pyLDAvis ValidationError: Not all rows (distributions) in doc_topic_dists sum to 1 #80
Description
i am getting the below error when trying to visualize HDP model trained on gensim
**_---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
in ()
----> 1 vis_data_hdp = gensimvis.prepare(hdpmodel, corpus, dictionary)
2 #pyLDAvis.display(vis_data_hdp)
C:\Anaconda2\lib\site-packages\pyLDAvis\gensim.pyc in prepare(topic_model, corpus, dictionary, doc_topic_dist, **kwargs)
110 """
111 opts = fp.merge(_extract_data(topic_model, corpus, dictionary, doc_topic_dist), kwargs)
--> 112 return vis_prepare(**opts)
C:\Anaconda2\lib\site-packages\pyLDAvis_prepare.pyc in prepare(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency, R, lambda_step, mds, n_jobs, plot_opts, sort_topics)
372 doc_lengths = _series_with_name(doc_lengths, 'doc_length')
373 vocab = _series_with_name(vocab, 'vocab')
--> 374 _input_validate(topic_term_dists, doc_topic_dists, doc_lengths, vocab, term_frequency)
375 R = min(R, len(vocab))
376
C:\Anaconda2\lib\site-packages\pyLDAvis_prepare.pyc in _input_validate(*args)
63 res = _input_check(*args)
64 if res:
---> 65 raise ValidationError('\n' + '\n'.join([' * ' + s for s in res]))
66
67
ValidationError:
- Not all rows (distributions) in doc_topic_dists sum to 1._**
To train hdp model i have used the following syntax:
hdpmodel = models.hdpmodel.HdpModel(corpus, dictionary)
corpus looks like this:
[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 2), (12, 1), (13, 2), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 4), (34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 2), (40, 1), (41, 2), (42, 1), (43, 2), (44, 1), (45, 1), (46, 1), (47, 3), (48, 1), (49, 1), (50, 2), (51, 1), (52, 1), (53, 1), (54, 1), (55, 1), (56, 1), (57, 1), (58, 1), (59, 1), (60, 1), (61, 1), (62, 1), (63, 1), (64, 1), (65, 1)]
dictionary looks like this:
[u'', u'dacteur', u'reallocations', u'advcompliance', u'resolveboth............