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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 | """held-out CV for panda. GroupKFold by dataset when possible, else StratifiedKFold."""
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
from sklearn.model_selection import GroupKFold, StratifiedKFold
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report
warnings.filterwarnings("ignore")
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,
)
from scripts.common.train_panda import prepare_batches, load_corpus, get_marker_gene_list
ROOT = Path(str(PANDA_ROOT))
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train_one_fold(Xpca, Xmark, y, y_dset, log10cz, classes, variant,
train_idx, epochs=6, batch=256, lr=1e-3, seed=0):
n_classes = len(classes)
n_datasets = int(max(y_dset[train_idx].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)
train_n = len(train_idx)
rng = np.random.default_rng(seed)
Xt = Xpca[train_idx]; yt = y[train_idx]; ydt = y_dset[train_idx]; dt = log10cz[train_idx]
Xmt = Xmark[train_idx] if Xmark is not None else None
for epoch in range(epochs):
stage = 0 if epoch < 1 else 1 if epoch < 3 else 2 if epoch < 6 else 3
for g in opt.param_groups: g["lr"] = lr * (0.5 if epoch >= epochs - 1 else 1.0)
perm = rng.permutation(train_n)
for bstart in range(0, train_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)
dd = torch.from_numpy(dt[idx]).float().to(DEVICE).unsqueeze(1)
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) + 0.3 * F.mse_loss(out["depth"], dd) \
+ 0.05 * hsic_biased(out["repr"], dd)
if stage >= 3:
L = L + 0.5 * prototype_repulsion(model.prototypes.detach().clone())
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_idx, classes):
model.eval()
preds, probs = [], []
Xv = Xpca[val_idx]; Xmv = Xmark[val_idx] if Xmark is not None else None
with torch.no_grad():
for i in range(0, len(val_idx), 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) # (B, K)
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_idx]
acc = accuracy_score(yv, preds)
f1 = f1_score(yv, preds, average="macro", zero_division=0)
# macro AUROC — nan if val has <2 classes
n_classes = len(classes)
try:
y_onehot = np.eye(n_classes)[yv]
auc = roc_auc_score(y_onehot, probs, average="macro", multi_class="ovr")
except Exception:
auc = float("nan")
rep = classification_report(yv, preds, labels=list(range(n_classes)),
target_names=classes, output_dict=True, zero_division=0)
return acc, f1, auc, rep
def cv(system, variant, folds=5, epochs=6, split_mode="auto", seed=0):
a, hvgs, mu, sig, pca = load_corpus(system)
marker_genes = get_marker_gene_list(system) if variant == "marker" else []
Xpca, Xmark, y, classes, y_dset, dset_classes, log10cz = prepare_batches(
a, hvgs, mu, sig, pca, marker_genes, variant
)
print(f"[cv] {system}/{variant} n={a.n_obs} K={len(classes)} datasets={len(dset_classes)}", flush=True)
use_group = (split_mode == "group") or (split_mode == "auto" and len(dset_classes) >= folds)
if use_group:
splitter = GroupKFold(n_splits=folds)
splits = list(splitter.split(np.zeros(len(y)), y, y_dset))
print(f"[cv] GroupKFold by dataset ({len(dset_classes)} groups)", flush=True)
else:
splitter = StratifiedKFold(n_splits=folds, shuffle=True, random_state=seed)
splits = list(splitter.split(np.zeros(len(y)), y))
print(f"[cv] StratifiedKFold on labels ({split_mode}) seed={seed}", flush=True)
per_fold_acc, per_fold_f1, per_fold_auc = [], [], []
last_report = None
for fold, (tr, va) in enumerate(splits):
print(f"[fold {fold+1}/{folds}] train={len(tr)} val={len(va)}", flush=True)
model = train_one_fold(Xpca, Xmark, y, y_dset, log10cz, classes, variant, tr,
epochs=epochs, seed=seed * 100 + fold)
acc, f1, auc, rep = evaluate(model, Xpca, Xmark, y, va, classes)
per_fold_acc.append(acc); per_fold_f1.append(f1); per_fold_auc.append(auc)
last_report = 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(a.n_obs), "n_classes": len(classes),
"per_fold_acc": per_fold_acc, "per_fold_f1": per_fold_f1, "per_fold_auc": per_fold_auc,
"mean_acc": float(np.mean(per_fold_acc)), "std_acc": float(np.std(per_fold_acc)),
"mean_f1": float(np.mean(per_fold_f1)), "std_f1": float(np.std(per_fold_f1)),
"mean_auc": float(np.nanmean(per_fold_auc)),
"std_auc": float(np.nanstd(per_fold_auc)),
"per_class_report": last_report,
}
out_dir = ROOT / f"discovery/{system}/{variant}"
out_dir.mkdir(parents=True, exist_ok=True)
suffix = f"_seed{seed}" if seed != 0 else ""
(out_dir / f"cv_{folds}fold{suffix}.json").write_text(json.dumps(result, indent=2, default=str))
print(f"\n[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("system", choices=["pan_skin", "hematopoiesis", "pancreas"])
ap.add_argument("--variant", choices=["pca", "marker"], required=True)
ap.add_argument("--folds", type=int, default=5)
ap.add_argument("--epochs", type=int, default=6)
ap.add_argument("--split", choices=["auto", "group", "stratified"], default="auto")
ap.add_argument("--seed", type=int, default=0)
args = ap.parse_args()
cv(args.system, args.variant, args.folds, args.epochs, args.split, args.seed)
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