ludwig/features/category_feature.py [344:378]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                            output_feature['name'],
                            all_rows_length,
                            output_feature['num_classes']
                        )
                    )

                similarities = np.array(similarities, dtype=np.float32)
                for i in range(len(similarities)):
                    similarities[i, :] = softmax(
                        similarities[i, :],
                        temperature=temperature
                    )

                output_feature[LOSS]['class_similarities'] = similarities
            else:
                raise ValueError(
                    'class_similarities_temperature > 0, '
                    'but no class_similarities are provided '
                    'for feature {}'.format(output_feature['name'])
                )

        if output_feature[LOSS][TYPE] == 'sampled_softmax_cross_entropy':
            output_feature[LOSS]['class_counts'] = [
                feature_metadata['str2freq'][cls]
                for cls in feature_metadata['idx2str']
            ]

    @staticmethod
    def calculate_overall_stats(
            test_stats,
            output_feature,
            dataset,
            train_set_metadata
    ):
        feature_name = output_feature['name']
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



ludwig/features/sequence_feature.py [341:374]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                            output_feature['name'],
                            all_rows_length,
                            output_feature['num_classes']
                        )
                    )

                similarities = np.array(similarities, dtype=np.float32)
                for i in range(len(similarities)):
                    similarities[i, :] = softmax(
                        similarities[i, :],
                        temperature=temperature
                    )
                output_feature[LOSS]['class_similarities'] = similarities
            else:
                raise ValueError(
                    'class_similarities_temperature > 0, '
                    'but no class_similarities are provided '
                    'for feature {}'.format(output_feature['name'])
                )

        if output_feature[LOSS][TYPE] == 'sampled_softmax_cross_entropy':
            output_feature[LOSS]['class_counts'] = [
                feature_metadata['str2freq'][cls]
                for cls in feature_metadata['idx2str']
            ]

    @staticmethod
    def calculate_overall_stats(
            test_stats,
            output_feature,
            dataset,
            train_set_metadata
    ):
        feature_name = output_feature['name']
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -