def test_NotFittedError_transform(self):
"""Checks that an error is returned when
trying to use the transform function
before the fit function"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=True)
with self.assertRaises(sklearn.exceptions.NotFittedError):
model.transform(X)
def test_shape_inconsistent_transform(self):
"""Checks that an error is returned when attempting
to use the 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.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())
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())