def testLearnModel2(self):
#We want to make sure the learnt tree with gamma = 0 maximise the
#empirical risk
minSplit = 20
maxDepth = 3
gamma = 0.01
learner = PenaltyDecisionTree(minSplit=minSplit,
maxDepth=maxDepth,
gamma=gamma,
pruning=False)
#Vary sampleSize
numpy.random.seed(21)
learner.setSampleSize(1)
learner.learnModel(self.X, self.y)
error1 = learner.treeObjective(self.X, self.y)
numpy.random.seed(21)
learner.setSampleSize(5)
learner.learnModel(self.X, self.y)
error2 = learner.treeObjective(self.X, self.y)
numpy.random.seed(21)
learner.setSampleSize(10)
learner.learnModel(self.X, self.y)
error3 = learner.treeObjective(self.X, self.y)
self.assertTrue(error1 >= error2)
self.assertTrue(error2 >= error3)
#Now vary max depth
learner.gamma = 0
numpy.random.seed(21)
learner.setSampleSize(1)
learner.minSplit = 1
learner.maxDepth = 3
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error1 = Evaluator.binaryError(self.y, predY)
numpy.random.seed(21)
learner.maxDepth = 5
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error2 = Evaluator.binaryError(self.y, predY)
numpy.random.seed(21)
learner.maxDepth = 10
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error3 = Evaluator.binaryError(self.y, predY)
self.assertTrue(error1 >= error2)
self.assertTrue(error2 >= error3)
def testLearnModel2(self):
#We want to make sure the learnt tree with gamma = 0 maximise the
#empirical risk
minSplit = 20
maxDepth = 3
gamma = 0.01
learner = PenaltyDecisionTree(minSplit=minSplit, maxDepth=maxDepth, gamma=gamma, pruning=False)
#Vary sampleSize
numpy.random.seed(21)
learner.setSampleSize(1)
learner.learnModel(self.X, self.y)
error1 = learner.treeObjective(self.X, self.y)
numpy.random.seed(21)
learner.setSampleSize(5)
learner.learnModel(self.X, self.y)
error2 = learner.treeObjective(self.X, self.y)
numpy.random.seed(21)
learner.setSampleSize(10)
learner.learnModel(self.X, self.y)
error3 = learner.treeObjective(self.X, self.y)
self.assertTrue(error1 >= error2)
self.assertTrue(error2 >= error3)
#Now vary max depth
learner.gamma = 0
numpy.random.seed(21)
learner.setSampleSize(1)
learner.minSplit = 1
learner.maxDepth = 3
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error1 = Evaluator.binaryError(self.y, predY)
numpy.random.seed(21)
learner.maxDepth = 5
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error2 = Evaluator.binaryError(self.y, predY)
numpy.random.seed(21)
learner.maxDepth = 10
learner.learnModel(self.X, self.y)
predY = learner.predict(self.X)
error3 = Evaluator.binaryError(self.y, predY)
self.assertTrue(error1 >= error2)
self.assertTrue(error2 >= error3)
