'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('==============================================')
# 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('==============================================')
'topK': 10, # the parameter of TopK similar users or services, the default
# value is topK = 10 as in the reference paper
'dimension': 10, # dimenisionality of the latent factors
'etaInit': 0.01, # inital learning rate. We use line search
# to find the best eta at each iteration
'lambda': 30, # L2 regularization parameter
'alpha': 0.4, # the parameter of combination, 0.4 as in the reference paper
'maxIter': 300, # the max iterations
'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('NIMF: Neighbourhood Integrated Matrix Factorization')
# load the dataset
dataMatrix = dataloader.load(para)
# evaluate QoS prediction algorithm
evaluator.execute(dataMatrix, para)
logger.info('All done. Elaspsed time: ' + utils.formatElapsedTime(time.time() - startTime)) # end timing
logger.info('==============================================')
