def testParallelPen(self):
#Check if penalisation == inf when treeSize < gamma
numExamples = 100
X, y = data.make_regression(numExamples)
learner = DecisionTreeLearner(pruneType="CART", maxDepth=10, minSplit=2)
paramDict = {}
paramDict["setGamma"] = numpy.array(numpy.round(2**numpy.arange(1, 10, 0.5)-1), dtype=numpy.int)
folds = 3
alpha = 1.0
Cvs = numpy.array([(folds-1)*alpha])
idx = Sampling.crossValidation(folds, X.shape[0])
resultsList = learner.parallelPen(X, y, idx, paramDict, Cvs)
learner, trainErrors, currentPenalties = resultsList[0]
learner.setGamma(2**10)
treeSize = 0
#Let's work out the size of the unpruned tree
for trainInds, testInds in idx:
trainX = X[trainInds, :]
trainY = y[trainInds]
learner.learnModel(trainX, trainY)
treeSize += learner.tree.size
treeSize /= float(folds)
self.assertTrue(numpy.isinf(currentPenalties[paramDict["setGamma"]>treeSize]).all())
self.assertTrue(not numpy.isinf(currentPenalties[paramDict["setGamma"]<treeSize]).all())
def testLearningRate(self):
numExamples = 100
trainX, trainY = data.make_regression(numExamples)
trainX = Standardiser().normaliseArray(trainX)
trainY = Standardiser().normaliseArray(trainY)
learner = DecisionTreeLearner(pruneType="CART", maxDepth=20, minSplit=1)
foldsSet = numpy.arange(2, 7, 2)
gammas = numpy.array(numpy.round(2**numpy.arange(1, 8, 1)-1), dtype=numpy.int)
paramDict = {}
paramDict["setGamma"] = gammas
betaGrid = learner.learningRate(trainX, trainY, foldsSet, paramDict)
#Compute beta more directly
numParams = gammas.shape[0]
sampleSize = trainX.shape[0]
sampleMethod = Sampling.crossValidation
Cvs = numpy.array([1])
penalties = numpy.zeros((foldsSet.shape[0], numParams))
betas = numpy.zeros(gammas.shape[0])
for k in range(foldsSet.shape[0]):
folds = foldsSet[k]
logging.debug("Folds " + str(folds))
idx = sampleMethod(folds, trainX.shape[0])
#Now try penalisation
resultsList = learner.parallelPen(trainX, trainY, idx, paramDict, Cvs)
bestLearner, trainErrors, currentPenalties = resultsList[0]
penalties[k, :] = currentPenalties
for i in range(gammas.shape[0]):
inds = numpy.logical_and(numpy.isfinite(penalties[:, i]), penalties[:, i]>0)
tempPenalties = penalties[:, i][inds]
tempfoldsSet = numpy.array(foldsSet, numpy.float)[inds]
if tempPenalties.shape[0] > 1:
x = numpy.log((tempfoldsSet-1)/tempfoldsSet*sampleSize)
y = numpy.log(tempPenalties)+numpy.log(tempfoldsSet)
clf = linear_model.LinearRegression()
clf.fit(numpy.array([x]).T, y)
betas[i] = clf.coef_[0]
betas = -betas
nptst.assert_array_equal(betaGrid, betas)
treeLeaveSizes = numpy.zeros(numParams)
for j in range(numRealisations):
print("")
logging.debug("j=" + str(j))
trainX, trainY, testX, testY = loadMethod(dataDir, datasetName, j)
logging.debug("Loaded dataset with " + str(trainX.shape) + " train and " + str(testX.shape) + " test examples")
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
trainX = trainX[trainInds,:]
trainY = trainY[trainInds]
idx = sampleMethod(folds, trainX.shape[0])
#Now try penalisation
resultsList = learner.parallelPen(trainX, trainY, idx, paramDict, Cvs)
bestLearner, trainErrors, currentPenalties = resultsList[0]
meanPenalties[k] += currentPenalties
meanTrainError += trainErrors
predY = bestLearner.predict(testX)
meanErrors[k] += bestLearner.getMetricMethod()(testY, predY)
#Compute ideal penalties and error on training data
meanIdealPenalities[k] += learner.parallelPenaltyGrid(trainX, trainY, testX, testY, paramDict)
for i in range(len(paramDict["setGamma"])):
allError = 0
learner.setGamma(paramDict["setGamma"][i])
for trainInds, testInds in idx:
validX = trainX[trainInds, :]
validY = trainY[trainInds]
