code/ster_run_v2.py

#!/usr/bin/env python3
"""
STER — NS-D2S Experiment Runner (Multi-City, GPU-optimized).

Runs the complete NS-D2S pipeline on any city benchmark:
  DDPM → CS-SDEdit → InfoNCE Contrastive → Zero-shot Evaluation

Usage:
    python ster_run_v2.py --city rotterdam --data_dir data/rotterdam_test --device cuda
"""
import argparse, json, os, time
import numpy as np
import torch, torch.nn as nn, torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
from ster_constraints import constraint_program

PROPERTY_NAMES = [
    "bounding_box_width","bounding_box_length","area","perimeter","perimeter_ind",
    "volume","convex_hull_area","convex_hull_volume","ave_centroid_distance",
    "height_diff","num_floors","axes_symmetry","compactness_2d","compactness_3d",
    "density","elongation","shape_ind","hemisphericality","fractality","cubeness",
    "circumference","aligned_bounding_box_width","aligned_bounding_box_length",
    "aligned_bounding_box_height","num_vertices",
]

# ─── Denoiser ───
class Denoiser(nn.Module):
    def __init__(self, d=25, hidden=256):
        super().__init__()
        self.time_mlp = nn.Sequential(nn.Linear(1,256), nn.SiLU(), nn.Linear(256,256))
        self.net = nn.Sequential(
            nn.Linear(d+256,hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden,hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden,hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden,d))
    def forward(self, x_t, t):
        te = self.time_mlp(t.float().unsqueeze(-1)/1000.)
        return self.net(torch.cat([x_t, te], dim=-1))

# ─── DDPM ───
class DDPM:
    def __init__(self, denoiser, T=1000, b1=1e-4, b2=0.02, device='cuda'):
        self.denoiser = denoiser.to(device)
        self.T, self.device = T, device
        self.betas = torch.linspace(b1, b2, T, device=device)
        self.alphas = 1.-self.betas
        self.alpha_bars = torch.cumprod(self.alphas, 0)

    def diffuse(self, x0, t):
        ab = self.alpha_bars[t].view(-1,1)
        eps = torch.randn_like(x0)
        return torch.sqrt(ab)*x0 + torch.sqrt(1-ab)*eps, eps

    @torch.no_grad()
    def sdedit(self, x0, t0=150, constraint_fn=None, eta=0.01, delta=0.5, S=3):
        self.denoiser.eval()
        B = x0.shape[0]
        ab0 = self.alpha_bars[t0]
        x_t = torch.sqrt(ab0)*x0 + torch.sqrt(1-ab0)*torch.randn_like(x0)

        for t in range(t0, 0, -1):
            tt = torch.full((B,), t, device=self.device, dtype=torch.long)
            eps_pred = self.denoiser(x_t, tt)
            ab = self.alpha_bars[t]

            # Tweedie estimate of clean sample
            x0_hat = (x_t - torch.sqrt(1-ab)*eps_pred) / (torch.sqrt(ab)+1e-8)
            x0_orig = x0_hat.clone()

            # Constraint-guided refinement
            if constraint_fn is not None:
                x0_hat_ref = x0_hat.clone().detach().requires_grad_(True)
                for s in range(S):
                    C = constraint_fn(x0_hat_ref)
                    if C.sum() > 0:
                        g = torch.autograd.grad(C.sum(), x0_hat_ref, retain_graph=(s<S-1))[0]
                        x0_hat_ref = x0_hat_ref - eta * g
                    # Trust radius
                    diff = x0_hat_ref - x0_orig
                    n = torch.norm(diff, dim=1, keepdim=True).clamp(min=1e-8)
                    x0_hat_ref = torch.where(n > delta, x0_orig + delta*diff/n, x0_hat_ref)
                    if s < S-1:
                        x0_hat_ref = x0_hat_ref.detach().requires_grad_(True)
                x0_hat = x0_hat_ref.detach()

