| """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): |
| |
| 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))) |
|
|
| |
| print("\n=== ndcg@10 by #conditions ===") |
| for bench in benches: |
| dim = "n_cond" |
| |
| 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() |
|
|
