def create_transformation(self) -> Transformation:
return Chain([
AsNumpyArray(
field=FieldName.TARGET,
expected_ndim=2,
),
# maps the target to (1, T)
# if the target data is uni dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME],
),
SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0]),
TargetDimIndicator(
field_name="target_dimension_indicator",
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(num_instances=1),
past_length=self.history_length,
future_length=self.prediction_length,
time_series_fields=[
FieldName.FEAT_TIME,
FieldName.OBSERVED_VALUES,
],
pick_incomplete=self.pick_incomplete,
),
RenameFields({
f"past_{FieldName.TARGET}":
f"past_{FieldName.TARGET}_cdf",
f"future_{FieldName.TARGET}":
f"future_{FieldName.TARGET}_cdf",
}),
])
def create_transformation(self) -> Transformation:
def use_marginal_transformation(
marginal_transformation: bool, ) -> Transformation:
if marginal_transformation:
return CDFtoGaussianTransform(
target_field=FieldName.TARGET,
observed_values_field=FieldName.OBSERVED_VALUES,
max_context_length=self.conditioning_length,
target_dim=self.target_dim,
)
else:
return RenameFields({
f"past_{FieldName.TARGET}":
f"past_{FieldName.TARGET}_cdf",
f"future_{FieldName.TARGET}":
f"future_{FieldName.TARGET}_cdf",
})
return Chain([
AsNumpyArray(
field=FieldName.TARGET,
expected_ndim=1 + len(self.distr_output.event_shape),
),
# maps the target to (1, T)
# if the target data is uni dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=0 if self.distr_output.event_shape[0] == 1 else None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME],
),
SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0]),
TargetDimIndicator(
field_name="target_dimension_indicator",
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(num_instances=1),
past_length=self.history_length,
future_length=self.prediction_length,
time_series_fields=[
FieldName.FEAT_TIME,
FieldName.OBSERVED_VALUES,
],
pick_incomplete=self.pick_incomplete,
),
use_marginal_transformation(self.use_marginal_transformation),
])