def testUnlock(self):
fileLock = FileLock(self.fileName)
fileLock.lock()
self.assertTrue(fileLock.isLocked())
fileLock.unlock()
self.assertTrue(not fileLock.isLocked())
def testUnlock(self):
fileLock = FileLock(self.fileName)
fileLock.lock()
self.assertTrue(fileLock.isLocked())
fileLock.unlock()
self.assertTrue(not fileLock.isLocked())
def saveWeightVectorResults(self, X, Y, learner, paramDict, fileName):
"""
Save the results of the variable importance
"""
filelock = FileLock(fileName)
gc.collect()
if not filelock.isLocked() and not filelock.fileExists():
filelock.lock()
try:
logging.debug("Computing weights file " + fileName)
logging.debug("Shape of examples: " + str(X.shape) + ", number of +1: " + str(numpy.sum(Y==1)) + ", -1: " + str(numpy.sum(Y==-1)))
tempLearner = learner.copy()
logging.debug("Initial learner is " + str(tempLearner))
idx = StratifiedKFold(Y, self.innerFolds)
tempLearner.processes = self.numProcesses
bestLearner, cvGrid = tempLearner.parallelModelSelect(X, Y, idx, paramDict)
bestLearner = tempLearner.getBestLearner(cvGrid, paramDict, X, Y, idx, best="max")
logging.debug("Best learner is " + str(bestLearner))
bestLearner.learnModel(X, Y)
weightVector = bestLearner.variableImportance(X, Y)
numpy.save(fileName, weightVector)
logging.debug("Saved results as : " + fileName)
finally:
filelock.unlock()
else:
logging.debug("File exists, or is locked: " + fileName)
def saveStats(args):
i, theta = args
resultsFileName = outputDir + "SimStats" + str(i) + ".pkl"
lock = FileLock(resultsFileName)
if not lock.fileExists() and not lock.isLocked():
lock.lock()
model = HIVModelUtils.createModel(targetGraph, startDate, endDate, recordStep, M, matchAlpha, breakSize, matchAlg, theta=thetaArray[i])
times, infectedIndices, removedIndices, graph, compTimes, graphMetrics = HIVModelUtils.simulate(model)
times = numpy.arange(startDate, endDate+1, recordStep)
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats, finalRemovedDegrees = HIVModelUtils.generateStatistics(graph, times)
stats = times, vertexArray, infectedIndices, removedGraphStats, finalRemovedDegrees, graphMetrics.objectives, compTimes
Util.savePickle(stats, resultsFileName)
lock.unlock()
else:
logging.debug("Results already computed: " + str(resultsFileName))
def saveResult(self, X, Y, learner, paramDict, fileName):
"""
Save a single result to file, checking if the results have already been computed
"""
filelock = FileLock(fileName)
gc.collect()
if not filelock.isLocked() and not filelock.fileExists():
filelock.lock()
try:
logging.debug("Computing file " + fileName)
logging.debug("Shape of examples: " + str(X.shape) + ", number of +1: " + str(numpy.sum(Y==1)) + ", -1: " + str(numpy.sum(Y==-1)))
#idxFull = Sampling.crossValidation(self.outerFolds, X.shape[0])
idxFull = StratifiedKFold(Y, self.outerFolds)
errors = numpy.zeros(self.outerFolds)
for i, (trainInds, testInds) in enumerate(idxFull):
logging.debug("Outer fold: " + str(i))
trainX, trainY = X[trainInds, :], Y[trainInds]
testX, testY = X[testInds, :], Y[testInds]
#idx = Sampling.crossValidation(self.innerFolds, trainX.shape[0])
idx = StratifiedKFold(trainY, self.innerFolds)
logging.debug("Initial learner is " + str(learner))
bestLearner, cvGrid = learner.parallelModelSelect(trainX, trainY, idx, paramDict)
