def train(self, trainset):
"""
Trains an ensemble of tree with Adaboost.M1.
"""
self.n_classes = len(trainset.metadata['targets'])
trainset_orange = make_orange_dataset(trainset)
self.trainset_domain = trainset_orange.domain
tree = orngTree.TreeLearner(max_majority=self.max_majority,
max_depth=self.max_depth,
min_instances=self.min_leaf_size,
skip_prob=self.skip_prob)
adaboost = orngEnsemble.BoostedLearner(learner=tree,
t=self.n_trees,
name="AdaBoost.M1")
self.boosted_trees = adaboost(instances=trainset_orange)
def setUp(self):
import orngEnsemble, orngTree
self.learner = orngEnsemble.BoostedLearner(orngTree.TreeLearner)
# Description: Demonstrates the use of boosting and bagging from orngEnsemble module
# Category: classification, ensembles
# Classes: BoostedLearner, BaggedLearner
# Uses: lymphography.tab
# Referenced: orngEnsemble.htm
import orange, orngEnsemble, orngTree
import orngTest, orngStat
tree = orngTree.TreeLearner(mForPruning=2, name="tree")
bs = orngEnsemble.BoostedLearner(tree, name="boosted tree")
bg = orngEnsemble.BaggedLearner(tree, name="bagged tree")
data = orange.ExampleTable("lymphography.tab")
learners = [tree, bs, bg]
results = orngTest.crossValidation(learners, data, folds=3)
print "Classification Accuracy:"
for i in range(len(learners)):
print ("%15s: %5.3f") % (learners[i].name, orngStat.CA(results)[i])
# Description: Bagging and boosting with k-nearest neighbors
# Category: modelling
# Uses: promoters.tab
# Classes: orngTest.crossValidation, orngEnsemble.BaggedLearner, orngEnsemble.BoostedLearner
# Referenced: o_ensemble.htm
import orange, orngTest, orngStat, orngEnsemble
data = orange.ExampleTable("promoters")
majority = orange.MajorityLearner()
majority.name = "default"
knn = orange.kNNLearner(k=11)
knn.name = "k-NN (k=11)"
bagged_knn = orngEnsemble.BaggedLearner(knn, t=10)
bagged_knn.name = "bagged k-NN"
boosted_knn = orngEnsemble.BoostedLearner(knn, t=10)
boosted_knn.name = "boosted k-NN"
learners = [majority, knn, bagged_knn, boosted_knn]
results = orngTest.crossValidation(learners, data, folds=10)
print " Learner CA Brier Score"
for i in range(len(learners)):
print("%15s: %5.3f %5.3f") % (learners[i].name, orngStat.CA(results)[i],
orngStat.BrierScore(results)[i])