plot_kernel_approximation.txt

.. _sphx_glr_auto_examples_plot_kernel_approximation.py:


==================================================
Explicit feature map approximation for RBF kernels
==================================================

An example illustrating the approximation of the feature map
of an RBF kernel.

.. currentmodule:: sklearn.kernel_approximation

It shows how to use :class:`RBFSampler` and :class:`Nystroem` to
approximate the feature map of an RBF kernel for classification with an SVM on
the digits dataset. Results using a linear SVM in the original space, a linear
SVM using the approximate mappings and using a kernelized SVM are compared.
Timings and accuracy for varying amounts of Monte Carlo samplings (in the case
of :class:`RBFSampler`, which uses random Fourier features) and different sized
subsets of the training set (for :class:`Nystroem`) for the approximate mapping
are shown.

Please note that the dataset here is not large enough to show the benefits
of kernel approximation, as the exact SVM is still reasonably fast.

Sampling more dimensions clearly leads to better classification results, but
comes at a greater cost. This means there is a tradeoff between runtime and
accuracy, given by the parameter n_components. Note that solving the Linear
SVM and also the approximate kernel SVM could be greatly accelerated by using
stochastic gradient descent via :class:`sklearn.linear_model.SGDClassifier`.
This is not easily possible for the case of the kernelized SVM.

The second plot visualized the decision surfaces of the RBF kernel SVM and
the linear SVM with approximate kernel maps.
The plot shows decision surfaces of the classifiers projected onto
the first two principal components of the data. This visualization should
be taken with a grain of salt since it is just an interesting slice through
the decision surface in 64 dimensions. In particular note that
a datapoint (represented as a dot) does not necessarily be classified
into the region it is lying in, since it will not lie on the plane
that the first two principal components span.

The usage of :class:`RBFSampler` and :class:`Nystroem` is described in detail
in :ref:`kernel_approximation`.




.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_kernel_approximation_001.png
            :scale: 47

    *

      .. image:: /auto_examples/images/sphx_glr_plot_kernel_approximation_002.png
            :scale: 47





.. code-block:: python

    print(__doc__)

    # Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
    #         Andreas Mueller <amueller@ais.uni-bonn.de>
    # License: BSD 3 clause

    # Standard scientific Python imports
    import matplotlib.pyplot as plt
    import numpy as np
    from time import time

    # Import datasets, classifiers and performance metrics
    from sklearn import datasets, svm, pipeline
    from sklearn.kernel_approximation import (RBFSampler,
                                              Nystroem)
    from sklearn.decomposition import PCA

    # The digits dataset
    digits = datasets.load_digits(n_class=9)

    # To apply an classifier on this data, we need to flatten the image, to
    # turn the data in a (samples, feature) matrix:
    n_samples = len(digits.data)
    data = digits.data / 16.
    data -= data.mean(axis=0)

    # We learn the digits on the first half of the digits
    data_train, targets_train = (data[:n_samples // 2],
                                 digits.target[:n_samples // 2])


    # Now predict the value of the digit on the second half:
    data_test, targets_test = (data[n_samples // 2:],
                               digits.target[n_samples // 2:])
    # data_test = scaler.transform(data_test)

    # Create a classifier: a support vector classifier
    kernel_svm = svm.SVC(gamma=.2)
    linear_svm = svm.LinearSVC()

    # create pipeline from kernel approximation
    # and linear svm
    feature_map_fourier = RBFSampler(gamma=.2, random_state=1)
    feature_map_nystroem = Nystroem(gamma=.2, random_state=1)
    fourier_approx_svm = pipeline.Pipeline([("feature_map", feature_map_fourier),
                                            ("svm", svm.LinearSVC())])

    nystroem_approx_svm = pipeline.Pipeline([("feature_map", feature_map_nystroem),
                                            ("svm", svm.LinearSVC())])

