bj 0.0.6

Generating hierarchical data

bj

Generating hierarchical data for machine learning and data science applications

Installation

pip install bj

Description

bj is a Python package that provides tools for generating synthetic hierarchical cluster data. It extends scikit-learn's make_blobs function to create nested, hierarchical blob clusters useful for:

• Testing hierarchical clustering algorithms
• Evaluating dimensionality reduction techniques
• Creating visualization examples for nested data structures
• Benchmarking clustering algorithms on hierarchical data

Usage

The main function is make_hblobs, which generates hierarchical blob clusters with configurable depth and branching.

from bj import make_hblobs
import matplotlib.pyplot as plt

# Generate a simple two-level hierarchy:
# 2 main clusters, each with 3 sub-clusters
X, y = make_hblobs(n_samples=300, centers=[2, 3])

# Plot the result
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis', alpha=0.8)
plt.title('Hierarchical Blob Clusters (2x3)')
plt.show()

# Create a more complex three-level hierarchy with varying cluster spreads:
# 2 main clusters, each with 3 sub-clusters, each with 4 sub-sub-clusters
X_complex, y_complex = make_hblobs(
    n_samples=500, 
    centers=[2, 3, 4],
    cluster_std=[1.0, 0.5, 0.2]  # Decreasing spread at each level
)

Parameters

The make_hblobs function supports:

• n_samples: Total number of points to generate
• n_features: Number of features for each sample
• centers: Integer or list of integers representing the hierarchy structure
• cluster_std: Float or list of floats controlling the spread at each level
• center_box: Bounding box for cluster centers
• shuffle: Whether to shuffle the samples
• random_state: For reproducibility

Examples

Simple Two-Level Hierarchy

from bj import make_hblobs

# Create a two-level hierarchy with 2 main clusters, each with 3 sub-clusters
X, y = make_hblobs(n_samples=200, centers=[2, 3])

Complex Multi-Level Hierarchy

# Create a three-level hierarchy with varying cluster spread
X, y = make_hblobs(
    n_samples=500,
    centers=[2, 3, 4],  # 2 main clusters -> 3 sub-clusters -> 4 sub-sub-clusters
    cluster_std=[1.0, 0.5, 0.3]  # Decreasing spread at each level
)

License

MIT