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))