def test_wrong_estimators_exceptions():
X = np.array([[0.]])
y = np.array([0.])
with pytest.raises(ValueError,
match="`classifier` has to be a classifier."):
zir = ZeroInflatedRegressor(ExtraTreesRegressor(),
ExtraTreesRegressor())
zir.fit(X, y)
with pytest.raises(ValueError, match="`regressor` has to be a regressor."):
zir = ZeroInflatedRegressor(ExtraTreesClassifier(),
ExtraTreesClassifier())
zir.fit(X, y)
def test_zero_inflated_example():
from sklearn.model_selection import cross_val_score
np.random.seed(0)
X = np.random.randn(10000, 4)
y = ((X[:, 0] > 0) & (X[:, 1] > 0)) * np.abs(
X[:, 2] * X[:, 3]**2) # many zeroes here, in about 75% of the cases.
zir = ZeroInflatedRegressor(
classifier=ExtraTreesClassifier(random_state=0),
regressor=ExtraTreesRegressor(random_state=0))
zir_score = cross_val_score(zir, X, y).mean()
et_score = cross_val_score(ExtraTreesRegressor(), X, y).mean()
assert zir_score > 0.85
assert zir_score > et_score
def test_estimator_checks(test_fn):
test_fn(
ZeroInflatedRegressor.__name__,
ZeroInflatedRegressor(classifier=ExtraTreesClassifier(random_state=0),
regressor=ExtraTreesRegressor(random_state=0)))
def test_zir(test_fn):
regr = ZeroInflatedRegressor(
classifier=ExtraTreesClassifier(random_state=0),
regressor=ExtraTreesRegressor(random_state=0))
test_fn(ZeroInflatedRegressor.__name__, regr)