modelSelect = args.modelSelect folds = 3 ks = numpy.array([64, 128, 256]) rhosSi = numpy.linspace(1.0, 0.0, 5) overwrite = args.overwrite datasets = ["Keyword", "Document"] resultsDir = PathDefaults.getOutputDir() + "coauthors/" contactsFilename = PathDefaults.getDataDir() + "reference/contacts_anonymised.tsv" interestsFilename = PathDefaults.getDataDir() + "reference/author_interest" #Create all the recommendation algorithms softImpute = IterativeSoftImpute(k=k, postProcess=True, svdAlg="rsvd") softImpute.maxIterations = maxIterations softImpute.metric = "f1" softImpute.q = 3 softImpute.p = 10 softImpute.rho = 0.1 softImpute.eps = 10**-4 softImpute.numProcesses = args.processes wrmf = WeightedMf(k=k, maxIterations=maxIterations, alpha=1.0) wrmf.ks = ks wrmf.folds = folds wrmf.lmbdas = 2.0**-numpy.arange(-1, 12, 2) wrmf.metric = "f1" wrmf.numProcesses = args.processes maxLocalAuc = MaxLocalAUC(k=k, w=0.9, maxIterations=50, lmbdaU=0.1, lmbdaV=0.1, stochastic=True)
def runExperiment(self, X):
"""
Run the selected ranking experiments and save results
"""
logging.debug("Splitting into train and test sets")
#Make sure different runs get the same train/test split
numpy.random.seed(21)
m, n = X.shape
#colProbs = (X.sum(0)+1)/float(m+1)
#colProbs = colProbs**-self.algoArgs.itemExp
#colProbs = numpy.ones(n)/float(n)
trainTestXs = Sampling.shuffleSplitRows(X, 1, self.algoArgs.testSize)
trainX, testX = trainTestXs[0]
logging.debug("Train X shape and nnz: " + str(trainX.shape) + " " + str(trainX.nnz))
logging.debug("Test X shape and nnz: " + str(testX.shape) + " " + str(testX.nnz))
#Have scipy versions of each array
trainXScipy = trainX.toScipyCsc()
testXScipy = testX.toScipyCsc()
if self.algoArgs.runSoftImpute:
logging.debug("Running soft impute")
resultsFileName = self.resultsDir + "ResultsSoftImpute.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainXScipy, self.algoArgs.modelSelectSamples, prune=True)
try:
learner = IterativeSoftImpute(self.algoArgs.rhoSi, eps=self.algoArgs.epsSi, k=self.algoArgs.k, svdAlg=self.algoArgs.svdAlg, postProcess=self.algoArgs.postProcess, p=self.algoArgs.pSi, q=self.algoArgs.qSi)
learner.folds = self.algoArgs.folds
learner.metric = self.algoArgs.metric
learner.numProcesses = self.algoArgs.processes
learner.recommendSize = self.algoArgs.recommendSize
learner.validationSize = self.algoArgs.validationSize
if self.algoArgs.modelSelect:
cvInds = Sampling.randCrossValidation(self.algoArgs.folds, modelSelectX.nnz)
meanErrors, stdErrors = learner.modelSelect2(modelSelectX, self.algoArgs.rhosSi, self.algoArgs.ks, cvInds)
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanErrors, stdErrors)
logging.debug("Saved model selection grid as " + modelSelectFileName)
logging.debug(learner)
self.recordResults(X, trainXScipy, testXScipy, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runMaxLocalAuc:
logging.debug("Running max local AUC")
if self.algoArgs.loss != "tanh":
resultsFileName = self.resultsDir + "ResultsMaxLocalAUC_loss=" + self.algoArgs.loss + ".npz"
else:
resultsFileName = self.resultsDir + "ResultsMaxLocalAUC_loss=" + self.algoArgs.loss + "_rho=" + str(self.algoArgs.rhoMlauc) + ".npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
try:
learner = MaxLocalAUC(self.algoArgs.k, 1-self.algoArgs.u, lmbdaU=self.algoArgs.lmbdaUMlauc, lmbdaV=self.algoArgs.lmbdaVMlauc, eps=self.algoArgs.epsMlauc, stochastic=not self.algoArgs.fullGradient)
learner.alpha = self.algoArgs.alpha
learner.alphas = self.algoArgs.alphas
learner.eta = self.algoArgs.eta
learner.folds = self.algoArgs.folds
learner.initialAlg = self.algoArgs.initialAlg
learner.itemExpP = self.algoArgs.itemExpP
learner.itemExpQ = self.algoArgs.itemExpQ
learner.ks = self.algoArgs.ks
learner.lmbdas = self.algoArgs.lmbdasMlauc
learner.loss = self.algoArgs.loss
learner.maxIterations = self.algoArgs.maxIterations
