xmcluster 0.0.1

XMPro Vector Insight is a Python library for analyzing and visualizing embedding data. It provides tools for calculating various similarity measures (cosine, Euclidean, inner product) between embeddings and reference vectors, applying different scaling techniques, and performing basic statistical analysis on embedding data. The library is designed with flexibility in mind, allowing for easy extension and customization of its core functionalities.

XMCluster

XMCluster is a Python library designed for performing density-based clustering on embedding data using DBSCAN (Density-Based Spatial Clustering of Applications with Noise). It provides a flexible and extensible framework for conducting cluster analysis, optimizing clustering parameters, and analyzing cluster characteristics.

Features

• DBSCAN Clustering: Perform density-based clustering on your embedding data.
• Cluster Statistics: Get basic statistics about the clusters, including the number of clusters, number of noise points, and cluster sizes.
• Cluster Quality Metrics: Calculate various cluster quality metrics such as Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index.
• Optimal Parameter Finding: Find the optimal epsilon parameter for DBSCAN using a grid search and the Silhouette score.
• Cluster Centroids: Calculate the centroids of each cluster.
• Intra-cluster Distances: Calculate the average distance of points to their cluster centroid.

Installation

Install XMCluster using pip:

pip install xmcluster

Usage

Here's a basic example of how to use XMCluster:

from xmcluster import DBSCANAnalyzer, ClusterMetric

# Sample embeddings
embeddings = {
    'key1': [[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]],
    'key2': [[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]]
}

# Create a DBSCANAnalyzer instance
analyzer = DBSCANAnalyzer(embeddings)

# Perform DBSCAN clustering
cluster_labels = analyzer.perform_dbscan(eps=3, min_samples=2)

# Get cluster statistics
stats = analyzer.get_cluster_statistics()

# Calculate silhouette score
silhouette = analyzer.calculate_cluster_metric(ClusterMetric.SILHOUETTE)

# Find optimal epsilon
optimal_eps = analyzer.find_optimal_epsilon(min_samples=2, eps_range=(0.1, 10.0), n_steps=20)

print("Cluster Labels:", cluster_labels)
print("Cluster Statistics:", stats)
print("Silhouette Scores:", silhouette)
print("Optimal Epsilon:", optimal_eps)

Advanced Usage

Cluster Centroids and Intra-cluster Distances

centroids = analyzer.get_cluster_centroids()
intra_distances = analyzer.get_intra_cluster_distances()

print("Cluster Centroids:", centroids)
print("Intra-cluster Distances:", intra_distances)

Different Cluster Quality Metrics

calinski_harabasz = analyzer.calculate_cluster_metric(ClusterMetric.CALINSKI_HARABASZ)
davies_bouldin = analyzer.calculate_cluster_metric(ClusterMetric.DAVIES_BOULDIN)

print("Calinski-Harabasz Index:", calinski_harabasz)
print("Davies-Bouldin Index:", davies_bouldin)

Dependencies

• numpy
• scikit-learn

Contributing

We welcome contributions! Please see our contributing guidelines for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any queries or support, please contact [your contact information].