Feature Forge

This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, etc.), and particularly helpful if you use scikit-learn (although this can work if you have a different algorithm).

Most machine learning problems involve an step of feature definition and preprocessing. Feature Forge helps you with:

• Defining and documenting features
• Testing your features against specified cases and against randomly generated
  cases (stress-testing). This helps you making your application more robust
  against invalid/misformatted input data. This also helps you checking that
  low-relevance results when doing feature analysis is actually because the
  feature is bad, and not because there's a slight bug in your feature code.
• Evaluating your features on a data set, producing a feature evaluation
  matrix. The evaluator has a robust mode that allows you some tolerance both
  for invalid data and buggy features.
• Experimentation: running, registering, classifying and reproducing
  experiments for determining best settings for your problems.

Installation

Just pip install featureforge.

Documentation

Documentation is available at http://feature-forge.readthedocs.org/en/latest/

Contact information

Feature Forge is copyright 2014 Machinalis (http://www.machinalis.com/). Its primary authors are:

• Javier Mansilla [email protected] (jmansilla at github)
• Daniel Moisset [email protected] (dmoisset at github)
• Rafael Carrascosa [email protected] (rafacarrascosa at github)

Any contributions or suggestions are welcome, the official channel for this is submitting github pull requests or issues.

Changelog

0.1.7: - StatsManager api change (order of arguments swapped) - For experimentation, enabled a way of booking experiments forever.

0.1.6: - Bug fixes related to sparse matrices. - Small documentation improvements. - Reduced default logging verbosity.

0.1.5: - Using sparse numpy matrices by default.

0.1.4: - Discarded the need of using forked version of Schema library.

0.1.3: - Added support for running and generating stats for experiments

0.1.2: - Fixing installer dependencies

0.1.1: - Added support for python 3 - Added support for bag-of-words features

0.1: - Initial release