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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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
The Topic Modeling Toolkit depends on the BigARTM C++ library. Therefore first you should first build and install it
either by following the instructions here or by using
the 'build_artm.sh' script provided. For example, for python3 you can use the following
either by following the instructions here or by using
the 'build_artm.sh' script provided. For example, for python3 you can use the following
$ 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*.whlNow you should have the 'bigartm' executable in PATH and you can find a built python wheel in 'bigartm/build/python/'
You should install the wheel in your environment, for example with command
You should install the wheel in your environment, for example with command
python -m pip install bigartm/build/python/path-python-wheelYou can install the package with the following command
When the package gets hosted on PyPI, it should be installed
When the package gets hosted on PyPI, it should be installed
$ 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 .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*- Vorontsov, K. and Potapenko, A. (2015). Additive regularization of topic models. Machine Learning, 101(1):303–323.