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Mark only the T case as xfail in the logscore distribution test #437

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Merged
alejandroschuler merged 3 commits into from
Nov 10, 2026
Merged

Mark only the T case as xfail in the logscore distribution test #437

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4952ace
Mark only the T case as xfail in the logscore distribution test
alejandroschuler Oct 27, 2026
8e8194d
Describe the T failure without quoting the error message
alejandroschuler Nov 10, 2026
371b2a9
Merge master into fix/test-distns-xfail
alejandroschuler Nov 10, 2026
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25 changes: 15 additions & 10 deletions tests/test_distns.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,12 +57,6 @@ def classification_data():
return X_train, X_test, y_train, y_test


def is_t_distribution(
dist, learner, regression_data
): # pylint: disable=unused-argument
return dist == T


@pytest.mark.slow
@pytest.mark.parametrize(
"dist",
Expand All @@ -74,7 +68,21 @@ def is_t_distribution(
LogNormal,
Exponential,
Gamma,
T,
# This case fails for both learners, so the marker is strict: if T starts
# to pass, the run fails and the marker has to go. Only a ValueError
# counts as the expected failure.
pytest.param(
T,
marks=pytest.mark.xfail(
reason=(
"NGBRegressor(Dist=T) raises a ValueError: the natural "
"gradient for log df runs away as df grows, and the "
"gradient goes to NaN (#275)"
),
raises=ValueError,
strict=True,
),
),
TFixedDf,
TFixedDfFixedVar,
Cauchy,
Expand All @@ -88,9 +96,6 @@ def is_t_distribution(
DecisionTreeRegressor(criterion="squared_error", max_depth=5),
],
)
@pytest.mark.xfail(
condition=is_t_distribution, reason="Known to fail with T distribution"
)
def test_dists_runs_on_examples_logscore(dist: Distn, learner, regression_data):
X_train, X_test, y_train, y_test = regression_data
# TODO: test early stopping features
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