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IPython is not visualizing the gensim model. #33
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Alternatively I tried to use display() function: not that much better. |
Odd.. what version of ipython are you using? You can trying using the |
I thought I did mentioned in my original post: I am on Windows 10, with WinPython-64bit-3.4.3.5Qt5 |
Ben Apparently I have gotten a "position out of range" error, perhaps causing the graphs not to display. QTextCursor::setPosition: Position '29941' out of range Any help would be really-really-really appreciated. |
Only the web/browser notebook is supported. It looks like you are using the QT notebook so it is not expected to work at all.
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Mr. Mabey If you can find it in your heart and in your schedule and tell with exact version of Python or WinPython for that matter I should use in order for this to work correctly? Thanks |
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Sorry I can't be of more help. I've never heard of WinPython and I don't have access to a Windows box. I did have to use |
Mr. Mabey This helps. WinPython is a comprehensive assembly of Python packages for Windows bums, like myself. I chose it because I'm very new to Python and don't have a ready access to the Unix box. Thank you very much |
Hi I want to reopen this issue I am in a GNU/Linux box and I have the same problem with
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First and foremost wanted to thank everyone for helping me get this far. I am able to generate a gensim model, run it in IPython notebook, and get to see some results - but not the beautiful graphic we all were hoping for. I'm running WinPython 3.4 QT5 (latest I believe) and I installed both the genism and pyLDAvis also today, so everything is fresh. Here is what my output looks like:
In[9]: pyLDAvis.enable_notebook()
In[10]: pyLDAvis.gensim.prepare(lda, corpus, dictionary)
C:\WinPython\python-3.4.3.amd64\lib\site-packages\skbio\stats\ordination_principal_coordinate_analysis.py:109: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -0.009952346420900118 and the largest is 0.034359155356682575.
RuntimeWarning
Out[10]:
PreparedData(topic_coordinates= Freq cluster topics x y
topic
24 20.249823 1 1 0.055595 0.006318
3 18.859849 1 2 0.003016 0.038028
17 16.519686 1 3 -0.117297 0.009020
18 9.578099 1 4 -0.014738 0.006581
....
13 0.000586 1 24 0.003268 0.007722
9 0.000586 1 25 -0.008638 -0.009829, topic_info= Category Freq Term Total loglift logprob
2300 Default 1280.000000 gladia 1280 30.0000 30.0000
5920 Default 984.000000 giskard 984 29.0000 29.0000
1512 Default 676.000000 amadiro 676 28.0000 28.0000
...
2252 Topic25 0.000562 anacreon 117 -0.3992 -6.2565
9440 Topic25 0.000626 madam 372 -1.5745 -6.2751
[1929 rows x 6 columns], token_table= Topic Freq Term
term
3268 1 0.181818 abilities
3268 2 0.318182 abilities
...
1155 10 0.019608 york
[1984 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[25, 4, 18, 19, 21, 11, 23, 17, 22, 8, 12, 24, 15, 13, 2, 7, 20, 9, 16, 3, 6, 5, 1, 14, 10])
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