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Angelos Chatzimparmpas 5 years ago
parent 03deab186f
commit e3a4a400b3
3 changed files with 446 additions and 49 deletions
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  1. BIN
      __pycache__/run.cpython-37.pyc
  2. 11
      frontend/src/components/Main.vue
  3. 484
      run.py

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frontend/src/components/Main.vue
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@ -18,7 +18,7 @@
</b-col>
<b-col cols="6">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Provenance</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">History and Algorithms/Models Selector</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 359px">
</mdb-card-text>
@ -27,7 +27,7 @@
</b-col>
<b-col cols="3">
<mdb-card >
<mdb-card-header color="primary-color" tag="h5" class="text-center">Majority-Voting Ensemble's Results</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Final Results of Majority-Voting Ensemble </mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 359px">
</mdb-card-text>
@ -119,7 +119,7 @@
<b-row class="md-3">
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Hyper-Parameters' Space
<mdb-card-header color="primary-color" tag="h5" class="text-center">Solution Space of Hyper-Parameters
[Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"><active-scatter/></small><span class="badge badge-info badge-pill float-right">Projection<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1</span></span>
</mdb-card-header>
<mdb-card-body>
@ -145,7 +145,7 @@
<b-row class="md-3">
<b-col cols="3">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Manipulation of Algorithms<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1&2</span></span>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Overall Performance for Each Algorithm/Model<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1&2</span></span>
</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 270px">
@ -921,6 +921,7 @@ export default Vue.extend({
console.log('Sent the unselected points for crossover and mutation.')
this.getDatafromtheBackEnd()
this.getCMComputedData()
this.changeActiveTo2()
})
.catch(error => {
console.log(error)
@ -979,7 +980,7 @@ export default Vue.extend({
EventBus.$on('RemainingPoints', this.changeActiveTo1)
EventBus.$on('RemainingPoints', data => { this.unselectedRemainingPoints = data })
EventBus.$on('InitializeCrossoverMutation', this.changeActiveTo2)
EventBus.$on('InitializeCrossoverMutation', this.sendPointsCrossMutat)
EventBus.$on('RemainingPointsCM', this.changeActiveTo2)

484
run.py
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@ -301,27 +301,15 @@ def retrieveFileName():
# models
global KNNModels
global SVCModels
global GausNBModels
global MLPModels
global LRModels
global LDAModels
global QDAModels
global RFModels
global ExtraTModels
global AdaBModels
global GradBModels
KNNModels = []
SVCModels = []
GausNBModels = []
MLPModels = []
LRModels = []
LDAModels = []
QDAModels = []
RFModels = []
ExtraTModels = []
AdaBModels = []
GradBModels = []
global results
@ -1051,12 +1039,15 @@ def CrossoverMutateFun():
EnsembleActive = json.loads(EnsembleActive)
EnsembleActive = EnsembleActive['StoreEnsemble']
random.seed(RANDOM_SEED)
global XData
global yData
global LRModelsCount
global addKNN
global addLR
global addMLP
global addRF
global addGradB
global countAllModels
# loop through the algorithms
@ -1066,14 +1057,23 @@ def CrossoverMutateFun():
KNNIDs = list(filter(lambda k: 'KNN' in k, RemainingIds))
LRIDs = list(filter(lambda k: 'LR' in k, RemainingIds))
