Datasets:
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f78cbef | 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 46 47 48 49 50 51 52 53 54 55 | from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
from sklearn.metrics import accuracy_score, f1_score
def read_rows(path: str):
with open(path, encoding="utf-8", newline="") as f:
return list(csv.DictReader(f))
def prediction_map(path: str):
rows = read_rows(path)
required = {"id", "prediction"}
if not rows or not required.issubset(rows[0]):
raise ValueError("Prediction CSV must contain columns: id,prediction")
return {row["id"]: row["prediction"] for row in rows}
def save_or_print(metrics, output):
text = json.dumps(metrics, ensure_ascii=False, indent=2)
print(text)
if output:
Path(output).write_text(text + "\n", encoding="utf-8")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--gold", required=True)
parser.add_argument("--predictions", required=True)
parser.add_argument("--output")
args = parser.parse_args()
gold_rows = read_rows(args.gold)
preds = prediction_map(args.predictions)
missing = [row["id"] for row in gold_rows if row["id"] not in preds]
if missing:
raise ValueError(f"Missing predictions for {len(missing)} IDs; first: {missing[:5]}")
y_true = [row["label_id"] for row in gold_rows]
y_pred = [str(preds[row["id"]]).strip() for row in gold_rows]
if True:
y_true = [int(x) for x in y_true]
y_pred = [int(x) for x in y_pred]
metrics = {
"n_examples": len(y_true),
"accuracy": accuracy_score(y_true, y_pred),
"macro_f1": f1_score(y_true, y_pred, average="macro", zero_division=0),
}
save_or_print(metrics, args.output)
if __name__ == "__main__":
main()
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