---

clustermatic is a Python library designed to accelerate clustering tasks using scikit-learn. It serves as a quick tool for selecting the optimal clustering algorithm and its hyperparameters, providing visualizations and metrics for comparison.

Features

• Clustering Algorithms: Analyzes six clustering algorithms from scikit-learn:
  • KMeans
  • DBSCAN
  • MiniBatchKMeans
  • AgglomerativeClustering
  • OPTICS
  • SpectralClustering
• Optimization Methods: Includes Bayesian optimization and random search for hyperparameter tuning.
• Flexible Preprocessing: Allows users to customize how the data is meant to be preprocessed, adjusting methods such as scaling, normalization, and dimensionality reduction.
• Evaluation Metrics: Supports evaluation with silhouette, calinski_harabasz, and davies_bouldin scores.
• Report Generation: Generates reports in HTML format after optimization.

Installation

To install clustermatic, use pip:

pip install clustermatic

Usage

For a quick start, use the following code snippet:

from clustermatic import AutoClusterizer

# Load data
from sklearn.datasets import make_moons
X, _ = make_moons(n_samples=200, noise=0.1, random_state=42)

# Initialize AutoClusterizer
ac = AutoClusterizer()

# Fit the data
ac.fit(X)

# Generate report
ac.evaluate()

For more detailed walkthrough, check out this example Jupyter Notebook