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feat: add tests, fix logic
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@madtoinou
madtoinou committed Nov 18, 2024
1 parent 960d239 commit 79c1cd7
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Showing 2 changed files with 107 additions and 10 deletions.
4 changes: 2 additions & 2 deletions darts/dataprocessing/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -230,7 +230,7 @@ def inverse_transform(
idx_params=idx_params
if (
isinstance(transformer, FittableDataTransformer)
and transformer._global_fit
and not transformer._global_fit
)
else None,
)
Expand All @@ -243,7 +243,7 @@ def inverse_transform(
idx_params=idx_params
if (
isinstance(transformer, FittableDataTransformer)
and transformer._global_fit
and not transformer._global_fit
)
else None,
)
Expand Down
113 changes: 105 additions & 8 deletions darts/tests/dataprocessing/test_pipeline.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -121,13 +121,13 @@ def ts_fit(

@staticmethod
def ts_transform(data: TimeSeries, params: Mapping[str, Any]) -> TimeSeries:
return data.append_values(params["fitted"])
return data + params["fitted"]

@staticmethod
def ts_inverse_transform(
data: TimeSeries, params: Mapping[str, Any]
) -> TimeSeries:
return data[:-1]
return data - params["fitted"]

def test_transform(self):
# given
Expand All @@ -148,6 +148,11 @@ def test_transform(self):
assert not t.inverse_transform_called

def test_transform_prefitted(self):
"""Check that when multiple series are passed to fit transformers with global_fit=False,
transform behave as expected when idx_params is specified.
Note: the transformers are fitted independently to make the expected results more intuitive
"""
data = [
constant_timeseries(value=0, length=2),
constant_timeseries(value=10, length=2),
Expand All @@ -166,11 +171,13 @@ def get_transf(global_fit: bool, fit: bool, coef: int):
])
transformed = p.transform(data)

# ts + (data[0][0] + 1) + (data[0][0] + 5) = 6
np.testing.assert_array_almost_equal(
transformed[0].values(), np.array([[0, 0, 1, 5]]).T
transformed[0].values(), np.array([[6, 6]]).T
)
# ts + (data[1][0] + 1) + (data[1][0] + 5) = 10 + 11 + 15 = 36
np.testing.assert_array_almost_equal(
transformed[1].values(), np.array([[10, 10, 11, 15]]).T
transformed[1].values(), np.array([[36, 36]]).T
)
# implicitely use the first params of each transformer
np.testing.assert_array_almost_equal(
Expand All @@ -181,8 +188,9 @@ def get_transf(global_fit: bool, fit: bool, coef: int):
transformed[0].values(), p.transform(data[0], idx_params=[0]).values()
)
# implicitely use the first params of each transformer
# ts + (data[0][0] + 1) + (data[0][0] + 5) = 10 + 1 + 5 = 16
np.testing.assert_array_almost_equal(
np.array([[10, 10, 1, 5]]).T, p.transform(data[1]).values()
np.array([[16, 16]]).T, p.transform(data[1]).values()
)
# explicitely use the second params of each transformer
np.testing.assert_array_almost_equal(
Expand All @@ -195,11 +203,13 @@ def get_transf(global_fit: bool, fit: bool, coef: int):
get_transf(global_fit=True, fit=True, coef=90),
])
transformed = p.transform(data)
# ts + (data[0][0] + 1) + (sum(data[;, 0]) + 90) = 0 + 1 + 100
np.testing.assert_array_almost_equal(
transformed[0].values(), np.array([[0, 0, 1, 100]]).T
transformed[0].values(), np.array([[101, 101]]).T
)
# ts + (data[1][0] + 1) + (sum(data[;, 0]) + 90) = 10 + 11 + 100
np.testing.assert_array_almost_equal(
transformed[1].values(), np.array([[10, 10, 11, 100]]).T
transformed[1].values(), np.array([[121, 121]]).T
)
# implicitely use the first params of first transformer, the second is global
np.testing.assert_array_almost_equal(
Expand All @@ -210,8 +220,9 @@ def get_transf(global_fit: bool, fit: bool, coef: int):
transformed[0].values(), p.transform(data[0], idx_params=[0]).values()
)
# implicitely use the first params of first transformer, the second is global
# ts + (data[0][0] + 1) + (sum(data[;, 0]) + 90) = 10 + 1 + 100
np.testing.assert_array_almost_equal(
np.array([[10, 10, 1, 100]]).T, p.transform(data[1]).values()
np.array([[111, 111]]).T, p.transform(data[1]).values()
)
# explicitely use the second params of first transformer, the second is global
np.testing.assert_array_almost_equal(
Expand Down Expand Up @@ -309,6 +320,92 @@ def test_inverse_transform(self):
# then
assert data == back

