def executeOneSetting(tensor, density, roundId, para):
logger.info('density=%.2f, %2d-round starts.'%(density, roundId + 1))
(numUser, numService, numTime) = tensor.shape
# remove the entries of data to generate trainTensor and testTensor
(trainTensor, testTensor) = evallib.removeTensor(tensor, density, roundId, para)
# invocation to the prediction function
startTime = time.clock() # to record the running time for one round
predictedTensor = Average.predict(trainTensor, para)
runningTime = float(time.clock() - startTime) / numTime
# evaluate the prediction error
for sliceId in xrange(numTime):
testMatrix = testTensor[:, :, sliceId]
predictedMatrix = predictedTensor[:, :, sliceId]
(testVecX, testVecY) = np.where(testMatrix)
testVec = testMatrix[testVecX, testVecY]
predVec = predictedMatrix[testVecX, testVecY]
evalResult = evallib.errMetric(testVec, predVec, para['metrics'])
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%02d_%.2f_round%02d.tmp'%(para['outPath'],
para['dataName'], para['dataType'], sliceId + 1, density, roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, %2d-round done.'%(density, roundId + 1))
logger.info('----------------------------------------------')
def executeOneSetting(matrix, density, roundId, sliceId, para):
(numUser, numService) = matrix.shape
dim = para['dimension']
# remove data entries to generate trainMatrix and testMatrix
seedID = roundId + sliceId * 100
(trainMatrix, testMatrix) = evallib.removeEntries(matrix, density, seedID)
(testVecX, testVecY) = np.where(testMatrix)
testVec = testMatrix[testVecX, testVecY]
# invocation to the prediction function
startTime = time.clock()
predictedMatrix = PMF.predict(trainMatrix, para)
runningTime = float(time.clock() - startTime)
# evaluate the prediction error
predVec = predictedMatrix[testVecX, testVecY]
evalResult = evallib.errMetric(testVec, predVec, para['metrics'])
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%02d_%.2f_round%02d.tmp' % (
para['outPath'], para['dataName'], para['dataType'], sliceId + 1,
density, roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, sliceId=%02d, %2d-round done.'\
%(density, sliceId + 1, roundId + 1))
def executeOneSetting(tensor, density, roundId, para):
logger.info('density=%.2f, %2d-round starts.' % (density, roundId + 1))
(numUser, numService, numTime) = tensor.shape
dim = para['dimension']
# remove the entries of data to generate trainTensor and testTensor
(trainTensor, testTensor) = evallib.removeTensor(tensor, density, roundId,
para)
# invocation to the prediction function
startTime = time.clock() # to record the running time for one round
predictedTensor = NTF.predict(trainTensor, para)
runningTime = float(time.clock() - startTime) / numTime
# evaluate the prediction error
for sliceId in xrange(numTime):
testMatrix = testTensor[:, :, sliceId]
predictedMatrix = predictedTensor[:, :, sliceId]
(testVecX, testVecY) = np.where(testMatrix)
testVec = testMatrix[testVecX, testVecY]
predVec = predictedMatrix[testVecX, testVecY]
evalResult = evallib.errMetric(testVec, predVec, para['metrics'])
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%02d_%.2f_round%02d.tmp' % (
para['outPath'], para['dataName'], para['dataType'], sliceId + 1,
density, roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, %2d-round done.' % (density, roundId + 1))
logger.info('----------------------------------------------')
def executeOneSetting(matrix, density, roundId, sliceId, para):
(numUser, numService) = matrix.shape
dim = para['dimension']
# remove data entries to generate trainMatrix and testMatrix
seedID = roundId + sliceId * 100
(trainMatrix, testMatrix) = evallib.removeEntries(matrix, density, seedID)
(testVecX, testVecY) = np.where(testMatrix)
testVec = testMatrix[testVecX, testVecY]
# invocation to the prediction function
startTime = time.clock()
predictedMatrix = PMF.predict(trainMatrix, para)
runningTime = float(time.clock() - startTime)
# evaluate the prediction error
predVec = predictedMatrix[testVecX, testVecY]
evalResult = evallib.errMetric(testVec, predVec, para['metrics'])
result = (evalResult, runningTime)
# dump the result at each density
outFile = '%s%s_%s_result_%02d_%.2f_round%02d.tmp'%(para['outPath'],
para['dataName'], para['dataType'], sliceId + 1, density, roundId + 1)
evallib.dumpresult(outFile, result)
logger.info('density=%.2f, sliceId=%02d, %2d-round done.'\
%(density, sliceId + 1, roundId + 1))