def test_iris(self, basis):
X, y = load_iris(return_X_y=True)
scml = SCML_Supervised(basis=basis, n_basis=85, k_genuine=7, k_impostor=5,
random_state=42)
scml.fit(X, y)
csep = class_separation(scml.transform(X), y)
assert csep < 0.24
def test_big_n_features(self):
X, y = make_classification(n_samples=100, n_classes=3, n_features=60,
n_informative=60, n_redundant=0, n_repeated=0,
random_state=42)
X = StandardScaler().fit_transform(X)
scml = SCML_Supervised(random_state=42)
scml.fit(X, y)
csep = class_separation(scml.transform(X), y)
assert csep < 0.7
def test_iris(self, basis):
"""
SCML applied to Iris dataset should give better results when
computing class separation.
"""
X, y = load_iris(return_X_y=True)
before = class_separation(X, y)
scml = SCML_Supervised(basis=basis, n_basis=85, k_genuine=7, k_impostor=5,
random_state=42)
scml.fit(X, y)
after = class_separation(scml.transform(X), y)
assert before > after + 0.03 # It's better by a margin of 0.03