# Kolmogorov–Arnold Networks for Tabular Data Synthesis

**KAN_synth** is an open-source Python package for generating high-fidelity synthetic tabular data using Kolmogorov–Arnold Networks (KANs). It extends the original [CTGAN](https://github.com/sdv-dev/CTGAN) and [TVAE](https://github.com/sdv-dev/CTGAN) models from the Synthetic Data Vault (SDV) by replacing their MLP-based architectures with KAN-based components. 

This project was developed for research (undergraduate thesis) and practical evaluation of KAN-based generative models, particularly in comparison to traditional GANs and VAEs on tabular data synthesis tasks.

## Overview

The KAN_synth repository is structured into several components:

### Models (`models/`)
KAN-based generative models for tabular data:
- **KAN_CTGAN**: Fully KAN-based implementation of CTGAN.
- **HYBRID_KAN_CTGAN**: CTGAN with only the first layer replaced by KAN.
- **Disc_KAN_CTGAN**: CTGAN with a KAN-based discriminator only.
- **Gen_KAN_CTGAN**: CTGAN with a KAN-based generator only.
- **KAN_TVAE**: Full KAN-based variant of TVAE.
- **HYBRID_KAN_TVAE**: TVAE where one intermediate layer is replaced by a KAN block.

### Evaluation Scripts (`benchmarks/`)
Non-reusable scripts developed to:
- Train and evaluate each model on various datasets.
- Measure similarity and ML utility.
- Aggregate and visualize results.

### Data Generation Scripts (`data_gen/`)
Standalone scripts for generating synthetic datasets using each model for internal benchmarking and experimentation purposes.

### Utilities (`utilities/`)
Functions to compute:
- Overall similarity between real and synthetic datasets
- Model evaluation metrics (MAE, RMSE, R² for regression; Accuracy, F1, etc. for classification)
- Visualizations of ML utility scores across models

### Tests (`test/`)
Unit tests for model importability, synthetic generation routines, and ML pipeline sanity checks using `pytest`.

The full model descriptions can be found in the thesis work at: [Not yet published].

## Installation

Once the package is published on [PyPI](https://pypi.org), it can be installed via `pip`:

```bash
pip install kan-synth
```

All required packages are listed in `requirements.txt`, and will be automatically installed when using:

```bash
pip install -r requirements.txt
```

Since these models are based on the original CTGAN and TVAE architectures, the same data preprocessing principles apply:

- Continuous columns must be represented as floats.
- Discrete columns must be represented as integers or strings.
- The input dataset should not contain any missing values.

For additional details, refer to the original CTGAN repository: [https://github.com/sdv-dev/CTGAN](https://github.com/sdv-dev/CTGAN)

### Local Development Installation
if you'd like to install the package locally for development or experimentation:

```bash
git clone https://github.com/cris1618/KAN_synth.git
cd KAN_synth
pip install -e .
```

The `-e` flag installs the package in "editable" mode, meaning any changes you make to the code will immediately affect the installed version without needing to reinstall.

## Usage Example
Here's a minimal working example to train a KAN-based synthesizer and generate synthetic tabular data using the `KAN_CTGAN` model.

```python
from KAN_synth import KAN_CTGAN
import pandas as pd

# Load real tabular data (pandas DataFrame)
df = pd.read_csv("your_dataset.csv")

# Define the discrete columns (if any)
discrete_columns = ["column_a", "column_b"]  # Modify as needed

# Initialize the synthesizer
synthesizer = KAN_CTGAN(
    epochs=100, 
    verbose=True, 
    grid_size_gen=5,  
    spline_order_gen=3,
    grid_size_desc=5,
    spline_order_desc=3
)

# Train the model on your data
synthesizer.fit(df, discrete_columns)

# Sample 1000 synthetic rows
synthetic_data = synthesizer.sample(1000)

# Save or explore the results
synthetic_data.to_csv("synthetic_output.csv", index=False)
```

You can replace KAN_CTGAN with any of the other available models, such as:

- `HYBRID_KAN_CTGAN`
- `Disc_KAN_CTGAN`
- `Gen_KAN_CTGAN`
- `KAN_TVAE`
- `HYBRID_KAN_TVAE`

Each supports similar training and sampling APIs compatible with the original CTGAN and TVAE interfaces.