MLPIDs = list(filter(lambda k: 'MLP' in k, RemainingIds))
RFIDs = list(filter(lambda k: 'RF' in k, RemainingIds))
GradBIDs = list(filter(lambda k: 'GradB' in k, RemainingIds))
countKNN = 0
countLR = 0
countMLP = 0
countRF = 0
countGradB = 0
setMaxLoopValue = 5
paramAllAlgs = PreprocessingParam()
KNNIntIndex = []
LRIntIndex = []
MLPIntIndex = []
RFIntIndex = []
GradBIntIndex = []
localCrossMutr = []
allParametersPerfCrossMutrKNNC = []
@ -1099,7 +1099,7 @@ def CrossoverMutateFun():
countKNN += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + 5
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
@ -1146,7 +1146,7 @@ def CrossoverMutateFun():
countKNN += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + 5
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
@ -1187,7 +1187,7 @@ def CrossoverMutateFun():
countLR += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + 5
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
@ -1234,7 +1234,7 @@ def CrossoverMutateFun():
countLR += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + 5
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
@ -1248,12 +1248,277 @@ def CrossoverMutateFun():
allParametersPerfCrossMutrLRM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRM
localCrossMutr.clear()
allParametersPerfCrossMutrMLPC = []
while countMLP < setMaxLoopValue:
for dr in MLPIDs:
MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
MLPPickPair = random.sample(MLPIntIndex,2)
pairDF = paramAllAlgs.iloc[MLPPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
randomZeroOne = random.randint(0, 1)
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['hidden_layer_sizes'] == crossoverDF['hidden_layer_sizes'].iloc[0]) & (paramAllAlgs['alpha'] == crossoverDF['alpha'].iloc[0]) & (paramAllAlgs['tol'] == crossoverDF['tol'].iloc[0]) & (paramAllAlgs['max_iter'] == crossoverDF['max_iter'].iloc[0]) & (paramAllAlgs['activation'] == crossoverDF['activation'].iloc[0]) & (paramAllAlgs['solver'] == crossoverDF['solver'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrMLPC.append(localCrossMutr[0])
allParametersPerfCrossMutrMLPC.append(localCrossMutr[1])
allParametersPerfCrossMutrMLPC.append(localCrossMutr[2])
allParametersPerfCrossMutrMLPC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrMLPC
countMLP = 0
MLPIntIndex = []
localCrossMutr.clear()
allParametersPerfCrossMutrMLPM = []
while countMLP < setMaxLoopValue:
for dr in MLPIDs:
MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
MLPPickPair = random.sample(MLPIntIndex,1)
pairDF = paramAllAlgs.iloc[MLPPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
if (column == 'hidden_layer_sizes'):
randomNumber = (random.randint(10,60), random.randint(4,10))
listData.append(randomNumber)
crossoverDF[column] = listData
else:
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['hidden_layer_sizes'] == crossoverDF['hidden_layer_sizes'].iloc[0]) & (paramAllAlgs['alpha'] == crossoverDF['alpha'].iloc[0]) & (paramAllAlgs['tol'] == crossoverDF['tol'].iloc[0]) & (paramAllAlgs['max_iter'] == crossoverDF['max_iter'].iloc[0]) & (paramAllAlgs['activation'] == crossoverDF['activation'].iloc[0]) & (paramAllAlgs['solver'] == crossoverDF['solver'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrMLPM.append(localCrossMutr[0])
allParametersPerfCrossMutrMLPM.append(localCrossMutr[1])
allParametersPerfCrossMutrMLPM.append(localCrossMutr[2])
allParametersPerfCrossMutrMLPM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrMLPM
localCrossMutr.clear()
allParametersPerfCrossMutrRFC = []
while countRF < setMaxLoopValue:
for dr in RFIDs:
