"""Train, calibrate, and checkpoint WoFS elastic-net logistic models.""" import argparse import json import os import sys from pathlib import Path import numpy as np import torch import yaml from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, Dataset, DistributedSampler, Subset ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(ROOT)) from model.wofsstormcal import WoFSStormCal class HazardDataset(Dataset): def __init__(self, path, config): self.data = np.load(path) if str(self.data["format_version"]) != config["data"]["format_version"]: raise ValueError("incompatible WoFS storm-object data format") count = len(self.data["features"]) if self.data["features"].shape != (count, 113): raise ValueError("features must have shape [N,113]") if self.data["targets"].shape != (count, 3): raise ValueError("targets must have shape [N,3]") if self.data["lead_group"].shape != (count,): raise ValueError("lead_group must have shape [N]") if not np.isfinite(self.data["features"]).all() or not np.isfinite(self.data["targets"]).all(): raise ValueError("data contain NaN or Inf") def __len__(self): return len(self.data["features"]) def __getitem__(self, index): return (torch.from_numpy(self.data["features"][index]).float(), torch.from_numpy(self.data["targets"][index]).float(), torch.as_tensor(self.data["lead_group"][index], dtype=torch.long)) def device_from_config(config, rank=0): requested = config["runtime"]["device"] if requested == "auto": return torch.device("cuda", rank) if torch.cuda.is_available() else torch.device("cpu") return torch.device(requested) def main(): parser = argparse.ArgumentParser() parser.add_argument("--resume", action="store_true", help="restore model and optimizer state before training") args = parser.parse_args() config = yaml.safe_load((ROOT / "conf/config.yaml").read_text()) seed = int(config["seed"]) np.random.seed(seed); torch.manual_seed(seed) distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1 local_rank = int(os.environ.get("LOCAL_RANK", "0")) if distributed: torch.distributed.init_process_group("nccl" if torch.cuda.is_available() else "gloo") rank = torch.distributed.get_rank() if distributed else 0 device = device_from_config(config, local_rank) if device.type == "cuda": torch.cuda.set_device(device) dataset = HazardDataset(ROOT / config["data"]["root"] / "train.npz", config) split = int(len(dataset) * (1 - float(config["train"]["calibration_fraction"]))) fit_set = Subset(dataset, range(split)) sampler = DistributedSampler(fit_set, shuffle=True) if distributed else None loader = DataLoader(fit_set, batch_size=int(config["train"]["batch_size"]), sampler=sampler, shuffle=sampler is None, num_workers=int(config["train"]["num_workers"])) model = WoFSStormCal(int(config["model"]["calibration_points"])).to(device) features = dataset.data["features"][:split] groups = dataset.data["lead_group"][:split] means, scales = [], [] for group in range(2): group_features = features[groups == group] means.append(group_features.mean(0)); scales.append(group_features.std(0).clip(1e-6)) model.set_normalization(torch.from_numpy(np.stack(means)).to(device), torch.from_numpy(np.stack(scales)).to(device)) wrapped = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None) if distributed else model optimizer = torch.optim.Adam(wrapped.parameters(), lr=float(config["train"]["learning_rate"])) checkpoint_path = ROOT / config["paths"]["checkpoint"] start_epoch, history = 0, [] if args.resume: checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) model.load_state_dict(checkpoint["model"]) optimizer.load_state_dict(checkpoint["optimizer"]) start_epoch = int(checkpoint["epoch"]) history = checkpoint.get("history", []) for epoch in range(start_epoch, start_epoch + int(config["train"]["epochs"])): if sampler is not None: sampler.set_epoch(epoch) total, steps = 0.0, 0 for batch_features, targets, lead_group in loader: batch_features, targets, lead_group = batch_features.to(device), targets.to(device), lead_group.to(device) active_model = wrapped.module if distributed else wrapped logits = wrapped(batch_features, lead_group, False) logits = torch.logit(logits.clamp(1e-6, 1 - 1e-6)) loss = active_model.elastic_net_loss(logits, targets, config["train"]["l1_strength"], config["train"]["l2_strength"]) optimizer.zero_grad(set_to_none=True); loss.backward() torch.nn.utils.clip_grad_norm_(wrapped.parameters(), float(config["train"]["gradient_clip_norm"])) optimizer.step(); total += float(loss.detach()); steps += 1 if rank == 0: history.append({"epoch": epoch + 1, "elastic_net_loss": total / max(steps, 1)}) model = wrapped.module if distributed else wrapped if rank == 0: calibration_features = torch.from_numpy(dataset.data["features"][split:]).float().to(device) calibration_targets = torch.from_numpy(dataset.data["targets"][split:]).float().to(device) calibration_groups = torch.from_numpy(dataset.data["lead_group"][split:]).long().to(device) model.fit_calibration(calibration_features, calibration_targets, calibration_groups) checkpoint_path.parent.mkdir(parents=True, exist_ok=True) payload = {"model": model.state_dict(), "optimizer": optimizer.state_dict(), "epoch": start_epoch + int(config["train"]["epochs"]), "history": history, "format_version": config["data"]["format_version"], "model_metadata": {"input_shape": ["N", 113], "output_shape": ["N", 3], "hazards": model.hazards, "lead_groups": model.lead_groups, "ensemble_members": 18, "grid_spacing_km": 3, "forecast_window_minutes": 30, "forecast_interval_minutes": 5}} torch.save(payload, checkpoint_path) metrics = ROOT / config["paths"]["training_metrics"] metrics.parent.mkdir(parents=True, exist_ok=True) metrics.write_text(json.dumps({"history": history, "calibration_samples": len(dataset) - split}, indent=2) + "\n") print(f"checkpoint={checkpoint_path.relative_to(ROOT)} output_shape=(N,3) epoch={payload['epoch']}") if distributed: torch.distributed.destroy_process_group() if __name__ == "__main__": main()