def test_LinearSVM(self):
# This warning is irrelevant here
warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
learn = LinearSVMLearner()
res = CrossValidation(self.data, [learn], k=2)
self.assertGreater(CA(res)[0], 0.8)
self.assertLess(CA(res)[0], 0.9)
def test_LinearSVM(self):
n = int(0.7 * self.data.X.shape[0])
learn = LinearSVMLearner()
clf = learn(self.data[:n])
z = clf(self.data[n:])
self.assertTrue(
np.sum(z.reshape((-1, 1)) == self.data.Y[n:]) > 0.7 * len(z))
def test_reprs(self):
lr = LogisticRegressionLearner(tol=0.0002)
m = MajorityLearner()
nb = NaiveBayesLearner()
rf = RandomForestLearner(bootstrap=False, n_jobs=3)
st = SimpleTreeLearner(seed=1, bootstrap=True)
sm = SoftmaxRegressionLearner()
svm = SVMLearner(shrinking=False)
lsvm = LinearSVMLearner(tol=0.022, dual=False)
nsvm = NuSVMLearner(tol=0.003, cache_size=190)
osvm = OneClassSVMLearner(degree=2)
tl = TreeLearner(max_depth=3, min_samples_split=1)
knn = KNNLearner(n_neighbors=4)
el = EllipticEnvelopeLearner(store_precision=False)
srf = SimpleRandomForestLearner(n_estimators=20)
learners = [lr, m, nb, rf, st, sm, svm,
lsvm, nsvm, osvm, tl, knn, el, srf]
for l in learners:
repr_str = repr(l)
new_l = eval(repr_str)
self.assertEqual(repr(new_l), repr_str)
def test_LinearSVM(self):
learn = LinearSVMLearner()
res = CrossValidation(self.data, [learn], k=2)
self.assertGreater(CA(res)[0], 0.8)
self.assertLess(CA(res)[0], 0.9)
def test_LinearSVM(self):
learn = LinearSVMLearner()
res = Orange.evaluation.CrossValidation(self.data, [learn], k=2)
self.assertTrue(0.8 < Orange.evaluation.CA(res)[0] < 0.9)
print("##########TASK 1####################")
data_tab = Table('iris')
feature_vars = list(data_tab.domain[:-1])
class_label_var = data_tab.domain[len(data_tab.domain) - 1]
iris_domain = Domain(feature_vars, class_label_var)
data_tab = Table.from_table(domain=iris_domain, source=data_tab)
print("DOMAIN: %s \nVARIABLES: %s \nATTRIBUTES: %s \nCLASS_VAR: %s"
% (data_tab.domain, data_tab.domain.variables, data_tab.domain.attributes,
data_tab.domain.class_var))
print(len(data_tab))
print("###########TASK 2###################")
svm_learner = LinearSVMLearner()
#Accuracy of cross validation: 0.940
#AUC: 0.955
eval_results = CrossValidation(data_tab, [svm_learner], k=10)
print("Accuracy of cross validation: {:.3f}".format(scoring.CA(eval_results)[0]))
print("AUC: {:.3f}".format(scoring.AUC(eval_results)[0]))
print("###########TASK 3###################")
data_tab_2d = data_tab[:50, ['sepal width', 'sepal length', 'iris']]
data_tab_2d.extend(data_tab[100:, ['sepal width', 'sepal length', 'iris']])
learner = LinearSVMLearner()
results = learner(data_tab_2d)
area_x_min = np.min(data_tab_2d[:, 'sepal width']) - 0.2
area_x_max = np.max(data_tab_2d[:, 'sepal width']) + 0.2
area_y_min = np.min(data_tab_2d[:, 'sepal length']) - 0.2