def testLearnModel(self):
maxDepth = 2
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setMaxDepth(maxDepth)
treeRankForest.learnModel(self.X, self.y)
forest = treeRankForest.getForest()
self.assertEquals(len(forest), treeRankForest.getNumTrees())
for treeRank in forest:
tree = treeRank.getTree()
self.assertTrue(tree.depth() <= maxDepth)
def testPredict(self):
maxDepth = 2
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setMaxDepth(maxDepth)
treeRankForest.learnModel(self.X, self.y)
scores = treeRankForest.predict(self.X)
scores2 = numpy.zeros(self.X.shape[0])
forest = treeRankForest.getForest()
for i in range(len(forest)):
scores2 += forest[i].predict(self.X)
scores2 /= treeRankForest.getNumTrees()
self.assertTrue((scores==scores2).all())
def testVariableImportance(self):
X, y, c = ExamplesGenerator().generateBinaryExamples(numExamples=100, verbose=True)
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setFeatureSize(0.5)
treeRankForest.setNumTrees(20)
treeRankForest.setSampleSize(1.0)
treeRankForest.learnModel(X, y)
weightVector = treeRankForest.variableImportance(X, y)
#Seems to work, sort of
print(c)
print(weightVector)
print(numpy.argsort(c))
print(numpy.argsort(weightVector))
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]
y = y*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]
testY = testY*2-1
X = Standardiser().standardiseArray(X)
testX = Standardiser().standardiseArray(testX)
numTrees = 5
minSplit = 50
maxDepths = range(3, 10)
trainAucs = numpy.array([0.7252582, 0.7323278, 0.7350289, 0.7372529, 0.7399985, 0.7382176, 0.7395104, 0.7386347])
testAucs = numpy.array([0.6806122, 0.6851614, 0.6886183, 0.6904147, 0.6897266, 0.6874600, 0.6875980, 0.6878801])
i = 0
#The results are approximately the same, but not exactly
for maxDepth in maxDepths:
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setMaxDepth(maxDepth)
treeRankForest.setMinSplit(minSplit)
treeRankForest.setNumTrees(numTrees)
treeRankForest.learnModel(X, y)
trainScores = treeRankForest.predict(X)
testScores = treeRankForest.predict(testX)
print(Evaluator.auc(trainScores, y), Evaluator.auc(testScores, testY))
self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 1)
self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1)
i+=1
