def test_invalid_sample_weights(self):
"""Checks that weights must be 1D array with the same length as the number of samples"""
X = self.random_state.uniform(0, 100, size=(3, 3))
wts_len = np.ones(len(X) + 1)
wts_dim = np.ones((len(X), 2))
model = StandardFlexibleScaler()
with self.assertRaises(ValueError):
model.fit_transform(X, sample_weight=wts_len)
with self.assertRaises(ValueError):
model.fit_transform(X, sample_weight=wts_dim)
def test_sample_weights(self):
"""Checks that sample weights of one are equal to the unweighted case and that the nonuniform weights are different from the unweighted case"""
X = self.random_state.uniform(0, 100, size=(3, 3))
equal_wts = np.ones(len(X))
nonequal_wts = self.random_state.uniform(0, 100, size=(len(X),))
model = StandardFlexibleScaler()
weighted_model = StandardFlexibleScaler()
X_unweighted = model.fit_transform(X)
X_equal_weighted = weighted_model.fit_transform(X, sample_weight=equal_wts)
self.assertTrue((np.isclose(X_unweighted, X_equal_weighted, atol=1e-12)).all())
X_nonequal_weighted = weighted_model.fit_transform(
X, sample_weight=nonequal_wts
)
self.assertFalse(
(np.isclose(X_unweighted, X_nonequal_weighted, atol=1e-12)).all()
)
def test_fit_transform_pf(self):
"""Checks that in the case of normalization by columns,
the result is the same as in the case of using the package from sklearn
"""
X = self.random_state.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=True)
transformed_skcosmo = model.fit_transform(X)
transformed_sklearn = StandardScaler().fit_transform(X)
self.assertTrue(
(np.isclose(transformed_sklearn, transformed_skcosmo, atol=1e-12)).all()
)
def test_fit_transform_npf(self):
"""Checks that the entire matrix is correctly normalized
(not column-wise). Compare with the value calculated
directly from the equation.
"""
X = self.random_state.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=False)
X_tr = model.fit_transform(X)
mean = X.mean(axis=0)
var = ((X - mean) ** 2).mean(axis=0)
scale = np.sqrt(var.sum())
X_ex = (X - mean) / scale
self.assertTrue((np.isclose(X_ex, X_tr, atol=1e-12)).all())