AutoML Pipeline

A simplified machine learning automation framework that handles the complexities of ML workflows through configuration. The project provides an easy-to-use interface for training machine learning models while automatically managing optimization, visualization, and training processes.

[!IMPORTANT] This framework is designed for rapid ML prototyping and experimentation. For production deployments, please review the performance metrics and model validation sections carefully.

🚀 Quick Start

```python from lightningauto import AutoML from config import getclassification_config

Get configuration

config = getclassificationconfig()

Initialize and train

automl = AutoML(config) automl.fit(traindata, valdata) ```

[!TIP] Check the examples/ directory for complete notebook demonstrations of common use cases.

✨ Key Features

• Automated Training: Simplified training process with minimal user intervention
• Configuration Templates: Easy-to-use configuration templates for common ML tasks
• Visualization: Built-in visualization tools for performance analysis

📊 Performance Metrics

[!NOTE] These metrics represent baseline performance and may vary based on your dataset and configuration.

• Training Loss: 1.6422
• Validation Loss: 1.6169
• Learning Rate: 0.000896

📈 Visualization Analysis

1. Class Distribution

[!NOTE] The distribution plot shows: - Class 2 dominates with ~50 samples - Class 0 follows with ~27 samples - Class 4 has the least representation with ~20 samples - Clear sign of class imbalance that may need addressing

2. Classification Results

[!NOTE] Key observations from the confusion matrix: - Strong diagonal pattern indicates good overall classification - Class 2 shows the highest confidence with 10–14 correct predictions - Some classifications between neighboring classes - Class 3 shows room for improvement in discrimination

3. Training Dynamics

[!NOTE] The loss plot reveals: - Training loss (blue) shows healthy fluctuation between 1.54–1.70 - Validation loss (orange) maintains stability around 1.62 - No significant overfitting as validation loss remains stable - Good model convergence with occasional exploration spikes

4. Learning Rate Schedule

[!NOTE] The learning rate schedule demonstrates: - Smooth cosine decay from 2e-3 to 9e-4 - Gradual learning rate reduction for fine-tuning - Proper annealing behavior for optimization stability

📁 Project Structure

automl/ ├── lightning_auto.py # Core AutoML engine ├── config.py # Configuration templates ├── train.py # Training script ├── WriterSide/ # Documentation └── examples/ # Example notebooks (Coming soon!)

⚙️ Configuration Templates

[!WARNING] Always validate configuration parameters against your specific use case before training.

Linear Classification Example

python config = { "model": { "type": "classification", "input_dim": 10, "output_dim": 5, "task": "classification" }, "training": { "learning_rate": 0.002, "epochs": 30 } # ... other parameters }

🤝 Contributing

[!CAUTION] Before submitting large changes, please open an issue to discuss the proposed modifications.

Adding New Configuration Templates

1. Create a new function in config.py: python def get_custom_config(): return { "model": { # model specifications }, "training": { # training parameters } }
2. Add documentation and example usage
3. Submit a pull request

Backend Development

1. For new features:
   • Fork the repository
   • Create feature branch
   • Add tests
   • Submit pull request

📝 License

This project is licensed under the MIT License—see the LICENSE file for details.