https://github.com/cmry/topbox
Python 3 wrapper around the Stanford Topic Modeling Toolbox
https://github.com/cmry/topbox
Last synced: 2 months ago
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Python 3 wrapper around the Stanford Topic Modeling Toolbox
- Host: GitHub
- URL: https://github.com/cmry/topbox
- Owner: cmry
- License: gpl-2.0
- Created: 2015-06-24T06:25:46.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2017-01-10T09:43:44.000Z (over 8 years ago)
- Last Synced: 2025-02-08T09:19:19.217Z (4 months ago)
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# topbox
A small Python 3 wrapper around the Stanford Topic Modeling Toolbox (STMT) that makes working with L-LDA a bit easier; no need to leave the Python environment. More information on its workings can be found on [my blog](https://cmry.github.io/notes/topbox).
# Setting up
Just [download](http://nlp.stanford.edu/software/tmt/tmt-0.4/tmt-0.4.0.jar) STMT and put it in the `box` directory. After, import `topbox` from wherever you left it.
On Linux, this would look something like this:
``` shell
$ cd ~
$ git clone https://github.com/cmry/topbox
$ cd ~/topbox/box
$ wget http://nlp.stanford.edu/software/tmt/tmt-0.4/tmt-0.4.0.jar
$ cd ~
$ vi some_topbox_script.py
```
You can paste the code below in the script file to test if it's working.
# Example
``` python
import topbox
stmt = topbox.STMT('bit_of_testing', epochs=10, mem=15000)
space = ['text text more text', 'things to do with text']
labels = ['label1 label2', 'label1 label3']
stmt.train(space, labels)
infer = ['this is a text', 'things with more text']
gs = ['label1 label2', 'label1 label3']
stmt.test(infer, gs)
from sklearn.metrics import average_precision_score
# array requires numpy and scipy
y_true, y_score = stmt.results(gs, array=True)
print(average_precision_score(y_true, y_score))
```