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"""Aggregate focused KSL+CASL+NSL experiment results."""
from __future__ import annotations
import argparse
import csv
import json
import statistics
from pathlib import Path
from typing import Any, Iterable
POOLED_NAME = "E9.1 pooled pose-only"
PROPOSED_NAMES = {
"exp9_kcn_proposed_pose": "E9.2 proposed pose-only",
"exp9_kcn_proposed_rgb": "E9.3 proposed RGB/image-only",
"exp9_kcn_proposed_multimodal": "E9.4 proposed RGB+pose/image",
"exp9_kcn_v2_pose": "E9.5 research-v2 pose-only",
"exp9_kcn_v2_rgb": "E9.6 research-v2 RGB/image-only",
"exp9_kcn_v2_multimodal": "E9.7 research-v2 RGB+pose/image",
}
def as_float(value: Any) -> float | None:
try:
return float(value)
except (TypeError, ValueError):
return None
def mean_std(values: Iterable[Any]) -> tuple[float | None, float | None]:
nums = [float(v) for v in values if as_float(v) is not None]
if not nums:
return None, None
return sum(nums) / len(nums), statistics.stdev(nums) if len(nums) > 1 else 0.0
def pct(value: Any) -> str:
num = as_float(value)
return "" if num is None else f"{num * 100:.2f}%"
def add_pooled_rows(rows: list[dict[str, Any]], root: Path) -> None:
for path in sorted((root / "exp9_kcn_pooled_pose").glob("**/pooled_arch_results.json")):
item = json.loads(path.read_text(encoding="utf-8"))
args = item.get("args", {})
seed = args.get("seed", "")
metrics = item.get("final_metrics", {})
base = {
"model": POOLED_NAME,
"run_name": "kcn_pooled_pose_only",
"seed": seed,
"split": "test",
"source": str(path),
"params": item.get("params", ""),
}
for key, metric in metrics.items():
if not isinstance(metric, dict):
continue
if key.startswith("macro"):
rows.append(
{
**base,
"row_type": "macro",
"task_key": key,
"language_code": "",
"modality": "pose",
"level": "",
"top1": metric.get("accuracy"),
"top5": metric.get("top5"),
"macro_f1": metric.get("macro_f1"),
"n": "",
"num_classes": "",
}
)
else:
rows.append(
{
**base,
"row_type": "task",
"task_key": f"pose_{key}",
"language_code": "casl" if key == "casl_si" else key,
"modality": "pose",
"level": "word" if key in {"casl_si", "casl", "ksl"} else "image",
"top1": metric.get("accuracy"),
"top5": metric.get("top5"),
"macro_f1": metric.get("macro_f1"),
"n": metric.get("n", ""),
"num_classes": (item.get("num_classes") or {}).get(key, ""),
}
)
def add_proposed_rows(rows: list[dict[str, Any]], root: Path) -> None:
for dirname, model_name in PROPOSED_NAMES.items():
for path in sorted((root / dirname).glob("*_results.json")):
item = json.loads(path.read_text(encoding="utf-8"))
base = {
"model": model_name,
"run_name": item.get("run_name", dirname),
"seed": item.get("seed", ""),
"source": str(path),
"params": item.get("params", ""),
}
for split in ("val", "test"):
for key, metric in (item.get(split) or {}).items():
if not isinstance(metric, dict):
continue
rows.append(
{
**base,
"split": split,
"row_type": "macro" if key.startswith("macro_") else "task",
"task_key": key,
"language_code": metric.get("language_code", ""),
"modality": metric.get("modality", ""),
"level": metric.get("level", ""),
"top1": metric.get("top1"),
"top5": metric.get("top5"),
"macro_f1": metric.get("macro_f1"),
"n": metric.get("n", ""),
"num_classes": metric.get("num_classes", metric.get("n_tasks", "")),
}
)
def summarize(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
groups: dict[tuple[str, str, str, str], list[dict[str, Any]]] = {}
for row in rows:
key = (row["model"], row["split"], row["row_type"], row["task_key"])
