def testAucFromRoc(self):
self.treeRankForest = TreeRankForestR()
self.treeRankForest.setLeafRank(self.treeRankForest.getTreeRankLib().LRsvm)
self.treeRankForest.learnModel(self.X, self.Y)
roc = self.treeRankForest.predictROC(self.X, self.Y)
auc = self.treeRankForest.aucFromROC(roc)
logging.debug(self.treeRankForest)
self.assertAlmostEquals(auc, 0.7603246, 1)
self.assertTrue(0 <= auc <= 1)
def setUp(self):
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
self.baseLib = importr('base')
self.treeRankLib = importr('TreeRank')
utilLib = importr("utils")
utilLib.data("TRdata")
XY = numpy.array(robjects.r["Gauss2D.learn"]).T
self.X = XY[:, 0:2]
self.Y = numpy.array([XY[:, 2]], numpy.int).T
trainExamples = 500
self.trainX = self.X[0:trainExamples, :]
self.trainY = self.Y[0:trainExamples, :]
self.testX = self.X[trainExamples:, :]
self.testY = self.Y[trainExamples:, :]
self.treeRankForest = TreeRankForestR()
def profileLearnModel(self):
treeRankForest = TreeRankForestR()
treeRankForest.printMemStats = True
treeRankForest.setMaxDepth(2)
treeRankForest.setNumTrees(5)
numExamples = 650
numFeatures = 950
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.array(numpy.random.rand(numExamples) < 0.1, numpy.int)
def run():
for i in range(10):
print("Iteration " + str(i))
treeRankForest.learnModel(X, Y)
#print(treeRank.getTreeSize())
#print(treeRank.getTreeDepth())
ProfileUtils.profile('run()', globals(), locals())
def testInit(self):
self.treeRankForest = TreeRankForestR()
