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141bacd | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | """5-fold stratified CV, 3 systems x 2 variants. 5 epochs/fold (shorter than train_panda)."""
from __future__ import annotations
import argparse, sys, json, pickle, warnings, numpy as np, pandas as pd, torch, torch.nn.functional as F
from pathlib import Path
import anndata as ad, scanpy as sc, scipy.sparse as sp, yaml
warnings.filterwarnings("ignore"); sc.settings.verbosity = 0
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report
import os as _os
from pathlib import Path as _Path
PANDA_ROOT = _Path(_os.environ.get("PANDA_ROOT", str(_Path(__file__).resolve().parents[2])))
sys.path.insert(0, str(PANDA_ROOT))
from panda import (PANDAEncoder, supcon_loss, vicreg_loss, hsic_biased,
subcenter_angular_infonce, prototype_repulsion)
ROOT = Path(str(PANDA_ROOT))
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def load_and_prepare(system, variant):
"""canonical corpus + features (PCA, optional marker channel)."""
a = ad.read_h5ad(ROOT / f"data/corpus/{system}/harmonized/corpus.h5ad")
stats = np.load(ROOT / f"data/corpus/{system}/harmonized/corpus_stats.npz", allow_pickle=True)
pca = pickle.load(open(ROOT / f"data/corpus/{system}/harmonized/pca_basis.pkl", "rb"))
hvgs = [str(g) for g in stats["shared_hvgs"]]
hvg2i = {g: i for i, g in enumerate(hvgs)}
common = [g for g in a.var_names.astype(str) if g in hvg2i]
a_c = a[:, common].copy()
sc.pp.normalize_total(a_c, target_sum=1e4); sc.pp.log1p(a_c)
X = a_c.X.toarray().astype(np.float32) if sp.issparse(a_c.X) else a_c.X.astype(np.float32)
Xf = np.zeros((a.n_obs, len(hvgs)), dtype=np.float32)
Xf[:, np.array([hvg2i[g] for g in common])] = X
Xz = np.clip((Xf - stats["mean"].astype(np.float32)) / stats["std"].astype(np.float32), -10, 10)
Xpca = pca.transform(Xz).astype(np.float32)
Xmark = None; marker_genes = []
if variant == "marker":
marker_genes = yaml.safe_load(open(ROOT / "panda/markers.yaml"))[system]
mv = np.zeros((a.n_obs, len(marker_genes)), dtype=np.float32)
for j, g in enumerate(marker_genes):
if g in a.var_names:
col = a[:, g].X
if sp.issparse(col): col = col.toarray()
mv[:, j] = col.flatten().astype(np.float32)
mmu = mv.mean(axis=0, keepdims=True); msig = mv.std(axis=0, keepdims=True) + 1e-6
Xmark = np.clip((mv - mmu) / msig, -5, 5).astype(np.float32)
labels = a.obs["canonical_label"].astype(str).values
classes = sorted(set(labels))
y = np.array([classes.index(l) for l in labels], dtype=np.int64)
datasets = sorted(set(a.obs["dataset"].astype(str).values))
y_dset = np.array([datasets.index(d) for d in a.obs["dataset"].astype(str).values], dtype=np.int64)
return Xpca, Xmark, y, classes, y_dset, datasets, marker_genes
def train_fold(Xpca, Xmark, y, y_dset, classes, variant, tr_ix, epochs=5, batch=256, lr=1e-3, seed=0):
n_classes = len(classes)
n_datasets = int(max(y_dset[tr_ix].max() + 1, 1))
n_markers = Xmark.shape[1] if Xmark is not None else 0
torch.manual_seed(seed); np.random.seed(seed)
model = PANDAEncoder(variant=variant, n_pca=50, n_markers=n_markers,
n_classes=n_classes, n_sub=3, n_datasets=n_datasets, dropout=0.2).to(DEVICE)
opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
Xt = Xpca[tr_ix]; yt = y[tr_ix]; ydt = y_dset[tr_ix]
Xmt = Xmark[tr_ix] if Xmark is not None else None
rng = np.random.default_rng(seed)
n = len(tr_ix)
for epoch in range(epochs):
stage = 0 if epoch < 1 else 1 if epoch < 3 else 2
perm = rng.permutation(n)
for bstart in range(0, n, batch):
idx = perm[bstart:bstart+batch]
x = torch.from_numpy(Xt[idx]).to(DEVICE)
xm = torch.from_numpy(Xmt[idx]).to(DEVICE) if Xmt is not None else None
yy = torch.from_numpy(yt[idx]).to(DEVICE)
yd = torch.from_numpy(ydt[idx]).to(DEVICE)
aux = torch.zeros(len(idx), 2, device=DEVICE)
lam = 0.1 if stage >= 2 else 0.0
out = model(x, aux, x_markers=xm, lam_dann=lam)
z = out["z"]
