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MAINT Follow-up on a fix for criterion="poisson" in decision trees #22306

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merged 3 commits into from
Jan 26, 2022
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MAINT Follow-up on a fix for criterion="poisson" in decision trees #22306

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5a97045
Fix changelog section order for svm / trees
ogrisel Jan 26, 2022
fc9f976
Make test_forest.test_poisson_vs_mse robust to random seed change
ogrisel Jan 26, 2022
6842b05
Update sklearn/ensemble/tests/test_forest.py
glemaitre Jan 26, 2022
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14 changes: 7 additions & 7 deletions doc/whats_new/v1.1.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -520,24 +520,24 @@ Changelog
`n_iter_`, the number of iterations of the libsvm optimization routine.
:pr:`21408` by :user:`Juan Martín Loyola <jmloyola>`.

- |Enhancement| :func:`svm.SVR`, :func:`svm.SVC`, :func:`svm.NuSVR`,
:func:`svm.OneClassSVM`, :func:`svm.NuSVC` now raise an error
when the dual-gap estimation produce non-finite parameter weights.
:pr:`22149` by :user:`Christian Ritter <chritter>` and
:user:`Norbert Preining <norbusan>`.

- |Fix| :class:`smv.NuSVC`, :class:`svm.NuSVR`, :class:`svm.SVC`,
:class:`svm.SVR`, :class:`svm.OneClassSVM` now validate input
parameters in `fit` instead of `__init__`.
:pr:`21436` by :user:`Haidar Almubarak <Haidar13 >`.

:mod:`sklearn.tree`
.......................
...................

- |Fix| Fix a bug in the Poisson splitting criterion for
:class:`tree.DecisionTreeRegressor`.
:pr:`22191` by :user:`Christian Lorentzen <lorentzenchr>`.

- |Enhancement| :func:`svm.SVR`, :func:`svm.SVC`, :func:`svm.NuSVR`,
:func:`svm.OneClassSVM`, :func:`svm.NuSVC` now raise an error
when the dual-gap estimation produce non-finite parameter weights.
:pr:`22149` by :user:`Christian Ritter <chritter>` and
:user:`Norbert Preining <norbusan>`.

:mod:`sklearn.utils`
....................

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6 changes: 3 additions & 3 deletions sklearn/ensemble/tests/test_forest.py
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Expand Up @@ -229,7 +229,7 @@ def test_poisson_vs_mse():
forest_mse.fit(X_train, y_train)
dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)

for X, y, val in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
for X, y, data_name in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
metric_poi = mean_poisson_deviance(y, forest_poi.predict(X))
# squared_error forest might produce non-positive predictions => clip
# If y = 0 for those, the poisson deviance gets too good.
Expand All @@ -244,9 +244,9 @@ def test_poisson_vs_mse():
# As squared_error might correctly predict 0 in train set, its train
# score can be better than Poisson. This is no longer the case for the
# test set. But keep the above comment for clipping in mind.
if val == "test":
if data_name == "test":
assert metric_poi < metric_mse
assert metric_poi < 0.5 * metric_dummy
assert metric_poi < 0.8 * metric_dummy


@pytest.mark.parametrize("criterion", ("poisson", "squared_error"))
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