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FIX: Regression in DecisionBoundaryDisplay.from_estimator with colors and plot_method='contour' #31553

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FIX: Regression in DecisionBoundaryDisplay.from_estimator with colors and plot_method='contour' #31553

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1afc804
Fix: DecisionBoundaryDisplay for `contour` and `cmap` (issue scikit-l…
jshn9515 Jun 16, 2025
a80ef4c
Update corresponding tests
jshn9515 Jun 16, 2025
c72e425
Reformat with ruff
jshn9515 Jun 16, 2025
1a9d4e0
Merge branch 'scikit-learn:main' into fix-DecisionBoundary
jshn9515 Jun 21, 2025
1be1a98
Fix: add handling logic when `colors` is a list
jshn9515 Jun 21, 2025
7ca868f
ENH: add tests for cmap and colors handling in DecisionBoundaryDisplay
jshn9515 Jun 21, 2025
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108 changes: 62 additions & 46 deletions sklearn/inspection/_plot/decision_boundary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,60 +221,76 @@
self.surface_ = plot_func(self.xx0, self.xx1, self.response, **kwargs)
else: # self.response.ndim == 3
n_responses = self.response.shape[-1]
if (
isinstance(self.multiclass_colors, str)
or self.multiclass_colors is None
):
if isinstance(self.multiclass_colors, str):
cmap = self.multiclass_colors
else:
if n_responses <= 10:
cmap = "tab10"
if self.multiclass_colors is None:
if "cmap" in kwargs and "colors" in kwargs:
raise ValueError(
"Cannot specify both 'cmap' and 'colors' in kwargs. "
"Please use only one of them."
Comment on lines +227 to +228
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isn't one deprecated upstream? We should recommend the new way.

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Thanks for the suggestion!
To clarify: matplotlib hasn't deprecated either cmap or colors — both are still valid and serve different purposes.
That said, I agree it's better to recommend cmap as the preferred option, especially for continuous or gradient-based plots. I’ll update the error message accordingly to reflect that.

)
if "cmap" in kwargs:
cmap = plt.get_cmap(kwargs.pop("cmap"), n_responses)
if not hasattr(cmap, "colors"):
# For LinearSegmentedColormap
colors = cmap(np.linspace(0, 1, n_responses))
else:
cmap = "gist_rainbow"

# Special case for the tab10 and tab20 colormaps that encode a
# discrete set of colors that are easily distinguishable
# contrary to other colormaps that are continuous.
if cmap == "tab10" and n_responses <= 10:
colors = plt.get_cmap("tab10", 10).colors[:n_responses]
elif cmap == "tab20" and n_responses <= 20:
colors = plt.get_cmap("tab20", 20).colors[:n_responses]
colors = cmap.colors
elif "colors" in kwargs:
if isinstance(kwargs["colors"], str):
colors = mpl.colors.to_rgba(kwargs.pop("colors"))
colors = [colors for _ in range(n_responses)]
else:
colors = kwargs.pop("colors")
colors = [mpl.colors.to_rgba(color) for color in colors]
else:
cmap = "tab10" if n_responses <= 10 else "gist_rainbow"
colors = plt.get_cmap(cmap, n_responses).colors
elif isinstance(self.multiclass_colors, str):
colors = colors = plt.get_cmap(
self.multiclass_colors, n_responses
).colors
else:
colors = [mpl.colors.to_rgba(color) for color in self.multiclass_colors]
if "cmap" in kwargs:
warnings.warn("'cmap' is ignored when 'multiclass_colors' is set.")
del kwargs["cmap"]
if "colors" in kwargs:
warnings.warn(
"'colors' is ignored when 'multiclass_colors' is set."
)
del kwargs["colors"]
if isinstance(self.multiclass_colors, str):
cmap = plt.get_cmap(self.multiclass_colors, n_responses)
if not hasattr(cmap, "colors"):
# For LinearSegmentedColormap
colors = cmap(np.linspace(0, 1, n_responses))

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else:
colors = cmap.colors
elif isinstance(self.multiclass_colors, list):
colors = [
mpl.colors.to_rgba(color) for color in self.multiclass_colors
]
else:
raise ValueError("'multiclass_colors' must be a list or a str.")

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self.multiclass_colors_ = colors
multiclass_cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
if plot_method == "contour":
# Plot only argmax map for contour
class_map = self.response.argmax(axis=2)
self.surface_ = plot_func(
self.xx0, self.xx1, class_map, colors=colors, **kwargs
)
for class_idx, (r, g, b, _) in enumerate(colors)
]

self.surface_ = []
for class_idx, cmap in enumerate(multiclass_cmaps):
response = np.ma.array(
self.response[:, :, class_idx],
mask=~(self.response.argmax(axis=2) == class_idx),
)
# `cmap` should not be in kwargs
safe_kwargs = kwargs.copy()
if "cmap" in safe_kwargs:
del safe_kwargs["cmap"]
warnings.warn(
"Plotting max class of multiclass 'decision_function' or "
"'predict_proba', thus 'multiclass_colors' used and "
"'cmap' kwarg ignored."
else:
multiclass_cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
)
for class_idx, (r, g, b, _) in enumerate(colors)
]

