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)
def test_pipeline_n_jobs(self):
"""
Checks if the n_jobs param applied to the pipeline is changing the verbosity level in the
contained transformers.
"""
def plus_ten(x):
return x + 10
mapper = Mapper(fn=plus_ten, n_jobs=1)
mapper_inv = InvertibleMapper(fn=lambda x: x + 2,
inverse_fn=lambda x: x - 2,
n_jobs=2)
n_jobs_value = -1
pipeline = Pipeline([mapper, mapper_inv], n_jobs=n_jobs_value)
for transformer in pipeline:
self.assertEqual(transformer._n_jobs, n_jobs_value)
def test_pipeline_verbose(self):
"""
Checks if the verbose param applied to the pipeline is changing the verbosity level in the
contained transformers.
"""
def plus_ten(x):
return x + 10
mapper = Mapper(fn=plus_ten, verbose=True)
mapper_inv = InvertibleMapper(fn=lambda x: x + 2,
inverse_fn=lambda x: x - 2,
verbose=True)
verbose_value = False
pipeline = Pipeline([mapper, mapper_inv], verbose=verbose_value)
for transformer in pipeline:
self.assertEqual(transformer._verbose, verbose_value)
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)