"""Text encoder comparison + fusion under the final protocol (clean real TCGA texts only; synthetic / virtual = test only). Text encoders (frozen, cache/alltxt_text_.npy and cache/orion_gentext_text_.npy; 45_ / 45b_ / 08_): biomedclip (current; raw features as in 42_), conchclip, plip, medcpt, biolord, pubmedbert, medembed, bge, qwen3-0.6b, qwen3-4b, keep, quiltnet (whatever exists for both sets). Non-baseline inputs are z-scored with TCGA-train stats. cat:+ early fusion (z-scored blocks scaled by 1/sqrt(dim), one head) ens:+ late fusion (mean of the L2-normalised outputs of the single-encoder heads; same frozen UNI2 space) Head: linear or MLP into the frozen UNI2 space, multi-positive InfoNCE (several texts per image), AdamW 3e-4 / wd 0.05 / OneCycle, batch 256, 1000 steps, early stopping on val R@1 every 50 steps (= 42_ S4 text recipe). Per fold k (splits/final/tcga_fold.csv; one random text per test image, = 33_/42_): trained H&E<->text TCGA test images (real text) emergent mIF<->text(synthetic) ORION patient_5fold test tiles, mIF = S4 MLP head (runs/rnafm/other_fold.pt, 44_) emergent text<->RNA(virtual) TCGA test images, RNA = hvg linear head of fold k (runs/rnafm/rna_hvg_fold.pt) emergent text<->mIF(MIPHEI) TCGA test images, MIPHEI virtual mIF through the same mIF head Usage: python 46_text_enc_eval.py [--encs ...] [--heads linear mlp] [--folds 0 1 2 3 4] Output results/textenc_long.csv (resumable), results/textenc_summary.csv """ import argparse import os import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F from scipy.optimize import linear_sum_assignment from common import CACHE, ROOT, SPLITS ap = argparse.ArgumentParser() ap.add_argument("--encs", nargs="+", default=None) ap.add_argument("--heads", nargs="+", default=["linear", "mlp"]) ap.add_argument("--folds", nargs="+", type=int, default=[0, 1, 2, 3, 4]) args = ap.parse_args() dev = "cuda" H = os.path.join(CACHE, "hest") G100, DRAWS, BS, LR, EVAL_EVERY, STEPS = 100, 50, 256, 3e-4, 50, 1000 f32 = lambda a: torch.from_numpy(np.asarray(a, dtype=np.float32)) nz = lambda x: F.normalize(x, dim=-1) HE_O = f32(np.load(os.path.join(CACHE, "he_uni2.npy"))).to(dev) IF_O = f32(np.load(os.path.join(CACHE, "if_virtues_m16.npy"))).to(dev) IF_V_T = f32(np.load(os.path.join(CACHE, "if_virtues_m16_virtual_miphei_tcga_text.npy"))[:, 0]).to(dev) HE_H = f32(np.load(os.path.join(H, "he_uni2.npy"))[:, 0]).to(dev) RNA_H = f32(np.load(os.path.join(H, "hvg.npy"))).to(dev) HE_T = f32(np.load(os.path.join(CACHE, "allimg_he_uni2.npy"))).to(dev) T = pd.read_csv(os.path.join(CACHE, "alltxt_meta.csv")) T_IMG = torch.as_tensor(T.img_idx.values, device=dev) T = T[~pd.read_csv(os.path.join(CACHE, "alltxt_junk.csv")).junk.values] hvg = pd.read_csv(os.path.join(H, "hvg_genes.csv")).gene.values def virtual_rna(st): # = 42_ g = pd.read_csv(os.path.join(CACHE, "stpath", f"{st}_genes.csv")).gene.values c = np.expm1(np.load(os.path.join(CACHE, "stpath", f"{st}_pred.npy")).astype(np.float32)).clip(0) c = np.log1p(c / c.sum(1, keepdims=True) * 1e4) return f32(c[:, pd.Series(range(len(g)), index=g).loc[hvg].values]).to(dev) RNA_V_T = virtual_rna("tcga_text") def load_enc(e): e = e.split("@")[0] # "@": same input, head trained with another seed (seed-ensemble control) return {s: f32(np.load(os.path.join(CACHE, f"{s}_text_{e}.npy"))[:, 0]).to(dev) for s in ["alltxt", "orion_gentext"]} def mlp(din, dout=1536, hid=512): return nn.Sequential(nn.Linear(din, hid), nn.GELU(), nn.Dropout(0.1), nn.Linear(hid, hid), nn.GELU(), nn.LayerNorm(hid), nn.Linear(hid, dout)) def one_text_per_image(t_ids, rng): pick = T.loc[t_ids].groupby("img_idx").apply(lambda g: g.index[rng.integers(len(g))], include_groups=False) return pick.index.values, pick.values def g100(a, b, seed=0): # = 33_ / 42_ a, b = a.cpu(), b.cpu() rng = np.random.default_rng(seed) out = {m: [] for m in ["R@1", "R@5", "R@10", "MRR", "pair_acc"]} for _ in range(DRAWS): s = torch.from_numpy(rng.choice(len(a), min(G100, len(a)), replace=False)) sim = (a[s] @ b[s].T).numpy(); d = np.diag(sim) for S in (sim, sim.T): rank = (S > d[:, None]).sum(1) for k_ in (1, 5, 10): out[f"R@{k_}"].append((rank < k_).mean() / 2) out["MRR"].append((1 / (rank + 1)).mean() / 2) ri, ci = linear_sum_assignment(-sim) out["pair_acc"].append((ci[np.argsort(ri)] == np.arange(len(s))).mean()) return {m: float(np.sum(v) / DRAWS) for m, v in out.items()} def r1(a, b): s = a @ b.T return float((((s > s.diag()[:, None]).sum(1) == 0).float().mean() + ((s.T > s.diag()[:, None]).sum(1) == 0).float().mean()) / 2) def train_head(X_tr, A_tr, X_va, A_va, kind, steps, seed, pos_tr=None): # = 44_ torch.manual_seed(seed); rng = np.random.default_rng(seed) h = (mlp(X_tr.shape[1]) if kind == "mlp" else nn.Linear(X_tr.shape[1], 1536)).to(dev) logit = nn.Parameter(torch.tensor(np.log(1 / 0.07), dtype=torch.float32, device=dev)) opt = torch.optim.AdamW(list(h.parameters()) + [logit], lr=LR, weight_decay=0.05) sch = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=LR, total_steps=steps, pct_start=0.05) za_va = nz(A_va[:, 0] if A_va.dim() == 3 else A_va) best, state = -1, None for step in range(1, steps + 1): h.train() b = torch.as_tensor(rng.choice(len(X_tr), BS, replace=False), device=dev) a = A_tr[b, torch.randint(0, A_tr.shape[1], (BS,), device=dev)] if A_tr.dim() == 3 else A_tr[b] lg = logit.exp().clamp(max=100) * nz(a) @ nz(h(X_tr[b])).T if pos_tr is None: t = torch.arange(BS, device=dev) loss = (F.cross_entropy(lg, t) + F.cross_entropy(lg.T, t)) / 2 else: ii = pos_tr[b]; pos = (ii[:, None] == ii[None, :]).float(); tgt = pos / pos.sum(1, keepdim=True) loss = (torch.sum(-tgt * F.log_softmax(lg, 1), 1).mean() + torch.sum(-tgt * F.log_softmax(lg.T, 1), 1).mean()) / 2 opt.zero_grad(set_to_none=True); loss.backward(); opt.step(); sch.step() if step % EVAL_EVERY == 0 or step == steps: h.eval() with torch.no_grad(): v = r1(za_va, nz(h(X_va))) if v > best: best, state = v, {k_: x.detach().clone() for k_, x in h.state_dict().items()} h.load_state_dict(state); h.eval() return h, best class Fold: def __init__(self, k): s = pd.read_csv(os.path.join(SPLITS, "patient_5fold", f"fold{k}.csv")) self.o = {x: s.tile_id[s.split == x].values for x in ["train", "val", "test"]} self.if_mu, self.if_sd = IF_O[self.o["train"]].mean((0, 1)), IF_O[self.o["train"]].std((0, 1)) + 1e-3 h = pd.read_csv(os.path.join(SPLITS, "final", f"hest_fold{k}.csv")) self.h = {x: h.spot_idx[h.split == x].values for x in ["train", "val", "test"]} sp = pd.read_csv(os.path.join(SPLITS, "final", f"tcga_fold{k}.csv")).set_index("img_idx").split tsp = sp.loc[T.img_idx].values self.t = {x: T.index[tsp == x].values for x in ["train", "val", "test"]} self.t_va = one_text_per_image(self.t["val"], np.random.default_rng(0)) self.t_te = one_text_per_image(self.t["test"], np.random.default_rng(k)) def nif(self, x): return (x - self.if_mu) / self.if_sd def mif_head(self, k): f = os.path.join(ROOT, "runs", "rnafm", f"other_fold{k}.pt") # written by 44_rnafm_eval.py hm = mlp(512).to(dev) hm.load_state_dict(torch.load(f, map_location=dev)["mif"]) return hm.eval() def rna_head(self, k): f = os.path.join(ROOT, "runs", "rnafm", f"rna_hvg_fold{k}.pt") hr = nn.Linear(2000, 1536).to(dev) if os.path.exists(f): hr.load_state_dict(torch.load(f, map_location=dev)) return