self.surface_ = []
for class_idx, cmap in enumerate(multiclass_cmaps):
response = np.ma.array(
self.response[:, :, class_idx],
mask=~(self.response.argmax(axis=2) == class_idx),
)
self.surface_.append(
plot_func(self.xx0, self.xx1, response, cmap=cmap, **kwargs)
)
self.surface_.append(
plot_func(self.xx0, self.xx1, response, cmap=cmap, **safe_kwargs)
)

if xlabel is not None or not ax.get_xlabel():
xlabel = self.xlabel if xlabel is None else xlabel
Expand Down
127 changes: 113 additions & 14 deletions sklearn/inspection/_plot/tests/test_boundary_decision_display.py
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Original file line number Diff line number Diff line change
Expand Up @@ -645,28 +645,127 @@ def test_multiclass_colors_cmap(pyplot, plot_method, multiclass_colors):
else:
colors = [mpl.colors.to_rgba(color) for color in multiclass_colors]

cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
if plot_method != "contour":
cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
)
for class_idx, (r, g, b, _) in enumerate(colors)
]
for idx, quad in enumerate(disp.surface_):
assert quad.cmap == cmaps[idx]
else:
assert_allclose(disp.surface_.colors, colors)


def test_cmap_and_colors_logic(pyplot):
"""Check the handling logic for `cmap` and `colors`."""
X, y = load_iris_2d_scaled()
clf = LogisticRegression().fit(X, y)

with pytest.raises(
ValueError,
match="Cannot specify both 'cmap' and 'colors' in kwargs.",
):
DecisionBoundaryDisplay.from_estimator(
clf,
X,
colors="black",
cmap="Blues",
)
for class_idx, (r, g, b, _) in enumerate(colors)
]

for idx, quad in enumerate(disp.surface_):
assert quad.cmap == cmaps[idx]
with pytest.warns(
UserWarning,
match="'cmap' is ignored when 'multiclass_colors' is set.",
):
DecisionBoundaryDisplay.from_estimator(
clf,
X,
multiclass_colors="plasma",
cmap="Blues",
)

with pytest.warns(
UserWarning,
match="'colors' is ignored when 'multiclass_colors' is set.",
):
DecisionBoundaryDisplay.from_estimator(
clf,
X,
multiclass_colors="plasma",
colors="blue",
)


@pytest.mark.parametrize("plot_method", ["contourf", "contour", "pcolormesh"])
@pytest.mark.parametrize("kwargs", [{"cmap": "tab10"}, {"cmap": "Blues"}])
def test_multiclass_cmap(pyplot, plot_method, kwargs):
"""Check that `cmap` is correctly applied to DecisionBoundaryDisplay."""
import matplotlib as mpl
import matplotlib.pyplot as plt

def test_multiclass_plot_max_class_cmap_kwarg(pyplot):
"""Check `cmap` kwarg ignored when using plotting max multiclass class."""
X, y = load_iris_2d_scaled()
clf = LogisticRegression().fit(X, y)

msg = (
"Plotting max class of multiclass 'decision_function' or 'predict_proba', "
"thus 'multiclass_colors' used and 'cmap' kwarg ignored."
disp = DecisionBoundaryDisplay.from_estimator(
clf,
X,
cmap=kwargs["cmap"],
plot_method=plot_method,
)

cmap = plt.get_cmap(kwargs["cmap"], len(clf.classes_))
if not hasattr(cmap, "colors"):
colors = cmap(np.linspace(0, 1, len(clf.classes_)))
else:
colors = cmap.colors

if plot_method != "contour":
cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
)
for class_idx, (r, g, b, _) in enumerate(colors)
]
for idx, quad in enumerate(disp.surface_):
assert quad.cmap == cmaps[idx]
else:
assert_allclose(disp.surface_.colors, colors)


@pytest.mark.parametrize("plot_method", ["contourf", "contour", "pcolormesh"])
@pytest.mark.parametrize("kwargs", [{"colors": "black"}, {"colors": ["r", "g", "b"]}])
def test_multiclass_colors(pyplot, plot_method, kwargs):
"""Check that `cmap` is correctly applied to DecisionBoundaryDisplay."""
import matplotlib as mpl

X, y = load_iris_2d_scaled()
clf = LogisticRegression().fit(X, y)

disp = DecisionBoundaryDisplay.from_estimator(
clf,
X,
colors=kwargs["colors"],
plot_method=plot_method,
)
with pytest.warns(UserWarning, match=msg):
DecisionBoundaryDisplay.from_estimator(clf, X, cmap="viridis")

if isinstance(kwargs["colors"], str):
colors = mpl.colors.to_rgba(kwargs["colors"])
colors = [colors for _ in range(len(clf.classes_))]
else:
colors = [mpl.colors.to_rgba(color) for color in kwargs["colors"]]

if plot_method != "contour":
cmaps = [
mpl.colors.LinearSegmentedColormap.from_list(
f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)]
)
for class_idx, (r, g, b, _) in enumerate(colors)
]
for idx, quad in enumerate(disp.surface_):
assert quad.cmap == cmaps[idx]
else:
assert_allclose(disp.surface_.colors, colors)


def test_subclass_named_constructors_return_type_is_subclass(pyplot):
Expand Down
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