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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
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