learner.maxNorms = self.algoArgs.maxNorms
learner.maxNormU = self.algoArgs.maxNorm
learner.maxNormV = self.algoArgs.maxNorm
learner.metric = self.algoArgs.metric
learner.normalise = self.algoArgs.normalise
learner.numAucSamples = self.algoArgs.numAucSamples
learner.numProcesses = self.algoArgs.processes
learner.numRowSamples = self.algoArgs.numRowSamples
learner.rate = self.algoArgs.rate
learner.recommendSize = self.algoArgs.recommendSize
learner.recordStep = self.algoArgs.recordStep
learner.rho = self.algoArgs.rhoMlauc
learner.rhos = self.algoArgs.rhosMlauc
learner.startAverage = self.algoArgs.startAverage
learner.t0 = self.algoArgs.t0
learner.t0s = self.algoArgs.t0s
learner.validationSize = self.algoArgs.validationSize
learner.validationUsers = self.algoArgs.validationUsers
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
if self.algoArgs.modelSelect and not os.path.isfile(modelSelectFileName):
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainX, self.algoArgs.modelSelectSamples, prune=True)
#
meanMetricsLR, paramDictLR = learner.learningRateSelect(modelSelectX)
meanMetricsMS, paramDictMS = learner.modelSelectLmbda(modelSelectX)
numpy.savez(modelSelectFileName, meanMetricsLR, meanMetricsMS)
logging.debug("Saved model selection grid as " + modelSelectFileName)
elif self.algoArgs.modelSelect:
data = numpy.load(modelSelectFileName)
logging.debug("Read model selection file " + modelSelectFileName)
meanMetricsLR = data["arr_0"]
meanMetricsMS = data["arr_1"]
learner.learningRateSelect(meanMetrics=meanMetricsLR)
learner.modelSelectLmbda(meanMetrics=meanMetricsMS)
#Turn on (optionally) parallel SGD only at the final learning stage
learner.parallelSGD = self.algoArgs.parallelSGD
learner.maxIterations *= 2
logging.debug(learner)
self.recordResults(X, trainX, testX, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runWarpMf:
logging.debug("Running WARP loss MF")
resultsFileName = self.resultsDir + "ResultsWarpMf.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
try:
learner = WarpMf(self.algoArgs.k, self.algoArgs.lmbdas[0], u=self.algoArgs.u)
learner.ks = self.algoArgs.ks
learner.numProcesses = self.algoArgs.processes
learner.recommendSize = self.algoArgs.recommendSize
learner.validationSize = self.algoArgs.validationSize
if self.algoArgs.modelSelect:
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainX, self.algoArgs.modelSelectSamples, prune=True)
meanAucs, stdAucs = learner.modelSelect(modelSelectX)
logging.debug("Mean local AUCs = " + str(meanAucs))
logging.debug("Std local AUCs = " + str(stdAucs))
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanAucs, stdAucs)
logging.debug("Saved model selection grid as " + modelSelectFileName)
logging.debug(learner)
self.recordResults(X, trainXScipy, testXScipy, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runWrMf:
logging.debug("Running Weighted Regularized Matrix Factorization")
resultsFileName = self.resultsDir + "ResultsWrMf.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
trainXScipy = trainXScipy.tocsr()
testXScipy = testXScipy.tocsr()
try:
learner = WeightedMf(self.algoArgs.k, alpha=self.algoArgs.alphaWrMf, lmbda=self.algoArgs.lmbdasWrMf[0], maxIterations=self.algoArgs.maxIterationsWrMf)
learner.folds = self.algoArgs.folds
learner.ks = self.algoArgs.ks
learner.lmbdas = self.algoArgs.lmbdasWrMf
learner.metric = self.algoArgs.metric
learner.numProcesses = self.algoArgs.processes
learner.numRecordAucSamples = self.algoArgs.numRecordAucSamples
learner.recommendSize = self.algoArgs.recommendSize
learner.validationSize = self.algoArgs.validationSize
if self.algoArgs.modelSelect:
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainX, self.algoArgs.modelSelectSamples, prune=True)
meanAucs, stdAucs = learner.modelSelect(modelSelectX)
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanAucs, stdAucs)
