tsne-pso 1.1.0

t-Distributed Stochastic Neighbor Embedding with Particle Swarm Optimization

TSNE-PSO

t-Distributed Stochastic Neighbor Embedding with Particle Swarm Optimization (TSNE-PSO) is an enhanced version of t-SNE that uses Particle Swarm Optimization instead of gradient descent for the optimization step.

Installation

pip install tsne-pso

Usage

from tsne_pso import TSNEPSO
import numpy as np
from sklearn.datasets import load_iris

# Load data
iris = load_iris()
X = iris.data

# Apply TSNE-PSO
tsne_pso = TSNEPSO(
    n_components=2,
    perplexity=30.0,
    n_particles=10,
    n_iter=500,
    random_state=42
)
X_embedded = tsne_pso.fit_transform(X)

# Visualize results
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 8))
scatter = plt.scatter(X_embedded[:, 0], X_embedded[:, 1], c=iris.target)
plt.legend(handles=scatter.legend_elements()[0], labels=iris.target_names)
plt.title('TSNE-PSO visualization of Iris dataset')
plt.show()

Features

• Uses Particle Swarm Optimization for better optimization
• Supports multiple initialization strategies (PCA, UMAP, t-SNE)
• Optional hybrid approach using both PSO and gradient descent
• Customizable parameters for optimization (particles, inertia, cognitive/social weights)

Dependencies

• numpy
• scipy
• scikit-learn
• umap-learn (optional)
• tqdm (optional, for progress bars)

Citation

If you use this package in your research, please cite the following paper:

@article{allaoui2025t,
  title={t-SNE-PSO: Optimizing t-SNE using particle swarm optimization},
  author={Allaoui, Mebarka and Belhaouari, Samir Brahim and Hedjam, Rachid and Bouanane, Khadra and Kherfi, Mohammed Lamine},
  journal={Expert Systems with Applications},
  volume={269},
  pages={126398},
  year={2025},
  publisher={Elsevier}
}

License

BSD-3-Clause License (same as scikit-learn)