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import mlflow
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from src.data.loader import load_clinc150, load_splits, save_splits
from src.data.preprocessor import preprocess
from src.evaluation.metrics import (
compute_classification_metrics,
compute_latency,
get_classification_report,
)
from src.models.neural import IntentDatasetNN, LSTMModel, RNNModel, TextCNN, Vocabulary
from src.storage.s3 import upload_artifact
from src.utils.config import load_config
from src.utils.mlflow_utils import (
get_or_create_experiment,
log_confusion_matrix,
log_metrics,
)
from src.utils.settings import settings
DATA_DIR = "data/raw"
VOCAB_PATH = "artifacts/models/vocab.pkl"
MAX_LENGTH = 32
def load_or_download_data(config: dict) -> tuple:
train_path = os.path.join(DATA_DIR, "train.csv")
if os.path.exists(train_path):
print("loading data from disk...")
splits = load_splits(DATA_DIR)
else:
print("downloading CLINC150...")
splits = load_clinc150(config["data"]["subset"])
save_splits(splits, DATA_DIR)
processed, label_map = preprocess(splits)
return processed, label_map
def build_dataloaders(
processed: dict,
vocab: Vocabulary,
batch_size: int,
) -> tuple[DataLoader, DataLoader, DataLoader]:
train_dataset = IntentDatasetNN(
processed["train"]["text"].tolist(),
processed["train"]["label_id"].tolist(),
vocab,
MAX_LENGTH,
)
val_dataset = IntentDatasetNN(
processed["validation"]["text"].tolist(),
processed["validation"]["label_id"].tolist(),
vocab,
MAX_LENGTH,
)
test_dataset = IntentDatasetNN(
processed["test"]["text"].tolist(),
processed["test"]["label_id"].tolist(),
vocab,
MAX_LENGTH,
)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader, test_loader
def run_epoch(
model: nn.Module,
loader: DataLoader,
criterion: nn.CrossEntropyLoss,
optimizer: torch.optim.Optimizer | None,
device: torch.device,
training: bool,
grad_clip: float | None = None,
) -> tuple[float, float]:
model.train() if training else model.eval()
total_loss = 0.0
correct = 0
total = 0
context = torch.enable_grad() if training else torch.no_grad()
with context:
for inputs, labels in loader:
inputs = inputs.to(device)
labels = labels.to(device)
logits = model(inputs)
loss = criterion(logits, labels)
if training and optimizer is not None:
optimizer.zero_grad()
loss.backward()
if grad_clip is not None:
nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
optimizer.step()
total_loss += loss.item() * len(labels)
correct += (logits.argmax(dim=1) == labels).sum().item()
total += len(labels)
return total_loss / total, correct / total
def predict_all(
model: nn.Module,
loader: DataLoader,
device: torch.device,
) -> tuple[np.ndarray, np.ndarray]:
model.eval()
all_preds = []
all_labels = []
with torch.no_grad():
for inputs, labels in loader:
inputs = inputs.to(device)
logits = model(inputs)
preds = logits.argmax(dim=1).cpu().numpy()
all_preds.extend(preds)
all_labels.extend(labels.numpy())
return np.array(all_preds), np.array(all_labels)
def build_model(model_type: str, model_cfg: dict, vocab_size: int, num_classes: int) -> nn.Module:
if model_type == "textcnn":
return TextCNN(
vocab_size=vocab_size,
embedding_dim=model_cfg["embedding_dim"],
num_filters=model_cfg["num_filters"],
kernel_sizes=model_cfg["kernel_sizes"],
num_classes=num_classes,
dropout=model_cfg["dropout"],
)
if model_type == "rnn":
return RNNModel(
vocab_size=vocab_size,
embedding_dim=model_cfg["embedding_dim"],
hidden_dim=model_cfg["hidden_dim"],
num_layers=model_cfg["num_layers"],
num_classes=num_classes,
dropout=model_cfg["dropout"],
)
if model_type == "lstm":
return LSTMModel(
vocab_size=vocab_size,
embedding_dim=model_cfg["embedding_dim"],
hidden_dim=model_cfg["hidden_dim"],
num_layers=model_cfg["num_layers"],
num_classes=num_classes,
dropout=model_cfg["dropout"],
)
raise ValueError(f"unknown model type: {model_type}")
def train_model(
model_type: str,
config: dict,
train_loader: DataLoader,
val_loader: DataLoader,
test_loader: DataLoader,
vocab: Vocabulary,
label_map: dict,
device: torch.device,
) -> dict:
num_classes = len(label_map)
model_cfg = config["model"][model_type]
save_path = f"artifacts/models/{model_type}.pt"
grad_clip = config["training"].get("grad_clip", None)
model = build_model(model_type, model_cfg, len(vocab), num_classes).to(device)
print(f"\n{model_type} parameters: {sum(p.numel() for p in model.parameters()):,}")
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=config["training"]["learning_rate"])
mlflow.set_tracking_uri(settings.mlflow_tracking_uri)
experiment_id = get_or_create_experiment(config["mlflow"]["experiment_name"])
with mlflow.start_run(experiment_id=experiment_id, run_name=model_type) as run:
flat_config = {
"model.type": model_type,
**{f"model.{k}": v for k, v in model_cfg.items()},
**{f"training.{k}": v for k, v in config["training"].items()},
}
mlflow.log_params(flat_config)
epochs = config["training"]["epochs"]
patience = config["training"]["patience"]
best_val_loss = float("inf")
epochs_no_improve = 0
print(f"training {model_type} for up to {epochs} epochs (patience={patience}, grad_clip={grad_clip})...")
