| """Aggregate runs/*/metrics.json into results/summary_*.csv (mean ± std over folds).""" |
| import glob |
| import json |
| import os |
|
|
| import pandas as pd |
|
|
| from common import ROOT, RUNS |
|
|
| PAIRS = ["he->if", "if->he", "he->nuclei", "nuclei->he", "he->markers", "markers->he", |
| "if->nuclei", "nuclei->if", "if->markers", "markers->if", "nuclei->markers", "markers->nuclei"] |
| rows = [] |
| for f in sorted(glob.glob(os.path.join(RUNS, "*", "metrics.json"))): |
| m = json.load(open(f)) |
| a = m["args"] |
| for subset in ["test", "test_tissue"]: |
| for pr in PAIRS: |
| r = m[subset][pr] |
| rows.append({"anchor": a.get("anchor", "imagebind"), "split": a["split"], "fold": a["fold"], "loss": a["loss"], "subset": subset, "pair": pr, |
| "trained_directly": pr.startswith("he") or pr.endswith("he") or a["loss"] == "allpairs", |
| **{k: r[k] for k in ["N", "chance_R@1", "R@1", "R@5", "R@10", "MRR", "MedR", "pair_acc", |
| "R@1_g100", "pair_acc_g100"]}}) |
| df = pd.DataFrame(rows) |
| out = os.path.join(ROOT, "results") |
| df.to_csv(os.path.join(out, "all_runs_long.csv"), index=False) |
|
|
| met = ["R@1", "R@5", "R@10", "MRR", "pair_acc", "R@1_g100", "pair_acc_g100"] |
| g = df.groupby(["anchor", "loss", "subset", "split", "pair"], sort=False) |
| agg = g[met + ["N"]].mean() |
| sd = g[met].std().fillna(0) |
| tab = agg.copy() |
| for c in met: |
| tab[c] = [f"{x:.3f}±{s:.3f}" if n > 1 else f"{x:.3f}" for x, s, n in zip(agg[c], sd[c], g.size())] |
| tab["n_folds"] = g.size() |
| tab.reset_index().to_csv(os.path.join(out, "summary_by_pair.csv"), index=False) |
|
|
| |
| h = df.groupby(["anchor", "loss", "subset", "split", "fold", "trained_directly"])[met].mean().reset_index() |
| h = h.groupby(["anchor", "loss", "subset", "split", "trained_directly"])[met].agg(["mean", "std"]).round(3) |
| h.to_csv(os.path.join(out, "summary_headline.csv")) |
| pd.set_option("display.width", 250) |
| print(h[[("R@1", "mean"), ("R@1", "std"), ("R@1_g100", "mean"), ("pair_acc", "mean"), ("pair_acc_g100", "mean")]]) |
|
|
| |
| ab = df[(df.loss == "anchor") & (df.subset == "test")] |
| fm = ab.pivot_table(index=["split", "pair"], columns="anchor", values="R@1_g100", aggfunc="mean").round(3) |
| fm.to_csv(os.path.join(out, "anchor_ablation_R1_g100.csv")) |
| fm2 = ab.pivot_table(index=["split", "pair"], columns="anchor", values="pair_acc", aggfunc="mean").round(3) |
| fm2.to_csv(os.path.join(out, "anchor_ablation_pair_acc.csv")) |
|
|
