Datasets:
File size: 3,106 Bytes
cbb33d5 | 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 90 91 92 93 94 | """Aggregate runs/full/<model>/<benchmark>/*.json into comparison tables.
- Per-benchmark macro-averaged metrics across that benchmark's tasks, per model.
- For multi-condition stratified view: average a chosen metric by n_cond.
Usage:
PYTHONPATH=src python scripts/aggregate.py [runs_dir]
"""
from __future__ import annotations
import glob
import json
import os
import sys
from collections import defaultdict
RUNS = sys.argv[1] if len(sys.argv) > 1 else "runs/full"
HEADLINE = ["ndcg@10", "recall@10", "mrr@10", "recall@100"]
def load_reports(runs_dir):
# model -> benchmark -> list[report]
out = defaultdict(lambda: defaultdict(list))
for path in glob.glob(os.path.join(runs_dir, "*", "*", "*.json")):
if os.path.basename(path) == "summary.json":
continue
try:
rep = json.load(open(path))
except Exception:
continue
if "dense" not in rep:
continue
model = path.split(os.sep)[-3]
bench = rep.get("benchmark") or path.split(os.sep)[-2]
out[model][bench].append(rep)
return out
def macro(reports, metric, stage="dense"):
vals = [r[stage]["metrics"].get(metric) for r in reports if r.get(stage)]
vals = [v for v in vals if v is not None]
return sum(vals) / len(vals) if vals else None
def fmt(v):
return f"{v:.4f}" if isinstance(v, float) else " - "
def main():
data = load_reports(RUNS)
if not data:
print(f"(no results yet under {RUNS})")
return
benches = sorted({b for m in data.values() for b in m})
models = sorted(data)
for bench in benches:
print(f"\n=== {bench} (macro over tasks) ===")
print("model".ljust(12) + "".join(h.ljust(12) for h in HEADLINE) + "n_tasks")
for m in models:
reps = data[m].get(bench)
if not reps:
continue
row = "".join(fmt(macro(reps, h)).ljust(12) for h in HEADLINE)
print(m.ljust(12) + row + str(len(reps)))
# stratified ndcg@10 by n_cond (MultiConIR / MERIT)
print("\n=== ndcg@10 by #conditions ===")
for bench in benches:
dim = "n_cond"
# collect per model: bucket -> [vals]
per_model = {}
for m in models:
buckets = defaultdict(list)
for r in data[m].get(bench, []):
strat = r["dense"].get("stratified", {}).get(dim, {})
for k, mm in strat.items():
if mm.get("ndcg@10") is not None:
buckets[k].append(mm["ndcg@10"])
if buckets:
per_model[m] = {k: sum(v) / len(v) for k, v in buckets.items()}
if not per_model:
continue
keys = sorted({k for d in per_model.values() for k in d}, key=lambda x: float(x))
print(f"\n[{bench}]")
print("model".ljust(12) + "".join(str(k).ljust(8) for k in keys))
for m, d in per_model.items():
print(m.ljust(12) + "".join((f"{d[k]:.3f}" if k in d else " - ").ljust(8) for k in keys))
if __name__ == "__main__":
main()
|