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e078b1d | 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 | from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
from src.evaluate.metrics import compute_rouge_single, compression_ratio
from src.evaluate.tables import aggregate_metrics_table
def evaluate_outputs(df: pd.DataFrame) -> pd.DataFrame:
rows = []
for _, row in df.iterrows():
item = row.to_dict()
source = str(item.get("Description", ""))
prediction = str(item.get("generated_summary", ""))
reference = str(item.get("reference_summary", "")).strip()
metrics = {"compression_ratio": compression_ratio(source, prediction)}
if reference:
metrics.update(compute_rouge_single(reference, prediction))
else:
metrics.update({"rouge1_f1": None, "rouge2_f1": None, "rougeL_f1": None})
item.update(metrics)
rows.append(item)
return pd.DataFrame(rows)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--detailed-output", default=None)
args = parser.parse_args()
df = pd.read_csv(args.input)
detailed = evaluate_outputs(df)
if args.detailed_output:
Path(args.detailed_output).parent.mkdir(parents=True, exist_ok=True)
detailed.to_csv(args.detailed_output, index=False)
aggregate = aggregate_metrics_table(detailed)
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
aggregate.to_csv(args.output, index=False)
print(aggregate.to_string(index=False))
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