src/trainer.py

"""Training loops for ACSA and baseline models."""
import json
import logging
from pathlib import Path
from typing import Optional

import numpy as np
import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, get_linear_schedule_with_warmup
from tqdm import tqdm

from . import config as cfg
from .models import (
    GatedAspectSemanticMetaFusionACSAModel, BertMetaFusionACSAModel, BertACSAModel, BertOverallModel,
    compute_class_weights,
)
from .dataset import ACSADataset, MetaACSADataset, OverallSentimentDataset
from .meta_encoder import MetaEncoder


logger = logging.getLogger(__name__)


def get_device():
    if torch.cuda.is_available():
        return torch.device("cuda")
    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


# ---------------------------------------------------------------------------
# Generic per-aspect evaluation (used by both Proposed and Baseline 3)
# ---------------------------------------------------------------------------

def _evaluate_per_aspect(model, loader, device, with_meta: bool):
    from sklearn.metrics import f1_score, accuracy_score
    model.eval()
    all_preds = [[] for _ in range(cfg.NUM_ASPECTS)]
    all_labels = [[] for _ in range(cfg.NUM_ASPECTS)]
    with torch.no_grad():
        for batch in loader:
            batch = {k: v.to(device) for k, v in batch.items()}
            if with_meta:
                out = model(batch["input_ids"], batch["attention_mask"],
                            batch["meta_features"])
            else:
                out = model(batch["input_ids"], batch["attention_mask"])
            preds = out["logits"].argmax(dim=-1).cpu().numpy()
            labels = batch["labels"].cpu().numpy()
            for i in range(cfg.NUM_ASPECTS):
                all_preds[i].extend(preds[:, i].tolist())
                all_labels[i].extend(labels[:, i].tolist())

    metrics = {}
    f1s, accs = [], []
    for i, aspect in enumerate(cfg.ASPECTS):
        f1 = f1_score(all_labels[i], all_preds[i], average="macro", zero_division=0)
        acc = accuracy_score(all_labels[i], all_preds[i])
        metrics[f"f1_{aspect}"] = float(f1)
        metrics[f"acc_{aspect}"] = float(acc)
        f1s.append(f1); accs.append(acc)
    metrics["macro_f1_mean"] = float(np.mean(f1s)) if f1s else 0.0
    metrics["accuracy_mean"] = float(np.mean(accs)) if accs else 0.0
    return metrics


def _evaluate_overall_head(model, loader, device):
    """Evaluate the overall sentiment auxiliary head on a dataloader.

    Returns metrics prefixed with 'overall_' so they don't collide with
    per-aspect metric keys.
    """
    from sklearn.metrics import f1_score, accuracy_score
    model.eval()
    all_preds, all_labels = [], []
    with torch.no_grad():
        for batch in loader:
            batch = {k: v.to(device) for k, v in batch.items()}
            if "overall_labels" not in batch:
                return {}
            out = model(batch["input_ids"], batch["attention_mask"],
                        batch["meta_features"])
            preds = out["overall_logits"].argmax(dim=-1).cpu().numpy()
            all_preds.extend(preds.tolist())
            all_labels.extend(batch["overall_labels"].cpu().numpy().tolist())
    if not all_labels:
        return {}
    return {
        "overall_macro_f1": float(f1_score(all_labels, all_preds, average="macro", zero_division=0)),
        "overall_accuracy": float(accuracy_score(all_labels, all_preds)),
    }


# ---------------------------------------------------------------------------
# Train: Proposed (BERT + Meta Cross-Attention + multi-head)
# ---------------------------------------------------------------------------

def train_meta_acsa(
    train_df, val_df, meta_encoder: MetaEncoder,
    bert_name: str = cfg.BERT_MODEL_NAME,
    epochs: int = cfg.DEFAULT_EPOCHS,
    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
    lr_bert: float = cfg.DEFAULT_LR_BERT,
    lr_heads: float = cfg.DEFAULT_LR_HEADS,
    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
    use_class_weights: bool = True,
    output_dir: Optional[Path] = None,
    seed: int = cfg.RANDOM_SEED,
):
    if output_dir is None:
        output_dir = cfg.CHECKPOINT_DIR / "meta_acsa"
    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)

    torch.manual_seed(seed); np.random.seed(seed)
    device = get_device()
    logger.info("Device: %s", device)

    tokenizer = AutoTokenizer.from_pretrained(bert_name)

    aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
    class_weights = (compute_class_weights(train_df, aspect_cols, cfg.NUM_CLASSES).to(device)
                     if use_class_weights else None)
    if class_weights is not None:
        logger.info("Per-aspect class weights:\n%s", class_weights.cpu().numpy())

    model = GatedAspectSemanticMetaFusionACSAModel(
        bert_name=bert_name,
        meta_in_dim=meta_encoder.total_dim,
        class_weights=class_weights,
    ).to(device)

