Kernel SGD
==========

Usage
-----

The kernel sgd module can be imported as:

.. code-block:: python

    import sklearn_extensions as ske

    mdl = ske.kernel_sgd.KernelSGD()
    mdl.fit_predict(X, y)

It also comes along with two helper classes:

.. code-block:: python

    ske.kernel_sgd.HingeLoss()
    ske.kernel_sgd.GaussianKernel()

Which can be used to create KernelSGD models, or you can bring your own loss and kernel classes.

Examples
--------

.. code-block:: python

    from sklearn_extensions.kernel_sgd.kernel_sgd import GaussianKernel, KernelSGD, HingeLoss
    import numpy as np


    def gen_non_lin_separable_data():
        mean1 = [-1, 2]
        mean2 = [1, -1]
        mean3 = [4, -4]
        mean4 = [-4, 4]
        cov = [[1.0,0.8], [0.8, 1.0]]
        X1 = np.random.multivariate_normal(mean1, cov, 50)
        X1 = np.vstack((X1, np.random.multivariate_normal(mean3, cov, 50)))
        y1 = np.ones(len(X1))
        X2 = np.random.multivariate_normal(mean2, cov, 50)
        X2 = np.vstack((X2, np.random.multivariate_normal(mean4, cov, 50)))
        y2 = np.ones(len(X2)) * -1
        return X1, y1, X2, y2


    def split_train(X1, y1, X2, y2):
        X1_train = X1[:90]
        y1_train = y1[:90]
        X2_train = X2[:90]
        y2_train = y2[:90]
        X_train = np.vstack((X1_train, X2_train))
        y_train = np.hstack((y1_train, y2_train))
        return X_train, y_train


    def split_test(X1, y1, X2, y2):
        X1_test = X1[90:]
        y1_test = y1[90:]
        X2_test = X2[90:]
        y2_test = y2[90:]
        X_test = np.vstack((X1_test, X2_test))
        y_test = np.hstack((y1_test, y2_test))
        return X_test, y_test


    def test():
        X1, y1, X2, y2 = gen_non_lin_separable_data()
        X_train, y_train = split_train(X1, y1, X2, y2)
        X_test, y_test = split_test(X1, y1, X2, y2)

        clf = KernelSGD(kernel=GaussianKernel(), loss=HingeLoss(threshold=0), n_iter=5)
        clf.fit(X_train, y_train)

        y_predict = clf.predict(X_test)
        correct = np.sum(y_predict == y_test)
        print("%d out of %d predictions correct" % (correct, len(y_predict)))

    test()

Which yields the output:

.. code-block:: python

    8 support vectors out of 180 points
    20 out of 20 predictions correct


Third Party Docs
----------------

The original unmodified version of this module's code if from a gist that can be found here: `Kernel SGD <https://gist.github.com/mblondel/2573392>`_