tabkan 1.0.2
TabKAN: A Framework for Advancing Tabular Data Analysis using Kolmogorov-Arnold Networks
Requires Python: >=3.9

# TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Networks

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**TabKAN** is a Python framework that implements a family of Kolmogorov-Arnold Network (KAN) based architectures specifically designed for tabular data analysis. This library is the official implementation of the research paper: [**TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network**](https://arxiv.org/abs/2504.06559).

Traditional deep learning models often struggle to outperform tree-based methods like XGBoost on structured data. TabKAN bridges this gap by leveraging the expressive power of KANs, which use learnable activation functions on the edges of the neural network instead of fixed activations on the nodes.

This library provides a unified API for various KAN variants, making it easy to experiment, train, and evaluate state-of-the-art models for tabular tasks.

## Key Features

- **Multiple KAN Variants:** Includes implementations and wrappers for:
  - **ChebyshevKAN**: Uses Chebyshev polynomials for function approximation.
  - **FourierKAN**: Uses Fourier series for capturing periodic patterns.
  - **SplineKAN**: The original KAN architecture based on B-splines (via `kan-python`).
  - **Rational KANs**: `JacobiKAN` and `PadeKAN` for modeling complex rational functions (via `rkan`).
  - **FractionalKAN**: Utilizes fractional-order Jacobi functions for enhanced flexibility (via `fkan`).
- **Advanced Architectures**: A generic and powerful `KANMixer` architecture that replaces MLPs in a Mixer design with any KAN layer, enhancing expressivity.
- **Unified and Simple API**: All models inherit from a base `KAN` class, providing consistent `.fit()` and `.tune()` methods.
- **Built-in Hyperparameter Tuning**: Seamlessly find the best model architecture using an integrated Optuna-based tuner.
- **Model Interpretability**: Includes methods like `.get_feature_importance()` as described in the paper.

