# parameter config area para = {'dataPath': '../data/', # data path 'dataName': 'Orangelab_sense_temperature', # set the dataset name 'outPath': 'result/', # output path for results 'metrics': ['MAE', 'NMAE', 'RMSE', 'MRE', 'NNPRE', 'SNR'], # evaluation metrics 'samplingRate': np.arange(0.05, 0.96, 0.05), # sampling rate 'rounds': 1, # how many runs to perform at each sampling rate 'lmbda': 1e-5, # sparisty regularization parameter 'trainingPeriod': 33, # training time periods 'saveTimeInfo': False, # whether to keep track of the running time 'saveLog': False, # whether to save log into file 'debugMode': False, #whether to record the debug info 'parallelMode': False # whether to leverage multiprocessing for speedup } startTime = time.time() # start timing utils.setConfig(para) # set configuration logger.info('==============================================') logger.info('CS-PCA: [Quer et al., TWC\'2012]') # load the dataset dataMatrix = dataloader.load(para) # evaluate compressive monitoring algorithm evaluator.execute(dataMatrix, para) logger.info('All done. Elaspsed time: ' + utils.formatElapsedTime(time.time() - startTime)) # end timing logger.info('==============================================')
'dataPath': '../../../data/', 'dataName': 'dataset#2', 'dataType': 'rt', # set the dataType as 'rt' or 'tp' 'outPath': 'result/', 'metrics': ['MAE', 'NMAE', 'RMSE', 'MRE', 'NPRE'], # delete where appropriate 'density': np.arange(0.05, 0.31, 0.05), # matrix density 'rounds': 20, # how many runs are performed at each matrix density 'topK': 10, # the parameter of TopK similar users or services 'lambda': 0.8, # the combination coefficient of UPCC and IPCC 'saveTimeInfo': False, # whether to keep track of the running time 'saveLog': True, # whether to save log into file 'debugMode': False, # whether to record the debug info 'parallelMode': True # whether to leverage multiprocessing for speedup } startTime = time.time() # start timing utils.setConfig(para) # set configuration logger.info('==============================================') logger.info('Approach: [UPCC, IPCC, UIPCC][TSC 2011]') # load the dataset dataTensor = dataloader.load(para) # evaluate QoS prediction algorithm evaluator.execute(dataTensor, para) logger.info('All done. Elaspsed time: ' + utils.formatElapsedTime(time.time() - startTime)) # end timing logger.info('==============================================')