📊 Spectral Bridges

sbcluster is a Python package that implements a novel clustering algorithm combining k-means and spectral clustering techniques, called Spectral Bridges. It leverages efficient affinity matrix computation and merges clusters based on a connectivity measure inspired by SVM's margin concept. This package is designed to provide robust clustering solutions, particularly suited for large datasets.

✨ Features

• Spectral Bridges Clustering Algorithm: Integrates k-means and spectral clustering with efficient affinity matrix calculation for improved clustering results.
• Scalability: Designed to handle large datasets by optimizing cluster formation through advanced affinity matrix computations.
• Customizable: Parameters such as number of clusters, iterations, and random state allow flexibility in clustering configurations.
• Model selection: Automatic model selection for number of nodes (m) according to a normalized eigengap metric.
• scikit-learn integration: Native BaseEstimator integration with fit, predict, fit_predict, get_params, and model-selection scorers.

⚡ Speed

Spectral Bridges utilizes fastkmeanspp's efficient implementation for KMeans, which makes it remarkably fast even with large scale datasets.

🚀 Installation

pip install sbcluster

🔧 Usage

Example

from time import time

import matplotlib.pyplot as plt
import numpy as np
from sbcluster import SpectralBridges, ngap_scorer
from sklearn.cluster import SpectralClustering
from sklearn.metrics import adjusted_rand_score
from sklearn.model_selection import GridSearchCV

# Load some synthetic data
data = np.genfromtxt("data/impossible.csv", delimiter=",")
X, y = data[:, :-1], data[:, -1]

# Define the parameter grid
param_grid = {"n_clusters": [2, 3, 4, 5, 6, 7, 8, 9, 10]}
cv = [(np.arange(X.shape[0]), np.arange(X.shape[0]))] * 5

# Perform grid search for optimal parameters
grid_search = GridSearchCV(
    estimator=SpectralBridges(n_clusters=2, n_nodes=250),
    param_grid=param_grid,
    scoring=ngap_scorer,
    cv=cv,
    verbose=1,
)

# Fit the grid search
grid_search.fit(X)

# Print the results
print(grid_search.cv_results_["mean_test_score"])
print(grid_search.best_params_)

# Make predictions with the best model
guess = grid_search.best_estimator_.predict(X)
ari = adjusted_rand_score(y, guess)

# Print the ARI
print(f"Adjusted Rand Index: {ari}")

# Visualize the clustering results
plt.scatter(X[:, 0], X[:, 1], c=guess, alpha=0.1)
plt.scatter(
    grid_search.best_estimator_.subcluster_centers_[:, 0],
    grid_search.best_estimator_.subcluster_centers_[:, 1],
    c=grid_search.best_estimator_.subcluster_labels_,
    marker="X",
)
plt.title("Clustered data and centroids with best SpectralBridges fit")
plt.show()

# Compare with sklearn's SpectralClustering
sc_low = SpectralClustering(n_clusters=7).fit(X)

plt.scatter(X[:, 0], X[:, 1], c=sc_low.labels_)
plt.title("Spectral Clustering of the original dataset, gamma=1.0")
plt.show()

sc_high = SpectralClustering(n_clusters=7, gamma=5).fit(X)

plt.scatter(X[:, 0], X[:, 1], c=sc_high.labels_)
plt.title("Spectral Clustering of the original dataset, gmma=5.0")
plt.show()

# Comapre times
start = time()
grid_search.best_estimator_.fit(X)
end = time()
print("SpectralBridges fit time:", end - start)

start = time()
sc_low.fit(X)
end = time()
print("SpectralClustering fit time:", end - start)

At prediction time, SpectralBridges first assigns each sample to the nearest learned subcluster center, then maps that subcluster to its final spectral cluster label.

Results Comparison

📖 Learn More

For tutorials, API reference, visit the official site:
👉 sbcluster's documentation