def testCalculateAccuracyMixedSamples(self): """ Tests testCalculateAccuracy() method of classification model base class for test samples with mixed classifications. """ model = ClassificationModel() actualLabels = [numpy.array([0, 1, 2])] predictedLabels1 = [numpy.array([1, 2, 0])] predictedLabels2 = [numpy.array([1])] predictedLabels3 = [None] classifications1 = [predictedLabels1, actualLabels] classifications2 = [predictedLabels2, actualLabels] classifications3 = [predictedLabels3, actualLabels] self.assertAlmostEqual(model.calculateAccuracy(classifications1), 1.0) self.assertAlmostEqual(model.calculateAccuracy(classifications2), float(1) / 3) self.assertAlmostEqual(model.calculateAccuracy(classifications3), 0.0)
def testCalculateAccuracyMultipleSamples(self): """ Tests testCalculateAccuracy() method of classification model base class for three test samples. """ model = ClassificationModel() actualLabels = [numpy.array([0]), numpy.array([0, 2]), numpy.array([0, 1, 2])] predictedLabels = [numpy.array([0]), [None], numpy.array([1, 2, 0])] classifications = [predictedLabels, actualLabels] self.assertAlmostEqual(model.calculateAccuracy(classifications), float(2) / 3)
def testCalculateAccuracyMultipleSamples(self): """ Tests testCalculateAccuracy() method of classification model base class for three test samples. """ model = ClassificationModel() actualLabels = [ numpy.array([0]), numpy.array([0, 2]), numpy.array([0, 1, 2]) ] predictedLabels = [numpy.array([0]), [None], numpy.array([1, 2, 0])] classifications = [predictedLabels, actualLabels] self.assertAlmostEqual(model.calculateAccuracy(classifications), float(2) / 3)