def test_linear_classifier_weights_on(self, dataset):
# Test get_linear_svm_weights
classifier = self.LEARNER(dataset)
weights = get_linear_svm_weights(classifier, sum=True)
weights = get_linear_svm_weights(classifier, sum=False)
n_class = len(classifier.class_var.values)
def class_pairs(n_class):
for i in range(n_class - 1):
for j in range(i + 1, n_class):
yield i, j
l_map = classifier._get_libsvm_labels_map()
for inst in dataset[:20]:
dec_values = classifier.get_decision_values(inst)
for dec_v, weight, rho, pair in zip(dec_values, weights,
classifier.rho,
class_pairs(n_class)):
t_inst = Orange.data.Instance(classifier.domain, inst)
dec_v1 = example_weighted_sum(t_inst, weight) - rho
self.assertAlmostEqual(dec_v, dec_v1, 4)
def test_linear_regression_weights_on(self, dataset):
predictor = self.LEARNER(dataset)
weights = get_linear_svm_weights(predictor)
for inst in dataset[:20]:
t_inst = Orange.data.Instance(predictor.domain, inst)
prediction = predictor(inst)
w_sum = example_weighted_sum(t_inst, weights)
self.assertAlmostEqual(float(prediction), w_sum - predictor.rho[0], places=4)
def test_linear_regression_weights_on(self, dataset):
predictor = self.LEARNER(dataset)
weights = get_linear_svm_weights(predictor)
for inst in dataset[:20]:
t_inst = Orange.data.Instance(predictor.domain, inst)
prediction = predictor(inst)
w_sum = example_weighted_sum(t_inst, weights)
self.assertAlmostEqual(float(prediction),
w_sum - predictor.rho[0],
places=4)
def test_linear_classifier_weights_on(self, dataset):
# Test get_linear_svm_weights
classifier = self.LEARNER(dataset)
weights = get_linear_svm_weights(classifier, sum=True)
weights = get_linear_svm_weights(classifier, sum=False)
n_class = len(classifier.class_var.values)
def class_pairs(n_class):
for i in range(n_class - 1):
for j in range(i + 1, n_class):
yield i, j
l_map = classifier._get_libsvm_labels_map()
for inst in dataset[:20]:
dec_values = classifier.get_decision_values(inst)
for dec_v, weight, rho, pair in zip(dec_values, weights, classifier.rho, class_pairs(n_class)):
t_inst = Orange.data.Instance(classifier.domain, inst)
dec_v1 = example_weighted_sum(t_inst, weight) - rho
self.assertAlmostEqual(dec_v, dec_v1, 4)