# bitermplus

**Repository Path**: ma-lechi/bitermplus

## Basic Information

- **Project Name**: bitermplus
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-16
- **Last Updated**: 2021-06-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Biterm Topic Model

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*Bitermplus* implements [Biterm topic model](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.4032&rep=rep1&type=pdf) for short texts introduced by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. Actually, it is a cythonized version of [BTM](https://github.com/xiaohuiyan/BTM). This package is also capable of computing *perplexity* and *semantic coherence* metrics.

## Development

Please note that bitermplus is actively improved.
Refer to [documentation](https://bitermplus.readthedocs.io) 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.

```bash
pip install bitermplus
```

Or from this repo:

```bash
pip install git+https://github.com/maximtrp/bitermplus.git
```

### Mac OS

First, you need to install XCode CLT and [Homebrew](https://brew.sh).
Then, install `libomp` using `brew`:

```bash
xcode-select --install
brew install libomp
pip3 install bitermplus
```

## Example

```python
import bitermplus as btm
import numpy as np
import pandas as pd
import pyLDAvis as plv

# 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
# Turning on displaying in Jupyter notebook
plv.enable_notebook()
# Preparing our results for visualization
vis = btm.vis_prepare_model(
    model.matrix_topics_words_,
    p_zd,
    docs_lens,
    model.vocabulary_,
    tf
)
# Displaying the results
plv.display(vis)
```

## Tutorial

There is a [tutorial](https://bitermplus.readthedocs.io/en/latest/tutorial.html)
in documentation that covers the important steps of topic modeling (including
stability measures and results visualization).