def computeErrorIndependently():
# rvSet = range(0, 50)
rvSet = range(50, 100)
T = utils.properties.timeSpan
tWin = utils.properties.tWin
topology = utils.properties.dbn_topology
numTrials = utils.properties.numTrials
trainset, testset = DataProvider.provide_data()
predictionModel = MultivariateDiscreteKalmanFilter()
for obsrate in utils.properties.obsrateList:
evidencepath = utils.properties.outputDirPath + str(obsrate) + '/evidences/'
predictionpath = utils.properties.outputDirPath + str(obsrate) + '/predictions/'
errorpath = utils.properties.outputDirPath + str(obsrate) + '/errors/'
if 0.0 == obsrate:
currentNumTrials = 1
else:
currentNumTrials = numTrials
errResults = np.empty(shape=(currentNumTrials, T, 3))
for trial in range(currentNumTrials):
evidMat = np.loadtxt(evidencepath + '{}_activeInf_model={}_T={}_trial={}_obsrate={}.csv'.
format('evidMat', utils.properties.prediction_model, T, trial, obsrate),
delimiter=',').astype(np.bool_)
predResults = np.loadtxt(predictionpath + 'mean/{}_activeInf_model={}_T={}_trial={}_obsRate={}.csv'.
format('predResults', utils.properties.prediction_model, T, trial, obsrate),
delimiter=',')
for t in range(T):
errResults[trial, t, 0] = predictionModel.compute_mean_absolute_error(testset[rvSet, t],
predResults[rvSet, t], type_=0,
evidence_mat=evidMat[rvSet, t])
errResults[trial, t, 1] = predictionModel.compute_mean_absolute_error(testset[rvSet, t],
predResults[rvSet, t], type_=1,
evidence_mat=evidMat[rvSet, t])
errResults[trial, t, 2] = predictionModel.compute_mean_absolute_error(testset[rvSet, t],
predResults[rvSet, t], type_=2,
evidence_mat=evidMat[rvSet, t])
np.savetxt(errorpath +
'{}_activeInfo_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format('mae_humid', utils.properties.prediction_model, topology, tWin, T, obsrate,
trial), errResults[trial], delimiter=',')
np.savetxt(errorpath +
'{}_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format('meanMAE_humid', utils.properties.prediction_model, topology,tWin,T,obsrate, 'mean'),
np.mean(errResults, axis=0), delimiter=',')
np.savetxt(errorpath +
'{}_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format('stderrMAE_humid', utils.properties.prediction_model, topology, tWin, T, obsrate,
'mean'), standard_error(errResults, axis=0), delimiter=',')
def testActiveInferenceGaussianDBNParallel():
start = time()
print 'Process started at:', datetime.datetime.fromtimestamp(start).strftime('%H:%M, %m/%d/%Y')
tWin = utils.properties.tWin
topology = utils.properties.dbn_topology
T = utils.properties.timeSpan
numTrials = utils.properties.numTrials
trainset, testset = DataProvider.provide_data()
if 'gp' == utils.properties.prediction_model:
prediction_model = GaussianProcessLocal()
elif 'dgbn' == utils.properties.prediction_model:
prediction_model = GaussianDBN()
elif 'kf' == utils.properties.prediction_model:
prediction_model = MultivariateDiscreteKalmanFilter()
elif 'lc-linear' == utils.properties.prediction_model:
prediction_model = LinearChain(regressionMethod='linear')
elif 'lc-ridge' == utils.properties.prediction_model:
prediction_model = LinearChain(regressionMethod='ridge')
elif 'lc-lasso' == utils.properties.prediction_model:
prediction_model = LinearChain(regressionMethod='lasso')
else:
raise ValueError('Unrecognized prediction model name')
print 'Prediction model selected: ', prediction_model.__class__
prediction_model.fit(trainset, topology=topology)
print 'Prediction model was trained.'
