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| import json | |
| import os | |
| import pandas as pd | |
| 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) | |
| # ------------------------------------------------------------------ | |
| # Fallback: if no evaluation results are found we populate the | |
| # leaderboard with a single example model. This guarantees that a | |
| # freshly deployed Space shows a non-empty leaderboard and it serves | |
| # as a template for the expected columns/values. | |
| # ------------------------------------------------------------------ | |
| if df.empty: | |
| example_row = {} | |
| # Populate benchmark metrics with the default value 0.5 using internal column names | |
| for metric in benchmark_cols: | |
| example_row[metric] = 0.5 | |
| # Minimal metadata so that the row displays nicely | |
| example_row[AutoEvalColumn.model.name] = make_clickable_model("example/model") | |
| example_row[AutoEvalColumn.average.name] = 0.5 | |
| example_row[AutoEvalColumn.model_type_symbol.name] = "🟢" | |
| example_row[AutoEvalColumn.model_type.name] = "pretrained" | |
| example_row[AutoEvalColumn.precision.name] = "float16" | |
| example_row[AutoEvalColumn.weight_type.name] = "Original" | |
| example_row[AutoEvalColumn.still_on_hub.name] = True | |
| example_row[AutoEvalColumn.architecture.name] = "Transformer" | |
| example_row[AutoEvalColumn.revision.name] = "main" | |
| example_row[AutoEvalColumn.license.name] = "apache-2.0" | |
| # Any missing columns will be created later in the function | |
| df = pd.DataFrame([example_row]) | |
| # Sort primarily by LLM exact-match Pass@1 metric; if not present, fall back to average | |
| preferred_cols = [] | |
| if hasattr(AutoEvalColumn, "pass_at_1"): | |
| preferred_cols.append(AutoEvalColumn.pass_at_1.name) | |
| preferred_cols.append(AutoEvalColumn.average.name) | |
| for col in preferred_cols: | |
| if col in df.columns: | |
| df = df.sort_values(by=[col], ascending=False) | |
| break | |
| # Ensure all expected columns exist, add missing ones with NaN so selection does not fail | |
| for expected in cols: | |
| if expected not in df.columns: | |
| df[expected] = pd.NA | |
| 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 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"]) | |
| 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 os.path.isfile(e) and 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] | |