Hi, I installed topik 0.3.0 on ubuntu 15.0, however, I got this error when running topik. Anyone has idea why and how to fix it?

Error in `/home/usr/anaconda2/bin/python': free(): invalid pointer:

Thanks

Hello, I was trying out the topik package and ran into some problems with the basic examples in the tutorial (http://topik.readthedocs.org/en/latest/example.html). Specifically, I was trying to get an LDAvis visualization using a variation of your basic code:

from topik.run import run_model run_model("reviews", content_field="text", r_ldavis=True, dir_path="./topic_model")

The parameters don't seem to match what's on the documentation, so I'm going by trial and error. With the present code, I get the error below. Could you kindly let me know how to properly invoke lDAvis services? Thanks and best regards

Alex

----> 1 run_model("reviews", content_field="text", r_ldavis=True, dir_path="./topic_model") /Users/alexmckenzie/anaconda/lib/python2.7/site-packages/topik-0.1.0-py2.7.egg/topik/run.pyc in run_model(data_source, source_type, year_field, start_year, stop_year, content_field, clear_es_index, tokenizer, n_topics, dir_path, model, termite_plot, output_file, r_ldavis, json_prefix, seed, **kwargs) 116 117 if r_ldavis: --> 118 to_r_ldavis(processed_data, dir_name=os.path.join(dir_path, 'ldavis'), lda=lda) 119 os.environ["LDAVIS_DIR"] = os.path.join(dir_path, 'ldavis') 120 try: /Users/alexmckenzie/anaconda/lib/python2.7/site-packages/topik-0.1.0-py2.7.egg/topik/utils.pyc in to_r_ldavis(corpus_bow, lda, dir_name) 40 np.savetxt(os.path.join(dir_name, 'topicTermDist'), tt_dist, delimiter=',', newline='\n',) 41 ---> 42 corpus_file = corpus_bow.filename 43 corpus = gensim.corpora.MmCorpus(corpus_file) 44 docTopicProbMat = lda.model[corpus] AttributeError: 'DigestedDocumentCollection' object has no attribute 'filename'

Hi, I just installed the 0.3.0 update with conda, however I get an error msg even when executing a simple command-line "help". This is the error:

$ topik --help Traceback (most recent call last): File "/Users/alexmckenzie/anaconda/bin/topik", line 4, in from topik.cli import run ImportError: No module named cli

I'd rather keep using conda and not download the source zip. Thanks for your help Alex

Added numpy 1.9.4 as requirement (argpartition bug was showing up in termite parsing code; it was fixed in np 1.9.4, numpy issue 5524) Added requirement for nose, stop_words In fileio/in_document_folder.py - Added support to ignore invalid UTF; but progress normally + log the fact that we encountered an error Added suitable test data (_junk) and test case to test_in_document_folder Added connectionerror handling for elasticsearch tests; if elasticsearch is not running, simply skip the tests Corrected tokenizer names in simple_run/cli.py Added stopword support to simple_run/run.py Corrected tokenizer names in simple_run/run.py Added logging in simple_run/run.py Tee generator in entities.py to avoid exhaustion. Support quadgrams and refactored code in ngrams.py Tee generator in ngrams.py + added some logging Added appropriate test case for quadgrams + tweaked test data in test_ngrams.py Added test case using a generator that will demonstrate exhaustion problem. All tests now succeeding (NB: ElasticSearch ones not tested - no changes aside from exception handling in tests though.)

I get this identical error on both Ubuntu 15 + pip install (or directly from the latest github) AND on Ubuntu 14 LTS + conda2; so I am pretty sure this is not an issue with my environment.