RFIntIndex.append(int(re.findall('\d+', dr)[0]))
RFPickPair = random.sample(RFIntIndex,2)
pairDF = paramAllAlgs.iloc[RFPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
randomZeroOne = random.randint(0, 1)
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrRFC.append(localCrossMutr[0])
allParametersPerfCrossMutrRFC.append(localCrossMutr[1])
allParametersPerfCrossMutrRFC.append(localCrossMutr[2])
allParametersPerfCrossMutrRFC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrRFC
countRF = 0
RFIntIndex = []
localCrossMutr.clear()
allParametersPerfCrossMutrRFM = []
while countRF < setMaxLoopValue:
for dr in RFIDs:
RFIntIndex.append(int(re.findall('\d+', dr)[0]))
RFPickPair = random.sample(RFIntIndex,1)
pairDF = paramAllAlgs.iloc[RFPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
if (column == 'n_estimators'):
randomNumber = random.randint(100, 200)
listData.append(randomNumber)
crossoverDF[column] = listData
else:
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrRFM.append(localCrossMutr[0])
allParametersPerfCrossMutrRFM.append(localCrossMutr[1])
allParametersPerfCrossMutrRFM.append(localCrossMutr[2])
allParametersPerfCrossMutrRFM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrRFM
localCrossMutr.clear()
allParametersPerfCrossMutrGradBC = []
while countGradB < setMaxLoopValue:
for dr in GradBIDs:
GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
GradBPickPair = random.sample(GradBIntIndex,2)
pairDF = paramAllAlgs.iloc[GradBPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
randomZeroOne = random.randint(0, 1)
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrGradBC.append(localCrossMutr[0])
allParametersPerfCrossMutrGradBC.append(localCrossMutr[1])
allParametersPerfCrossMutrGradBC.append(localCrossMutr[2])
allParametersPerfCrossMutrGradBC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrGradBC
countGradB = 0
GradBIntIndex = []
localCrossMutr.clear()
allParametersPerfCrossMutrGradBM = []
while countGradB < setMaxLoopValue:
for dr in GradBIDs:
GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
GradPickPair = random.sample(GradBIntIndex,1)
pairDF = paramAllAlgs.iloc[GradBPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
if (column == 'n_estimators'):
randomNumber = random.randint(100, 200)
listData.append(randomNumber)
crossoverDF[column] = listData
else:
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue
for loop in range(setMaxLoopValue - 1):
localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
localCrossMutr[2] = pd.concat([localCrossMutr[2], localCrossMutr[(loop+1)*4+2]], ignore_index=True)
localCrossMutr[3] = pd.concat([localCrossMutr[3], localCrossMutr[(loop+1)*4+3]], ignore_index=True)
allParametersPerfCrossMutrGradBM.append(localCrossMutr[0])
allParametersPerfCrossMutrGradBM.append(localCrossMutr[1])
allParametersPerfCrossMutrGradBM.append(localCrossMutr[2])
allParametersPerfCrossMutrGradBM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrGradBM
localCrossMutr.clear()
allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM + allParametersPerfCrossMutrMLPC + allParametersPerfCrossMutrMLPM + allParametersPerfCrossMutrRFC + allParametersPerfCrossMutrRFM + allParametersPerfCrossMutrGradBC + allParametersPerfCrossMutrGradBM
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
@ -1273,27 +1538,45 @@ def CrossoverMutateFun():
allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRC[3]], ignore_index=True)
allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRM[3]], ignore_index=True)
addKNN = addLR
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrMLPC[0] + allParametersPerfCrossMutrMLPM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrMLPC[1]], ignore_index=True)
