def create_transformation()

in src/gluonts/torch/model/deepar/estimator.py [0:0]

• 64 lines of code
• 7 McCabe index (conditional complexity)

    def create_transformation(self) -> Transformation:
        remove_field_names = []
        if self.num_feat_static_real == 0:
            remove_field_names.append(FieldName.FEAT_STATIC_REAL)
        if self.num_feat_dynamic_real == 0:
            remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)

        return Chain(
            [RemoveFields(field_names=remove_field_names)]
            + (
                [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])]
                if not self.num_feat_static_cat > 0
                else []
            )
            + (
                [
                    SetField(
                        output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
                    )
                ]
                if not self.num_feat_static_real > 0
                else []
            )
            + [
                AsNumpyArray(
                    field=FieldName.FEAT_STATIC_CAT,
                    expected_ndim=1,
                    dtype=np.long,
                ),
                AsNumpyArray(
                    field=FieldName.FEAT_STATIC_REAL,
                    expected_ndim=1,
                ),
                AsNumpyArray(
                    field=FieldName.TARGET,
                    # in the following line, we add 1 for the time dimension
                    expected_ndim=1 + len(self.distr_output.event_shape),
                ),
                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,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.num_feat_dynamic_real > 0
                        else []
                    ),
                ),
            ]
        )