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)
for vertexId in learner.tree.getAllVertexIds():
alpha = learner.tree.getVertex(vertexId).alpha
if alpha < minAlpha:
minAlpha = alpha
if alpha > maxAlpha:
maxAlpha = alpha
numAlphas = 100
alphas = numpy.linspace(maxAlpha+0.1, minAlpha, numAlphas)
errors = numpy.zeros(numAlphas)
for i in range(alphas.shape[0]):
#learner.learnModel(trainX, trainY)
learner.setAlphaThreshold(alphas[i])
learner.cvPrune(trainX, trainY)
#learner.cvPrune(validX, validY, alphas[numpy.argmin(errors)])
#learner.prune(validX, validY, alphas[i])
predY = learner.predict(testX)
errors[i] = Evaluator.rootMeanSqError(predY, testY)
plt.figure(3)
plt.scatter(alphas, errors)
#Now plot best tree
plt.figure(4)
learner.learnModel(trainX, trainY)
#learner.cvPrune(validX, validY, alphas[numpy.argmin(errors)])
learner.setAlphaThreshold(alphas[numpy.argmin(errors)])
learner.cvPrune(trainX, trainY)
rootId = learner.tree.getRootId()
