This is the official paper version.

master
parent 1bcd645e54
commit c975540945
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{"duration": 5.343116044998169, "input_args": {"RemainingIds": "['KNN0', 'KNN1', 'KNN2', 'KNN3', 'KNN4', 'KNN5', 'KNN6', 'KNN7', 'KNN8', 'KNN9', 'KNN10', 'KNN11', 'KNN12', 'KNN13', 'KNN14', 'KNN15', 'KNN16', 'KNN17', 'KNN18', 'KNN19', 'KNN20', 'KNN21', 'KNN22', 'KNN23', 'KNN24', 'KNN25', 'KNN26', 'KNN27', 'KNN28', 'KNN29', 'KNN30', 'KNN31', 'KNN32', 'KNN33', 'KNN34', 'KNN35', 'KNN36', 'KNN37', 'KNN38', 'KNN39', 'KNN40', 'KNN41', 'KNN42', 'KNN43', 'KNN44', 'KNN45', 'KNN46', 'KNN47', 'KNN48', 'KNN49', 'KNN50', 'KNN51', 'KNN52', 'KNN53', 'KNN54', 'KNN55', 'KNN56', 'KNN57', 'KNN58', 'KNN59', 'KNN60', 'KNN61', 'KNN62', 'KNN63', 'KNN64', 'KNN65', 'KNN66', 'KNN67', 'KNN68', 'KNN69', 'KNN70', 'KNN71', 'KNN72', 'KNN73', 'KNN74', 'KNN75', 'KNN76', 'KNN77', 'KNN78', 'KNN79', 'KNN80', 'KNN81', 'KNN82', 'KNN83', 'KNN84', 'KNN85', 'KNN86', 'KNN87', 'KNN88', 'KNN89', 'KNN90', 'KNN91', 'KNN92', 'KNN93', 'KNN94', 'KNN95', 'KNN96', 'KNN97', 'KNN98', 'KNN99', 'LR100', 'LR101', 'LR102', 'LR103', 'LR104', 'LR105', 'LR106', 'LR107', 'LR108', 'LR109', 'LR110', 'LR111', 'LR112', 'LR113', 'LR114', 'LR115', 'LR116', 'LR117', 'LR118', 'LR119', 'LR120', 'LR121', 'LR122', 'LR123', 'LR124', 'LR125', 'LR126', 'LR127', 'LR128', 'LR129', 'LR130', 'LR131', 'LR132', 'LR133', 'LR134', 'LR135', 'LR136', 'LR137', 'LR138', 'LR139', 'LR140', 'LR141', 'LR142', 'LR143', 'LR144', 'LR145', 'LR146', 'LR147', 'LR148', 'LR149', 'LR150', 'LR151', 'LR152', 'LR153', 'LR154', 'LR155', 'LR156', 'LR157', 'LR158', 'LR159', 'LR160', 'LR161', 'LR162', 'LR163', 'LR164', 'LR165', 'LR166', 'LR167', 'LR168', 'LR169', 'LR170', 'LR171', 'LR172', 'LR173', 'LR174', 'LR175', 'LR176', 'LR177', 'LR178', 'LR179', 'LR180', 'LR181', 'LR182', 'LR183', 'LR184', 'LR185', 'LR186', 'LR187', 'LR188', 'LR189', 'LR190', 'LR191', 'LR192', 'LR193', 'LR194', 'LR195', 'LR196', 'LR197', 'LR198', 'LR199', 'MLP200', 'MLP201', 'MLP202', 'MLP203', 'MLP204', 'MLP205', 'MLP206', 'MLP207', 'MLP208', 'MLP209', 'MLP210', 'MLP211', 'MLP212', 'MLP213', 'MLP214', 'MLP215', 'MLP216', 'MLP217', 'MLP218', 'MLP219', 'MLP220', 'MLP221', 'MLP222', 'MLP223', 'MLP224', 'MLP225', 'MLP226', 'MLP227', 'MLP228', 'MLP229', 'MLP230', 'MLP231', 'MLP232', 'MLP233', 'MLP234', 'MLP235', 'MLP236', 'MLP237', 'MLP238', 'MLP239', 'MLP240', 'MLP241', 'MLP242', 'MLP243', 'MLP244', 'MLP245', 'MLP246', 'MLP247', 'MLP248', 'MLP249', 'MLP250', 'MLP251', 'MLP252', 'MLP253', 'MLP254', 'MLP255', 'MLP256', 'MLP257', 'MLP258', 'MLP259', 'MLP260', 'MLP261', 'MLP262', 'MLP263', 'MLP264', 'MLP265', 'MLP266', 'MLP267', 'MLP268', 'MLP269', 'MLP270', 'MLP271', 'MLP272', 'MLP273', 'MLP274', 'MLP275', 'MLP276', 'MLP277', 'MLP278', 'MLP279', 'MLP280', 'MLP281', 'MLP282', 'MLP283', 'MLP284', 'MLP285', 'MLP286', 'MLP287', 'MLP288', 'MLP289', 'MLP290', 'MLP291', 'MLP292', 'MLP293', 'MLP294', 'MLP295', 'MLP296', 'MLP297', 'MLP298', 'MLP299', 'RF300', 'RF302', 'RF303', 'RF304', 'RF305', 'RF306', 'RF307', 'RF308', 'RF309', 'RF310', 'RF311', 'RF312', 'RF313', 'RF314', 'RF315', 'RF316', 'RF317', 'RF318', 'RF319', 'RF320', 'RF321', 'RF322', 'RF323', 'RF324', 'RF325', 'RF326', 'RF327', 'RF328', 'RF329', 'RF330', 'RF331', 'RF332', 'RF333', 'RF334', 'RF335', 'RF336', 'RF337', 'RF338', 'RF339', 'RF340', 'RF341', 'RF342', 'RF343', 'RF344', 'RF345', 'RF346', 'RF347', 'RF348', 'RF349', 'RF350', 'RF351', 'RF352', 'RF353', 'RF354', 'RF355', 'RF356', 'RF358', 'RF359', 'RF360', 'RF361', 'RF362', 'RF363', 'RF364', 'RF365', 'RF366', 'RF367', 'RF368', 'RF369', 'RF370', 'RF371', 'RF372', 'RF373', 'RF375', 'RF376', 'RF377', 'RF378', 'RF379', 'RF380', 'RF381', 'RF383', 'RF385', 'RF386', 'RF387', 'RF388', 'RF389', 'RF390', 'RF391', 'RF392', 'RF393', 'RF394', 'RF395', 'RF396', 'RF397', 'RF398', 'RF399', 'GradB400', 'GradB401', 'GradB402', 'GradB403', 'GradB404', 'GradB405', 'GradB406', 'GradB407', 