<jupyter_start><jupyter_text>50% labeled data<jupyter_code>print('50% Data')
allMetrics = []
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet0, performanceHistory0, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.5)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory0[0],performanceHistory0[1],performanceHistory0[2],
                performanceHistory0[3],performanceHistory0[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet1, performanceHistory1, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.5)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory1[0],performanceHistory1[1],performanceHistory1[2],
                performanceHistory1[3],performanceHistory1[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet2, performanceHistory2, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.5)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory2[0],performanceHistory2[1],performanceHistory2[2],
                performanceHistory2[3],performanceHistory2[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet3, performanceHistory3, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.5)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory3[0],performanceHistory3[1],performanceHistory3[2],
                performanceHistory3[3],performanceHistory3[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet4, performanceHistory4, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.5)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory4[0],performanceHistory4[1],performanceHistory4[2],
                performanceHistory4[3],performanceHistory4[4])
# all results

# 0: accuracy
# 1: f1_score
# 2: precision_score
# 3: recall_score
# 4: training_time
# 5: testingTime
# 6: nHiddenLayer
# 7: nHiddenNode

meanResults = np.round_(np.mean(allMetrics,0), decimals=2)
stdResults  = np.round_(np.std(allMetrics,0), decimals=2)

print('\n')
print('========== Performance occupancy ==========')
print('Preq Accuracy: ', meanResults[0].item(), '(+/-)',stdResults[0].item())
print('F1 score: ', meanResults[1].item(), '(+/-)',stdResults[1].item())
print('Precision: ', meanResults[2].item(), '(+/-)',stdResults[2].item())
print('Recall: ', meanResults[3].item(), '(+/-)',stdResults[3].item())
print('Training time: ', meanResults[4].item(), '(+/-)',stdResults[4].item())
print('Testing time: ', meanResults[5].item(), '(+/-)',stdResults[5].item())

print('\n')
print('========== Network ==========')
print('Number of hidden layers: ', meanResults[6].item(), '(+/-)',stdResults[6].item())
print('Number of features: ', meanResults[7].item(), '(+/-)',stdResults[7].item())<jupyter_output><empty_output><jupyter_text>25% Labeled Data<jupyter_code>print('25% Data')
allMetrics = []
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet0, performanceHistory0, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.25)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory0[0],performanceHistory0[1],performanceHistory0[2],
                performanceHistory0[3],performanceHistory0[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet1, performanceHistory1, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.25)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory1[0],performanceHistory1[1],performanceHistory1[2],
                performanceHistory1[3],performanceHistory1[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet2, performanceHistory2, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.25)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory2[0],performanceHistory2[1],performanceHistory2[2],
                performanceHistory2[3],performanceHistory2[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet3, performanceHistory3, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.25)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory3[0],performanceHistory3[1],performanceHistory3[2],
                performanceHistory3[3],performanceHistory3[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet4, performanceHistory4, allPerformance = DEVDANmain(DevdanNet,dataStreams,labeled = False, nLabeled = 0.25)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory4[0],performanceHistory4[1],performanceHistory4[2],
                performanceHistory4[3],performanceHistory4[4])
# all results

# 0: accuracy
# 1: f1_score
# 2: precision_score
# 3: recall_score
# 4: training_time
# 5: testingTime
# 6: nHiddenLayer
# 7: nHiddenNode

meanResults = np.round_(np.mean(allMetrics,0), decimals=2)
stdResults  = np.round_(np.std(allMetrics,0), decimals=2)

print('\n')
print('========== Performance occupancy ==========')
print('Preq Accuracy: ', meanResults[0].item(), '(+/-)',stdResults[0].item())
print('F1 score: ', meanResults[1].item(), '(+/-)',stdResults[1].item())
print('Precision: ', meanResults[2].item(), '(+/-)',stdResults[2].item())
print('Recall: ', meanResults[3].item(), '(+/-)',stdResults[3].item())
print('Training time: ', meanResults[4].item(), '(+/-)',stdResults[4].item())
print('Testing time: ', meanResults[5].item(), '(+/-)',stdResults[5].item())

print('\n')
print('========== Network ==========')
print('Number of hidden layers: ', meanResults[6].item(), '(+/-)',stdResults[6].item())
print('Number of features: ', meanResults[7].item(), '(+/-)',stdResults[7].item())<jupyter_output><empty_output><jupyter_text>Infinite Delay<jupyter_code>print('Infinite Delay')
allMetrics = []
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet0, performanceHistory0, allPerformance = DEVDANmainID(DevdanNet,dataStreams)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory0[0],performanceHistory0[1],performanceHistory0[2],
                performanceHistory0[3],performanceHistory0[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet1, performanceHistory1, allPerformance = DEVDANmainID(DevdanNet,dataStreams)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory1[0],performanceHistory1[1],performanceHistory1[2],
                performanceHistory1[3],performanceHistory1[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet2, performanceHistory2, allPerformance = DEVDANmainID(DevdanNet,dataStreams)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory2[0],performanceHistory2[1],performanceHistory2[2],
                performanceHistory2[3],performanceHistory2[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet3, performanceHistory3, allPerformance = DEVDANmainID(DevdanNet,dataStreams)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory3[0],performanceHistory3[1],performanceHistory3[2],
                performanceHistory3[3],performanceHistory3[4])
# initialization
DevdanNet = DEVDAN(dataStreams.nInput,dataStreams.nOutput)
DevdanNet4, performanceHistory4, allPerformance = DEVDANmainID(DevdanNet,dataStreams)
allMetrics.append(allPerformance)
plotPerformance(performanceHistory4[0],performanceHistory4[1],performanceHistory4[2],
                performanceHistory4[3],performanceHistory4[4])
# all results

# 0: accuracy
# 1: f1_score
# 2: precision_score
# 3: recall_score
# 4: training_time
# 5: testingTime
# 6: nHiddenLayer
# 7: nHiddenNode

meanResults = np.round_(np.mean(allMetrics,0), decimals=2)
stdResults  = np.round_(np.std(allMetrics,0), decimals=2)

print('\n')
print('========== Performance occupancy ==========')
print('Preq Accuracy: ', meanResults[0].item(), '(+/-)',stdResults[0].item())
print('F1 score: ', meanResults[1].item(), '(+/-)',stdResults[1].item())
print('Precision: ', meanResults[2].item(), '(+/-)',stdResults[2].item())
print('Recall: ', meanResults[3].item(), '(+/-)',stdResults[3].item())
print('Training time: ', meanResults[4].item(), '(+/-)',stdResults[4].item())
print('Testing time: ', meanResults[5].item(), '(+/-)',stdResults[5].item())

print('\n')
print('========== Network ==========')
print('Number of hidden layers: ', meanResults[6].item(), '(+/-)',stdResults[6].item())
print('Number of features: ', meanResults[7].item(), '(+/-)',stdResults[7].item())<jupyter_output><empty_output>
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