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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 | """train panda on the canonical paper-labeled corpus for one system + variant."""
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
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_corpus(system):
"""canonical loader; alias for load_corpus_v3 after finalize_rename."""
return load_corpus_v3(system)
def load_corpus_v3(system):
# kept for backward-compat with older scripts that import load_corpus_v3
p = 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"))
a = ad.read_h5ad(p)
hvgs = [str(g) for g in stats["shared_hvgs"]]
return a, hvgs, stats["mean"], stats["std"], pca
def get_marker_gene_list(system):
y = yaml.safe_load(open(ROOT / "panda/markers.yaml"))
return y[system]
def prepare_batches(adata, hvgs, mu, sig, pca, marker_genes=None, variant="pca",
legacy_double_norm=True):
# corpus.h5ad X is already normalize_total+log1p'd by the corpus builders, and the
# published checkpoints were trained with a second normalize/log1p applied on top
# (against per-gene stats computed from singly-normalized data). legacy_double_norm=True
# reproduces that behaviour bit-for-bit; pass False to train on the corpus values the
# stats were actually computed from. Do not mix: a checkpoint must be evaluated under
# the same setting it was trained with.
hvg2i = {g: i for i, g in enumerate(hvgs)}
common = [g for g in adata.var_names.astype(str) if g in hvg2i]
a_c = adata[:, common].copy()
if legacy_double_norm:
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((adata.n_obs, len(hvgs)), dtype=np.float32)
cols = np.array([hvg2i[g] for g in common])
Xf[:, cols] = X_
Xz = np.clip((Xf - mu.astype(np.float32)) / sig.astype(np.float32), -10, 10)
Xpca = pca.transform(Xz).astype(np.float32)
Xmark = None
if variant == "marker" and marker_genes:
mvals = np.zeros((adata.n_obs, len(marker_genes)), dtype=np.float32)
for j, g in enumerate(marker_genes):
if g in adata.var_names:
col = adata[:, g].X
if sp.issparse(col): col = col.toarray()
mvals[:, j] = col.flatten().astype(np.float32)
mmu = mvals.mean(axis=0, keepdims=True); msig = mvals.std(axis=0, keepdims=True) + 1e-6
Xmark = np.clip((mvals - mmu) / msig, -5, 5).astype(np.float32)
# expose the training-corpus marker stats so the checkpoint can carry them;
# inference must reuse these rather than refit on the target dataset
prepare_batches.last_marker_stats = (mmu, msig)
labels = adata.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(adata.obs["dataset"].astype(str).values))
y_dset = np.array([datasets.index(d) for d in adata.obs["dataset"].astype(str).values], dtype=np.int64)
counts = np.asarray(adata.X.sum(axis=1)).ravel()
log10cz = ((np.log10(counts + 1) - np.log10(counts + 1).mean()) /
(np.log10(counts + 1).std() + 1e-6)).astype(np.float32)
return Xpca, Xmark, y, classes, y_dset, datasets, log10cz
def train(system, variant, epochs=8, batch=256, lr=1e-3):
a, hvgs, mu, sig, pca = load_corpus_v3(system)
marker_genes = get_marker_gene_list(system) if variant == "marker" else []
Xpca, Xmark, y, classes, y_dset, datasets, log10cz = prepare_batches(
a, hvgs, mu, sig, pca, marker_genes, variant
)
print(f"[train] {system}/{variant} n={a.n_obs} K={len(classes)} datasets={len(datasets)}", flush=True)
print(f"[train] classes: {classes}", flush=True)
n_markers = Xmark.shape[1] if Xmark is not None else 0
model = PANDAEncoder(variant=variant, n_pca=50, n_markers=n_markers,
n_classes=len(classes), n_sub=3, n_datasets=len(datasets), dropout=0.2).to(DEVICE)
opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
rng = np.random.default_rng(0)
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(a.n_obs)
losses = []
for bstart in range(0, a.n_obs, batch):
idx = perm[bstart:bstart+batch]
x = torch.from_numpy(Xpca[idx]).to(DEVICE)
xm = torch.from_numpy(Xmark[idx]).to(DEVICE) if Xmark is not None else None
yy = torch.from_numpy(y[idx]).to(DEVICE)
yd = torch.from_numpy(y_dset[idx]).to(DEVICE)
dd = torch.from_numpy(log10cz[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)
# NOTE: earlier revisions added 0.5 * prototype_repulsion(model.prototypes.detach())
# at stage 3. prototypes is a gradient-free EMA buffer and the tensor was detached,
# so the term contributed exactly zero gradient — it only inflated the printed loss.
# Removed from the objective (behaviour-preserving); prototype_repulsion() remains
# in panda.model as an analysis metric.
opt.zero_grad(); L.backward(); opt.step()
if stage >= 1:
with torch.no_grad(): model.update_prototypes(z.detach(), yy)
losses.append(float(L))
print(f"[train {system}/{variant}] epoch {epoch}/{epochs} stage={stage} loss={np.mean(losses):.4f}", flush=True)
# save to the same path every consumer (zero_shot, run_all_zero_shot, extract_prototypes)
# loads from. Earlier revisions wrote to checkpoints/{system}_v3/ while consumers read
# checkpoints/{system}/ — a retrain silently never propagated.
ck_dir = ROOT / f"checkpoints/{system}/{variant}"
ck_dir.mkdir(parents=True, exist_ok=True)
mstats = getattr(prepare_batches, "last_marker_stats", None) if variant == "marker" else None
torch.save({"model": model.state_dict(), "classes": classes, "datasets": datasets,
"marker_genes": marker_genes if variant == "marker" else [],
"marker_mu": mstats[0] if mstats else None,
"marker_sig": mstats[1] if mstats else None,
"legacy_double_norm": True,
"prototypes": model.prototypes.detach().cpu().numpy()},
ck_dir / "panda_final.pt")
print(f"[save] {ck_dir}/panda_final.pt", 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("--epochs", type=int, default=8)
ap.add_argument("--batch", type=int, default=256)
ap.add_argument("--lr", type=float, default=1e-3)
args = ap.parse_args()
train(args.system, args.variant, args.epochs, args.batch, args.lr)
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