bestLearner = learner.getBestLearner(cvGrid, paramDict, trainX, trainY, idx, best="max")
logging.debug("Best learner is " + str(bestLearner))
bestLearner.learnModel(trainX, trainY)
predY = bestLearner.predict(testX)
errors[i] = Evaluator.auc(predY, testY)
logging.debug("Mean auc: " + str(numpy.mean(errors)))
numpy.save(fileName, errors)
logging.debug("Saved results as : " + fileName)
finally:
filelock.unlock()
else:
logging.debug("File exists, or is locked: " + fileName)
def computeLearningRates(datasetNames, numProcesses, fileNameSuffix, learnerName, sampleSizes, foldsSet):
dataDir = PathDefaults.getDataDir() + "modelPenalisation/"
outputDir = PathDefaults.getOutputDir() + "modelPenalisation/"
learner, loadMethod, dataDir, outputDir, paramDict = getSetup(learnerName, dataDir, outputDir, numProcesses)
for i in range(len(datasetNames)):
logging.debug("Learning using dataset " + datasetNames[i][0])
outfileName = outputDir + datasetNames[i][0] + fileNameSuffix
fileLock = FileLock(outfileName + ".npz")
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
numRealisations = datasetNames[i][1]
gridShape = [numRealisations, sampleSizes.shape[0]]
gridShape.extend(list(learner.gridShape(paramDict)))
gridShape = tuple(gridShape)
betaGrids = numpy.zeros(gridShape)
for k in range(sampleSizes.shape[0]):
sampleSize = sampleSizes[k]
logging.debug("Using sample size " + str(sampleSize))
for j in range(numRealisations):
Util.printIteration(j, 1, numRealisations, "Realisation: ")
trainX, trainY, testX, testY = loadMethod(dataDir, datasetNames[i][0], j)
numpy.random.seed(21)
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
betaGrids[j, k, :] = learner.learningRate(validX, validY, foldsSet, paramDict)
numpy.savez(outfileName, betaGrids)
logging.debug("Saved results as file " + outfileName + ".npz")
fileLock.unlock()
def testLock(self):
fileLock = FileLock(self.fileName)
fileLock.lock()
def runToyExp(datasetNames, sampleSizes, foldsSet, cvScalings, sampleMethods, numProcesses, fileNameSuffix):
dataDir = PathDefaults.getDataDir() + "modelPenalisation/toy/"
outputDir = PathDefaults.getOutputDir() + "modelPenalisation/"
svm = LibSVM()
numCs = svm.getCs().shape[0]
numGammas = svm.getGammas().shape[0]
numMethods = 1 + (1 + cvScalings.shape[0])
numParams = 2
runIdeal = True
runCv = True
runVfpen = True
for i in range(len(datasetNames)):
datasetName = datasetNames[i][0]
numRealisations = datasetNames[i][1]
logging.debug("Learning using dataset " + datasetName)
for s in range(len(sampleMethods)):
sampleMethod = sampleMethods[s][1]
outfileName = outputDir + datasetName + sampleMethods[s][0] + fileNameSuffix
fileLock = FileLock(outfileName + ".npz")
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
errors = numpy.zeros((numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods))
params = numpy.zeros((numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods, numParams))
errorGrids = numpy.zeros(
(numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods, numCs, numGammas)
)
approxGrids = numpy.zeros(
(numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods, numCs, numGammas)
)
idealGrids = numpy.zeros((numRealisations, len(sampleSizes), foldsSet.shape[0], numCs, numGammas))
data = numpy.load(dataDir + datasetName + ".npz")
gridPoints, trainX, trainY, pdfX, pdfY1X, pdfYminus1X = (
data["arr_0"],
data["arr_1"],
data["arr_2"],
