def test_lda_toy(self):
expected_n_basis = 7
model = SCML_Supervised(n_basis=expected_n_basis)
X = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
y = np.array([0, 0, 1, 1])
basis, n_basis = model._generate_bases_LDA(X, y)
# All points are along the same line, so the only possible basis will be
# the vector along that line normalized. In this case it is possible to
# obtain it with positive or negative orientations.
expected_basis = np.ones((expected_n_basis, 2)) / np.sqrt(2)
assert n_basis == expected_n_basis
np.testing.assert_allclose(np.abs(basis), expected_basis)
def test_lda(self, n_samples, n_features, n_classes):
X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
n_features=n_features, n_informative=n_features,
n_redundant=0, n_repeated=0)
X = StandardScaler().fit_transform(X)
model = SCML_Supervised()
basis, n_basis = model._generate_bases_LDA(X, y)
num_eig = min(n_classes - 1, n_features)
expected_n_basis = min(20 * n_features, n_samples * 2 * num_eig - 1)
assert n_basis == expected_n_basis
assert basis.shape == (expected_n_basis, n_features)
def test_lda(self, n_samples, n_features, n_classes):
"""
Test that when n_basis=None, the correct n_basis is generated,
for SCML_Supervised and different values of n_samples, n_features
and n_classes.
"""
X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
n_features=n_features, n_informative=n_features,
n_redundant=0, n_repeated=0)
X = StandardScaler().fit_transform(X)
msg = "As no value for `n_basis` was selected, "
with pytest.warns(UserWarning) as raised_warning:
model = SCML_Supervised(n_basis=None) # Explicit n_basis=None
basis, n_basis = model._generate_bases_LDA(X, y)
assert msg in str(raised_warning[0].message)
num_eig = min(n_classes - 1, n_features)
expected_n_basis = min(20 * n_features, n_samples * 2 * num_eig - 1)
assert n_basis == expected_n_basis
assert basis.shape == (expected_n_basis, n_features)