import json import os import time import argparse import math from dataclasses import asdict, dataclass from typing import Literal, Sequence from pathlib import Path import torch import torch.nn as nn from torch.optim import AdamW from torch.utils.data import DataLoader from torch.utils.data import random_split from torch.nn.utils import clip_grad_norm_ from tqdm import tqdm from dataset import HealthDataset, health_collate_fn from model import DelphiFork, SapDelphi from losses import ExponentialNLLLoss, WeibullNLLLoss, get_valid_pairs_and_dt @dataclass class TrainConfig: # Model Parameters model_type: Literal['sap_delphi', 'delphi_fork'] = 'delphi_fork' loss_type: Literal['exponential', 'weibull'] = 'weibull' age_encoder: Literal['sinusoidal', 'learned'] = 'learned' full_cov: bool = False n_embd: int = 120 n_head: int = 12 n_layer: int = 12 pdrop: float = 0.1 lambda_reg: float = 1e-4 # SapDelphi specific pretrained_emd_path: str = "icd10_sapbert_embeddings.npy" # Data Parameters data_prefix: str = "ukb" train_ratio: float = 0.7 val_ratio: float = 0.15 random_seed: int = 42 # Training Parameters batch_size: int = 128 max_epochs: int = 200 warmup_epochs: int = 10 patience: int = 10 min_lr: float = 1e-5 max_lr: float = 5e-4 grad_clip: float = 1.0 weight_decay: float = 1e-2 device: str = 'cuda' if torch.cuda.is_available() else 'cpu' # EMA parameters ema_decay: float = 0.999 def parse_args() -> TrainConfig: parser = argparse.ArgumentParser(description="Train Delphi Model") parser.add_argument("--model_type", type=str, choices=[ 'sap_delphi', 'delphi_fork'], default='delphi_fork', help="Type of model to use.") parser.add_argument("--loss_type", type=str, choices=[ 'exponential', 'weibull'], default='weibull', help="Type of loss function to use.") parser.add_argument("--age_encoder", type=str, choices=[ 'sinusoidal', 'learned'], default='learned', help="Type of age encoder to use.") parser.add_argument("--n_embd", type=int, default=120, help="Embedding dimension.") parser.add_argument("--n_head", type=int, default=12, help="Number of attention heads.") parser.add_argument("--n_layer", type=int, default=12, help="Number of transformer layers.") parser.add_argument("--pdrop", type=float, default=0.1, help="Dropout probability.") parser.add_argument("--lambda_reg", type=float, default=1e-4, help="Regularization weight.") parser.add_argument("--pretrained_emd_path", type=str, default="icd10_sapbert_embeddings.npy", help="Path to pretrained embeddings for SapDelphi.") parser.add_argument("--data_prefix", type=str, default="ukb", help="Prefix for dataset files.") parser.add_argument("--full_cov", action='store_true', help="Whether to use full covariates.") parser.add_argument("--train_ratio", type=float, default=0.7, help="Training data ratio.") parser.add_argument("--val_ratio", type=float, default=0.15, help="Validation data ratio.") parser.add_argument("--random_seed", type=int, default=42, help="Random seed for data splitting.") parser.add_argument("--batch_size", type=int, default=128, help="Batch size.") parser.add_argument("--max_epochs", type=int, default=200, help="Maximum number of epochs.") parser.add_argument("--warmup_epochs", type=int, default=10, help="Number of warmup epochs.") parser.add_argument("--patience", type=int, default=10, help="Early stopping patience.") parser.add_argument("--min_lr", type=float, default=1e-5, help="Minimum learning rate.") parser.add_argument("--max_lr", type=float, default=5e-4, help="Maximum learning rate.") parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value.") parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay for optimizer.") parser.add_argument("--ema_decay", type=float, default=0.999, help="EMA decay rate.") parser.add_argument("--device", type=str, default='cuda' if torch.cuda.is_available() else 'cpu', help="Device to use for training.") args = parser.parse_args() return TrainConfig(**vars(args)) def get_num_params(model: nn.Module) -> int: return sum(p.numel() for p in model.parameters() if p.requires_grad) class Trainer: def __init__( self, cfg: TrainConfig, ): self.cfg = cfg self.device = cfg.device self.global_step = 0 if cfg.full_cov: cov_list = None else: cov_list = ["bmi", "smoking", "alcohol"] dataset = HealthDataset( data_prefix=cfg.data_prefix, covariate_list=cov_list, ) print("Dataset loaded.") n_total = len(dataset) print(f"Total samples in dataset: {n_total}") print(f"Number of diseases: {dataset.n_disease}") print(f"Number of continuous covariates: {dataset.n_cont}") print(f"Number of categorical covariates: {dataset.n_cate}") self.train_data, self.val_data, _ = random_split( dataset, [ int(n_total * cfg.train_ratio), int(n_total * cfg.val_ratio), n_total - int(n_total * cfg.train_ratio) - int(n_total * cfg.val_ratio), ], generator=torch.Generator().manual_seed(cfg.random_seed), ) self.train_loader = DataLoader( self.train_data, batch_size=cfg.batch_size, shuffle=True, collate_fn=health_collate_fn, ) self.val_loader = DataLoader( self.val_data, batch_size=cfg.batch_size, shuffle=False, collate_fn=health_collate_fn, ) if cfg.loss_type == "exponential": self.criterion = ExponentialNLLLoss( lambda_reg=cfg.lambda_reg, ).to(self.device) n_dim = 1 elif cfg.loss_type == "weibull": self.criterion = WeibullNLLLoss( lambda_reg=cfg.lambda_reg, ).to(self.device) n_dim = 2 else: raise ValueError(f"Unsupported loss type: {cfg.loss_type}") if cfg.model_type == "delphi_fork": self.model = DelphiFork( n_disease=dataset.n_disease, n_embd=cfg.n_embd, n_head=cfg.n_head, n_layer=cfg.n_layer, pdrop=cfg.pdrop, age_encoder=cfg.age_encoder, n_dim=n_dim, n_cont=dataset.n_cont, n_cate=dataset.n_cate, ).to(self.device) elif cfg.model_type == "sap_delphi": self.model = SapDelphi( n_disease=dataset.n_disease, n_embd=cfg.n_embd, n_head=cfg.n_head, n_layer=cfg.n_layer, pdrop=cfg.pdrop, age_encoder=cfg.age_encoder, n_dim=n_dim, n_cont=dataset.n_cont, n_cate=dataset.n_cate, pretrained_emd_path=cfg.pretrained_emd_path, freeze_pretrained_emd=True, ).to(self.device) else: raise ValueError(f"Unsupported model type: {cfg.model_type}") print(f"Model initialized: {cfg.model_type}") print(f"Number of trainable parameters: {get_num_params(self.model)}") # Initialize EMA model self.ema_model = None if cfg.ema_decay < 1.0: if cfg.model_type == "delphi_fork": self.ema_model = DelphiFork( n_disease=dataset.n_disease, n_embd=cfg.n_embd, n_head=cfg.n_head, n_layer=cfg.n_layer, pdrop=cfg.pdrop, age_encoder=cfg.age_encoder, n_dim=n_dim, n_cont=dataset.n_cont, n_cate=dataset.n_cate, ).to(self.device) elif cfg.model_type == "sap_delphi": self.ema_model = SapDelphi( n_disease=dataset.n_disease, n_embd=cfg.n_embd, n_head=cfg.n_head, n_layer=cfg.n_layer, pdrop=cfg.pdrop, age_encoder=cfg.age_encoder, n_dim=n_dim, n_cont=dataset.n_cont, n_cate=dataset.n_cate, pretrained_emd_path=cfg.pretrained_emd_path, freeze_pretrained_emd=True, ).to(self.device) else: raise ValueError(f"Unsupported model type: {cfg.model_type}") self.ema_model.load_state_dict(self.model.state_dict()) for param in self.ema_model.parameters(): param.requires_grad = False print("EMA model initialized.") self.optimizer = AdamW( self.model.parameters(), lr=cfg.max_lr, weight_decay=cfg.weight_decay, betas=(0.9, 0.99), ) self.total_steps = (len(self.train_loader) * cfg.max_epochs) print(f"Total optimization steps: {self.total_steps}") while True: cov_suffix = "fullcov" if cfg.full_cov else "partcov" name = f"{cfg.model_type}_{cfg.loss_type}_{cfg.age_encoder}_{cov_suffix}" timestamp = time.strftime("%Y%m%d-%H%M%S") model_dir = os.path.join("runs", f"{name}_{timestamp}") if not os.path.exists(model_dir): self.out_dir = model_dir os.makedirs(model_dir) break time.sleep(1) print(f"Output directory: {self.out_dir}") self.best_path = os.path.join(self.out_dir, "best_model.pt") self.global_step = 0 self.save_config() def save_config(self): cfg_path = os.path.join(self.out_dir, "train_config.json") with open(cfg_path, 'w') as f: json.dump(asdict(self.cfg), f, indent=4) print(f"Configuration saved to {cfg_path}") def update_ema(self): if self.ema_model is None: return decay = self.cfg.ema_decay with torch.no_grad(): model_params = dict(self.model.named_parameters()) ema_params = dict(self.ema_model.named_parameters()) for name in model_params.keys(): ema_params[name].data.mul_(decay).add_( model_params[name].data, alpha=1 - decay) def compute_lr(self, current_step: int) -> float: cfg = self.cfg if current_step < cfg.warmup_epochs * len(self.train_loader): lr = cfg.max_lr * (current_step / (cfg.warmup_epochs * len(self.train_loader))) else: denom = (cfg.max_epochs - cfg.warmup_epochs) * \ len(self.train_loader) progress = (current_step - cfg.warmup_epochs * len(self.train_loader)) / denom lr = cfg.min_lr + 0.5 * \ (cfg.max_lr - cfg.min_lr) * (1 + math.cos(math.pi * progress)) return lr def train(self) -> None: history = [] best_val_score = float('inf') patience_counter = 0 for epoch in range(1, self.cfg.max_epochs + 1): self.model.train() running_nll = 0.0 running_reg = 0.0 pbar = tqdm(self.train_loader, desc=f"Epoch {epoch}/{self.cfg.max_epochs} - Training", ncols=100) batch_count = 0 for batch in pbar: ( event_seq, time_seq, cont_feats, cate_feats, sexes, ) = batch event_seq = event_seq.to(self.device) time_seq = time_seq.to(self.device) cont_feats = cont_feats.to(self.device) cate_feats = cate_feats.to(self.device) sexes = sexes.to(self.device) res = get_valid_pairs_and_dt(event_seq, time_seq, 2) if res is None: continue dt, b_prev, t_prev, b_next, t_next = res self.optimizer.zero_grad() lr = self.compute_lr(self.global_step) for param_group in self.optimizer.param_groups: param_group['lr'] = lr logits = self.model( event_seq, time_seq, sexes, cont_feats, cate_feats, b_prev=b_prev, t_prev=t_prev, ) target_event = event_seq[b_next, t_next] - 2 nll_vec, reg = self.criterion( logits, target_event, dt, reduction="none", ) finite_mask = torch.isfinite(nll_vec) if not finite_mask.any(): continue nll_vec = nll_vec[finite_mask] nll = nll_vec.mean() loss = nll + reg batch_count += 1 running_nll += nll.item() running_reg += reg.item() pbar.set_postfix({ "lr": lr, "NLL": running_nll / batch_count, "Reg": running_reg / batch_count, }) loss.backward() if self.cfg.grad_clip > 0: clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip) self.optimizer.step() self.update_ema() self.global_step += 1 if batch_count == 0: print("No valid batches in this epoch, skipping validation.") continue train_nll = running_nll / batch_count train_reg = running_reg / batch_count self.ema_model.eval() total_val_pairs = 0 total_val_nll = 0.0 total_val_reg = 0.0 with torch.no_grad(): val_pbar = tqdm(self.val_loader, desc="Validation") for batch in val_pbar: ( event_seq, time_seq, cont_feats, cate_feats, sexes, ) = batch event_seq = event_seq.to(self.device) time_seq = time_seq.to(self.device) cont_feats = cont_feats.to(self.device) cate_feats = cate_feats.to(self.device) sexes = sexes.to(self.device) res = get_valid_pairs_and_dt(event_seq, time_seq, 2) if res is None: continue dt, b_prev, t_prev, b_next, t_next = res num_pairs = dt.size(0) logits = self.ema_model( event_seq, time_seq, sexes, cont_feats, cate_feats, b_prev=b_prev, t_prev=t_prev ) target_events = event_seq[b_next, t_next] - 2 nll, reg = self.criterion( logits, target_events, dt, reduction="none", ) batch_nll_sum = nll.sum().item() total_val_nll += batch_nll_sum total_val_reg += reg.item() * num_pairs total_val_pairs += num_pairs current_val_avg_nll = total_val_nll / \ total_val_pairs if total_val_pairs > 0 else 0.0 current_val_avg_reg = total_val_reg / \ total_val_pairs if total_val_pairs > 0 else 0.0 val_pbar.set_postfix({ "NLL": f"{current_val_avg_nll:.4f}", "Reg": f"{current_val_avg_reg:.4f}", }) val_nll = total_val_nll / total_val_pairs if total_val_pairs > 0 else 0.0 val_reg = total_val_reg / total_val_pairs if total_val_pairs > 0 else 0.0 history.append({ "epoch": epoch, "train_nll": train_nll, "train_reg": train_reg, "val_nll": val_nll, "val_reg": val_reg, }) tqdm.write(f"\nEpoch {epoch+1}/{self.cfg.max_epochs} Stats:") tqdm.write(f" Train NLL: {train_nll:.4f}") tqdm.write(f" Val NLL: {val_nll:.4f} ā† PRIMARY METRIC") with open(os.path.join(self.out_dir, "training_history.json"), "w") as f: json.dump(history, f, indent=4) # Check for improvement if val_nll < best_val_score: best_val_score = val_nll patience_counter = 0 tqdm.write(" āœ“ New best validation score. Saving checkpoint.") torch.save({ "epoch": epoch, "global_step": self.global_step, "model_state_dict": self.ema_model.state_dict(), "criterion_state_dict": self.criterion.state_dict(), "optimizer_state_dict": self.optimizer.state_dict(), }, self.best_path) else: patience_counter += 1 if epoch >= self.cfg.warmup_epochs and patience_counter >= self.cfg.patience: tqdm.write( f"\nāš  No improvement in validation score for {patience_counter} epochs. Early stopping.") return tqdm.write( f" No improvement (patience: {patience_counter}/{self.cfg.patience})") tqdm.write("\nšŸŽ‰ Training complete!") if __name__ == "__main__": cfg = parse_args() trainer = Trainer(cfg) trainer.train()