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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:
metadata=json.load(open(f"{requests_path}/metadata.json"))
raw_data = get_raw_eval_results(results_path, requests_path, metadata)
all_data_json = [v.to_dict() for v in raw_data]
# print(all_data_json)
json.dump(all_data_json, open("all_data.json", "w"), indent=2, ensure_ascii=False)
df = pd.DataFrame.from_records(all_data_json)
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
df = df[cols].round(decimals=2)
# set column rank to pd.DataFrame df
df['rank'] = df[AutoEvalColumn.average.name].rank(ascending=False, method="min")
# df.insert(0, "rank", df[AutoEvalColumn.average.name].rank(ascending=False, method="min"), True)
# filter out if any of the benchmarks have not been produced
#df2 = 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]:
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]
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