8000 FIX: Fix for binary classifier prediction shape in partial dependence function by stephenpardy · Pull Request #30287 · scikit-learn/scikit-learn · GitHub
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FIX: Fix for binary classifier prediction shape in partial dependence function #30287

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FIX: Fix for binary classifier prediction shape in partial dependence function #30287

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8f13653
Fix for binary classifier prediction shape
stephenpardy Nov 16, 2024
df19ddd
Merge branch 'main' into 30271-partial-dependence-grid-fix
stephenpardy Nov 16, 2024
5ee74de
Add doc file
stephenpardy Nov 16, 2024
564809d
Bug in test
stephenpardy Nov 17, 2024
195324f
Update doc/whats_new/upcoming_changes/sklearn.inspection/302871.fix.rst
stephenpardy Nov 18, 2024
c82aa96
Simplify tests
stephenpardy Nov 18, 2024
3625af8
Merge branch '30271-partial-dependence-grid-fix' of https://github.co…
stephenpardy Nov 18, 2024
ee3e74d
Merge branch 'main' into 30271-partial-dependence-grid-fix
stephenpardy Nov 19, 2024
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3 changes: 3 additions & 0 deletions doc/whats_new/upcoming_changes/sklearn.inspection/302871.fix.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,3 @@
- Fixes an issue where the partial dependence function accidentally tries to select
positive class from binary predictions when they have already been selected.
By :user:`Stephen Pardy <stephenpardy>`
14 changes: 6 additions & 8 deletions sklearn/inspection/tests/test_partial_dependence.py
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Expand Up @@ -739,15 +739,13 @@ def test_partial_dependence_pipeline():


@pytest.mark.parametrize(
"features, grid_resolution, n_vals_expected",
"grid_resolution",
[
(["a"], 10, 10),
(["a"], 2, 2),
10,
2,
],
)
def test_partial_dependence_binary_model_grid_resolution(
features, grid_resolution, n_vals_expected
):
def test_partial_dependence_binary_model_grid_resolution(grid_resolution):
pd = pytest.importorskip("pandas")
model = DummyClassifier()

Expand All @@ -763,11 +761,11 @@ def test_partial_dependence_binary_model_grid_resolution(
part_dep = partial_dependence(
model,
X,
features=features,
features=["a"],
grid_resolution=grid_resolution,
kind="average",
)
assert part_dep["average"].size == n_vals_expected
assert part_dep["average"].size == grid_resolution


@pytest.mark.parametrize(
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0