scikit-learn
diff --git a/‎doc/modules/model_evaluation.rst
Lines changed: 4 additions & 3 deletions b/‎doc/modules/model_evaluation.rst
Lines changed: 4 additions & 3 deletions
diff --git a/‎doc/whats_new/v1.0.rst
Lines changed: 8 additions & 0 deletions b/‎doc/whats_new/v1.0.rst
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diff --git a/‎examples/model_selection/plot_precision_recall.py
Lines changed: 104 additions & 88 deletions b/‎examples/model_selection/plot_precision_recall.py
Lines changed: 104 additions & 88 deletions
@@ -796,9 +796,10 @@ score:
 
 Note that the :func:`precision_recall_curve` function is restricted to the
 binary case. The :func:`average_precision_score` function works only in
-binary classification and multilabel indicator format. The
-:func:`plot_precision_recall_curve` function plots the precision recall as
-follows.
+binary classification and multilabel indicator format.
+The :func:`PredictionRecallDisplay.from_estimator` and
+:func:`PredictionRecallDisplay.from_predictions` functions will plot the
+precision-recall curve as follows.
 
 .. image:: ../auto_examples/model_selection/images/sphx_glr_plot_precision_recall_001.png
         :target: ../auto_examples/model_selection/plot_precision_recall.html#plot-the-precision-recall-curve
 
@@ -589,6 +589,14 @@ Changelog
   class methods and will be removed in 1.2.
   :pr:`18543` by `Guillaume Lemaitre`_.
 
+- |API| :class:`metrics.PrecisionRecallDisplay` exposes two class methods
+  :func:`~metrics.PrecisionRecallDisplay.from_estimator` and
+  :func:`~metrics.PrecisionRecallDisplay.from_predictions` allowing to create
+  a precision-recall curve using an estimator or the predictions.
+  :func:`metrics.plot_precision_recall_curve` is deprecated in favor of these
+  two class methods and will be removed in 1.2.
+  :pr:`20552` by `Guillaume Lemaitre`_.
+
 - |API| :class:`metrics.DetCurveDisplay` exposes two class methods
   :func:`~metrics.DetCurveDisplay.from_estimator` and
   :func:`~metrics.DetCurveDisplay.from_predictions` allowing to create
 
@@ -92,64 +92,80 @@
 """
 # %%
 # In binary classification settings
-# --------------------------------------------------------
+# ---------------------------------
 #
-# Create simple data
-# ..................
+# Dataset and model
+# .................
 #
-# Try to differentiate the two first classes of the iris data
-from sklearn import svm, datasets
-from sklearn.model_selection import train_test_split
+# We will use a Linear SVC classifier to differentiate two types of irises.
 import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
 
-iris = datasets.load_iris()
-X = iris.data
-y = iris.target
+X, y = load_iris(return_X_y=True)
 
 # Add noisy features
 random_state = np.random.RandomState(0)
 n_samples, n_features = X.shape
-X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
+X = np.concatenate([X, random_state.randn(n_samples, 200 * n_features)], axis=1)
 
 # Limit to the two first classes, and split into training and test
-X_train, X_test, y_train, y_test = train_test_split(X[y < 2], y[y < 2],
-                                                    test_size=.5,
-                                                    random_state=random_state)
+X_train, X_test, y_train, y_test = train_test_split(
+    X[y < 2], y[y < 2], test_size=0.5, random_state=random_state
+)
 
-# Create a simple classifier
-classifier = svm.LinearSVC(random_state=random_state)
+# %%
+# Linear SVC will expect each feature to have a similar range of values. Thus,
+# we will first scale the data using a
+# :class:`~sklearn.preprocessing.StandardScaler`.
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import LinearSVC
+
+classifier = make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
 classifier.fit(X_train, y_train)
-y_score = classifier.decision_function(X_test)
 
 # %%
-# Compute the average precision score
-# ...................................
-from sklearn.metrics import average_precision_score
-average_precision = average_precision_score(y_test, y_score)
+# Plot the Precision-Recall curve
+# ...............................
+#
+# To plot the precision-recall curve, you should use
+# :class:`~sklearn.metrics.PrecisionRecallDisplay`. Indeed, there is two
+# methods available depending if you already computed the predictions of the
 # classifier or not.
+#
+# Let's first plot the precision-recall curve without the classifier
+# predictions. We use
+# :func:`~sklearn.metrics.PrecisionRecallDisplay.from_estimator` that
+# computes the predictions for us before plotting the curve.
+from sklearn.metrics import PrecisionRecallDisplay
 
-print('Average precision-recall score: {0:0.2f}'.format(
-      average_precision))
+display = PrecisionRecallDisplay.from_estimator(
+    classifier, X_test, y_test, name="LinearSVC"
+)
+_ = display.ax_.set_title("2-class Precision-Recall curve")
 
 # %%
-# Plot the Precision-Recall curve
-# ................................
-from sklearn.metrics import precision_recall_curve
-from sklearn.metrics import plot_precision_recall_curve
-import matplotlib.pyplot as plt
+# If we already got the estimated probabilities or scores for
+# our model, then we can use
+# :func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions`.
+y_score = classifier.decision_function(X_test)
 
-disp = plot_precision_recall_curve(classifier, X_test, y_test)
-disp.ax_.set_title('2-class Precision-Recall curve: '
-                   'AP={0:0.2f}'.format(average_precision))
+display = PrecisionRecallDisplay.from_predictions(y_test, y_score, name="LinearSVC")
+_ = display.ax_.set_title("2-class Precision-Recall curve")
 
