freediscovery.cluster.ClusterLabels

class freediscovery.cluster.ClusterLabels(vect, model, pars, lsi=None, cluster_indices=None)

Calculate the cluster labels.

This is an internal class that is called by Clustering

Parameters:

• vect (VectorizerMixin object) – a scikit-learn’s text vectorizer
• model (ClusterMixin object) – the cluster object
• pars (dict) – clustering algorithms parameters
• lsi_components (TruncatedSVD object or None) – LSA object if it was used for clustering
• cluster_indices (list, default=None) – if not None, ignore clustering given by the clustering model and compute terms for the cluster provided by the given indices

__init__(vect, model, pars, lsi=None, cluster_indices=None)

Methods

__init__(vect, model, pars[, lsi, ...])
predict([method, n_top_words])	Compute the cluster labels

predict(method=u'centroid-frequency', n_top_words=6)

Compute the cluster labels

Parameters:

• method (str, optional, default='centroid-frequency') – the method used to compute the centroid labels Must be one of ‘centroid-frequency’,
• n_top_words (int, default=10) – keep only most relevant n_top_words words

Returns:

cluster_labels

Return type:

array [n_samples]