def test_calling_fit_reinitializes():
est = LinearSVC(random_state=0)
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
transformer.set_params(estimator__C=100)
transformer.fit(data, y)
assert_equal(transformer.estimator_.C, 100)
def test_calling_fit_reinitializes():
est = LinearSVC(random_state=0)
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
transformer.set_params(estimator__C=100)
transformer.fit(data, y)
assert transformer.estimator_.C == 100
def test_prefit_max_features():
"""Check the interaction between `prefit` and `max_features`."""
# case 1: an error should be raised at `transform` if `fit` was not called to
# validate the attributes
estimator = RandomForestClassifier(n_estimators=5, random_state=0)
estimator.fit(data, y)
model = SelectFromModel(estimator,
prefit=True,
max_features=lambda X: X.shape[1])
err_msg = (
"When `prefit=True` and `max_features` is a callable, call `fit` "
"before calling `transform`.")
with pytest.raises(NotFittedError, match=err_msg):
model.transform(data)
# case 2: `max_features` is not validated and different from an integer
# FIXME: we cannot validate the upper bound of the attribute at transform
# and we should force calling `fit` if we intend to force the attribute
# to have such an upper bound.
max_features = 2.5
model.set_params(max_features=max_features)
with pytest.raises(ValueError, match="`max_features` must be an integer"):
model.transform(data)
