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TST Improves testing for missing value support in random forest #26939

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Aug 2, 2023
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TST Improves testing for missing value support in random forest #26939

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5 changes: 4 additions & 1 deletion sklearn/ensemble/tests/test_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1819,7 +1819,7 @@ def test_round_samples_to_one_when_samples_too_low(class_weight):
],
)
def test_missing_values_is_resilient(make_data, Forest):
"""Check that forest can deal with missing values and have decent performance."""
"""Check that forest can deal with missing values and has decent performance."""

rng = np.random.RandomState(0)
n_samples, n_features = 1000, 10
Expand All @@ -1828,6 +1828,8 @@ def test_missing_values_is_resilient(make_data, Forest):
# Create dataset with missing values
X_missing = X.copy()
X_missing[rng.choice([False, True], size=X.shape, p=[0.95, 0.05])] = np.nan
assert np.isnan(X_missing).any()

X_missing_train, X_missing_test, y_train, y_test = train_test_split(
X_missing, y, random_state=0
)
Expand Down Expand Up @@ -1864,6 +1866,7 @@ def test_missing_value_is_predictive(Forest):

predictive_feature = rng.standard_normal(size=n_samples)
predictive_feature[y_mask] = np.nan
assert np.isnan(predictive_feature).any()

X_predictive = X_non_predictive.copy()
X_predictive[:, 5] = predictive_feature
Expand Down