def test_precision_recall_and_f_calculation(self):
ede = ClusteringEvaluator()
targets = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 , 1, 2, 2, 2, 2, 2, 2]
predictions = [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1 , 2, 2, 2, 2, 2, 0, 2]
confusion_matrix = ede.create_confusion_matrix(targets, predictions, 3)
rp_rates = ede.calculate_precision_recall(confusion_matrix)
fs = ede.calculate_f_measure(rp_rates)
self.assertEquals(sum(fs), [2.5])
def test_confusion_matrix_creation(self):
ede = ClusteringEvaluator()
targets = [1, 1, 1, 1, 1 , 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2]
predictions = [1, 1, 1, 1, 1 , 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2]
confusion_matrix = ede.create_confusion_matrix(targets, predictions, 3)
expected = numpy.array([[ 6., 0., 0.],[ 0., 6., 0.],[ 0., 0., 6.]])
self.assertTrue(numpy.in1d((numpy.sum(confusion_matrix-expected, axis=1)), [0., 0., 0.]).all())
