githubmirror/scikit-learn

doc/visualizations.rst
138 строк5 KB
Jérémie du Boisberranger
DOC Don't use deprecated RocCurveDisplay kwargs (#31482)
05 июн 2025, 03:32
05 июн 2025, 03:325d2ad4f
.. _visualizations:

==============
Visualizations
==============

Scikit-learn defines a simple API for creating visualizations for machine
learning. The key feature of this API is to allow for quick plotting and
visual adjustments without recalculation. We provide `Display` classes that
expose two methods for creating plots: `from_estimator` and
`from_predictions`.

The `from_estimator` method generates a `Display` object from a fitted estimator,
input data (`X`, `y`), and a plot.
The `from_predictions` method creates a `Display` object from true and predicted
values (`y_test`, `y_pred`), and a plot.

Using `from_predictions` avoids having to recompute predictions,
but the user needs to take care that the prediction values passed correspond
to the `pos_label`. For :term:`predict_proba`, select the column corresponding
to the `pos_label` class while for :term:`decision_function`, revert the score
(i.e. multiply by -1) if `pos_label` is not the last class in the
`classes_` attribute of your estimator.

The `Display` object stores the computed values (e.g., metric values or
feature importance) required for plotting with Matplotlib. These values are the
results derived from the raw predictions passed to `from_predictions`, or
an estimator and `X` passed to `from_estimator`.

Display objects have a plot method that creates a matplotlib plot once the display
object has been initialized (note that we recommend that display objects are created
via `from_estimator` or `from_predictions` instead of initialized directly).
The plot method allows adding to an existing plot by passing the existing plots
:class:`matplotlib.axes.Axes` to the `ax` parameter.

In the following example, we plot a ROC curve for a fitted Logistic Regression
model `from_estimator`:

.. plot::
   :context: close-figs
   :align: center

    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import RocCurveDisplay
    from sklearn.datasets import load_iris

    X, y = load_iris(return_X_y=True)
    y = y == 2  # make binary
    X_train, X_test, y_train, y_test = train_test_split(
       X, y, test_size=.8, random_state=42
    )
    clf = LogisticRegression(random_state=42, C=.01)
    clf.fit(X_train, y_train)

    clf_disp = RocCurveDisplay.from_estimator(clf, X_test, y_test)

If you already have the prediction values, you could instead use
`from_predictions` to do the same thing (and save on compute):


.. plot::
   :context: close-figs
   :align: center

    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import RocCurveDisplay
    from sklearn.datasets import load_iris

    X, y = load_iris(return_X_y=True)
    y = y == 2  # make binary
    X_train, X_test, y_train, y_test = train_test_split(
       X, y, test_size=.8, random_state=42
    )
    clf = LogisticRegression(random_state=42, C=.01)
    clf.fit(X_train, y_train)

    # select the probability of the class that we considered to be the positive label
    y_pred = clf.predict_proba(X_test)[:, 1]

    clf_disp = RocCurveDisplay.from_predictions(y_test, y_pred)


The returned `clf_disp` object allows us to add another curve to the already computed
ROC curve. In this case, the `clf_disp` is a :class:`~sklearn.metrics.RocCurveDisplay`
that stores the computed values as attributes called `roc_auc`, `fpr`, and `tpr`.

Next, we train a random forest classifier and plot the previously computed ROC curve
again by using the `plot` method of the `Display` object.

.. plot::
   :context: close-figs
   :align: center

    import matplotlib.pyplot as plt
    from sklearn.ensemble import RandomForestClassifier

    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
    rfc.fit(X_train, y_train)

    ax = plt.gca()
    rfc_disp = RocCurveDisplay.from_estimator(
      rfc, X_test, y_test, ax=ax, curve_kwargs={"alpha": 0.8}
    )
    clf_disp.plot(ax=ax, curve_kwargs={"alpha": 0.8})

Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
values of the curves.


.. rubric:: Examples

* :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
* :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
* :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`

Available Plotting Utilities
============================

Display Objects
---------------

.. currentmodule:: sklearn

.. autosummary::

   calibration.CalibrationDisplay
   inspection.PartialDependenceDisplay
   inspection.DecisionBoundaryDisplay
   metrics.ConfusionMatrixDisplay
   metrics.DetCurveDisplay
   metrics.PrecisionRecallDisplay
   metrics.PredictionErrorDisplay
   metrics.RocCurveDisplay
   model_selection.LearningCurveDisplay
   model_selection.ValidationCurveDisplay