def test_boxcox_inverse(self):
boxcox = BoxCox()
transformed = boxcox.fit_transform(self.multi_series)
back = boxcox.inverse_transform(transformed)
pd.testing.assert_frame_equal(self.multi_series.pd_dataframe(),
back.pd_dataframe(),
check_exact=False)
def test_boxcox_transform(self):
log_mapper = Mapper(lambda x: log(x))
boxcox = BoxCox(lmbda=0)
transformed1 = log_mapper.transform(self.sine_series)
transformed2 = boxcox.fit(self.sine_series).transform(self.sine_series)
self.assertEqual(transformed1, transformed2)
def test_multivariate_stochastic_series(self):
transformer = BoxCox()
vals = np.random.rand(10, 5, 10)
series = TimeSeries.from_values(vals)
new_series = transformer.fit_transform(series)
series_back = transformer.inverse_transform(new_series)
# Test inverse transform
np.testing.assert_allclose(series.all_values(),
series_back.all_values())
def test_boxcox_transform(self):
log_mapper = Mapper(lambda x: np.log(x))
boxcox = BoxCox(lmbda=0)
transformed1 = log_mapper.transform(self.sine_series)
transformed2 = boxcox.fit(self.sine_series).transform(self.sine_series)
np.testing.assert_almost_equal(
transformed1.all_values(copy=False),
transformed2.all_values(copy=False),
decimal=4,
)
def test_boxcox_multi_ts(self):
test_cases = [
([[0.2, 0.4], [0.3, 0.6]]), # full lambda
(0.4), # single value
None # None
]
for lmbda in test_cases:
box_cox = BoxCox(lmbda=lmbda)
transformed = box_cox.fit_transform([self.multi_series, self.multi_series])
back = box_cox.inverse_transform(transformed)
pd.testing.assert_frame_equal(self.multi_series._df, back[0]._df, check_exact=False)
pd.testing.assert_frame_equal(self.multi_series._df, back[1]._df, check_exact=False)
def test_boxcox_multiple_calls_to_fit(self):
"""
This test checks whether calling the scaler twice is calculating new lambdas instead of
keeping the old ones
"""
box_cox = BoxCox()
box_cox.fit(self.sine_series)
lambda1 = deepcopy(box_cox._fitted_params)[0].tolist()
box_cox.fit(self.lin_series)
lambda2 = deepcopy(box_cox._fitted_params)[0].tolist()
self.assertNotEqual(lambda1, lambda2, "Lambdas should change when the transformer is retrained")
def test_boxbox_lambda(self):
boxcox = BoxCox(lmbda=0.3)
boxcox.fit(self.multi_series)
self.assertEqual(boxcox._fitted_params, [[0.3, 0.3]])
boxcox = BoxCox(lmbda=[0.3, 0.4])
boxcox.fit(self.multi_series)
self.assertEqual(boxcox._fitted_params, [[0.3, 0.4]])
with self.assertRaises(ValueError):
boxcox = BoxCox(lmbda=[0.2, 0.4, 0.5])
boxcox.fit(self.multi_series)
boxcox = BoxCox(optim_method="mle")
boxcox.fit(self.multi_series)
lmbda1 = boxcox._fitted_params[0].tolist()
boxcox = BoxCox(optim_method="pearsonr")
boxcox.fit(self.multi_series)
lmbda2 = boxcox._fitted_params[0].tolist()
self.assertNotEqual(lmbda1, lmbda2)
def test_boxbox_lambda(self):
boxcox = BoxCox()
boxcox.fit(self.multi_series, 0.3)
self.assertEqual(boxcox._lmbda, [0.3, 0.3])
boxcox.fit(self.multi_series, [0.3, 0.4])
self.assertEqual(boxcox._lmbda, [0.3, 0.4])
with self.assertRaises(ValueError):
boxcox.fit(self.multi_series, [0.2, 0.4, 0.5])
boxcox.fit(self.multi_series, optim_method='mle')
lmbda1 = boxcox._lmbda
boxcox.fit(self.multi_series, optim_method='pearsonr')
lmbda2 = boxcox._lmbda
self.assertNotEqual(lmbda1.array, lmbda2.array)