print(f"{'epoch':<8} {'train_loss':<14} {'train_acc':<14} {'val_loss':<14} {'val_acc'}")
print("-" * 65)
for epoch in range(1, epochs + 1):
train_loss, train_acc = run_epoch(
model,
train_loader,
criterion,
optimizer,
device,
training=True,
grad_clip=grad_clip,
)
val_loss, val_acc = run_epoch(
model,
val_loader,
criterion,
None,
device,
training=False,
)
log_metrics(
{
"train_loss": round(train_loss, 4),
"train_accuracy": round(train_acc, 4),
"val_loss": round(val_loss, 4),
"val_accuracy": round(val_acc, 4),
},
step=epoch,
)
print(f"{epoch:<8} {train_loss:<14.4f} {train_acc:<14.4f} {val_loss:<14.4f} {val_acc:.4f}")
if val_loss < best_val_loss:
best_val_loss = val_loss
epochs_no_improve = 0
model.save(save_path)
else:
epochs_no_improve += 1
if epochs_no_improve >= patience:
print(f"early stopping at epoch {epoch}")
break
print("loading best checkpoint...")
model.load(save_path)
id_to_label = {v: k for k, v in label_map.items()}
label_names = [id_to_label[i] for i in range(num_classes)]
test_preds, test_labels = predict_all(model, test_loader, device)
test_metrics = compute_classification_metrics(test_labels, test_preds)
test_metrics_logged = {f"test_{k}": v for k, v in test_metrics.items()}
log_metrics(test_metrics_logged)
def predict_fn(loader):
predict_all(model, loader, device)
latency = compute_latency(predict_fn, test_loader, n_runs=50)
log_metrics(latency)
report = get_classification_report(test_labels, test_preds, label_names)
report_path = f"artifacts/{model_type}_report.txt"
with open(report_path, "w") as f:
f.write(report)
mlflow.log_artifact(report_path)
log_confusion_matrix(
test_labels,
test_preds,
label_names,
save_path=f"artifacts/{model_type}_confusion_matrix.png",
)
mlflow.log_artifact(save_path)
upload_artifact(save_path, f"{config['s3']['prefix']}/{model_type}.pt")
print(f" test accuracy : {test_metrics['accuracy']}")
print(f" test macro_f1 : {test_metrics['macro_f1']}")
print(f" latency p50 : {latency['latency_p50_ms']}ms")
print(f" run id : {run.info.run_id}")
return {**test_metrics_logged, **latency}
def main():
config = load_config("neural")
torch.manual_seed(config["training"]["random_state"])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"device: {device}")
processed, label_map = load_or_download_data(config)
print("building vocabulary on train set only...")
vocab = Vocabulary()
vocab.build(processed["train"]["text"].tolist())
vocab.save(VOCAB_PATH)
print(f"vocabulary size: {len(vocab)}")
batch_size = config["training"]["batch_size"]
train_loader, val_loader, test_loader = build_dataloaders(processed, vocab, batch_size)
all_results = {}
for model_type in ["textcnn", "rnn", "lstm"]:
metrics = train_model(
model_type=model_type,
config=config,
train_loader=train_loader,
val_loader=val_loader,
test_loader=test_loader,
vocab=vocab,
label_map=label_map,
device=device,
)
all_results[model_type] = metrics
print("\n\nfinal comparison:")
print(f"{'model':<12} {'test_acc':<12} {'macro_f1':<12} {'p50_ms'}")
print("-" * 50)
for model_type, metrics in all_results.items():
print(
f"{model_type:<12} "
f"{metrics['test_accuracy']:<12} "
f"{metrics['test_macro_f1']:<12} "
f"{metrics['latency_p50_ms']}ms"
)
if __name__ == "__main__":
main()
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