from Orange import data
from Orange import evaluation
from Orange.classification.svm import SVMLearner, kernels
from Orange.distance import Euclidean
from Orange.distance import Hamming
iris = data.Table("iris.tab")
l1 = SVMLearner()
l1.kernel_func = kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5)
l1.kernel_type = SVMLearner.Custom
l1.probability = True
c1 = l1(iris)
l1.name = "SVM - RBF(Euclidean)"
l2 = SVMLearner()
l2.kernel_func = kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5)
l2.kernel_type = SVMLearner.Custom
l2.probability = True
c2 = l2(iris)
l2.name = "SVM - RBF(Hamming)"
l3 = SVMLearner()
l3.kernel_func = kernels.CompositeKernelWrapper(
kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5),
kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5), l=0.5)
l3.kernel_type = SVMLearner.Custom
l3.probability = True
c3 = l1(iris)
l3.name = "SVM - Composite"
from Orange import data
from Orange import evaluation
from Orange.classification.svm import SVMLearner, kernels
from Orange.distance import Euclidean
from Orange.distance import Hamming
iris = data.Table("iris.tab")
l1 = SVMLearner()
l1.kernel_func = kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5)
l1.kernel_type = SVMLearner.Custom
l1.probability = True
c1 = l1(iris)
l1.name = "SVM - RBF(Euclidean)"
l2 = SVMLearner()
l2.kernel_func = kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5)
l2.kernel_type = SVMLearner.Custom
l2.probability = True
c2 = l2(iris)
l2.name = "SVM - RBF(Hamming)"
l3 = SVMLearner()
l3.kernel_func = kernels.CompositeKernelWrapper(
kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5),
kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5),
l=0.5)
l3.kernel_type = SVMLearner.Custom
l3.probability = True
c3 = l1(iris)
l3.name = "SVM - Composite"
# Check the accuracy
print "\n" + "*" * 50
print "DEFAULT SVM"
print "Training Accuracy: " + str(compute_learner_accuracy(svm, trn_data))
print "Testing Accuracy: " + str(compute_learner_accuracy(svm, tst_data))
# Cross Validation
from Orange.classification.svm import SVMLearner, kernels
from Orange.distance import Euclidean
from Orange.distance import Hamming
svm1 = SVMLearner()
svm2 = SVMLearner()
svm2.kernel_func = kernels.RBFKernelWrapper(Hamming(trn_data), gamma=0.5)
svm2.kernel_type = SVMLearner.Custom
svm2.probability = True
svm3 = SVMLearner(kernel_type=SVMLearner.Custom,
kernel_func=kernels.CompositeKernelWrapper(
kernels.RBFKernelWrapper(Euclidean(trn_data), gamma=0.5),
kernels.RBFKernelWrapper(Hamming(trn_data), gamma=0.5), l=0.5),
probability=False)
res = Orange.evaluation.testing.cross_validation([svm1, svm2, svm3], trn_data, folds=5)
print "\n" + "-" * 30
print "CROSS VALIDATION"
print "SVM 1 Accuracy: %.2f" % Orange.evaluation.scoring.CA(res)[0]
print "SVM 2 Accuracy: %.2f" % Orange.evaluation.scoring.CA(res)[1]
print "SVM 3 Accuracy: %.2f" % Orange.evaluation.scoring.CA(res)[2]