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrMLPM[1]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrMLPC[2]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrMLPM[2]], ignore_index=True)
allParametersPerformancePerModel[11] = pd.concat([allParametersPerformancePerModel[11], allParametersPerfCrossMutrMLPC[3]], ignore_index=True)
allParametersPerformancePerModel[11] = pd.concat([allParametersPerformancePerModel[11], allParametersPerfCrossMutrMLPM[3]], ignore_index=True)
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrRFC[0] + allParametersPerfCrossMutrRFM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrRFC[1]], ignore_index=True)
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrRFM[1]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrRFC[2]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrRFM[2]], ignore_index=True)
allParametersPerformancePerModel[15] = pd.concat([allParametersPerformancePerModel[15], allParametersPerfCrossMutrRFC[3]], ignore_index=True)
allParametersPerformancePerModel[15] = pd.concat([allParametersPerformancePerModel[15], allParametersPerfCrossMutrRFM[3]], ignore_index=True)
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrGradBC[0] + allParametersPerfCrossMutrGradBM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrGradBC[1]], ignore_index=True)
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrGradBM[1]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrGradBC[2]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrGradBM[2]], ignore_index=True)
allParametersPerformancePerModel[19] = pd.concat([allParametersPerformancePerModel[19], allParametersPerfCrossMutrGradBC[3]], ignore_index=True)
allParametersPerformancePerModel[19] = pd.concat([allParametersPerformancePerModel[19], allParametersPerfCrossMutrGradBM[3]], ignore_index=True)
addLR = addLR + 10
addKNN = addLR
# KNNIntIndex = []
# for dr in KNNIDs:
# KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
addLR = addLR + setMaxLoopValue*2
# allParametersPerformancePerModel[0] = [j for i, j in enumerate(allParametersPerformancePerModel[0]) if i not in KNNIntIndex]
# allParametersPerformancePerModel[1].drop(allParametersPerformancePerModel[1].index[KNNIntIndex], inplace=True)
# allParametersPerformancePerModel[2].drop(allParametersPerformancePerModel[2].index[KNNIntIndex], inplace=True)
# allParametersPerformancePerModel[3].drop(allParametersPerformancePerModel[3].index[KNNIntIndex], inplace=True)
addMLP = addLR + setMaxLoopValue*2
# LRIntIndex = []
# for dr in LRIDs:
# LRIntIndex.append(int(re.findall('\d+', dr)[0]) - 100)
addRF = addMLP + setMaxLoopValue*2
# allParametersPerformancePerModel[4] = [j for i, j in enumerate(allParametersPerformancePerModel[4]) if i not in LRIntIndex]
# allParametersPerformancePerModel[5].drop(allParametersPerformancePerModel[5].index[LRIntIndex], inplace=True)
# allParametersPerformancePerModel[6].drop(allParametersPerformancePerModel[6].index[LRIntIndex], inplace=True)
# allParametersPerformancePerModel[7].drop(allParametersPerformancePerModel[7].index[LRIntIndex], inplace=True)
addGradB = addRF + setMaxLoopValue*2
return 'Everything Okay'
@ -1391,8 +1674,15 @@ def PreprocessingIDsCM():
dicKNNM = allParametersPerfCrossMutr[4]
dicLRC = allParametersPerfCrossMutr[8]
dicLRM = allParametersPerfCrossMutr[12]
dicMLPC = allParametersPerfCrossMutr[16]
dicMLPM = allParametersPerfCrossMutr[20]
dicRFC = allParametersPerfCrossMutr[24]
dicRFM = allParametersPerfCrossMutr[28]
dicGradBC = allParametersPerfCrossMutr[32]