'GradB408', 'GradB409', 'GradB410', 'GradB411', 'GradB412', 'GradB413', 'GradB414', 'GradB415', 'GradB416', 'GradB417', 'GradB418', 'GradB419', 'GradB420', 'GradB421', 'GradB422', 'GradB423', 'GradB424', 'GradB425', 'GradB426', 'GradB427', 'GradB428', 'GradB429', 'GradB430', 'GradB431', 'GradB432', 'GradB433', 'GradB434', 'GradB435', 'GradB436', 'GradB437', 'GradB438', 'GradB439', 'GradB440', 'GradB441', 'GradB442', 'GradB443', 'GradB444', 'GradB445', 'GradB446', 'GradB447', 'GradB448', 'GradB449', 'GradB450', 'GradB451', 'GradB452', 'GradB453', 'GradB454', 'GradB455', 'GradB456', 'GradB457', 'GradB458', 'GradB459', 'GradB460', 'GradB461', 'GradB462', 'GradB463', 'GradB464', 'GradB465', 'GradB466', 'GradB467', 'GradB468', 'GradB469', 'GradB470', 'GradB471', 'GradB472', 'GradB473', 'GradB474', 'GradB475', 'GradB476', 'GradB477', 'GradB478', 'GradB479', 'GradB480', 'GradB481', 'GradB482', 'GradB483', 'GradB484', 'GradB485', 'GradB486', 'GradB487', 'GradB488', 'GradB489', 'GradB490', 'GradB491', 'GradB492', 'GradB493', 'GradB494', 'GradB495', 'GradB496', 'GradB497', 'GradB498', 'GradB499']", "setMaxLoopValue": "[0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0]"}}

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# first line: 1312
@memory.cache
def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
random.seed(RANDOM_SEED)
global XData
global yData
global addKNN
global addLR
global addMLP
global addRF
global addGradB
global countAllModels
# loop through the algorithms
global allParametersPerfCrossMutr
global HistoryPreservation
global randomSearchVar
greater = randomSearchVar*5
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
paramAllAlgs = PreprocessingParam()
KNNIntIndex = []
LRIntIndex = []
MLPIntIndex = []
RFIntIndex = []
GradBIntIndex = []
localCrossMutr = []
allParametersPerfCrossMutrKNNC = []
while countKNN < setMaxLoopValue[16]:
for dr in KNNIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN)
else:
KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
KNNPickPair = random.sample(KNNIntIndex,2)
pairDF = paramAllAlgs.iloc[KNNPickPair]
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['algorithm'] == crossoverDF['algorithm'].iloc[0]) & (paramAllAlgs['metric'] == crossoverDF['metric'].iloc[0]) & (paramAllAlgs['n_neighbors'] == crossoverDF['n_neighbors'].iloc[0]) & (paramAllAlgs['weights'] == crossoverDF['weights'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_C', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[16]
for loop in range(setMaxLoopValue[16] - 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)
allParametersPerfCrossMutrKNNC.append(localCrossMutr[0])
allParametersPerfCrossMutrKNNC.append(localCrossMutr[1])
allParametersPerfCrossMutrKNNC.append(localCrossMutr[2])
allParametersPerfCrossMutrKNNC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrKNNC
countKNN = 0
KNNIntIndex = []
localCrossMutr.clear()
allParametersPerfCrossMutrKNNM = []
while countKNN < setMaxLoopValue[10]:
for dr in KNNIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN)
else:
KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
KNNPickPair = random.sample(KNNIntIndex,1)
pairDF = paramAllAlgs.iloc[KNNPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
if (column == 'n_neighbors'):
randomNumber = random.randint(101, math.floor(((len(yData)/crossValidation)*(crossValidation-1)))-1)
listData.append(randomNumber)
crossoverDF[column] = listData
else:
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['algorithm'] == crossoverDF['algorithm'].iloc[0]) & (paramAllAlgs['metric'] == crossoverDF['metric'].iloc[0]) & (paramAllAlgs['n_neighbors'] == crossoverDF['n_neighbors'].iloc[0]) & (paramAllAlgs['weights'] == crossoverDF['weights'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_M', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[10]
for loop in range(setMaxLoopValue[10] - 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)
