def test_obvious_usecase():
X = np.concatenate([
np.random.normal(-10, 1, (100, 2)),
np.random.normal(10, 1, (100, 2))
])
y = np.concatenate([np.zeros(100), np.ones(100)])
assert (GMMClassifier().fit(X, y).predict(X) == y).all()
def test_value_error_threshold():
X = np.concatenate([
np.random.normal(-10, 1, (100, 2)),
np.random.normal(10, 1, (100, 2))
])
y = np.concatenate([np.zeros(100), np.ones(100)])
with pytest.raises(ValueError):
GMMClassifier(megatondinosaurhead=1).fit(X, y)
def test_estimator_checks(test_fn):
clf = GMMClassifier()
test_fn(GMMClassifier.__name__, clf)
clf = BayesianGMMClassifier()
test_fn(BayesianGMMClassifier.__name__, clf)
@pytest.mark.parametrize("estimator", [
RandomRegressor(strategy="uniform"),
RandomRegressor(strategy="normal"),
DeadZoneRegressor(effect="linear", n_iter=100),
DeadZoneRegressor(effect="quadratic", n_iter=100),
],
ids=id_func)
def test_shape_regression(estimator, random_xy_dataset_regr):
X, y = random_xy_dataset_regr
assert estimator.fit(X, y).predict(X).shape[0] == y.shape[0]
pipe = Pipeline(steps=[('scaler', StandardScaler()), ('clf', estimator)])
assert pipe.fit(X, y).predict(X).shape[0] == y.shape[0]
@pytest.mark.parametrize("estimator", [
GMMClassifier(),
BayesianGMMClassifier(),
GMMOutlierDetector(threshold=0.999, method="quantile"),
GMMOutlierDetector(threshold=2, method="stddev"),
BayesianGMMOutlierDetector(threshold=0.999, method="quantile"),
BayesianGMMOutlierDetector(threshold=2, method="stddev")
],
ids=id_func)
def test_shape_classification(estimator, random_xy_dataset_clf):
X, y = random_xy_dataset_clf
assert estimator.fit(X, y).predict(X).shape[0] == y.shape[0]
pipe = Pipeline(steps=[('scaler', StandardScaler()), ('clf', estimator)])
assert pipe.fit(X, y).predict(X).shape[0] == y.shape[0]