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:math:`\mu\text{TC}`
==================================

.. image:: https://github.com/INGEOTEC/microtc/actions/workflows/test.yaml/badge.svg
	   :target: https://github.com/INGEOTEC/microtc/actions/workflows/test.yaml

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   :target: https://coveralls.io/github/INGEOTEC/microtc?branch=develop

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   :target: https://badge.fury.io/py/microtc

.. image:: https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/microtc-feedstock?branchName=main
   :target: https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=16441&branchName=main

.. image:: https://img.shields.io/conda/vn/conda-forge/microtc.svg
   :target: https://anaconda.org/conda-forge/microtc

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   :target: https://anaconda.org/conda-forge/microtc

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   :target: https://microtc.readthedocs.io/en/docs/?badge=docs

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   :target: https://colab.research.google.com/github/INGEOTEC/microtc/blob/master/docs/microTC_Quickstart.ipynb	    

A great variety of text tasks such as topic or spam identification,
user profiling, and sentiment analysis can be posed as a supervised
learning problem and tackled using a text classifier. A text
classifier consists of several subprocesses, some of them are general
enough to be applied to any supervised learning problem, whereas
others are specifically designed to tackle a particular task using
complex and computational expensive processes such as lemmatization,
syntactic analysis, etc. Contrary to traditional approaches,
:math:`\mu\text{TC}` is a minimalist and multi-propose text-classifier able to tackle
tasks independently of domain and language.

:math:`\mu\text{TC}` core entry point is :py:class:`microtc.textmodel.TextModel` which
can be seen as a function with the form
:math:`m(\text{text}) \rightarrow \Re ^d` where :math:`d`
is the vocabulary, i.e., the dimension of the *vector space*. As can
be seen, :math:`m` can be used to transform a text into a vector, and,
consequently, it can be used to transform a training set of pairs,
text and label, into a training set of pairs, vectors and label, which
can be directly used by any supervised
learning algorithm to obtain a **text classifier**.

:py:class:`microtc.textmodel.TextModel` follows the idea of http://scikit-learn.org
transformers. That is, it implements a method
:py:func:`microtc.textmodel.TextModel.fit` that receives the training
set and a method :py:func:`microtc.textmodel.TextModel.transform` that
receives a list of texts and returns and sparse matrix that correspond
to the representation of the given texts in the vector space.

:math:`\mu\text{TC}` is described in
`An Automated Text Categorization Framework based on Hyperparameter Optimization <https://www.sciencedirect.com/science/article/pii/S0950705118301217>`_.
Eric S. Tellez, Daniela Moctezuma, Sabino Miranda-Jímenez, Mario Graff. Knowledge-Based Systems
Volume 149, 1 June 2018, Pages 110-123

Quickstart Guide
===================

We have decided to make a live quickstart guide, it covers the
installation, and the use of :math:`\mu\text{TC}` with different parameters. 
Finally, the notebook can be found at the docs directory on GitHub.

.. raw:: html

	<iframe width="560" height="315" src="https://www.youtube.com/embed/4Am1IiGEgW4" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


Citing
======

If you find :math:`\mu\text{TC}` useful for any academic/scientific purpose, we
would appreciate citations to the following reference:

.. code:: bibtex
	
	  @article{Tellez2018110,
	  title = "An automated text categorization framework based on hyperparameter optimization",
	  journal = "Knowledge-Based Systems",
	  volume = "149",
	  pages = "110--123",
	  year = "2018",
	  issn = "0950-7051",
	  doi = "10.1016/j.knosys.2018.03.003",
	  url = "https://www.sciencedirect.com/science/article/pii/S0950705118301217",
	  author = "Eric S. Tellez and Daniela Moctezuma and Sabino Miranda-Jiménez and Mario Graff",
	  keywords = "Text classification",
	  keywords = "Hyperparameter optimization",
	  keywords = "Text modelling"
	  }

Installing :math:`\mu\text{TC}`
===============================

:math:`\mu\text{TC}` can be easly install using anaconda

.. code:: bash

	  conda install -c conda-forge microtc

or can be install using pip, it depends on numpy, scipy and
scikit-learn.

.. code:: bash
	  
	  pip install numpy
	  pip install scipy
	  pip install scikit-learn
	  pip install microtc


Text Model
=============

This is class is :math:`\mu\text{TC}` main entry, it receives a
corpus, i.e., a list of text and builds a text model from it.

.. autoclass:: microtc.textmodel.TextModel
   :members:

.. autofunction:: microtc.textmodel.norm_chars
.. autofunction:: microtc.textmodel.get_word_list
.. autofunction:: microtc.textmodel.expand_qgrams
.. autofunction:: microtc.textmodel.expand_qgrams_word_list
.. autofunction:: microtc.textmodel.expand_skipgrams_word_list		  
		  
Modules
==================	      

.. toctree::
   :maxdepth: 2
   
   cli	      
   weighting
   utils