def testDepth(self):
dictTree = DictTree()
self.assertEquals(dictTree.depth(), 0)
dictTree.setVertex("a")
self.assertEquals(dictTree.depth(), 0)
dictTree.addEdge("a", "b")
dictTree.addEdge("a", "c")
dictTree.addEdge("d", "a")
self.assertEquals(dictTree.depth(), 2)
dictTree.addEdge("c", "e")
self.assertEquals(dictTree.depth(), 3)
def testDepth(self):
dictTree = DictTree()
self.assertEquals(dictTree.depth(), 0)
dictTree.setVertex("a")
self.assertEquals(dictTree.depth(), 0)
dictTree.addEdge("a", "b")
dictTree.addEdge("a", "c")
dictTree.addEdge("d", "a")
self.assertEquals(dictTree.depth(), 2)
dictTree.addEdge("c", "e")
self.assertEquals(dictTree.depth(), 3)
def testSplitNode(self):
d = 0
k = 0
maxDepth = 1
inds = numpy.arange(self.y.shape[0])
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
node = RankNode(inds, numpy.arange(self.X.shape[1]))
tree = DictTree()
tree.setVertex((0, 0), node)
tree = treeRank.splitNode(tree, self.X, self.y, d, k)
self.assertEquals(tree.getNumVertices(), 3)
self.assertEquals(tree.getNumEdges(), 2)
self.assertEquals(tree.getRootId(), (0, 0))
self.assertTrue(not tree.getVertex((0, 0)).isLeafNode())
self.assertTrue(tree.getVertex((1, 0)).isLeafNode())
self.assertTrue(tree.getVertex((1, 1)).isLeafNode())
self.assertTrue(tree.depth() <= maxDepth)
def testSplitNode(self):
d = 0
k = 0
maxDepth = 1
inds = numpy.arange(self.y.shape[0])
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
node = RankNode(inds, numpy.arange(self.X.shape[1]))
tree = DictTree()
tree.setVertex((0, 0), node)
tree = treeRank.splitNode(tree, self.X, self.y, d, k)
self.assertEquals(tree.getNumVertices(), 3)
self.assertEquals(tree.getNumEdges(), 2)
self.assertEquals(tree.getRootId(), (0, 0))
self.assertTrue(not tree.getVertex((0, 0)).isLeafNode())
self.assertTrue(tree.getVertex((1, 0)).isLeafNode())
self.assertTrue(tree.getVertex((1, 1)).isLeafNode())
self.assertTrue(tree.depth() <= maxDepth)
class PenaltyDecisionTree(AbstractPredictor):
def __init__(self, criterion="gain", maxDepth=10, minSplit=30, learnType="reg", pruning=True, gamma=0.01, sampleSize=10):
"""
Learn a decision tree with penalty proportional to the root of the size
of the tree as in Nobel 2002. We use a stochastic approach in which we
learn a set of trees randomly and choose the best one.
:param criterion: The splitting criterion which is only informaiton gain currently
:param maxDepth: The maximum depth of the tree
:type maxDepth: `int`
:param minSplit: The minimum size of a node for it to be split.
:type minSplit: `int`
:param type: The type of learning to perform. Currently only regression
:param pruning: Whether to perform pruning or not.
:type pruning: `boolean`
:param gamma: The weight on the penalty factor between 0 and 1
:type gamma: `float`
:param sampleSize: The number of trees to learn in the stochastic search.