 # %%
 # In multi-label settings
-# ------------------------
+# -----------------------
+#
+# The precision-recall curve does not support the multilabel setting. However,
+# one can decide how to handle this case. We show such an example below.
 #
 # Create multi-label data, fit, and predict
-# ...........................................
+# .........................................
 #
 # We create a multi-label dataset, to illustrate the precision-recall in
-# multi-label settings
+# multi-label settings.
 
 from sklearn.preprocessing import label_binarize
 
@@ -158,95 +174,95 @@
 n_classes = Y.shape[1]
 
 # Split into training and test
-X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5,
-                                                    random_state=random_state)
+X_train, X_test, Y_train, Y_test = train_test_split(
+    X, Y, test_size=0.5, random_state=random_state
+)
 
-# We use OneVsRestClassifier for multi-label prediction
+# %%
+# We use :class:`~sklearn.multiclass.OneVsRestClassifier` for multi-label
+# prediction.
 from sklearn.multiclass import OneVsRestClassifier
 
-# Run classifier
-classifier = OneVsRestClassifier(svm.LinearSVC(random_state=random_state))
+classifier = OneVsRestClassifier(
+    make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
+)
 classifier.fit(X_train, Y_train)
 y_score = classifier.decision_function(X_test)
 
 
 # %%
 # The average precision score in multi-label settings
-# ....................................................
+# ...................................................
+from sklearn.metrics import precision_recall_curve
 from sklearn.metrics import average_precision_score
 
 # For each class
 precision = dict()
 recall = dict()
 average_precision = dict()
 for i in range(n_classes):
-    precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i],
-                                                        y_score[:, i])
+    precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i])
     average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])
 
 # A "micro-average": quantifying score on all classes jointly
-precision["micro"], recall["micro"], _ = precision_recall_curve(Y_test.ravel(),
-                                                                y_score.ravel())
-average_precision["micro"] = average_precision_score(Y_test, y_score,
-                                                     average="micro")
-print('Average precision score, micro-averaged over all classes: {0:0.2f}'
-      .format(average_precision["micro"]))
+precision["micro"], recall["micro"], _ = precision_recall_curve(
+    Y_test.ravel(), y_score.ravel()
+)
+average_precision["micro"] = average_precision_score(Y_test, y_score, average="micro")
 
 # %%
 # Plot the micro-averaged Precision-Recall curve
-# ...............................................
-#
-
-plt.figure()
-plt.step(recall['micro'], precision['micro'], where='post')
-
-plt.xlabel('Recall')
-plt.ylabel('Precision')
-plt.ylim([0.0, 1.05])
-plt.xlim([0.0, 1.0])
-plt.title(
-    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'
-    .format(average_precision["micro"]))
+# ..............................................
+display = PrecisionRecallDisplay(
+    recall=recall["micro"],
+    precision=precision["micro"],
+    average_precision=average_precision["micro"],
+)
+display.plot()
+_ = display.ax_.set_title("Micro-averaged over all classes")
 
 # %%
 # Plot Precision-Recall curve for each class and iso-f1 curves
-# .............................................................
-#
+# ............................................................
+import matplotlib.pyplot as plt
 from itertools import cycle
+
 # setup plot details
-colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])
+colors = cycle(["navy", "turquoise", "darkorange", "cornflowerblue", "teal"])
+
+_, ax = plt.subplots(figsize=(7, 8))
 
-plt.figure(figsize=(7, 8))
 f_scores = np.linspace(0.2, 0.8, num=4)
-lines = []
-labels = []
+lines, labels = [], []
 for f_score in f_scores:
     x = np.linspace(0.01, 1)
     y = f_score * x / (2 * x - f_score)
-    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)
-    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))
+    (l,) = plt.plot(x[y >= 0], y[y >= 0], color="gray", alpha=0.2)
+    plt.annotate("f1={0:0.1f}".format(f_score), xy=(0.9, y[45] + 0.02))
 
-lines.append(l)
-labels.append('iso-f1 curves')
-l, = plt.plot(recall["micro"], precision["micro"], color='gold', lw=2)
-lines.append(l)
-labels.append('micro-average Precision-recall (area = {0:0.2f})'
-              ''.format(average_precision["micro"]))
+display = PrecisionRecallDisplay(
+    recall=recall["micro"],
+    precision=precision["micro"],
+    average_precision=average_precision["micro"],
+)
+display.plot(ax=ax, name="Micro-average precision-recall", color="gold")
 
 for i, color in zip(range(n_classes), colors):
-    l, = plt.plot(recall[i], precision[i], color=color, lw=2)
-    lines.append(l)
-    labels.append('Precision-recall for class {0} (area = {1:0.2f})'
-                  ''.format(i, average_precision[i]))
-
-fig = plt.gcf()
-fig.subplots_adjust(bottom=0.25)
-plt.xlim([0.0, 1.0])
-plt.ylim([0.0, 1.05])
-plt.xlabel('Recall')
-plt.ylabel('Precision')
-plt.title('Extension of Precision-Recall curve to multi-class')
-plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14))
-
+    display = PrecisionRecallDisplay(
+        recall=recall[i],
+        precision=precision[i],
+        average_precision=average_precision[i],
+    )
+    display.plot(ax=ax, name=f"Precision-recall for class {i}", color=color)
+
+# add the legend for the iso-f1 curves
+handles, labels = display.ax_.get_legend_handles_labels()
+handles.extend([l])
+labels.extend(["iso-f1 curves"])
+# set the legend and the axes
+ax.set_xlim([0.0, 1.0])
+ax.set_ylim([0.0, 1.05])
+ax.legend(handles=handles, labels=labels, loc="best")
+ax.set_title("Extension of Precision-Recall curve to multi-class")
 
 plt.show()