Datasets:
File size: 2,914 Bytes
0338a7c | 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 76 77 78 79 80 81 | #!/usr/bin/env python3
"""Strict offline scoring of versioned answer manifests and saved choices."""
import argparse
import json
from pathlib import Path
DOMAINS = {"mc4": {"A", "B", "C", "D"}, "binary": {"是", "否"}}
def normalize(value):
if not isinstance(value, str):
raise ValueError("Answers must be strings")
return value.strip().upper()
def read_rows(path):
with Path(path).open(encoding="utf-8") as stream:
rows = [json.loads(line) for line in stream if line.strip()]
if not rows:
raise ValueError("Input must not be empty")
indexed = {}
for row in rows:
item_id = row["item_id"]
if not isinstance(item_id, str) or not item_id:
raise ValueError("item_id must be a nonempty string")
if item_id in indexed:
raise ValueError("Duplicate item_id: " + item_id)
indexed[item_id] = row
return indexed
def score(manifest_path, predictions_path):
manifest = read_rows(manifest_path)
predictions = read_rows(predictions_path)
if manifest.keys() != predictions.keys():
raise ValueError(
"ID coverage mismatch: missing=%d unexpected=%d"
% (len(manifest.keys() - predictions.keys()), len(predictions.keys() - manifest.keys()))
)
by_task = {}
invalid = 0
for item_id, expected in manifest.items():
prediction = predictions[item_id]
if expected["task_name"] != prediction["task_name"]:
raise ValueError("Task mismatch: " + item_id)
domain = DOMAINS[expected["answer_type"]]
truth = normalize(expected["ground_truth"])
answer = normalize(prediction["model_answer"])
if truth not in domain:
raise ValueError("Ground truth outside answer domain: " + item_id)
if answer and answer not in domain:
raise ValueError("Prediction outside answer domain: " + item_id)
invalid += not answer
task = by_task.setdefault(expected["task_name"], {"correct": 0, "total": 0})
task["total"] += 1
task["correct"] += int(answer == truth)
for counts in by_task.values():
counts["accuracy_percent"] = 100 * counts["correct"] / counts["total"]
correct = sum(counts["correct"] for counts in by_task.values())
return {
"correct": correct,
"total": len(manifest),
"accuracy_percent": 100 * correct / len(manifest),
"empty_incorrect_predictions": invalid,
"by_task": dict(sorted(by_task.items())),
}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--manifest", required=True, type=Path)
parser.add_argument("--predictions", required=True, type=Path)
arguments = parser.parse_args()
print(json.dumps(score(arguments.manifest, arguments.predictions), ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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