:type sampleSize: `int`
"""
super(PenaltyDecisionTree, self).__init__()
self.maxDepth = maxDepth
self.minSplit = minSplit
self.criterion = criterion
self.learnType = learnType
self.setGamma(gamma)
self.setSampleSize(sampleSize)
self.pruning = pruning
self.alphaThreshold = 0.0
def setGamma(self, gamma):
Parameter.checkFloat(gamma, 0.0, 1.0)
self.gamma = gamma
def setSampleSize(self, sampleSize):
Parameter.checkInt(sampleSize, 1, float("inf"))
self.sampleSize = sampleSize
def setAlphaThreshold(self, alphaThreshold):
Parameter.checkFloat(alphaThreshold, -float("inf"), float("inf"))
self.alphaThreshold = alphaThreshold
def getAlphaThreshold(self):
return self.alphaThreshold
def getLeftChildId(self, nodeId):
leftChildId = list(nodeId)
leftChildId.append(0)
leftChildId = tuple(leftChildId)
return leftChildId
def getRightChildId(self, nodeId):
rightChildId = list(nodeId)
rightChildId.append(1)
rightChildId = tuple(rightChildId)
return rightChildId
def getTree(self):
return self.tree
def learnModel(self, X, y):
if numpy.unique(y).shape[0] != 2:
raise ValueError("Must provide binary labels")
if y.dtype != numpy.int:
raise ValueError("Labels must be integers")
self.shapeX = X.shape
argsortX = numpy.zeros(X.shape, numpy.int)
for i in range(X.shape[1]):
argsortX[:, i] = numpy.argsort(X[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
rootId = (0,)
idStack = [rootId]
self.tree = DictTree()
rootNode = DecisionNode(numpy.arange(X.shape[0]), Util.mode(y))
self.tree.setVertex(rootId, rootNode)
bestError = float("inf")
bestTree = self.tree
#First grow a selection of trees
while len(idStack) != 0:
#Prune the current node away and grow from that node
nodeId = idStack.pop()
for i in range(self.sampleSize):
self.tree = bestTree.deepCopy()
try:
node = self.tree.getVertex(nodeId)
except ValueError:
print(nodeId)
print(self.tree)
raise
self.tree.pruneVertex(nodeId)
self.growTree(X, y, argsortX, nodeId)
self.prune(X, y)
error = self.treeObjective(X, y)
if error < bestError:
bestError = error
bestTree = self.tree.deepCopy()
children = bestTree.children(nodeId)
idStack.extend(children)
self.tree = bestTree
def growTree(self, X, y, argsortX, startId):
"""
Grow a tree using a stack. Give a sample of data and a node index, we
find the best split and add children to the tree accordingly. We perform
pre-pruning based on the penalty.
"""
eps = 10**-4
idStack = [startId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
accuracies, thresholds = findBestSplitRisk(self.minSplit, X, y, node.getTrainInds(), argsortX)
#Choose best feature based on gains
accuracies += eps
bestFeatureInd = Util.randomChoice(accuracies)[0]
bestThreshold = thresholds[bestFeatureInd]
nodeInds = node.getTrainInds()
bestLeftInds = numpy.sort(nodeInds[numpy.arange(nodeInds.shape[0])[X[:, bestFeatureInd][nodeInds]<bestThreshold]])
bestRightInds = numpy.sort(nodeInds[numpy.arange(nodeInds.shape[0])[X[:, bestFeatureInd][nodeInds]>=bestThreshold]])
#The split may have 0 items in one set, so don't split
if bestLeftInds.sum() != 0 and bestRightInds.sum() != 0 and self.tree.depth() < self.maxDepth:
node.setError(1-accuracies[bestFeatureInd])
node.setFeatureInd(bestFeatureInd)
node.setThreshold(bestThreshold)
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(bestLeftInds, Util.mode(y[bestLeftInds]))
self.tree.addChild(nodeId, leftChildId, leftChild)
if leftChild.getTrainInds().shape[0] >= self.minSplit:
idStack.append(leftChildId)
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(bestRightInds, Util.mode(y[bestRightInds]))
self.tree.addChild(nodeId, rightChildId, rightChild)
if rightChild.getTrainInds().shape[0] >= self.minSplit:
idStack.append(rightChildId)
def predict(self, X, y=None):
"""
Make a prediction for the set of examples given in the matrix X. If
one passes in a label vector y then we set the errors for each node. On
the other hand if y=None, no errors are set.