data["arr_3"],
data["arr_4"],
data["arr_5"],
)
# We form a test set from the grid points
testX = numpy.zeros((gridPoints.shape[0] ** 2, 2))
for m in range(gridPoints.shape[0]):
testX[m * gridPoints.shape[0] : (m + 1) * gridPoints.shape[0], 0] = gridPoints
testX[m * gridPoints.shape[0] : (m + 1) * gridPoints.shape[0], 1] = gridPoints[m]
for j in range(numRealisations):
Util.printIteration(j, 1, numRealisations, "Realisation: ")
for k in range(sampleSizes.shape[0]):
sampleSize = sampleSizes[k]
for m in range(foldsSet.shape[0]):
folds = foldsSet[m]
logging.debug("Using sample size " + str(sampleSize) + " and " + str(folds) + " folds")
perm = numpy.random.permutation(trainX.shape[0])
trainInds = perm[0:sampleSize]
validX = trainX[trainInds, :]
validY = trainY[trainInds]
svm = LibSVM(processes=numProcesses)
# Find ideal penalties
if runIdeal:
logging.debug("Finding ideal grid of penalties")
idealGrids[j, k, m, :, :] = parallelPenaltyGridRbf(
svm, validX, validY, testX, gridPoints, pdfX, pdfY1X, pdfYminus1X
)
# Cross validation
if runCv:
logging.debug("Running V-fold cross validation")
methodInd = 0
idx = sampleMethod(folds, validY.shape[0])
if sampleMethod == Sampling.bootstrap:
bootstrap = True
else:
bootstrap = False
bestSVM, cvGrid = svm.parallelVfcvRbf(validX, validY, idx, True, bootstrap)
predY, decisionsY = bestSVM.predict(testX, True)
decisionGrid = numpy.reshape(
decisionsY, (gridPoints.shape[0], gridPoints.shape[0]), order="F"
)
errors[j, k, m, methodInd] = ModelSelectUtils.bayesError(
gridPoints, decisionGrid, pdfX, pdfY1X, pdfYminus1X
)
params[j, k, m, methodInd, :] = numpy.array([bestSVM.getC(), bestSVM.getKernelParams()])
errorGrids[j, k, m, methodInd, :, :] = cvGrid
# v fold penalisation
if runVfpen:
logging.debug("Running penalisation")
# BIC penalisation
Cv = float((folds - 1) * numpy.log(validX.shape[0]) / 2)
tempCvScalings = cvScalings * (folds - 1)
tempCvScalings = numpy.insert(tempCvScalings, 0, Cv)
# Use cross validation
idx = sampleMethod(folds, validY.shape[0])
svmGridResults = svm.parallelVfPenRbf(validX, validY, idx, tempCvScalings)
for n in range(len(tempCvScalings)):
bestSVM, trainErrors, approxGrid = svmGridResults[n]
methodInd = n + 1
predY, decisionsY = bestSVM.predict(testX, True)
decisionGrid = numpy.reshape(
decisionsY, (gridPoints.shape[0], gridPoints.shape[0]), order="F"
)
errors[j, k, m, methodInd] = ModelSelectUtils.bayesError(
gridPoints, decisionGrid, pdfX, pdfY1X, pdfYminus1X
)
params[j, k, m, methodInd, :] = numpy.array(
[bestSVM.getC(), bestSVM.getKernelParams()]
)
errorGrids[j, k, m, methodInd, :, :] = trainErrors + approxGrid
approxGrids[j, k, m, methodInd, :, :] = approxGrid
meanErrors = numpy.mean(errors, 0)
print(meanErrors)
meanParams = numpy.mean(params, 0)
print(meanParams)
meanErrorGrids = numpy.mean(errorGrids, 0)
stdErrorGrids = numpy.std(errorGrids, 0)
meanIdealGrids = numpy.mean(idealGrids, 0)
stdIdealGrids = numpy.std(idealGrids, 0)
meanApproxGrids = numpy.mean(approxGrids, 0)
stdApproxGrids = numpy.std(approxGrids, 0)
numpy.savez(
outfileName,
errors,
params,
meanErrorGrids,
stdErrorGrids,
meanIdealGrids,
stdIdealGrids,
meanApproxGrids,
stdApproxGrids,
)
logging.debug("Saved results as file " + outfileName + ".npz")
fileLock.unlock()
else:
logging.debug("Results already computed")
logging.debug("All done!")
