def _create_instance_splitter(self, mode: str):
assert mode in ["training", "validation", "test"]
instance_sampler = {
"training": self.train_sampler,
"validation": self.validation_sampler,
"test": TestSplitSampler(),
}[mode]
return InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=instance_sampler,
past_length=self.history_length,
future_length=self.prediction_length,
time_series_fields=[
FieldName.FEAT_TIME,
FieldName.OBSERVED_VALUES,
],
) + (CDFtoGaussianTransform(
target_field=FieldName.TARGET,
observed_values_field=FieldName.OBSERVED_VALUES,
max_context_length=self.conditioning_length,
target_dim=self.target_dim,
) if self.use_marginal_transformation else RenameFields(
{
f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
}))
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",
})