Source code for pySPACE.tests.unittests.nodes.classification.test_linear_classifier

#!/usr/bin/python

"""
This module contains unittests which test linear classifier nodes.

:Author: Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
:Created: 2008/08/26
"""
import numpy

import unittest

if __name__ == '__main__':
    import sys
    import os
    # The root of the code
    file_path = os.path.dirname(os.path.abspath(__file__))
    sys.path.append(file_path[:file_path.rfind('pySPACE') - 1])

from pySPACE.missions.nodes.classification.linear_classifier import FDAClassifierNode
from pySPACE.resources.data_types.feature_vector import FeatureVector


class FDAClassifierTestCase(unittest.TestCase):

    """ unittest for FDAClassifierNode """

    def setUp(self):
        """ Generate separable training data """
        # to avoid the very unlikely case that the data is not
        # linearly separable
        numpy.random.seed(0)
        self.x_a = numpy.random.multivariate_normal(numpy.array([0.0, 0.0]),
                                                    numpy.array([[1.0, 1.0],
                                                                 [1.0, 0.0]]),
                                                    200)
        self.x_b = numpy.random.multivariate_normal(numpy.array([10.0, 10.0]),
                                                    numpy.array([[1.0, 1.0],
                                                                 [1.0, 0.0]]),
                                                    200)

    def test_fda(self):
        """ Train FDA and test on training data """
        fda_node = FDAClassifierNode(
        )  # (generalized) Fisher Discriminant Analysis
        for x in self.x_b:
            fda_node.train(FeatureVector(x), 'b')
        for x in self.x_a:
            fda_node.train(FeatureVector(x), 'a')
        fda_node.stop_training()

        # for calling execute we need FeatureVectors since meta data is handled
        # there
        self.x_a = [FeatureVector(numpy.atleast_2d(elem)) for elem in self.x_a]
        self.x_b = [FeatureVector(numpy.atleast_2d(elem)) for elem in self.x_b]
        classification_a = [fda_node.execute(fv).label for fv in self.x_a]
        classification_b = [fda_node.execute(fv).label for fv in self.x_b]

        self.assert_(numpy.alltrue(map(lambda x: x == 'a', classification_a)))
        self.assert_(numpy.alltrue(map(lambda x: x == 'b', classification_b)))

if __name__ == '__main__':
    suite = unittest.TestLoader().loadTestsFromName('test_linear_classifier')
    unittest.TextTestRunner(verbosity=2).run(suite)