Following the tutorial on the movie reviews data (not sure if that matters).. I get..

brian@niihau:[~]$ topik -d reviews -c text 2016-04-18 14:29:00,880 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy Traceback (most recent call last): File "/home/brian/anaconda2/bin/topik", line 6, in sys.exit(run()) File "/home/brian/anaconda2/lib/python2.7/site-packages/click/core.py", line 716, in call return self.main(_args, *_kwargs) File "/home/brian/anaconda2/lib/python2.7/site-packages/click/core.py", line 696, in main rv = self.invoke(ctx) File "/home/brian/anaconda2/lib/python2.7/site-packages/click/core.py", line 889, in invoke return ctx.invoke(self.callback, *_ctx.params) File "/home/brian/anaconda2/lib/python2.7/site-packages/click/core.py", line 534, in invoke return callback(_args, *_kwargs) File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/simple_run/cli.py", line 27, in run termite_plot=termite) File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/simple_run/run.py", line 67, in run_pipeline model = models.registered_models[model](vectorized_data, ntopics=ntopics, **kwargs) File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/lda.py", line 82, in lda return ModelOutput(vectorized_corpus=vectorized_output, model_func=_LDA, ntopics=ntopics, *_kwargs) File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/base_model_output.py", line 20, in init vectorized_corpus, **kwargs) File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/lda.py", line 72, in _LDA for topic_no in range(ntopics)} File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/lda.py", line 72, in for topic_no in range(ntopics)} File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/lda.py", line 12, in _topic_term_to_array term_scores = {term: float(score) for score, term in topic} File "/home/brian/anaconda2/lib/python2.7/site-packages/topik/models/lda.py", line 12, in term_scores = {term: float(score) for score, term in topic} ValueError: could not convert string to float: s

I consistently encounter a KeyError for the "token_..." field when using the 'elastic' output_type. I can see that the field exists if I manually view an individual document in the browser, but it appears there is some lag between appending the tokenized document and actually being able to retrieve it back. I added a 1-second wait after the append loop and that appears to have solved the problem.

Obviously, per-class specific. I envision dictionary storage doing some serialization, but the Elasticsearch backend should store a file with only connection details and current field selections.

Example: https://github.com/ContinuumIO/topik/blob/master/topik/tokenizers/entities.py#L87

np is commonly used to point at NumPy, via import numpy as np.

Eliminate all such occurrences (as well as other common ones, like sp for scipy, pd for pandas, etc.).

adds vectorization to CLI changed project run_model default to lda changed the datatype of the individual weight values in the lda matrices from numpy.float64 to float in order to match plsa and more importantly successfully decode from file using jsonpickle. minor documentation updates

-renames run.run_model to run.run_pipeline, updates imports and function calls accordingly -changes default visualization for run_pipeline to lda_vis -fixes some default parameters in models.run_model -minor updates to documentation code examples -prevents TFIDF/LDA combination when using projects -(full fix including storage of corpus parameter strings coming in separate PR)

Added plots to documentation. This is a workaround to keep using readthedocs for now, and I am intentionally not closing the associated issue because it will need to be solved again once we switch doc hosting platforms.

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............

I encountered the error saying "Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so." when running topik --help.

I installed topik using conda install -c memex topik and running with Python 2.7.11 :: Anachonda 2.5.0

The two files in question are in /home/user/anaconda2/lib directory and they look intact, 36M and 30M respectively in size. and the directory path is in my LD_LIBRARY_PATH and DYLD_LIBRARY_PATH env variable.

Is there anything I am missing here? Any help?

There are several issues with the various data structures that need fixing. These fixes will make them much more coherent. I'll list them here:

• [ ] prepared_model_vis_data.token_table uses a non-unique index, namely the unique id for each term. This needs to be a proper index, as it causes attempts to serialize the DataFrame to fail.

We should exclude empty documents because they generate useless output at best, and crashes at worst.

However, we must not silently drop the document, as it may be useful for the user to know that there is an empty document in the database.

In some domains, certain expressions (phrases, compound words) are very common and meaningful. Having the simple tokenizer recognize such words would be very useful, and it could be done simply by passing all tokens through a transformation that recognizes those expressions and replaces sequences of tokens with said expressions. Reference:

http://www.mimno.org/articles/phrases/

That would improve the performance of models using tokenizers.simple, especially in certain domains.