Files
mask-ddpm/example/program_stats.py

143 lines
4.6 KiB
Python

#!/usr/bin/env python3
"""Compute program-style stats (dwell, change count, step size) for selected features."""
import argparse
import csv
import gzip
import json
from pathlib import Path
from typing import Dict, List
def parse_args():
base_dir = Path(__file__).resolve().parent
parser = argparse.ArgumentParser(description="Program stats for setpoints/demands.")
parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
parser.add_argument("--reference", default=str(base_dir / "config.json"))
parser.add_argument("--features", default="", help="comma-separated list; empty = auto from eval")
parser.add_argument("--config", default=str(base_dir / "config.json"))
parser.add_argument("--out", default=str(base_dir / "results" / "program_stats.json"))
parser.add_argument("--max-rows", type=int, default=200000)
return parser.parse_args()
def resolve_reference_glob(ref_arg: str) -> str:
ref_path = Path(ref_arg)
if ref_path.suffix == ".json":
cfg = json.loads(ref_path.read_text(encoding="utf-8"))
data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
if not data_glob:
raise SystemExit("reference config has no data_glob/data_path")
combined = ref_path.parent / data_glob
# avoid resolve on glob patterns
if "*" in str(combined) or "?" in str(combined):
return str(combined)
return str(combined.resolve())
return str(ref_path)
def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
vals = {c: [] for c in cols}
opener = gzip.open if str(path).endswith(".gz") else open
with opener(path, "rt", newline="") as fh:
reader = csv.DictReader(fh)
for i, row in enumerate(reader):
for c in cols:
try:
vals[c].append(float(row[c]))
except Exception:
pass
if max_rows > 0 and i + 1 >= max_rows:
break
return vals
def dwell_and_steps(series: List[float]):
if not series:
return {
"num_changes": 0,
"mean_dwell": None,
"median_dwell": None,
"mean_step": None,
"median_step": None,
}
changes = 0
dwells = []
steps = []
current = series[0]
dwell = 1
for v in series[1:]:
if v == current:
dwell += 1
continue
changes += 1
dwells.append(dwell)
steps.append(abs(v - current))
current = v
dwell = 1
dwells.append(dwell)
def mean(x):
return sum(x) / len(x) if x else None
def median(x):
if not x:
return None
xs = sorted(x)
mid = len(xs) // 2
return xs[mid] if len(xs) % 2 == 1 else 0.5 * (xs[mid - 1] + xs[mid])
return {
"num_changes": changes,
"mean_dwell": mean(dwells),
"median_dwell": median(dwells),
"mean_step": mean(steps),
"median_step": median(steps),
}
def main():
args = parse_args()
out_path = Path(args.out)
eval_path = Path("results") / "eval.json"
auto_feats = []
if eval_path.exists():
data = json.loads(eval_path.read_text(encoding="utf-8"))
ks = data.get("continuous_ks", {})
auto_feats = [k for k, v in ks.items() if v >= 0.6]
features = [f.strip() for f in args.features.split(",") if f.strip()] or auto_feats
if not features and Path(args.config).exists():
cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
features = cfg.get("type1_features", []) or []
if not features:
raise SystemExit("no features specified and no eval.json with ks>=0.6")
# generated series
gen_vals = read_series(Path(args.generated), features, args.max_rows)
# reference series (aggregate across files)
ref_glob = resolve_reference_glob(args.reference)
ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
if not ref_paths:
raise SystemExit(f"no reference files matched: {ref_glob}")
real_vals = {c: [] for c in features}
for p in ref_paths:
vals = read_series(p, features, args.max_rows)
for c in features:
real_vals[c].extend(vals[c])
out = {"features": features, "generated": {}, "reference": {}}
for c in features:
out["generated"][c] = dwell_and_steps(gen_vals[c])
out["reference"][c] = dwell_and_steps(real_vals[c])
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
print("wrote", out_path)
if __name__ == "__main__":
main()