Spaces:
Running
Running
File size: 1,528 Bytes
e148f6b 0e8f5d6 e148f6b 0e8f5d6 b233f03 0e8f5d6 b233f03 0e8f5d6 e148f6b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import pandas as pd
import os
from src.display.utils import AutoEvalColumn, ModelType
from src.about import Tasks
def get_leaderboard_df(cols: list, benchmark_cols: list) -> pd.DataFrame:
"""Creates a dataframe from the static results.csv"""
csv_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "results.csv")
df = pd.read_csv(csv_path)
# Add model_type_symbol based on model_type
def get_type_with_symbol(mtype):
return ModelType.from_str(str(mtype)).to_str()
df["model_type"] = df["model_type"].apply(get_type_with_symbol)
# Sort by pass_rate
if "pass_rate" in df.columns:
df = df.sort_values(by=["pass_rate"], ascending=False)
# Rename columns to match the expected names in AutoEvalColumn
rename_map = {
"model": AutoEvalColumn.model.name,
"model_type": AutoEvalColumn.model_type.name,
"params": AutoEvalColumn.params.name,
"license": AutoEvalColumn.license.name,
}
# Add tasks to rename map
for task in Tasks:
# task.value.benchmark is the column name in csv (e.g., "pass_rate")
# task.value.col_name is the display name (e.g., "Pass Rate (%)")
rename_map[task.value.benchmark] = task.value.col_name
df = df.rename(columns=rename_map)
# Ensure all required columns exist, fill missing with NaN
for col in cols:
if col not in df.columns:
df[col] = None
df = df[cols].round(decimals=2)
return df
|