def monitoring(matrix, rate, roundId, para):
startTime = time.clock()
logger.info('rate=%.2f, %2d-round starts.'%(rate, roundId + 1))
# sampling
logger.info('CS-PCA sampling...')
startTime = time.clock() # to record the running time for one round
(trainMatrix, observedMatrix, testMatrix) = CS_PCA.sampling(matrix, rate, roundId, para)
# CS-PCA algorithm
logger.info('CS-PCA estimation...')
recoveredMatrix = CS_PCA.recover(trainMatrix, observedMatrix, para)
runningTime = float(time.clock() - startTime)
# evaluate the estimation error
evalResult = evallib.evaluate(testMatrix, recoveredMatrix, para)
result = (evalResult, runningTime)
# dump the result at each rate
outFile = '%s%s%s_result_%.2f%s.tmp'%(para['outPath'], para['dataName'],
('_%s'%para['dataType'] if ('dataType' in para.keys()) else ''), rate,
'_round%02d'%(roundId + 1) if (para['rounds'] > 1) else '')
evallib.dumpresult(outFile, result)
logger.info('rate=%.2f, %2d-round done.'%(rate, roundId + 1))
logger.info('----------------------------------------------')
def executeOneSetting(matrix, density, roundId, para):
logger.info('density=%.2f, %2d-round starts.' % (density, roundId + 1))
# remove data matrix
(trainMatrix, testMatrix) = evallib.removeEntries(matrix, density, roundId)
# QoS prediction
startTime = time.clock() # to record the running time for one round
predictedMatrix1 = UIPCC.UPCC(trainMatrix, para)
predictedMatrix2 = UIPCC.IPCC(trainMatrix, para)
predictedMatrix = UIPCC.UIPCC(trainMatrix, predictedMatrix1,
predictedMatrix2, para)
runningTime = float(time.clock() - startTime)
# evaluate the estimation error
evalResult = evallib.evaluate(testMatrix, predictedMatrix, para)
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%.2f_round%02d.tmp' % (
para['outPath'], para['dataName'], para['dataType'], density,
roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, %2d-round done.' % (density, roundId + 1))
logger.info('----------------------------------------------')
def monitoring(matrix, rate, para):
startTime = time.clock()
logger.info("rate=%.2f starts." % rate)
# JGD algorithm
startTime = time.clock() # to record the running time for one round
trainingPeriod = para["trainingPeriod"]
trainMatrix = matrix[:, 0:trainingPeriod]
testMatrix = matrix[:, trainingPeriod:]
logger.info("monitor selection...")
(selectedMonitors, toEstimateNodes) = JGD.selectMonitor(trainMatrix, rate, para)
observedMatrix = testMatrix[selectedMonitors, :]
logger.info("JGD estimation...")
recoveredMatrix = JGD.recover(trainMatrix, observedMatrix, selectedMonitors, toEstimateNodes)
runningTime = float(time.clock() - startTime)
# evaluate the estimation error
evalResult = evallib.evaluate(testMatrix, recoveredMatrix, para)
result = (evalResult, runningTime)
# dump the result at each rate
outFile = "%s%s%s_result_%.2f.tmp" % (
para["outPath"],
para["dataName"],
"_%s" % para["dataType"] if ("dataType" in para.keys()) else "",
rate,
)
evallib.dumpresult(outFile, result)
logger.info("rate=%.2f done." % rate)
logger.info("----------------------------------------------")
def monitoring(matrix, rate, roundId, para):
startTime = time.clock()
logger.info('rate=%.2f, %2d-round starts.'%(rate, roundId + 1))
# sampling
logger.info('CS sampling...')
startTime = time.clock() # to record the running time for one round
# sorting in ascending and permutation
idx = np.argsort(-matrix[:, 0])
matrix = matrix[idx, :]
(trainMatrix, observedMatrix, testMatrix) = CS.sampling(matrix, rate, roundId, para)
# CS algorithm
logger.info('CS estimation...')
recoveredMatrix = CS.recover(trainMatrix, observedMatrix, para)
plot((matrix[:,30]), '-o')
plot(recoveredMatrix[:,30], '-x')
show()
# sys.exit()
runningTime = float(time.clock() - startTime)
# evaluate the estimation error
evalResult = evallib.evaluate(testMatrix, recoveredMatrix, para)
result = (evalResult, runningTime)
# dump the result at each rate
outFile = '%s%s%s_result_%.2f%s.tmp'%(para['outPath'], para['dataName'],
('_%s'%para['dataType'] if ('dataType' in para.keys()) else ''), rate,
'_round%02d'%(roundId + 1) if (para['rounds'] > 1) else '')
evallib.dumpresult(outFile, result)
logger.info('rate=%.2f, %2d-round done.'%(rate, roundId + 1))
logger.info('----------------------------------------------')
def executeOneSetting(matrix, density, roundId, para):
logger.info('density=%.2f, %2d-round starts.'%(density, roundId + 1))
# remove data matrix
(trainMatrix, testMatrix) = evallib.removeEntries(matrix, density, roundId)
# QoS prediction
startTime = time.clock() # to record the running time for one round
predictedMatrix = NMF.predict(trainMatrix, para)
runningTime = float(time.clock() - startTime)
# evaluate the estimation error
evalResult = evallib.evaluate(testMatrix, predictedMatrix, para)
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%.2f_round%02d.tmp'%(para['outPath'], para['dataName'],
para['dataType'], density, roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, %2d-round done.'%(density, roundId + 1))
logger.info('----------------------------------------------')
