8000 TST catch UserWarning in test_predictions for HGBT by lorentzenchr · Pull Request #26312 · scikit-learn/scikit-learn · GitHub
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TST catch UserWarning in test_predictions for HGBT #26312

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Merged
merged 2 commits into from
May 23, 2023
Merged

TST catch UserWarning in test_predictions for HGBT #26312

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TST catch UserWarning in test_predictions
lorentzenchr May 1, 2023
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CLN apply reviewer suggestions
lorentzenchr May 2, 2023
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11 changes: 8 additions & 3 deletions sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
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()

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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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