def testGenerate(self): generate = DecisionTree.generate(5, 50) learner = generate() learner.learnModel(self.X, self.y) self.assertEquals(learner.getMaxDepth(), 5) self.assertEquals(learner.getMinSplit(), 50)
def testPredict(self): decisionTree = DecisionTree() decisionTree.learnModel(self.X, self.y) predY = decisionTree.predict(self.X) inds = numpy.random.permutation(self.X.shape[0]) predY2 = decisionTree.predict(self.X[inds, :]) self.assertTrue((predY[inds] == predY2).all()) #Let's test on -1, +1 labels y2 = (self.y*2)-1 decisionTree.learnModel(self.X, y2) predY2 = decisionTree.predict(self.X) self.assertTrue((predY2 == predY*2-1).all())
def testParallelPenaltyGrid(self): folds = 3 idx = Sampling.crossValidation(folds, self.X.shape[0]) decisionTree = DecisionTree() bestLearner, meanErrors = decisionTree.parallelVfcv(self.X, self.y, idx) trainX = self.X[0:40, :] trainY = self.y[0:40] paramDict = {} paramDict["setMinSplit"] = decisionTree.getMinSplits() paramDict["setMaxDepth"] = decisionTree.getMaxDepths() idealPenalties = decisionTree.parallelPenaltyGrid(trainX, trainY, self.X, self.y, paramDict)
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))
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 testMinSplit(self): decisionTree = DecisionTree() decisionTree.setMinSplit(20) decisionTree.learnModel(self.X, self.y) size = decisionTree.getTree().node_count #orngTree.printTree(decisionTree.getClassifier()) decisionTree.setMinSplit(1) decisionTree.learnModel(self.X, self.y) size2 = decisionTree.getTree().node_count #orngTree.printTree(decisionTree.getClassifier()) self.assertTrue(size < size2)
def testLearnModel(self): decisionTree = DecisionTree() decisionTree.learnModel(self.X, self.y) tree = decisionTree.getClassifier()
def testParallelVfcv(self): folds = 3 idx = Sampling.crossValidation(folds, self.X.shape[0]) decisionTree = DecisionTree() bestLearner, meanErrors = decisionTree.parallelVfcv(self.X, self.y, idx)
def testSetMaxDepth(self): maxDepth = 20 decisionTree = DecisionTree() decisionTree.setMaxDepth(maxDepth) decisionTree.learnModel(self.X, self.y) #self.assertTrue(DecisionTree.depth(decisionTree.getClassifier().tree) <= maxDepth+1) maxDepth = 5 decisionTree = DecisionTree() decisionTree.setMaxDepth(maxDepth) decisionTree.learnModel(self.X, self.y)