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)
logging.debug("Dataset " + datasetName)
learner = DecisionTreeLearner(criterion="mse", maxDepth=100, minSplit=1, pruneType="CART", processes=numProcesses)
learner.setChunkSize(3)
outfileName = outputDir + datasetName + "Beta"
for m in range(sampleSizes.shape[0]):
sampleSize = sampleSizes[m]
logging.debug("Sample size " + str(sampleSize))
penalties = numpy.zeros((foldsSet.shape[0], numParams))
betas = numpy.zeros((gammas.shape[0], sampleSizes.shape[0]))
for j in range(numRealisations):
logging.debug("Realisation: " + str(j))
trainX, trainY, testX, testY = loadMethod(dataDir, datasetName, j)
numpy.random.seed(21)
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
betas = learner.learningRate(validX, validY, foldsSet, paramDict)
print(betas)
plt.plot(gammas, betas)
plt.show()
break
