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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 AutoEvalColumnQA, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, FullEvalResult
from typing import Tuple, List
def get_leaderboard_df(raw_data: List[FullEvalResult], cols: list, benchmark_cols: list, task: str, metric: str) -> pd.DataFrame:
"""Creates a dataframe from all the individual experiment results"""
all_data_json = []
for v in raw_data:
all_data_json += v.to_dict(task=task, metric=metric)
df = pd.DataFrame.from_records(all_data_json)
print(f'dataframe created: {df.shape}')
# calculate the average score for selected benchmarks
_benchmark_cols = frozenset(benchmark_cols).intersection(frozenset(df.columns.to_list()))
df[AutoEvalColumnQA.average.name] = df[list(_benchmark_cols)].mean(axis=1).round(decimals=2)
df = df.sort_values(by=[AutoEvalColumnQA.average.name], ascending=False)
df.reset_index(inplace=True)
_cols = frozenset(cols).intersection(frozenset(df.columns.to_list()))
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 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]
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