return
Y_test_allT = np.vectorize(lambda x: x.true_label)(testset)
parameterList = list()
sampleSize = utils.properties.mh_sampleSize
burnInCount = utils.properties.mh_burnInCount
for obsrate in utils.properties.obsrateList:
obsCount = int(obsrate * prediction_model.rvCount)
evidencepath = utils.properties.outputDirPath + str(obsrate) + '/evidences/'
if not os.path.exists(evidencepath):
os.makedirs(evidencepath)
meanPredictionPath = utils.properties.outputDirPath + str(obsrate) + '/predictions/mean/'
if not os.path.exists(meanPredictionPath):
os.makedirs(meanPredictionPath)
varPredictionPath = utils.properties.outputDirPath + str(obsrate) + '/predictions/var/'
if not os.path.exists(varPredictionPath):
os.makedirs(varPredictionPath)
errorpath = utils.properties.outputDirPath + str(obsrate) + '/errors/'
if not os.path.exists(errorpath):
os.makedirs(errorpath)
selection_strategy_name = utils.properties.selectionStrategy
if 0.0 == obsrate:
trial = 0
parameterList.append({'trial': trial, 'prediction_model': prediction_model,
'selection_strategy_name': selection_strategy_name, 'T': T, 'tWin': tWin,
'testset': testset, 'Y_test_allT': Y_test_allT, 'sampleSize': sampleSize,
'burnInCount': burnInCount, 'topology': topology, 'obsrate': obsrate,
'obsCount': obsCount, 'evidencepath': evidencepath,
'meanPredictionPath': meanPredictionPath, 'varPredictionPath': varPredictionPath,
'errorpath': errorpath})
else:
for trial in range(0, numTrials):
parameterList.append({'trial': trial, 'prediction_model': prediction_model,
'selection_strategy_name': selection_strategy_name, 'T': T, 'tWin': tWin,
'testset': testset, 'Y_test_allT': Y_test_allT, 'sampleSize': sampleSize,
'burnInCount': burnInCount, 'topology': topology, 'obsrate': obsrate,
'obsCount': obsCount, 'evidencepath': evidencepath,
'meanPredictionPath': meanPredictionPath, 'varPredictionPath': varPredictionPath,
'errorpath': errorpath})
print 'Tasks for parallel computation were created.'
pool = mp.Pool(processes=utils.properties.numParallelThreads)
print 'Tasks in parallel are being started.'
pool.map(trialFuncStar, parameterList)
# for params in parameterList:
# trialFuncStar(params)
for obsrate in utils.properties.obsrateList:
errorpath = utils.properties.outputDirPath + str(obsrate) + '/errors/'
if 0.0 == obsrate:
trial = 0
errResults = np.loadtxt(errorpath +
'mae_activeInfo_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format(utils.properties.prediction_model, topology, tWin, T, obsrate,
trial), delimiter=',')
np.savetxt(errorpath +
'meanMAE_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format(utils.properties.prediction_model, topology, tWin, T, obsrate, 'mean'),
errResults, delimiter=',')
np.savetxt(errorpath +
'stderrMAE_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format(utils.properties.prediction_model, topology, tWin, T, obsrate, 'mean'),
np.zeros(shape=errResults.shape), delimiter=',')
else:
errResults = np.empty(shape=(numTrials, T, 6))
for trial in range(numTrials):
errResults[trial] = np.loadtxt(errorpath + ('mae_activeInfo_model={}_topology={}_window={}_' +
'T={}_obsRate={}_trial={}.csv').
format(utils.properties.prediction_model, topology,tWin,T,obsrate,
trial), delimiter=',')
np.savetxt(errorpath +
'meanMAE_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format(utils.properties.prediction_model, topology,tWin,T,obsrate, 'mean'),
np.mean(errResults,axis=0), delimiter=',')
np.savetxt(errorpath +
'stderrMAE_activeInf_model={}_topology={}_window={}_T={}_obsRate={}_trial={}.csv'.
format(utils.properties.prediction_model, topology,tWin,T,obsrate, 'mean'),
standard_error(errResults, axis=0), delimiter=',')
print 'End of process, duration: {} secs'.format(time() - start)