import json import os import pandas as pd import numpy as np from src.display.formatting import has_no_nan_values, make_clickable_model from src.display.utils import AutoEvalColumn, EvalQueueColumn 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) -> pd.DataFrame: """Creates a dataframe from all the individual experiment results""" raw_data = get_raw_eval_results(results_path, requests_path) all_data_json = [v.to_dict() for v in raw_data] df = pd.DataFrame.from_records(all_data_json) # Add category average columns with default values category_avg_columns = { "Average IE ⬆️": "average_IE", "Average TA ⬆️": "average_TA", "Average QA ⬆️": "average_QA", "Average TG ⬆️": "average_TG", "Average RM ⬆️": "average_RM", "Average FO ⬆️": "average_FO", "Average DM ⬆️": "average_DM", "Average Spanish ⬆️": "average_Spanish" } for display_name, internal_name in category_avg_columns.items(): df[display_name] = df[internal_name] df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) # Apply the transformation for MCC values mcc_tasks = ["German", "Australian", "LendingClub", "ccf", "ccfraud", "polish", "taiwan", "portoseguro", "travelinsurance"] for task in mcc_tasks: if task in df.columns: df[task] = df.apply(lambda row: (row[task] + 100) / 2.0 if row[task] != "missing" else row[task], axis=1) for index, row in df.iterrows(): if "FinTrade" in row and row["FinTrade"] != "missing": df.loc[index, "FinTrade"] = (row["FinTrade"] + 300) / 6 # Now, select the columns that were passed to the function df = df[cols] # Function to round numeric values, including those in string format def round_numeric(x): try: return round(float(x), 1) except ValueError: return x # Apply rounding to all columns except 'T' and 'Model' for col in df.columns: if col not in ['T', 'Model']: df[col] = df[col].apply(round_numeric) # 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 requests""" 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"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") 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) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") all_evals.append(data) pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]] running_list = [e for e in all_evals if e["status"] == "RUNNING"] finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"] 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]