dicGradBM = allParametersPerfCrossMutr[36]
df_concatIDs = dicKNNC + dicKNNM + dicLRC + dicLRM
df_concatIDs = dicKNNC + dicKNNM + dicLRC + dicLRM + dicMLPC + dicMLPM + dicRFC + dicRFM + dicGradBC + dicGradBM
return df_concatIDs
def PreprocessingMetricsCM():
@ -1400,13 +1690,25 @@ def PreprocessingMetricsCM():
dicKNNM = allParametersPerfCrossMutr[6]
dicLRC = allParametersPerfCrossMutr[10]
dicLRM = allParametersPerfCrossMutr[14]
dicMLPC = allParametersPerfCrossMutr[18]
dicMLPM = allParametersPerfCrossMutr[22]
dicRFC = allParametersPerfCrossMutr[26]
dicRFM = allParametersPerfCrossMutr[30]
dicGradBC = allParametersPerfCrossMutr[34]
dicGradBM = allParametersPerfCrossMutr[38]
dfKNNC = pd.DataFrame.from_dict(dicKNNC)
dfKNNM = pd.DataFrame.from_dict(dicKNNM)
dfLRC = pd.DataFrame.from_dict(dicLRC)
dfLRM = pd.DataFrame.from_dict(dicLRM)
df_concatMetrics = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM])
dfMLPC = pd.DataFrame.from_dict(dicMLPC)
dfMLPM = pd.DataFrame.from_dict(dicMLPM)
dfRFC = pd.DataFrame.from_dict(dicRFC)
dfRFM = pd.DataFrame.from_dict(dicRFM)
dfGradBC = pd.DataFrame.from_dict(dicGradBC)
dfGradBM = pd.DataFrame.from_dict(dicGradBM)
df_concatMetrics = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM])
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
return df_concatMetrics
@ -1415,17 +1717,35 @@ def PreprocessingPredCM():
dicKNNM = allParametersPerfCrossMutr[7]
dicLRC = allParametersPerfCrossMutr[11]
dicLRM = allParametersPerfCrossMutr[15]
dicMLPC = allParametersPerfCrossMutr[19]
dicMLPM = allParametersPerfCrossMutr[23]
dicRFC = allParametersPerfCrossMutr[27]
dicRFM = allParametersPerfCrossMutr[31]
dicGradBC = allParametersPerfCrossMutr[35]
dicGradBM = allParametersPerfCrossMutr[39]
dfKNNC = pd.DataFrame.from_dict(dicKNNC)
dfKNNM = pd.DataFrame.from_dict(dicKNNM)
dfLRC = pd.DataFrame.from_dict(dicLRC)
dfLRM = pd.DataFrame.from_dict(dicLRM)
dfMLPC = pd.DataFrame.from_dict(dicMLPC)
dfMLPM = pd.DataFrame.from_dict(dicMLPM)
dfRFC = pd.DataFrame.from_dict(dicRFC)
dfRFM = pd.DataFrame.from_dict(dicRFM)
dfGradBC = pd.DataFrame.from_dict(dicGradBC)
dfGradBM = pd.DataFrame.from_dict(dicGradBM)
dfKNN = pd.concat([dfKNNC, dfKNNM])
dfLR = pd.concat([dfLRC, dfLRM])
df_concatProbs = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM])
dfMLP = pd.concat([dfMLPC, dfMLPM])
dfRF = pd.concat([dfRFC, dfRFM])
dfGradB = pd.concat([dfGradBC, dfGradBM])
df_concatProbs = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM])
predictionsKNN = []
for column, content in dfKNN.items():
@ -1436,41 +1756,87 @@ def PreprocessingPredCM():
for column, content in dfLR.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsLR.append(el)
predictionsMLP = []
for column, content in dfMLP.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsMLP.append(el)
predictionsRF = []
for column, content in dfRF.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsRF.append(el)
predictionsGradB = []
for column, content in dfGradB.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsGradB.append(el)
predictions = []
for column, content in df_concatProbs.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictions.append(el)
return [predictionsKNN, predictionsLR, predictions]
return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
def PreprocessingParamCM():
dicKNNC = allParametersPerfCrossMutr[1]
dicKNNM = allParametersPerfCrossMutr[5]
dicLRC = allParametersPerfCrossMutr[9]
dicLRM = allParametersPerfCrossMutr[13]
dicMLPC = allParametersPerfCrossMutr[17]
dicMLPM = allParametersPerfCrossMutr[21]
dicRFC = allParametersPerfCrossMutr[25]
dicRFM = allParametersPerfCrossMutr[29]
