README

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SuPyLearner
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An implementation of the SuperLearner algorithm in Python built on scikit-learn.  

Typical useage:

    import supylearner as sl
    from sklearn import datasets, svm, linear_model, neighbors, svm
    import numpy as np

    # generate dataset
    np.random.seed(100)
    X, y = datasets.make_friedman1(1000)

    ols = linear_model.LinearRegression()
    elnet = linear_model.ElasticNetCV(l1_ratio = .1)
    ridge = linear_model.RidgeCV()
    lars = linear_model.LarsCV()
    lasso = linear_model.LassoCV()
    nn = neighbors.KNeighborsRegressor()
    svm1 = svm.SVR(kernel = 'rbf') 
    svm2 = svm.SVR(kernel = 'poly')
    lib = [ols, elnet, ridge,lars, lasso, nn, svm1, svm2]
    libnames = ["OLS", "ElasticNet", "Ridge", "LARS", "LASSO", "kNN", "SVM rbf", "SVM poly"]

    sl_inst = sl.SuperLearner(lib, libnames, loss = "L2")

    sl_inst.fit(X, y)

    sl_inst.summarize()

    sl.cv_superlearner(sl_inst, X, y, K = 5)