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        "author": "Mason Gallo",
        "author_email": "masongallo@gatech.edu",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "sk-modelcurves\n==============\n\nA Python wrapper built for software engineers and researchers to facilitate\neasy creation of learning and validation curve plots from scikit-learn. \n\nThe module is meant to complement your workflow in scikit-learn and ease the\nprocess of evaluating your models. \n\nThe module includes many quality of life features that should save you precious\ntime whenever you want to plot a learning curve to check for bias/variance or \nplot a validation curve to see the effect of tuning a hyperparameter.\n\n\nBackground\n==========\n\nFor those not familiar with learning curves, check out Andrew Ng's excellent \ndiscussion of their use at http://cs229.stanford.edu/materials/ML-advice.pdf\n\nOver the process of writing many research papers and building many models, I\nfound myself using boilerplate code that I would copy paste for almost every\nproject whenever I wanted to plot a learning curve or validation curve to\nevaluate models.\n\nHopefully, this module will save you a few minutes each time you need to plot\na learning or validation curve so you can focus on other things.\n\n\nInstall\n=======\n\nPython's pip is the recommended method of installation. From the terminal::\n\n   $ pip install sk_modelcurves\n\n\n\nExample Usage\n=============\n\nGenerate a learning curve using accuracy as a metric and 5-fold cross validation.\n\nAssumes a sklearn estimator called knn, training data matrix called X and\ntraining labels called y::\n\n   $ from sk_modelcurves.learning_curve import draw_learning_curve\n   $ draw_learning_curve(knn, X, y, scoring='accuracy', cv=5)\n   $ plt.show()\n\nGenerate multiple learning curves for several estimators using F1 score as a \nmetric, 5-fold cross validation, and names for each of the estimators.\n\nAssumes 3 sklearn estimators called knn2, knn20, knn40, training data matrix\ncalled X and training labels called y::\n\n   $ from sk_modelcurves.learning_curve import draw_learning_curve\n   $ draw_learning_curve([knn2, knn20, knn40], X, y, scoring='f1', cv=5,\n     estimator_titles=['2 Neighbors', '20 Neighbors', '40 Neighbors'])\n   $ plt.show()\n\nMany other options are available. Check out the source code docstrings or the\nupcoming documentation.\n\n\nImportant Links\n===============\n\n- Official source code repo: https://github.com/MasonGallo/sk-modelcurve\n- HTML documentation: coming soon!\n- Issue tracker: https://github.com/MasonGallo/sk-modelcurve/issues\n\n\nDependencies\n============\n\nsk-modelcurves is tested to work for Python 2.6 and Python 2.7. Python 3.3+ has\nnot been tested and is assumed to not work until tested.\n\nThe required dependencies include scikit-learn (of course!), numpy >= 1.6.1,\nand matplotlib >= 1.1.1.\n\nTo run tests, you will need nose >= 1.1.2.\n\n\nContributing\n============\n\nAnyone is welcome!\n\nIf you find a bug or would like to discuss a potential feature, please file an\nissue first.\n\n\nTesting\n=======\n\nAfter installation, you can launch the test suite from outside the source \ndirectory (you will need to have the ``nose`` package installed)::\n\n   $ nosetests -v sk_modelcurves\n\n",
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