc`, `fpr`, and `tpr`. Be aware that we could get
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the predictions from the support vector machine and then use `from_predictions`
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instead of `from_estimator`. Next, we train a random forest classifier and plot
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the previously computed roc curve again by using the `plot` method of the
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`Display` object.
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.. plot::
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:context: close-figs
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:align: center
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import matplotlib.pyplot as plt
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from sklearn.ensemble import RandomForestClassifier
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rfc = RandomForestClassifier(n_estimators=10, random_state=42)
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rfc.fit(X_train, y_train)
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ax = plt.gca()
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rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
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svc_disp.plot(ax=ax, alpha=0.8)
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Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
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values of the curves.
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.. rubric:: Examples
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
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* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`
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Available Plotting Utilities
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============================
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Display Objects
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---------------
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.. currentmodule:: sklearn
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.. autosummary::
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calibration.CalibrationDisplay
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inspection.PartialDependenceDisplay
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inspection.DecisionBoundaryDisplay
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metrics.ConfusionMatrixDisplay
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metrics.DetCurveDisplay
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metrics.PrecisionRecallDisplay
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metrics.PredictionErrorDisplay
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metrics.RocCurveDisplay
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model_selection.LearningCurveDisplay
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model_selection.ValidationCurveDisplay
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