import Orange
data = Orange.data.Table('multitarget:bridges.tab')
cl1 = Orange.multitarget.binary.BinaryRelevanceLearner( \
learner = Orange.classification.majority.MajorityLearner, name="Majority")
cl2 = Orange.multitarget.tree.ClusteringTreeLearner(name="CTree")
learners = [cl1,cl2]
results = Orange.evaluation.testing.cross_validation(learners, data)
print "%18s %7s %6s %10s %8s %8s" % \
("Learner ", "LogLoss", "Brier", "Inf. Score", "Mean Acc", "Glob Acc")
for i in range(len(learners)):
print "%18s %1.4f %1.4f %+2.4f %1.4f %1.4f" % (learners[i].name,
# Calculate average logloss
Orange.multitarget.scoring.mt_average_score(results, \
Orange.evaluation.scoring.logloss)[i],
# Calculate average Brier score
Orange.multitarget.scoring.mt_average_score(results, \
Orange.evaluation.scoring.Brier_score)[i],
# Calculate average Information Score
Orange.multitarget.scoring.mt_average_score(results, \
Orange.evaluation.scoring.IS)[i],
# Calculate mean accuracy
Orange.multitarget.scoring.mt_mean_accuracy(results)[i],
# Calculate global accuracy
Orange.multitarget.scoring.mt_global_accuracy(results)[i])