Comments (3)
Hi, Jorge,
The reason is the newer version of numpy enforces the data type to be explicitly defined, even creating from an all integer list.
Anyway, please try to run the code from a different repo https://github.com/kzhai/PyInfVoc. It basically contains the exact same code, but much cleaner.
I have just ported this repo to python3, so you should be able to run it without any problem.
kzhai@MININT-LPGA1J7:/mnt/c/Users/kezhai/Workspace/PyInfVoc$ python3 -m launch_train --input_directory=./de-news/ --output_directory=./ --truncation_level=4000 --number_of_pics=10 --number_of_documents=9800 --training_iterations=100 --vocab_prune_interval=10 --batch_size=98 --alpha_beta=100
========== ========== ========== ========== ==========
output_directory=./de-news/18Oct03-231005-D9800-K10-T4000-P10-I10-B98-O100-t64-k0.75-at0.1-ab100/
input_directory=./de-news
corpus_name=de-news
dictionary_file=None
number_of_documents=9800
number_of_topics=10
truncation_level=4000
vocab_prune_interval=10
snapshot_interval=10
batch_size=98
training_iterations=100
tau=64.0
kappa=0.75
alpha_theta=0.1
alpha_beta=100.0
========== ========== ========== ========== ==========
successfully load all training documents...
select documents from 0 to 98
P-step, E-step and M-step take 0, 2, 0 seconds respectively...
vocabulary size = [1725, 1725, 1725, 1725, 1725, 1725, 1725, 1725, 1725, 1725]
training iteration 1 finished in 2.888820 seconds: epsilon = 0.043683
select documents from 98 to 196
P-step, E-step and M-step take 0, 2, 0 seconds respectively...
vocabulary size = [2582, 2582, 2582, 2582, 2582, 2582, 2582, 2582, 2582, 2582]
training iteration 2 finished in 3.002949 seconds: epsilon = 0.043186
select documents from 196 to 294
P-step, E-step and M-step take 0, 2, 0 seconds respectively...
vocabulary size = [3194, 3194, 3194, 3194, 3194, 3194, 3194, 3194, 3194, 3194]
training iteration 3 finished in 2.965397 seconds: epsilon = 0.042702
select documents from 294 to 392
P-step, E-step and M-step take 0, 4, 0 seconds respectively...
vocabulary size = [3654, 3654, 3654, 3654, 3654, 3654, 3654, 3654, 3654, 3654]
training iteration 4 finished in 4.734318 seconds: epsilon = 0.042230
select documents from 392 to 490
P-step, E-step and M-step take 0, 3, 0 seconds respectively...
Thanks for digging into this, and feel free to let me know for any bugs.
Best,
Ke
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Hi kzhai, thanks for answering
FInally i launch with python 2.7 and fixed the tab/space conversion via untabify spaces but I keep getting another error type:
Traceback (most recent call last):
File "C:\Python27\lib\runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "C:\Python27\lib\runpy.py", line 72, in _run_code
exec code in run_globals
File "C:\Users\Jorge Castillo\(...)l\InfVocLDA-master\src\infvoc\launch.py", line 248, in <module>
main()
File "C:\Users\Jorge Castillo\(...)\InfVocLDA-master\src\infvoc\launch.py", line 218, in main
batch_gamma = olda.learning(docset);
File "infvoc\hybrid.py", line 448, in learning
sufficient_statistics, batch_document_topic_distribution = self.e_step(wordids);
File "infvoc\hybrid.py", line 335, in e_step
sufficient_statistics[k][0, id] += temp_phi[k, 0];
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
Clearly, I must edit the lines 335 and 448 of hybrid.py in order to recognize valid indexes
Edit: now I see, for both lines, that wordids is the problem but I don't know how to fix it.
Edit 2: Curiosly, between lines 309 to 317 there is no error, specifically in 310 it's defined wordid and in 317 the sentence temp_phi[k, 0] *= exp_weights[k][0, id];
don't give error, I don't know why.
Thanks for your help and code developing.
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