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 and semantic coherence 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
Example
import 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_
# RESULTS VISUALIZATION
# You need to install tmplot first
import tmplot as tmp
tmp.report(model=model, docs=texts)
