getting 'too many indices for array' error when trying to print out topic results

[tgrover2]

Hi there,

I'm trying to run this program using my own data, and the actual guided topic modeling fit as expected, but now using your code to print out the resulting seeded topics:

n_top_words = 10 topic_word = model.topic_word_ for i, topic_dist in enumerate(topic_word): topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words+1):-1] print('Topic {}: {}'.format(i, ' '.join(topic_words)))

I am getting an error at topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words+1):-1] of IndexError: too many indices for array.

My vocab object is a python dictionary as expected with the word as the key and the value as the ID, like in your tutorial.

{'level': 23949, 'nationalsozialistische': 27680, 'boyish': 4847, 'uprising': 44406, 'reached': 34053, 'infinitesimal': 20852, 'humiliated': 19720, 'fundraise': 16348, 'reprogram': 35089, 'nwf': 28830, 'impolite': 20381, 'upmu': 44393, 'stomp': 40042, 'reassertion': 34162, 'matthjews': 25541, 'kokesh': 23156, 'seize': 37167, 'proven': 32956, 'rted': 36093, 'streams': 40190, 'jvx': 22572, 'deformation': 10161, 'schoolkids': 36798, 'agonising': 865, 'skellington': 38332, 'xvideos': 46943, 'hills': 19027, 'francoist': 15947, 'hitters': 19140, 'urination': 44472, 'crowdfund': 9114, 'fivethirtyeight': 15321, 'flagbearers': 15362, 'shoah': 37862, 'uncritically': 43738, 'heretics': 18837, 'congressional': 8097, 'slayin': 38487, 'kickerdaily': 22901, 'blogging': 4382, 'riot': 35685, 'consciously': 8154, 'attention': 2656, 'tik': 42227, 'pfft': 31040, 'steppe': 39913, 'eigene': 12762, 'drag': 12040, 'insectivore': 21073, 'premiere': 32308, 'outing': 29750, 'citizenry': 6985, 'repute': 35126, 'savvy': 36620, 'artfag': 2289, 'twinkies': 43330, 'supporting': 40785, 'escaped': 13642, 'shhiiiieeeetttt': 37692, 'yellow': 47058, 'rationality': 33954, 'sighting': 38107, 'negotiation': 27908, 'adults': 612, 'overflowing': 29884 etc, etc...

Any insight into what I might be missing here or doing wrong would be greatly appreciated. I am more experienced with R than python so I'm not used to all the nuances of python.

Thanks in advance!

[deepakkumar98355]

worked for me by using vocab = cv.get_feature_names()

model = guidedlda.GuidedLDA(n_topics=10, n_iter=500, random_state=7, refresh=20) model.fit(X)

topic_word = model.topicword n_top_words = 20 for i, topic_dist in enumerate(topic_word): topic_words = np.array(cv.get_feature_names())[np.argsort(topic_dist)][:-(n_top_words+1):-1] print('Topic {}: {}'.format(i, ' '.join(topic_words)))

[arthi-rajendran24]

Hi @deepakkumar98355 What does "cv" represent here? I know cv doesn't have a function"get_feature_names".

[arthi-rajendran24]

Hi @tgrover2 where you able to fix your issue? I am getting the same error. Please share your solution if you have any.