dicGradBC = allParametersPerfCrossMutr[33]
dicGradBM = allParametersPerfCrossMutr[37]
dicKNNC = dicKNNC['params']
dicKNNM = dicKNNM['params']
dicLRC = dicLRC['params']
dicLRM = dicLRM['params']
dicMLPC = dicMLPC['params']
dicMLPM = dicMLPM['params']
dicRFC = dicRFC['params']
dicRFM = dicRFM['params']
dicGradBC = dicGradBC['params']
dicGradBM = dicGradBM['params']
dicKNNC = {int(k):v for k,v in dicKNNC.items()}
dicKNNM = {int(k):v for k,v in dicKNNM.items()}
dicLRC = {int(k):v for k,v in dicLRC.items()}
dicLRM = {int(k):v for k,v in dicLRM.items()}
dicMLPC = {int(k):v for k,v in dicMLPC.items()}
dicMLPM = {int(k):v for k,v in dicMLPM.items()}
dicRFC = {int(k):v for k,v in dicRFC.items()}
dicRFM = {int(k):v for k,v in dicRFM.items()}
dicGradBC = {int(k):v for k,v in dicGradBC.items()}
dicGradBM = {int(k):v for k,v in dicGradBM.items()}
dfKNNC = pd.DataFrame.from_dict(dicKNNC)
dfKNNM = pd.DataFrame.from_dict(dicKNNM)
dfLRC = pd.DataFrame.from_dict(dicLRC)
dfLRM = pd.DataFrame.from_dict(dicLRM)
dfMLPC = pd.DataFrame.from_dict(dicMLPC)
dfMLPM = pd.DataFrame.from_dict(dicMLPM)
dfRFC = pd.DataFrame.from_dict(dicRFC)
dfRFM = pd.DataFrame.from_dict(dicRFM)
dfGradBC = pd.DataFrame.from_dict(dicGradBC)
dfGradBM = pd.DataFrame.from_dict(dicGradBM)
dfKNNC = dfKNNC.T
dfKNNM = dfKNNM.T
dfLRC = dfLRC.T
dfLRM = dfLRM.T
df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM])
dfMLPC = dfMLPC.T
dfMLPM = dfMLPM.T
dfRFC = dfRFC.T
dfRFM = dfRFM.T
dfGradBC = dfGradBC.T
dfGradBM = dfGradBM.T
df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM])
df_params = df_params.reset_index(drop=True)
return df_params
@ -1479,28 +1845,58 @@ def PreprocessingParamSepCM():
dicKNNM = allParametersPerfCrossMutr[5]
dicLRC = allParametersPerfCrossMutr[9]
dicLRM = allParametersPerfCrossMutr[13]
dicMLPC = allParametersPerfCrossMutr[17]
dicMLPM = allParametersPerfCrossMutr[21]
dicRFC = allParametersPerfCrossMutr[25]
dicRFM = allParametersPerfCrossMutr[29]
dicGradBC = allParametersPerfCrossMutr[33]
dicGradBM = allParametersPerfCrossMutr[37]
dicKNNC = dicKNNC['params']
dicKNNM = dicKNNM['params']
dicLRC = dicLRC['params']
dicLRM = dicLRM['params']
dicMLPC = dicMLPC['params']
dicMLPM = dicMLPM['params']
dicRFC = dicRFC['params']
dicRFM = dicRFM['params']
dicGradBC = dicGradBC['params']
dicGradBM = dicGradBM['params']
dicKNNC = {int(k):v for k,v in dicKNNC.items()}
dicKNNM = {int(k):v for k,v in dicKNNM.items()}
dicLRC = {int(k):v for k,v in dicLRC.items()}
dicLRM = {int(k):v for k,v in dicLRM.items()}
dicMLPC = {int(k):v for k,v in dicMLPC.items()}
dicMLPM = {int(k):v for k,v in dicMLPM.items()}
dicRFC = {int(k):v for k,v in dicRFC.items()}
dicRFM = {int(k):v for k,v in dicRFM.items()}
dicGradBC = {int(k):v for k,v in dicGradBC.items()}
dicGradBM = {int(k):v for k,v in dicGradBM.items()}
dfKNNC = pd.DataFrame.from_dict(dicKNNC)
dfKNNM = pd.DataFrame.from_dict(dicKNNM)
dfLRC = pd.DataFrame.from_dict(dicLRC)
dfLRM = pd.DataFrame.from_dict(dicLRM)
dfMLPC = pd.DataFrame.from_dict(dicMLPC)
dfMLPM = pd.DataFrame.from_dict(dicMLPM)
dfRFC = pd.DataFrame.from_dict(dicRFC)
dfRFM = pd.DataFrame.from_dict(dicRFM)
dfGradBC = pd.DataFrame.from_dict(dicGradBC)
dfGradBM = pd.DataFrame.from_dict(dicGradBM)
dfKNNC = dfKNNC.T
dfKNNM = dfKNNM.T
dfLRC = dfLRC.T
dfLRM = dfLRM.T
return [dfKNNC, dfKNNM, dfLRC, dfLRM]
dfMLPC = dfMLPC.T
dfMLPM = dfMLPM.T
dfRFC = dfRFC.T
dfRFM = dfRFM.T
dfGradBC = dfGradBC.T
dfGradBM = dfGradBM.T
return [dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM]
# remove that maybe!
def preProcsumPerMetricCM(factors):

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