logging.debug("Saved model selection grid as " + modelSelectFileName)
logging.debug(learner)
self.recordResults(X, trainXScipy, testXScipy, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runBpr:
logging.debug("Running Bayesian Personalised Recommendation")
resultsFileName = self.resultsDir + "ResultsBpr.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
try:
#trainX = trainX.toScipyCsr()
#testX = testX.toScipyCsr()
learner = BprRecommender(self.algoArgs.k, lmbdaUser=self.algoArgs.lmbdaUserBpr, lmbdaPos=self.algoArgs.lmbdaItemBpr, lmbdaNeg=self.algoArgs.lmbdaItemBpr, gamma=self.algoArgs.gammaBpr)
learner.folds = self.algoArgs.folds
learner.gammas = self.algoArgs.gammasBpr
learner.ks = self.algoArgs.ks
learner.lmbdaItems = self.algoArgs.lmbdaItems
learner.lmbdaUsers = self.algoArgs.lmbdaUsers
learner.maxIterations = self.algoArgs.maxIterationsBpr
learner.metric = self.algoArgs.metric
#learner.numAucSamples = self.algoArgs.numAucSamples
learner.numProcesses = self.algoArgs.processes
learner.recommendSize = self.algoArgs.recommendSize
learner.recordStep = self.algoArgs.recordStep
learner.validationSize = self.algoArgs.validationSize
if self.algoArgs.modelSelect:
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainX, self.algoArgs.modelSelectSamples, prune=True)
meanAucs, stdAucs = learner.modelSelect(modelSelectX)
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanAucs, stdAucs)
logging.debug("Saved model selection grid as " + modelSelectFileName)
logging.debug(learner)
self.recordResults(X, trainX, testX, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runKnn:
logging.debug("Running kNN")
resultsFileName = self.resultsDir + "ResultsKnn.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
try:
trainX = trainX.toScipyCsr()
testX = testX.toScipyCsr()
learner = KNNRecommender(self.algoArgs.kns[0])
learner.numProcesses = self.algoArgs.processes
logging.debug(learner)
self.recordResults(X, trainX, testX, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
if self.algoArgs.runCLiMF:
# !!!! no model selection
logging.debug("Running CLiMF")
resultsFileName = self.resultsDir + "ResultsCLiMF.npz"
fileLock = FileLock(resultsFileName)
if not (fileLock.isLocked() or fileLock.fileExists()) or self.algoArgs.overwrite:
fileLock.lock()
try:
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
modelSelectX, userInds = Sampling.sampleUsers2(trainX, self.algoArgs.modelSelectSamples, prune=True)
modelSelectX = scipy.sparse.csr_matrix(modelSelectX.toScipyCsr(), dtype=numpy.float64)
trainX = scipy.sparse.csr_matrix(trainX.toScipyCsr(), dtype=numpy.float64)
testX = testX.toScipyCsr()
learner = CLiMF(self.algoArgs.k, self.algoArgs.lmbdaCLiMF, self.algoArgs.gammaCLiMF)
learner.folds = self.algoArgs.folds
learner.gammas = self.algoArgs.gammasCLiMF
learner.ks = self.algoArgs.ks
learner.lmbdas = self.algoArgs.lmbdasCLiMF
learner.max_iters = self.algoArgs.maxIterCLiMF
learner.metric = self.algoArgs.metric
learner.numProcesses = self.algoArgs.processes
learner.numRecordAucSamples = self.algoArgs.numRecordAucSamples
learner.recommendSize = self.algoArgs.recommendSize
learner.validationSize = self.algoArgs.validationSize
learner.verbose = self.algoArgs.verbose
if self.algoArgs.modelSelect:
logging.debug("Performing model selection, taking sample size " + str(self.algoArgs.modelSelectSamples))
meanObjs, stdObjs = learner.modelSelect(modelSelectX)
modelSelectFileName = resultsFileName.replace("Results", "ModelSelect")
numpy.savez(modelSelectFileName, meanObjs, stdObjs)
logging.debug("Saved model selection grid as " + modelSelectFileName)
logging.debug(learner)
self.recordResults(X, trainX, testX, learner, resultsFileName)
finally:
fileLock.unlock()
else:
logging.debug("File is locked or already computed: " + resultsFileName)
logging.info("All done: see you around!")
