scikit-learn · Charlie-XIAO · Mar 24, 2023 · Mar 24, 2023 · Mar 25, 2023 · Mar 25, 2023
diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst
@@ -318,6 +318,11 @@ Changelog
   curves.
   :pr:`24668` by :user:`dberenbaum`.
 
+- |Enhancement| :class:`RocCurveDisplay` now plots the ROC curve with both axes
+  limited to [0, 1] and a loosely dotted frame. There is also an additional
+  parameter `plot_chance_level` to determine whether to plot the chance level.
+  :pr:`25972` by :user:`Yao Xiao <Charlie-XIAO>`.
+
 - |Fix| :func:`log_loss` raises a warning if the values of the parameter `y_pred` are
   not normalized, instead of actually normalizing them in the metric. Starting from
   1.5 this will raise an error. :pr:`25299` by :user:`Omar Salman <OmarManzoor`.

diff --git a/sklearn/metrics/_plot/roc_curve.py b/sklearn/metrics/_plot/roc_curve.py
@@ -43,6 +43,9 @@ class RocCurveDisplay:
     line_ : matplotlib Artist
         ROC Curve.
 
+    chance_level_ : matplotlib Artist
+        The chance level line or None if the chance level is not plotted.
+
     ax_ : matplotlib Axes
         Axes with ROC Curve.
 
@@ -81,7 +84,7 @@ def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=Non
         self.roc_auc = roc_auc
         self.pos_label = pos_label
 
-    def plot(self, ax=None, *, name=None, **kwargs):
+    def plot(self, ax=None, *, name=None, plot_chance_level=True, **kwargs):
         """Plot visualization.
 
         Extra keyword arguments will be passed to matplotlib's ``plot``.
@@ -96,6 +99,9 @@ def plot(self, ax=None, *, name=None, **kwargs):
             Name of ROC Curve for labeling. If `None`, use `estimator_name` if
             not `None`, otherwise no labeling is shown.
 
+        plot_chance_level : bool, default=True
+            Whether to plot the chance level.
+
         **kwargs : dict
             Keyword arguments to be passed to matplotlib's `plot`.
 
@@ -123,6 +129,24 @@ def plot(self, ax=None, *, name=None, **kwargs):
         if ax is None:
             fig, ax = plt.subplots()
 
+        # Set limits of axes to [0, 1] and fix aspect ratio to squared
+        ax.set_xlim((0, 1))
+        ax.set_ylim((0, 1))
+        ax.set_aspect(1)
+
+        # Plot the frame in dotted line, so that the curve can be
+        # seen better when values are close to 0 or 1
+        for s in ["right", "left", "top", "bottom"]:
+            ax.spines[s].set_linestyle((0, (1, 5)))
+            ax.spines[s].set_linewidth(0.5)
+
+        if plot_chance_level:
+            (self.chance_level_,) = ax.plot(
+                (0, 1), (0, 1), linestyle="dotted", label="Chance level"
+            )
+        else:
+            self.chance_level_ = None
+
         (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 ""
@@ -152,6 +176,7 @@ def from_estimator(
         pos_label=None,
         name=None,
         ax=None,
+        plot_chance_level=True,
         **kwargs,
     ):
         """Create a ROC Curve display from an estimator.
@@ -195,6 +220,9 @@ def from_estimator(
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is created.
 
+        plot_chance_level : bool, default=True
+            Whether to plot the chance level.
+
         **kwargs : dict
             Keyword arguments to be passed to matplotlib's `plot`.
 
@@ -245,6 +273,7 @@ def from_estimator(
             name=name,
             ax=ax,
             pos_label=pos_label,
+            plot_chance_level=plot_chance_level,
             **kwargs,
         )
 
@@ -259,6 +288,7 @@ def from_predictions(
         pos_label=None,
         name=None,
         ax=None,
+        plot_chance_level=True,
         **kwargs,
     ):
         """Plot ROC curve given the true and predicted values.
@@ -298,6 +328,9 @@ def from_predictions(
             Axes object to plot on. If `None`, a new figure and axes is
             created.
 
+        plot_chance_level : bool, default=True
+            Whether to plot the chance level.
+
         **kwargs : dict
             Additional keywords arguments passed to matplotlib `plot` function.
 
@@ -348,4 +381,4 @@ def from_predictions(
             fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=name, pos_label=pos_label
         )
 
-        return viz.plot(ax=ax, name=name, **kwargs)
+        return viz.plot(ax=ax, name=name, plot_chance_level=plot_chance_level, **kwargs)
diff --git a/sklearn/metrics/_plot/tests/test_roc_curve_display.py b/sklearn/metrics/_plot/tests/test_roc_curve_display.py
@@ -36,6 +36,7 @@ def data_binary(data):
 @pytest.mark.parametrize("with_sample_weight", [True, False])
 @pytest.mark.parametrize("drop_intermediate", [True, False])
 @pytest.mark.parametrize("with_strings", [True, False])
+@pytest.mark.parametrize("plot_chance_level", [True, False])
 @pytest.mark.parametrize(
     "constructor_name, default_name",
     [
@@ -50,6 +51,7 @@ def test_roc_curve_display_plotting(
     with_sample_weight,
     drop_intermediate,
     with_strings,
+    plot_chance_level,
     constructor_name,
     default_name,
 ):
@@ -82,6 +84,7 @@ def test_roc_curve_display_plotting(
             drop_intermediate=drop_intermediate,
             pos_label=pos_label,
             alpha=0.8,
+            plot_chance_level=plot_chance_level,
         )
     else:
         display = RocCurveDisplay.from_predictions(
@@ -91,6 +94,7 @@ def test_roc_curve_display_plotting(
             drop_intermediate=drop_intermediate,
             pos_label=pos_label,
             alpha=0.8,
+            plot_chance_level=plot_chance_level,
         )
 
     fpr, tpr, _ = roc_curve(
@@ -114,6 +118,13 @@ def test_roc_curve_display_plotting(
     assert isinstance(display.ax_, mpl.axes.Axes)
     assert isinstance(display.figure_, mpl.figure.Figure)
 
+    if plot_chance_level:
+        assert isinstance(display.chance_level_, mpl.lines.Line2D)
+        assert tuple(display.chance_level_.get_xdata()) == (0, 1)
+        assert tuple(display.chance_level_.get_ydata()) == (0, 1)
+    else:
+        assert display.chance_level_ is None
+
     expected_label = f"{default_name} (AUC = {display.roc_auc:.2f})"
     assert display.line_.get_label() == expected_label
 
@@ -124,6 +135,15 @@ def test_roc_curve_display_plotting(
     assert display.ax_.get_ylabel() == expected_ylabel
     assert display.ax_.get_xlabel() == expected_xlabel
 
+    assert display.ax_.get_xlim() == (0, 1)
+    assert display.ax_.get_ylim() == (0, 1)
+    assert display.ax_.get_aspect() == 1
+
+    # Check frame styles
+    for s in ["right", "left", "top", "bottom"]:
+        assert display.ax_.spines[s].get_linestyle() == (0, (1, 5))
+        assert display.ax_.spines[s].get_linewidth() <= 0.5
+
 
 @pytest.mark.parametrize(
     "clf",