textdirectory 0.3.1

TextDirectory allows you to combine multiple text files into one.

============= TextDirectory

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.. image:: https://user-images.githubusercontent.com/16179317/39367680-cd409a00-4a37-11e8-8d42-0bed5a4e814b.png :alt: TextDirectory

TextDirectory allows you to combine multiple text files into one aggregated file. TextDirectory also supports matching files for certain criteria and applying transformations to the aggregated text.

TextDirectory can be used as a mere tool (via the CLI) and as a Python library.

Of course, everything TextDirectory does could be achieved in bash or PowerShell. However, there are certain use-cases (e.g. when used as a library) in which it might be useful.

• Free software: MIT license
• Documentation: https://textdirectory.readthedocs.io.

Features

• Aggregating multiple text files
• Filtering documents/texts based on various parameters such as length, content, and random sampling
• Transforming the aggregated text (e.g. transforming the text to lowercase)

.. csv-table:: :header: "Version", "Filters", "Transformations" :widths: 10, 30, 30

0.1.0, filter_by_max_chars(n int); filter_by_min_chars(n int); filter_by_max_tokens(n int); filter_by_min_tokens(n int); filter_by_contains(str); filter_by_not_contains(str); filter_by_random_sampling(n int; replace=False), transformation_lowercase 0.1.1, filter_by_chars_outliers(n sigmas int), transformation_remove_nl 0.1.2, filter_by_filename_contains(str), transformation_usas_en_semtag; transformation_uppercase; transformation_postag(spacy_model str) 0.1.3, filter_by_similar_documents(reference_file str; threshold float), transformation_remove_non_ascii; transformation_remove_non_alphanumerical 0.2.0, filter_by_max_filesize(max_kb int); filter_by_min_filesize(min_kb int), transformation_to_leetspeak; transformation_crude_spellchecker(language model str) 0.2.1, None, transformation_remove_stopwords(stopwords_source str; stopwords str [en]; spacy_model str; custom_stopwords str); transformation_remove_htmltags 0.3.0, None, transformation_remove_weird_tokens(spaCy model; remove_double_space=False); transformation_lemmatizer(spaCy model)

Quickstart

Install TextDirectory via pip: pip install textdirectory

TextDirectory, as exemplified below, works with a two-stage model. After loading in your data (directory) you can iteratively select the files you want to process. In a second step you can perform transformations on the text before finally aggregating it.

.. image:: https://user-images.githubusercontent.com/16179317/39367589-7f774116-4a37-11e8-9a09-5cbdf5f3311b.png :alt: TextDirectory

As a Command-Line Tool

*TextDirectory* comes equipped with a CLI.

The syntax for both the *filters* and *tranformations* works similarly. They are chained by adding slashes (/) and
parameters are passed via commas (,): ``filter_by_min_tokens,5/filter_by_random_sampling,2``.

**Example 1: A Very Simple Aggregation**

``textdirectory --directory testdata --output_file aggregated.txt``

This will take all files (.txt) in *testdata* and then aggregates the files into a file called *aggregated.txt*.

**Example 2: Applying Filters and Transformations**

In this example we want to filter the files based on their token count, perform a random sampling and finally transform all text to lowercase.

``textdirectory --directory testdata --output_file aggregated.txt --filters filter_by_min_tokens,5/filter_by_random_sampling,2 --transformations transformation_lowercase``

After passing two filters (*filter_by_min_tokens* and *filter_by_random_sampling*) we've applied the *transform_lowercase* transformation.

The resulting file will contain the content of two files that each have at least five tokens.

