CLustering In Multiphase Boundaries (CLiMB)

A versatile two-phase clustering algorithm designed for datasets with both known and exploratory components.

Features

Two-Phase Clustering: Combines constrained clustering with exploratory clustering to identify both known and novel patterns.
Density-Aware: Uses local density estimation to intelligently filter and assign points.
Flexible Exploratory Phase: Supports multiple clustering algorithms (DBSCAN, HDBSCAN, OPTICS) through a strategy pattern.
Visualization Tools: Built-in 2D and 3D visualization capabilities for cluster analysis.
Parameter Tuning: Builder pattern for flexible parameter adjustment.
Customizable Distance Metrics: Supports various distance metrics such as Euclidean, Mahalanobis, and custom metrics.
Advanced Seed Points: Ability to initialize clustering with known seed points provided in a dictionary structure.

Installation

Install via pip:

pip install climb-astro

Or install from source:

git clone https://github.com/LorenzoMonti/CLiMB.git
cd CLiMB
pip install -e .

Quick Start

Here is a basic example of how to use CLiMB with synthetic data:

import numpy as np
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from CLiMB.core.CLiMB import CLiMB
from CLiMB.exploratory.DBSCANExploratory import DBSCANExploratory

# The number of centers to generate
centers = 4

# Generate synthetic data with 5 dimensions
X, y = make_blobs(n_samples=500, centers=centers, n_features=5, random_state=42)

# Scale the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Create seed points (optional)
seed_points = np.array([
    X[y == i].mean(axis=0) for i in range(centers)
])
seed_points_scaled = scaler.transform(seed_points)

# Example of seed points as a dictionary for more precise control
seed_dict_scaled = {
    tuple(seed_points_scaled[0]): [tuple(X_scaled[y == 0][0]), tuple(X_scaled[y == 0][1])],
    tuple(seed_points_scaled[1]): [tuple(X_scaled[y == 1][0])],
    tuple(seed_points_scaled[2]): [],
    tuple(seed_points_scaled[3]): [tuple(X_scaled[y == 3][0]), tuple(X_scaled[y == 3][1]), tuple(X_scaled[y == 3][2])]
}

# Initialize and fit CLiMB
climb = CLiMB(
    constrained_clusters=4,
    seed_points=seed_dict_scaled,
    density_threshold=0.15,
    distance_threshold=2.5,
    radial_threshold=1.2,
    convergence_tolerance=0.05,
    distance_metric='euclidean',
    metric_params=None,
    exploratory_algorithm=DBSCANExploratory(0.5)
)
climb.fit(X_scaled)

# Get cluster labels
labels = climb.get_labels()

# Visualize results (only possible in lower dimensions)
# Note: visualization requires dimensionality reduction if features > 3
climb.inverse_transform(scaler)
# fig = climb.plot_comprehensive_3d(save_path="./3d")
# fig2 = climb.plot_comprehensive_2d(save_path="./2d")

API Reference

Here you can find complete documentation for all classes and methods.

CLiMB package
- Subpackages
- Module contents

How It Works

CLiMB operates in two phases:

Constrained Phase (KBound): A modified K-means that:
- Uses seed points to guide initial clustering.
- Applies density and distance constraints.
- Prevents centroids from drifting too far using radial thresholds.
- Supports customizable distance metrics.
- Handles advanced seed points via a dictionary structure.
Exploratory Phase: Uses density-based clustering methods to discover patterns in points not assigned during the first phase.

Advanced Usage

Using Different Exploratory Algorithms

from CLiMB.core.CLiMB import CLiMB
from CLiMB.exploratory.HDBSCANExploratory import HDBSCANExploratory

# Create HDBSCAN exploratory algorithm
hdbscan = HDBSCANExploratory(min_cluster_size=5, min_samples=3)

# Use it with CLIMB
climb = CLiMB(
    constrained_clusters=3,
    exploratory_algorithm=hdbscan
)

Parameter Tuning with Builder Pattern

climb = CLiMB()
climb.set_density(0.3) \
     .set_distance(2.5) \
     .set_radial(1.0) \
     .set_convergence(0.1)

Using Custom Distance Metrics

To use distance metrics other than Euclidean, you can use the distance_metric and metric_params parameters.

Example with Mahalanobis Metric:

import numpy as np
from CLiMB.core.KBound import KBound

# ... (Load or generate your data X) ...

# Calculate the inverse covariance matrix (VI)
covariance_matrix = np.cov(X.T)
inv_covariance_matrix = np.linalg.inv(covariance_matrix)

kbound = KBound(
    n_clusters=3,
    distance_metric='mahalanobis',
    metric_params={'VI': inv_covariance_matrix}
)
kbound.fit(X)

Example with Custom Metric:

import numpy as np
from scipy.spatial.distance import euclidean
from CLiMB.core.KBound import KBound

def custom_distance(u, v):
    # Example: weighted Euclidean distance
    weight = np.array([2, 1, 1])
    return euclidean(u * weight, v * weight)

kbound = KBound(
    n_clusters=3,
    distance_metric='custom',
    metric_params={'func': custom_distance}
)
kbound.fit(X)