Biterm Topic Model

Bitermplus implements Biterm topic model for short texts introduced by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. Actually, it is a cythonized version of BTM. This package is also capable of computing perplexity, semantic coherence, and entropy metrics.

Development

Please note that bitermplus is actively improved. Refer to documentation to stay up to date.

Requirements

• cython
• numpy
• pandas
• scipy
• scikit-learn
• tqdm

Setup

Linux and Windows

There should be no issues with installing bitermplus under these OSes. You can install the package directly from PyPi.

pip install bitermplus

Or from this repo:

pip install git+https://github.com/maximtrp/bitermplus.git

Mac OS

First, you need to install XCode CLT and Homebrew. Then, install libomp using brew:

xcode-select --install
brew install libomp
pip3 install bitermplus

If you have the following issue with libomp (fatal error: 'omp.h' file not found), run brew info libomp in the console:

brew info libomp

You should see the following output:

libomp: stable 15.0.5 (bottled) [keg-only]
LLVM's OpenMP runtime library
https://openmp.llvm.org/
/opt/homebrew/Cellar/libomp/15.0.5 (7 files, 1.6MB)
Poured from bottle on 2022-11-19 at 12:16:49
From: https://github.com/Homebrew/homebrew-core/blob/HEAD/Formula/libomp.rb
License: MIT
==> Dependencies
Build: cmake ✘, lit ✘
==> Caveats
libomp is keg-only, which means it was not symlinked into /opt/homebrew,
because it can override GCC headers and result in broken builds.
For compilers to find libomp you may need to set:
export LDFLAGS="-L/opt/homebrew/opt/libomp/lib"
export CPPFLAGS="-I/opt/homebrew/opt/libomp/include"
==> Analytics
install: 192,197 (30 days), 373,389 (90 days), 1,285,192 (365 days)
install-on-request: 24,388 (30 days), 48,013 (90 days), 164,666 (365 days)
build-error: 0 (30 days)

Export LDFLAGS and CPPFLAGS as suggested in brew output:

export LDFLAGS="-L/opt/homebrew/opt/libomp/lib"export CPPFLAGS="-I/opt/homebrew/opt/libomp/include"

Example

Model fitting

importbitermplusasbtmimportnumpyasnpimportpandasaspd# IMPORTING DATAdf=pd.read_csv(
'dataset/SearchSnippets.txt.gz', header=None, names=['texts'])
texts=df['texts'].str.strip().tolist()
# PREPROCESSING# Obtaining terms frequency in a sparse matrix and corpus vocabularyX, vocabulary, vocab_dict=btm.get_words_freqs(texts)
tf=np.array(X.sum(axis=0)).ravel()
# Vectorizing documentsdocs_vec=btm.get_vectorized_docs(texts, vocabulary)
docs_lens=list(map(len, docs_vec))
# Generating bitermsbiterms=btm.get_biterms(docs_vec)
# INITIALIZING AND RUNNING MODELmodel=btm.BTM(
X, vocabulary, seed=12321, T=8, M=20, alpha=50/8, beta=0.01)
model.fit(biterms, iterations=20)
p_zd=model.transform(docs_vec)
# METRICSperplexity=btm.perplexity(model.matrix_topics_words_, p_zd, X, 8)
coherence=btm.coherence(model.matrix_topics_words_, X, M=20)
# orperplexity=model.perplexity_coherence=model.coherence_# LABELSmodel.labels_# orbtm.get_docs_top_topic(texts, model.matrix_docs_topics_)

Results visualization

You need to install tmplot first.

importtmplotastmptmp.report(model=model, docs=texts)

Tutorial

There is a tutorial in documentation that covers the important steps of topic modeling (including stability measures and results visualization).