            # Reconstruct eps and step
            eps_eff = (x_t - torch.sqrt(ab)*x0_hat) / (torch.sqrt(1-ab)+1e-8)
            bt = self.betas[t]; at = self.alphas[t]
            ab_p = self.alpha_bars[t-1] if t>1 else torch.tensor(1., device=self.device)
            sigma = torch.sqrt(bt*(1-ab_p)/(1-ab+1e-8))
            noise = torch.randn_like(x_t) if t > 1 else 0.
            x_t = (1/torch.sqrt(at))*(x_t - bt/torch.sqrt(1-ab+1e-8)*eps_eff) + sigma*noise

        return x_t

# ─── Encoder ───
class Encoder(nn.Module):
    def __init__(self, d=25):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(d,128), nn.BatchNorm1d(128), nn.ReLU(),
            nn.Linear(128,128), nn.BatchNorm1d(128), nn.ReLU(),
            nn.Linear(128,64))
    def forward(self, x):
        return F.normalize(self.net(x), dim=-1)

# ─── InfoNCE ───
def info_nce(za, zb, tau=0.1):
    za, zb = F.normalize(za,dim=-1), F.normalize(zb,dim=-1)
    B = za.shape[0]; labels = torch.arange(B, device=za.device)
    return (F.cross_entropy(za@zb.T/tau, labels) + F.cross_entropy(zb@za.T/tau, labels))/2

# ─── Load ───
def load_props(path):
    df = None
    for ext, loader in [('.parquet', lambda p: __import__('pandas').read_parquet(p)),
                         ('.json', lambda p: __import__('pandas').DataFrame.from_dict(
                             __import__('json').load(open(p)), orient='index'))]:
        fp = path + ext
        if os.path.exists(fp):
            df = loader(fp); break
    if df is None: raise FileNotFoundError(f"No properties at {path}")
    X = df[PROPERTY_NAMES].values.astype(np.float32)
    if X.max() > 100: X = np.log1p(X)  # auto log-normalize
    return X

# ─── Main ───
def run(city, data_dir, device='cuda', epochs_diff=300, epochs_enc=200,
        lambda_c=0.05, delta=0.5, t0=150, S=3, eta=0.01, bs=256, lr=3e-4):
    print(f"\n{'='*60}\n  NS-D2S: {city}  |  device={device}\n{'='*60}")

    # 1. Load
    X = load_props(os.path.join(data_dir, 'properties_lod22'))
    n = len(X); n_tr = int(0.7*n)
    np.random.seed(42); idx = np.random.permutation(n)
    X_tr = torch.tensor(X[idx[:n_tr]], dtype=torch.float32)
    X_te = torch.tensor(X[idx[n_tr:]], dtype=torch.float32)
    print(f"  Buildings: {n} (train={n_tr}, test={n-n_tr})")

    # 2. Train DDPM
    print(f"\n  [DDPM] T=1000 λ_C={lambda_c}")
    dns = Denoiser(); ddpm = DDPM(dns, device=device)
    opt = torch.optim.AdamW(dns.parameters(), lr=lr, wd=1e-5)
    dl = DataLoader(TensorDataset(X_tr), batch_size=bs, shuffle=True)
    t0_t = time.time()
    for ep in range(epochs_diff):
        tl = 0.
        for (x0b,) in dl:
            x0b = x0b.to(device); B = x0b.shape[0]
            t = torch.randint(1, ddpm.T, (B,), device=device)
            xt, eps = ddpm.diffuse(x0b, t)
            ep_pred = dns(xt, t)
            L = F.mse_loss(ep_pred, eps)
            if lambda_c > 0:
                ab = ddpm.alpha_bars[t].view(-1,1)
                x0h = (xt - torch.sqrt(1-ab)*ep_pred) / (torch.sqrt(ab)+1e-8)
                L = L + lambda_c * constraint_program(x0h).mean()
            opt.zero_grad(); L.backward(); opt.step(); tl += L.item()
        if (ep+1)%50==0: print(f"    ep {ep+1}/{epochs_diff} loss={tl/len(dl):.4f}")
    dt = time.time()-t0_t; print(f"  DDPM done in {dt:.0f}s")

    # 3. CS-SDEdit
    print(f"\n  [CS-SDEdit] t0={t0} δ={delta} S={S}")
    dl2 = DataLoader(TensorDataset(X_te), batch_size=bs, shuffle=False)
    srcs, sibs = [], []
    for (x0b,) in dl2:
        x0b = x0b.to(device)
        sib = ddpm.sdedit(x0b, t0=t0, constraint_fn=constraint_program, eta=eta, delta=delta, S=S)
        srcs.append(x0b.cpu()); sibs.append(sib.cpu())
    X_src = torch.cat(srcs); X_sib = torch.cat(sibs)
    cvr = (constraint_program(X_sib.to(device))>0).float().mean().item()
    print(f"  CVR(sibs)={cvr:.4f}")

