learners = [("SoftImpute", softImpute), ("WRMF", wrmf), ("KNN", knn), ("MLAUC", maxLocalAuc), ("SLIM", slim)]
#Figure out the correct learner
for tempLearnerName, tempLearner in learners:
if args.alg == tempLearnerName:
learnerName = tempLearnerName
learner = tempLearner
if "learner" not in globals():
raise ValueError("Learner not found: " + learnerName)
os.system('taskset -p 0xffffffff %d' % os.getpid())
for dataset in datasets:
X = DatasetUtils.mendeley2(minNnzRows=0, dataset=dataset)
outputFilename = resultsDir + "Results_" + learnerName + "_" + dataset + ".npz"
similaritiesFileName = resultsDir + "Recommendations_" + learnerName + "_" + dataset + ".csv"
fileLock = FileLock(outputFilename)
if not (fileLock.isLocked() or fileLock.fileExists()) or overwrite:
fileLock.lock()
logging.debug(learner)
try:
#Do some recommendation
if type(learner) == IterativeSoftImpute:
trainX = X.toScipyCsc()
trainIterator = iter([trainX])
