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| import json | |
| import os | |
| import pandas as pd | |
| from src.display.formatting import has_no_nan_values, make_clickable_model | |
| # changes to be made here | |
| from src.display.utils import AutoEvalColumn, EvalQueueColumn, OpenEndedColumns, MedSafetyColumns, MedicalSummarizationColumns, ACIColumns, SOAPColumns | |
| from src.leaderboard.read_evals import get_raw_eval_results | |
| def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list, evaluation_metric:str, subset:str) -> pd.DataFrame: | |
| """Creates a dataframe from all the individual experiment results""" | |
| raw_data = get_raw_eval_results(results_path, requests_path, evaluation_metric) | |
| # print(raw_data) | |
| # raise Exception("stop") | |
| all_data_json = [v.to_dict(subset=subset) for v in raw_data] | |
| df = pd.DataFrame.from_records(all_data_json) | |
| # changes to be made here | |
| if subset == "datasets": | |
| df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) | |
| elif subset == "med_safety": | |
| df = df.sort_values(by=["Harmfulness Score"], ascending=True) | |
| elif subset == "open_ended": | |
| df = df.sort_values(by=["ELO"], ascending=False) | |
| elif subset == "medical_summarization": | |
| df = df.sort_values(by=[AutoEvalColumn.overall.name], ascending=False) | |
| elif subset == "aci": | |
| df = df.sort_values(by=[AutoEvalColumn.overall.name], ascending=False) | |
| elif subset == "soap": | |
| df = df.sort_values(by=[AutoEvalColumn.overall.name], ascending=False) | |
| cols = list(set(df.columns).intersection(set(cols))) | |
| df = df[cols].round(decimals=2) | |
| # filter out if any of the benchmarks have not been produced | |
| df = df[has_no_nan_values(df, benchmark_cols)] | |
| return raw_data, df | |
| def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]: | |
| """Creates the different dataframes for the evaluation queues requestes""" | |
| entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")] | |
| all_evals = [] | |
| for entry in entries: | |
| if ".json" in entry: | |
| file_path = os.path.join(save_path, entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model_name"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| # changes to be made here | |
| data[EvalQueueColumn.closed_ended_status.name] = data["status"]["closed-ended"] | |
| data[EvalQueueColumn.open_ended_status.name] = data["status"]["open-ended"] | |
| data[EvalQueueColumn.med_safety_status.name] = data["status"]["med-safety"] | |
| data[EvalQueueColumn.medical_summarization_status.name] = data["status"]["medical-summarization"] | |
| data[EvalQueueColumn.note_generation_status.name] = data["status"]["note-generation"] | |
| all_evals.append(data) | |
| elif ".md" not in entry: | |
| # this is a folder | |
| sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")] | |
| for sub_entry in sub_entries: | |
| file_path = os.path.join(save_path, entry, sub_entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| # print(data) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model_name"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| data[EvalQueueColumn.closed_ended_status.name] = data["status"]["closed-ended"] | |
| data[EvalQueueColumn.open_ended_status.name] = data["status"]["open-ended"] | |
| data[EvalQueueColumn.med_safety_status.name] = data["status"]["med-safety"] | |
| data[EvalQueueColumn.medical_summarization_status.name] = data["status"]["medical-summarization"] | |
| data[EvalQueueColumn.note_generation_status.name] = data["status"]["note-generation"] | |
| all_evals.append(data) | |
| # breakpoint() | |
| pending_list = [] | |
| running_list = [] | |
| finished_list = [] | |
| for run in all_evals: | |
| # changes to be made here | |
| status_list = [run["status"]["closed-ended"], run["status"]["open-ended"], run["status"]["med-safety"], run["status"]["medical-summarization"], run["status"]["note-generation"]] | |
| # status_list = status_list | |
| if "RUNNING" in status_list: | |
| running_list.append(run) | |
| elif "PENDING" in status_list or "RERUN" in status_list: | |
| pending_list.append(run) | |
| else: | |
| finished_list.append(run) | |
| # breakpoint() | |
| df_pending = pd.DataFrame.from_records(pending_list, columns=cols) | |
| df_running = pd.DataFrame.from_records(running_list, columns=cols) | |
| df_finished = pd.DataFrame.from_records(finished_list, columns=cols) | |
| return df_finished[cols], df_running[cols], df_pending[cols] | |