TST catch UserWarning in test_predictions for HGBT #26312
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thomasjpfan
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May 2, 2023
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Thank you for the PR! In this case, I think it's better to fix the warnings themselves by using a dataframe during predict:
diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
index 9456b9d993..f11bec3bd7 100644
--- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
+++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
@@ -14,6 +14,7 @@ from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
+from sklearn.utils._testing import _convert_container
n_threads = _openmp_effective_n_threads()
@@ -212,9 +213,9 @@ def test_predictions(global_random_seed, use_feature_names):
f_0 = rng.rand(n_samples) # positive correlation with y
f_1 = rng.rand(n_samples) # negative correslation with y
X = np.c_[f_0, f_1]
- if use_feature_names:
- pd = pytest.importorskip("pandas")
- X = pd.DataFrame(X, columns=["f_0", "f_1"])
+ columns_name = ["f_0", "f_1"]
+ constructor_name = "dataframe" if use_feature_names else "array"
+ X = _convert_container(X, constructor_name, columns_name=columns_name)
noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
y = 5 * f_0 + np.sin(10 * np.pi * f_0) - 5 * f_1 - np.cos(10 * np.pi * f_1) + noise
@@ -244,20 +245,24 @@ def test_predictions(global_random_seed, use_feature_names):
# First feature (POS)
# assert pred is all increasing when f_0 is all increasing
X = np.c_[linspace, constant]
+ X = _convert_container(X, constructor_name, columns_name=columns_name)
pred = gbdt.predict(X)
assert is_increasing(pred)
# assert pred actually follows the variations of f_0
X = np.c_[sin, constant]
+ X = _convert_container(X, constructor_name, columns_name=columns_name)
pred = gbdt.predict(X)
assert np.all((np.diff(pred) >= 0) == (np.diff(sin) >= 0))
# Second feature (NEG)
# assert pred is all decreasing when f_1 is all increasing
X = np.c_[constant, linspace]
+ X = _convert_container(X, constructor_name, columns_name=columns_name)
pred = gbdt.predict(X)
assert is_decreasing(pred)
# assert pred actually follows the inverse variations of f_1
X = np.c_[constant, sin]
+ X = _convert_container(X, constructor_name, columns_name=columns_name)
pred = gbdt.predict(X)
assert ((np.diff(pred) <= 0) == (np.diff(sin) >= 0)).all()
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Reference Issues/PRs
None
What does this implement/fix? Explain your changes.
This PR catches
UserWarning: X does not have valid feature namesfor a test for HGBT.Any other comments?
With this PR
pytest -x -Werror sklearn/ensemble/_hist_gradient_boostingsucceeds.