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 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('----------------------------------------------')
