snf.metrics.rank_feature_by_nmi

snf.metrics.rank_feature_by_nmi(inputs, W, *, K=20, mu=0.5, n_clusters=None)[source]

Calculates NMI of each feature in inputs with W

Parameters:
  • inputs (list-of-tuple) – Each tuple should contain (1) an (N, M) data array, where N is samples M is features, and (2) a string indicating the metric to use to compute a distance matrix for the given data. This MUST be one of the options available in scipy.spatial.distance.cdist()
  • W ((N, N) array_like) – Similarity array generated by snf.compute.snf()
  • K ((0, N) int, optional) – Hyperparameter normalization factor for scaling. Default: 20
  • mu ((0, 1) float, optional) – Hyperparameter normalization factor for scaling. Default: 0.5
  • n_clusters (int, optional) – Number of desired clusters. Default: determined by eigengap (see snf.get_n_clusters())
Returns:

nmi – Normalized mutual information scores for each feature of input arrays
Return type:

list of (M,) np.ndarray