As a Python Library
~~~~~~~~~~~~~~~~~~~
In order to demonstrate *TextDirectory* as a Python library, we'll recreate the second example from above:

.. code:: python

    import textdirectory
    td = textdirectory.TextDirectory(directory='testdata')
    td.load_files(recursive=False, filetype='txt', sort=True)
    td.filter_by_min_tokens(5)
    td.filter_by_random_sampling(2)
    td.stage_transformation(['transformation_lowercase'])
    td.aggregate_to_file('aggregated.txt')

If we wanted to keep working with the actual aggregated text, we could have called ``text = td.aggregate_to_memory()``.

Every applied filter will create a *state* (i.e. a checkpoint). If we want to go back to a previous state, we can print
all states by calling ``td.print_saved_states()``. Previous states can then be loaded by
calling ``td.load_aggregation_state(state=0)``.


It's also possible to pass arguments to the individual transformations. In order to do this (at the moment) you have to adhere to the correct order of arguments.

.. code:: python

    # def transformation_remove_stopwords(text, stopwords_source='internal', stopwords='en', spacy_model='en_core_web_sm', custom_stopwords=None, *args)
    td.stage_transformation(['transformation_remove_stopwords', 'internal', 'en', 'en_core_web_sm', 'dolor'])

In the above example, we are adding additional custom stopwords to the transformer.

Notes for Developers
====================
If you want to run tests, please use `python setup.py test`.

To-Do
=======
* Increasing test coverage
* Writing better documentation
* Adding better error handling (raw exception are, well ...)
* Adding logging
* Better handling of non-unicode files (e.g. by detecting and reporting the encoding)
* Contemplating whether it makes sense to stage filters similarly to transformations
* Allowing users to pass keyword arguments to transformers
* Implementing autodoc (via Sphinx)

Behaviour
=========
We are not holding the actual texts in memory. This leads to much more disk read activity (and time inefficiency), but
saves memory.

``transformation_usas_en_semtag`` relies on the web versionof `Paul Rayson's USAS Tagger
<http://ucrel.lancs.ac.uk/usas/>`_. Don't use this transformation for large amounts of text, give credit, and
consider using their commercial product `Wmatrix <http://ucrel.lancs.ac.uk/wmatrix/>`_.

Credits
=======
This package is based on the `audreyr/cookiecutter-pypackage`_ coockiecutter template. The *crude spellchecker*
(transformation) is implemented following Peter Norvig's excellent `tutorial`_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _`tutorial`: http://norvig.com/spell-correct.html


=======
History
=======

0.1.0 (2018-04-26)
------------------

* Initial release
* First release on PyPI.

0.1.1 (2018-04-27)
------------------

* added filter_by_chars_outliers
* added transformation_remove_nl

0.1.2 (2018-04-29)
------------------
* added transformation_postag
* added transformation_usas_en_semtag
* added transformation_uppercase
* added filter_by_filename_contains
* added parameter support for transformations

0.1.3 (2018-04-30)
------------------
* filter_by_random_sampling now has a "replacement" option
* changed from tabulate to an embedded function
* added transformation_remove_non_ascii
* added transformation_remove_non_alphanumerical
* added filter_by_similar_documents

0.1.4 (2018-04-02)
------------------
* fixed an object mutation problem in the tabulate function

0.2.0 (2018-05-13)
------------------
* added transform_to_memory() function
* added transformation_to_leetspeak() function
* added transformation_crude_spellchecker
* added filter_by_max_filesize
* added filter_by_min_filesize
* fixed a bug where load_files() would fail if there were no files

0.2.1 (2019-06-13)
------------------
added transformation_remove_stopwords
added transformation_remove_htmltags
fixed some minor bugs

0.2.2 (2019-06-13)
------------------
changed the data packaging

0.3.0 (2020-01-19)
------------------
* added transformation_remove_weird_tokens
* added transformation_lemmatizer
* fixed some minor bugs
* added a function to revert applied filters
* added a function that prints the current pipeline
* added a function that clears all transformations
* added helper functions to list available filters and transformations
* fixed a bug in which ``tabulate_flat_list_of_dicts`` would fail if the dictionary was empty
* ``self.aggregation`` does not hold a copy of the files anymore but references to ``self.files``
* transformations relying on spaCy are now estimating a max_length based on available memory
* TextDirectory objects are now iterable

0.3.1 (2020-01-20)
------------------
* added long_description_content_type to setup.py