File size: 2,993 Bytes
a4326d6 | 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 82 83 84 85 86 87 88 89 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import csv
import json
import math
import sys
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parents[4]
SHARED_SCRIPTS = REPO_ROOT / "fi1503_baseline_reproduction" / "soft_radgraph" / "scripts"
sys.path.insert(0, str(SHARED_SCRIPTS))
from soft_radgraph import phrase_similarity # noqa: E402
def line_count(path: Path) -> int:
with path.open(encoding="utf-8") as handle:
return sum(1 for line in handle if line.strip())
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest-dir", required=True, type=Path)
parser.add_argument("--results-dir", required=True, type=Path)
args = parser.parse_args()
manifest_summary = json.loads((args.manifest_dir / "manifest_summary.json").read_text())
manifest_n = sum(
line_count(path)
for path in sorted(args.manifest_dir.glob("annotation_manifest_shard*.jsonl"))
)
annotation_n = sum(
line_count(path)
for path in sorted((args.results_dir / "annotations").glob("annotations_shard*.jsonl"))
)
assert manifest_n == annotation_n == manifest_summary["unique_nonblank_texts"]
rows = list(
csv.DictReader(
(args.results_dir / "soft_radgraph_summary.csv").open(
newline="", encoding="utf-8"
)
)
)
assert len(rows) == manifest_summary["models"] == 12
expected_cases = 4_394
for row in rows:
assert int(row["n_total"]) == int(row["pairs_n"]) == expected_cases
per_sample = args.results_dir / row["model_id"] / "soft_radgraph_per_sample.csv"
assert line_count(per_sample) - 1 == expected_cases
for key in (
"soft_radgraph_simple_f1",
"soft_radgraph_partial_f1",
"soft_radgraph_complete_f1",
):
value = float(row[key])
assert math.isfinite(value) and 0.0 <= value <= 1.0
assert float(row["soft_radgraph_partial_f1"]) >= float(
row["official_radgraph_partial_f1"]
)
assert phrase_similarity("heart", "cardiac silhouette", "ANAT-DP") == 0.0
assert phrase_similarity("mild", "mildly", "OBS-DP") == 1.0
assert phrase_similarity("effusion", "effusions", "OBS-DP") == 1.0
assert phrase_similarity("left", "left lower", "ANAT-DP") == 0.5
report = {
"status": "passed",
"models": len(rows),
"cases_per_model": expected_cases,
"manifest_annotations": manifest_n,
"output_annotations": annotation_n,
"per_sample_rows_verified": sum(int(row["n_total"]) for row in rows),
"soft_partial_not_below_official": True,
"semantic_synonym_nonmatch_verified": "heart != cardiac silhouette",
}
(args.results_dir / "validation.json").write_text(
json.dumps(report, indent=2) + "\n"
)
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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