def test_ValueError_full(self):
"""Checks that the matrix cannot be normalized
if there is a zero variation matrix."""
X = np.array([2, 2, 2]).reshape(-1, 1)
model = StandardFlexibleScaler(column_wise=False)
with self.assertRaises(ValueError):
model.fit(X)
def test_NotFittedError_inverse(self):
"""Checks that an error is returned when
trying to use the inverse transform function
before the fit function"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler()
with self.assertRaises(sklearn.exceptions.NotFittedError):
model.inverse_transform(X)
def test_ValueError_column_wise(self):
"""Checks that the matrix cannot be normalized
across columns if there is a zero variation column."""
X = np.random.uniform(0, 100, size=(3, 3))
X[0][0] = X[1][0] = X[2][0] = 2
model = StandardFlexibleScaler(column_wise=True)
with self.assertRaises(ValueError):
model.fit(X)
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 = np.random.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 = np.random.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())
def test_transform(self):
"""Checks the transformation relative
to the reference matrix.
"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=True)
model.fit(X)
Y = np.random.uniform(0, 100, size=(3, 3))
Y_tr = model.transform(Y)
mean = X.mean(axis=0)
var = ((X - mean)**2).mean(axis=0)
scale = np.sqrt(var)
Y_ex = (Y - mean) / scale
self.assertTrue((np.isclose(Y_tr, Y_ex, atol=1e-12)).all())
def test_shape_inconsistent_inverse(self):
"""Checks that an error is returned when attempting
to use the inverse transform function with mismatched matrix sizes."""
X = np.random.uniform(0, 100, size=(3, 3))
X_test = np.random.uniform(0, 100, size=(4, 4))
model = StandardFlexibleScaler(column_wise=True)
model.fit(X)
with self.assertRaises(ValueError):
model.inverse_transform(X_test)
def test_inverse_transform(self):
"""Checks the inverse transformation with
respect to the reference matrix.
"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=True)
model.fit(X)
Y = np.random.uniform(0, 100, size=(3, 3))
Y_tr = model.transform(Y)
Y = np.around(Y, decimals=4)
Y_inv = np.around((model.inverse_transform(Y_tr)), decimals=4)
self.assertTrue((np.isclose(Y, Y_inv, atol=1e-12)).all())
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=False)
model.fit(X)
Y = np.random.uniform(0, 100, size=(3, 3))
Y_tr = model.transform(Y)
Y = np.around(Y, decimals=4)
Y_inv = np.around((model.inverse_transform(Y_tr)), decimals=4)
self.assertTrue((np.isclose(Y, Y_inv, atol=1e-12)).all())