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MNT Use check_scalar in AdaBoostRegressor #21605

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Merged
merged 11 commits into from
Dec 16, 2021
Merged

MNT Use check_scalar in AdaBoostRegressor #21605

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12e1627
Add tests
genvalen Nov 9, 2021
8482313
Validate scalar parameters with check_scalar
genvalen Nov 9, 2021
68fd69c
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
genvalen Nov 24, 2021
9b8a673
Move checks from AdaBoostRegressor to BaseWeightBoosting
genvalen Nov 24, 2021
28e2af0
Update tests
genvalen Dec 10, 2021
0335ea2
Remove redundant checks from AdaBoostClassifer
genvalen Dec 10, 2021
94d420d
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
genvalen Dec 10, 2021
2cabe56
Minor clean up and format with black
genvalen Dec 10, 2021
563c5f0
Update sklearn/ensemble/tests/test_weight_boosting.py
genvalen Dec 14, 2021
2ae607d
Update sklearn/ensemble/tests/test_weight_boosting.py
genvalen Dec 14, 2021
360e7d0
Ensure validation happens in
genvalen Dec 14, 2021
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40 changes: 20 additions & 20 deletions sklearn/ensemble/_weight_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,9 +111,22 @@ def fit(self, X, y, sample_weight=None):
-------
self : object
"""
# Check parameters
if self.learning_rate <= 0:
raise ValueError("learning_rate must be greater than zero")
# Validate scalar parameters
check_scalar(
self.n_estimators,
"n_estimators",
target_type=numbers.Integral,
min_val=1,
include_boundaries="left",
)

check_scalar(
self.learning_rate,
"learning_rate",
target_type=numbers.Real,
min_val=0,
include_boundaries="neither",
)

X, y = self._validate_data(
X,
Expand Down Expand Up @@ -480,22 +493,6 @@ def fit(self, X, y, sample_weight=None):
self : object
Fitted estimator.
"""
check_scalar(
self.n_estimators,
"n_estimators",
target_type=numbers.Integral,
min_val=1,
include_boundaries="left",
)

check_scalar(
self.learning_rate,
"learning_rate",
target_type=numbers.Real,
min_val=0,
include_boundaries="neither",
)

# Check that algorithm is supported
if self.algorithm not in ("SAMME", "SAMME.R"):
raise ValueError(
Expand Down Expand Up @@ -1080,7 +1077,10 @@ def fit(self, X, y, sample_weight=None):
"""
# Check loss
if self.loss not in ("linear", "square", "exponential"):
raise ValueError("loss must be 'linear', 'square', or 'exponential'")
raise ValueError(
"loss must be 'linear', 'square', or 'exponential'"
f" Got {self.loss!r} instead."
)

# Fit
return super().fit(X, y, sample_weight)
Expand Down
27 changes: 19 additions & 8 deletions sklearn/ensemble/tests/test_weight_boosting.py
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Expand Up @@ -273,6 +273,14 @@ def test_importances():
def test_error():
# Test that it gives proper exception on deficient input.

reg = AdaBoostRegressor(loss="foo")
with pytest.raises(ValueError):
reg.fit(X, y_class)

clf = AdaBoostClassifier(algorithm="foo")
with pytest.raises(ValueError):
clf.fit(X, y_class)
Comment on lines +276 to +282
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@reshamas: I should have been clearer.

Here for instance, there's a check for ValueErrors being raised but their error messages aren't checked.

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Should we re-open this one? Error messages are important and helpful, correct?

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Here we go: #22144


with pytest.raises(ValueError):
AdaBoostClassifier().fit(X, y_class, sample_weight=np.asarray([-1]))

Expand Down Expand Up @@ -556,17 +564,20 @@ def test_adaboostregressor_sample_weight():
),
({"learning_rate": -1}, ValueError, "learning_rate == -1, must be > 0."),
({"learning_rate": 0}, ValueError, "learning_rate == 0, must be > 0."),
(
{"algorithm": "unknown"},
ValueError,
"Algorithm must be 'SAMME' or 'SAMME.R'.",
),
],
)
def test_adaboost_classifier_params_validation(params, err_type, err_msg):
"""Check the parameters validation in `AdaBoostClassifier`."""
@pytest.mark.parametrize(
"model, X, y",
[
(AdaBoostClassifier, X, y_class),
(AdaBoostRegressor, X, y_regr),
],
)
def test_adaboost_params_validation(model, X, y, params, err_type, err_msg):
"""Check input parameter validation in weight boosting."""
est = model(**params)
with pytest.raises(err_type, match=err_msg):
AdaBoostClassifier(**params).fit(X, y_class)
est.fit(X, y)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
Expand Down