def runBenchmarkExp(datasetNames, sampleSizes, foldsSet, cvScalings, sampleMethods, numProcesses, fileNameSuffix, learnerName, betaNameSuffix):
dataDir = PathDefaults.getDataDir() + "modelPenalisation/"
outputDir = PathDefaults.getOutputDir() + "modelPenalisation/"
learner, loadMethod, dataDir, outputDir, paramDict = getSetup(learnerName, dataDir, outputDir, numProcesses)
numParams = len(paramDict.keys())
numMethods = 1 + (cvScalings.shape[0] + 1)
runCv = True
runVfpen = True
for i in range(len(datasetNames)):
datasetName = datasetNames[i][0]
numRealisations = datasetNames[i][1]
logging.debug("Learning using dataset " + datasetName)
#Load learning rates for penalisation
betafileName = outputDir + datasetNames[i][0] + betaNameSuffix + ".npz"
betaGrids = numpy.load(betafileName)["arr_0"]
betaGrids = numpy.clip(betaGrids, 0, 1)
for s in range(len(sampleMethods)):
sampleMethod = sampleMethods[s][1]
outfileName = outputDir + datasetName + sampleMethods[s][0] + fileNameSuffix
fileLock = FileLock(outfileName + ".npz")
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
errors = numpy.zeros((numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods))
params = numpy.zeros((numRealisations, len(sampleSizes), foldsSet.shape[0], numMethods, numParams))
errorShape = [numRealisations, len(sampleSizes), foldsSet.shape[0] ,numMethods]
errorShape.extend(list(learner.gridShape(paramDict)))
errorShape = tuple(errorShape)
gridShape = [numRealisations, len(sampleSizes), foldsSet.shape[0] ,numMethods]
gridShape.extend(list(learner.gridShape(paramDict)))
gridShape = tuple(gridShape)
idealErrorShape = [numRealisations, len(sampleSizes)]
idealErrorShape.extend(list(learner.gridShape(paramDict)))
idealErrorShape = tuple(idealErrorShape)
errorGrids = numpy.zeros(errorShape)
approxGrids = numpy.zeros(errorShape)
for j in range(numRealisations):
Util.printIteration(j, 1, numRealisations, "Realisation: ")
trainX, trainY, testX, testY = loadMethod(dataDir, datasetName, j)
for k in range(sampleSizes.shape[0]):
sampleSize = sampleSizes[k]
for m in range(foldsSet.shape[0]):
if foldsSet[m] < sampleSize:
folds = foldsSet[m]
else:
folds = sampleSize
logging.debug("Using sample size " + str(sampleSize) + " and " + str(folds) + " folds")
numpy.random.seed(21)
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
#Cross validation
if runCv:
logging.debug("Running simple sampling using " + str(sampleMethod))
methodInd = 0
idx = sampleMethod(folds, validY.shape[0])
bestLearner, cvGrid = learner.parallelModelSelect(validX, validY, idx, paramDict)
predY = bestLearner.predict(testX)
errors[j, k, m, methodInd] = bestLearner.getMetricMethod()(testY, predY)
params[j, k, m, methodInd, :] = bestLearner.getParamsArray(paramDict)
errorGrids[j, k, m, methodInd, :] = cvGrid
#v fold penalisation
if runVfpen:
logging.debug("Running penalisation using " + str(sampleMethod))
#Corrected penalisation give by using learning rate
tempCvScalings = list(cvScalings * (folds-1))
tempCvScalings.insert(0, betaGrids[j, k, :])
idx = sampleMethod(folds, validY.shape[0])
learnerGridResults = learner.parallelPen(validX, validY, idx, paramDict, tempCvScalings)
for n in range(len(tempCvScalings)):
bestLearner, trainErrors, approxGrid = learnerGridResults[n]
predY = bestLearner.predict(testX)
methodInd = n + 1
