bitermplus 0.6.1

Biterm Topic Model

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
• 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
import pandas as pd

# Importing data
df = pd.read_csv(
    'dataset/SearchSnippets.txt.gz', header=None, names=['texts'])
texts = df['texts'].str.strip().tolist()

# Vectorizing documents, obtaining full vocabulary and biterms
X, vocabulary, vocab_dict = btm.get_words_freqs(texts)
docs_vec = btm.get_vectorized_docs(texts, vocabulary)
biterms = btm.get_biterms(docs_vec)

# Initializing and running model
model = btm.BTM(
    X, vocabulary, seed=12321, T=8, W=vocabulary.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_topics_words_, p_zd, X, 8)
coherence = btm.coherence(model.matrix_topics_words_, X, M=20)
# or
perplexity = model.perplexity_
coherence = model.coherence_