sk_modelcurves 0.4

sk-modelcurves

A Python wrapper built for software engineers and researchers to facilitate easy creation of learning and validation curve plots from scikit-learn.

The module is meant to complement your workflow in scikit-learn and ease the process of evaluating your models.

The module includes many quality of life features that should save you precious time whenever you want to plot a learning curve to check for bias/variance or plot a validation curve to see the effect of tuning a hyperparameter.

Background

For those not familiar with learning curves, check out Andrew Ng’s excellent discussion of their use at http://cs229.stanford.edu/materials/ML-advice.pdf

Over the process of writing many research papers and building many models, I found myself using boilerplate code that I would copy paste for almost every project whenever I wanted to plot a learning curve or validation curve to evaluate models.

Hopefully, this module will save you a few minutes each time you need to plot a learning or validation curve so you can focus on other things.

Install

Python’s pip is the recommended method of installation. From the terminal:

$ pip install sk_modelcurves

Example Usage

Generate a learning curve using accuracy as a metric and 5-fold cross validation.

Assumes a sklearn estimator called knn, training data matrix called X and training labels called y:

$ from sk_modelcurves.learning_curve import draw_learning_curve
$ draw_learning_curve(knn, X, y, scoring='accuracy', cv=5)
$ plt.show()

Generate multiple learning curves for several estimators using F1 score as a metric, 5-fold cross validation, and names for each of the estimators.

Assumes 3 sklearn estimators called knn2, knn20, knn40, training data matrix called X and training labels called y:

$ from sk_modelcurves.learning_curve import draw_learning_curve
$ draw_learning_curve([knn2, knn20, knn40], X, y, scoring='f1', cv=5,
  estimator_titles=['2 Neighbors', '20 Neighbors', '40 Neighbors'])
$ plt.show()

Many other options are available. Check out the source code docstrings or the upcoming documentation.

Important Links

• Official source code repo: https://github.com/MasonGallo/sk-modelcurve
• HTML documentation: coming soon!
• Issue tracker: https://github.com/MasonGallo/sk-modelcurve/issues

Dependencies

sk-modelcurves is tested to work for Python 2.6 and Python 2.7. Python 3.3+ has not been tested and is assumed to not work until tested.

The required dependencies include scikit-learn (of course!), numpy >= 1.6.1, and matplotlib >= 1.1.1.

To run tests, you will need nose >= 1.1.2.

Contributing

Anyone is welcome!

If you find a bug or would like to discuss a potential feature, please file an issue first.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):

$ nosetests -v sk_modelcurves