errors[j, k, m, methodInd] = bestLearner.getMetricMethod()(testY, predY)
params[j, k, m, methodInd, :] = bestLearner.getParamsArray(paramDict)
errorGrids[j, k, m, methodInd, :] = trainErrors + approxGrid
approxGrids[j, k, m, methodInd, :] = approxGrid
meanErrors = numpy.mean(errors, 0)
print(meanErrors)
meanParams = numpy.mean(params, 0)
print(meanParams)
#When using CART trees the penalty can be inf in which case std is undefined
#In this case we set to zero any infinite values
meanErrorGrids = numpy.mean(errorGrids, 0)
try:
stdErrorGrids = numpy.std(errorGrids, 0)
except FloatingPointError:
errorGrids[numpy.isinf(errorGrids)] = 0
stdErrorGrids = numpy.std(errorGrids, 0)
meanApproxGrids = numpy.mean(approxGrids, 0)
try:
stdApproxGrids = numpy.std(approxGrids, 0)
except FloatingPointError:
approxGrids[numpy.isinf(approxGrids)] = 0
stdApproxGrids = numpy.std(approxGrids, 0)
numpy.savez(outfileName, errors, params, meanErrorGrids, stdErrorGrids, meanApproxGrids, stdApproxGrids)
logging.debug("Saved results as file " + outfileName + ".npz")
fileLock.unlock()
else:
logging.debug("Results already computed")
logging.debug("All done!")
def findErrorGrid(datasetNames, numProcesses, fileNameSuffix, learnerName, sampleSizes):
dataDir = PathDefaults.getDataDir() + "modelPenalisation/"
outputDir = PathDefaults.getOutputDir() + "modelPenalisation/"
learner, loadMethod, dataDir, outputDir, paramDict = getSetup(learnerName, dataDir, outputDir, numProcesses)
numParams = len(paramDict.keys())
runIdeal = True
runTest = True
for i in range(len(datasetNames)):
logging.debug("Learning using dataset " + datasetNames[i][0])
outfileName = outputDir + datasetNames[i][0] + fileNameSuffix
fileLock = FileLock(outfileName + ".npz")
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
numRealisations = datasetNames[i][1]
gridShape = [numRealisations, sampleSizes.shape[0]]
gridShape.extend(list(learner.gridShape(paramDict)))
gridShape = tuple(gridShape)
idealPenGrids = numpy.zeros(gridShape)
idealErrorGrids = numpy.zeros(gridShape)
idealErrors = numpy.zeros((numRealisations, sampleSizes.shape[0]))
params = numpy.zeros((numRealisations, len(sampleSizes), numParams))
for k in range(sampleSizes.shape[0]):
sampleSize = sampleSizes[k]
logging.debug("Using sample size " + str(sampleSize))
for j in range(numRealisations):
Util.printIteration(j, 1, numRealisations, "Realisation: ")
trainX, trainY, testX, testY = loadMethod(dataDir, datasetNames[i][0], j)
numpy.random.seed(21)
trainInds = numpy.random.permutation(trainX.shape[0])[0:sampleSize]
validX = trainX[trainInds,:]
validY = trainY[trainInds]
#Find ideal penalties
if runIdeal:
logging.debug("Finding ideal grid of penalties")
idealPenGrids[j, k, :] = learner.parallelPenaltyGrid(validX, validY, testX, testY, paramDict)
#Find ideal model using the test set
if runTest:
logging.debug("Running test set sampling")
cvGrid = learner.parallelSplitGrid(validX, validY, testX, testY, paramDict)
bestLearner = learner.getBestLearner(cvGrid, paramDict, validX, validY)
predY = bestLearner.predict(testX)
idealErrors[j, k] = bestLearner.getMetricMethod()(testY, predY)
params[j, k, :] = bestLearner.getParamsArray(paramDict)
idealErrorGrids[j, k, :] = cvGrid
meanIdealPenGrids = idealPenGrids.mean(0)
stdIdealPenGrids = idealPenGrids.std(0)
meanIdealErrorGrids = idealErrorGrids.mean(0)
