def testSetWeight(self):
#Try weight = 0 and weight = 1
decisionTree = DecisionTree()
decisionTree.setWeight(0.0)
decisionTree.learnModel(self.X, self.y)
predY = decisionTree.predict(self.X)
self.assertTrue((predY == numpy.zeros(predY.shape[0])).all())
decisionTree.setWeight(1.0)
decisionTree.learnModel(self.X, self.y)
predY = decisionTree.predict(self.X)
self.assertTrue((predY == numpy.ones(predY.shape[0])).all())
def testPredict2(self):
#We play around with parameters to maximise AUC on the IGF1_0-Haar data
dataDir = PathDefaults.getDataDir()
fileName = dataDir + "IGF1_0-Haar.npy"
XY = numpy.load(fileName)
X = XY[:, 0:XY.shape[1]-1]
y = XY[:, XY.shape[1]-1].ravel()
weight = numpy.bincount(numpy.array(y, numpy.int))[0]/float(y.shape[0])
#weight = 0.5
#weight = 0.9
folds = 3
decisionTree = DecisionTree()
decisionTree.setWeight(weight)
decisionTree.setMaxDepth(50)
#decisionTree.setMinSplit(100)
mean, var = decisionTree.evaluateCv(X, y, folds, Evaluator.auc)
logging.debug("AUC = " + str(mean))
logging.debug("Var = " + str(var))
