def SLCT(para,dataPath,data,curData):
util.config(para)
startTime = time.time() # start timing
# load the dataset
loglines = dataloader.load(para)
# log template extraction
slct.extract(loglines, para)
timeInterval=time.time() - startTime
print('execution time is: %f (precision calculation,data preprocessing and templates splitting not counted)'%(timeInterval))
tempParameter=tempPara(dataname=data[curData]+'/',savePath=dataPath)
tempProcess(tempParameter)
return timeInterval
# 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('==============================================')
'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
'dimension': 10, # dimenisionality of the latent factors
'etaInit': 0.001, # inital learning rate. We use line search
# to find the best eta at each iteration
'lambda': 200, # regularization parameter
'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('PMF: Probabilistic Matrix Factorization')
# 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('==============================================')
'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('==============================================')
'outPath': 'result/',
'metrics': ['NDCG', 'Precision'], # delete where appropriate
'metric_parameter': [1, 5, 10, 50, 100],
'density': [0.01, 0.1, 0.3], # matrix density
'rounds': 10, # how many runs are performed at each matrix density
'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': 0.1, # regularization parameter
'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('PMF: Probabilistic 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('==============================================')
import os, sys, time
sys.path.append('../../')
sys.path.append('../')
from logparser import slct
from commons.util import logger
from commons import util
from commons import dataloader
#########################################################
# config area
#
para = {'dataPath': '../data/', # data path
'dataName': 'redlug.com', # set the dataset name
'outPath': 'result/', # output path for results
'supportThreshold': 1, # set support threshold
'saveLog': True, # whether to save log into file
}
util.config(para)
#########################################################
startTime = time.time() # start timing
# load the dataset
loglines = dataloader.load(para)
# log template extraction
slct.extract(loglines, para)
logger.info('All done. Elaspsed time: ' + util.formatElapsedTime(time.time() - startTime)) # end timing
logger.info('==============================================')