def testVariableImportance(self):
X, y, c = ExamplesGenerator().generateBinaryExamples(numExamples=500, verbose=True)
treeRank = TreeRank(self.leafRanklearner)
treeRank.learnModel(X, y)
weightVector = treeRank.variableImportance(X, y)
def testEvaluateCvOuter(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
folds = 3
(bestParams, allMetrics, bestMetaDicts) = treeRank.evaluateCvOuter(self.X, self.y, folds)
def testVariableImportance(self):
X, y, c = ExamplesGenerator().generateBinaryExamples(numExamples=500,
verbose=True)
treeRank = TreeRank(self.leafRanklearner)
treeRank.learnModel(X, y)
weightVector = treeRank.variableImportance(X, y)
def testEvaluateCvOuter(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
folds = 3
(bestParams, allMetrics,
bestMetaDicts) = treeRank.evaluateCvOuter(self.X, self.y, folds)
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 testPredict(self):
maxDepth = 2
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
scores = treeRank.predict(self.X)
scores2 = numpy.zeros(self.X.shape[0])
#Test if train and test indices are the same
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
self.assertTrue((node.getTrainInds() == node.getTestInds()).all())
(d, k) = vertexId
if node.isLeafNode():
self.assertEquals(node.getScore(),
(1 - float(k) / 2**d) * 2**maxDepth)
scores2[node.getTestInds()] = node.getScore()
self.assertTrue((scores == scores2).all())
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 testPredict(self):
maxDepth = 2
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
scores = treeRank.predict(self.X)
scores2 = numpy.zeros(self.X.shape[0])
# Test if train and test indices are the same
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
self.assertTrue((node.getTrainInds() == node.getTestInds()).all())
(d, k) = vertexId
if node.isLeafNode():
self.assertEquals(node.getScore(), (1 - float(k) / 2 ** d) * 2 ** maxDepth)
scores2[node.getTestInds()] = node.getScore()
self.assertTrue((scores == scores2).all())
def testPredict2(self):
#Test on Gauss2D dataset
dataDir = PathDefaults.getDataDir()
fileName = dataDir + "Gauss2D_learn.csv"
XY = numpy.loadtxt(fileName,
skiprows=1,
usecols=(1, 2, 3),
delimiter=",")
X = XY[:, 0:2]
y = XY[:, 2] * 2 - 1
fileName = dataDir + "Gauss2D_test.csv"
testXY = numpy.loadtxt(fileName,
skiprows=1,
usecols=(1, 2, 3),
delimiter=",")
testX = testXY[:, 0:2]
testY = testXY[:, 2] * 2 - 1
#X = Standardiser().standardiseArray(X)
#testX = Standardiser().standardiseArray(testX)
maxDepths = range(3, 10)
trainAucs = numpy.array([
0.7194734, 0.7284824, 0.7332185, 0.7348198, 0.7366152, 0.7367508,
0.7367508, 0.7367508
])
testAucs = numpy.array([
0.6789078, 0.6844632, 0.6867918, 0.6873420, 0.6874820, 0.6874400,
0.6874400, 0.6874400
])
i = 0
#The results are approximately the same, but not exactly
for maxDepth in maxDepths:
treeRank = TreeRank(DecisionTree)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(X, y)
trainScores = treeRank.predict(X)
testScores = treeRank.predict(testX)
#print(Evaluator.auc(trainScores, y), Evaluator.auc(testScores, testY))
#self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 2)
#self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1)
i += 1
#Compare tree to that of R version
tree = treeRank.getTree()
def testPredict2(self):
# Test on Gauss2D dataset
dataDir = PathDefaults.getDataDir()
fileName = dataDir + "Gauss2D_learn.csv"
XY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",")
X = XY[:, 0:2]
y = XY[:, 2]
fileName = dataDir + "Gauss2D_test.csv"
testXY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",")
testX = testXY[:, 0:2]
testY = testXY[:, 2]
X = Standardiser().standardiseArray(X)
testX = Standardiser().standardiseArray(testX)
maxDepths = range(3, 10)
trainAucs = numpy.array(
[0.7194734, 0.7284824, 0.7332185, 0.7348198, 0.7366152, 0.7367508, 0.7367508, 0.7367508]
)
testAucs = numpy.array([0.6789078, 0.6844632, 0.6867918, 0.6873420, 0.6874820, 0.6874400, 0.6874400, 0.6874400])
i = 0
# The results are approximately the same, but not exactly
for maxDepth in maxDepths:
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(X, y)
trainScores = treeRank.predict(X)
testScores = treeRank.predict(testX)
self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 2)
self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1)