"""
rootId = (0,)
predY = numpy.zeros(X.shape[0])
self.tree.getVertex(rootId).setTestInds(numpy.arange(X.shape[0]))
idStack = [rootId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
testInds = node.getTestInds()
if y!=None:
node.setTestError(self.vertexTestError(y[testInds], node.getValue()))
if self.tree.isLeaf(nodeId):
predY[testInds] = node.getValue()
else:
for childId in [self.getLeftChildId(nodeId), self.getRightChildId(nodeId)]:
if self.tree.vertexExists(childId):
child = self.tree.getVertex(childId)
if childId[-1] == 0:
childInds = X[testInds, node.getFeatureInd()] < node.getThreshold()
else:
childInds = X[testInds, node.getFeatureInd()] >= node.getThreshold()
child.setTestInds(testInds[childInds])
idStack.append(childId)
return predY
def treeObjective(self, X, y):
"""
Return the empirical risk plus penalty for the tree.
"""
predY = self.predict(X)
(n, d) = X.shape
return (1-self.gamma)*numpy.sum(predY!=y)/float(n) + self.gamma*numpy.sqrt(self.tree.getNumVertices())
def prune(self, X, y):
"""
Do some post pruning greedily.
"""
self.predict(X, y)
self.computeAlphas()
#Do the pruning, recomputing alpha along the way
rootId = (0,)
idStack = [rootId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
if node.alpha > self.alphaThreshold:
self.tree.pruneVertex(nodeId)
self.computeAlphas()
else:
for childId in [self.getLeftChildId(nodeId), self.getRightChildId(nodeId)]:
if self.tree.vertexExists(childId):
idStack.append(childId)
def vertexTestError(self, trueY, predY):
"""
This is the error used for pruning. We compute it at each node.
"""
return numpy.sum(trueY != predY)
def computeAlphas(self):
"""
The alpha value at each vertex is the improvement in the objective by
pruning at that vertex.
"""
n = self.shapeX[0]
for vertexId in self.tree.getAllVertexIds():
currentNode = self.tree.getVertex(vertexId)
subtreeLeaves = self.tree.leaves(vertexId)
subtreeError = 0
for leaf in subtreeLeaves:
subtreeError += self.tree.getVertex(leaf).getTestError()
T = self.tree.getNumVertices()
T2 = T - len(self.tree.subtreeIds(vertexId)) + 1
currentNode.alpha = (1-self.gamma)*(subtreeError - currentNode.getTestError())
currentNode.alpha /= n
currentNode.alpha += self.gamma * numpy.sqrt(T)
currentNode.alpha -= self.gamma * numpy.sqrt(T2)
def copy(self):
"""
Create a new tree with the same parameters.
"""
newLearner = PenaltyDecisionTree(criterion=self.criterion, maxDepth=self.maxDepth, minSplit=self.minSplit, learnType=self.learnType, pruning=self.pruning, gamma=self.gamma, sampleSize=self.sampleSize)
return newLearner
def getMetricMethod(self):
"""
Returns a way to measure the performance of the classifier.
"""
return Evaluator.binaryError
class PenaltyDecisionTree(AbstractPredictor):
def __init__(self,
criterion="gain",
maxDepth=10,
minSplit=30,
learnType="reg",
pruning=True,
gamma=0.01,
sampleSize=10):
"""
Learn a decision tree with penalty proportional to the root of the size
of the tree as in Nobel 2002. We use a stochastic approach in which we
learn a set of trees randomly and choose the best one.
:param criterion: The splitting criterion which is only informaiton gain currently
:param maxDepth: The maximum depth of the tree
:type maxDepth: `int`
:param minSplit: The minimum size of a node for it to be split.
:type minSplit: `int`
:param type: The type of learning to perform. Currently only regression
:param pruning: Whether to perform pruning or not.
