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Commitfe6960b

Browse files
FIX: Regression in DecisionBoundaryDisplay.from_estimator with colors and plot_method='contour' (#31553)
Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>
1 parentbab34a0 commitfe6960b

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3 files changed

+93
-55
lines changed

3 files changed

+93
-55
lines changed
Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,7 @@
1+
- Fix multiple issues in the multiclass setting of:class:`inspection.DecisionBoundaryDisplay`:
2+
3+
- `contour` plotting now correctly shows the decision boundary.
4+
- `cmap` and `colors` are now properly ignored in favor of `multiclass_colors`.
5+
- Linear segmented colormaps are now fully supported.
6+
7+
By:user:`Yunjie Lin <jshn9515>`

‎sklearn/inspection/_plot/decision_boundary.py

Lines changed: 44 additions & 38 deletions
Original file line numberDiff line numberDiff line change
@@ -221,17 +221,22 @@ def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwar
221221
self.surface_=plot_func(self.xx0,self.xx1,self.response,**kwargs)
222222
else:# self.response.ndim == 3
223223
n_responses=self.response.shape[-1]
224-
if (
225-
isinstance(self.multiclass_colors,str)
226-
orself.multiclass_colorsisNone
224+
forkwargin ("cmap","colors"):
225+
ifkwarginkwargs:
226+
warnings.warn(
227+
f"'{kwarg}' is ignored in favor of 'multiclass_colors' "
228+
"in the multiclass case when the response method is "
229+
"'decision_function' or 'predict_proba'."
230+
)
231+
delkwargs[kwarg]
232+
233+
ifself.multiclass_colorsisNoneorisinstance(
234+
self.multiclass_colors,str
227235
):
228-
ifisinstance(self.multiclass_colors,str):
229-
cmap=self.multiclass_colors
236+
ifself.multiclass_colorsisNone:
237+
cmap="tab10"ifn_responses<=10else"gist_rainbow"
230238
else:
231-
ifn_responses<=10:
232-
cmap="tab10"
233-
else:
234-
cmap="gist_rainbow"
239+
cmap=self.multiclass_colors
235240

236241
# Special case for the tab10 and tab20 colormaps that encode a
237242
# discrete set of colors that are easily distinguishable
@@ -241,40 +246,41 @@ def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwar
241246
elifcmap=="tab20"andn_responses<=20:
242247
colors=plt.get_cmap("tab20",20).colors[:n_responses]
243248
else:
244-
colors=plt.get_cmap(cmap,n_responses).colors
245-
elifisinstance(self.multiclass_colors,str):
246-
colors=colors=plt.get_cmap(
247-
self.multiclass_colors,n_responses
248-
).colors
249-
else:
249+
cmap=plt.get_cmap(cmap,n_responses)
250+
ifnothasattr(cmap,"colors"):
251+
# For LinearSegmentedColormap
252+
colors=cmap(np.linspace(0,1,n_responses))
253+
else:
254+
colors=cmap.colors
255+
elifisinstance(self.multiclass_colors,list):
250256
colors= [mpl.colors.to_rgba(color)forcolorinself.multiclass_colors]
257+
else:
258+
raiseValueError("'multiclass_colors' must be a list or a str.")
251259

252260
self.multiclass_colors_=colors
253-
multiclass_cmaps= [
254-
mpl.colors.LinearSegmentedColormap.from_list(
255-
f"colormap_{class_idx}", [(1.0,1.0,1.0,1.0), (r,g,b,1.0)]
256-
)
257-
forclass_idx, (r,g,b,_)inenumerate(colors)
258-
]
259-
260-
self.surface_= []
261-
forclass_idx,cmapinenumerate(multiclass_cmaps):
262-
response=np.ma.array(
263-
self.response[:, :,class_idx],
264-
mask=~(self.response.argmax(axis=2)==class_idx),
261+
ifplot_method=="contour":
262+
# Plot only argmax map for contour
263+
class_map=self.response.argmax(axis=2)
264+
self.surface_=plot_func(
265+
self.xx0,self.xx1,class_map,colors=colors,**kwargs
265266
)
266-
# `cmap` should not be in kwargs
267-
safe_kwargs=kwargs.copy()
268-
if"cmap"insafe_kwargs:
269-
delsafe_kwargs["cmap"]
270-
warnings.warn(
271-
"Plotting max class of multiclass 'decision_function' or "
272-
"'predict_proba', thus 'multiclass_colors' used and "
273-
"'cmap' kwarg ignored."
267+
else:
268+
multiclass_cmaps= [
269+
mpl.colors.LinearSegmentedColormap.from_list(
270+
f"colormap_{class_idx}", [(1.0,1.0,1.0,1.0), (r,g,b,1.0)]
271+
)
272+
forclass_idx, (r,g,b,_)inenumerate(colors)
273+
]
274+
275+
self.surface_= []
276+
forclass_idx,cmapinenumerate(multiclass_cmaps):
277+
response=np.ma.array(
278+
self.response[:, :,class_idx],
279+
mask=~(self.response.argmax(axis=2)==class_idx),
280+
)
281+
self.surface_.append(
282+
plot_func(self.xx0,self.xx1,response,cmap=cmap,**kwargs)
274283
)
275-
self.surface_.append(
276-
plot_func(self.xx0,self.xx1,response,cmap=cmap,**safe_kwargs)
277-
)
278284

