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 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)