[MRG] EHN Provide a pos_label parameter in plot_roc_curve

doc/whats_new/v0.24.rst

@@ -117,6 +117,11 @@ Changelog
   :class:`metrics.median_absolute_error`. :pr:`17225` by
   :user:`Lucy Liu <lucyleeow>`.
 
+- |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 +43,151 @@ def _check_classifier_response_method(estimator, response_method):
                 estimator.__class__.__name__))
 
     return prediction_method
+
+
+def _get_target_scores(X, estimator, response_method, pos_label=None):
+    """Return target scores 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
+
+
+class CurveDisplay:
+    """Metrics visualization base class.
+
+    Parameters
+    -----------
+    estimator_name : str, default=None
+        Name of estimator. If None, then the estimator name is not shown.
+
+    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.
+
+    Attributes
+    ----------
+    line_ : matplotlib Artist
+        Metrics curve.
+
+    ax_ : matplotlib Axes
+        Axes with the curve.
+
+    figure_ : matplotlib Figure
+        Figure containing the curve.
+    """
+
+    def __init__(self, estimator_name=None, pos_label=None):
+        self.estimator_name = estimator_name
+        self.pos_label = pos_label
+
+    def _setup_display(self, x, y, line_kwargs,
+                       xlabel, ylabel, loc, ax=None):
+        """Setup visualization.
+
+        Parameters
+        ----------
+        x, y : array-like or scalar
+            The horizontal / vertical coordinates of the data points.
+
+        line_kwargs : dict
+            Keyword arguments to be passed to matplotlib's `plot`.
+
+        xlabel : str
+            Label of the horizontal axis
+
+        ylabel : str
+            Label of the vertical axis
+
+        loc : str
+            Location of the legend.
+
+        ax : Matplotlib Axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        Returns
+        -------
+        display : :class:`~sklearn.metrics.Display`
+            Object that stores computed values.
+        """
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig, ax = plt.subplots()
+
+        self.line_, = ax.plot(x, y, **line_kwargs)
+        info_pos_label = (f" (Positive label: {self.pos_label})"
+                          if self.pos_label is not None else "")
+        xlabel += info_pos_label
+        ylabel += info_pos_label
+        ax.set(xlabel=xlabel, ylabel=ylabel)
+
+        if "label" in line_kwargs:
+            ax.legend(loc=loc)
+
+        self.ax_ = ax
+        self.figure_ = ax.figure
+        return self

sklearn/metrics/_plot/precision_recall_curve.py

@@ -1,14 +1,14 @@
-from .base import _check_classifier_response_method
+from .base import _get_target_scores
+from .base import CurveDisplay
 
 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:
+class PrecisionRecallDisplay(CurveDisplay):
     """Precision Recall visualization.
 
     It is recommend to use :func:`~sklearn.metrics.plot_precision_recall_curve`
@@ -30,6 +30,13 @@ class PrecisionRecallDisplay:
     estimator_name : str, default=None
         Name of estimator. If None, then the estimator name is not shown.
 
+    pos_label : str or int, default=None
+        The class considered as the positive class when computing the precision
+        and recall metrics. By default, `estimators.classes_[1]` is considered
+        as the positive class.
+
+        .. versionadded:: 0.24
+
     Attributes
     ----------
     line_ : matplotlib Artist
@@ -59,12 +66,13 @@ class PrecisionRecallDisplay:
     >>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
     >>> disp.plot() # doctest: +SKIP
     """
+
     def __init__(self, precision, recall, *,
-                 average_precision=None, estimator_name=None):
+                 average_precision=None, estimator_name=None, pos_label=None):
+        super().__init__(estimator_name, pos_label)
         self.precision = precision
         self.recall = recall
         self.average_precision = average_precision
-        self.estimator_name = estimator_name
 
     @_deprecate_positional_args
     def plot(self, ax=None, *, name=None, **kwargs):
@@ -91,10 +99,6 @@ 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
 
@@ -109,21 +113,21 @@ def plot(self, ax=None, *, name=None, **kwargs):
             line_kwargs["label"] = name
         line_kwargs.update(**kwargs)
 
-        self.line_, = ax.plot(self.recall, self.precision, **line_kwargs)
-        ax.set(xlabel="Recall", ylabel="Precision")
-
-        if "label" in line_kwargs:
-            ax.legend(loc='lower left')
-
-        self.ax_ = ax
-        self.figure_ = ax.figure
-        return self
+        return self._setup_display(
+            x=self.recall,
+            y=self.precision,
+            line_kwargs=line_kwargs,
+            xlabel="Recall",
+            ylabel="Precision",
+            loc="lower left",
+            ax=ax
+        )
 
 
 @_deprecate_positional_args
 def plot_precision_recall_curve(estimator, X, y, *,
                                 sample_weight=None, response_method="auto",
-                                name=None, ax=None, **kwargs):
+                                name=None, ax=None, pos_label=None, **kwargs):
     """Plot Precision Recall Curve for binary classifiers.
 
     Extra keyword arguments will be passed to matplotlib's `plot`.
@@ -159,6 +163,13 @@ def plot_precision_recall_curve(estimator, X, y, *,
     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 precision
+        and recall metrics. By default, `estimators.classes_[1]` is considered
+        as the positive class.
+
+        .. versionadded:: 0.24
+
     **kwargs : dict
         Keyword arguments to be passed to matplotlib's `plot`.
 
@@ -174,31 +185,24 @@ 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 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_target_scores(
+        X, estimator, response_method, pos_label=pos_label)
 
-    pos_label = estimator.classes_[1]
     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)