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#!/usr/bin/env python3
import argparse
import os
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent))
import fev
import pandas as pd
from src.utils import format_leaderboard
# Constants from the main app
BASELINE_MODEL = "Seasonal Naive"
LEAKAGE_IMPUTATION_MODEL = "Chronos-Bolt"
SORT_COL = "win_rate"
N_RESAMPLES_FOR_CI = 1000
TOP_K_MODELS_TO_PLOT = 15
AVAILABLE_METRICS = ["SQL", "MASE", "WQL", "WAPE"]
def load_summaries(path="."):
csv_files = list(Path(path).glob("*.csv"))
if not csv_files:
raise FileNotFoundError(f"No CSV files found in {path}")
dfs = [pd.read_csv(file) for file in csv_files]
return pd.concat(dfs, ignore_index=True)
def compute_leaderboard(summaries: pd.DataFrame, metric_name: str) -> pd.DataFrame:
lb = fev.analysis.leaderboard(
summaries=summaries,
metric_column=metric_name,
missing_strategy="impute",
baseline_model=BASELINE_MODEL,
leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
)
lb = lb.astype("float64").reset_index()
lb["skill_score"] = lb["skill_score"] * 100
lb["win_rate"] = lb["win_rate"] * 100
lb["num_failures"] = lb["num_failures"] / summaries["task_name"].nunique() * 100
return lb
def compute_pairwise(summaries: pd.DataFrame, metric_name: str, included_models: list[str]) -> pd.DataFrame:
if BASELINE_MODEL not in included_models:
included_models = included_models + [BASELINE_MODEL]
return (
fev.analysis.pairwise_comparison(
summaries,
included_models=included_models,
metric_column=metric_name,
baseline_model=BASELINE_MODEL,
missing_strategy="impute",
n_resamples=N_RESAMPLES_FOR_CI,
leakage_imputation_model=LEAKAGE_IMPUTATION_MODEL,
)
.round(3)
.reset_index()
)
def compute_pivot_table(summaries: pd.DataFrame, metric_name: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
errors = fev.pivot_table(summaries=summaries, metric_column=metric_name, task_columns=["task_name"])
train_overlap = (
fev.pivot_table(summaries=summaries, metric_column="trained_on_this_dataset", task_columns=["task_name"])
.fillna(False)
.astype(bool)
)
is_imputed_baseline = errors.isna()
is_leakage_imputed = train_overlap
# Handle imputations
errors = errors.mask(train_overlap, errors[LEAKAGE_IMPUTATION_MODEL], axis=0)
for col in errors.columns:
if col != BASELINE_MODEL:
errors[col] = errors[col].fillna(errors[BASELINE_MODEL])
errors = errors[errors.rank(axis=1).mean().sort_values().index]
is_imputed_baseline = is_imputed_baseline[errors.columns]
is_leakage_imputed = is_leakage_imputed[errors.columns]
errors.index.rename("Task name", inplace=True)
is_imputed_baseline.index.rename("Task name", inplace=True)
is_leakage_imputed.index.rename("Task name", inplace=True)
return errors.reset_index(), is_imputed_baseline.reset_index(), is_leakage_imputed.reset_index()
def main():
parser = argparse.ArgumentParser(description="Generate leaderboard tables from CSV summaries")
parser.add_argument("-s", "--summaries-path", default=".", help="Path to directory containing CSV files")
args = parser.parse_args()
# Create tables directory
tables_dir = Path("tables")
tables_dir.mkdir(exist_ok=True)
print("Loading summaries...")
summaries = load_summaries(args.summaries_path)
for metric in AVAILABLE_METRICS:
print(f"Processing {metric}...")
# Compute leaderboard
leaderboard_df = compute_leaderboard(summaries, metric)
leaderboard_df.to_csv(tables_dir / f"leaderboard_{metric}.csv", index=False)
# Get top models for pairwise comparison
top_k_models = (
leaderboard_df.sort_values(by=SORT_COL, ascending=False).head(TOP_K_MODELS_TO_PLOT)["model_name"].tolist()
)
# Compute pairwise comparison
pairwise_df = compute_pairwise(summaries, metric, top_k_models)
pairwise_df.to_csv(tables_dir / f"pairwise_{metric}.csv", index=False)
# Compute pivot table
pivot_df, baseline_imputed, leakage_imputed = compute_pivot_table(summaries, metric)
pivot_df.to_csv(tables_dir / f"pivot_{metric}.csv", index=False)
baseline_imputed.to_csv(tables_dir / f"pivot_{metric}_baseline_imputed.csv", index=False)
leakage_imputed.to_csv(tables_dir / f"pivot_{metric}_leakage_imputed.csv", index=False)
print(f" Saved: leaderboard_{metric}.csv, pairwise_{metric}.csv, pivot_{metric}.csv")
print(f"All tables saved to {tables_dir}/")
if __name__ == "__main__":
main()
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