# Nebula Loss Function: One Loss Function to Rule Them All! 🌌

<img src="./nebula.png" width="1000px"></img>


Welcome to the **Nebula Loss Function**! A versatile and adaptive loss function that works with any model, dataset, or task. Experience the magic of a single loss function that dynamically adapts to your needs, making it easy and efficient to use in any deep learning project.

## Features 🚀
- Automatically determines the appropriate loss function for your task
- One-hot encoding for classification tasks
- Caching mechanism for efficient training
- Domain knowledge and user input integration
- Supports a variety of optimization techniques

## Getting Started 🌟

### Requirements
- Python 3.7 or higher
- PyTorch 1.9 or higher
- NumPy

### Installation
```sh
pip install nebula
```

### Usage

```python
import torch
from nebula_loss import Nebula

# Instantiate the Nebula loss function
loss_function = Nebula()

# Define your model, dataset, and other components here

# Calculate loss using the Nebula loss function
loss = loss_function.compute_loss(y_pred, y_true)
```

## How it works 🔭

The Nebula loss function works by analyzing the characteristics of your model's predictions (`y_pred`) and the ground truth labels (`y_true`). Based on these characteristics, it automatically selects the most appropriate loss function for your task, such as Mean Squared Error (MSE) for regression or Cross-Entropy Loss for classification.

### Optimization Techniques

The Nebula loss function supports a variety of optimization techniques, such as:

1. Analyzing the range of values in `y_true`
2. Checking the distribution of values in `y_true`
3. Analyzing the dimensions of `y_pred`
4. Checking the sparsity of `y_true`
5. Analyzing the correlation between `y_pred` and `y_true`
6. Leveraging domain knowledge and user input
7. Analyzing the distribution of values in `y_pred`
8. Checking the balance of classes in `y_true`
9. Model selection techniques (requires a model and a dataset)
10. Utilizing user input or metadata

# Roadmap
1. Add in 5 more detections to effectively determine which loss function to utilize

2. Add in 5 more loss functions that the detections map to.

3. Create an entirely polymorphic version that has no shape whatsoever and fills up the data, task, model environment at runtime.

## Contributing 🤝

We welcome contributions! If you'd like to contribute, feel free to open a pull request with your changes or additions. For major changes, please open an issue first to discuss your proposed changes.

## License 📄

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.