oikan 0.0.2.3

OIKAN: Optimized Interpretable Kolmogorov-Arnold Networks

OIKAN: Optimized Interpretable Kolmogorov-Arnold Networks

Overview

OIKAN (Optimized Interpretable Kolmogorov-Arnold Networks) is a neuro-symbolic ML framework that combines modern neural networks with classical Kolmogorov-Arnold representation theory. It provides interpretable machine learning solutions through automatic extraction of symbolic mathematical formulas from trained models.

Key Features

• 🧠 Neuro-Symbolic ML: Combines neural network learning with symbolic mathematics
• 📊 Automatic Formula Extraction: Generates human-readable mathematical expressions
• 🎯 Scikit-learn Compatible: Familiar .fit() and .predict() interface
• 🚀 Production-Ready: Export symbolic formulas for lightweight deployment
• 📈 Multi-Task: Supports both regression and classification problems

Scientific Foundation

OIKAN is based on Kolmogorov's superposition theorem, which states that any multivariate continuous function can be represented as a composition of single-variable functions. We leverage this theory by:

1. Using neural networks to learn optimal basis functions through interpretable edge transformations
2. Combining transformed features using learnable weights
3. Automatically extracting human-readable symbolic formulas

Quick Start

Installation

Method 1: Via PyPI (Recommended)

pip install -qU oikan

Method 2: Local Development

git clone https://github.com/silvermete0r/OIKAN.git
cd OIKAN
pip install -e .  # Install in development mode

Regression Example

from oikan.model import OIKANRegressor
from sklearn.model_selection import train_test_split

# Initialize model with optimal architecture
model = OIKANRegressor(
    hidden_dims=[16, 8],  # Network architecture
    dropout=0.1           # Regularization
)

# Fit model (sklearn-style)
model.fit(X_train, y_train, epochs=100, lr=0.01)

# Get predictions
y_pred = model.predict(X_test)

# Save interpretable formula to file with auto-generated guidelines
# The output file will contain:
# - Detailed symbolic formulas for each feature
# - Instructions for practical implementation
# - Recommendations for production deployment
model.save_symbolic_formula("regression_formula.txt")

Example of the saved symbolic formula instructions: outputs/regression_symbolic_formula.txt

Classification Example

from oikan.model import OIKANClassifier

# Similar sklearn-style interface for classification
model = OIKANClassifier(hidden_dims=[16, 8])
model.fit(X_train, y_train, epochs=100, lr=0.01)
probas = model.predict_proba(X_test)

# Save classification formulas with implementation guidelines
# The output file will contain:
# - Decision boundary formulas for each class
# - Softmax application instructions
# - Production deployment recommendations
model.save_symbolic_formula("classification_formula.txt")

Example of the saved symbolic formula instructions: outputs/classification_symbolic_formula.txt

Architecture Details

OIKAN implements a novel neuro-symbolic architecture based on Kolmogorov-Arnold representation theory through three specialized components:

1. Edge Symbolic Layer: Learns interpretable single-variable transformations

   • Adaptive basis function composition using 9 core functions:

     ADVANCED_LIB = {
         'x':    ('x', lambda x: x),
         'x^2':  ('x^2', lambda x: x**2),
         'x^3':  ('x^3', lambda x: x**3),
         'exp':  ('exp(x)', lambda x: np.exp(x)),
         'log':  ('log(x)', lambda x: np.log(abs(x) + 1)),
         'sqrt': ('sqrt(x)', lambda x: np.sqrt(abs(x))),
         'tanh': ('tanh(x)', lambda x: np.tanh(x)),
         'sin':  ('sin(x)', lambda x: np.sin(x)),
         'abs':  ('abs(x)', lambda x: np.abs(x))
     }
     

   • Each input feature is transformed through these basis functions
   • Learnable weights determine the optimal combination

2. Neural Composition Layer: Multi-layer feature aggregation

   • Direct feature-to-feature connections through KAN layers
   • Dropout regularization (p=0.1 default) for robust learning
   • Gradient clipping (max_norm=1.0) for stable training
   • User-configurable hidden layer dimensions

3. Symbolic Extraction Layer: Generates production-ready formulas

   • Weight-based term pruning (threshold=1e-4)
   • Automatic coefficient optimization
   • Human-readable mathematical expressions
   • Exportable to lightweight production code

Architecture Diagram

Key Design Principles

1. Interpretability First: All transformations maintain clear mathematical meaning
2. Scikit-learn Compatibility: Familiar .fit() and .predict() interface
3. Production Ready: Export formulas as lightweight mathematical expressions
4. Automatic Simplification: Remove insignificant terms (|w| < 1e-4)

Model Components

1. Symbolic Edge Functions

   class EdgeActivation(nn.Module):
       """Learnable edge activation with basis functions"""
       def forward(self, x):
           return sum(self.weights[i] * basis[i](x) for i in range(self.num_basis))
   

2. KAN Layer Implementation

   class KANLayer(nn.Module):
       """Kolmogorov-Arnold Network layer"""
       def forward(self, x):
           edge_outputs = [self.edges[i](x[:,i]) for i in range(self.input_dim)]
           return self.combine(edge_outputs)
   

3. Formula Extraction

   def get_symbolic_formula(self):
       """Extract interpretable mathematical expression"""
       terms = []
       for i, edge in enumerate(self.edges):
           if abs(self.weights[i]) > threshold:
               terms.append(f"{self.weights[i]:.4f} * {edge.formula}")
       return " + ".join(terms)
   

Key Design Principles

• Modular Architecture: Each component is independent and replaceable
• Interpretability First: All transformations maintain symbolic representations
• Automatic Simplification: Removes insignificant terms and combines similar expressions
• Production Ready: Export formulas for lightweight deployment

Contributing

We welcome contributions! Key areas of interest:

• Model architecture improvements
• Novel basis function implementations
• Improved symbolic extraction algorithms
• Real-world case studies and applications
• Performance optimizations

Please see CONTRIBUTING.md for guidelines.

Citation

If you use OIKAN in your research, please cite:

@software{oikan2025,
  title = {OIKAN: Optimized Interpretable Kolmogorov-Arnold Networks},
  author = {Zhalgasbayev, Arman},
  year = {2025},
  url = {https://github.com/silvermete0r/OIKAN}
}

License

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