def test_equality(self):
self.assertNotEqual(self.sample, self.sample_cl)
meta = [NumericAttribute(), NominalAttribute(animals)]
sample = Sample([1.2, meta[1].get_idx("dog")], meta)
self.assertEqual(self.sample, sample)
self.assertNotEqual(self.sample, self.sample_cl)
meta = [NumericAttribute(), NominalAttribute(animals), NumericAttribute()]
sample = Sample([1.2, meta[1].get_idx("dog"), 3.14], meta)
self.assertNotEqual(self.sample, sample)
self.assertNotEqual(self.sample_cl, sample)
meta = [NumericAttribute(), NominalAttribute(animals)]
sample = Sample([1.2, meta[1].get_idx("cat")], meta)
self.assertNotEqual(self.sample, sample)
self.assertNotEqual(self.sample_cl, sample)
sample = Sample([1.3, meta[1].get_idx("dog")], meta)
self.assertNotEqual(self.sample, sample)
self.assertNotEqual(self.sample_cl, sample)
sample = Sample([100, self.meta[1].get_idx("cat")], self.meta,
self.meta_cl.get_idx("duck"), self.meta_cl)
self.assertEqual(self.sample_cl, sample)
self.assertNotEqual(self.sample, sample)
sample = Sample([10.20, self.meta[1].get_idx("cat")], self.meta,
self.meta_cl.get_idx("duck"), self.meta_cl)
self.assertNotEqual(self.sample, sample)
self.assertNotEqual(self.sample_cl, sample)
def test_equality(self):
self.assertEqual(self.na, NominalAttribute(animals))
self.assertEqual(self.na,
NominalAttribute(["dog", "cat", "lion", "duck", "python:)"]))
self.assertNotEqual(self.na, NominalAttribute(animals + ["donkey"]))
self.assertEqual(NumericAttribute(), NumericAttribute())
self.assertNotEqual(self.na, NumericAttribute())
def test_classification(self):
cls_meta = NominalAttribute([0, 1])
meta = [NumericAttribute() for _ in xrange(3)]
train_set = [
Sample([0, 0, 0], meta, 0, cls_meta),
Sample([0, 1, 0], meta, 0, cls_meta),
Sample([0, 0, 1], meta, 0, cls_meta),
Sample([3, 0, 0], meta, 1, cls_meta),
Sample([3, 1, 0], meta, 1, cls_meta),
Sample([3, 0, 1], meta, 1, cls_meta),
]
classifier = self.knn1
# classifier = self.tree
classifier.train(train_set)
expected = [str(x) for x in [0, 0, 0, 1, 1, 1, 0, 1]]
samples = [s for s in train_set]
samples.extend([Sample([1, 0, 0], meta), Sample([2, 1, 0], meta)])
for e, s in izip(expected, samples):
self.assertEqual(e, classifier.classify(s))
k, p = classifier.classify_pval(s)
self.assertTrue(0. <= p <= 1.)
self.assertEqual(k, classifier.classify(s))
p2 = classifier.class_probabilities(s)
self.assertAlmostEqual(1., sum(p2.values()), delta=0.00001)
def test_loading_arff(self):
expected_meta = [NumericAttribute() for _ in xrange(4)]
expected_cls_meta = NominalAttribute(
["Iris-setosa", "Iris-versicolor", "Iris-virginica"])
sample = self.samples[0]
self.assertEqual(sample.meta, expected_meta)
self.assertEqual(sample.cls_meta, expected_cls_meta)
def setUp(self):
self.classifiers = [("BayesLearner", [], {}), ("TreeLearner", [], {}),
("kNNLearner", [], {
"k": 1
}), ("kNNLearner", [], {
"k": 3
}), ("TreeLearner", [], {})]
self.knn1 = OrangeClassifier(self.classifiers[2][0])
self.knn3 = OrangeClassifier(self.classifiers[3][0])
self.tree = OrangeClassifier(self.classifiers[4][0])
self.cls_meta = NominalAttribute([0, 1])
self.meta = [NumericAttribute() for _ in xrange(3)]
self.train_set = [
Sample([0, 0, 0], self.meta, 0, self.cls_meta),
Sample([0, 1, 0], self.meta, 0, self.cls_meta),
Sample([0, 0, 1], self.meta, 0, self.cls_meta),
Sample([3, 0, 0], self.meta, 1, self.cls_meta),
Sample([3, 1, 0], self.meta, 1, self.cls_meta),
Sample([3, 0, 1], self.meta, 1, self.cls_meta),
]
def setUp(self):
self.meta = [NumericAttribute(), NominalAttribute(animals)]
self.sample = Sample([1.2, self.meta[1].get_idx("dog")], self.meta)
self.meta_cl = NominalAttribute(animals)
self.sample_cl = Sample([100, self.meta[1].get_idx("cat")], self.meta,
self.meta_cl.get_idx("duck"), self.meta_cl)
def test_numeric_attr_getting_value(self):
na = NumericAttribute()
for i in xrange(10):
self.assertEqual(i, na.get_value(i))
def simple_meta_attrs(class_attrs=[0, 1]):
return [NumericAttribute(), NominalAttribute(list(class_attrs))]