## Installation

You can install TabKAN directly from PyPI:
```bash
pip install tabkan
```

## Usage

```python
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

from tabkan import ChebyshevKAN


device = 'cpu'

# --- 1. Data Setup: Simulating a Transfer Learning Scenario ---
print("\n--- [Step 1] Creating Synthetic Datasets ---")

# SOURCE DATASET: A larger dataset with more features and samples
X_source, y_source = make_classification(
    n_samples=5000,
    n_features=20,
    n_informative=10, # 10 features are useful
    n_redundant=5,    # 5 are linear combinations of informative features
    n_classes=2,
    random_state=42
)

X_target, y_target = make_classification(
    n_samples=500,
    n_features=15,
    n_informative=8, # 5 of these overlap with source, 3 are new
    n_redundant=2,
    n_classes=2,
    random_state=123
)

# Align features for transfer learning (pad target with zeros to match source dim)
source_dim = X_source.shape[1]
target_dim = X_target.shape[1]
if target_dim < source_dim:
    padding = np.zeros((X_target.shape[0], source_dim - target_dim))
    X_target_padded = np.hstack((X_target, padding))
else:
    X_target_padded = X_target

# Convert to Tensors
X_source, y_source = torch.tensor(X_source, dtype=torch.float32), torch.tensor(y_source, dtype=torch.long)
X_target, y_target = torch.tensor(X_target_padded, dtype=torch.float32), torch.tensor(y_target, dtype=torch.long)

# Create dataset dictionaries for TabKAN
def create_dataset_dict(X, y, test_size=0.2):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42, stratify=y)
    return {
        "train_input": X_train.to(device), "train_label": y_train.to(device),
        "test_input": X_test.to(device), "test_label": y_test.to(device),
    }

source_dataset = create_dataset_dict(X_source, y_source)
target_dataset = create_dataset_dict(X_target, y_target)

print(f"Source Dataset: {source_dataset['train_input'].shape[0]} train samples, {source_dim} features.")
print(f"Target Dataset: {target_dataset['train_input'].shape[0]} train samples, {target_dim} features (padded to {source_dim}).")

# --- 2. Hyperparameter Tuning on the Source Task ---
print("\n--- [Step 2] Hyperparameter Tuning for ChebyshevKAN ---")

# Define a search space for Optuna
search_space = {
    "depth": {"type": "int", "low": 1, "high": 2},
    "neurons_layer_0": {"type": "int", "low": 16, "high": 32},
    "neurons_layer_1": {"type": "int", "low": 8, "high": 16},
    "orders_layer_0": {"type": "int", "low": 2, "high": 4},
    "orders_layer_1": {"type": "int", "low": 2, "high": 4},
    "lr": {"type": "float", "low": 1e-2, "high": 1.0, "log": True},
    "steps": {"type": "categorical", "choices": [20]}
}

best_params = ChebyshevKAN.tune(
    model_class=ChebyshevKAN,
    dataset=source_dataset,
    search_space=search_space,
    n_trials=5,
    device=device
)
print("Best hyperparameters found:", best_params)

# --- 3. Pre-training on Source Dataset ---
print("\n--- [Step 3] Pre-training Model on Source Data ---")

# Build the model with the best parameters found
depth = best_params.pop("depth")
lr = best_params.pop("lr")
steps = best_params.pop("steps")
layers = [source_dim] + [best_params[f'neurons_layer_{i}'] for i in range(depth)] + [2]
orders = [best_params[f'orders_layer_{i}'] for i in range(depth)]

pretrained_model = ChebyshevKAN(layers=layers, orders=orders).to(device)

# Pre-train the model
pretrained_model.pretrain(
    source_dataset, 
    steps=steps, 
    loss_fn=nn.CrossEntropyLoss(),
    lr=lr
)

# --- 4. Interpretability: Analyze the Pre-trained Model ---
print("\n--- [Step 4] Analyzing Feature Importance from Pre-trained Model ---")

# Get feature importance from the first KAN layer
feature_importance = pretrained_model.get_feature_importance(layer_index=0)
print("Feature importances (first 10):", feature_importance.cpu().detach().numpy()[:10])

# Visualize the importance
plt.figure(figsize=(12, 6))
plt.bar(range(source_dim), feature_importance.cpu().detach().numpy())
plt.title("Feature Importance from Pre-trained ChebyshevKAN")
plt.xlabel("Feature Index")
plt.ylabel("Importance (L1 Norm of Coefficients)")
plt.axvspan(-0.5, 9.5, color='green', alpha=0.2, label='Informative Features')
plt.axvspan(9.5, 14.5, color='orange', alpha=0.2, label='Redundant Features')
plt.axvspan(14.5, 19.5, color='red', alpha=0.2, label='Noisy Features')
plt.legend()
plt.show()

# --- 5. Transfer Learning & Comparison ---
print("\n--- [Step 5] Fine-Tuning and Comparison ---")

# Helper function for evaluation
def evaluate(model, dataset, model_name):
    model.eval()
    with torch.no_grad():
        preds = torch.argmax(model(dataset['test_input']), dim=1)
        accuracy = (preds == dataset['test_label']).float().mean().item()
        print(f"[{model_name}] Test Accuracy: {accuracy:.4f}")
    return accuracy

# Scenario A: Standard Fine-Tuning
from copy import deepcopy
model_ft_standard = deepcopy(pretrained_model)
model_ft_standard.finetune(
    target_dataset, 
    method='standard',
    steps=100,
    loss_fn=nn.CrossEntropyLoss(),
    lr=0.1
)
acc_ft_standard = evaluate(model_ft_standard, target_dataset, "Fine-tuned (Standard)")

# Scenario B: GRPO Fine-Tuning (Wrapped in try-except as it's a new feature)
try:
    model_ft_grpo = deepcopy(pretrained_model)
    model_ft_grpo.finetune(
        target_dataset,
        method='grpo',
        steps=200, # GRPO often benefits from more steps
        lr=1e-3,   # And a smaller learning rate
        batch=32
    )
    acc_ft_grpo = evaluate(model_ft_grpo, target_dataset, "Fine-tuned (GRPO)")
except NotImplementedError as e:
    print(f"[Fine-tuned (GRPO)] SKIPPED: {e}")
    acc_ft_grpo = 0.0

# Scenario C: Training from Scratch on Target Data
print("\nTraining a model from scratch on the small target dataset...")
scratch_model = ChebyshevKAN(layers=layers, orders=orders).to(device)
scratch_model.fit(
    target_dataset, 
    steps=steps, 
    loss_fn=nn.CrossEntropyLoss(), 
    lr=lr
)
acc_scratch = evaluate(scratch_model, target_dataset, "Trained from Scratch")

# --- 6. Final Results ---
print("\n--- [Step 6] Final Performance Comparison ---")
results = {
    "Trained from Scratch": acc_scratch,
    "Fine-tuned (Standard)": acc_ft_standard,
    "Fine-tuned (GRPO)": acc_ft_grpo
}
results_df = pd.DataFrame.from_dict(results, orient='index', columns=['Accuracy'])
print(results_df)
```