Datasets:
File size: 2,655 Bytes
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | from __future__ import annotations
import argparse
import ast
import csv
import json
from pathlib import Path
from sklearn.metrics import f1_score, precision_score, recall_score
def read_rows(path):
with open(path, encoding="utf-8", newline="") as f:
return list(csv.DictReader(f))
def parse_boundary_prediction(value, word):
value = value.strip()
if value.startswith("["):
parsed = json.loads(value)
return [int(x) for x in parsed]
if "-" in value:
syllables = value.split("-")
if "".join(syllables) != word:
raise ValueError(f"Segmentation does not reconstruct word {word!r}: {value!r}")
labels = [0] * (len(word) - 1)
cursor = 0
for syllable in syllables[:-1]:
cursor += len(syllable)
labels[cursor - 1] = 1
return labels
try:
parsed = ast.literal_eval(value)
return [int(x) for x in parsed]
except Exception as exc:
raise ValueError(f"Invalid boundary prediction for {word!r}: {value!r}") from exc
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)
pred_rows = read_rows(args.predictions)
if not pred_rows or not {"id", "prediction"}.issubset(pred_rows[0]):
raise ValueError("Prediction CSV must contain columns: id,prediction")
preds = {row["id"]: row["prediction"] for row in pred_rows}
y_true, y_pred = [], []
exact = 0
for row in gold_rows:
if row["id"] not in preds:
raise ValueError(f"Missing prediction for {row['id']}")
gold = [int(x) for x in json.loads(row["boundary_labels"])]
pred = parse_boundary_prediction(preds[row["id"]], row["word"])
if len(pred) != len(gold):
raise ValueError(f"Boundary length mismatch for {row['id']}: {len(pred)} != {len(gold)}")
y_true.extend(gold)
y_pred.extend(pred)
exact += int(gold == pred)
metrics = {
"n_examples": len(gold_rows),
"boundary_precision": precision_score(y_true, y_pred, zero_division=0),
"boundary_recall": recall_score(y_true, y_pred, zero_division=0),
"boundary_f1": f1_score(y_true, y_pred, zero_division=0),
"exact_match": exact / len(gold_rows),
}
text = json.dumps(metrics, ensure_ascii=False, indent=2)
print(text)
if args.output:
Path(args.output).write_text(text + "\n", encoding="utf-8")
if __name__ == "__main__":
main()
|