# Scripts Directory

This directory contains reusable Python modules for the MNIST digit classification project.

## Modules

### Data Processing
- **`data_loader.py`** - MNIST data loading from IDX binary format
  - `MnistDataloader` class for loading train/test data
  
- **`preprocessing.py`** - Data preprocessing and PyTorch Dataset
  - `MnistDataset` - PyTorch Dataset with normalization
  - `create_train_val_split()` - Split training data into train/val
  
- **`data_quality.py`** - Data quality analysis functions
  - Quality checks: missing values, outliers, class balance
  - `generate_quality_report()` - Comprehensive quality report
  
- **`augmentation.py`** - Data augmentation pipeline
  - `get_augmentation_pipeline()` - Transform composition for training

### Model
- **`models.py`** - CNN architectures
  - `BaselineCNN` - 2-layer CNN baseline model
  
- **`train.py`** - Training pipeline
  - `train_epoch()` - Single epoch training
  - `validate()` - Validation evaluation
  - `train_model()` - Complete training loop with MLflow logging
  
- **`evaluate.py`** - Model evaluation
  - `evaluate_model()` - Comprehensive metrics computation
  - Accuracy, precision, recall, confusion matrix
  
- **`inference.py`** - Production inference
  - `DigitClassifier` - Inference wrapper for deployment

### Experiment Tracking
- **`mlflow_setup.py`** - MLflow configuration
  - `setup_mlflow()` - Initialize MLflow experiment
  - Tracking URI and experiment management

### Utilities
- **`launch_mlflow_ui.sh`** - Launch MLflow UI server
- **`docker_start.sh`** - Start Docker containers
- **`docker_stop.sh`** - Stop Docker containers
- **`docker_logs.sh`** - View Docker container logs

## Usage

All modules are designed to be imported and used in notebooks or other scripts:

```python
# Example: Load data
from scripts.data_loader import MnistDataloader

loader = MnistDataloader(
    training_images_filepath='data/raw/train-images.idx3-ubyte',
    training_labels_filepath='data/raw/train-labels.idx1-ubyte',
    test_images_filepath='data/raw/t10k-images.idx3-ubyte',
    test_labels_filepath='data/raw/t10k-labels.idx1-ubyte'
)
(x_train, y_train), (x_test, y_test) = loader.load_data()
```

## Development Guidelines

- All functions include type hints
- All public functions have docstrings
- Follow naming conventions (snake_case for functions, PascalCase for classes)
- Run `ruff check . --fix` before committing
- Add unit tests in `tests/` directory for critical functions