groups.setdefault(key, []).append(row)
summary: list[dict[str, Any]] = []
for (_model, _split, _row_type, _task), group in sorted(groups.items()):
first = group[0]
top1_mean, top1_std = mean_std(row.get("top1") for row in group)
f1_mean, f1_std = mean_std(row.get("macro_f1") for row in group)
top5_mean, top5_std = mean_std(row.get("top5") for row in group)
summary.append(
{
"model": first["model"],
"split": first["split"],
"row_type": first["row_type"],
"task_key": first["task_key"],
"language_code": first["language_code"],
"modality": first["modality"],
"level": first["level"],
"runs": len(group),
"top1_mean": top1_mean,
"top1_std": top1_std,
"top5_mean": top5_mean,
"top5_std": top5_std,
"macro_f1_mean": f1_mean,
"macro_f1_std": f1_std,
"n": first["n"],
"num_classes": first["num_classes"],
"params": first["params"],
}
)
return summary
def write_csv(path: Path, rows: list[dict[str, Any]]) -> None:
if not rows:
return
with path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys()))
writer.writeheader()
writer.writerows(rows)
def md_table(rows: list[dict[str, Any]]) -> str:
if not rows:
return "No rows yet."
lines = [
"| Model | Split | Row | Task | Modality | Runs | Top-1 | Macro-F1 | Classes/Tasks |",
"|---|---|---|---|---:|---:|---:|---:|---:|",
]
for row in rows:
lines.append(
f"| {row['model']} | {row['split']} | {row['row_type']} | {row['task_key']} | {row['modality']} | "
f"{row['runs']} | {pct(row['top1_mean'])} | {pct(row['macro_f1_mean'])} | {row.get('num_classes', '')} |"
)
return "\n".join(lines)
def write_markdown(path: Path, summary: list[dict[str, Any]]) -> None:
test_macros = [r for r in summary if r["split"] == "test" and r["row_type"] == "macro"]
test_tasks = [r for r in summary if r["split"] == "test" and r["row_type"] == "task"]
val_macros = [r for r in summary if r["split"] == "val" and r["row_type"] == "macro"]
test_macros.sort(key=lambda r: (r["model"], r["task_key"]))
test_tasks.sort(key=lambda r: (r["model"], r["modality"], r["task_key"]))
val_macros.sort(key=lambda r: (r["model"], r["task_key"]))
text = "# KSL + CASL + NSL Focused Experiments\n\n"
text += "## Test Macro Rows\n\n" + md_table(test_macros) + "\n\n"
text += "## Test Task Rows\n\n" + md_table(test_tasks) + "\n\n"
text += "## Validation Macro Rows\n\n" + md_table(val_macros) + "\n"
path.write_text(text, encoding="utf-8")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--results-root", type=Path, default=Path("results"))
parser.add_argument("--out-dir", type=Path, default=Path("results/exp9_kcn_focus_summary"))
return parser.parse_args()
def main() -> None:
args = parse_args()
args.out_dir.mkdir(parents=True, exist_ok=True)
rows: list[dict[str, Any]] = []
add_pooled_rows(rows, args.results_root)
add_proposed_rows(rows, args.results_root)
if not rows:
raise SystemExit(f"No KCN focused results found under {args.results_root}")
summary = summarize(rows)
raw = args.out_dir / "kcn_focus_raw_rows.csv"
csv_path = args.out_dir / "kcn_focus_summary.csv"
json_path = args.out_dir / "kcn_focus_summary.json"
md_path = args.out_dir / "kcn_focus_summary.md"
write_csv(raw, rows)
write_csv(csv_path, summary)
json_path.write_text(json.dumps({"raw_rows": rows, "summary": summary}, indent=2), encoding="utf-8")
write_markdown(md_path, summary)
print(md_path.read_text(encoding="utf-8"))
print("Saved:")
print(" ", raw)
print(" ", csv_path)
print(" ", json_path)
print(" ", md_path)
if __name__ == "__main__":
main()
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