TextDirectory allows you to combine multiple text files into one. While doing this, filters and transformations can be applied.
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.
- 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)
|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)|
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.
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.
You could also use ‘*’ as a wildcard for filetype if you need to include all files and not just .txt.
textdirectory --directory testdata --output_file aggregated.txt --filetype *
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:
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 don’t have special requirements, we can also call td = textdirectory.TextDirectory(directory=’testdata’, autoload=True) to skip manually callin load_files. If we wanted to keep working with the actual aggregated text, we could have called text = td.aggregate_to_memory() instead of aggregate_to_file.
import textdirectory td = textdirectory.TextDirectory(directory='testdata', autoload=True) td.get_text(0)
Sometimes we might want to get the actual text of a given file. This can be achieved as seen above. The get_text method will return the transformed text if it is available. Otherwise, it will simply read the file and return the text.
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.
# 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.
- 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)
We are not holding the actual texts in memory. This leads to much more disk read activity (and time inefficiency), but saves memory. Of course, this is not the case when using aggregate_to_memory.
transformation_usas_en_semtag relies on the web version of Paul Rayson’s USAS Tagger. Don’t use this transformation for large amounts of text, give credit, and consider using their commercial product Wmatrix.
- Initial release
- First release on PyPI.
- added filter_by_chars_outliers
- added transformation_remove_nl
- added transformation_postag
- added transformation_usas_en_semtag
- added transformation_uppercase
- added filter_by_filename_contains
- added parameter support for transformations
- 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
- fixed an object mutation problem in the tabulate function
- 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
- added transformation_remove_stopwords
- added transformation_remove_htmltags
- fixed some minor bugs
- changed the data packaging
- 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
- added long_description_content_type to setup.py
- added transformation_expand_english_contractions
- fixed some minor bugs
- added __str__ method to TextDirectory
- added filename to __str__ output
- added get_text method
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