def testLearnModelCut(self):
maxDepth = 5
minSplit = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.setMinSplit(minSplit)
treeRank.learnModelCut(self.X, self.y)
tree = treeRank.getTree()
self.assertTrue(tree.depth() <= maxDepth)
def testLearnModelCut(self):
maxDepth = 5
minSplit = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.setMinSplit(minSplit)
treeRank.learnModelCut(self.X, self.y)
tree = treeRank.getTree()
self.assertTrue(tree.depth() <= maxDepth)
def testMinSplit(self):
maxDepth = 10
minSplit = 100
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.setMinSplit(minSplit)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
if not node.isLeafNode():
self.assertTrue(node.getTrainInds().shape[0] >= minSplit)
def testMinSplit(self):
maxDepth = 10
minSplit = 100
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.setMinSplit(minSplit)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
if not node.isLeafNode():
self.assertTrue(node.getTrainInds().shape[0] >= minSplit)
def learnModel(self, X, y):
"""
Learn a model for a set of examples given as the rows of the matrix X,
with corresponding labels given in the elements of 1D array y.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
:param y: A vector of labels
:type y: :class:`ndarray`
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(y, numpy.ndarray)
Parameter.checkArray(X)
Parameter.checkArray(y)
labels = numpy.unique(y)
if labels.shape[0] != 2:
raise ValueError("Can only accept binary labelled data")
if (labels != numpy.array([-1, 1])).any():
raise ValueError("Labels must be -1/+1: " + str(labels))
forestList = []
indList = []
numSampledExamples = int(numpy.round(self.sampleSize * X.shape[0]))
for i in range(self.numTrees):
Util.printConciseIteration(i, 1, self.numTrees, "Tree: ")
if self.sampleReplace:
inds = numpy.random.randint(0, X.shape[0], numSampledExamples)
else:
inds = numpy.random.permutation(
X.shape[0])[0:numSampledExamples]
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(self.maxDepth)
treeRank.setMinSplit(self.minSplit)
treeRank.setFeatureSize(self.featureSize)
treeRank.setBestResponse(self.bestResponse)
treeRank.learnModel(X[inds, :], y[inds])
forestList.append(treeRank)
indList.append(inds)
self.forestList = forestList
self.indList = indList
def learnModel(self, X, y):
"""
Learn a model for a set of examples given as the rows of the matrix X,
with corresponding labels given in the elements of 1D array y.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
:param y: A vector of labels
:type y: :class:`ndarray`
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(y, numpy.ndarray)
Parameter.checkArray(X)
Parameter.checkArray(y)
labels = numpy.unique(y)
if labels.shape[0] != 2:
raise ValueError("Can only accept binary labelled data")
if (labels != numpy.array([-1, 1])).any():
raise ValueError("Labels must be -1/+1: " + str(labels))
forestList = []
indList = []
numSampledExamples = int(numpy.round(self.sampleSize*X.shape[0]))
for i in range(self.numTrees):
Util.printConciseIteration(i, 1, self.numTrees, "Tree: ")
if self.sampleReplace:
inds = numpy.random.randint(0, X.shape[0], numSampledExamples)
else:
inds = numpy.random.permutation(X.shape[0])[0:numSampledExamples]
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(self.maxDepth)
treeRank.setMinSplit(self.minSplit)
treeRank.setFeatureSize(self.featureSize)
treeRank.setBestResponse(self.bestResponse)
treeRank.learnModel(X[inds, :], y[inds])
forestList.append(treeRank)
indList.append(inds)
self.forestList = forestList
self.indList = indList
