[MRG] EHN Provide a pos_label parameter in plot_roc_curve

doc/whats_new/v0.24.rst

@@ -174,6 +174,11 @@ Changelog
   optional in the matplotlib plot by setting colorbar=False. :pr:`17192` by
   :user:`Avi Gupta <avigupta2612>`
 
+- |Enhancement| Add `pos_label` parameter in
+  :func:`metrics.plot_roc_curve` in order to specify the positive
+  class to be used when computing the roc auc statistics.
+  :pr:`17651` by :user:`Clara Matos <claramatos>`.
+
 :mod:`sklearn.model_selection`
 ..............................
 

sklearn/metrics/_plot/base.py

@@ -1,3 +1,8 @@
+import numpy as np
+
+from sklearn.base import is_classifier
+
+
 def _check_classifier_response_method(estimator, response_method):
     """Return prediction method from the response_method
 
@@ -38,3 +43,72 @@ def _check_classifier_response_method(estimator, response_method):
                 estimator.__class__.__name__))
 
     return prediction_method
+
+
+def _get_response(X, estimator, response_method, pos_label=None):
+    """Return response and positive label.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Input values.
+
+    estimator : estimator instance
+        Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
+        in which the last estimator is a classifier.
+
+    response_method: {'auto', 'predict_proba', 'decision_function'}
+        Specifies whether to use :term:`predict_proba` or
+        :term:`decision_function` as the target response. If set to 'auto',
+        :term:`predict_proba` is tried first and if it does not exist
+        :term:`decision_function` is tried next.
+
+    pos_label : str or int, default=None
+        The class considered as the positive class when computing
+        the metrics. By default, `estimators.classes_[1]` is
+        considered as the positive class.
+
+    Returns
+    -------
+    y_pred: ndarray of shape (n_samples,)
+        Target scores calculated from the provided response_method
+        and pos_label.
+
+    pos_label: str or int
+        The class considered as the positive class when computing
+        the metrics.
+    """
+    classification_error = (
+        "{} should be a binary classifier".format(estimator.__class__.__name__)
+    )
+
+    if not is_classifier(estimator):
+        raise ValueError(classification_error)
+
+    prediction_method = _check_classifier_response_method(
+        estimator, response_method)
+
+    y_pred = prediction_method(X)
+
+    if pos_label is not None and pos_label not in estimator.classes_:
+        raise ValueError(
+            f"The class provided by 'pos_label' is unknown. Got "
+            f"{pos_label} instead of one of {estimator.classes_}"
+        )
+
+    if y_pred.ndim != 1:  # `predict_proba`
+        if y_pred.shape[1] != 2:
+            raise ValueError(classification_error)
+        if pos_label is None:
+            pos_label = estimator.classes_[1]
+            y_pred = y_pred[:, 1]
+        else:
+            class_idx = np.flatnonzero(estimator.classes_ == pos_label)
+            y_pred = y_pred[:, class_idx]
+    else:
+        if pos_label is None:
+            pos_label = estimator.classes_[1]
+        elif pos_label == estimator.classes_[0]:
+            y_pred *= -1
+
+    return y_pred, pos_label

sklearn/metrics/_plot/precision_recall_curve.py

@@ -1,13 +1,10 @@
-import numpy as np
-
-from .base import _check_classifier_response_method
+from .base import _get_response
 
 from .. import average_precision_score
 from .. import precision_recall_curve
 
 from ...utils import check_matplotlib_support
 from ...utils.validation import _deprecate_positional_args
-from ...base import is_classifier
 
 
 class PrecisionRecallDisplay:
@@ -67,12 +64,13 @@ class PrecisionRecallDisplay:
     >>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
     >>> disp.plot() # doctest: +SKIP
     """
+
     def __init__(self, precision, recall, *,
                  average_precision=None, estimator_name=None, pos_label=None):
+        self.estimator_name = estimator_name
         self.precision = precision
         self.recall = recall
         self.average_precision = average_precision
-        self.estimator_name = estimator_name
         self.pos_label = pos_label
 