L = supcon_loss(z, yy, 0.1) + 1.0 * vicreg_loss(z) + 0.4 * F.cross_entropy(out["logits"], yy)
if stage >= 1:
L = L + 0.6 * subcenter_angular_infonce(z, yy, model.prototypes.detach().clone(),
margin=0.15, temperature=0.07)
if stage >= 2:
L = L + F.cross_entropy(out["dom"], yd)
opt.zero_grad(); L.backward(); opt.step()
if stage >= 1:
with torch.no_grad(): model.update_prototypes(z.detach(), yy)
return model
def evaluate(model, Xpca, Xmark, y, val_ix, classes):
model.eval()
preds, probs = [], []
Xv = Xpca[val_ix]; Xmv = Xmark[val_ix] if Xmark is not None else None
with torch.no_grad():
for i in range(0, len(val_ix), 2048):
xb = torch.from_numpy(Xv[i:i+2048]).to(DEVICE)
xmb = torch.from_numpy(Xmv[i:i+2048]).to(DEVICE) if Xmv is not None else None
aux = torch.zeros(len(xb), 2, device=DEVICE)
out = model(xb, aux, x_markers=xmb, lam_dann=0.0)
z = out["z"]
mc = model.max_sub_cos(z)
preds.append(mc.argmax(dim=1).cpu().numpy())
probs.append(F.softmax(mc / 0.07, dim=1).cpu().numpy())
preds = np.concatenate(preds); probs = np.concatenate(probs)
yv = y[val_ix]
acc = accuracy_score(yv, preds)
f1 = f1_score(yv, preds, average="macro", zero_division=0)
try:
auc = roc_auc_score(np.eye(len(classes))[yv], probs, average="macro", multi_class="ovr")
except Exception:
auc = float("nan")
rep = classification_report(yv, preds, labels=list(range(len(classes))),
target_names=classes, output_dict=True, zero_division=0)
return acc, f1, auc, rep
def cv(system, variant, folds=5, epochs=5, seed=0):
print(f"\n=== CV {system}/{variant} ({folds}-fold, {epochs} epochs) ===", flush=True)
Xpca, Xmark, y, classes, y_dset, datasets, _ = load_and_prepare(system, variant)
print(f"[cv] n={len(y):,} K={len(classes)}", flush=True)
skf = StratifiedKFold(n_splits=folds, shuffle=True, random_state=seed)
accs, f1s, aucs = [], [], []
last_rep = None
for fold, (tr, va) in enumerate(skf.split(np.zeros(len(y)), y)):
model = train_fold(Xpca, Xmark, y, y_dset, classes, variant, tr,
epochs=epochs, seed=seed * 100 + fold)
acc, f1, auc, rep = evaluate(model, Xpca, Xmark, y, va, classes)
accs.append(acc); f1s.append(f1); aucs.append(auc)
last_rep = rep
print(f"[fold {fold+1}] acc={acc:.4f} F1={f1:.4f} AUC={auc:.4f}", flush=True)
result = {
"system": system, "variant": variant, "folds": folds, "epochs": epochs, "seed": seed,
"n_cells": int(len(y)), "n_classes": len(classes),
"per_class_report_note": "per_class_report is from the LAST fold only, not aggregated",
"per_fold_acc": accs, "per_fold_f1": f1s, "per_fold_auc": aucs,
"mean_acc": float(np.mean(accs)), "std_acc": float(np.std(accs)),
"mean_f1": float(np.mean(f1s)), "std_f1": float(np.std(f1s)),
"mean_auc": float(np.nanmean(aucs)), "std_auc": float(np.nanstd(aucs)),
"per_class_report": last_rep,
}
out_dir = ROOT / f"discovery/{system}/{variant}"
out_dir.mkdir(parents=True, exist_ok=True)
# seed 0 is the canonical file; other seeds get a suffix so true seed-replicates
# (same corpus, same script, same curriculum) can be compared side by side
fname = "cv_5fold.json" if seed == 0 else f"cv_5fold_seed{seed}.json"
(out_dir / fname).write_text(json.dumps(result, indent=2, default=str))
print(f"[cv] mean acc={result['mean_acc']:.4f}±{result['std_acc']:.4f} "
f"F1={result['mean_f1']:.4f} AUC={result['mean_auc']:.4f}", flush=True)
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("--systems", nargs="*", default=["pan_skin", "hematopoiesis", "pancreas"])
ap.add_argument("--variants", nargs="*", default=["pca", "marker"])
ap.add_argument("--folds", type=int, default=5)
ap.add_argument("--epochs", type=int, default=5)
ap.add_argument("--seed", type=int, default=0,
help="fold-assignment + model-init seed; non-zero seeds write cv_5fold_seed{N}.json")
args = ap.parse_args()
for s in args.systems:
for v in args.variants:
cv(s, v, args.folds, args.epochs, seed=args.seed)
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