allParametersPerfCrossMutrKNNM.append(localCrossMutr[0])
allParametersPerfCrossMutrKNNM.append(localCrossMutr[1])
allParametersPerfCrossMutrKNNM.append(localCrossMutr[2])
allParametersPerfCrossMutrKNNM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrKNNM
localCrossMutr.clear()
allParametersPerfCrossMutrLRC = []
while countLR < setMaxLoopValue[15]:
for dr in LRIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar))
else:
LRIntIndex.append(int(re.findall('\d+', dr)[0]))
LRPickPair = random.sample(LRIntIndex,2)
pairDF = paramAllAlgs.iloc[LRPickPair]
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['C'] == crossoverDF['C'].iloc[0]) & (paramAllAlgs['max_iter'] == crossoverDF['max_iter'].iloc[0]) & (paramAllAlgs['solver'] == crossoverDF['solver'].iloc[0]) & (paramAllAlgs['penalty'] == crossoverDF['penalty'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_C', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[15]
for loop in range(setMaxLoopValue[15] - 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)
allParametersPerfCrossMutrLRC.append(localCrossMutr[0])
allParametersPerfCrossMutrLRC.append(localCrossMutr[1])
allParametersPerfCrossMutrLRC.append(localCrossMutr[2])
allParametersPerfCrossMutrLRC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRC
countLR = 0
LRIntIndex = []
localCrossMutr.clear()
allParametersPerfCrossMutrLRM = []
while countLR < setMaxLoopValue[9]:
for dr in LRIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar))
else:
LRIntIndex.append(int(re.findall('\d+', dr)[0]))
LRPickPair = random.sample(LRIntIndex,1)
pairDF = paramAllAlgs.iloc[LRPickPair]
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
if (column == 'C'):
randomNumber = random.randint(101, 1000)
listData.append(randomNumber)
crossoverDF[column] = listData
else:
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['C'] == crossoverDF['C'].iloc[0]) & (paramAllAlgs['max_iter'] == crossoverDF['max_iter'].iloc[0]) & (paramAllAlgs['solver'] == crossoverDF['solver'].iloc[0]) & (paramAllAlgs['penalty'] == crossoverDF['penalty'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_M', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[9]
for loop in range(setMaxLoopValue[9] - 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)
allParametersPerfCrossMutrLRM.append(localCrossMutr[0])
allParametersPerfCrossMutrLRM.append(localCrossMutr[1])
allParametersPerfCrossMutrLRM.append(localCrossMutr[2])
allParametersPerfCrossMutrLRM.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRM
localCrossMutr.clear()
allParametersPerfCrossMutrMLPC = []
while countMLP < setMaxLoopValue[14]:
for dr in MLPIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
MLPIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*2))
else:
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_C', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[14]
for loop in range(setMaxLoopValue[14] - 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[8]:
for dr in MLPIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
MLPIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*2))
else:
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_M', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[8]
for loop in range(setMaxLoopValue[8] - 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[13]:
for dr in RFIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
RFIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*3))
else:
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_C', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[13]
for loop in range(setMaxLoopValue[13] - 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[7]:
if (int(re.findall('\d+', dr)[0]) >= greater):
RFIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*3))
else:
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_M', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[7]
