tsne-pso 1.1.4

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. This implementation is based on the research paper by Allaoui et al. (2025).

Features

• Improved Optimization: Uses Particle Swarm Optimization for better optimization with less susceptibility to local minima
• Multiple Initialization Options: Supports initialization using PCA, UMAP, t-SNE, or custom embeddings
• Hybrid Approach: Optional hybrid optimization combining PSO with gradient descent steps
• Highly Customizable: Fine-tune parameters for particles, inertia, cognitive/social weights, and more
• scikit-learn Compatible: Follows scikit-learn's API conventions for easy integration

Installation

Install the latest stable version from PyPI:

pip install tsne-pso

Dependencies

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

Quick Start

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

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

# Create and fit the TSNE-PSO model
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 the 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()

Advanced Usage

Different Initialization Methods

# Using UMAP for initialization
model = TSNEPSO(init='umap', perplexity=30)

# Using t-SNE for initialization
model = TSNEPSO(init='tsne', perplexity=30)

# Using custom initialization
initial_embedding = np.random.normal(0, 0.0001, (n_samples, 2))
model = TSNEPSO(init=initial_embedding)

Tuning PSO Parameters

model = TSNEPSO(
    n_particles=20,           # Number of particles
    inertia_weight=0.7,       # Inertia weight
    h=1e-20,                  # Parameter for dynamic cognitive weight
    f=1e-21,                  # Parameter for dynamic social weight
    use_hybrid=True,          # Use hybrid PSO + gradient descent
    n_iter=1000               # Number of iterations
)

How It Works

TSNE-PSO enhances the original t-SNE algorithm by replacing gradient descent with Particle Swarm Optimization. The algorithm:

1. Initialization: Creates a swarm of particles with positions initialized via PCA, UMAP, t-SNE, or randomly
2. Optimization: Updates particles using:
   • Cognitive component (attraction to personal best position)
   • Social component (attraction to global best position)
   • Inertia (tendency to continue current trajectory)
3. Dynamic Parameters: Adapts cognitive and social weights over iterations
4. Hybrid Approach: Optionally applies gradient descent steps to accelerate convergence

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}
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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