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 and semantic coherence metrics.
Please note that bitermplus is actively improved. Refer to documentation to stay up to date.
- cython
- numpy
- pandas
- scipy
- scikit-learn
- tqdm
There should be no issues with installing bitermplus under these OSes. You can install the package directly from PyPi.
pip install bitermplusOr from this repo:
pip install git+https://github.com/maximtrp/bitermplus.gitFirst, you need to install XCode CLT and Homebrew.
Then, install libomp using brew:
xcode-select --install
brew install libomp
pip3 install bitermplusimport bitermplus as btm
import numpy as np
import pandas as pd
# IMPORTING DATA
df = 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 vocabulary
X, vocabulary, vocab_dict = btm.get_words_freqs(texts)
tf = np.array(X.sum(axis=0)).ravel()
# Vectorizing documents
docs_vec = btm.get_vectorized_docs(texts, vocabulary)
docs_lens = list(map(len, docs_vec))
# Generating biterms
biterms = btm.get_biterms(docs_vec)
# INITIALIZING AND RUNNING MODEL
model = 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)
# METRICS
perplexity = btm.perplexity(model.matrix_topics_words_, p_zd, X, 8)
coherence = btm.coherence(model.matrix_topics_words_, X, M=20)
# or
perplexity = model.perplexity_
coherence = model.coherence_You need to install tmplot first.
import tmplot as tmp
tmp.report(model=model, docs=texts)
There is a tutorial in documentation that covers the important steps of topic modeling (including stability measures and results visualization).
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