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import argparse
import json
import math
from collections import defaultdict
from pathlib import Path
HIGHER_BETTER = {
"accuracy",
"ap",
"auc",
"f1",
"mrr",
"rocauc",
}
LOWER_BETTER = {
"mae",
"raw-mae",
"rmse",
}
def _metric_direction(metric: str) -> int:
metric = metric.lower()
if metric in HIGHER_BETTER:
return 1
if metric in LOWER_BETTER:
return -1
if "mae" in metric or "rmse" in metric:
return -1
return 1
def _read(path: Path) -> dict | None:
try:
with path.open() as f:
rep = json.load(f)
except (OSError, json.JSONDecodeError):
return None
required = {"dataset", "view", "compute", "seed", "metric", "val"}
if not required.issubset(rep):
return None
return rep
def _score(rep: dict, split: str) -> float | None:
metric = rep.get("metric")
if not metric:
return None
value = rep.get(split, {}).get(metric)
if value is None:
return None
return float(value)
def _rank_key(rep: dict) -> tuple[int, int, int]:
return (
int(rep.get("epochs", 0)),
int(rep.get("hidden", 0)),
int(rep.get("ep", 0) or 0),
)
def _mean(xs: list[float]) -> float:
return sum(xs) / len(xs)
def _std(xs: list[float]) -> float:
if len(xs) < 2:
return 0.0
mu = _mean(xs)
return math.sqrt(sum((x - mu) ** 2 for x in xs) / (len(xs) - 1))
def _fmt(value: float | None, digits: int) -> str:
if value is None:
return ""
return f"{value:.{digits}f}"
def _is_classic_baseline(rep: dict) -> bool:
return rep.get("compute") == "classic" and int(rep.get("T", -1)) == 0 and int(rep.get("n_sup", -1)) == 1
def _compute_label(rep: dict) -> str:
compute = str(rep["compute"])
if _is_classic_baseline(rep):
label = "classic"
else:
label = f"{compute}-T{rep.get('T')}-ns{rep.get('n_sup')}"
ema = float(rep.get("ema", 0.0) or 0.0)
if ema > 0:
label += f"+ema{ema:g}"
return label
def _choose_runs(paths: list[Path], min_epochs: int, epochs: int | None) -> list[dict]:
candidates: dict[tuple[str, str, str, int], dict] = {}
for path in paths:
rep = _read(path)
if rep is None:
continue
if epochs is not None and int(rep.get("epochs", -1)) != epochs:
continue
if int(rep.get("epochs", 0)) < min_epochs:
continue
val = _score(rep, "val")
test = _score(rep, "test")
if val is None or test is None:
continue
key = (str(rep["dataset"]), str(rep["view"]), _compute_label(rep), int(rep["seed"]))
old = candidates.get(key)
if old is None or _rank_key(rep) > _rank_key(old):
candidates[key] = rep
return list(candidates.values())
def _group_by_cell(runs: list[dict]) -> dict[tuple[str, str, str], list[dict]]:
grouped: dict[tuple[str, str, str], list[dict]] = defaultdict(list)
for rep in runs:
grouped[(str(rep["dataset"]), str(rep["view"]), _compute_label(rep))].append(rep)
return dict(grouped)
def _summarize_cell(reps: list[dict], split: str) -> tuple[float, float, int]:
scores = [_score(rep, split) for rep in reps]
xs = [x for x in scores if x is not None]
return _mean(xs), _std(xs), len(xs)
def _markdown_table(headers: list[str], rows: list[list[str]]) -> str:
out = ["| " + " | ".join(headers) + " |", "| " + " | ".join(["---"] * len(headers)) + " |"]
out.extend("| " + " | ".join(row) + " |" for row in rows)
return "\n".join(out)
def print_tables(args) -> None:
paths = sorted(Path(args.runs_dir).glob(args.glob))
runs = _choose_runs(paths, args.min_epochs, args.epochs)
grouped = _group_by_cell(runs)
classic_cells = {
(task, view): reps
for (task, view, compute), reps in grouped.items()
if compute == "classic"
for reps in [[rep for rep in reps if _is_classic_baseline(rep)]]
if reps
}
baseline_rows = []
for (task, view), reps in sorted(classic_cells.items()):
metric = str(reps[0]["metric"])
val_mu, val_sd, n = _summarize_cell(reps, "val")
test_mu, test_sd, _ = _summarize_cell(reps, "test")
baseline_rows.append([
task,
view,
metric,
str(n),
f"{_fmt(val_mu, args.digits)} +/- {_fmt(val_sd, args.digits)}",
f"{_fmt(test_mu, args.digits)} +/- {_fmt(test_sd, args.digits)}",
])
delta_rows = []
for (task, view, compute), reps in sorted(grouped.items()):
if compute == "classic":
continue
base = classic_cells.get((task, view))
if not base:
continue
metric = str(reps[0]["metric"])
direction = _metric_direction(metric)
base_by_seed = {int(rep["seed"]): rep for rep in base}
paired = []
for rep in reps:
seed = int(rep["seed"])
if seed in base_by_seed:
paired.append((rep, base_by_seed[seed]))
if not paired:
base_test_mu, _, _ = _summarize_cell(base, "test")
base_val_mu, _, _ = _summarize_cell(base, "val")
paired = [(rep, None) for rep in reps]
else:
base_test_mu = None
base_val_mu = None
val_scores, test_scores, val_deltas, test_deltas = [], [], [], []
adaptive_steps = []
for rep, base_rep in paired:
val = _score(rep, "val")
test = _score(rep, "test")
if val is None or test is None:
continue
if base_rep is None:
base_val = base_val_mu
base_test = base_test_mu
else:
base_val = _score(base_rep, "val")
base_test = _score(base_rep, "test")
if base_val is None or base_test is None:
continue
val_scores.append(val)
test_scores.append(test)
val_deltas.append(direction * (val - base_val))
test_deltas.append(direction * (test - base_test))
if rep.get("adaptive_steps") is not None:
adaptive_steps.append(float(rep["adaptive_steps"]))
if not test_scores:
continue
delta_rows.append([
task,
view,
compute,
metric,
str(len(test_scores)),
f"{_fmt(_mean(val_scores), args.digits)} ({_fmt(_mean(val_deltas), args.digits)})",
f"{_fmt(_mean(test_scores), args.digits)} ({_fmt(_mean(test_deltas), args.digits)})",
_fmt(_mean(adaptive_steps), 2) if adaptive_steps else "",
])
print("\nClassic baseline: task x backbone")
print(_markdown_table(["task", "backbone", "metric", "n", "val", "test"], baseline_rows))
print("\nDelta vs matching classic")
print(_markdown_table([
"task", "backbone", "compute", "metric", "n", "val score (delta)", "test score (delta)", "steps"
], delta_rows))
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--runs-dir", default="runs")
ap.add_argument("--glob", default="*.json")
ap.add_argument("--min-epochs", type=int, default=10)
ap.add_argument("--epochs", type=int)
ap.add_argument("--digits", type=int, default=4)
args = ap.parse_args()
print_tables(args)
if __name__ == "__main__":
main()
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