Noviyourbae.zip · Popular

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# Noviyourbae
A tiny, self‑contained Python library that shows a clean, modular workflow for
building, training, and evaluating a simple neural network.
## Features
- Minimal dependencies (`torch`, `numpy`, `pandas`, `tqdm`)
- Clear folder layout (src, docs, tests, examples)
- Ready‑to‑run Jupyter notebooks
- Extensible utility helpers (logging, checkpointing)
## Installation
```bash
# Clone the repo (or just unzip Noviyourbae.zip)
git clone https://github.com/yourname/Noviyourbae.git
cd Noviyourbae
# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt

import argparse
import torch
import torch.nn as nn
from torch.optim import Adam
from tqdm import tqdm
from .utils.logger import get_logger
from .data_loader import CSVLoader
logger = get_logger(__name__)
class Trainer:
    """
    Orchestrates training / validation loops and checkpointing.
    """
    def __init__(
        self,
        model: nn.Module,
        train_loader,
        val_loader,
        lr: float = 1e-3,
        device: str = "cpu",
        checkpoint_path: str = "checkpoints/best.pt",
    ):
        self.model = model.to(device)
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.criterion = nn.MSELoss()            # change as needed
        self.optimizer = Adam(self.model.parameters(), lr=lr)
        self.device = device
        self.checkpoint_path = checkpoint_path
        self.best_val_loss = float("inf")
def _train_one_epoch(self):
        self.model.train()
        running_loss = 0.0
        for xb, yb in tqdm(self.train_loader, desc="Training", leave=False):
            xb, yb = xb.to(self.device), yb.to(self.device)
self.optimizer.zero_grad()
            preds = self.model(xb)
            loss = self.criterion(preds, yb)
            loss.backward()
            self.optimizer.step()
running_loss += loss.item() * xb.size(0)
        epoch_loss = running_loss / len(self.train_loader.dataset)
        return epoch_loss
def _validate(self):
        self.model.eval()
        running_loss = 0.0
        with torch.no_grad():
            for xb, yb in tqdm(self.val_loader, desc="Validation", leave=False):
                xb, yb = xb.to(self.device), yb.to(self.device)
                preds = self.model(xb)
                loss = self.criterion(preds, yb)
                running_loss += loss.item() * xb.size(0)
        val_loss = running_loss / len(self.val_loader.dataset)
        return val_loss
def run(self, epochs: int = 10):
        for epoch in range(1, epochs + 1):
            train_loss = self._train_one_epoch()
            val_loss = self._validate()
logger.info(
                f"Epoch epoch:02d | Train loss: train_loss:.4f | Val loss: val_loss:.4f"
            )
if val_loss < self.best_val_loss:
                self.best_val_loss = val_loss
                torch.save(self.model.state_dict(), self.checkpoint_path)
                logger.info(f"  → New best model saved to self.checkpoint_path")
@staticmethod
    def cli():
        parser = argparse.ArgumentParser(description="Train a SimpleNet on a CSV file.")
        parser.add_argument("--data-path", required=True, help="Path to the CSV dataset.")
        parser.add_argument(
            "--target-col", default=None, help="Name of the target column (default: last column)."
        )
        parser.add_argument("--epochs", type=int, default=10)
        parser.add_argument("--batch-size", type=int, default=32)
        parser.add_argument("--lr", type=float, default=1e-3)
        parser.add_argument("--device", default="cpu", choices=["cpu", "cuda"])
        parser.add_argument("--hidden-dim", type=int, default=64)
        args = parser.parse_args()
# Build data pipeline
        loader = CSVLoader(args.data_path, target_col=args.target_col)
        train_dl, val_dl = loader.get_dataloaders(
            batch_size=args.batch_size
        )
# Build model
        model = SimpleNet(
            input_dim=loader.num_features,
            hidden_dim=args.hidden_dim,
            output_dim=1,
        )
# Trainer & run
        trainer = Trainer(
            model=model,
            train_loader=train_dl,
            val_loader=val_dl,
            lr=args.lr,
            device=args.device,
        )
        trainer.run(epochs=args.epochs)
if __name__ == "__main__":
    Trainer.cli()