eva

• build_hierarchy
• classification
• clustering
• hierarchy_quality
• topic_coherence
• topic_diversity

Package Contents

_diversity(top_words)
multiaspect_diversity(top_words[, _type])
dynamic_diversity(top_words, train_bow, train_times, vocab)
_clustering(theta, labels)
hierarchical_clustering(test_theta, test_labels)
_cls(train_theta, test_theta, train_labels, test_labels)
crosslingual_cls(train_theta_en, train_theta_cn, ...)
hierarchical_cls(train_theta, test_theta, ...[, ...])
_coherence(reference_corpus, vocab, top_words[, ...])
dynamic_coherence(train_texts, train_times, vocab, ...)
hierarchy_quality(vocab, reference_bow, ...)

_diversity(top_words: List[str])

multiaspect_diversity(top_words: List[str], _type='TD')

dynamic_diversity(top_words: List[str], train_bow: numpy.ndarray, train_times: List[int], vocab: List[str], verbose=False)

_clustering(theta, labels)

hierarchical_clustering(test_theta, test_labels)

_cls(train_theta, test_theta, train_labels, test_labels, classifier='SVM', gamma='scale')

crosslingual_cls(train_theta_en, train_theta_cn, test_theta_en, test_theta_cn, train_labels_en, train_labels_cn, test_labels_en, test_labels_cn, classifier='SVM', gamma='scale')

hierarchical_cls(train_theta, test_theta, train_labels, test_labels, classifier='SVM', gamma='scale')

_coherence(reference_corpus: List[str], vocab: List[str], top_words: List[str], coherence_type='c_v', topn=20)

dynamic_coherence(train_texts, train_times, vocab, top_words_list, coherence_type='c_v', verbose=False)

hierarchy_quality(vocab, reference_bow, topic_str_list, beta_list, phi_list)