import pandas as pd import os from src.display.formatting import styled_error, styled_message, styled_warning from huggingface_hub import HfApi from src.display.utils import AutoEvalColumnQA, AutoEvalColumnLongDoc, COLS_QA, COLS_LONG_DOC, QA_BENCHMARK_COLS, LONG_DOC_BENCHMARK_COLS from src.benchmarks import BENCHMARK_COLS_QA, BENCHMARK_COLS_LONG_DOC, BenchmarksQA, BenchmarksLongDoc from src.leaderboard.read_evals import FullEvalResult, get_leaderboard_df from typing import List 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, task: str="qa") -> pd.DataFrame: if task == "qa": always_here_cols = [ AutoEvalColumnQA.retrieval_model.name, AutoEvalColumnQA.reranking_model.name, AutoEvalColumnQA.average.name ] cols = list(frozenset(COLS_QA).intersection(frozenset(BENCHMARK_COLS_QA))) elif task == "long_doc": always_here_cols = [ AutoEvalColumnLongDoc.retrieval_model.name, AutoEvalColumnLongDoc.reranking_model.name, AutoEvalColumnLongDoc.average.name ] cols = list(frozenset(COLS_LONG_DOC).intersection(frozenset(BENCHMARK_COLS_LONG_DOC))) selected_cols = [] for c in cols: if c not in df.columns: continue if task == "qa": eval_col = BenchmarksQA[c].value elif task == "long_doc": eval_col = BenchmarksLongDoc[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[always_here_cols[2]] = filtered_df[selected_cols].mean(axis=1).round(decimals=2) return filtered_df def update_table( hidden_df: pd.DataFrame, domains: list, langs: 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, domains, langs) return df def update_table_long_doc( hidden_df: pd.DataFrame, domains: list, langs: 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, domains, langs, task='long_doc') return df def update_metric( raw_data: List[FullEvalResult], task: str, metric: str, domains: list, langs: list, reranking_model: list, query: str, ) -> pd.DataFrame: if task == 'qa': leaderboard_df = get_leaderboard_df(raw_data, COLS_QA, QA_BENCHMARK_COLS, task=task, metric=metric) return update_table( leaderboard_df, domains, langs, reranking_model, query ) elif task == 'long_doc': leaderboard_df = get_leaderboard_df(raw_data, COLS_LONG_DOC, LONG_DOC_BENCHMARK_COLS, task=task, metric=metric) return update_table_long_doc( leaderboard_df, domains, langs, reranking_model, query ) def upload_file(files): file_paths = [file.name for file in files] print(f"file uploaded: {file_paths}") # for fp in file_paths: # # upload the file # print(file_paths) # HfApi(token="").upload_file(...) # os.remove(fp) return file_paths