    # 4. Contrastive
    print(f"\n  [InfoNCE] epochs={epochs_enc} τ=0.1")
    enc = Encoder().to(device)
    oe = torch.optim.AdamW(enc.parameters(), lr=lr, wd=1e-5)
    pl = DataLoader(TensorDataset(X_src, X_sib), batch_size=bs, shuffle=True)
    for ep in range(epochs_enc):
        tl=0.
        for xa,xb in pl:
            xa,xb = xa.to(device), xb.to(device)
            L = info_nce(enc(xa), enc(xb))
            oe.zero_grad(); L.backward(); oe.step(); tl+=L.item()
        if (ep+1)%50==0: print(f"    ep {ep+1}/{epochs_enc} loss={tl/len(pl):.4f}")

    # 5. Eval
    print(f"\n  [Eval]")
    enc.eval()
    with torch.no_grad():
        Nt = min(len(X_src), 2000)
        it = torch.randperm(len(X_src))[:Nt]
        pos = (enc(X_src[it].to(device))*enc(X_sib[it].to(device))).sum(-1)
        ni = torch.randperm(Nt)
        neg = (enc(X_src[it].to(device))*enc(X_sib[ni].to(device))).sum(-1)
        sc = torch.cat([pos, neg]); lb = torch.cat([torch.ones(Nt), torch.zeros(Nt)])
        best_f1, best_t = 0., 0.
        tp_opt = fp_opt = fn_opt = 0
        for th in np.linspace(0., 1., 200):
            pr = (sc>=th).float()
            tp = ((pr==1)&(lb==1)).sum().item(); fp = ((pr==1)&(lb==0)).sum().item()
            fn = ((pr==0)&(lb==1)).sum().item()
            p = tp/max(tp+fp,1); r = tp/max(tp+fn,1)
            f1 = 2*p*r/max(p+r,1e-6)
            if f1>best_f1: best_f1,best_t = f1,th; tp_opt,fp_opt,fn_opt = tp,fp,fn

    res = {"city":city, "n_buildings":n, "n_train":n_tr, "n_test":n-n_tr,
           "lambda_c":lambda_c, "delta":delta, "t0":t0, "S":S,
           "cvr":float(cvr), "f1":float(best_f1),
           "precision":float(tp_opt/max(tp_opt+fp_opt,1)),
           "recall":float(tp_opt/max(tp_opt+fn_opt,1)),
           "threshold":float(best_t), "diff_time":dt}
    print(f"  → F1={res['f1']:.4f} P={res['precision']:.4f} R={res['recall']:.4f} CVR={res['cvr']:.4f}")

    os.makedirs(os.path.join(data_dir,'results'), exist_ok=True)
    os.makedirs(os.path.join(data_dir,'models'), exist_ok=True)
    with open(os.path.join(data_dir,'results','nsd2s.json'),'w') as f: json.dump(res,f,indent=2)
    torch.save(dns.state_dict(), os.path.join(data_dir,'models','ddpm.pt'))
    torch.save(enc.state_dict(), os.path.join(data_dir,'models','encoder.pt'))
    np.savez(os.path.join(data_dir,'results','siblings.npz'),
             sources=X_src.numpy(), siblings=X_sib.numpy())
    print(f"  Saved → {data_dir}/results/ + models/")
    return res

if __name__ == '__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument('--city', required=True)
    ap.add_argument('--data_dir', default=None)
    ap.add_argument('--device', default='cuda')
    ap.add_argument('--lambda_c', type=float, default=0.05)
    ap.add_argument('--delta', type=float, default=0.5)
    ap.add_argument('--t0', type=int, default=150)
    ap.add_argument('--S', type=int, default=3)
    ap.add_argument('--epochs_diff', type=int, default=300)
    ap.add_argument('--epochs_enc', type=int, default=200)
    ap.add_argument('--batch_size', type=int, default=256)
    a = ap.parse_args()
    dd = a.data_dir or os.path.join('data', a.city)
    run(a.city, dd, a.device, a.epochs_diff, a.epochs_enc,
        a.lambda_c, a.delta, a.t0, a.S, batch_size=a.batch_size)