def _extract_keyword_entities_rltn_score()

in src/entity_relation_scorer.py [0:0]

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

def _extract_keyword_entities_rltn_score(df, entity_name, entity_type, relation, tgt_entity_name, score_col=None):
    sel_df = df.loc[~df[entity_name].isna(), [entity_name, tgt_entity_name]].reset_index(drop=True)
    for ers_info, tgt_val in zip(sel_df[entity_name].apply(json.loads), sel_df[tgt_entity_name]):
        for ers in ers_info:
            for key, val in ers.items():
                # print(key, val, tgt_val)
                if key == entity_type:
                    src_entity = val
                if score_col and key == score_col:
                    score = 1+val
                else:
                    score = None
            yield _generate_entity_rltn_score(src_entity, entity_type, relation, tgt_val, score)