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import json | |
import os | |
import copy | |
import pandas as pd | |
from src.display.formatting import has_no_nan_values, make_requests_clickable_model | |
from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row, proprietary_rows | |
from src.leaderboard.filter_models import filter_models_flags | |
from src.leaderboard.read_evals import get_raw_eval_results | |
def get_leaderboard_df(results_path: str, requests_path: str, dynamic_path: str, cols: list, benchmark_cols: list, show_incomplete=False) -> pd.DataFrame: | |
raw_data = get_raw_eval_results(results_path=results_path, requests_path=requests_path, dynamic_path=dynamic_path) | |
all_data_json = [v.to_dict() for v in raw_data] | |
all_data_json.append(baseline_row) | |
for proprietary_row in proprietary_rows: | |
all_data_json.append(proprietary_row) | |
filter_models_flags(all_data_json) | |
df = pd.DataFrame.from_records(all_data_json) | |
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) | |
df = df[cols].round(decimals=2) | |
# filter out if any of the benchmarks have not been produced | |
if not show_incomplete: | |
df = df[has_no_nan_values(df, benchmark_cols)] | |
return raw_data, df | |
def get_evaluation_queue_df(save_path: str, cols: list, show_incomplete=False) -> list[pd.DataFrame]: | |
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_requests_clickable_model(data["model"], entry) | |
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_requests_clickable_model(data["model"], os.path.join(entry, sub_entry)) | |
data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
all_evals.append(data) | |
cols_pending = copy.deepcopy(cols) | |
cols_pending.append('source') | |
cols_pending.append('submitted_time') | |
pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN", "PENDING_NEW_EVAL"]] | |
running_list = [e for e in all_evals if e["status"] == "RUNNING"] | |
finished_list = [e for e in all_evals if e["status"] in ["FINISHED", "PENDING_NEW_EVAL" if show_incomplete else "FINISHED"]] | |
failed_list = [e for e in all_evals if e["status"] == "FAILED"] | |
df_pending = pd.DataFrame.from_records(pending_list, columns=cols_pending) | |
df_running = pd.DataFrame.from_records(running_list, columns=cols) | |
df_finished = pd.DataFrame.from_records(finished_list, columns=cols) | |
df_failed = pd.DataFrame.from_records(failed_list, columns=cols) | |
df_pending['source_priority'] = df_pending["source"].apply(lambda x: {"manual": 0, "leaderboard": 1, "script": 2}.get(x, 3)) | |
df_pending['status_priority'] = df_pending["status"].apply(lambda x: {"PENDING": 2, "RERUN": 0, "PENDING_NEW_EVAL": 1}.get(x, 3)) | |
df_pending = df_pending.sort_values(['source_priority', 'status_priority', 'submitted_time']) | |
df_pending = df_pending.drop(['source_priority', 'status_priority', 'submitted_time', 'source'], axis=1) | |
return df_finished[cols], df_running[cols], df_pending[cols], df_failed[cols] | |