    train_ds = MetaACSADataset(train_df, tokenizer, meta_encoder)
    val_ds = MetaACSADataset(val_df, tokenizer, meta_encoder)
    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)

    bert_params = list(model.bert.named_parameters())
    other_params = [(n, p) for n, p in model.named_parameters()
                    if not n.startswith("bert.")]
    no_decay = ["bias", "LayerNorm.weight"]
    grouped = [
        {"params": [p for n, p in bert_params if not any(nd in n for nd in no_decay)],
         "weight_decay": weight_decay, "lr": lr_bert},
        {"params": [p for n, p in bert_params if any(nd in n for nd in no_decay)],
         "weight_decay": 0.0, "lr": lr_bert},
        {"params": [p for n, p in other_params if not any(nd in n for nd in no_decay)],
         "weight_decay": weight_decay, "lr": lr_heads},
        {"params": [p for n, p in other_params if any(nd in n for nd in no_decay)],
         "weight_decay": 0.0, "lr": lr_heads},
    ]
    optimizer = torch.optim.AdamW(grouped)
    total_steps = max(len(train_loader) * epochs, 1)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=int(cfg.WARMUP_RATIO * total_steps),
        num_training_steps=total_steps,
    )

    best_f1 = -1.0; history = []
    for epoch in range(epochs):
        model.train(); running = 0.0
        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] meta_acsa")
        for batch in pbar:
            batch = {k: v.to(device) for k, v in batch.items()}
            optimizer.zero_grad()
            out = model(batch["input_ids"], batch["attention_mask"],
                        batch["meta_features"], labels=batch["labels"],
                        overall_labels=batch.get("overall_labels"))
            out["loss"].backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step(); scheduler.step()
            running += out["loss"].item()
            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})

        avg_loss = running / max(len(train_loader), 1)
        val = _evaluate_per_aspect(model, val_loader, device, with_meta=True)

        # Also evaluate overall head on val set
        val_overall = _evaluate_overall_head(model, val_loader, device)
        val.update(val_overall)

        logger.info("Epoch %d | loss=%.4f | val_macro_f1=%.4f | val_acc=%.4f | val_overall_f1=%.4f",
                    epoch+1, avg_loss, val["macro_f1_mean"], val["accuracy_mean"],
                    val.get("overall_macro_f1", 0.0))
        history.append({"epoch": epoch+1, "train_loss": avg_loss, **val})

        if val["macro_f1_mean"] > best_f1:
            best_f1 = val["macro_f1_mean"]
            torch.save({"model_state_dict": model.state_dict(),
                        "config": {"bert_name": bert_name,
                                   "meta_in_dim": meta_encoder.total_dim,
                                   "architecture": "gated_aspect_semantic_meta_acsa",
                                   "meta_token_names": GatedAspectSemanticMetaFusionACSAModel.meta_token_names}},
                       output_dir / "best.pt")
            tokenizer.save_pretrained(output_dir / "tokenizer")
            logger.info("Saved new best meta_acsa (macro_f1=%.4f)", best_f1)

    with open(output_dir / "history.json", "w") as f:
        json.dump(history, f, indent=2)
    return model, history


# ---------------------------------------------------------------------------
# Train: Baseline 3 (BERT-ACSA, no meta 鈥?ablation)
# ---------------------------------------------------------------------------

def train_acsa(
    train_df, val_df,
    bert_name: str = cfg.BERT_MODEL_NAME,
    epochs: int = cfg.DEFAULT_EPOCHS,
    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
    lr_bert: float = cfg.DEFAULT_LR_BERT,
    lr_heads: float = cfg.DEFAULT_LR_HEADS,
    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
    use_class_weights: bool = True,
    output_dir: Optional[Path] = None,
    seed: int = cfg.RANDOM_SEED,
):
    if output_dir is None:
        output_dir = cfg.CHECKPOINT_DIR / "acsa"
    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)

    torch.manual_seed(seed); np.random.seed(seed)
    device = get_device()
    tokenizer = AutoTokenizer.from_pretrained(bert_name)

    aspect_cols = [f"aspect_{a}" for a in cfg.ASPECTS]
    class_weights = (compute_class_weights(train_df, aspect_cols, cfg.NUM_CLASSES).to(device)
                     if use_class_weights else None)
    model = BertACSAModel(bert_name=bert_name, class_weights=class_weights).to(device)

    train_ds = ACSADataset(train_df, tokenizer)
    val_ds = ACSADataset(val_df, tokenizer)
    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)

    bert_params = list(model.bert.named_parameters())
    head_params = list(model.heads.named_parameters())
    no_decay = ["bias", "LayerNorm.weight"]
    grouped = [
        {"params": [p for n, p in bert_params if not any(nd in n for nd in no_decay)],
         "weight_decay": weight_decay, "lr": lr_bert},
        {"params": [p for n, p in bert_params if any(nd in n for nd in no_decay)],
         "weight_decay": 0.0, "lr": lr_bert},
        {"params": [p for _, p in head_params], "weight_decay": weight_decay, "lr": lr_heads},
    ]
    optimizer = torch.optim.AdamW(grouped)
    total_steps = max(len(train_loader) * epochs, 1)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, int(cfg.WARMUP_RATIO * total_steps), total_steps,
    )

