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‎doc/developers/contributing.rst

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@@ -1657,24 +1657,24 @@ Plotting API
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Scikit-learn defines a simple API for creating visualizations for machine
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learning. The key features of this API is to run calculations once and to have
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the flexibility to adjust the visualizations after the fact. This logic is
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encapsulated into a visualizer object where the computed data is stored and
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the plotting is done in a `plot` method. The visualizer object's `__init__`
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encapsulated into a display object where the computed data is stored and
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the plotting is done in a `plot` method. The display object's `__init__`
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method contains only the data needed to create the visualization. The `plot`
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method takes in parameters that only have to do with visualization, such as a
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matplotlib axes. The `plot` method will store the matplotlib artists as
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attributes allowing for style adjustments through the visualizer object. A
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attributes allowing for style adjustments through the display object. A
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`plot_*` helper function accepts parameters to do the computation and the
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parameters used for plotting. After the helper function creates the visualizer
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with the computed values, it calls the visualizer's plot method. Note that the
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`plot` method defines attributes related to matplotlib, such as the line
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artist. This allows for customizations after calling the `plot` method.
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parameters used for plotting. After the helper function creates the display
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object with the computed values, it calls the display's plot method. Note
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that the `plot` method defines attributes related to matplotlib, such as the
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line artist. This allows for customizations after calling the `plot` method.
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For example, the `RocCurveVisualizer` defines the following methods and
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For example, the `RocCurveDisplay` defines the following methods and
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attributes:
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.. code-block:: python
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class RocCurveVisualizer:
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class RocCurveDisplay:
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def __init__(self, fpr, tpr, roc_auc, estimator_name):
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...
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self.fpr = fpr
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drop_intermediate=True, response_method="auto",
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name=None, ax=None, **kwargs):
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# do computation
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viz = RocCurveVisualizer(fpr, tpr, roc_auc,
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viz = RocCurveDisplay(fpr, tpr, roc_auc,
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estimator.__class__.__name__)
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return viz.plot(ax=ax, name=name, **kwargs)
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```

‎doc/modules/classes.rst

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@@ -1024,7 +1024,7 @@ See the :ref:`visualizations` section of the user guide for further details.
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:toctree: generated/
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:template: class.rst
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metrics.RocCurveVisualizer
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metrics.RocCurveDisplay
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.. _mixture_ref:

‎doc/visualizations.rst

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@@ -22,19 +22,19 @@ ROC curve for a fitted support vector machine:
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svc = SVC(random_state=42)
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svc.fit(X_train, y_train)
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viz_svc = plot_roc_curve(svc, X_test, y_test)
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svc_disp = plot_roc_curve(svc, X_test, y_test)
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.. figure:: ../auto_examples/images/sphx_glr_plot_roc_curve_visualizer_001.png
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:target: ../auto_examples/plot_roc_curve_visualizer.html
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.. figure:: ../auto_examples/images/sphx_glr_plot_roc_curve_visualization_api_001.png
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:target: ../auto_examples/plot_roc_curve_visualization_api.html
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:align: center
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:scale: 75%
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The returned `viz_svc` object allows us to continue using the already computed
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ROC curve for SVC in future plots. In this case, the `viz_svc` is a
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:class:`~sklearn.metrics.RocCurveVisualizer` that stores the computed values as
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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`. Next, we train a random forest
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classifier and plot the previously computed roc curve again by using the `plot`
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method of the `Visualizer` object.
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method of the `Display` object.
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.. code-block:: python
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rfc.fit(X_train, y_train)
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ax = plt.gca()
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viz_rfc = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8)
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viz_svc.plot(ax=ax, alpha=0.8)
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rfc_disp = plot_roc_curve(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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.. figure:: ../auto_examples/images/sphx_glr_plot_roc_curve_visualizer_002.png
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:target: ../auto_examples/plot_roc_curve_visualizer.html
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.. figure:: ../auto_examples/images/sphx_glr_plot_roc_curve_visualization_api_002.png
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:target: ../auto_examples/plot_roc_curve_visualization_api.html
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:align: center
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:scale: 75%
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_plot_roc_curve_visualizer.py`
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* :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
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Available Plotting Utilities
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============================
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metrics.plot_roc_curve
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Visualizers
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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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metrics.RocCurveVisualizer
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metrics.RocCurveDisplay

‎examples/plot_roc_curve_visualizer.py renamed to ‎examples/plot_roc_curve_visualization_api.py

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"""
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==========================
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ROC Curve with Visualizers
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==========================
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================================
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ROC Curve with Visualization API
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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 features of this API is to allow for quick plotting and
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visual adjustments without recalculation. In this example, we will demonstrate
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# Plotting the ROC Curve
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# ----------------------
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# Next, we plot the ROC curve with a single call to
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# :func:`sklearn.metrics.plot_roc_curve`. The returned `viz_svc` object allows
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# :func:`sklearn.metrics.plot_roc_curve`. The returned `svc_disp` object allows
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# us to continue using the already computed ROC curve for the SVC in future
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# plots.
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viz_svc = plot_roc_curve(svc, X_test, y_test)
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svc_disp = plot_roc_curve(svc, X_test, y_test)
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plt.show()
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##############################################################################
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# Training a Random Forest and Plotting the ROC Curve
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# --------------------------------------------------------
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# We train a random forest classifier and create a plot comparing it to the SVC
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# ROC curve. Notice how `viz_svc` uses
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# :func:`~sklearn.metrics.RocCurveVisualizer.plot` to plot the SVC ROC curve
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# without recomputing the values of the roc curve itself. Futhermore, how we
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# ROC curve. Notice how `svc_disp` uses
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# :func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve
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# without recomputing the values of the roc curve itself. Futhermore, we
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# pass `alpha=0.8` to the plot functions to adjust the alpha values of the
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# curves.
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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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viz_rfc = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8)
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viz_svc.plot(ax=ax, alpha=0.8)
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rfc_disp = plot_roc_curve(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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plt.show()

‎sklearn/metrics/__init__.py

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from .scorer import get_scorer
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from ._plot.roc_curve import plot_roc_curve
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from ._plot.roc_curve import RocCurveVisualizer
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from ._plot.roc_curve import RocCurveDisplay
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__all__ = [
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'precision_score',
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'r2_score',
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'recall_score',
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'RocCurveVisualizer',
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'RocCurveDisplay',
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'roc_auc_score',
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'roc_curve',
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'SCORERS',

‎sklearn/metrics/_plot/roc_curve.py

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from ...utils import check_matplotlib_support
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class RocCurveVisualizer:
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class RocCurveDisplay:
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"""ROC Curve visualization.
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It is recommend to use `sklearn.metrics.plot_roc_curve` to create a
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Returns
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-------
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viz : :class:`sklearn.metrics.plot.RocCurveVisualizer`
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viz : :class:`sklearn.metrics.plot.RocCurveDisplay`
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object that stores computed values
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"""
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if response_method not in ("predict_proba", "decision_function", "auto"):
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fpr, tpr, _ = roc_curve(y, y_pred, pos_label=pos_label,
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drop_intermediate=drop_intermediate)
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roc_auc = auc(fpr, tpr)
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viz = RocCurveVisualizer(fpr, tpr, roc_auc, estimator.__class__.__name__)
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viz = RocCurveDisplay(fpr, tpr, roc_auc, estimator.__class__.__name__)
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return viz.plot(ax=ax, name=name, **kwargs)

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