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import pandas as pd
from src.display.formatting import has_no_nan_values
from src.display.utils import AutoEvalColumn
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)
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
df = df[has_no_nan_values(df, benchmark_cols)]
return df
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