i += 1
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 testLearnModel(self):
maxDepth = 2
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
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)
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 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 testLearnModel(self):
maxDepth = 2
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
self.assertTrue(tree.depth() <= maxDepth)
def testClassifyNode(self):
# Try on a single split tree
maxDepth = 1
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
root = tree.getVertex(tree.getRootId())
root.setTestInds(numpy.arange(self.X.shape[0]))
treeRank.classifyNode(tree, self.X, 0, 0)
self.assertTrue((tree.getVertex((0, 0)).getTrainInds() == tree.getVertex((0, 0)).getTestInds()).all())
self.assertTrue((tree.getVertex((1, 0)).getTrainInds() == tree.getVertex((1, 0)).getTestInds()).all())
self.assertTrue((tree.getVertex((1, 1)).getTrainInds() == tree.getVertex((1, 1)).getTestInds()).all())
def testCut(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
depth = tree.depth()
cutTree = TreeRank.cut(tree, depth - 1)
self.assertEquals(cutTree.depth(), depth - 1)
#Check if leaves are marked correctly
for leaf in cutTree.leaves():
self.assertTrue(cutTree.getVertex(leaf).isLeafNode())
cutTree = TreeRank.cut(tree, 1)
for leaf in cutTree.leaves():
self.assertTrue(cutTree.getVertex(leaf).isLeafNode())
def testClassifyNode(self):
#Try on a single split tree
maxDepth = 1
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
root = tree.getVertex(tree.getRootId())
root.setTestInds(numpy.arange(self.X.shape[0]))
treeRank.classifyNode(tree, self.X, 0, 0)
self.assertTrue((tree.getVertex(
(0, 0)).getTrainInds() == tree.getVertex(
(0, 0)).getTestInds()).all())
self.assertTrue((tree.getVertex(
(1, 0)).getTrainInds() == tree.getVertex(
(1, 0)).getTestInds()).all())
self.assertTrue((tree.getVertex(
(1, 1)).getTrainInds() == tree.getVertex(
(1, 1)).getTestInds()).all())
def testFeaturesSize(self):
treeRank = TreeRank(self.leafRanklearner)
featureSize = 0.5
treeRank.setFeatureSize(featureSize)
treeRank.learnModel(self.X, self.y)
scores = treeRank.predict(self.X)
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
#print(node.getFeatureInds())
self.assertEquals(node.getFeatureInds().shape[0],
numpy.round(featureSize * self.X.shape[1]))
self.assertEquals(
numpy.unique(node.getFeatureInds()).shape[0],
node.getFeatureInds().shape[0])
def testFeaturesSize(self):
treeRank = TreeRank(self.leafRanklearner)
featureSize = 0.5
treeRank.setFeatureSize(featureSize)
treeRank.learnModel(self.X, self.y)
scores = treeRank.predict(self.X)
tree = treeRank.getTree()
vertexIds = tree.getAllVertexIds()
for vertexId in vertexIds:
node = tree.getVertex(vertexId)
# print(node.getFeatureInds())
self.assertEquals(node.getFeatureInds().shape[0], numpy.round(featureSize * self.X.shape[1]))
self.assertEquals(numpy.unique(node.getFeatureInds()).shape[0], node.getFeatureInds().shape[0])
def testCut(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
tree = treeRank.getTree()
depth = tree.depth()
cutTree = TreeRank.cut(tree, depth - 1)
self.assertEquals(cutTree.depth(), depth - 1)
# Check if leaves are marked correctly
for leaf in cutTree.leaves():
self.assertTrue(cutTree.getVertex(leaf).isLeafNode())
cutTree = TreeRank.cut(tree, 1)
for leaf in cutTree.leaves():
self.assertTrue(cutTree.getVertex(leaf).isLeafNode())
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 testInit(self):
treeRank = TreeRank(self.leafRanklearner)
def testMaxDepth(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
depth = treeRank.getTree().depth()
self.assertTrue(depth <= maxDepth)
maxDepth = 1
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
depth2 = treeRank.getTree().depth()
self.assertTrue(depth2 <= maxDepth)
self.assertTrue(depth > depth2)
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 testMaxDepth(self):
maxDepth = 10
treeRank = TreeRank(self.leafRanklearner)
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
depth = treeRank.getTree().depth()
self.assertTrue(depth <= maxDepth)
maxDepth = 1
treeRank.setMaxDepth(maxDepth)
treeRank.learnModel(self.X, self.y)
depth2 = treeRank.getTree().depth()
self.assertTrue(depth2 <= maxDepth)
self.assertTrue(depth > depth2)
