def test_inverse_raise_exception(self):
# given
mock = self.DataTransformerMock1()
p = Pipeline([mock])
# when & then
with self.assertRaises(ValueError):
p.inverse_transform(None)
def test_inverse_transform(self):
# given
data = constant_timeseries(0., 3)
transformers = [self.PlusTenTransformer(), self.TimesTwoTransformer()]
p = Pipeline(transformers)
# when
transformed = p.transform(data)
back = p.inverse_transform(transformed)
# then
self.assertEqual(data, back)
def test_pipeline_partial_inverse(self):
series = constant_timeseries(0., 3)
def plus_ten(x):
return x + 10
mapper = Mapper(fn=plus_ten)
mapper_inv = InvertibleMapper(fn=lambda x: x + 2,
inverse_fn=lambda x: x - 2)
series_plus_ten = mapper.transform(series)
pipeline = Pipeline([mapper, mapper_inv])
transformed = pipeline.transform(series)
# should fail, since partial is False by default
with self.assertRaises(ValueError):
pipeline.inverse_transform(transformed)
back = pipeline.inverse_transform(transformed, partial=True)
# while the +/- 2 is inverted, the +10 operation is not
self.assertEqual(series_plus_ten, back)
def test_multi_ts(self):
series1 = constant_timeseries(0., 3)
series2 = constant_timeseries(1., 3)
data = [series1, series2]
mapper1 = InvertibleMapper(fn=lambda x: x + 10,
inverse_fn=lambda x: x - 10)
mapper2 = InvertibleMapper(fn=lambda x: x * 10,
inverse_fn=lambda x: x / 10)
transformers = [mapper1, mapper2]
p = Pipeline(transformers)
# when
transformed = p.transform(data)
back = p.inverse_transform(transformed)
# then
self.assertEqual(data, back)