def _parse()

in tzrec/features/sequence_feature.py [0:0]

• 45 lines of code
• 6 McCabe index (conditional complexity)

    def _parse(self, input_data: Dict[str, pa.Array]) -> ParsedData:
        """Parse input data for the feature impl.

        Args:
            input_data (dict): raw input feature data.

        Return:
            parsed feature data.
        """
        if self.fg_mode == FgMode.FG_NONE:
            feat = input_data[self.name]
            if self.is_sparse:
                parsed_feat = _parse_fg_encoded_sequence_sparse_feature_impl(
                    self.name,
                    feat,
                    sequence_delim=self.sequence_delim,
                    **self._fg_encoded_kwargs,
                )
            else:
                parsed_feat = _parse_fg_encoded_sequence_dense_feature_impl(
                    self.name,
                    feat,
                    sequence_delim=self.sequence_delim,
                    value_dim=self.config.value_dim,
                    **self._fg_encoded_kwargs,
                )
        elif self.fg_mode == FgMode.FG_NORMAL:
            input_feat = input_data[self.inputs[0]]
            if pa.types.is_list(input_feat.type):
                input_feat = input_feat.fill_null([])
            input_feat = input_feat.tolist()
            if self._fg_op.is_sparse:
                values, lengths = self._fg_op.to_bucketized_jagged_tensor(input_feat)
                parsed_feat = SequenceSparseData(
                    name=self.name,
                    values=values,
                    key_lengths=np.array(
                        [self._fg_op.value_dimension()] * sum(lengths)
                    ),
                    seq_lengths=lengths,
                )
            else:
                values, lengths = self._fg_op.to_jagged_tensor(input_feat)
                parsed_feat = SequenceDenseData(
                    name=self.name,
                    values=values,
                    seq_lengths=lengths,
                )
        else:
            raise ValueError(
                f"fg_mode: {self.fg_mode} is not supported without fg handler."
            )
        return parsed_feat