     @_deprecate_positional_args
@@ -100,10 +98,6 @@ def plot(self, ax=None, *, name=None, **kwargs):
             Object that stores computed values.
         """
         check_matplotlib_support("PrecisionRecallDisplay.plot")
-        import matplotlib.pyplot as plt
-
-        if ax is None:
-            fig, ax = plt.subplots()
 
         name = self.estimator_name if name is None else name
 
@@ -118,15 +112,21 @@ def plot(self, ax=None, *, name=None, **kwargs):
             line_kwargs["label"] = name
         line_kwargs.update(**kwargs)
 
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig, ax = plt.subplots()
+
         self.line_, = ax.plot(self.recall, self.precision, **line_kwargs)
         info_pos_label = (f" (Positive label: {self.pos_label})"
                           if self.pos_label is not None else "")
+
         xlabel = "Recall" + info_pos_label
         ylabel = "Precision" + info_pos_label
         ax.set(xlabel=xlabel, ylabel=ylabel)
 
         if "label" in line_kwargs:
-            ax.legend(loc='lower left')
+            ax.legend(loc="lower left")
 
         self.ax_ = ax
         self.figure_ = ax.figure
@@ -194,46 +194,24 @@ def plot_precision_recall_curve(estimator, X, y, *,
     """
     check_matplotlib_support("plot_precision_recall_curve")
 
-    classification_error = ("{} should be a binary classifier".format(
-        estimator.__class__.__name__))
-    if not is_classifier(estimator):
-        raise ValueError(classification_error)
-
-    prediction_method = _check_classifier_response_method(estimator,
-                                                          response_method)
-    y_pred = prediction_method(X)
-
-    if pos_label is not None and pos_label not in estimator.classes_:
-        raise ValueError(
-            f"The class provided by 'pos_label' is unknown. Got "
-            f"{pos_label} instead of one of {estimator.classes_}"
-        )
-
-    if y_pred.ndim != 1:  # `predict_proba`
-        if y_pred.shape[1] != 2:
-            raise ValueError(classification_error)
-        if pos_label is None:
-            pos_label = estimator.classes_[1]
-            y_pred = y_pred[:, 1]
-        else:
-            class_idx = np.flatnonzero(estimator.classes_ == pos_label)
-            y_pred = y_pred[:, class_idx]
-    else:  # `decision_function`
-        if pos_label is None:
-            pos_label = estimator.classes_[1]
-        elif pos_label == estimator.classes_[0]:
-            y_pred *= -1
+    y_pred, pos_label = _get_response(
+        X, estimator, response_method, pos_label=pos_label)
 
     precision, recall, _ = precision_recall_curve(y, y_pred,
                                                   pos_label=pos_label,
                                                   sample_weight=sample_weight)
     average_precision = average_precision_score(y, y_pred,
                                                 pos_label=pos_label,
                                                 sample_weight=sample_weight)
+
     name = name if name is not None else estimator.__class__.__name__
+
     viz = PrecisionRecallDisplay(
-        precision=precision, recall=recall,
-        average_precision=average_precision, estimator_name=name,
+        precision=precision,
+        recall=recall,
+        average_precision=average_precision,
+        estimator_name=name,
         pos_label=pos_label,
     )
+
     return viz.plot(ax=ax, name=name, **kwargs)

sklearn/metrics/_plot/roc_curve.py

@@ -1,9 +1,9 @@
+from .base import _get_response
+
 from .. import auc
 from .. import roc_curve
 
-from .base import _check_classifier_response_method
 from ...utils import check_matplotlib_support
-from ...base import is_classifier
 from ...utils.validation import _deprecate_positional_args
 
 
@@ -29,6 +29,13 @@ class RocCurveDisplay:
     estimator_name : str, default=None
         Name of estimator. If None, the estimator name is not shown.
 
+    pos_label : str or int, default=None
+        The class considered as the positive class when computing the roc auc
+        metrics. By default, `estimators.classes_[1]` is considered
+        as the positive class.
+
+        .. versionadded:: 0.24
+
     Attributes
     ----------
     line_ : matplotlib Artist
@@ -54,11 +61,14 @@ class RocCurveDisplay:
     >>> display.plot()  # doctest: +SKIP
     >>> plt.show()      # doctest: +SKIP
     """
-    def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None):
+
+    def __init__(self, *, fpr, tpr,
+                 roc_auc=None, estimator_name=None, pos_label=None):
+        self.estimator_name = estimator_name
         self.fpr = fpr
         self.tpr = tpr
         self.roc_auc = roc_auc
-        self.estimator_name = estimator_name
+        self.pos_label = pos_label
 
