There are a large number of machine learning tools for effectively exploring and working with data that is given as vectors (ideally with a defined notion of distance as well). There is also a large volume of data that does not come neatly packaged as vectors. It could be text data, variable length sequence data (either numeric or categorical), dataframes of mixed data types, sets of point clouds, or more. Usually, one way or another, such data can be wrangled into vectors in a way that preserves some relevant properties of the original data. This library seeks to provide a suite of a wide variety of general purpose techniques for such wrangling, making it easier and faster for users to get various kinds of unstructured sequence data into vector formats for exploration and machine learning.
Why use Vectorizers?
Data wrangling can be tedious, error-prone, and fragile when trying to integrate it into production pipelines. The vectorizers library aims to provide a set of easy to use tools for turning various kinds of unstructured sequence data into vectors. By following the scikit-learn transformer API we ensure that any of the vectorizer classes can be trivially integrated into existing sklearn workflows or pipelines. By keeping the vectorization approaches as general as possible (as opposed to specialising on very specific data types), we aim to ensure that a very broad range of data can be handled efficiently. Finally we aim to provide robust techniques with sound mathematical foundations over potentially more powerful but black-box approaches for greater transparency in data processing and transformation.
How to use Vectorizers
Quick start examples to be added soon …
For further examples on using this library for text we recommend checking out the documentation written up in the EasyData reproducible data science framework by some of our colleagues over at: https://github.com/hackalog/vectorizers_playground
Vectorizers is designed to be easy to install being a pure python module with relatively light requirements:
- scikit-learn >= 0.22
- numba >= 0.51
In the near future the package should be pip installable — check back for updates:
pip install vectorizers
To manually install this package:
wget https://github.com/TutteInstitute/vectorizers/archive/master.zip unzip master.zip rm master.zip cd vectorizers-master python setup.py install
Help and Support
This project is still young. The documentation is still growing. In the meantime please open an issue and we will try to provide any help and guidance that we can. Please also check the docstrings on the code, which provide some descriptions of the parameters.
Contributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don’t feel you can’t contribute. We would greatly appreciate the contribution of tutorial notebooks applying vectorizer tools to diverse or interesting datasets. If you find vectorizers useful for your data please consider contributing an example showing how it can apply to the kind of data you work with!
To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
The vectorizers package is 2-clause BSD licensed.