279285
ifxlabelisnotNoneornotax.get_xlabel():
280286
xlabel=self.xlabelifxlabelisNoneelsexlabel

‎sklearn/inspection/_plot/tests/test_boundary_decision_display.py

Lines changed: 42 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -169,6 +169,10 @@ def test_input_validation_errors(pyplot, kwargs, error_msg, fitted_clf):
169169
@pytest.mark.parametrize(
170170
"kwargs, error_msg",
171171
[
172+
(
173+
{"multiclass_colors": {"dict":"not_list"}},
174+
"'multiclass_colors' must be a list or a str.",
175+
),
172176
({"multiclass_colors":"not_cmap"},"it must be a valid Matplotlib colormap"),
173177
({"multiclass_colors": ["red","green"]},"it must be of the same length"),
174178
(
@@ -617,6 +621,7 @@ def test_multiclass_plot_max_class(pyplot, response_method):
617621
"multiclass_colors",
618622
[
619623
"plasma",
624+
"Blues",
620625
["red","green","blue"],
621626
],
622627
)
@@ -642,31 +647,51 @@ def test_multiclass_colors_cmap(pyplot, plot_method, multiclass_colors):
642647

643648
ifmulticlass_colors=="plasma":
644649
colors=mpl.pyplot.get_cmap(multiclass_colors,len(clf.classes_)).colors
650+
elifmulticlass_colors=="Blues":
651+
cmap=mpl.pyplot.get_cmap(multiclass_colors,len(clf.classes_))
652+
colors=cmap(np.linspace(0,1,len(clf.classes_)))
645653
else:
646654
colors= [mpl.colors.to_rgba(color)forcolorinmulticlass_colors]
647655

648-
cmaps= [
649-
mpl.colors.LinearSegmentedColormap.from_list(
650-
f"colormap_{class_idx}", [(1.0,1.0,1.0,1.0), (r,g,b,1.0)]
651-
)
652-
forclass_idx, (r,g,b,_)inenumerate(colors)
653-
]
654-
655-
foridx,quadinenumerate(disp.surface_):
656-
assertquad.cmap==cmaps[idx]
656+
ifplot_method!="contour":
657+
cmaps= [
658+
mpl.colors.LinearSegmentedColormap.from_list(
659+
f"colormap_{class_idx}", [(1.0,1.0,1.0,1.0), (r,g,b,1.0)]
660+
)
661+
forclass_idx, (r,g,b,_)inenumerate(colors)
662+
]
663+
foridx,quadinenumerate(disp.surface_):
664+
assertquad.cmap==cmaps[idx]
665+
else:
666+
assert_allclose(disp.surface_.colors,colors)
657667

658668

659-
deftest_multiclass_plot_max_class_cmap_kwarg(pyplot):
660-
"""Check`cmap` kwarg ignored when using plotting max multiclass class."""
669+
deftest_cmap_and_colors_logic(pyplot):
670+
"""Checkthe handling logic for `cmap` and `colors`."""
661671
X,y=load_iris_2d_scaled()
662672
clf=LogisticRegression().fit(X,y)
663673

664-
msg= (
665-
"Plotting max class of multiclass 'decision_function' or 'predict_proba', "
666-
"thus 'multiclass_colors' used and 'cmap' kwarg ignored."
667-
)
668-
withpytest.warns(UserWarning,match=msg):
669-
DecisionBoundaryDisplay.from_estimator(clf,X,cmap="viridis")
674+
withpytest.warns(
675+
UserWarning,
676+
match="'cmap' is ignored in favor of 'multiclass_colors'",
677+
):
678+
DecisionBoundaryDisplay.from_estimator(
679+
clf,
680+
X,
681+
multiclass_colors="plasma",
682+
cmap="Blues",
683+
)
684+
685+
withpytest.warns(
686+
UserWarning,
687+
match="'colors' is ignored in favor of 'multiclass_colors'",
688+
):
689+
DecisionBoundaryDisplay.from_estimator(
690+
clf,
691+
X,
692+
multiclass_colors="plasma",
693+
colors="blue",
694+
)
670695

671696

672697
deftest_subclass_named_constructors_return_type_is_subclass(pyplot):

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