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))