def testSetMaxDepth(self):
maxDepth = 20
randomForest = RandomForest()
randomForest.setMaxDepth(maxDepth)
randomForest.learnModel(self.X, self.y)
#self.assertTrue(RandomForest.depth(randomForest.getClassifier().tree) <= maxDepth+1)
maxDepth = 5
randomForest = RandomForest()
randomForest.setMaxDepth(maxDepth)
randomForest.learnModel(self.X, self.y)
def testSetMaxDepth(self):
maxDepth = 20
randomForest = RandomForest()
randomForest.setMaxDepth(maxDepth)
randomForest.learnModel(self.X, self.y)
#self.assertTrue(RandomForest.depth(randomForest.getClassifier().tree) <= maxDepth+1)
maxDepth = 5
randomForest = RandomForest()
randomForest.setMaxDepth(maxDepth)
randomForest.learnModel(self.X, self.y)
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
randomForest = RandomForest()
randomForest.setWeight(weight)
randomForest.setMaxDepth(50)
#randomForest.setMinSplit(100)
mean, var = randomForest.evaluateCv(X, y, folds, Evaluator.auc)
logging.debug("AUC = " + str(mean))
logging.debug("Var = " + str(var))
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
randomForest = RandomForest()
randomForest.setWeight(weight)
randomForest.setMaxDepth(50)
#randomForest.setMinSplit(100)
mean, var = randomForest.evaluateCv(X, y, folds, Evaluator.auc)
logging.debug("AUC = " + str(mean))
logging.debug("Var = " + str(var))
