def testFindBestSplitRisk(self):
minSplit = 1
numExamples = 20
numFeatures = 10
X = numpy.zeros((numExamples, numFeatures))
y = numpy.ones(numExamples, numpy.int)
X[0:10, 2] = numpy.arange(10)
X[10:, 2] = numpy.arange(10)+10
y[0:10] = -1
y += 1
nodeInds = numpy.arange(X.shape[0])
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])
errors, thresholds = findBestSplitRisk(minSplit, X, y, nodeInds, argsortX)
print(errors, thresholds)
X = numpy.random.rand(numExamples, numFeatures)
errors, thresholds = findBestSplitRisk(minSplit, X, y, nodeInds, argsortX)
print(errors, thresholds)
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 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)