hr.eval() hr, _ = train_head(RNA_H[self.h["train"]], HE_H[self.h["train"]], RNA_H[self.h["val"]], HE_H[self.h["val"]], "linear", 2700, k) torch.save(hr.state_dict(), f) return hr def build(enc, tr): parts = enc.split(":", 1)[1].split("+") if enc.startswith("cat:") else [enc] blocks = [] for p in parts: X = CACHE_ENC[p] if p.split("@")[0] == "biomedclip" and len(parts) == 1: blocks.append(X); continue # = 42_ / 33_ (raw BiomedCLIP features) mu, sd = X["alltxt"][tr].mean(0), X["alltxt"][tr].std(0) + 1e-6 w = 1 / np.sqrt(X["alltxt"].shape[1]) if len(parts) > 1 else 1.0 blocks.append({s: (v - mu) / sd * w for s, v in X.items()}) return {s: torch.cat([b[s] for b in blocks], 1) for s in blocks[0]} avail = sorted({f[len("alltxt_text_"):-4] for f in os.listdir(CACHE) if f.startswith("alltxt_text_") and f.endswith(".npy") and os.path.exists(os.path.join(CACHE, "orion_gentext_text_" + f[len("alltxt_text_"):]))}) encs = sorted(args.encs or avail, key=lambda e: e.startswith("ens:")) # late fusions after their single heads singles = sorted({p for e in encs for p in (e.split(":", 1)[1].split("+") if ":" in e else [e])}) CACHE_ENC = {e: load_enc(e) for e in singles} print("encoders:", {e: CACHE_ENC[e]["alltxt"].shape[1] for e in singles}, flush=True) f_out = os.path.join(ROOT, "results", "textenc_long.csv") rows = pd.read_csv(f_out).to_dict("records") if os.path.exists(f_out) else [] done = {(r["enc"], r["head"], r["fold"]) for r in rows} for k in args.folds: F_ = Fold(k) hm, hr = F_.mif_head(k), F_.rna_head(k) te_o = F_.o["test"]; ii, tt = (torch.as_tensor(x, device=dev) for x in F_.t_te) vi, vt = (torch.as_tensor(x, device=dev) for x in F_.t_va) trt = torch.as_tensor(F_.t["train"], device=dev) with torch.no_grad(): z_mif = nz(hm(F_.nif(IF_O[te_o, 0]))); z_rna = nz(hr(RNA_V_T[ii])); z_vmif = nz(hm(F_.nif(IF_V_T[ii]))) z_he = nz(HE_T[ii, 0]) heads_out = {} for enc in encs: for hk in args.heads: parts = enc.split(":", 1)[1].split("+") if enc.startswith("ens:") else None need = any(enc in e.split(":", 1)[1].split("+") for e in encs if e.startswith("ens:")) if (enc, hk, k) in done and not (parts is None and need): continue if parts is not None: if not all((p, hk) in heads_out for p in parts): continue Z = {s: nz(sum(heads_out[(p, hk)][s] for p in parts)) for s in heads_out[(parts[0], hk)]} best = float("nan") else: X = build(enc, trt) sd_ = k + 1000 * int(enc.split("@")[1]) if "@" in enc else k h, best = train_head(X["alltxt"][trt], HE_T[T_IMG[trt]], X["alltxt"][vt], HE_T[vi, 0], hk, STEPS, sd_, pos_tr=T_IMG[trt]) with torch.no_grad(): Z = {"tcga": nz(h(X["alltxt"][tt])), "orion": nz(h(X["orion_gentext"][te_o]))} heads_out[(enc, hk)] = Z if (enc, hk, k) in done: continue res = {("H&E<->text (TCGA)", "trained"): g100(z_he, Z["tcga"]), ("mIF<->text(synthetic) (ORION)", "emergent"): g100(z_mif, Z["orion"]), ("text<->RNA(virtual) (TCGA)", "emergent"): g100(Z["tcga"], z_rna), ("text<->mIF(MIPHEI) (TCGA)", "emergent"): g100(Z["tcga"], z_vmif)} for (pair, kind), r in res.items(): rows.append({"enc": enc, "head": hk, "fold": k, "pair": pair, "kind": kind, "val_R@1": best, **r}) pd.DataFrame(rows).to_csv(f_out, index=False) print(f"fold {k} {enc:<34} {hk:<6} val {best:.3f} " + " ".join(f"{p.split(' (')[0]} {r['R@1']:.3f}" for (p, _), r in res.items()), flush=True) R = pd.DataFrame(rows) S = R.groupby(["enc", "head", "kind", "pair"], sort=False)[["R@1", "R@10", "pair_acc"]].agg(["mean", "std"]).round(4) S.to_csv(os.path.join(ROOT, "results", "textenc_summary.csv")) pd.set_option("display.width", 250) print(R.pivot_table(index=["enc", "head"], columns="pair", values="R@1", sort=False).round(3).to_string())