Topic Modeling Toolkit - Python Library
This library aims to automate Topic Modeling research-related activities.
Data preprocessing and dataset computing
Model training (with parameter grid-search), evaluating and comparing
Graph building
Computing KL-divergence between p(c|t) distributions
Datasets/models/kl-distances reporting
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Overview
This library serves as a higher level API around the BigARTM (artm python interface) library and exposes it conviniently through the command line.
Key features of the Library:
Flexible preprocessing pipelines
Optimization of classification scheme with an evolutionary algorithm
Fast model inference with parallel/multicore execution
Persisting of models and experimental results
Visualization
Installation
$ git clone https://github.com/boromir674/topic-modeling-toolkit.git $ chmod +x topic-modeling-toolkit/build_artm.sh $ # build and install BigARTM library in /usr/local and create python3 wheel $ topic-modeling-toolkit/build_artm.sh $ ls bigartm/build/python/bigartm*.whl
python -m pip install bigartm/build/python/path-python-wheel
$ cd topic-modeling-toolkit $ pip install .
If the above fails try again including manual installation of dependencies
$ cd topic-modeling-toolkit $ pip install -r requirements.txt $ pip install .
Usage
A sample example is below.
$ current_dir=$(echo $PWD) $ export COLLECTIONS_DIR=$current_dir/datasets-dir $ mkdir $COLLECTIONS_DIR $ transform posts pipeline.cfg my-dataset $ train my-dataset train.cfg plsa-model --save $ make-graphs --model-labels "plsa-model" --allmetrics --no-legend $ xdg-open $COLLECTIONS_DIR/plsa-model/graphs/plsa*prpl*
Citation
Vorontsov, K. and Potapenko, A. (2015). Additive regularization of topic models. Machine Learning, 101(1):303–323.
1 maintainer
Metadata
1 maintainer
Release files for topic-modeling-toolkit 0.5.6
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