Metadata-Version: 1.1
Name: sk_modelcurves
Version: 0.2
Summary: A wrapper for easy plots of learning and validation curves
Home-page: http://github.com/masongallo/sk-modelcurve
Author: Mason Gallo
Author-email: masongallo@gatech.edu
License: MIT
Description: 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
Keywords: sk_modelcurves learning curves validation curves
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
