bitermplus 0.6.7

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
• 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, W=vocabulary.size, 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)

Tutorial

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