startTime = time.clock() # start timing
logger.info('==============================================')
logger.info('Approach: [UMEAN, IMEAN, UPCC, IPCC, UIPCC].')
# load the dataset
dataTensor = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
numTimeSlice = dataTensor.shape[2]
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for cxtId in xrange(numTimeSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
pool.apply_async(evaluator.execute, (dataMatrix, density, para, cxtId))
pool.close()
pool.join()
else: # run on single processes
for cxtId in xrange(numTimeSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
evaluator.execute(dataMatrix, density, para, cxtId)
# result handling
resulthandler.averageStats(para, numTimeSlice)
logger.info(time.strftime('All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')
startTime = time.clock() # start timing
logger.info('==============================================')
logger.info('PMF: Probabilistic Matrix Factorization.')
# load the dataset
dataTensor = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for cxtId in range(dataTensor.shape[2]):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
pool.apply_async(evaluator.execute, (dataMatrix, density, para, cxtId))
pool.close()
pool.join()
else: # run on single processes
for cxtId in range(dataTensor.shape[2]):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
evaluator.execute(dataMatrix, density, para, cxtId)
# result handling
resulthandler.averageStats(para)
logger.info(time.strftime('All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')
# load the dataset
dataTensor = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
numTimeSlice = dataTensor.shape[2]
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for cxtId in xrange(numTimeSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
pool.apply_async(evaluator.execute,
(dataMatrix, density, para, cxtId))
pool.close()
pool.join()
else: # run on single processes
for cxtId in xrange(numTimeSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
evaluator.execute(dataMatrix, density, para, cxtId)
# result handling
resulthandler.averageStats(para, numTimeSlice)
logger.info(
time.strftime(
'All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')
logger.info('Approach: [UMEAN, IMEAN, UPCC, IPCC, UIPCC].')
# load the dataset
dataTensor = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for cxtId in range(dataTensor.shape[2]):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
pool.apply_async(evaluator.execute,
(dataMatrix, density, para, cxtId))
pool.close()
pool.join()
else: # run on single processes
for cxtId in range(dataTensor.shape[2]):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
evaluator.execute(dataMatrix, density, para, cxtId)
# result handling
resulthandler.averageStats(para, dataTensor.shape[2])
logger.info(
time.strftime(
'All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')
logger.info('==============================================')
logger.info('Approach: [UMEAN, IMEAN, UPCC, IPCC, UIPCC].')
# load the dataset
dataTensor = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
endSlice = dataTensor.shape[2]
startSlice = int(endSlice * (1 - para['slicesToTest']))
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for cxtId in xrange(startSlice, endSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
pool.apply_async(evaluator.execute, (dataMatrix, density, para, cxtId))
pool.close()
pool.join()
else: # run on single processes
for cxtId in xrange(startSlice, endSlice):
dataMatrix = dataTensor[:, :, cxtId]
for density in para['density']:
evaluator.execute(dataMatrix, density, para, cxtId)
# result handling
resulthandler.averageStats(para, endSlice)
logger.info(time.strftime('All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')