:type pruning: `boolean`
:param gamma: The weight on the penalty factor between 0 and 1
:type gamma: `float`
:param sampleSize: The number of trees to learn in the stochastic search.
:type sampleSize: `int`
"""
super(PenaltyDecisionTree, self).__init__()
self.maxDepth = maxDepth
self.minSplit = minSplit
self.criterion = criterion
self.learnType = learnType
self.setGamma(gamma)
self.setSampleSize(sampleSize)
self.pruning = pruning
self.alphaThreshold = 0.0
def setGamma(self, gamma):
Parameter.checkFloat(gamma, 0.0, 1.0)
self.gamma = gamma
def setSampleSize(self, sampleSize):
Parameter.checkInt(sampleSize, 1, float("inf"))
self.sampleSize = sampleSize
def setAlphaThreshold(self, alphaThreshold):
Parameter.checkFloat(alphaThreshold, -float("inf"), float("inf"))
self.alphaThreshold = alphaThreshold
def getAlphaThreshold(self):
return self.alphaThreshold
def getLeftChildId(self, nodeId):
leftChildId = list(nodeId)
leftChildId.append(0)
leftChildId = tuple(leftChildId)
return leftChildId
def getRightChildId(self, nodeId):
rightChildId = list(nodeId)
rightChildId.append(1)
rightChildId = tuple(rightChildId)
return rightChildId
def getTree(self):
return self.tree
def learnModel(self, X, y):
if numpy.unique(y).shape[0] != 2:
raise ValueError("Must provide binary labels")
if y.dtype != numpy.int:
raise ValueError("Labels must be integers")
self.shapeX = X.shape
argsortX = numpy.zeros(X.shape, numpy.int)
for i in range(X.shape[1]):
argsortX[:, i] = numpy.argsort(X[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
rootId = (0, )
idStack = [rootId]
self.tree = DictTree()
rootNode = DecisionNode(numpy.arange(X.shape[0]), Util.mode(y))
self.tree.setVertex(rootId, rootNode)
bestError = float("inf")
bestTree = self.tree
#First grow a selection of trees
while len(idStack) != 0:
#Prune the current node away and grow from that node
nodeId = idStack.pop()
for i in range(self.sampleSize):
self.tree = bestTree.deepCopy()
try:
node = self.tree.getVertex(nodeId)
except ValueError:
print(nodeId)
print(self.tree)
raise
self.tree.pruneVertex(nodeId)
self.growTree(X, y, argsortX, nodeId)
self.prune(X, y)
error = self.treeObjective(X, y)
if error < bestError:
bestError = error
bestTree = self.tree.deepCopy()
children = bestTree.children(nodeId)
idStack.extend(children)
self.tree = bestTree
def growTree(self, X, y, argsortX, startId):
"""
Grow a tree using a stack. Give a sample of data and a node index, we
find the best split and add children to the tree accordingly. We perform
pre-pruning based on the penalty.
"""
eps = 10**-4
idStack = [startId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
accuracies, thresholds = findBestSplitRisk(self.minSplit, X, y,
node.getTrainInds(),
argsortX)
#Choose best feature based on gains
accuracies += eps
bestFeatureInd = Util.randomChoice(accuracies)[0]
bestThreshold = thresholds[bestFeatureInd]
nodeInds = node.getTrainInds()
bestLeftInds = numpy.sort(nodeInds[numpy.arange(nodeInds.shape[0])[
X[:, bestFeatureInd][nodeInds] < bestThreshold]])
bestRightInds = numpy.sort(nodeInds[numpy.arange(
nodeInds.shape[0])[
X[:, bestFeatureInd][nodeInds] >= bestThreshold]])
#The split may have 0 items in one set, so don't split
if bestLeftInds.sum() != 0 and bestRightInds.sum(
) != 0 and self.tree.depth() < self.maxDepth:
node.setError(1 - accuracies[bestFeatureInd])
node.setFeatureInd(bestFeatureInd)
node.setThreshold(bestThreshold)
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(bestLeftInds,
Util.mode(y[bestLeftInds]))
self.tree.addChild(nodeId, leftChildId, leftChild)
if leftChild.getTrainInds().shape[0] >= self.minSplit:
idStack.append(leftChildId)
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(bestRightInds,
Util.mode(y[bestRightInds]))
self.tree.addChild(nodeId, rightChildId, rightChild)
if rightChild.getTrainInds().shape[0] >= self.minSplit:
idStack.append(rightChildId)
def predict(self, X, y=None):
"""
Make a prediction for the set of examples given in the matrix X. If
one passes in a label vector y then we set the errors for each node. On
the other hand if y=None, no errors are set.