stdIdealErrorGrids = idealErrorGrids.std(0)
numpy.savez(outfileName, idealErrors, params, meanIdealErrorGrids, stdIdealErrorGrids, meanIdealPenGrids, stdIdealPenGrids)
logging.debug("Saved results as file " + outfileName + ".npz")
fileLock.unlock()
else:
logging.debug("Results already computed")
def runExperiment(self):
"""
Run the selected clustering experiments and save results
"""
if self.algoArgs.runSoftImpute:
logging.debug("Running soft impute")
for svdAlg in self.algoArgs.svdAlgs:
if svdAlg == "rsvd" or svdAlg == "rsvdUpdate" or svdAlg == "rsvdUpdate2":
resultsFileName = self.resultsDir + "ResultsSoftImpute_alg=" + svdAlg + "_p=" + str(self.algoArgs.p)+ "_q=" + str(self.algoArgs.q) + "_updateAlg=" + self.algoArgs.updateAlg + ".npz"
else:
resultsFileName = self.resultsDir + "ResultsSoftImpute_alg=" + svdAlg + "_updateAlg=" + self.algoArgs.updateAlg + ".npz"
fileLock = FileLock(resultsFileName)
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
try:
learner = IterativeSoftImpute(svdAlg=svdAlg, logStep=self.logStep, kmax=self.algoArgs.kmax, postProcess=self.algoArgs.postProcess, weighted=self.algoArgs.weighted, p=self.algoArgs.p, q=self.algoArgs.q, verbose=self.algoArgs.verbose, updateAlg=self.algoArgs.updateAlg)
if self.algoArgs.modelSelect:
trainIterator = self.getTrainIterator()
#Let's find the optimal lambda using the first matrix
X = trainIterator.next()
logging.debug("Performing model selection, taking subsample of entries of size " + str(self.sampleSize))
X = SparseUtils.submatrix(X, self.sampleSize)
cvInds = Sampling.randCrossValidation(self.algoArgs.folds, X.nnz)
meanErrors, stdErrors = learner.modelSelect(X, self.algoArgs.rhos, self.algoArgs.ks, cvInds)
logging.debug("Mean errors = " + str(meanErrors))
logging.debug("Std errors = " + str(stdErrors))
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanErrors, stdErrors)
logging.debug("Saved model selection grid as " + modelSelectFileName)
rho = self.algoArgs.rhos[numpy.unravel_index(numpy.argmin(meanErrors), meanErrors.shape)[0]]
k = self.algoArgs.ks[numpy.unravel_index(numpy.argmin(meanErrors), meanErrors.shape)[1]]
else:
rho = self.algoArgs.rhos[0]
k = self.algoArgs.ks[0]
learner.setK(k)
learner.setRho(rho)
logging.debug(learner)
trainIterator = self.getTrainIterator()
ZIter = learner.learnModel(trainIterator)
self.recordResults(ZIter, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runSgdMf:
logging.debug("Running SGD MF")
resultsFileName = self.resultsDir + "ResultsSgdMf.npz"
fileLock = FileLock(resultsFileName)
if not fileLock.isLocked() and not fileLock.fileExists():
fileLock.lock()
try:
learner = IterativeSGDNorm2Reg(k=self.algoArgs.ks[0], lmbda=self.algoArgs.lmbdas[0], gamma=self.algoArgs.gammas[0], eps=self.algoArgs.eps)
if self.algoArgs.modelSelect:
# Let's find optimal parameters using the first matrix
learner.modelSelect(self.getTrainIterator().next(), self.algoArgs.ks, self.algoArgs.lmbdas, self.algoArgs.gammas, self.algoArgs.folds)
trainIterator = self.getTrainIterator()
trainIterator = self.getTrainIterator()
ZIter = learner.learnModel(trainIterator)
self.recordResults(ZIter, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
logging.info("All done: see you around!")
def testLock(self):
fileLock = FileLock(self.fileName)
fileLock.lock()
def testInit(self):
fileLock = FileLock(self.fileName)
