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