Biterm Topic Model
Project description
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.
Requirements
- Cython
- NumPy
- Pandas
- SciPy
- Scikit-learn
- pyLDAvis (optional)
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
from gzip import open as gzip_open
# Importing and vectorizing text data
with gzip_open('dataset/SearchSnippets.txt.gz', 'rb') as file:
texts = file.readlines()
# Vectorizing documents, obtaining full vocabulary and biterms
X, vocab = btm.get_words_freqs(texts)
docs_vec = btm.get_vectorized_docs(X)
biterms = btm.get_biterms(X)
# Initializing and running model
model = btm.BTM(X, T=8, W=vocab.size, M=20, alpha=50/8, beta=0.01)
model.fit(biterms, iterations=20)
p_zd = model.transform(docs_vec)
# Calculating metrics
perplexity = btm.perplexity(model.matrix_words_topics_, p_zd, X, 8)
coherence = btm.coherence(model.matrix_words_topics_, X, M=20)
# or
perplexity = model.perplexity_
coherence = model.coherence_
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
bitermplus-0.5.5.tar.gz
(598.3 kB
view hashes)