for loop in range(setMaxLoopValue[7] - 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[12]:
for dr in GradBIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
GradBIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*4))
else:
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_C', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[12]
for loop in range(setMaxLoopValue[12] - 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[6]:
for dr in GradBIDs:
if (int(re.findall('\d+', dr)[0]) >= greater):
GradBIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*4))
else:
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, 'GradB_M', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
countAllModels = countAllModels + setMaxLoopValue[6]
for loop in range(setMaxLoopValue[6] - 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[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNC[3]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNM[3]], ignore_index=True)
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrLRC[0] + allParametersPerfCrossMutrLRM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRC[1]], ignore_index=True)
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRM[1]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRC[2]], ignore_index=True)
allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRM[2]], ignore_index=True)
allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRC[3]], ignore_index=True)
allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRM[3]], ignore_index=True)
allParametersPerformancePerModel[8] = allParametersPerformancePerModel[8] + allParametersPerfCrossMutrMLPC[0] + allParametersPerfCrossMutrMLPM[0]
allParametersPerformancePerModel[9] = pd.concat([allParametersPerformancePerModel[9], allParametersPerfCrossMutrMLPC[1]], ignore_index=True)
allParametersPerformancePerModel[9] = pd.concat([allParametersPerformancePerModel[9], allParametersPerfCrossMutrMLPM[1]], ignore_index=True)
allParametersPerformancePerModel[10] = pd.concat([allParametersPerformancePerModel[10], allParametersPerfCrossMutrMLPC[2]], ignore_index=True)
allParametersPerformancePerModel[10] = pd.concat([allParametersPerformancePerModel[10], 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[12] = allParametersPerformancePerModel[12] + allParametersPerfCrossMutrRFC[0] + allParametersPerfCrossMutrRFM[0]
allParametersPerformancePerModel[13] = pd.concat([allParametersPerformancePerModel[13], allParametersPerfCrossMutrRFC[1]], ignore_index=True)
allParametersPerformancePerModel[13] = pd.concat([allParametersPerformancePerModel[13], allParametersPerfCrossMutrRFM[1]], ignore_index=True)
allParametersPerformancePerModel[14] = pd.concat([allParametersPerformancePerModel[14], allParametersPerfCrossMutrRFC[2]], ignore_index=True)
allParametersPerformancePerModel[14] = pd.concat([allParametersPerformancePerModel[14], 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[16] = allParametersPerformancePerModel[16] + allParametersPerfCrossMutrGradBC[0] + allParametersPerfCrossMutrGradBM[0]
allParametersPerformancePerModel[17] = pd.concat([allParametersPerformancePerModel[17], allParametersPerfCrossMutrGradBC[1]], ignore_index=True)
allParametersPerformancePerModel[17] = pd.concat([allParametersPerformancePerModel[17], allParametersPerfCrossMutrGradBM[1]], ignore_index=True)
allParametersPerformancePerModel[18] = pd.concat([allParametersPerformancePerModel[18], allParametersPerfCrossMutrGradBC[2]], ignore_index=True)
allParametersPerformancePerModel[18] = pd.concat([allParametersPerformancePerModel[18], 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)
addKNN = addGradB
addLR = addKNN + setMaxLoopValue[16] + setMaxLoopValue[10]
addMLP = addLR + setMaxLoopValue[15] + setMaxLoopValue[9]
addRF = addMLP + setMaxLoopValue[14] + setMaxLoopValue[8]
addGradB = addRF + setMaxLoopValue[13] + setMaxLoopValue[7]
return 'Everything Okay'

@ -1 +0,0 @@
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