xtrapnet 0.1.2

A robust package for extrapolation control in neural networks

XtrapNet - Extrapolation-Aware Neural Networks

XtrapNet is a cutting-edge deep learning framework designed to handle out-of-distribution (OOD) extrapolation in neural networks. Unlike traditional models that fail when encountering unseen data, XtrapNet:

• ✅ Detects OOD inputs and allows custom fallback behaviors
• ✅ Supports ensemble uncertainty quantification
• ✅ Offers multiple extrapolation control mechanisms
• ✅ Works with PyTorch and integrates seamlessly with any model

Installation

To install XtrapNet: pip install xtrapnet

Usage Example

import numpy as np 
from xtrapnet import XtrapNet, XtrapTrainer, XtrapController

# Generate dummy training data
features = np.random.uniform(-3.14, 3.14, (100, 2)).astype(np.float32) labels = np.sin(features[:, 0]) * np.cos(features[:, 1]).reshape(-1, 1)

# Train the model
net = XtrapNet(input_dim=2) trainer = XtrapTrainer(net) trainer.train(labels, features)

# Define an extrapolation-aware controller
controller = XtrapController( trained_model=net, train_features=features, train_labels=labels, mode='warn' )

# Test prediction with OOD handling
test_input = np.array([[5.0, -3.5]]) # OOD point prediction = controller.predict(test_input) print("Prediction:", prediction)

Extrapolation Handling Modes

XtrapNet allows you to control how the model reacts to out-of-distribution (OOD) inputs:

Mode	Behavior
clip	Restricts predictions within known value ranges
zero	Returns 0 for OOD inputs
nearest_data	Uses the closest training point's prediction
symmetry	Uses symmetry-based assumptions to infer values
warn	Prints a warning but still predicts
error	Raises an error when encountering OOD data
highest_confidence	Selects the lowest-variance prediction
backup	Uses a secondary model when uncertainty is high

Visualizing Extrapolation Behavior

import matplotlib.pyplot as plt 
x_test = np.linspace(-5, 5, 100).reshape(-1, 1) 
mean_pred, var_pred = controller.predict(x_test, return_variance=True)

plt.plot(x_test, mean_pred, label='Ensemble Mean', color='blue') 
plt.fill_between(x_test.flatten(), mean_pred - var_pred, mean_pred + var_pred, color='blue', alpha=0.2, label='Uncertainty (Variance)') 
plt.legend() 
plt.show()

This generates an extrapolation-aware prediction plot with uncertainty bands! 🔥

Future Roadmap

We are actively developing new features:

• ✅ Bayesian Neural Network support
• ✅ Physics-Informed Neural Networks
• ✅ Integration with Large Language Models (LLMs)
• 🚀 Adaptive learning for OOD generalization
• 🚀 Built-in anomaly detection for real-world data

Contributing

Want to improve XtrapNet? Feel free to submit a pull request! Contributions are welcome.
🔗 GitHub: https://github.com/cykurd/xtrapnet

License

This project is licensed under the MIT License.

Support

If you have any questions, feel free to open an issue on GitHub or reach out via [e-mail]. 🚀

🔥 Why Use XtrapNet? Traditional neural networks struggle with out-of-distribution (OOD) data.
🔥 XtrapNet is the first open-source library designed to intelligently control extrapolation!