     @_deprecate_positional_args
     def plot(self, ax=None, *, name=None, **kwargs):
@@ -82,10 +92,6 @@ def plot(self, ax=None, *, name=None, **kwargs):
             Object that stores computed values.
         """
         check_matplotlib_support('RocCurveDisplay.plot')
-        import matplotlib.pyplot as plt
-
-        if ax is None:
-            fig, ax = plt.subplots()
 
         name = self.estimator_name if name is None else name
 
@@ -99,12 +105,21 @@ def plot(self, ax=None, *, name=None, **kwargs):
 
         line_kwargs.update(**kwargs)
 
-        self.line_ = ax.plot(self.fpr, self.tpr, **line_kwargs)[0]
-        ax.set_xlabel("False Positive Rate")
-        ax.set_ylabel("True Positive Rate")
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig, ax = plt.subplots()
+
+        self.line_, = ax.plot(self.fpr, self.tpr, **line_kwargs)
+        info_pos_label = (f" (Positive label: {self.pos_label})"
+                          if self.pos_label is not None else "")
+
+        xlabel = "False Positive Rate" + info_pos_label
+        ylabel = "True Positive Rate" + info_pos_label
+        ax.set(xlabel=xlabel, ylabel=ylabel)
 
         if "label" in line_kwargs:
-            ax.legend(loc='lower right')
+            ax.legend(loc="lower right")
 
         self.ax_ = ax
         self.figure_ = ax.figure
@@ -114,7 +129,7 @@ def plot(self, ax=None, *, name=None, **kwargs):
 @_deprecate_positional_args
 def plot_roc_curve(estimator, X, y, *, sample_weight=None,
                    drop_intermediate=True, response_method="auto",
-                   name=None, ax=None, **kwargs):
+                   name=None, ax=None, pos_label=None, **kwargs):
     """Plot Receiver operating characteristic (ROC) curve.
 
     Extra keyword arguments will be passed to matplotlib's `plot`.
@@ -155,6 +170,13 @@ def plot_roc_curve(estimator, X, y, *, sample_weight=None,
     ax : matplotlib axes, default=None
         Axes object to plot on. If `None`, a new figure and axes is created.
 
+    pos_label : str or int, default=None
+        The class considered as the positive class when computing the roc auc
+        metrics. By default, `estimators.classes_[1]` is considered
+        as the positive class.
+
+        .. versionadded:: 0.24
+
     Returns
     -------
     display : :class:`~sklearn.metrics.RocCurveDisplay`
@@ -181,29 +203,22 @@ def plot_roc_curve(estimator, X, y, *, sample_weight=None,
     """
     check_matplotlib_support('plot_roc_curve')
 
-    classification_error = (
-        "{} should be a binary classifier".format(estimator.__class__.__name__)
-    )
-    if not is_classifier(estimator):
-        raise ValueError(classification_error)
-
-    prediction_method = _check_classifier_response_method(estimator,
-                                                         response_method)
-    y_pred = prediction_method(X)
-
-    if y_pred.ndim != 1:
-        if y_pred.shape[1] != 2:
-            raise ValueError(classification_error)
-        else:
-            y_pred = y_pred[:, 1]
+    y_pred, pos_label = _get_response(
+        X, estimator, response_method, pos_label=pos_label)
 
-    pos_label = estimator.classes_[1]
     fpr, tpr, _ = roc_curve(y, y_pred, pos_label=pos_label,
                             sample_weight=sample_weight,
                             drop_intermediate=drop_intermediate)
     roc_auc = auc(fpr, tpr)
+
     name = estimator.__class__.__name__ if name is None else name
+
     viz = RocCurveDisplay(
-        fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=name
+        fpr=fpr,
+        tpr=tpr,
+        roc_auc=roc_auc,
+        estimator_name=name,
+        pos_label=pos_label
     )
+
     return viz.plot(ax=ax, name=name, **kwargs)

sklearn/metrics/_plot/tests/test_plot_precision_recall.py

@@ -18,7 +18,6 @@
 from sklearn.utils import shuffle
 from sklearn.compose import make_column_transformer
 
-
 # TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved
 pytestmark = pytest.mark.filterwarnings(
     "ignore:In future, it will be an error for 'np.bool_':DeprecationWarning:"