dataeval.core.rank_hdbscan_distance¶

dataeval.core.rank_hdbscan_distance(embeddings, c=None, max_cluster_size=None, reference=None)¶

Rank samples using distance to HDBSCAN cluster centers.

Clusters embeddings using HDBSCAN and ranks by distance to assigned cluster centers. Returns samples in easy-first order (low distance = prototypical).

Parameters:¶

embeddings : NDArray[np.floating]¶

Embedding vectors to rank, shape (n_samples, n_features).

c : int | None, default None¶

Expected number of clusters (used as hint for min_cluster_size). If None, uses sqrt(n_samples).

max_cluster_size : int | None, default None¶

Maximum size limit for identified clusters.

reference : NDArray[np.floating] | None, default None¶

Reference embeddings for comparative ranking. If provided, samples are ranked relative to the reference set rather than themselves.

Returns:¶

Dictionary containing:

• indices: NDArray[np.intp] - Ranked indices in easy-first order

• scores: NDArray[np.float32] | None - Distance to cluster center for each sample

• method: str - “hdbscan_distance”

• policy: str - “easy_first”

Return type:¶

RankResult

Examples

>>> from dataeval.core import rank_hdbscan_distance
>>> import numpy as np
>>> embeddings = np.random.rand(100, 64).astype(np.float32)
>>> result = rank_hdbscan_distance(embeddings, c=10)