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])
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 = simple_meta_attrs(['-', '+'])
self.cs = lambda i, v: Sample(
[i, self.meta[1].set_value(v)], self.meta, last_is_class=True)
self.classifier = OrangeClassifier('kNNLearner', k=1)
test_samples = '+++-++-+-+--+---'
N = len(test_samples)
train_samples = ('+' * (N / 2)) + ('-' * (N / 2))
self.test_samples, self.train_samples = ([
self.cs(i, v) for i, v in enumerate(samples)
] for samples in [test_samples, train_samples])
random.shuffle(self.test_samples)
self.classifier.train(self.train_samples)
def test_classifier_creation(self):
""" Proper classifier creation """
for (c, args, kargs) in self.classifiers:
classifier = OrangeClassifier(c, *args, **kargs)
self.assertEqual(getattr(orange, c), type(classifier.classifier))
