tom_lib 0.2.1

A library for topic modeling and browsing

TOM
===

TOM (TOpic Modeling) is a Python 3 library for topic modeling and
browsing, licensed under the MIT license. Its objective is to allow for
an efficient analysis of a text corpus from start to finish, via the
discovery of latent topics. To this end, TOM features functions for
preparing and vectorizing a text corpus. It also offers a common
interface for two topic models (namely LDA using either variational
inference or Gibbs sampling, and NMF using alternating least-square with
a projected gradient method), and implements three state-of-the-art
methods for estimating the optimal number of topics to model a corpus.
What is more, TOM constructs an interactive Web-based browser that makes
it easy to explore a topic model and the related corpus.
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Features
--------

Vector space modeling
~~~~~~~~~~~~~~~~~~~~~

- Feature selection based on word frequency
- Weighting

- tf
- tf-idf

Topic modeling
~~~~~~~~~~~~~~

- Latent Dirichlet Allocation

- Standard variational Bayesian inference (Latent Dirichlet
Allocation. Blei et al, 2003)
- Online variational Bayesian inference (Online learning for Latent
Dirichlet Allocation. Hoffman et al, 2010)
- Collapsed Gibbs sampling (Finding scientific topics. Griffiths &
Steyvers, 2004)

- Non-negative Matrix Factorization (NMF)

- Alternating least-square with a projected gradient method
(Projected gradient methods for non-negative matrix factorization.
Lin, 2007)

Estimating the optimal number of topics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- Stability analysis (How many topics? Stability analysis for topic
models. Greene et al, 2014)
- Spectral analysis (On finding the natural number of topics with
latent dirichlet allocation: Some observations. Arun et al, 2010)
- Consensus-based analysis (Metagenes and molecular pattern discovery
using matrix factorization. Brunet et al, 2004)
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Installation
------------

We recommend you to install Anaconda (https://www.continuum.io) which
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will automatically install most of the required dependencies
(i.e. pandas, numpy, scipy, scikit-learn, matplotlib, flask). You should
then install the lda module (pip install lda). Eventually, clone or
download this repo and run the following command:
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will automatically install most of the required dependencies (i.e.
pandas, numpy, scipy, scikit-learn, matplotlib, flask). You should then
install the lda module (pip install lda). Eventually, clone or download
this repo and run the following command:
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::

python setup.py install

Or, install it directly from PyPi:

::

pip install tom_lib

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Usage
-----

We provide two sample programs, topic\_model.py (which shows you how to
load and prepare a corpus, estimate the optimal number of topics, infer
the topic model and then manipulate it) and topic\_model\_browser.py
(which shows you how to generate a topic model browser to explore a
corpus), to help you get started using TOM.

Load and prepare a textual corpus
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following code snippet shows how to load a corpus of French
documents and vectorize them using tf-idf with unigrams.

::

corpus = Corpus(source_file_path='input/raw_corpus.csv',
language='french',
vectorization='tfidf',
n_gram=1,
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max_relative_frequency=0.8,
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max_relative_frequency=0.8,
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min_absolute_frequency=4)
print('corpus size:', corpus.size)
print('vocabulary size:', len(corpus.vocabulary))
print('Vector representation of document 0:\n', corpus.vector_for_document(0))
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Instantiate a topic model and infer topics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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Instantiate a topic model and infer topics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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It is possible to instantiate a NMF or LDA object then infer topics.

NMF:

::

topic_model = NonNegativeMatrixFactorization(corpus)
topic_model.infer_topics(num_topics=15)

LDA (using either the standard variational Bayesian inference or Gibbs
sampling):

::

topic_model = LatentDirichletAllocation(corpus)
topic_model.infer_topics(num_topics=15, algorithm='variational')

::

topic_model = LatentDirichletAllocation(corpus)
topic_model.infer_topics(num_topics=15, algorithm='gibbs')

Instantiate a topic model and estimate the optimal number of topics
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Here we instantiate a NMF object, then generate plots with the three
metrics for estimating the optimal number of topics.

::

topic_model = NonNegativeMatrixFactorization(corpus)
viz = Visualization(topic_model)
viz.plot_greene_metric(min_num_topics=5,
max_num_topics=50,
tao=10, step=1,
top_n_words=10)
viz.plot_arun_metric(min_num_topics=5,
max_num_topics=50,
iterations=10)
viz.plot_brunet_metric(min_num_topics=5,
max_num_topics=50,
iterations=10)

Save/load a topic model
~~~~~~~~~~~~~~~~~~~~~~~

To allow reusing previously learned topics models, TOM can save them on
disk, as shown below.

::

utils.save_topic_model(topic_model, 'output/NMF_15topics.tom')
topic_model = utils.load_topic_model('output/NMF_15topics.tom')

Print information about a topic model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This code excerpt illustrates how one can manipulate a topic model,
e.g. get the topic distribution for a document or the word distribution
for a topic.

::

print('\nTopics:')
topic_model.print_topics(num_words=10)
print('\nTopic distribution for document 0:',
topic_model.topic_distribution_for_document(0))
print('\nMost likely topic for document 0:',
topic_model.most_likely_topic_for_document(0))
print('\nFrequency of topics:',
topic_model.topics_frequency())
print('\nTop 10 most relevant words for topic 2:',
topic_model.top_words(2, 10))

Topic model browser: screenshots
--------------------------------

Topic cloud
~~~~~~~~~~~

|image0| ### Topic details |image1| ### Document details |image2|

.. |image0| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/topic_cloud.jpg
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.. |image1| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/topic_0.jpg
.. |image2| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/document_31.jpg
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.. |image1| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/topic_details.jpg
.. |image2| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/document_details.jpg
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.. |image1| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/topic_0.jpg
.. |image2| image:: http://mediamining.univ-lyon2.fr/people/guille/tom_resources/document_31.jpg
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