Datasets:
File size: 2,623 Bytes
3c366de | 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Aggregate all results/<dataset>/<model>/metrics.json into one comparison table.
Writes results/summary.csv and prints a readable table grouped by dataset.
"""
import os, json, glob, csv
from collections import defaultdict
PROJ = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image"
RESULTS = os.environ.get("RESULTS_DIR", f"{PROJ}/results")
MODELS = ["retfound", "resnet", "vit"]
# metric key shown per task type
COMMON = ["accuracy", "balanced_accuracy", "f1_macro", "precision_macro",
"recall_macro", "cohen_kappa", "quadratic_weighted_kappa", "mcc"]
BIN = ["auroc", "auprc", "sensitivity", "specificity"]
MULTI = ["auroc_macro_ovr", "auprc_macro"]
def fmt(v):
return "" if v is None else (f"{v:.4f}" if isinstance(v, (int, float)) else str(v))
def main():
rows = []
by_ds = defaultdict(dict)
for mj in sorted(glob.glob(os.path.join(RESULTS, "*", "*", "metrics.json"))):
parts = mj.split(os.sep)
ds, model = parts[-3], parts[-2]
m = json.load(open(mj))
cols = COMMON + (BIN if m.get("task") == "binary" else MULTI)
row = {"dataset": ds, "model": model, "task": m.get("task"), "n_test": m.get("n_test")}
for k in COMMON + BIN + MULTI:
row[k] = m.get(k)
rows.append(row)
by_ds[ds][model] = m
# write full CSV
os.makedirs(RESULTS, exist_ok=True)
csv_path = os.path.join(RESULTS, "summary.csv")
allcols = ["dataset", "model", "task", "n_test"] + COMMON + BIN + MULTI
with open(csv_path, "w", newline="") as f:
w = csv.DictWriter(f, fieldnames=allcols)
w.writeheader()
for r in rows:
w.writerow({k: fmt(r.get(k)) for k in allcols})
print(f"wrote {csv_path} ({len(rows)} runs)\n")
# pretty per-dataset table
for ds in sorted(by_ds):
task = next(iter(by_ds[ds].values())).get("task")
keys = ["accuracy", "f1_macro"] + (["auroc", "auprc", "sensitivity", "specificity"]
if task == "binary"
else ["auroc_macro_ovr", "quadratic_weighted_kappa"])
print(f"### {ds} ({task})")
header = " " + "model".ljust(10) + "".join(k[:14].ljust(15) for k in keys)
print(header)
for model in MODELS:
if model not in by_ds[ds]:
continue
m = by_ds[ds][model]
line = " " + model.ljust(10) + "".join(fmt(m.get(k)).ljust(15) for k in keys)
print(line)
print()
if __name__ == "__main__":
main()
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