"""
rootId = (0, )
predY = numpy.zeros(X.shape[0])
self.tree.getVertex(rootId).setTestInds(numpy.arange(X.shape[0]))
idStack = [rootId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
testInds = node.getTestInds()
if y != None:
node.setTestError(
self.vertexTestError(y[testInds], node.getValue()))
if self.tree.isLeaf(nodeId):
predY[testInds] = node.getValue()
else:
for childId in [
self.getLeftChildId(nodeId),
self.getRightChildId(nodeId)
]:
if self.tree.vertexExists(childId):
child = self.tree.getVertex(childId)
if childId[-1] == 0:
childInds = X[
testInds,
node.getFeatureInd()] < node.getThreshold()
else:
childInds = X[
testInds,
node.getFeatureInd()] >= node.getThreshold()
child.setTestInds(testInds[childInds])
idStack.append(childId)
return predY
def treeObjective(self, X, y):
"""
Return the empirical risk plus penalty for the tree.
"""
predY = self.predict(X)
(n, d) = X.shape
return (1 - self.gamma) * numpy.sum(predY != y) / float(
n) + self.gamma * numpy.sqrt(self.tree.getNumVertices())
def prune(self, X, y):
"""
Do some post pruning greedily.
"""
self.predict(X, y)
self.computeAlphas()
#Do the pruning, recomputing alpha along the way
rootId = (0, )
idStack = [rootId]
while len(idStack) != 0:
nodeId = idStack.pop()
node = self.tree.getVertex(nodeId)
if node.alpha > self.alphaThreshold:
self.tree.pruneVertex(nodeId)
self.computeAlphas()
else:
for childId in [
self.getLeftChildId(nodeId),
self.getRightChildId(nodeId)
]:
if self.tree.vertexExists(childId):
idStack.append(childId)
def vertexTestError(self, trueY, predY):
"""
This is the error used for pruning. We compute it at each node.
"""
return numpy.sum(trueY != predY)
def computeAlphas(self):
"""
The alpha value at each vertex is the improvement in the objective by
pruning at that vertex.
"""
n = self.shapeX[0]
for vertexId in self.tree.getAllVertexIds():
currentNode = self.tree.getVertex(vertexId)
subtreeLeaves = self.tree.leaves(vertexId)
subtreeError = 0
for leaf in subtreeLeaves:
subtreeError += self.tree.getVertex(leaf).getTestError()
T = self.tree.getNumVertices()
T2 = T - len(self.tree.subtreeIds(vertexId)) + 1
currentNode.alpha = (1 - self.gamma) * (subtreeError -
currentNode.getTestError())
currentNode.alpha /= n
currentNode.alpha += self.gamma * numpy.sqrt(T)
currentNode.alpha -= self.gamma * numpy.sqrt(T2)
def copy(self):
"""
Create a new tree with the same parameters.
"""
newLearner = PenaltyDecisionTree(criterion=self.criterion,
maxDepth=self.maxDepth,
minSplit=self.minSplit,
learnType=self.learnType,
pruning=self.pruning,
gamma=self.gamma,
sampleSize=self.sampleSize)
return newLearner
def getMetricMethod(self):
"""
Returns a way to measure the performance of the classifier.
"""
return Evaluator.binaryError