    # fit and predict using linear and kernel svm:

    kernel_svm_time = time()
    kernel_svm.fit(data_train, targets_train)
    kernel_svm_score = kernel_svm.score(data_test, targets_test)
    kernel_svm_time = time() - kernel_svm_time

    linear_svm_time = time()
    linear_svm.fit(data_train, targets_train)
    linear_svm_score = linear_svm.score(data_test, targets_test)
    linear_svm_time = time() - linear_svm_time

    sample_sizes = 30 * np.arange(1, 10)
    fourier_scores = []
    nystroem_scores = []
    fourier_times = []
    nystroem_times = []

    for D in sample_sizes:
        fourier_approx_svm.set_params(feature_map__n_components=D)
        nystroem_approx_svm.set_params(feature_map__n_components=D)
        start = time()
        nystroem_approx_svm.fit(data_train, targets_train)
        nystroem_times.append(time() - start)

        start = time()
        fourier_approx_svm.fit(data_train, targets_train)
        fourier_times.append(time() - start)

        fourier_score = fourier_approx_svm.score(data_test, targets_test)
        nystroem_score = nystroem_approx_svm.score(data_test, targets_test)
        nystroem_scores.append(nystroem_score)
        fourier_scores.append(fourier_score)

    # plot the results:
    plt.figure(figsize=(8, 8))
    accuracy = plt.subplot(211)
    # second y axis for timeings
    timescale = plt.subplot(212)

    accuracy.plot(sample_sizes, nystroem_scores, label="Nystroem approx. kernel")
    timescale.plot(sample_sizes, nystroem_times, '--',
                   label='Nystroem approx. kernel')

    accuracy.plot(sample_sizes, fourier_scores, label="Fourier approx. kernel")
    timescale.plot(sample_sizes, fourier_times, '--',
                   label='Fourier approx. kernel')

    # horizontal lines for exact rbf and linear kernels:
    accuracy.plot([sample_sizes[0], sample_sizes[-1]],
                  [linear_svm_score, linear_svm_score], label="linear svm")
    timescale.plot([sample_sizes[0], sample_sizes[-1]],
                   [linear_svm_time, linear_svm_time], '--', label='linear svm')

    accuracy.plot([sample_sizes[0], sample_sizes[-1]],
                  [kernel_svm_score, kernel_svm_score], label="rbf svm")
    timescale.plot([sample_sizes[0], sample_sizes[-1]],
                   [kernel_svm_time, kernel_svm_time], '--', label='rbf svm')

    # vertical line for dataset dimensionality = 64
    accuracy.plot([64, 64], [0.7, 1], label="n_features")

    # legends and labels
    accuracy.set_title("Classification accuracy")
    timescale.set_title("Training times")
    accuracy.set_xlim(sample_sizes[0], sample_sizes[-1])
    accuracy.set_xticks(())
    accuracy.set_ylim(np.min(fourier_scores), 1)
    timescale.set_xlabel("Sampling steps = transformed feature dimension")
    accuracy.set_ylabel("Classification accuracy")
    timescale.set_ylabel("Training time in seconds")
    accuracy.legend(loc='best')
    timescale.legend(loc='best')

    # visualize the decision surface, projected down to the first
    # two principal components of the dataset
    pca = PCA(n_components=8).fit(data_train)

    X = pca.transform(data_train)

    # Generate grid along first two principal components
    multiples = np.arange(-2, 2, 0.1)
    # steps along first component
    first = multiples[:, np.newaxis] * pca.components_[0, :]
    # steps along second component
    second = multiples[:, np.newaxis] * pca.components_[1, :]
    # combine
    grid = first[np.newaxis, :, :] + second[:, np.newaxis, :]
    flat_grid = grid.reshape(-1, data.shape[1])

    # title for the plots
    titles = ['SVC with rbf kernel',
              'SVC (linear kernel)\n with Fourier rbf feature map\n'
              'n_components=100',
              'SVC (linear kernel)\n with Nystroem rbf feature map\n'
              'n_components=100']

    plt.tight_layout()
    plt.figure(figsize=(12, 5))

    # predict and plot
    for i, clf in enumerate((kernel_svm, nystroem_approx_svm,
                             fourier_approx_svm)):
        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max]x[y_min, y_max].
        plt.subplot(1, 3, i + 1)
        Z = clf.predict(flat_grid)

        # Put the result into a color plot
        Z = Z.reshape(grid.shape[:-1])
        plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)
        plt.axis('off')

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired)

        plt.title(titles[i])
    plt.tight_layout()
    plt.show()

**Total running time of the script:**
(0 minutes 2.322 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_kernel_approximation.py <plot_kernel_approximation.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_kernel_approximation.ipynb <plot_kernel_approximation.ipynb>`