    best_f1 = -1.0; history = []
    for epoch in range(epochs):
        model.train(); running = 0.0
        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] acsa-no-meta")
        for batch in pbar:
            batch = {k: v.to(device) for k, v in batch.items()}
            optimizer.zero_grad()
            out = model(batch["input_ids"], batch["attention_mask"], labels=batch["labels"])
            out["loss"].backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step(); scheduler.step()
            running += out["loss"].item()
            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})

        val = _evaluate_per_aspect(model, val_loader, device, with_meta=False)
        logger.info("Epoch %d | val_macro_f1=%.4f | val_acc=%.4f",
                    epoch+1, val["macro_f1_mean"], val["accuracy_mean"])
        history.append({"epoch": epoch+1, "train_loss": running/max(len(train_loader),1),
                        **val})

        if val["macro_f1_mean"] > best_f1:
            best_f1 = val["macro_f1_mean"]
            torch.save({"model_state_dict": model.state_dict(),
                        "config": {"bert_name": bert_name}},
                       output_dir / "best.pt")
            tokenizer.save_pretrained(output_dir / "tokenizer")

    with open(output_dir / "history.json", "w") as f:
        json.dump(history, f, indent=2)
    return model, history


# ---------------------------------------------------------------------------
# Train: Baseline 2 (BERT-overall)
# ---------------------------------------------------------------------------

def train_bert_overall(
    train_df, val_df,
    bert_name: str = cfg.BERT_MODEL_NAME,
    epochs: int = cfg.DEFAULT_EPOCHS,
    batch_size: int = cfg.DEFAULT_BATCH_SIZE,
    lr: float = cfg.DEFAULT_LR_BERT,
    weight_decay: float = cfg.DEFAULT_WEIGHT_DECAY,
    output_dir: Optional[Path] = None,
    seed: int = cfg.RANDOM_SEED,
):
    from sklearn.metrics import f1_score, accuracy_score
    if output_dir is None:
        output_dir = cfg.CHECKPOINT_DIR / "bert_overall"
    output_dir = Path(output_dir); output_dir.mkdir(parents=True, exist_ok=True)

    torch.manual_seed(seed); np.random.seed(seed)
    device = get_device()
    tokenizer = AutoTokenizer.from_pretrained(bert_name)
    model = BertOverallModel(bert_name=bert_name).to(device)

    train_ds = OverallSentimentDataset(train_df, tokenizer)
    val_ds = OverallSentimentDataset(val_df, tokenizer)
    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
    val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=0)

    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
    total_steps = max(len(train_loader) * epochs, 1)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, int(cfg.WARMUP_RATIO * total_steps), total_steps,
    )

    best_f1 = -1.0; history = []
    for epoch in range(epochs):
        model.train(); running = 0.0
        pbar = tqdm(train_loader, desc=f"[epoch {epoch+1}/{epochs}] bert-overall")
        for batch in pbar:
            batch = {k: v.to(device) for k, v in batch.items()}
            optimizer.zero_grad()
            out = model(**batch)
            out["loss"].backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step(); scheduler.step()
            running += out["loss"].item()
            pbar.set_postfix({"loss": f"{out['loss'].item():.4f}"})

        model.eval(); all_p, all_l = [], []
        with torch.no_grad():
            for batch in val_loader:
                batch = {k: v.to(device) for k, v in batch.items()}
                out = model(**batch)
                all_p.extend(out["logits"].argmax(dim=-1).cpu().numpy().tolist())
                all_l.extend(batch["labels"].cpu().numpy().tolist())
        metrics = {
            "macro_f1": float(f1_score(all_l, all_p, average="macro", zero_division=0)),
            "accuracy": float(accuracy_score(all_l, all_p)),
            "weighted_f1": float(f1_score(all_l, all_p, average="weighted", zero_division=0)),
        }
        logger.info("Epoch %d | val_macro_f1=%.4f | val_acc=%.4f",
                    epoch+1, metrics["macro_f1"], metrics["accuracy"])
        history.append({"epoch": epoch+1, "train_loss": running/max(len(train_loader),1),
                        **metrics})

        if metrics["macro_f1"] > best_f1:
            best_f1 = metrics["macro_f1"]
            torch.save({"model_state_dict": model.state_dict(),
                        "config": {"bert_name": bert_name}},
                       output_dir / "best.pt")
            tokenizer.save_pretrained(output_dir / "tokenizer")

    with open(output_dir / "history.json", "w") as f:
        json.dump(history, f, indent=2)
    return model, history