def testPrune(self):
startId = (0, )
minSplit = 20
maxDepth = 5
gamma = 0.05
learner = PenaltyDecisionTree(minSplit=minSplit,
maxDepth=maxDepth,
gamma=gamma,
pruning=False)
trainX = self.X[100:, :]
trainY = self.y[100:]
testX = self.X[0:100, :]
testY = self.y[0:100]
argsortX = numpy.zeros(trainX.shape, numpy.int)
for i in range(trainX.shape[1]):
argsortX[:, i] = numpy.argsort(trainX[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
learner.tree = DictTree()
rootNode = DecisionNode(numpy.arange(trainX.shape[0]),
Util.mode(trainY))
learner.tree.setVertex(startId, rootNode)
learner.growTree(trainX, trainY, argsortX, startId)
learner.shapeX = trainX.shape
learner.predict(trainX, trainY)
learner.computeAlphas()
obj1 = learner.treeObjective(trainX, trainY)
size1 = learner.tree.getNumVertices()
#Now we'll prune
learner.prune(trainX, trainY)
obj2 = learner.treeObjective(trainX, trainY)
size2 = learner.tree.getNumVertices()
self.assertTrue(obj1 >= obj2)
self.assertTrue(size1 >= size2)
#Check there are no nodes with alpha>alphaThreshold
for vertexId in learner.tree.getAllVertexIds():
self.assertTrue(
learner.tree.getVertex(vertexId).alpha <=
learner.alphaThreshold)
def testPrune(self):
startId = (0, )
minSplit = 20
maxDepth = 5
gamma = 0.05
learner = PenaltyDecisionTree(minSplit=minSplit, maxDepth=maxDepth, gamma=gamma, pruning=False)
trainX = self.X[100:, :]
trainY = self.y[100:]
testX = self.X[0:100, :]
testY = self.y[0:100]
argsortX = numpy.zeros(trainX.shape, numpy.int)
for i in range(trainX.shape[1]):
argsortX[:, i] = numpy.argsort(trainX[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
learner.tree = DictTree()
rootNode = DecisionNode(numpy.arange(trainX.shape[0]), Util.mode(trainY))
learner.tree.setVertex(startId, rootNode)
learner.growTree(trainX, trainY, argsortX, startId)
learner.shapeX = trainX.shape
learner.predict(trainX, trainY)
learner.computeAlphas()
obj1 = learner.treeObjective(trainX, trainY)
size1 = learner.tree.getNumVertices()
#Now we'll prune
learner.prune(trainX, trainY)
obj2 = learner.treeObjective(trainX, trainY)
size2 = learner.tree.getNumVertices()
self.assertTrue(obj1 >= obj2)
self.assertTrue(size1 >= size2)
#Check there are no nodes with alpha>alphaThreshold
for vertexId in learner.tree.getAllVertexIds():
self.assertTrue(learner.tree.getVertex(vertexId).alpha <= learner.alphaThreshold)
def testGrowTree(self):
startId = (0, )
minSplit = 20
maxDepth = 3
gamma = 0.01
learner = PenaltyDecisionTree(minSplit=minSplit,
maxDepth=maxDepth,
gamma=gamma,
pruning=False)
trainX = self.X[100:, :]
trainY = self.y[100:]
testX = self.X[0:100, :]
testY = self.y[0:100]
argsortX = numpy.zeros(trainX.shape, numpy.int)
for i in range(trainX.shape[1]):
argsortX[:, i] = numpy.argsort(trainX[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
learner.tree = DictTree()
rootNode = DecisionNode(numpy.arange(trainX.shape[0]),
Util.mode(trainY))
learner.tree.setVertex(startId, rootNode)
#Note that this matches with the case where we create a new tree each time
numpy.random.seed(21)
bestError = float("inf")
for i in range(20):
learner.tree.pruneVertex(startId)
learner.growTree(trainX, trainY, argsortX, startId)
predTestY = learner.predict(testX)
error = Evaluator.binaryError(predTestY, testY)
#print(Evaluator.binaryError(predTestY, testY), learner.tree.getNumVertices())
if error < bestError:
bestError = error
bestTree = learner.tree.copy()
self.assertTrue(learner.tree.depth() <= maxDepth)
for vertexId in learner.tree.nonLeaves():
self.assertTrue(
learner.tree.getVertex(vertexId).getTrainInds().shape[0] >=
minSplit)
bestError1 = bestError
learner.tree = bestTree
#Now we test growing a tree from a non-root vertex
numpy.random.seed(21)
for i in range(20):
learner.tree.pruneVertex((0, 1))
learner.growTree(trainX, trainY, argsortX, (0, 1))
self.assertTrue(
learner.tree.getVertex((0, )) == bestTree.getVertex((0, )))
self.assertTrue(
learner.tree.getVertex((0, 0)) == bestTree.getVertex((0, 0)))
predTestY = learner.predict(testX)
error = Evaluator.binaryError(predTestY, testY)
if error < bestError:
bestError = error
bestTree = learner.tree.copy()
#print(Evaluator.binaryError(predTestY, testY), learner.tree.getNumVertices())
self.assertTrue(bestError1 >= bestError)
def testGrowTree(self):
startId = (0, )
minSplit = 20
maxDepth = 3
gamma = 0.01
learner = PenaltyDecisionTree(minSplit=minSplit, maxDepth=maxDepth, gamma=gamma, pruning=False)
trainX = self.X[100:, :]
trainY = self.y[100:]
testX = self.X[0:100, :]
testY = self.y[0:100]
argsortX = numpy.zeros(trainX.shape, numpy.int)
for i in range(trainX.shape[1]):
argsortX[:, i] = numpy.argsort(trainX[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
learner.tree = DictTree()
rootNode = DecisionNode(numpy.arange(trainX.shape[0]), Util.mode(trainY))
learner.tree.setVertex(startId, rootNode)
#Note that this matches with the case where we create a new tree each time
numpy.random.seed(21)
bestError = float("inf")
for i in range(20):
learner.tree.pruneVertex(startId)
learner.growTree(trainX, trainY, argsortX, startId)
predTestY = learner.predict(testX)
error = Evaluator.binaryError(predTestY, testY)
#print(Evaluator.binaryError(predTestY, testY), learner.tree.getNumVertices())
if error < bestError:
bestError = error
bestTree = learner.tree.copy()
self.assertTrue(learner.tree.depth() <= maxDepth)
for vertexId in learner.tree.nonLeaves():
self.assertTrue(learner.tree.getVertex(vertexId).getTrainInds().shape[0] >= minSplit)
bestError1 = bestError
learner.tree = bestTree
#Now we test growing a tree from a non-root vertex
numpy.random.seed(21)
for i in range(20):
learner.tree.pruneVertex((0, 1))
learner.growTree(trainX, trainY, argsortX, (0, 1))
self.assertTrue(learner.tree.getVertex((0,)) == bestTree.getVertex((0,)))
self.assertTrue(learner.tree.getVertex((0,0)) == bestTree.getVertex((0,0)))
predTestY = learner.predict(testX)
error = Evaluator.binaryError(predTestY, testY)
if error < bestError:
bestError = error
bestTree = learner.tree.copy()
#print(Evaluator.binaryError(predTestY, testY), learner.tree.getNumVertices())
self.assertTrue(bestError1 >= bestError )