def test_inverse_transform_prefitted(self):
"""Check that when multiple series are passed to fit transformers with global_fit=False,
inverse_transform behave as expected when idx_params is specified.
Note: the transformers are fitted independently to make the expected results more intuitive
"""
data = [
constant_timeseries(value=0, length=2),
constant_timeseries(value=10, length=2),
]

def get_transf(global_fit: bool, fit: bool, coef: int):
transf = self.ExtendTransformer(global_fit=global_fit, coef=coef)
if fit:
transf.fit(data)
return transf

# multiple series, global_fit=False
p = Pipeline([
get_transf(global_fit=False, fit=True, coef=1),
get_transf(global_fit=False, fit=True, coef=5),
])
transformed = p.transform(data)

# implicitely use the first params of each transformer
np.testing.assert_array_almost_equal(
data[0].values(), p.inverse_transform(transformed[0]).values()
)
# explicitely use the first params of each transformer
np.testing.assert_array_almost_equal(
data[0].values(),
p.inverse_transform(transformed[0], idx_params=[0]).values(),
)

# 10 + 11 + 15
np.testing.assert_array_almost_equal(
np.array([[36, 36]]).T, transformed[1].values()
)
# implicitely use the first params of each transformer
# inverse_transform[0][0] = lambda x: x - 1, inverse_transform[1][0] = lamdda x: x - 5
np.testing.assert_array_almost_equal(
np.array([[30, 30]]).T, p.inverse_transform(transformed[1]).values()
)
np.testing.assert_array_almost_equal(
np.array([[30, 30]]).T,
p.inverse_transform(transformed[1], idx_params=0).values(),
)
# explicitely use the second params of each transformer
# inverse_transform[0][0] = lambda x: x - 11, inverse_transform[1][0] = lamdda x: x - 15
np.testing.assert_array_almost_equal(
data[1].values(), p.inverse_transform(transformed[1], idx_params=1).values()
)

# multiple series, mixture of local and global transformers
p = Pipeline([
get_transf(global_fit=False, fit=True, coef=1),
get_transf(global_fit=True, fit=True, coef=90),
])
transformed = p.transform(data)

# implicitely use the first params of each transformer
np.testing.assert_array_almost_equal(
data[0].values(), p.inverse_transform(transformed[0]).values()
)
# explicitely use the first params of each transformer
np.testing.assert_array_almost_equal(
data[0].values(),
p.inverse_transform(transformed[0], idx_params=[0]).values(),
)
# 10 + 11 + 100
np.testing.assert_array_almost_equal(
np.array([[121, 121]]).T, transformed[1].values()
)

# implicitely use the first params of each transformer
# inverse_transform[0][0] = lambda x: x - 1, inverse_transform = lamdda x: x - 100
np.testing.assert_array_almost_equal(
np.array([[20, 20]]).T, p.inverse_transform(transformed[1]).values()
)
# explicitely use the second params of each transformer
# inverse_transform[0][1] = lambda x: x - 11, inverse_transform = lamdda x: x - 100
np.testing.assert_array_almost_equal(
data[1].values(),
p.inverse_transform(transformed[1], idx_params=[1]).values(),
)

def test_getitem(self):
# given

Expand Down

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