Source code for denspp.offline.metric.cluster_index

import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import calinski_harabasz_score, silhouette_score




def calculate_euclidean_distance(point1: np.ndarray, point2: np.ndarray) -> float:
    """Calculate Euclidean distance between two points.

    Args:
        point1 (npt.NDArray): Point 1
        point2 (npt.NDArray): Point 2

    Raises:
        ValueError: If point1 and point2 are not of equal size.

    Returns:
        float: Euclidean distance between point1 and point2
    """
    return np.linalg.norm(point1 - point2)





def calculate_dunn_index(data: np.ndarray, labels: np.ndarray) -> float:
    """Calculate the Dunn-index for a given dataset.

    Args:
        data (np.ndarray): array representing a cluster
                     -> each row describes a sample
                     -> each column represents a different feature
        labels (np.ndarray): label of each sample
    Returns:
        float: Floating with metric value
    """
    unique_labels = np.unique(labels)
    if len(unique_labels) < 2:
        return 0.0

    clusters = [data[labels == label] for label in unique_labels]
    intra_dists = []
    for cluster in clusters:
        if len(cluster) > 1:
            diameter = np.max(cdist(cluster, cluster, metric="euclidean"))
            intra_dists.append(diameter)

    if not intra_dists:
        return 0.0

    # --- Inter-cluster distances ---
    # Min. distance between any two clusters
    inter_dists = []
    for i in range(len(clusters)):
        for j in range(i + 1, len(clusters)):
            min_dist = np.min(cdist(clusters[i], clusters[j], metric="euclidean"))
            inter_dists.append(min_dist)

    # Calculate Dunn Index
    max_intra = max(intra_dists)
    min_inter = min(inter_dists) if inter_dists else 0.0

    # Avoid division by zero
    if max_intra == 0:
        return 0.0
    return float(min_inter / max_intra)





def calculate_silhouette(data: np.ndarray, labels: np.ndarray) -> float:
    """Calculate the Silhouette index for a given dataset.

    Args:
        data (np.ndarray): array representing a cluster
                     -> each row describes a sample
                     -> each column represents a different feature
        labels (np.ndarray): label of each sample
    Returns:
        float: Floating with metric value
    """
    return silhouette_score(data, labels)





def calculate_harabasz(data: np.ndarray, labels: np.ndarray) -> float:
    """Calculate the Calinski-Harabasz index for a given dataset.

    Args:
        data (np.ndarray): array representing a cluster
                     -> each row describes a sample
                     -> each column represents a different feature
        labels (np.ndarray): label of each sample
    Returns:
        float: Floating with metric value
    """
    return calinski_harabasz_score(data, labels)