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), ])