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_int_inputs_supervised(self, name):
value = 1.0
d = {name: value}
scml = SCML_Supervised(**d)
X = np.array([[0, 0], [1, 1], [3, 3], [4, 4]])
y = np.array([1, 1, 0, 0])
msg = ("%s should be an integer, instead it is of type"
" %s" % (name, type(value)))
with pytest.raises(ValueError) as raised_error:
scml.fit(X, y)
assert msg == raised_error.value.args[0]
def test_small_n_basis_lda(self):
X = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
y = np.array([0, 0, 1, 1])
n_class = 2
scml = SCML_Supervised(n_basis=n_class-1)
msg = ("The number of basis is less than the number of classes, which may"
" lead to poor discriminative performance.")
with pytest.warns(UserWarning) as raised_warning:
scml.fit(X, y)
assert msg == raised_warning[0].message.args[0]
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
def test_big_n_basis_lda(self):
X = np.array([[0, 0], [1, 1], [3, 3]])
y = np.array([1, 2, 3])
n_class = 3
num_eig = min(n_class - 1, X.shape[1])
n_basis = X.shape[0] * 2 * num_eig
scml = SCML_Supervised(n_basis=n_basis)
msg = ("Not enough samples to generate %d LDA bases, n_basis"
"should be smaller than %d" % (n_basis, n_basis))
with pytest.raises(ValueError) as raised_error:
scml.fit(X, y)
assert msg == raised_error.value.args[0]