def testCvPrune(self):
numExamples = 500
X, y = data.make_regression(numExamples)
y = Standardiser().standardiseArray(y)
numTrain = numpy.round(numExamples * 0.33)
numValid = numpy.round(numExamples * 0.33)
trainX = X[0:numTrain, :]
trainY = y[0:numTrain]
validX = X[numTrain:numTrain+numValid, :]
validY = y[numTrain:numTrain+numValid]
testX = X[numTrain+numValid:, :]
testY = y[numTrain+numValid:]
learner = DecisionTreeLearner()
learner.learnModel(trainX, trainY)
error1 = Evaluator.rootMeanSqError(learner.predict(testX), testY)
#print(learner.getTree())
unprunedTree = learner.tree.copy()
learner.setGamma(1000)
learner.cvPrune(trainX, trainY)
self.assertEquals(unprunedTree.getNumVertices(), learner.tree.getNumVertices())
learner.setGamma(100)
learner.cvPrune(trainX, trainY)
#Test if pruned tree is subtree of current:
for vertexId in learner.tree.getAllVertexIds():
self.assertTrue(vertexId in unprunedTree.getAllVertexIds())
#The error should be better after pruning
learner.learnModel(trainX, trainY)
#learner.cvPrune(validX, validY, 0.0, 5)
learner.repPrune(validX, validY)
error2 = Evaluator.rootMeanSqError(learner.predict(testX), testY)
self.assertTrue(error1 >= error2)
trainX = X[0:numTrainExamples, :] trainY = y[0:numTrainExamples] validX = X[numTrainExamples:numTrainExamples+numValidExamples, :] validY = y[numTrainExamples:numTrainExamples+numValidExamples] testX = X[numTrainExamples+numValidExamples:, :] testY = y[numTrainExamples+numValidExamples:] learner = DecisionTreeLearner(minSplit=1, maxDepth=50) learner.learnModel(trainX, trainY) #Seem to be optimal alphaThreshold = 100.0 learner.setAlphaThreshold(alphaThreshold) learner.repPrune(validX, validY) #learner.tree = learner.tree.cut(3) predY = learner.predict(testX) plt.figure(0) plt.scatter(testX[:, 0], testX[:, 1], c=testY, s=50, vmin=0, vmax=1) plt.colorbar() plt.figure(1) plt.scatter(testX[:, 0], testX[:, 1], c=predY, s=50, vmin=0, vmax=1) plt.colorbar() colormap = matplotlib.cm.get_cmap() def displayTree(learner, vertexId, minX0, maxX0, minX1, maxX1, colormap):
