import pandas as pd from src.display.utils import AutoEvalColumnQA, COLS from src.benchmarks import BENCHMARK_COLS_QA, BenchmarksQA def filter_models(df: pd.DataFrame, reranking_query: list) -> pd.DataFrame: return df.loc[df["Reranking Model"].isin(reranking_query)] def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: final_df = [] if query != "": queries = [q.strip() for q in query.split(";")] for _q in queries: _q = _q.strip() if _q != "": temp_filtered_df = search_table(filtered_df, _q) if len(temp_filtered_df) > 0: final_df.append(temp_filtered_df) if len(final_df) > 0: filtered_df = pd.concat(final_df) filtered_df = filtered_df.drop_duplicates( subset=[ AutoEvalColumnQA.retrieval_model.name, AutoEvalColumnQA.reranking_model.name, ] ) return filtered_df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumnQA.retrieval_model.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, domain_query: list, language_query: list) -> pd.DataFrame: always_here_cols = [ AutoEvalColumnQA.retrieval_model.name, AutoEvalColumnQA.reranking_model.name, AutoEvalColumnQA.average.name ] selected_cols = [] for c in COLS: if c not in df.columns: continue if c not in BENCHMARK_COLS_QA: continue eval_col = BenchmarksQA[c].value if eval_col.domain not in domain_query: continue if eval_col.lang not in language_query: continue selected_cols.append(c) # We use COLS to maintain sorting filtered_df = df[always_here_cols + selected_cols] filtered_df[AutoEvalColumnQA.average.name] = filtered_df[selected_cols].mean(axis=1) return filtered_df def update_table( hidden_df: pd.DataFrame, columns: list, reranking_query: list, query: str, ): filtered_df = filter_models(hidden_df, reranking_query) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns) return df