def recursiveSplit(self, X, y, argsortX, nodeId):
"""
Give a sample of data and a node index, we find the best split and
add children to the tree accordingly.
"""
if len(nodeId) - 1 >= self.maxDepth:
return
node = self.tree.getVertex(nodeId)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(
self.minSplit, X, y, node.getTrainInds(), argsortX)
#The split may have 0 items in one set, so don't split
if bestLeftInds.sum() != 0 and bestRightInds.sum() != 0:
node.setError(bestError)
node.setFeatureInd(bestFeatureInd)
node.setThreshold(bestThreshold)
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(bestLeftInds, y[bestLeftInds].mean())
self.tree.addChild(nodeId, leftChildId, leftChild)
if leftChild.getTrainInds().shape[0] >= self.minSplit:
self.recursiveSplit(X, y, argsortX, leftChildId)
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(bestRightInds, y[bestRightInds].mean())
self.tree.addChild(nodeId, rightChildId, rightChild)
if rightChild.getTrainInds().shape[0] >= self.minSplit:
self.recursiveSplit(X, y, argsortX, rightChildId)
def recursiveSplit(self, X, y, argsortX, nodeId):
"""
Give a sample of data and a node index, we find the best split and
add children to the tree accordingly.
"""
if len(nodeId)-1 >= self.maxDepth:
return
node = self.tree.getVertex(nodeId)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(self.minSplit, X, y, node.getTrainInds(), argsortX)
#The split may have 0 items in one set, so don't split
if bestLeftInds.sum() != 0 and bestRightInds.sum() != 0:
node.setError(bestError)
node.setFeatureInd(bestFeatureInd)
node.setThreshold(bestThreshold)
leftChildId = self.getLeftChildId(nodeId)
leftChild = DecisionNode(bestLeftInds, y[bestLeftInds].mean())
self.tree.addChild(nodeId, leftChildId, leftChild)
if leftChild.getTrainInds().shape[0] >= self.minSplit:
self.recursiveSplit(X, y, argsortX, leftChildId)
rightChildId = self.getRightChildId(nodeId)
rightChild = DecisionNode(bestRightInds, y[bestRightInds].mean())
self.tree.addChild(nodeId, rightChildId, rightChild)
if rightChild.getTrainInds().shape[0] >= self.minSplit:
self.recursiveSplit(X, y, argsortX, rightChildId)
def testFindBestSplit2(self):
minSplit = 1
X = numpy.zeros((20, 10))
y = numpy.ones(20)
X[0:10, 2] = numpy.arange(10)
X[10:, 2] = numpy.arange(10)+10
y[0:10] = -1
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit2(minSplit, X, y)
self.assertEquals(bestError, 0.0)
self.assertEquals(bestFeatureInd, 2)
self.assertEquals(bestThreshold, 9.5)
self.assertTrue((bestLeftInds == numpy.arange(0, 10)).all())
self.assertTrue((bestRightInds == numpy.arange(10, 20)).all())
#Test case where all values are the same
X = numpy.zeros((20, 10))
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit2(minSplit, X, y)
self.assertTrue(bestRightInds.shape[0]==0)
#Another simple example
X = numpy.random.rand(20, 1)
y = numpy.random.rand(20)
inds = [1, 3, 7, 12, 14, 15]
X[inds, 0] += 10
y[inds] += 1
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit2(minSplit, X, y)
for i in range(10):
numExamples = numpy.random.randint(1, 200)
numFeatures = numpy.random.randint(1, 10)
X = numpy.random.rand(numExamples, numFeatures)
y = numpy.random.rand(numExamples)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y)
bestError2, bestFeatureInd2, bestThreshold2, bestLeftInds2, bestRightInds2 = findBestSplit2(minSplit, X, y)
self.assertEquals(bestFeatureInd, bestFeatureInd2)
self.assertAlmostEquals(bestThreshold, bestThreshold2)
nptst.assert_array_equal(bestLeftInds, bestLeftInds2)
nptst.assert_array_equal(bestRightInds, bestRightInds2)
def testFindBestSplit3(self):
minSplit = 1
numExamples = 20
X = numpy.zeros((numExamples, 2), order="F")
y = numpy.ones(numExamples)
X[0:10, 0] = numpy.random.permutation(10)
X[10:, 0] = numpy.random.permutation(10)+10
y[0:10] = -1
argsortX = numpy.zeros(X.shape, numpy.int, order="F")
for i in range(X.shape[1]):
argsortX[:, i] = numpy.argsort(X[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
inds = numpy.arange(numExamples-2, dtype=numpy.int)
print(X)
print(y)
print(inds)
print(X[inds, :], y[inds])
tempX = X[inds, :]
tempY = y[inds]
print(tempY[tempX[:, 0]<9.5])
print(tempY[tempX[:, 0]>9.5])
print("Calling function")
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit3(minSplit, X, y, inds, argsortX)
print(bestError, bestFeatureInd, bestThreshold)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, inds, argsortX)
print(bestError, bestFeatureInd, bestThreshold)
def testFindBestSplit(self):
minSplit = 1
X = numpy.zeros((20, 10))
y = numpy.ones(20)
X[0:10, 2] = numpy.arange(10)
X[10:, 2] = numpy.arange(10)+10
y[0:10] = -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])
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
self.assertEquals(bestError, 0.0)
self.assertEquals(bestFeatureInd, 2)
self.assertEquals(bestThreshold, 9.5)
self.assertTrue((bestLeftInds == numpy.arange(0, 10)).all())
self.assertTrue((bestRightInds == numpy.arange(10, 20)).all())
#Test case where all values are the same
X = numpy.zeros((20, 10))
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])
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
self.assertTrue(bestLeftInds.shape[0]==0)
self.assertTrue(bestRightInds.shape[0]==X.shape[0])
#Another simple example
X = numpy.random.rand(20, 1)
y = numpy.random.rand(20)
inds = [1, 3, 7, 12, 14, 15]
X[inds, 0] += 10
y[inds] += 1
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])
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
nptst.assert_array_equal(bestRightInds, numpy.array(inds))
#Test minSplit
minSplit = 10
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
self.assertTrue(bestLeftInds.shape[0] >= minSplit)
self.assertTrue(bestRightInds.shape[0] >= minSplit)
#Vary nodeInds
minSplit = 1
nodeInds = numpy.arange(16)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
nptst.assert_array_equal(bestRightInds, numpy.array(inds))
nptst.assert_array_equal(bestLeftInds, numpy.setdiff1d(nodeInds, numpy.array(inds)))
nodeInds = numpy.arange(10)
bestError, bestFeatureInd, bestThreshold, bestLeftInds, bestRightInds = findBestSplit(minSplit, X, y, nodeInds, argsortX)
nptst.assert_array_equal(bestRightInds, numpy.array([1,3,7]))
nptst.assert_array_equal(bestLeftInds, numpy.setdiff1d(nodeInds, numpy.array([1,3,7])))
